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L. Billinge and Thomas Proffen</h2><p class="issue_description" style="">This virtual collection gathers together articles from various IUCr journals, illustrating the application of artificial intelligence and machine learning in structural science. [A related virtual collection of recent articles in <i>Journal of Applied Crystallography</i> is also available at <a href="https://journals.iucr.org/special_issues/2024/ANNs/">https://journals.iucr.org/special_issues/2024/ANNs/</a>.]<br /></p> <img class="issue_coverill" style="" alt="Highlighted illustration" src="/special_issues/2024/ML/graphics/coverill.gif"/><p class="issue_coverillcaption"><span class="inlineheading">Cover illustration:</span> An image generated by DALL·E using the prompt `A depiction of molecules surrounded by abstract representations of digital data and AI algorithms, highlighting the historical improvements in the data-driven approach to crystallography'.</p> <hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2024). </span>A<b><a href="/a/services/archive.html" style="">80</a></b>, <span><a href="/a/issues/2024/02/00/me6264/index.html" style="">139-145</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273324000172" style="">https://doi.org/10.1107/S2053273324000172</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="me6264cr" class="addthis_button_compact" href="#" addthis:description="" addthis:title="Machine learning in crystallography and structural science" addthis:url="https://doi.org/10.1107/S2053273324000172"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; 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J. L. Billinge</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Proffen%2C%20Th%2E"><span class="au">Th. Proffen</span></a></div> <div class="ica_synopsis">An overview of the virtual collection on machine learning (ML) in crystallography and structural science, as represented in <span class="it"><i>Acta Crystallographica Sections A</i></span>, <span class="it"><i>B</i></span> and <span class="it"><i>D</i></span>, <span class="it"><i>IUCrJ</i></span> and <span class="it"><i>Journal of Synchrotron Radiation</i></span>, is presented. Some terms and concepts related to artificial intelligence and machine learning are briefly introduced and described, and a short history of ML in structural science as it appeared in these IUCr journals is given to whet the appetite for the rest of the collection.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2024/02/00/me6264/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (1977). </span>A<b><a href="/a/services/archive.html" style="">33</a></b>, <span><a href="/a/issues/1977/01/00/a14037/a14037.pdf" style="">13-18</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S0567739477000047" style="">https://doi.org/10.1107/S0567739477000047</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="a14037cr" class="addthis_button_compact" href="#" addthis:description="Artificial intelligence research, as a part of computer science, has produced a variety of programs of experimental and applications interest: programs for scientific inference, chemical synthesis, planning robot control, extraction of meaning from English sentences, speech understanding, interpretation of visual images, and so on. The symbolic manipulation techniques used in artificial intelligence provide a framework for analyzing and coding the knowledge base of a problem independently of an algorithmic implementation. A possible application of artificial intelligence methodology to protein crystallography is described." addthis:title="A correlation between crystallographic computing and artificial intelligence research" addthis:url="https://doi.org/10.1107/S0567739477000047"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#a14037pr').css('display','none')" /> </a> </div> <div id="a14037pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#a14037cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?a14037" title="Statistics"> <img id="a14037_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="a14037_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?a14037">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?a14037" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="a14037_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?a14037">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="a14037cl" class="addthis_button_compact" href="#" addthis:description="Artificial intelligence research, as a part of computer science, has produced a variety of programs of experimental and applications interest: programs for scientific inference, chemical synthesis, planning robot control, extraction of meaning from English sentences, speech understanding, interpretation of visual images, and so on. The symbolic manipulation techniques used in artificial intelligence provide a framework for analyzing and coding the knowledge base of a problem independently of an algorithmic implementation. A possible application of artificial intelligence methodology to protein crystallography is described." addthis:title="A correlation between crystallographic computing and artificial intelligence research" addthis:url="https://doi.org/10.1107/S0567739477000047"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#a14037pl').css('display','none')" /> </a> </div> <div id="a14037pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#a14037cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/1977/01/00/a14037/a14037.pdf"><img src="/a/issues/1977/01/00/a14037/a14037thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/1977/01/00/a14037/a14037.pdf">A correlation between crystallographic computing and artificial intelligence research</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Feigenbaum%2C%20E%2EA%2E"><span class="au">E. A. Feigenbaum</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Engelmore%2C%20R%2ES%2E"><span class="au">R. S. Engelmore</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Johnson%2C%20C%2EK%2E"><span class="au">C. K. Johnson</span></a></div> <div class="ica_synopsis"></div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/1977/01/00/a14037/a14037.pdf">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2002). </span>A<b><a href="/a/services/archive.html" style="">58</a></b>, <span><a href="/a/issues/2002/02/00/mm0016/index.html" style="">171-179</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S0108767301020311" style="">https://doi.org/10.1107/S0108767301020311</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mm0016cr" class="addthis_button_compact" href="#" addthis:description="When identifying the correct atom types to occupy the specific atomic locations within newly observed structures or when assessing the plausibility of new suggested structures with specific locations for specific types of atoms, any information quantifying geometrically the local environments around those locations is valuable, provided known characteristic differences exist, with respect to this geometric information, between the different atom types. A powerful tool for quantifying such geometries is the Voronoi tessellation; this has been used in a pilot study of polynuclear aromatic hydrocarbons. It has been found that perfect identification of all C and H atoms may be achieved through the examination of polyhedral volumes and surface areas. The use of a weighted face-area average is also found to be a useful measure of local structure. Simple neural network models that may be used for atom-type prediction are given in the paper. It is expected that the present approach will be useful in distinguishing between atoms that have close scattering curves whilst displaying similar crystallographic behaviour." addthis:title="Analysis of polynuclear aromatic hydrocarbons via the Voronoi tessellation approach: classification of atom types using artificial neural networks" addthis:url="https://doi.org/10.1107/S0108767301020311"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#mm0016pr').css('display','none')" /> </a> </div> <div id="mm0016pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mm0016cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?mm0016" title="Statistics"> <img id="mm0016_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mm0016_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mm0016">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mm0016cl" class="addthis_button_compact" href="#" addthis:description="When identifying the correct atom types to occupy the specific atomic locations within newly observed structures or when assessing the plausibility of new suggested structures with specific locations for specific types of atoms, any information quantifying geometrically the local environments around those locations is valuable, provided known characteristic differences exist, with respect to this geometric information, between the different atom types. A powerful tool for quantifying such geometries is the Voronoi tessellation; this has been used in a pilot study of polynuclear aromatic hydrocarbons. It has been found that perfect identification of all C and H atoms may be achieved through the examination of polyhedral volumes and surface areas. The use of a weighted face-area average is also found to be a useful measure of local structure. Simple neural network models that may be used for atom-type prediction are given in the paper. It is expected that the present approach will be useful in distinguishing between atoms that have close scattering curves whilst displaying similar crystallographic behaviour." addthis:title="Analysis of polynuclear aromatic hydrocarbons via the Voronoi tessellation approach: classification of atom types using artificial neural networks" addthis:url="https://doi.org/10.1107/S0108767301020311"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#mm0016pl').css('display','none')" /> </a> </div> <div id="mm0016pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mm0016cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2002/02/00/mm0016/index.html"><img src="/a/issues/2002/02/00/mm0016/mm0016thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2002/02/00/mm0016/index.html">Analysis of polynuclear aromatic hydrocarbons <span class="it"><i>via</i></span> the Voronoi tessellation approach: classification of atom types using artificial neural networks</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Christensen%2C%20S%2EW%2E"><span class="au">S. W. Christensen</span></a></div> <div class="ica_synopsis">It is shown how Voronoi polyhedra for C and H atoms in polynuclear aromatic hydrocarbons display characteristic differences that allow perfect atom-type prediction from knowledge of the atomic coordinates alone.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2002/02/00/mm0016/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2016). </span>A<b><a href="/a/services/archive.html" style="">72</a></b>, <span><a href="/a/issues/2016/05/00/vk5006/index.html" style="">557-569</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273316009657" style="">https://doi.org/10.1107/S2053273316009657</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vk5006cr" class="addthis_button_compact" href="#" addthis:description="Inferring structural information from the intensity of a small-angle scattering (SAS) experiment is an ill-posed inverse problem. Thus, the determination of a solution is in general non-trivial. In this work, the indirect Fourier transform (IFT), which determines the pair distance distribution function from the intensity and hence yields structural information, is discussed within two different statistical inference approaches, namely a frequentist one and a Bayesian one, in order to determine a solution objectively From the frequentist approach the cross-validation method is obtained as a good practical objective function for selecting an IFT solution. Moreover, modern machine learning methods are employed to suppress oscillatory behaviour of the solution, hence extracting only meaningful features of the solution. By comparing the results yielded by the different methods presented here, the reliability of the outcome can be improved and thus the approach should enable more reliable information to be deduced from SAS experiments." addthis:title="Indirect Fourier transform in the context of statistical inference" addthis:url="https://doi.org/10.1107/S2053273316009657"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#vk5006pr').css('display','none')" /> </a> </div> <div id="vk5006pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vk5006cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?vk5006" title="Statistics"> <img id="vk5006_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vk5006_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vk5006">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vk5006cl" class="addthis_button_compact" href="#" addthis:description="Inferring structural information from the intensity of a small-angle scattering (SAS) experiment is an ill-posed inverse problem. Thus, the determination of a solution is in general non-trivial. In this work, the indirect Fourier transform (IFT), which determines the pair distance distribution function from the intensity and hence yields structural information, is discussed within two different statistical inference approaches, namely a frequentist one and a Bayesian one, in order to determine a solution objectively From the frequentist approach the cross-validation method is obtained as a good practical objective function for selecting an IFT solution. Moreover, modern machine learning methods are employed to suppress oscillatory behaviour of the solution, hence extracting only meaningful features of the solution. By comparing the results yielded by the different methods presented here, the reliability of the outcome can be improved and thus the approach should enable more reliable information to be deduced from SAS experiments." addthis:title="Indirect Fourier transform in the context of statistical inference" addthis:url="https://doi.org/10.1107/S2053273316009657"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#vk5006pl').css('display','none')" /> </a> </div> <div id="vk5006pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vk5006cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2016/05/00/vk5006/index.html"><img src="/a/issues/2016/05/00/vk5006/vk5006thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2016/05/00/vk5006/index.html">Indirect Fourier transform in the context of statistical inference</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Muthig%2C%20M%2E"><span class="au">M. Muthig</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Prevost%2C%20S%2E"><span class="au">S. Prévost</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Orglmeister%2C%20R%2E"><span class="au">R. Orglmeister</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gradzielski%2C%20M%2E"><span class="au">M. Gradzielski</span></a></div> <div class="ica_synopsis">The indirect Fourier transform is discussed in the context of complementary statistical inference frameworks in order to determine a solution objectively, which then allows one to automate model-free analysis of small-angle scattering data. Modern machine-learning methods are used to obtain the most robust solution.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2016/05/00/vk5006/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2019). </span>A<b><a href="/a/services/archive.html" style="">75</a></b>, <span><a href="/a/issues/2019/02/00/sc5122/index.html" style="">214-222</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273318017618" style="">https://doi.org/10.1107/S2053273318017618</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="sc5122cr" class="addthis_button_compact" href="#" addthis:description="The increasing efficiency of detectors and brightness of X-rays in both laboratory and large-scale facilities allow the collection of full single-crystal X-ray data sets within minutes. The analysis of these `crystallographic big data' requires new tools and approaches. To answer these needs, the use of principal component analysis (PCA) is proposed to improve the efficiency and speed of the analysis. Potentialities and limitations of PCA were investigated using single-crystal X-ray diffraction (XRD) data collected in situ on Y zeolite, in which CO2, acting as an active species, is thermally adsorbed while cooling from 300 to 200 K. For the first time, thanks to the high sensitivity of single-crystal XRD, it was possible to determine the sites where CO2 is adsorbed, the increase in their occupancy while the temperature is decreased, and the correlated motion of active species, i.e. CO2, H2O and Na+. PCA allowed identification and elimination of problematic data sets, and better understanding of the trends of the occupancies of CO2, Na+ and water. The quality of the data allowed for the first time calculation of the enthalpy ([Delta]H) and entropy ([Delta]S) of the CO2 adsorption by applying the van 't Hoff equation to in situ single-crystal data. The calculation of thermodynamic values was carried out by both traditional and PCA-based approaches, producing comparable results. The obtained [Delta]H value is significant and involves systems (CO2 and Y zeolite) with no toxicity, superb stability and chemical inertness. Such features, coupled with the absence of carbonate formation and framework inertness upon adsorption, were demonstrated for the bulk crystal by the single-crystal experiment, and suggest that the phenomenon can be easily reversed for a large number of cycles, with CO2 released on demand. The main advantages of PCA-assisted analysis reside in its speed and in the possibility of it being applied directly to raw data, possibly as an `online' data-quality test during data collection, without any a priori knowledge of the crystal structure." addthis:title="CO2 adsorption in Y zeolite: a structural and dynamic view by a novel principal-component-analysis-assisted in situ single-crystal X-ray diffraction experiment" addthis:url="https://doi.org/10.1107/S2053273318017618"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#sc5122pr').css('display','none')" /> </a> </div> <div id="sc5122pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#sc5122cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?sc5122" title="Statistics"> <img id="sc5122_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 115px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="sc5122_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?sc5122">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="sc5122cl" class="addthis_button_compact" href="#" addthis:description="The increasing efficiency of detectors and brightness of X-rays in both laboratory and large-scale facilities allow the collection of full single-crystal X-ray data sets within minutes. The analysis of these `crystallographic big data' requires new tools and approaches. To answer these needs, the use of principal component analysis (PCA) is proposed to improve the efficiency and speed of the analysis. Potentialities and limitations of PCA were investigated using single-crystal X-ray diffraction (XRD) data collected in situ on Y zeolite, in which CO2, acting as an active species, is thermally adsorbed while cooling from 300 to 200 K. For the first time, thanks to the high sensitivity of single-crystal XRD, it was possible to determine the sites where CO2 is adsorbed, the increase in their occupancy while the temperature is decreased, and the correlated motion of active species, i.e. CO2, H2O and Na+. PCA allowed identification and elimination of problematic data sets, and better understanding of the trends of the occupancies of CO2, Na+ and water. The quality of the data allowed for the first time calculation of the enthalpy ([Delta]H) and entropy ([Delta]S) of the CO2 adsorption by applying the van 't Hoff equation to in situ single-crystal data. The calculation of thermodynamic values was carried out by both traditional and PCA-based approaches, producing comparable results. The obtained [Delta]H value is significant and involves systems (CO2 and Y zeolite) with no toxicity, superb stability and chemical inertness. Such features, coupled with the absence of carbonate formation and framework inertness upon adsorption, were demonstrated for the bulk crystal by the single-crystal experiment, and suggest that the phenomenon can be easily reversed for a large number of cycles, with CO2 released on demand. The main advantages of PCA-assisted analysis reside in its speed and in the possibility of it being applied directly to raw data, possibly as an `online' data-quality test during data collection, without any a priori knowledge of the crystal structure." addthis:title="CO2 adsorption in Y zeolite: a structural and dynamic view by a novel principal-component-analysis-assisted in situ single-crystal X-ray diffraction experiment" addthis:url="https://doi.org/10.1107/S2053273318017618"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 138px; width: 20px;" onclick="$('#sc5122pl').css('display','none')" /> </a> </div> <div id="sc5122pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#sc5122cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2019/02/00/sc5122/index.html"><img src="/a/issues/2019/02/00/sc5122/sc5122thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2019/02/00/sc5122/index.html">CO<span class="inf"><sub>2</sub></span> adsorption in Y zeolite: a structural and dynamic view by a novel principal-component-analysis-assisted <span class="it"><i>in situ</i></span> single-crystal X-ray diffraction experiment</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Conterosito%2C%20E%2E"><span class="au">E. Conterosito</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Palin%2C%20L%2E"><span class="au">L. Palin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Caliandro%2C%20R%2E"><span class="au">R. Caliandro</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=van%20Beek%2C%20W%2E"><span class="au">W. van Beek</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chernyshov%2C%20D%2E"><span class="au">D. Chernyshov</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Milanesio%2C%20M%2E"><span class="au">M. Milanesio</span></a></div> <div class="ica_synopsis"><span class="it"><i>In situ</i></span> single-crystal X-ray diffraction data were used to unravel the structural dynamics and enthalpy and entropy of adsorption of CO<span class="inf"><sub>2</sub></span> into Y zeolite. A principal-component-analysis- (PCA) based approach is applied in an innovative way to single-crystal X-ray diffraction data analysis, allowing one to selectively detect the information from the subset of active atoms. The potential of and limitations of PCA in single-crystal diffraction are discussed.</div><div class="ica_codelinks" style=""> <div class="ica_codelinks_csd"><span class="ica_codelinks_head" style="">CCDC reference</span>: <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=csd&csdid=1884781">1884781</a> </div> </div> <div class="ica_readmore"> <a href="/a/issues/2019/02/00/sc5122/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2019). </span>A<b><a href="/a/services/archive.html" style="">75</a></b>, <span><a href="/a/issues/2019/04/00/ae5065/index.html" style="">633-643</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273319005606" style="">https://doi.org/10.1107/S2053273319005606</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5065cr" class="addthis_button_compact" href="#" addthis:description="A method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials." addthis:title="Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function" addthis:url="https://doi.org/10.1107/S2053273319005606"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ae5065pr').css('display','none')" /> </a> </div> <div id="ae5065pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5065cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ae5065" title="Statistics"> <img id="ae5065_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5065_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5065">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5065cl" class="addthis_button_compact" href="#" addthis:description="A method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials." addthis:title="Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function" addthis:url="https://doi.org/10.1107/S2053273319005606"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ae5065pl').css('display','none')" /> </a> </div> <div id="ae5065pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5065cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2019/04/00/ae5065/index.html"><img src="/a/issues/2019/04/00/ae5065/ae5065thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2019/04/00/ae5065/index.html">Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20C%2E%2DH%2E"><span class="au">C.-H. Liu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tao%2C%20Y%2E"><span class="au">Y. Tao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hsu%2C%20D%2E"><span class="au">D. Hsu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Du%2C%20Q%2E"><span class="au">Q. Du</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Billinge%2C%20S%2EJ%2EL%2E"><span class="au">S. J. L. Billinge</span></a></div> <div class="ica_synopsis">We present applications of machine learning models for predicting the space group of the underlying structure from its atomic pair distribution function (PDF).</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2019/04/00/ae5065/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2019). </span>A<b><a href="/a/services/archive.html" style="">75</a></b>, <span><a href="/a/issues/2019/06/00/ae5069/index.html" style="">851-860</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273319011446" style="">https://doi.org/10.1107/S2053273319011446</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5069cr" class="addthis_button_compact" href="#" addthis:description="Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I(+) and I(-) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods." addthis:title="Bayesian machine learning improves single-wavelength anomalous diffraction phasing" addthis:url="https://doi.org/10.1107/S2053273319011446"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ae5069pr').css('display','none')" /> </a> </div> <div id="ae5069pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5069cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ae5069" title="Statistics"> <img id="ae5069_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 92px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5069_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5069">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5069cl" class="addthis_button_compact" href="#" addthis:description="Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I(+) and I(-) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods." addthis:title="Bayesian machine learning improves single-wavelength anomalous diffraction phasing" addthis:url="https://doi.org/10.1107/S2053273319011446"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 115px; width: 20px;" onclick="$('#ae5069pl').css('display','none')" /> </a> </div> <div id="ae5069pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5069cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2019/06/00/ae5069/index.html"><img src="/a/issues/2019/06/00/ae5069/ae5069thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2019/06/00/ae5069/index.html">Bayesian machine learning improves single-wavelength anomalous diffraction phasing</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Garcia%2DBonete%2C%20M%2E%2DJ%2E"><span class="au">M.-J. Garcia-Bonete</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Katona%2C%20G%2E"><span class="au">G. Katona</span></a></div> <div class="ica_synopsis">The <span class="it"><i>a posteriori</i></span> probability densities of anomalous structure-factor amplitude differences were estimated by the Markov chain Monte Carlo machine-learning method. The model incorporated the correlation between the different Bijvoet pairs and the improved estimates were shown to be beneficial for SAD phasing.</div><div class="ica_codelinks" style=""> <div class="ica_codelinks_pdb"><span class="ica_codelinks_head" style="">PDB references</span>: <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=6sho">human survivin, 6sho</a>; <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=6sij">hen egg-white lysozyme, continuous rotation data collection and multivariate analysis of Friedel pairs, 6sij</a>; <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=6sik">hen egg-white lysozyme, continuous rotation, univariate, 6sik</a>; <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=6sil">hen egg-white lysozyme, inverse-beam geometry data collection and multivariate analysis of Friedel pairs, 6sil</a>; <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=6sim">hen egg-white lysozyme, inverse-beam geometry, univariate, 6sim</a> </div> </div> <div class="ica_readmore"> <a href="/a/issues/2019/06/00/ae5069/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2019). </span>A<b><a href="/a/services/archive.html" style="">75</a></b>, <span><a href="/a/issues/2019/06/00/ib5076/index.html" style="">876-888</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273319012804" style="">https://doi.org/10.1107/S2053273319012804</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ib5076cr" class="addthis_button_compact" href="#" addthis:description="A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO3, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms." addthis:title="Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis" addthis:url="https://doi.org/10.1107/S2053273319012804"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ib5076pr').css('display','none')" /> </a> </div> <div id="ib5076pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ib5076cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ib5076" title="Statistics"> <img id="ib5076_divwide" alt="" class="art_icon downloads" style="top: 0px; 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The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO3, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms." addthis:title="Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis" addthis:url="https://doi.org/10.1107/S2053273319012804"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ib5076pl').css('display','none')" /> </a> </div> <div id="ib5076pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ib5076cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2019/06/00/ib5076/index.html"><img src="/a/issues/2019/06/00/ib5076/ib5076thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2019/06/00/ib5076/index.html">Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Song%2C%20Y%2E"><span class="au">Y. Song</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tamura%2C%20N%2E"><span class="au">N. Tamura</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20C%2E"><span class="au">C. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Karami%2C%20M%2E"><span class="au">M. Karami</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chen%2C%20X%2E"><span class="au">X. Chen</span></a></div> <div class="ica_synopsis">A novel data-driven approach for synchrotron Laue X-ray microdiffraction scans is presented based on machine learning techniques.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2019/06/00/ib5076/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2020). </span>A<b><a href="/a/services/archive.html" style="">76</a></b>, <span><a href="/a/issues/2020/03/00/vk5039/index.html" style="">395-409</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273320002028" style="">https://doi.org/10.1107/S2053273320002028</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vk5039cr" class="addthis_button_compact" href="#" addthis:description="A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search criteria and performs structure refinements on them without human intervention. It supports both X-ray and neutron PDFs. Tests on various material systems show the effectiveness and robustness of the algorithm in finding the correct atomic crystal structure. It works on crystalline and nanocrystalline materials including complex oxide nanoparticles and nanowires, low-symmetry and locally distorted structures, and complicated doped and magnetic materials. This approach could greatly reduce the traditional structure searching work and enable the possibility of high-throughput real-time auto-analysis PDF experiments in the future." addthis:title="Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models" addthis:url="https://doi.org/10.1107/S2053273320002028"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#vk5039pr').css('display','none')" /> </a> </div> <div id="vk5039pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vk5039cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?vk5039" title="Statistics"> <img id="vk5039_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vk5039_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vk5039">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vk5039cl" class="addthis_button_compact" href="#" addthis:description="A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search criteria and performs structure refinements on them without human intervention. It supports both X-ray and neutron PDFs. Tests on various material systems show the effectiveness and robustness of the algorithm in finding the correct atomic crystal structure. It works on crystalline and nanocrystalline materials including complex oxide nanoparticles and nanowires, low-symmetry and locally distorted structures, and complicated doped and magnetic materials. This approach could greatly reduce the traditional structure searching work and enable the possibility of high-throughput real-time auto-analysis PDF experiments in the future." addthis:title="Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models" addthis:url="https://doi.org/10.1107/S2053273320002028"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#vk5039pl').css('display','none')" /> </a> </div> <div id="vk5039pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vk5039cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2020/03/00/vk5039/index.html"><img src="/a/issues/2020/03/00/vk5039/vk5039thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2020/03/00/vk5039/index.html">Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20L%2E"><span class="au">L. Yang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Juhas%2C%20P%2E"><span class="au">P. Juhás</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Terban%2C%20M%2EW%2E"><span class="au">M. W. Terban</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tucker%2C%20M%2EG%2E"><span class="au">M. G. Tucker</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Billinge%2C%20S%2EJ%2EL%2E"><span class="au">S. J. L. Billinge</span></a></div> <div class="ica_synopsis">Structure-mining finds and returns the best-fit structures from structural databases given a measured pair distribution function data set. Using databases and heuristics for automation, it has the potential to save experimenters a large amount of time as they explore candidate structures from the literature.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2020/03/00/vk5039/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2021). </span>A<b><a href="/a/services/archive.html" style="">77</a></b>, <span><a href="/a/issues/2021/01/00/ae5091/index.html" style="">2-6</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273320013066" style="">https://doi.org/10.1107/S2053273320013066</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5091cr" class="addthis_button_compact" href="#" addthis:description="A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data (PDFitc) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarityMapping, are described. In the first and second the user uploads a single PDF and the application returns a list of best-fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy-to-use workflow on PDFitc. In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform." addthis:title="A cloud platform for atomic pair distribution function analysis: PDFitc" addthis:url="https://doi.org/10.1107/S2053273320013066"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ae5091pr').css('display','none')" /> </a> </div> <div id="ae5091pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5091cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ae5091" title="Statistics"> <img id="ae5091_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5091_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5091">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5091cl" class="addthis_button_compact" href="#" addthis:description="A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data (PDFitc) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarityMapping, are described. In the first and second the user uploads a single PDF and the application returns a list of best-fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy-to-use workflow on PDFitc. In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform." addthis:title="A cloud platform for atomic pair distribution function analysis: PDFitc" addthis:url="https://doi.org/10.1107/S2053273320013066"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ae5091pl').css('display','none')" /> </a> </div> <div id="ae5091pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5091cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2021/01/00/ae5091/index.html"><img src="/a/issues/2021/01/00/ae5091/ae5091thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2021/01/00/ae5091/index.html">A cloud platform for atomic pair distribution function analysis: <span class="it"><i>PDFitc</i></span></a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20L%2E"><span class="au">L. Yang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Culbertson%2C%20E%2EA%2E"><span class="au">E. A. Culbertson</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Thomas%2C%20N%2EK%2E"><span class="au">N. K. Thomas</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Vuong%2C%20H%2ET%2E"><span class="au">H. T. Vuong</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kjaer%2C%20E%2ET%2ES%2E"><span class="au">E. T. S. Kjær</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jensen%2C%20K%2EM%2EO%2E"><span class="au">K. M. Ø. Jensen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tucker%2C%20M%2EG%2E"><span class="au">M. G. Tucker</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Billinge%2C%20S%2EJ%2EL%2E"><span class="au">S. J. L. Billinge</span></a></div> <div class="ica_synopsis">A new web platform is presented for the pair distribution function (PDF) community to use and share advanced PDF analysis software in the cloud.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2021/01/00/ae5091/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2022). </span>A<b><a href="/a/services/archive.html" style="">78</a></b>, <span><a href="/a/issues/2022/05/00/ae5116/index.html" style="">386-394</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273322007483" style="">https://doi.org/10.1107/S2053273322007483</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5116cr" class="addthis_button_compact" href="#" addthis:description="A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges." addthis:title="Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern" addthis:url="https://doi.org/10.1107/S2053273322007483"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ae5116pr').css('display','none')" /> </a> </div> <div id="ae5116pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5116cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ae5116" title="Statistics"> <img id="ae5116_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5116_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5116">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?ae5116" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5116_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5116">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5116cl" class="addthis_button_compact" href="#" addthis:description="A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges." addthis:title="Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern" addthis:url="https://doi.org/10.1107/S2053273322007483"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ae5116pl').css('display','none')" /> </a> </div> <div id="ae5116pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5116cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2022/05/00/ae5116/index.html"><img src="/a/issues/2022/05/00/ae5116/ae5116thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2022/05/00/ae5116/index.html">Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ozer%2C%20B%2E"><span class="au">B. Özer</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Karlsen%2C%20M%2EA%2E"><span class="au">M. A. Karlsen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Thatcher%2C%20Z%2E"><span class="au">Z. Thatcher</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lan%2C%20L%2E"><span class="au">L. Lan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McMahon%2C%20B%2E"><span class="au">B. McMahon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Strickland%2C%20P%2ER%2E"><span class="au">P. R. Strickland</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Westrip%2C%20S%2EP%2E"><span class="au">S. P. Westrip</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sang%2C%20K%2ES%2E"><span class="au">K. S. Sang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Billing%2C%20D%2EG%2E"><span class="au">D. G. Billing</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ravnsbaek%2C%20D%2EB%2E"><span class="au">D. B. Ravnsbæk</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Billinge%2C%20S%2EJ%2EL%2E"><span class="au">S. J. L. Billinge</span></a></div> <div class="ica_synopsis">A prototype application, <span class="it"><i>pyDataRecognition</i></span>, is described and tested. It has the goal that, given a measured powder diffraction pattern, it will return a list of publications from the IUCr Journals database that might be related based on the similarity to powder diffraction data deposited for those publications. This explores the possibility of a machine-readable literature where, for example, relevant studies may be found automatically through data similarity matches of online databases.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2022/05/00/ae5116/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>A<b><a href="/a/services/archive.html" style="">79</a></b>, <span><a href="/a/issues/2023/02/00/lu5022/index.html" style="">132-144</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273323000268" style="">https://doi.org/10.1107/S2053273323000268</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="lu5022cr" class="addthis_button_compact" href="#" addthis:description="Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical-physical properties of crystals, for instance, the formation of co-crystals, polymorph stability and solubility." addthis:title="A general force field by machine learning on experimental crystal structures. Calculations of intermolecular Gibbs energy with FlexCryst" addthis:url="https://doi.org/10.1107/S2053273323000268"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#lu5022pr').css('display','none')" /> </a> </div> <div id="lu5022pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#lu5022cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?lu5022" title="Statistics"> <img id="lu5022_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="lu5022_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?lu5022">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?lu5022" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Download citation" id="art_issuecontentsfunctions_narrow__horizontal_citation" src="/logos/buttonlogos/citation.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <span class="popuphd">Download citation</span> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <form action="//scripts.iucr.org/cgi-bin/biblio" method="post"><table class="citation"><tbody><tr><td align="right">Format<input name="cnor" value="" type="hidden"/></td><td align="right"><input name="saveas" value="BIBTeX" checked="checked" type="radio"/></td><td align="left">BIBTeX</td></tr><tr><td></td><td align="right"><input name="saveas" value="EndNote" type="radio"/></td><td align="left">EndNote</td></tr><tr><td></td><td align="right"><input name="saveas" value="RefMan" type="radio"/></td><td align="left">RefMan</td></tr><tr><td></td><td align="right"><input name="saveas" value="Refer" type="radio"/></td><td align="left">Refer</td></tr><tr><td></td><td align="right"><input name="saveas" value="Medline" type="radio"/></td><td align="left">Medline</td></tr><tr><td></td><td align="right"><input name="saveas" value="CIF" type="radio"/></td><td align="left">CIF</td></tr><tr><td></td><td align="right"><input name="saveas" value="SGML" type="radio"/></td><td align="left">SGML</td></tr><tr><td></td><td align="right"><input name="saveas" value="plaintext" type="radio"/></td><td align="left">Plain Text</td></tr><tr><td></td><td align="right"><input name="saveas" type="radio" value="text"/></td><td align="left">Text</td></tr><tr><td colspan="3" align="center"><input class="downloadbutton" name="Action" type="submit" value="download" /></td></tr></tbody></table></form> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?lu5022" title="Statistics"> <img id="lu5022_divnrw" alt="" class="art_icon downloads" style="top: 0px; left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="lu5022_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?lu5022">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="lu5022cl" class="addthis_button_compact" href="#" addthis:description="Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical-physical properties of crystals, for instance, the formation of co-crystals, polymorph stability and solubility." addthis:title="A general force field by machine learning on experimental crystal structures. Calculations of intermolecular Gibbs energy with FlexCryst" addthis:url="https://doi.org/10.1107/S2053273323000268"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#lu5022pl').css('display','none')" /> </a> </div> <div id="lu5022pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#lu5022cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2023/02/00/lu5022/index.html"><img src="/a/issues/2023/02/00/lu5022/lu5022thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2023/02/00/lu5022/index.html">A general force field by machine learning on experimental crystal structures. Calculations of intermolecular Gibbs energy with <span class="it"><i>FlexCryst</i></span></a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hofmann%2C%20D%2EW%2EM%2E"><span class="au">D. W. M. Hofmann</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kuleshova%2C%20L%2EN%2E"><span class="au">L. N. Kuleshova</span></a></div> <div class="ica_synopsis">Machine learning was employed on the Cambridge Structural Database to derive a general force field for all observed atom–atom interactions. The force field parameters, <span class="it"><i>i.e.</i></span> interatomic potentials and `critical bond distances', are derived to calculate the intermolecular Gibbs energy, which is important for the prediction of crystal structures, solubility and other thermodynamic properties.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2023/02/00/lu5022/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>A<b><a href="/a/services/archive.html" style="">79</a></b>, <span><a href="/a/issues/2023/04/00/ik5007/index.html" style="">331-338</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273323001948" style="">https://doi.org/10.1107/S2053273323001948</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ik5007cr" class="addthis_button_compact" href="#" addthis:description="To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images." addthis:title="Crystal search - feasibility study of a real-time deep learning process for crystallization well images" addthis:url="https://doi.org/10.1107/S2053273323001948"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ik5007pr').css('display','none')" /> </a> </div> <div id="ik5007pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ik5007cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ik5007" title="Statistics"> <img id="ik5007_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ik5007_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ik5007">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ik5007cl" class="addthis_button_compact" href="#" addthis:description="To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images." addthis:title="Crystal search - feasibility study of a real-time deep learning process for crystallization well images" addthis:url="https://doi.org/10.1107/S2053273323001948"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ik5007pl').css('display','none')" /> </a> </div> <div id="ik5007pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ik5007cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2023/04/00/ik5007/index.html"><img src="/a/issues/2023/04/00/ik5007/ik5007thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2023/04/00/ik5007/index.html">Crystal search – feasibility study of a real-time deep learning process for crystallization well images</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Thielmann%2C%20Y%2E"><span class="au">Y. Thielmann</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Luft%2C%20T%2E"><span class="au">T. Luft</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zint%2C%20N%2E"><span class="au">N. Zint</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Koepke%2C%20J%2E"><span class="au">J. Koepke</span></a></div> <div class="ica_synopsis">Four deep learning architectures were applied and SqueezeNet scored best. It was combined with the grid programming system BOINC to realize automatic real-time scoring of crystallization well images. Scores are written to a database and displayed to facilitate image inspection for users.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2023/04/00/ik5007/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>A<b><a href="/a/services/archive.html" style="">79</a></b>, <span><a href="/a/issues/2023/04/00/lu5027/index.html" style="">360-368</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273323004680" style="">https://doi.org/10.1107/S2053273323004680</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="lu5027cr" class="addthis_button_compact" href="#" addthis:description="As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small. This necessitates novel concepts for quick and accurate data selection. For this purpose, a set of machine learning algorithms for diffraction data classification has been implemented and tested. The proposed pre-processing and analysis workflow efficiently distinguished between amorphous ice and carbon support, providing proof of the principle of machine learning based identification of positions of interest. While limited in its current context, this approach exploits inherent characteristics of narrow electron beam diffraction patterns and can be extended for protein data classification and feature extraction." addthis:title="Machine learning for classifying narrow-beam electron diffraction data" addthis:url="https://doi.org/10.1107/S2053273323004680"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#lu5027pr').css('display','none')" /> </a> </div> <div id="lu5027pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#lu5027cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?lu5027" title="Statistics"> <img id="lu5027_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 23px; width: 20px;" title="Download citation" id="art_issuecontentsfunctions_narrow__horizontal_citation" src="/logos/buttonlogos/citation.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <span class="popuphd">Download citation</span> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <form action="//scripts.iucr.org/cgi-bin/biblio" method="post"><table class="citation"><tbody><tr><td align="right">Format<input name="cnor" value="" type="hidden"/></td><td align="right"><input name="saveas" value="BIBTeX" checked="checked" type="radio"/></td><td align="left">BIBTeX</td></tr><tr><td></td><td align="right"><input name="saveas" value="EndNote" type="radio"/></td><td align="left">EndNote</td></tr><tr><td></td><td align="right"><input name="saveas" value="RefMan" type="radio"/></td><td align="left">RefMan</td></tr><tr><td></td><td align="right"><input name="saveas" value="Refer" type="radio"/></td><td align="left">Refer</td></tr><tr><td></td><td align="right"><input name="saveas" value="Medline" type="radio"/></td><td align="left">Medline</td></tr><tr><td></td><td align="right"><input name="saveas" value="CIF" type="radio"/></td><td align="left">CIF</td></tr><tr><td></td><td align="right"><input name="saveas" value="SGML" type="radio"/></td><td align="left">SGML</td></tr><tr><td></td><td align="right"><input name="saveas" value="plaintext" type="radio"/></td><td align="left">Plain Text</td></tr><tr><td></td><td align="right"><input name="saveas" type="radio" value="text"/></td><td align="left">Text</td></tr><tr><td colspan="3" align="center"><input class="downloadbutton" name="Action" type="submit" value="download" /></td></tr></tbody></table></form> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?lu5027" title="Statistics"> <img id="lu5027_divnrw" alt="" class="art_icon downloads" style="top: 0px; left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="lu5027_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?lu5027">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="lu5027cl" class="addthis_button_compact" href="#" addthis:description="As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small. This necessitates novel concepts for quick and accurate data selection. For this purpose, a set of machine learning algorithms for diffraction data classification has been implemented and tested. The proposed pre-processing and analysis workflow efficiently distinguished between amorphous ice and carbon support, providing proof of the principle of machine learning based identification of positions of interest. While limited in its current context, this approach exploits inherent characteristics of narrow electron beam diffraction patterns and can be extended for protein data classification and feature extraction." addthis:title="Machine learning for classifying narrow-beam electron diffraction data" addthis:url="https://doi.org/10.1107/S2053273323004680"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#lu5027pl').css('display','none')" /> </a> </div> <div id="lu5027pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#lu5027cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2023/04/00/lu5027/index.html"><img src="/a/issues/2023/04/00/lu5027/lu5027thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2023/04/00/lu5027/index.html">Machine learning for classifying narrow-beam electron diffraction data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Matinyan%2C%20S%2E"><span class="au">S. Matinyan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Demir%2C%20B%2E"><span class="au">B. Demir</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Filipcik%2C%20P%2E"><span class="au">P. Filipcik</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Abrahams%2C%20J%2EP%2E"><span class="au">J. P. Abrahams</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=van%20Genderen%2C%20E%2E"><span class="au">E. van Genderen</span></a></div> <div class="ica_synopsis">Neural networks were trained for robust classification of narrow electron beam diffraction patterns and may significantly decrease the need for storage space.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2023/04/00/lu5027/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2024). </span>A<b><a href="/a/services/archive.html" style="">80</a></b>, <span><a href="/a/issues/2024/01/00/ae5136/index.html" style="">1-17</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2053273323009300" style="">https://doi.org/10.1107/S2053273323009300</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ae5136cr" class="addthis_button_compact" href="#" addthis:description="Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field." addthis:title="Deep learning applications in protein crystallography" addthis:url="https://doi.org/10.1107/S2053273323009300"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ae5136pr').css('display','none')" /> </a> </div> <div id="ae5136pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5136cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ae5136" title="Statistics"> <img id="ae5136_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ae5136_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ae5136">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?ae5136" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field." addthis:title="Deep learning applications in protein crystallography" addthis:url="https://doi.org/10.1107/S2053273323009300"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ae5136pl').css('display','none')" /> </a> </div> <div id="ae5136pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ae5136cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/a/issues/2024/01/00/ae5136/index.html"><img src="/a/issues/2024/01/00/ae5136/ae5136thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/a/issues/2024/01/00/ae5136/index.html">Deep learning applications in protein crystallography</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Matinyan%2C%20S%2E"><span class="au">S. Matinyan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Filipcik%2C%20P%2E"><span class="au">P. Filipcik</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Abrahams%2C%20J%2EP%2E"><span class="au">J. P. Abrahams</span></a></div> <div class="ica_synopsis">Deep learning applications are increasingly dominating many areas of science. This paper reviews their relevance for and impact on protein crystallography.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/a/issues/2024/01/00/ae5136/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2015). </span>B<b><a href="/b/services/archive.html" style="">71</a></b>, <span><a href="/b/issues/2015/05/00/wf5121/index.html" style="">507-513</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052520615013979" style="">https://doi.org/10.1107/S2052520615013979</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="wf5121cr" class="addthis_button_compact" href="#" addthis:description="We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure." addthis:title="Classification of ABO3 perovskite solids: a machine learning study" addthis:url="https://doi.org/10.1107/S2052520615013979"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#wf5121pr').css('display','none')" /> </a> </div> <div id="wf5121pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wf5121cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?wf5121" title="Statistics"> <img id="wf5121_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="wf5121_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?wf5121">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="wf5121cl" class="addthis_button_compact" href="#" addthis:description="We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure." addthis:title="Classification of ABO3 perovskite solids: a machine learning study" addthis:url="https://doi.org/10.1107/S2052520615013979"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#wf5121pl').css('display','none')" /> </a> </div> <div id="wf5121pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wf5121cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/b/issues/2015/05/00/wf5121/index.html"><img src="/b/issues/2015/05/00/wf5121/wf5121thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/b/issues/2015/05/00/wf5121/index.html">Classification of <span class="it"><i>AB</i></span>O<span class="inf"><sub>3</sub></span> perovskite solids: a machine learning study</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pilania%2C%20G%2E"><span class="au">G. Pilania</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Balachandran%2C%20P%2EV%2E"><span class="au">P. V. Balachandran</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gubernatis%2C%20J%2EE%2E"><span class="au">J. E. Gubernatis</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lookman%2C%20T%2E"><span class="au">T. Lookman</span></a></div> <div class="ica_synopsis">This article explores gradient tree boosting based machine learning methods for classifying whether a particular <span class="it"><i>AB</i></span>O<span class="inf"><sub>3</sub></span> chemistry forms a perovskite or non-perovskite structured solid. A 95% test set classification accuracy is shown to be achieved for the best performing feature set.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/b/issues/2015/05/00/wf5121/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2017). </span>B<b><a href="/b/services/archive.html" style="">73</a></b>, <span><a href="/b/issues/2017/05/00/wf5138/index.html" style="">962-967</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052520617011945" style="">https://doi.org/10.1107/S2052520617011945</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="wf5138cr" class="addthis_button_compact" href="#" addthis:description="In ferroelectric perovskites, displacements of cations from the high-symmetry lattice positions in the paraelectric phase break the spatial inversion symmetry. Furthermore, the relative magnitude of ionic displacements correlate strongly with ferroelectric properties such as the Curie temperature. As a result, there is interest in predicting the relative displacements of cations prior to experiments. Here, machine learning is used to predict the average displacement of octahedral cations from its high-symmetry position in ferroelectric perovskites. Published octahedral cation displacements data from density functional theory (DFT) calculations are used to train machine learning models, where each cation is represented by features such as Pauling electronegativity, Martynov-Batsanov electronegativity and the ratio of valence electron number to nominal charge. Average displacements for ten new octahedral cations for which DFT data do not exist are predicted. Predictions are validated by comparing them with new DFT calculations and existing experimental data. The outcome of this work has implications in the design and discovery of novel ferroelectric perovskites." addthis:title="Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning" addthis:url="https://doi.org/10.1107/S2052520617011945"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#wf5138pr').css('display','none')" /> </a> </div> <div id="wf5138pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wf5138cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?wf5138" title="Statistics"> <img id="wf5138_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="wf5138_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?wf5138">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?wf5138" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="wf5138_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?wf5138">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="wf5138cl" class="addthis_button_compact" href="#" addthis:description="In ferroelectric perovskites, displacements of cations from the high-symmetry lattice positions in the paraelectric phase break the spatial inversion symmetry. Furthermore, the relative magnitude of ionic displacements correlate strongly with ferroelectric properties such as the Curie temperature. As a result, there is interest in predicting the relative displacements of cations prior to experiments. Here, machine learning is used to predict the average displacement of octahedral cations from its high-symmetry position in ferroelectric perovskites. Published octahedral cation displacements data from density functional theory (DFT) calculations are used to train machine learning models, where each cation is represented by features such as Pauling electronegativity, Martynov-Batsanov electronegativity and the ratio of valence electron number to nominal charge. Average displacements for ten new octahedral cations for which DFT data do not exist are predicted. Predictions are validated by comparing them with new DFT calculations and existing experimental data. The outcome of this work has implications in the design and discovery of novel ferroelectric perovskites." addthis:title="Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning" addthis:url="https://doi.org/10.1107/S2052520617011945"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#wf5138pl').css('display','none')" /> </a> </div> <div id="wf5138pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wf5138cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/b/issues/2017/05/00/wf5138/index.html"><img src="/b/issues/2017/05/00/wf5138/wf5138thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/b/issues/2017/05/00/wf5138/index.html">Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Balachandran%2C%20P%2EV%2E"><span class="au">P. V. Balachandran</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shearman%2C%20T%2E"><span class="au">T. Shearman</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Theiler%2C%20J%2E"><span class="au">J. Theiler</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lookman%2C%20T%2E"><span class="au">T. Lookman</span></a></div> <div class="ica_synopsis">A machine learning approach is developed to rapidly predict the displacements of previously unexplored octahedral cations in PbTiO<span class="inf"><sub>3</sub></span>-based ferroelectric perovskites. Thus, expanding the knowledge base on ionic displacement data, which are important for the rational design of novel ferroelectric perovskites.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/b/issues/2017/05/00/wf5138/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (1993). </span>D<b><a href="/d/services/archive.html" style="">49</a></b>, <span><a href="/d/issues/1993/01/00/bu0306/bu0306.pdf" style="">168-178</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S090744499200917X" style="">https://doi.org/10.1107/S090744499200917X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="bu0306cr" class="addthis_button_compact" href="#" addthis:description="A knowledge-based approach to crystal structure determination is presented. The approach integrates direct-methods and artificial-intelligence strategies to rephrase the structure determination process as an exercise in scene analysis. A general joint probability distribution framework, which allows the incorporation of isomorphous replacement, anomalous scattering and a priori structural information, forms the basis of the direct-methods strategies. The accumulated knowledge on crystal and molecular structures is exploited through the use of artificial-intelligence strategies, which include techniques of knowledge representation, search and machine learning." addthis:title="Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure" addthis:url="https://doi.org/10.1107/S090744499200917X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#bu0306pr').css('display','none')" /> </a> </div> <div id="bu0306pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#bu0306cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?bu0306" title="Statistics"> <img id="bu0306_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="bu0306_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?bu0306">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="bu0306cl" class="addthis_button_compact" href="#" addthis:description="A knowledge-based approach to crystal structure determination is presented. The approach integrates direct-methods and artificial-intelligence strategies to rephrase the structure determination process as an exercise in scene analysis. A general joint probability distribution framework, which allows the incorporation of isomorphous replacement, anomalous scattering and a priori structural information, forms the basis of the direct-methods strategies. The accumulated knowledge on crystal and molecular structures is exploited through the use of artificial-intelligence strategies, which include techniques of knowledge representation, search and machine learning." addthis:title="Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure" addthis:url="https://doi.org/10.1107/S090744499200917X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#bu0306pl').css('display','none')" /> </a> </div> <div id="bu0306pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#bu0306cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/1993/01/00/bu0306/bu0306.pdf"><img src="/d/issues/1993/01/00/bu0306/bu0306thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/1993/01/00/bu0306/bu0306.pdf">Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fortier%2C%20S%2E"><span class="au">S. Fortier</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Castleden%2C%20I%2E"><span class="au">I. Castleden</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Glasgow%2C%20J%2E"><span class="au">J. Glasgow</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Conklin%2C%20D%2E"><span class="au">D. Conklin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Walmsley%2C%20C%2E"><span class="au">C. Walmsley</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Leherte%2C%20L%2E"><span class="au">L. Leherte</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Allen%2C%20F%2EH%2E"><span class="au">F. H. Allen</span></a></div> <div class="ica_synopsis"></div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/1993/01/00/bu0306/bu0306.pdf">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2002). </span>D<b><a href="/d/services/archive.html" style="">58</a></b>, <span><a href="/d/issues/2002/12/00/en0065/index.html" style="">2043-2054</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S0907444902016724" style="">https://doi.org/10.1107/S0907444902016724</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="en0065cr" class="addthis_button_compact" href="#" addthis:description="Most crystallographers today solve protein structures by first building as much of the protein backbone as possible and then modeling the side chains. Automating the determination of backbone coordinates by computer-based interpretation of the electron density would enhance the speed and possibly improve the accuracy of the structure-solution process. In this paper, a new computational procedure called CAPRA is described that predicts coordinates of C[alpha] atoms in density maps and outputs chains of C[alpha] atoms representing the backbone of the protein. The result constitutes a significant step beyond tracing the density, because there is ideally a one-to-one correspondence between atoms predicted in the chains output by CAPRA and C[alpha] atoms in the true structure (refined model). CAPRA is based on pattern-recognition techniques, including extraction of rotation-invariant numeric features to represent patterns in the density and use of a neural network to predict which pseudo-atoms in the trace are closest to true C[alpha] atoms. Experiments with several MAD and MIR electron-density maps of 2.4-2.8 A resolution reveal that CAPRA is capable of building ~90% of the backbone of a protein molecule, with an r.m.s. error for C[alpha] coordinates of around 0.9 A." addthis:title="Automatic modeling of protein backbones in electron-density maps via prediction of C[alpha] coordinates" addthis:url="https://doi.org/10.1107/S0907444902016724"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#en0065pr').css('display','none')" /> </a> </div> <div id="en0065pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#en0065cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?en0065" title="Statistics"> <img id="en0065_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="en0065_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?en0065">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="en0065cl" class="addthis_button_compact" href="#" addthis:description="Most crystallographers today solve protein structures by first building as much of the protein backbone as possible and then modeling the side chains. Automating the determination of backbone coordinates by computer-based interpretation of the electron density would enhance the speed and possibly improve the accuracy of the structure-solution process. In this paper, a new computational procedure called CAPRA is described that predicts coordinates of C[alpha] atoms in density maps and outputs chains of C[alpha] atoms representing the backbone of the protein. The result constitutes a significant step beyond tracing the density, because there is ideally a one-to-one correspondence between atoms predicted in the chains output by CAPRA and C[alpha] atoms in the true structure (refined model). CAPRA is based on pattern-recognition techniques, including extraction of rotation-invariant numeric features to represent patterns in the density and use of a neural network to predict which pseudo-atoms in the trace are closest to true C[alpha] atoms. Experiments with several MAD and MIR electron-density maps of 2.4-2.8 A resolution reveal that CAPRA is capable of building ~90% of the backbone of a protein molecule, with an r.m.s. error for C[alpha] coordinates of around 0.9 A." addthis:title="Automatic modeling of protein backbones in electron-density maps via prediction of C[alpha] coordinates" addthis:url="https://doi.org/10.1107/S0907444902016724"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#en0065pl').css('display','none')" /> </a> </div> <div id="en0065pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#en0065cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2002/12/00/en0065/index.html"><img src="/d/issues/2002/12/00/en0065/en0065thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2002/12/00/en0065/index.html">Automatic modeling of protein backbones in electron-density maps <span class="it"><i>via</i></span> prediction of C<span class="sup"><sup>α</sup></span> coordinates</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ioerger%2C%20T%2ER%2E"><span class="au">T. R. Ioerger</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sacchettini%2C%20J%2EC%2E"><span class="au">J. C. Sacchettini</span></a></div> <div class="ica_synopsis">A new computational procedure called <span class="it"><i>CAPRA</i></span> is described that predicts coordinates of C<span class="sup"><sup>α</sup></span> atoms in density maps and outputs chains of C<span class="sup"><sup>α</sup></span> atoms representing the backbone of the protein.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2002/12/00/en0065/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2004). </span>D<b><a href="/d/services/archive.html" style="">60</a></b>, <span><a href="/d/issues/2004/10/00/av5008/index.html" style="">1705-1716</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S090744490401683X" style="">https://doi.org/10.1107/S090744490401683X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="av5008cr" class="addthis_button_compact" href="#" addthis:description="Systematizing belief systems regarding macromolecular crystallization has two major advantages: automation and clarification. In this paper, methodologies are presented for systematizing and representing knowledge about the chemical and physical properties of additives used in crystallization experiments. A novel autonomous discovery program is introduced as a method to prune rule-based models produced from crystallization data augmented with such knowledge. Computational experiments indicate that such a system can retain and present informative rules pertaining to protein crystallization that warrant further confirmation via experimental techniques." addthis:title="Machine-learning techniques for macromolecular crystallization data" addthis:url="https://doi.org/10.1107/S090744490401683X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#av5008pr').css('display','none')" /> </a> </div> <div id="av5008pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#av5008cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?av5008" title="Statistics"> <img id="av5008_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="av5008_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?av5008">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="av5008cl" class="addthis_button_compact" href="#" addthis:description="Systematizing belief systems regarding macromolecular crystallization has two major advantages: automation and clarification. In this paper, methodologies are presented for systematizing and representing knowledge about the chemical and physical properties of additives used in crystallization experiments. A novel autonomous discovery program is introduced as a method to prune rule-based models produced from crystallization data augmented with such knowledge. Computational experiments indicate that such a system can retain and present informative rules pertaining to protein crystallization that warrant further confirmation via experimental techniques." addthis:title="Machine-learning techniques for macromolecular crystallization data" addthis:url="https://doi.org/10.1107/S090744490401683X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#av5008pl').css('display','none')" /> </a> </div> <div id="av5008pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#av5008cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2004/10/00/av5008/index.html"><img src="/d/issues/2004/10/00/av5008/av5008thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2004/10/00/av5008/index.html">Machine-learning techniques for macromolecular crystallization data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gopalakrishnan%2C%20V%2E"><span class="au">V. Gopalakrishnan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Livingston%2C%20G%2E"><span class="au">G. Livingston</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hennessy%2C%20D%2E"><span class="au">D. Hennessy</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Buchanan%2C%20B%2E"><span class="au">B. Buchanan</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Rosenberg%2C%20J%2EM%2E"><span class="au">J. M. Rosenberg</span></a></div> <div class="ica_synopsis">Methodologies are presented for systematizing and representing knowledge about the chemical and physical properties of additives used in crystallization experiments. A novel machine-learning and discovery program is introduced as a method that uses such knowledge for automatic analysis of augmented macromolecular crystallization data in order to categorize and find interesting relationships that can potentially aid the growth of new crystals.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2004/10/00/av5008/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2008). </span>D<b><a href="/d/services/archive.html" style="">64</a></b>, <span><a href="/d/issues/2008/12/00/yt5007/index.html" style="">1187-1195</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S090744490802982X" style="">https://doi.org/10.1107/S090744490802982X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yt5007cr" class="addthis_button_compact" href="#" addthis:description="The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures." addthis:title="Image-based crystal detection: a machine-learning approach" addthis:url="https://doi.org/10.1107/S090744490802982X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#yt5007pr').css('display','none')" /> </a> </div> <div id="yt5007pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yt5007cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?yt5007" title="Statistics"> <img id="yt5007_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Download citation" id="art_issuecontentsfunctions_narrow__horizontal_citation" src="/logos/buttonlogos/citation.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <span class="popuphd">Download citation</span> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <form action="//scripts.iucr.org/cgi-bin/biblio" method="post"><table class="citation"><tbody><tr><td align="right">Format<input name="cnor" value="" type="hidden"/></td><td align="right"><input name="saveas" value="BIBTeX" checked="checked" type="radio"/></td><td align="left">BIBTeX</td></tr><tr><td></td><td align="right"><input name="saveas" value="EndNote" type="radio"/></td><td align="left">EndNote</td></tr><tr><td></td><td align="right"><input name="saveas" value="RefMan" type="radio"/></td><td align="left">RefMan</td></tr><tr><td></td><td align="right"><input name="saveas" value="Refer" type="radio"/></td><td align="left">Refer</td></tr><tr><td></td><td align="right"><input name="saveas" value="Medline" type="radio"/></td><td align="left">Medline</td></tr><tr><td></td><td align="right"><input name="saveas" value="CIF" type="radio"/></td><td align="left">CIF</td></tr><tr><td></td><td align="right"><input name="saveas" value="SGML" type="radio"/></td><td align="left">SGML</td></tr><tr><td></td><td align="right"><input name="saveas" value="plaintext" type="radio"/></td><td align="left">Plain Text</td></tr><tr><td></td><td align="right"><input name="saveas" type="radio" value="text"/></td><td align="left">Text</td></tr><tr><td colspan="3" align="center"><input class="downloadbutton" name="Action" type="submit" value="download" /></td></tr></tbody></table></form> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?yt5007" title="Statistics"> <img id="yt5007_divnrw" alt="" class="art_icon downloads" style="top: 0px; left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="yt5007_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?yt5007">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yt5007cl" class="addthis_button_compact" href="#" addthis:description="The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures." addthis:title="Image-based crystal detection: a machine-learning approach" addthis:url="https://doi.org/10.1107/S090744490802982X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#yt5007pl').css('display','none')" /> </a> </div> <div id="yt5007pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yt5007cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2008/12/00/yt5007/index.html"><img src="/d/issues/2008/12/00/yt5007/yt5007thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2008/12/00/yt5007/index.html">Image-based crystal detection: a machine-learning approach</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20R%2E"><span class="au">R. Liu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Freund%2C%20Y%2E"><span class="au">Y. Freund</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Spraggon%2C%20G%2E"><span class="au">G. Spraggon</span></a></div> <div class="ica_synopsis">A system for scoring images based on the likelihood of containing crystalline material is described. A simulation carried out on a real set of crystallization images demonstrates the utility of such a system in high-throughput environments by substantially reducing the manual workload necessary to detect crystals for X-ray screening.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2008/12/00/yt5007/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2014). </span>D<b><a href="/d/services/archive.html" style="">70</a></b>, <span><a href="/d/issues/2014/03/00/wd5222/index.html" style="">627-635</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1399004713032070" style="">https://doi.org/10.1107/S1399004713032070</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="wd5222cr" class="addthis_button_compact" href="#" addthis:description="Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007), Protein Sci. 16, 2472-2482] was developed. XtalPred classifies proteins into five `crystallization classes' based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets." addthis:title="Improving the chances of successful protein structure determination with a random forest classifier" addthis:url="https://doi.org/10.1107/S1399004713032070"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#wd5222pr').css('display','none')" /> </a> </div> <div id="wd5222pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wd5222cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?wd5222" title="Statistics"> <img id="wd5222_divwide" alt="" class="art_icon downloads" style="top: 0px; 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The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007), Protein Sci. 16, 2472-2482] was developed. XtalPred classifies proteins into five `crystallization classes' based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets." addthis:title="Improving the chances of successful protein structure determination with a random forest classifier" addthis:url="https://doi.org/10.1107/S1399004713032070"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#wd5222pl').css('display','none')" /> </a> </div> <div id="wd5222pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#wd5222cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2014/03/00/wd5222/index.html"><img src="/d/issues/2014/03/00/wd5222/wd5222thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2014/03/00/wd5222/index.html">Improving the chances of successful protein structure determination with a random forest classifier</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jahandideh%2C%20S%2E"><span class="au">S. Jahandideh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jaroszewski%2C%20L%2E"><span class="au">L. Jaroszewski</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Godzik%2C%20A%2E"><span class="au">A. Godzik</span></a></div> <div class="ica_synopsis">Using an extended set of protein features calculated separately for protein surface and interior, a new version of <span class="it"><i>XtalPred</i></span> based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2014/03/00/wd5222/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2019). </span>D<b><a href="/d/services/archive.html" style="">75</a></b>, <span><a href="/d/issues/2019/08/00/rr5180/index.html" style="">753-763</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798319009392" style="">https://doi.org/10.1107/S2059798319009392</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5180cr" class="addthis_button_compact" href="#" addthis:description="The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP." addthis:title="Sequence assignment for low-resolution modelling of protein crystal structures" addthis:url="https://doi.org/10.1107/S2059798319009392"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rr5180pr').css('display','none')" /> </a> </div> <div id="rr5180pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5180cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rr5180" title="Statistics"> <img id="rr5180_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rr5180_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rr5180">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5180cl" class="addthis_button_compact" href="#" addthis:description="The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP." addthis:title="Sequence assignment for low-resolution modelling of protein crystal structures" addthis:url="https://doi.org/10.1107/S2059798319009392"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#rr5180pl').css('display','none')" /> </a> </div> <div id="rr5180pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5180cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2019/08/00/rr5180/index.html"><img src="/d/issues/2019/08/00/rr5180/rr5180thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2019/08/00/rr5180/index.html">Sequence assignment for low-resolution modelling of protein crystal structures</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chojnowski%2C%20G%2E"><span class="au">G. Chojnowski</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pereira%2C%20J%2E"><span class="au">J. Pereira</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lamzin%2C%20V%2ES%2E"><span class="au">V. S. Lamzin</span></a></div> <div class="ica_synopsis">Recent advances in automated protein model building using <span class="it"><i>ARP</i></span>/<span class="it"><i>wARP</i></span> are presented. The new methods include machine-learning-enhanced sequence assignment and loop building using a fragment database.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2019/08/00/rr5180/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2020). </span>D<b><a href="/d/services/archive.html" style="">76</a></b>, <span><a href="/d/issues/2020/08/00/qj5003/index.html" style="">713-723</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798320009080" style="">https://doi.org/10.1107/S2059798320009080</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qj5003cr" class="addthis_button_compact" href="#" addthis:description="Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures." addthis:title="Predicting protein model correctness in Coot using machine learning" addthis:url="https://doi.org/10.1107/S2059798320009080"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#qj5003pr').css('display','none')" /> </a> </div> <div id="qj5003pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qj5003cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?qj5003" title="Statistics"> <img id="qj5003_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="qj5003_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?qj5003">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qj5003cl" class="addthis_button_compact" href="#" addthis:description="Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures." addthis:title="Predicting protein model correctness in Coot using machine learning" addthis:url="https://doi.org/10.1107/S2059798320009080"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#qj5003pl').css('display','none')" /> </a> </div> <div id="qj5003pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qj5003cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2020/08/00/qj5003/index.html"><img src="/d/issues/2020/08/00/qj5003/qj5003thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2020/08/00/qj5003/index.html">Predicting protein model correctness in <span class="it"><i>Coot</i></span> using machine learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bond%2C%20P%2ES%2E"><span class="au">P. S. Bond</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wilson%2C%20K%2ES%2E"><span class="au">K. S. Wilson</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cowtan%2C%20K%2ED%2E"><span class="au">K. D. Cowtan</span></a></div> <div class="ica_synopsis">Two neural networks were trained to predict the correctness of protein residues by combining multiple validation metrics in <span class="it"><i>Coot</i></span>. Using the predicted correctness to automatically prune models led to significant improvements in the <span class="it"><i>Buccaneer</i></span> pipeline.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2020/08/00/qj5003/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2021). </span>D<b><a href="/d/services/archive.html" style="">77</a></b>, <span><a href="/d/issues/2021/01/00/ba5309/index.html" style="">1-10</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798320014746" style="">https://doi.org/10.1107/S2059798320014746</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ba5309cr" class="addthis_button_compact" href="#" addthis:description="Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies." addthis:title="Phasertng: directed acyclic graphs for crystallographic phasing" addthis:url="https://doi.org/10.1107/S2059798320014746"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ba5309pr').css('display','none')" /> </a> </div> <div id="ba5309pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ba5309cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ba5309" title="Statistics"> <img id="ba5309_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ba5309_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ba5309">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ba5309cl" class="addthis_button_compact" href="#" addthis:description="Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies." addthis:title="Phasertng: directed acyclic graphs for crystallographic phasing" addthis:url="https://doi.org/10.1107/S2059798320014746"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ba5309pl').css('display','none')" /> </a> </div> <div id="ba5309pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ba5309cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2021/01/00/ba5309/index.html"><img src="/d/issues/2021/01/00/ba5309/ba5309thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2021/01/00/ba5309/index.html"><span class="it"><i>Phasertng</i></span>: directed acyclic graphs for crystallographic phasing</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McCoy%2C%20A%2EJ%2E"><span class="au">A. J. McCoy</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Stockwell%2C%20D%2EH%2E"><span class="au">D. H. Stockwell</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sammito%2C%20M%2ED%2E"><span class="au">M. D. Sammito</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Oeffner%2C%20R%2ED%2E"><span class="au">R. D. Oeffner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hatti%2C%20K%2ES%2E"><span class="au">K. S. Hatti</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Croll%2C%20T%2EI%2E"><span class="au">T. I. Croll</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Read%2C%20R%2EJ%2E"><span class="au">R. J. Read</span></a></div> <div class="ica_synopsis">The employment of directed acyclic graphs to advance the tracking, control and appraisal of crystallographic phasing strategies is discussed.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2021/01/00/ba5309/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2021). </span>D<b><a href="/d/services/archive.html" style="">77</a></b>, <span><a href="/d/issues/2021/08/00/rr5212/index.html" style="">982-991</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798321007531" style="">https://doi.org/10.1107/S2059798321007531</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5212cr" class="addthis_button_compact" href="#" addthis:description="The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed." addthis:title="Protein structure prediction by AlphaFold2: are attention and symmetries all you need?" addthis:url="https://doi.org/10.1107/S2059798321007531"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rr5212pr').css('display','none')" /> </a> </div> <div id="rr5212pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5212cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rr5212" title="Statistics"> <img id="rr5212_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rr5212_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rr5212">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5212cl" class="addthis_button_compact" href="#" addthis:description="The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed." addthis:title="Protein structure prediction by AlphaFold2: are attention and symmetries all you need?" addthis:url="https://doi.org/10.1107/S2059798321007531"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#rr5212pl').css('display','none')" /> </a> </div> <div id="rr5212pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5212cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2021/08/00/rr5212/index.html"><img src="/d/issues/2021/08/00/rr5212/rr5212thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2021/08/00/rr5212/index.html">Protein structure prediction by <span class="it"><i>AlphaFold</i></span>2: are attention and symmetries all you need?</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bouatta%2C%20N%2E"><span class="au">N. Bouatta</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sorger%2C%20P%2E"><span class="au">P. Sorger</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=AlQuraishi%2C%20M%2E"><span class="au">M. AlQuraishi</span></a></div> <div class="ica_synopsis">This review discusses the <span class="it"><i>AlphaFold</i></span>2 system for protein structure prediction, including its conceptual and methodological advances, its amenability to interpretation and its achievements in the last Critical Assessment of protein Structure Prediction (CASP14) experiment.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2021/08/00/rr5212/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2021). </span>D<b><a href="/d/services/archive.html" style="">77</a></b>, <span><a href="/d/issues/2021/12/00/rr5209/index.html" style="">1564-1578</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798321010949" style="">https://doi.org/10.1107/S2059798321010949</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5209cr" class="addthis_button_compact" href="#" addthis:description="[beta]-Galactosidases catalyse the hydrolysis of lactose into galactose and glucose; as an alternative reaction, some [beta]-galactosidases also catalyse the formation of galactooligosaccharides by transglycosylation. Both reactions have industrial importance: lactose hydrolysis is used to produce lactose-free milk, while galactooligosaccharides have been shown to act as prebiotics. For some multi-domain [beta]-galactosidases, the hydrolysis/transglycosylation ratio can be modified by the truncation of carbohydrate-binding modules. Here, an analysis of BbgIII, a multidomain [beta]-galactosidase from Bifidobacterium bifidum, is presented. The X-ray structure has been determined of an intact protein corresponding to a gene construct of eight domains. The use of evolutionary covariance-based predictions made sequence docking in low-resolution areas of the model spectacularly easy, confirming the relevance of this rapidly developing deep-learning-based technique for model building. The structure revealed two alternative orientations of the CBM32 carbohydrate-binding module relative to the GH2 catalytic domain in the six crystallographically independent chains. In one orientation the CBM32 domain covers the entrance to the active site of the enzyme, while in the other orientation the active site is open, suggesting a possible mechanism for switching between the two activities of the enzyme, namely lactose hydrolysis and transgalactosylation. The location of the carbohydrate-binding site of the CBM32 domain on the opposite site of the module to where it comes into contact with the catalytic GH2 domain is consistent with its involvement in adherence to host cells. The role of the CBM32 domain in switching between hydrolysis and transglycosylation modes offers protein-engineering opportunities for selective [beta]-galactosidase modification for industrial purposes in the future." addthis:title="Multitasking in the gut: the X-ray structure of the multidomain BbgIII from Bifidobacterium bifidum offers possible explanations for its alternative functions" addthis:url="https://doi.org/10.1107/S2059798321010949"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rr5209pr').css('display','none')" /> </a> </div> <div id="rr5209pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5209cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rr5209" title="Statistics"> <img id="rr5209_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 92px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rr5209_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rr5209">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rr5209cl" class="addthis_button_compact" href="#" addthis:description="[beta]-Galactosidases catalyse the hydrolysis of lactose into galactose and glucose; as an alternative reaction, some [beta]-galactosidases also catalyse the formation of galactooligosaccharides by transglycosylation. Both reactions have industrial importance: lactose hydrolysis is used to produce lactose-free milk, while galactooligosaccharides have been shown to act as prebiotics. For some multi-domain [beta]-galactosidases, the hydrolysis/transglycosylation ratio can be modified by the truncation of carbohydrate-binding modules. Here, an analysis of BbgIII, a multidomain [beta]-galactosidase from Bifidobacterium bifidum, is presented. The X-ray structure has been determined of an intact protein corresponding to a gene construct of eight domains. The use of evolutionary covariance-based predictions made sequence docking in low-resolution areas of the model spectacularly easy, confirming the relevance of this rapidly developing deep-learning-based technique for model building. The structure revealed two alternative orientations of the CBM32 carbohydrate-binding module relative to the GH2 catalytic domain in the six crystallographically independent chains. In one orientation the CBM32 domain covers the entrance to the active site of the enzyme, while in the other orientation the active site is open, suggesting a possible mechanism for switching between the two activities of the enzyme, namely lactose hydrolysis and transgalactosylation. The location of the carbohydrate-binding site of the CBM32 domain on the opposite site of the module to where it comes into contact with the catalytic GH2 domain is consistent with its involvement in adherence to host cells. The role of the CBM32 domain in switching between hydrolysis and transglycosylation modes offers protein-engineering opportunities for selective [beta]-galactosidase modification for industrial purposes in the future." addthis:title="Multitasking in the gut: the X-ray structure of the multidomain BbgIII from Bifidobacterium bifidum offers possible explanations for its alternative functions" addthis:url="https://doi.org/10.1107/S2059798321010949"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 115px; width: 20px;" onclick="$('#rr5209pl').css('display','none')" /> </a> </div> <div id="rr5209pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rr5209cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2021/12/00/rr5209/index.html"><img src="/d/issues/2021/12/00/rr5209/rr5209thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2021/12/00/rr5209/index.html">Multitasking in the gut: the X-ray structure of the multidomain BbgIII from <span class="it"><i>Bifidobacterium bifidum</i></span> offers possible explanations for its alternative functions</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Moroz%2C%20O%2EV%2E"><span class="au">O. V. Moroz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Blagova%2C%20E%2E"><span class="au">E. Blagova</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lebedev%2C%20A%2EA%2E"><span class="au">A. A. Lebedev</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sanchez%20Rodriguez%2C%20F%2E"><span class="au">F. Sánchez Rodríguez</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Rigden%2C%20D%2EJ%2E"><span class="au">D. J. Rigden</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tams%2C%20J%2EW%2E"><span class="au">J. W. Tams</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wilting%2C%20R%2E"><span class="au">R. Wilting</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Vester%2C%20J%2EK%2E"><span class="au">J. K. Vester</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Longhin%2C%20E%2E"><span class="au">E. Longhin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hansen%2C%20G%2EH%2E"><span class="au">G. H. Hansen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Krogh%2C%20K%2EB%2ER%2EM%2E"><span class="au">K. B. R. M. Krogh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pache%2C%20R%2EA%2E"><span class="au">R. A. Pache</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Davies%2C%20G%2EJ%2E"><span class="au">G. J. Davies</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wilson%2C%20K%2ES%2E"><span class="au">K. S. Wilson</span></a></div> <div class="ica_synopsis">The X-ray structure of BbgIII, a multidomain β-galactosidase from <span class="it"><i>B. bifidum</i></span>, determined using an intact protein corresponding to a gene construct of eight domains is reported.</div><div class="ica_codelinks" style=""> <div class="ica_codelinks_pdb"><span class="ica_codelinks_head" style="">PDB reference</span>: <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=7nit">BbgIII from <span class="it"><i>Bifidobacterium bifidum</i></span>, 7nit</a> </div> </div> <div class="ica_readmore"> <a href="/d/issues/2021/12/00/rr5209/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2022). </span>D<b><a href="/d/services/archive.html" style="">78</a></b>, <span><a href="/d/issues/2022/01/00/qg5003/index.html" style="">1-13</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798321012122" style="">https://doi.org/10.1107/S2059798321012122</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qg5003cr" class="addthis_button_compact" href="#" addthis:description="The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models." addthis:title="Implications of AlphaFold2 for crystallographic phasing by molecular replacement" addthis:url="https://doi.org/10.1107/S2059798321012122"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#qg5003pr').css('display','none')" /> </a> </div> <div id="qg5003pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qg5003cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?qg5003" title="Statistics"> <img id="qg5003_divwide" alt="" class="art_icon downloads" style="top: 0px; 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Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models." addthis:title="Implications of AlphaFold2 for crystallographic phasing by molecular replacement" addthis:url="https://doi.org/10.1107/S2059798321012122"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#qg5003pl').css('display','none')" /> </a> </div> <div id="qg5003pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qg5003cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2022/01/00/qg5003/index.html"><img src="/d/issues/2022/01/00/qg5003/qg5003thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2022/01/00/qg5003/index.html">Implications of <span class="it"><i>AlphaFold</i></span>2 for crystallographic phasing by molecular replacement</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McCoy%2C%20A%2EJ%2E"><span class="au">A. J. McCoy</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sammito%2C%20M%2ED%2E"><span class="au">M. D. Sammito</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Read%2C%20R%2EJ%2E"><span class="au">R. J. Read</span></a></div> <div class="ica_synopsis">The implications of the <span class="it"><i>AlphaFold</i></span>2 protein structure-modelling software for crystallographic phasing strategies are discussed.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2022/01/00/qg5003/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2022). </span>D<b><a href="/d/services/archive.html" style="">78</a></b>, <span><a href="/d/issues/2022/02/00/gm5087/index.html" style="">187-195</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S205979832101202X" style="">https://doi.org/10.1107/S205979832101202X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="gm5087cr" class="addthis_button_compact" href="#" addthis:description="Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software." addthis:title="Detecting ice artefacts in processed macromolecular diffraction data with machine learning" addthis:url="https://doi.org/10.1107/S205979832101202X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#gm5087pr').css('display','none')" /> </a> </div> <div id="gm5087pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gm5087cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?gm5087" title="Statistics"> <img id="gm5087_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="gm5087_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?gm5087">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="gm5087cl" class="addthis_button_compact" href="#" addthis:description="Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software." addthis:title="Detecting ice artefacts in processed macromolecular diffraction data with machine learning" addthis:url="https://doi.org/10.1107/S205979832101202X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#gm5087pl').css('display','none')" /> </a> </div> <div id="gm5087pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gm5087cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2022/02/00/gm5087/index.html"><img src="/d/issues/2022/02/00/gm5087/gm5087thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2022/02/00/gm5087/index.html">Detecting ice artefacts in processed macromolecular diffraction data with machine learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nolte%2C%20K%2E"><span class="au">K. Nolte</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gao%2C%20Y%2E"><span class="au">Y. Gao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Stab%2C%20S%2E"><span class="au">S. Stäb</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kollmannsberger%2C%20P%2E"><span class="au">P. Kollmannsberger</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Thorn%2C%20A%2E"><span class="au">A. Thorn</span></a></div> <div class="ica_synopsis">A program utilizing artificial learning and convolutional neural networks, named <span class="it"><i>Helcaraxe</i></span>, has been developed which can detect ice-crystal artefacts in processed macromolecular diffraction data with unprecedented accuracy.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2022/02/00/gm5087/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2022). </span>D<b><a href="/d/services/archive.html" style="">78</a></b>, <span><a href="/d/issues/2022/04/00/jc5047/index.html" style="">517-531</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798322002157" style="">https://doi.org/10.1107/S2059798322002157</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="jc5047cr" class="addthis_button_compact" href="#" addthis:description="The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 A resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement." addthis:title="The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report" addthis:url="https://doi.org/10.1107/S2059798322002157"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#jc5047pr').css('display','none')" /> </a> </div> <div id="jc5047pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#jc5047cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?jc5047" title="Statistics"> <img id="jc5047_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="jc5047_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?jc5047">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?jc5047" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 A resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement." addthis:title="The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report" addthis:url="https://doi.org/10.1107/S2059798322002157"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 115px; width: 20px;" onclick="$('#jc5047pl').css('display','none')" /> </a> </div> <div id="jc5047pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#jc5047cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2022/04/00/jc5047/index.html"><img src="/d/issues/2022/04/00/jc5047/jc5047thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2022/04/00/jc5047/index.html">The X-ray crystallography phase problem solved thanks to <span class="it"><i>AlphaFold</i></span> and <span class="it"><i>RoseTTAFold</i></span> models: a case-study report</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Barbarin%2DBocahu%2C%20I%2E"><span class="au">I. Barbarin-Bocahu</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Graille%2C%20M%2E"><span class="au">M. Graille</span></a></div> <div class="ica_synopsis">The new artificial intelligence-based protein structure modeling programs such as <span class="it"><i>AlphaFold</i></span> and <span class="it"><i>RoseTTAFold</i></span> have raised great enthusiasm in the scientific community. Here, it is shown that the excellent overall quality of these models can solve the phase problem faced by structural biology using X-ray diffraction. This study also validates these <span class="it"><i>in silico</i></span> models.</div><div class="ica_codelinks" style=""> <div class="ica_codelinks_pdb"><span class="ica_codelinks_head" style="">PDB reference</span>: <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=7qhy"><span class="it"><i>Kluyveromyces lactis</i></span> Nmd4, 7qhy</a> </div> </div> <div class="ica_readmore"> <a href="/d/issues/2022/04/00/jc5047/index.html">Read article</a><a href="https://scripts.iucr.org/cgi-bin/paper?cnor=jc9047">Correction</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2022). </span>D<b><a href="/d/services/archive.html" style="">78</a></b>, <span><a href="/d/issues/2022/12/00/qo5002/index.html" style="">1412-1427</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798322010415" style="">https://doi.org/10.1107/S2059798322010415</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qo5002cr" class="addthis_button_compact" href="#" addthis:description="Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org)." addthis:title="Using deep-learning predictions of inter-residue distances for model validation" addthis:url="https://doi.org/10.1107/S2059798322010415"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#qo5002pr').css('display','none')" /> </a> </div> <div id="qo5002pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qo5002cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?qo5002" title="Statistics"> <img id="qo5002_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="qo5002_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?qo5002">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?qo5002" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="qo5002_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?qo5002">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qo5002cl" class="addthis_button_compact" href="#" addthis:description="Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org)." addthis:title="Using deep-learning predictions of inter-residue distances for model validation" addthis:url="https://doi.org/10.1107/S2059798322010415"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#qo5002pl').css('display','none')" /> </a> </div> <div id="qo5002pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qo5002cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2022/12/00/qo5002/index.html"><img src="/d/issues/2022/12/00/qo5002/qo5002thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2022/12/00/qo5002/index.html">Using deep-learning predictions of inter-residue distances for model validation</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sanchez%20Rodriguez%2C%20F%2E"><span class="au">F. Sánchez Rodríguez</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chojnowski%2C%20G%2E"><span class="au">G. Chojnowski</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Keegan%2C%20R%2EM%2E"><span class="au">R. M. Keegan</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Rigden%2C%20D%2EJ%2E"><span class="au">D. J. Rigden</span></a></div> <div class="ica_synopsis">The use of <span class="it"><i>AlphaFold</i></span>2 predictions for the detection and correction of sequence-register errors among protein structures determined using cryo-EM deposited in the Protein Data Bank is described.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2022/12/00/qo5002/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>D<b><a href="/d/services/archive.html" style="">79</a></b>, <span><a href="/d/issues/2023/01/00/ji5028/index.html" style="">10-21</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798322011676" style="">https://doi.org/10.1107/S2059798322011676</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ji5028cr" class="addthis_button_compact" href="#" addthis:description="As more protein structure models have been determined from cryogenic electron microscopy (cryo-EM) density maps, establishing how to evaluate the model accuracy and how to correct models in cases where they contain errors is becoming crucial to ensure the quality of the structural models deposited in the public database, the PDB. Here, a new protocol is presented for evaluating a protein model built from a cryo-EM map and applying local structure refinement in the case where the model has potential errors. Firstly, model evaluation is performed using a deep-learning-based model-local map assessment score, DAQ, that has recently been developed. The subsequent local refinement is performed by a modified AlphaFold2 procedure, in which a trimmed template model and a trimmed multiple sequence alignment are provided as input to control which structure regions to refine while leaving other more confident regions of the model intact. A benchmark study showed that this protocol, DAQ-refine, consistently improves low-quality regions of the initial models. Among 18 refined models generated for an initial structure, DAQ shows a high correlation with model quality and can identify the best accurate model for most of the tested cases. The improvements obtained by DAQ-refine were on average larger than other existing methods." addthis:title="Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score" addthis:url="https://doi.org/10.1107/S2059798322011676"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ji5028pr').css('display','none')" /> </a> </div> <div id="ji5028pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ji5028cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ji5028" title="Statistics"> <img id="ji5028_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ji5028_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ji5028">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?ji5028" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ji5028_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ji5028">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ji5028cl" class="addthis_button_compact" href="#" addthis:description="As more protein structure models have been determined from cryogenic electron microscopy (cryo-EM) density maps, establishing how to evaluate the model accuracy and how to correct models in cases where they contain errors is becoming crucial to ensure the quality of the structural models deposited in the public database, the PDB. Here, a new protocol is presented for evaluating a protein model built from a cryo-EM map and applying local structure refinement in the case where the model has potential errors. Firstly, model evaluation is performed using a deep-learning-based model-local map assessment score, DAQ, that has recently been developed. The subsequent local refinement is performed by a modified AlphaFold2 procedure, in which a trimmed template model and a trimmed multiple sequence alignment are provided as input to control which structure regions to refine while leaving other more confident regions of the model intact. A benchmark study showed that this protocol, DAQ-refine, consistently improves low-quality regions of the initial models. Among 18 refined models generated for an initial structure, DAQ shows a high correlation with model quality and can identify the best accurate model for most of the tested cases. The improvements obtained by DAQ-refine were on average larger than other existing methods." addthis:title="Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score" addthis:url="https://doi.org/10.1107/S2059798322011676"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ji5028pl').css('display','none')" /> </a> </div> <div id="ji5028pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ji5028cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2023/01/00/ji5028/index.html"><img src="/d/issues/2023/01/00/ji5028/ji5028thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2023/01/00/ji5028/index.html">Protein model refinement for cryo-EM maps using <span class="it"><i>AlphaFold</i></span>2 and the DAQ score</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Terashi%2C%20G%2E"><span class="au">G. Terashi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20X%2E"><span class="au">X. Wang</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kihara%2C%20D%2E"><span class="au">D. Kihara</span></a></div> <div class="ica_synopsis">A new protocol, <span class="it"><i>DAQ-refine</i></span>, for evaluating a protein model built from a cryo-EM map and applying local structure refinement is described.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2023/01/00/ji5028/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>D<b><a href="/d/services/archive.html" style="">79</a></b>, <span><a href="/d/issues/2023/03/00/nz5011/index.html" style="">234-244</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S205979832300102X" style="">https://doi.org/10.1107/S205979832300102X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="nz5011cr" class="addthis_button_compact" href="#" addthis:description="Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C[alpha] atoms matching those in the deposited models within 2 A. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested." addthis:title="Accelerating crystal structure determination with iterative AlphaFold prediction" addthis:url="https://doi.org/10.1107/S205979832300102X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#nz5011pr').css('display','none')" /> </a> </div> <div id="nz5011pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#nz5011cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?nz5011" title="Statistics"> <img id="nz5011_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="nz5011_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?nz5011">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="nz5011cl" class="addthis_button_compact" href="#" addthis:description="Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C[alpha] atoms matching those in the deposited models within 2 A. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested." addthis:title="Accelerating crystal structure determination with iterative AlphaFold prediction" addthis:url="https://doi.org/10.1107/S205979832300102X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#nz5011pl').css('display','none')" /> </a> </div> <div id="nz5011pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#nz5011cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2023/03/00/nz5011/index.html"><img src="/d/issues/2023/03/00/nz5011/nz5011thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2023/03/00/nz5011/index.html">Accelerating crystal structure determination with iterative <span class="it"><i>AlphaFold</i></span> prediction</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Terwilliger%2C%20T%2EC%2E"><span class="au">T. C. Terwilliger</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Afonine%2C%20P%2EV%2E"><span class="au">P. V. Afonine</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liebschner%2C%20D%2E"><span class="au">D. Liebschner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Croll%2C%20T%2EI%2E"><span class="au">T. I. Croll</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McCoy%2C%20A%2EJ%2E"><span class="au">A. J. McCoy</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Oeffner%2C%20R%2ED%2E"><span class="au">R. D. Oeffner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Williams%2C%20C%2EJ%2E"><span class="au">C. J. Williams</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Poon%2C%20B%2EK%2E"><span class="au">B. K. Poon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Richardson%2C%20J%2ES%2E"><span class="au">J. S. Richardson</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Read%2C%20R%2EJ%2E"><span class="au">R. J. Read</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Adams%2C%20P%2ED%2E"><span class="au">P. D. Adams</span></a></div> <div class="ica_synopsis"><span class="it"><i>AlphaFold</i></span> predictions can be used both as a starting point for structure determination and as a method of model optimization. The <span class="it"><i>Phenix</i></span> <span class="it"><i>PredictAndBuild</i></span> tool automates iterative prediction and model building, yielding a density map and model starting with sequence information and crystallographic data.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2023/03/00/nz5011/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>D<b><a href="/d/services/archive.html" style="">79</a></b>, <span><a href="/d/issues/2023/04/00/di5061/index.html" style="">326-338</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S205979832300181X" style="">https://doi.org/10.1107/S205979832300181X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="di5061cr" class="addthis_button_compact" href="#" addthis:description="Tracing the backbone is a critical step in protein model building, as incorrect tracing leads to poor protein models. Here, a neural network trained to identify unfavourable fragments and remove them from the model-building process in order to improve backbone tracing is presented. Moreover, a decision tree was trained to select an optimal threshold to eliminate unfavourable fragments. The neural network was tested on experimental phasing data sets from the Joint Center for Structural Genomics (JCSG), recently deposited experimental phasing data sets (from 2015 to 2021) and molecular-replacement data sets. The experimental results show that using the neural network in the Buccaneer protein-model-building software can produce significantly more complete protein models than those built using Buccaneer alone. In particular, Buccaneer with the neural network built protein models with a completeness that was at least 5% higher for 25% and 50% of the original and truncated resolution JCSG experimental phasing data sets, respectively, for 28% of the recently collected experimental phasing data sets and for 43% of the molecular-replacement data sets." addthis:title="Buccaneer model building with neural network fragment selection" addthis:url="https://doi.org/10.1107/S205979832300181X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#di5061pr').css('display','none')" /> </a> </div> <div id="di5061pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#di5061cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?di5061" title="Statistics"> <img id="di5061_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="di5061_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?di5061">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="di5061cl" class="addthis_button_compact" href="#" addthis:description="Tracing the backbone is a critical step in protein model building, as incorrect tracing leads to poor protein models. Here, a neural network trained to identify unfavourable fragments and remove them from the model-building process in order to improve backbone tracing is presented. Moreover, a decision tree was trained to select an optimal threshold to eliminate unfavourable fragments. The neural network was tested on experimental phasing data sets from the Joint Center for Structural Genomics (JCSG), recently deposited experimental phasing data sets (from 2015 to 2021) and molecular-replacement data sets. The experimental results show that using the neural network in the Buccaneer protein-model-building software can produce significantly more complete protein models than those built using Buccaneer alone. In particular, Buccaneer with the neural network built protein models with a completeness that was at least 5% higher for 25% and 50% of the original and truncated resolution JCSG experimental phasing data sets, respectively, for 28% of the recently collected experimental phasing data sets and for 43% of the molecular-replacement data sets." addthis:title="Buccaneer model building with neural network fragment selection" addthis:url="https://doi.org/10.1107/S205979832300181X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#di5061pl').css('display','none')" /> </a> </div> <div id="di5061pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#di5061cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2023/04/00/di5061/index.html"><img src="/d/issues/2023/04/00/di5061/di5061thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2023/04/00/di5061/index.html"><span class="it"><i>Buccaneer</i></span> model building with neural network fragment selection</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Alharbi%2C%20E%2E"><span class="au">E. Alharbi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Calinescu%2C%20R%2E"><span class="au">R. Calinescu</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cowtan%2C%20K%2E"><span class="au">K. Cowtan</span></a></div> <div class="ica_synopsis">A neural network trained to identify unfavourable fragments and therefore improve protein model building in the <span class="it"><i>Buccaneer</i></span> software is described.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2023/04/00/di5061/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2023). </span>D<b><a href="/d/services/archive.html" style="">79</a></b>, <span><a href="/d/issues/2023/09/00/qi5002/index.html" style="">796-805</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798323005776" style="">https://doi.org/10.1107/S2059798323005776</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qi5002cr" class="addthis_button_compact" href="#" addthis:description="X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods." addthis:title="Correcting systematic errors in diffraction data with modern scaling algorithms" addthis:url="https://doi.org/10.1107/S2059798323005776"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#qi5002pr').css('display','none')" /> </a> </div> <div id="qi5002pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qi5002cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?qi5002" title="Statistics"> <img id="qi5002_divwide" alt="" class="art_icon downloads" style="top: 0px; 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Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods." addthis:title="Correcting systematic errors in diffraction data with modern scaling algorithms" addthis:url="https://doi.org/10.1107/S2059798323005776"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#qi5002pl').css('display','none')" /> </a> </div> <div id="qi5002pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qi5002cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2023/09/00/qi5002/index.html"><img src="/d/issues/2023/09/00/qi5002/qi5002thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2023/09/00/qi5002/index.html">Correcting systematic errors in diffraction data with modern scaling algorithms</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Aldama%2C%20L%2EA%2E"><span class="au">L. A. Aldama</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Dalton%2C%20K%2EM%2E"><span class="au">K. M. Dalton</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hekstra%2C%20D%2ER%2E"><span class="au">D. R. Hekstra</span></a></div> <div class="ica_synopsis">Emerging algorithms based on machine learning offer promise in processing new diffraction experiments.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2023/09/00/qi5002/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">Acta Cryst.</span></span><span style="color:#000;"> (2024). </span>D<b><a href="/d/services/archive.html" style="">80</a></b>, <span><a href="/d/issues/2024/01/00/qi5003/index.html" style="">26-43</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2059798323010586" style="">https://doi.org/10.1107/S2059798323010586</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qi5003cr" class="addthis_button_compact" href="#" addthis:description="The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis." addthis:title="Deep residual networks for crystallography trained on synthetic data" addthis:url="https://doi.org/10.1107/S2059798323010586"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#qi5003pr').css('display','none')" /> </a> </div> <div id="qi5003pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qi5003cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?qi5003" title="Statistics"> <img id="qi5003_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="qi5003_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?qi5003">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="qi5003cl" class="addthis_button_compact" href="#" addthis:description="The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis." addthis:title="Deep residual networks for crystallography trained on synthetic data" addthis:url="https://doi.org/10.1107/S2059798323010586"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#qi5003pl').css('display','none')" /> </a> </div> <div id="qi5003pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#qi5003cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/d/issues/2024/01/00/qi5003/index.html"><img src="/d/issues/2024/01/00/qi5003/qi5003thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/d/issues/2024/01/00/qi5003/index.html">Deep residual networks for crystallography trained on synthetic data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mendez%2C%20D%2E"><span class="au">D. Mendez</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Holton%2C%20J%2EM%2E"><span class="au">J. M. Holton</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lyubimov%2C%20A%2EY%2E"><span class="au">A. Y. Lyubimov</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hollatz%2C%20S%2E"><span class="au">S. Hollatz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mathews%2C%20I%2EI%2E"><span class="au">I. I. Mathews</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cichosz%2C%20A%2E"><span class="au">A. Cichosz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Martirosyan%2C%20V%2E"><span class="au">V. Martirosyan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zeng%2C%20T%2E"><span class="au">T. Zeng</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Stofer%2C%20R%2E"><span class="au">R. Stofer</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20R%2E"><span class="au">R. Liu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Song%2C%20J%2E"><span class="au">J. Song</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McPhillips%2C%20S%2E"><span class="au">S. McPhillips</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Soltis%2C%20M%2E"><span class="au">M. Soltis</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cohen%2C%20A%2EE%2E"><span class="au">A. E. Cohen</span></a></div> <div class="ica_synopsis">Artificial intelligence was used to characterize the diffraction in images from serial and rotation crystallography experiments. Forward simulations were used to train models to infer <span class="it"><i>B</i></span> factors, resolutions and the presence of crystal splitting from single diffraction images.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/d/issues/2024/01/00/qi5003/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2003). </span><b><a href="/s/services/archive.html" style="">10</a></b>, <span><a href="/s/issues/2003/06/00/he0321/index.html" style="">445-449</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S0909049503020855" style="">https://doi.org/10.1107/S0909049503020855</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="he0321cr" class="addthis_button_compact" href="#" addthis:description="High-throughput protein crystallography requires the automation of multiple steps used in the protein structure determination. One crucial step is to find and monitor the crystal quality on the basis of its diffraction pattern. It is often time-consuming to scan protein crystals when selecting a good candidate for exposure. The use of neural networks for this purpose is explored. A dynamic neural network algorithm to achieve a fast convergence and high-speed image recognition has been developed. On the test set a 96% success rate in identifying properly the quality of the crystal has been achieved." addthis:title="Application of a neural network in high-throughput protein crystallography" addthis:url="https://doi.org/10.1107/S0909049503020855"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#he0321pr').css('display','none')" /> </a> </div> <div id="he0321pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#he0321cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?he0321" title="Statistics"> <img id="he0321_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="he0321_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?he0321">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?he0321" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="he0321_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?he0321">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="he0321cl" class="addthis_button_compact" href="#" addthis:description="High-throughput protein crystallography requires the automation of multiple steps used in the protein structure determination. One crucial step is to find and monitor the crystal quality on the basis of its diffraction pattern. It is often time-consuming to scan protein crystals when selecting a good candidate for exposure. The use of neural networks for this purpose is explored. A dynamic neural network algorithm to achieve a fast convergence and high-speed image recognition has been developed. On the test set a 96% success rate in identifying properly the quality of the crystal has been achieved." addthis:title="Application of a neural network in high-throughput protein crystallography" addthis:url="https://doi.org/10.1107/S0909049503020855"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#he0321pl').css('display','none')" /> </a> </div> <div id="he0321pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#he0321cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2003/06/00/he0321/index.html"><img src="/s/issues/2003/06/00/he0321/he0321thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2003/06/00/he0321/index.html">Application of a neural network in high-throughput protein crystallography</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Berntson%2C%20A%2E"><span class="au">A. Berntson</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Stojanoff%2C%20V%2E"><span class="au">V. Stojanoff</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Takai%2C%20H%2E"><span class="au">H. Takai</span></a></div> <div class="ica_synopsis">Protein crystal quality evaluation using cascade correlation neural networks.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2003/06/00/he0321/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2010). </span><b><a href="/s/services/archive.html" style="">17</a></b>, <span><a href="/s/issues/2010/01/00/vv5003/index.html" style="">86-92</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S0909049509042824" style="">https://doi.org/10.1107/S0909049509042824</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vv5003cr" class="addthis_button_compact" href="#" addthis:description="The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on-site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time-consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis." addthis:title="Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data" addthis:url="https://doi.org/10.1107/S0909049509042824"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#vv5003pr').css('display','none')" /> </a> </div> <div id="vv5003pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vv5003cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?vv5003" title="Statistics"> <img id="vv5003_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vv5003_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vv5003">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vv5003cl" class="addthis_button_compact" href="#" addthis:description="The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on-site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time-consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis." addthis:title="Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data" addthis:url="https://doi.org/10.1107/S0909049509042824"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#vv5003pl').css('display','none')" /> </a> </div> <div id="vv5003pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vv5003cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2010/01/00/vv5003/index.html"><img src="/s/issues/2010/01/00/vv5003/vv5003thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2010/01/00/vv5003/index.html">Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Planckaert%2C%20N%2E"><span class="au">N. Planckaert</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Demeulemeester%2C%20J%2E"><span class="au">J. Demeulemeester</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Laenens%2C%20B%2E"><span class="au">B. Laenens</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Smeets%2C%20D%2E"><span class="au">D. Smeets</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Meersschaut%2C%20J%2E"><span class="au">J. Meersschaut</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=L%27abbe%2C%20C%2E"><span class="au">C. L'abbé</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Temst%2C%20K%2E"><span class="au">K. Temst</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Vantomme%2C%20A%2E"><span class="au">A. Vantomme</span></a></div> <div class="ica_synopsis">The capabilities of artificial neural networks for the automatic and instantaneous analysis of nuclear resonant scattering spectra obtained at a synchrotron source are discussed.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2010/01/00/vv5003/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2017). </span><b><a href="/s/services/archive.html" style="">24</a></b>, <span><a href="/s/issues/2017/02/00/vv5155/index.html" style="">469-475</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577516020117" style="">https://doi.org/10.1107/S1600577516020117</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vv5155cr" class="addthis_button_compact" href="#" addthis:description="This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided." addthis:title="A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography" addthis:url="https://doi.org/10.1107/S1600577516020117"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#vv5155pr').css('display','none')" /> </a> </div> <div id="vv5155pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vv5155cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?vv5155" title="Statistics"> <img id="vv5155_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vv5155_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vv5155">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?vv5155" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vv5155_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vv5155">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vv5155cl" class="addthis_button_compact" href="#" addthis:description="This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided." addthis:title="A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography" addthis:url="https://doi.org/10.1107/S1600577516020117"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#vv5155pl').css('display','none')" /> </a> </div> <div id="vv5155pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vv5155cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2017/02/00/vv5155/index.html"><img src="/s/issues/2017/02/00/vv5155/vv5155thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2017/02/00/vv5155/index.html">A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20X%2E"><span class="au">X. Yang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=De%20Carlo%2C%20F%2E"><span class="au">F. De Carlo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Phatak%2C%20C%2E"><span class="au">C. Phatak</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gursoy%2C%20D%2E"><span class="au">D. Gürsoy</span></a></div> <div class="ica_synopsis">Use of convolutional neural networks for automated calibration of rotation axes is described.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2017/02/00/vv5155/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2018). </span><b><a href="/s/services/archive.html" style="">25</a></b>, <span><a href="/s/issues/2018/03/00/fv5080/index.html" style="">655-670</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577518004873" style="">https://doi.org/10.1107/S1600577518004873</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fv5080cr" class="addthis_button_compact" href="#" addthis:description="A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization." addthis:title="A convolutional neural network-based screening tool for X-ray serial crystallography" addthis:url="https://doi.org/10.1107/S1600577518004873"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fv5080pr').css('display','none')" /> </a> </div> <div id="fv5080pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fv5080cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fv5080" title="Statistics"> <img id="fv5080_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fv5080_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fv5080">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fv5080cl" class="addthis_button_compact" href="#" addthis:description="A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization." addthis:title="A convolutional neural network-based screening tool for X-ray serial crystallography" addthis:url="https://doi.org/10.1107/S1600577518004873"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#fv5080pl').css('display','none')" /> </a> </div> <div id="fv5080pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fv5080cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2018/03/00/fv5080/index.html"><img src="/s/issues/2018/03/00/fv5080/fv5080thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2018/03/00/fv5080/index.html">A convolutional neural network-based screening tool for X-ray serial crystallography</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ke%2C%20T%2E%2DW%2E"><span class="au">T.-W. Ke</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Brewster%2C%20A%2ES%2E"><span class="au">A. S. Brewster</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yu%2C%20S%2EX%2E"><span class="au">S. X. Yu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ushizima%2C%20D%2E"><span class="au">D. Ushizima</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20C%2E"><span class="au">C. Yang</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sauter%2C%20N%2EK%2E"><span class="au">N. K. Sauter</span></a></div> <div class="ica_synopsis">Deep learning provides one possible avenue to reduce the data stream generated by serial macromolecular X-ray crystallography. Convolutional neural networks can be trained to recognize the presence or absence of Bragg spots, forming a criterion to veto events prior to downstream data processing.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2018/03/00/fv5080/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2019). </span><b><a href="/s/services/archive.html" style="">26</a></b>, <span><a href="/s/issues/2019/03/00/yi5063/index.html" style="">839-853</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577519003448" style="">https://doi.org/10.1107/S1600577519003448</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yi5063cr" class="addthis_button_compact" href="#" addthis:description="X-ray computed tomography and, specifically, time-resolved volumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process. For this paper these highly sampled data are used to aid feature detection in the rapidly collected tomograms by assisting with the upsampling of their projections, which is equivalent to upscaling the [theta]-axis of the sinograms. In this paper, a super-resolution approach is proposed based on deep learning (termed an upscaling Deep Neural Network, or UDNN) that aims to upscale the sinogram space of individual tomograms in a 4D dataset of a sample. This is done using learned behaviour from a dataset containing a high number of projections, taken of the same sample and occurring at the beginning or the end of the data collection. The prior provided by the highly sampled tomogram allows the application of an upscaling process with better accuracy than existing interpolation techniques. This upscaling process subsequently permits an increase in the quality of the tomogram's reconstruction, especially in situations that require capture of only a limited number of projections, as is the case in high-frequency time-series capture. The increase in quality can prove very helpful for researchers, as downstream it enables easier segmentation of the tomograms in areas of interest, for example. The method itself comprises a convolutional neural network which through training learns an end-to-end mapping between sinograms with a low and a high number of projections. Since datasets can differ greatly between experiments, this approach specifically develops a lightweight network that can easily and quickly be retrained for different types of samples. As part of the evaluation of our technique, results with different hyperparameter settings are presented, and the method has been tested on both synthetic and real-world data. In addition, accompanying real-world experimental datasets have been released in the form of two 80 GB tomograms depicting a metallic pin that undergoes corruption from a droplet of salt water. Also a new engineering-based phantom dataset has been produced and released, inspired by the experimental datasets." addthis:title="A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram" addthis:url="https://doi.org/10.1107/S1600577519003448"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#yi5063pr').css('display','none')" /> </a> </div> <div id="yi5063pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yi5063cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?yi5063" title="Statistics"> <img id="yi5063_divwide" alt="" class="art_icon downloads" style="top: 0px; 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This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process. For this paper these highly sampled data are used to aid feature detection in the rapidly collected tomograms by assisting with the upsampling of their projections, which is equivalent to upscaling the [theta]-axis of the sinograms. In this paper, a super-resolution approach is proposed based on deep learning (termed an upscaling Deep Neural Network, or UDNN) that aims to upscale the sinogram space of individual tomograms in a 4D dataset of a sample. This is done using learned behaviour from a dataset containing a high number of projections, taken of the same sample and occurring at the beginning or the end of the data collection. The prior provided by the highly sampled tomogram allows the application of an upscaling process with better accuracy than existing interpolation techniques. This upscaling process subsequently permits an increase in the quality of the tomogram's reconstruction, especially in situations that require capture of only a limited number of projections, as is the case in high-frequency time-series capture. The increase in quality can prove very helpful for researchers, as downstream it enables easier segmentation of the tomograms in areas of interest, for example. The method itself comprises a convolutional neural network which through training learns an end-to-end mapping between sinograms with a low and a high number of projections. Since datasets can differ greatly between experiments, this approach specifically develops a lightweight network that can easily and quickly be retrained for different types of samples. As part of the evaluation of our technique, results with different hyperparameter settings are presented, and the method has been tested on both synthetic and real-world data. In addition, accompanying real-world experimental datasets have been released in the form of two 80 GB tomograms depicting a metallic pin that undergoes corruption from a droplet of salt water. Also a new engineering-based phantom dataset has been produced and released, inspired by the experimental datasets." addthis:title="A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram" addthis:url="https://doi.org/10.1107/S1600577519003448"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#yi5063pl').css('display','none')" /> </a> </div> <div id="yi5063pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yi5063cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2019/03/00/yi5063/index.html"><img src="/s/issues/2019/03/00/yi5063/yi5063thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2019/03/00/yi5063/index.html">A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bellos%2C%20D%2E"><span class="au">D. Bellos</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Basham%2C%20M%2E"><span class="au">M. Basham</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pridmore%2C%20T%2E"><span class="au">T. Pridmore</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=French%2C%20A%2EP%2E"><span class="au">A. P. French</span></a></div> <div class="ica_synopsis">A convolutional neural network has been designed to quickly and accurately upscale the sinograms of X-ray tomograms captured with a low number of projections, effectively increasing the number of projections. This is particularly useful for tomograms that are part of a time-series as, in order to capture fast-occurring temporal events, tomograms have to be collected quickly, requiring a low number of projections. The upscaling process is facilitated using a single tomogram with a high number of projections for training, which is usually captured at the end or the beginning of the time-series when capturing the tomogram quickly is no longer needed.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2019/03/00/yi5063/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2019). </span><b><a href="/s/services/archive.html" style="">26</a></b>, <span><a href="/s/issues/2019/04/00/xl5030/index.html" style="">1058-1068</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577519004648" style="">https://doi.org/10.1107/S1600577519004648</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="xl5030cr" class="addthis_button_compact" href="#" addthis:description="The optical design of a novel spectrometer is presented, combining a cylindrically convex pre-mirror with a cylindrically concave varied-line-spacing grating (both in the meridional) to deliver a resolving power of 100000-200000 in the `water window' (2-5 nm). Most remarkably, the extremely high spectral resolution is achieved for an effective meridional source size of 50 microm (r.m.s.); this property could potentially be applied to diagnose SASE-FEL and well resolve individual single spikes in its radiation spectrum. The overall optical aberrations of the system are well analysed and compensated, providing an excellent flat-field at the detector domain throughout the whole spectral range. Also, a machine-learning scheme - SVM - is introduced to explore and reconstruct the optimal system with high efficiency." addthis:title="Towards an extremely high resolution broad-band flat-field spectrometer in the 'water window'" addthis:url="https://doi.org/10.1107/S1600577519004648"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#xl5030pr').css('display','none')" /> </a> </div> <div id="xl5030pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#xl5030cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?xl5030" title="Statistics"> <img id="xl5030_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="xl5030_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?xl5030">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?xl5030" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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Most remarkably, the extremely high spectral resolution is achieved for an effective meridional source size of 50 microm (r.m.s.); this property could potentially be applied to diagnose SASE-FEL and well resolve individual single spikes in its radiation spectrum. The overall optical aberrations of the system are well analysed and compensated, providing an excellent flat-field at the detector domain throughout the whole spectral range. Also, a machine-learning scheme - SVM - is introduced to explore and reconstruct the optimal system with high efficiency." addthis:title="Towards an extremely high resolution broad-band flat-field spectrometer in the 'water window'" addthis:url="https://doi.org/10.1107/S1600577519004648"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#xl5030pl').css('display','none')" /> </a> </div> <div id="xl5030pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#xl5030cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2019/04/00/xl5030/index.html"><img src="/s/issues/2019/04/00/xl5030/xl5030thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2019/04/00/xl5030/index.html">Towards an extremely high resolution broad-band flat-field spectrometer in the `water window'</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Li%2C%20Z%2E"><span class="au">Z. Li</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Li%2C%20B%2E"><span class="au">B. Li</span></a></div> <div class="ica_synopsis">An original and novel design scheme has been formulated to achieve an extremely high resolving power for a broad-band X-ray spectrometer with a relatively large source size, implementing a meridional pre-convex mirror to enhance the resolving power substantially while maintaining minimal intrinsic optical aberrations for the whole system to cast a decent flat-field at the detector domain throughout the spectral range.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2019/04/00/xl5030/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2019). </span><b><a href="/s/services/archive.html" style="">26</a></b>, <span><a href="/s/issues/2019/04/00/rv5105/index.html" style="">1361-1366</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S160057751900434X" style="">https://doi.org/10.1107/S160057751900434X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rv5105cr" class="addthis_button_compact" href="#" addthis:description="High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography." addthis:title="DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography" addthis:url="https://doi.org/10.1107/S160057751900434X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rv5105pr').css('display','none')" /> </a> </div> <div id="rv5105pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rv5105cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rv5105" title="Statistics"> <img id="rv5105_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rv5105_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rv5105">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rv5105cl" class="addthis_button_compact" href="#" addthis:description="High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography." addthis:title="DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography" addthis:url="https://doi.org/10.1107/S160057751900434X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#rv5105pl').css('display','none')" /> </a> </div> <div id="rv5105pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rv5105cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2019/04/00/rv5105/index.html"><img src="/s/issues/2019/04/00/rv5105/rv5105thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2019/04/00/rv5105/index.html">DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ito%2C%20S%2E"><span class="au">S. Ito</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ueno%2C%20G%2E"><span class="au">G. Ueno</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yamamoto%2C%20M%2E"><span class="au">M. Yamamoto</span></a></div> <div class="ica_synopsis">A fully automated crystal centering system using deep learning is presented. Using this system, a fully automated crystal structure determination pipeline has also been developed.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2019/04/00/rv5105/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2020). </span><b><a href="/s/services/archive.html" style="">27</a></b>, <span><a href="/s/issues/2020/02/00/hf5394/index.html" style="">477-485</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S160057752000017X" style="">https://doi.org/10.1107/S160057752000017X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="hf5394cr" class="addthis_button_compact" href="#" addthis:description="In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100degrees limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 x 10-3 microm-1 in the FBP reconstruction to 1.21 x 10-3 microm-1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 x 10-3 microm-1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science." addthis:title="Limited angle tomography for transmission X-ray microscopy using deep learning" addthis:url="https://doi.org/10.1107/S160057752000017X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#hf5394pr').css('display','none')" /> </a> </div> <div id="hf5394pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#hf5394cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?hf5394" title="Statistics"> <img id="hf5394_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="hf5394_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?hf5394">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="hf5394cl" class="addthis_button_compact" href="#" addthis:description="In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100degrees limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 x 10-3 microm-1 in the FBP reconstruction to 1.21 x 10-3 microm-1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 x 10-3 microm-1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science." addthis:title="Limited angle tomography for transmission X-ray microscopy using deep learning" addthis:url="https://doi.org/10.1107/S160057752000017X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#hf5394pl').css('display','none')" /> </a> </div> <div id="hf5394pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#hf5394cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2020/02/00/hf5394/index.html"><img src="/s/issues/2020/02/00/hf5394/hf5394thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2020/02/00/hf5394/index.html">Limited angle tomography for transmission X-ray microscopy using deep learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Huang%2C%20Y%2E"><span class="au">Y. Huang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20S%2E"><span class="au">S. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Guan%2C%20Y%2E"><span class="au">Y. Guan</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Maier%2C%20A%2E"><span class="au">A. Maier</span></a></div> <div class="ica_synopsis">A deep-learning method for limited angle tomography in synchrotron radiation transmission X-ray microscopies and a demonstration of its application in 3D visualization of a chlorella cell.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2020/02/00/hf5394/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2020). </span><b><a href="/s/services/archive.html" style="">27</a></b>, <span><a href="/s/issues/2020/02/00/il5048/index.html" style="">486-493</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577520000831" style="">https://doi.org/10.1107/S1600577520000831</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="il5048cr" class="addthis_button_compact" href="#" addthis:description="This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180degrees rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70degrees could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging." addthis:title="Tomographic reconstruction with a generative adversarial network" addthis:url="https://doi.org/10.1107/S1600577520000831"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#il5048pr').css('display','none')" /> </a> </div> <div id="il5048pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#il5048cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?il5048" title="Statistics"> <img id="il5048_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="il5048_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?il5048">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="il5048cl" class="addthis_button_compact" href="#" addthis:description="This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180degrees rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70degrees could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging." addthis:title="Tomographic reconstruction with a generative adversarial network" addthis:url="https://doi.org/10.1107/S1600577520000831"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#il5048pl').css('display','none')" /> </a> </div> <div id="il5048pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#il5048cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2020/02/00/il5048/index.html"><img src="/s/issues/2020/02/00/il5048/il5048thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2020/02/00/il5048/index.html">Tomographic reconstruction with a generative adversarial network</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20X%2E"><span class="au">X. Yang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kahnt%2C%20M%2E"><span class="au">M. Kahnt</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bruckner%2C%20D%2E"><span class="au">D. Brückner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schropp%2C%20A%2E"><span class="au">A. Schropp</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fam%2C%20Y%2E"><span class="au">Y. Fam</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Becher%2C%20J%2E"><span class="au">J. Becher</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Grunwaldt%2C%20J%2E%2DD%2E"><span class="au">J.-D. Grunwaldt</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sheppard%2C%20T%2EL%2E"><span class="au">T. L. Sheppard</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schroer%2C%20C%2EG%2E"><span class="au">C. G. Schroer</span></a></div> <div class="ica_synopsis">A generative adversarial network (GAN) is used to reconstruct the missing-wedge tomographic data of an <span class="it"><i>in situ</i></span> ptychographic measurement.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2020/02/00/il5048/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2020). </span><b><a href="/s/services/archive.html" style="">27</a></b>, <span><a href="/s/issues/2020/04/00/ju5007/index.html" style="">1069-1073</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577520005767" style="">https://doi.org/10.1107/S1600577520005767</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ju5007cr" class="addthis_button_compact" href="#" addthis:description="Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations." addthis:title="Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network" addthis:url="https://doi.org/10.1107/S1600577520005767"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ju5007pr').css('display','none')" /> </a> </div> <div id="ju5007pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ju5007cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ju5007" title="Statistics"> <img id="ju5007_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ju5007_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ju5007">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ju5007cl" class="addthis_button_compact" href="#" addthis:description="Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations." addthis:title="Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network" addthis:url="https://doi.org/10.1107/S1600577520005767"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ju5007pl').css('display','none')" /> </a> </div> <div id="ju5007pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ju5007cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2020/04/00/ju5007/index.html"><img src="/s/issues/2020/04/00/ju5007/ju5007thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2020/04/00/ju5007/index.html">Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ikemoto%2C%20H%2E"><span class="au">H. Ikemoto</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yamamoto%2C%20K%2E"><span class="au">K. Yamamoto</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Touyama%2C%20H%2E"><span class="au">H. Touyama</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yamashita%2C%20D%2E"><span class="au">D. Yamashita</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nakamura%2C%20M%2E"><span class="au">M. Nakamura</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Okuda%2C%20H%2E"><span class="au">H. Okuda</span></a></div> <div class="ica_synopsis">Convolutional neural networks are useful for classifying grazing-incidence small-angle X-ray scattering patterns. They are also useful for classifying real experimental data.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2020/04/00/ju5007/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2020). </span><b><a href="/s/services/archive.html" style="">27</a></b>, <span><a href="/s/issues/2020/05/00/mo5217/index.html" style="">1339-1346</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577520007407" style="">https://doi.org/10.1107/S1600577520007407</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5217cr" class="addthis_button_compact" href="#" addthis:description="Hard X-ray nanotomography enables 3D investigations of a wide range of samples with high resolution (<100 nm) with both synchrotron-based and laboratory-based setups. However, the advantage of synchrotron-based setups is the high flux, enabling time resolution, which cannot be achieved at laboratory sources. Here, the nanotomography setup at the imaging beamline P05 at PETRA III is presented, which offers high time resolution not only in absorption but for the first time also in Zernike phase contrast. Two test samples are used to evaluate the image quality in both contrast modalities based on the quantitative analysis of contrast-to-noise ratio (CNR) and spatial resolution. High-quality scans can be recorded in 15 min and fast scans down to 3 min are also possible without significant loss of image quality. At scan times well below 3 min, the CNR values decrease significantly and classical image-filtering techniques reach their limitation. A machine-learning approach shows promising results, enabling acquisition of a full tomography in only 6 s. Overall, the transmission X-ray microscopy instrument offers high temporal resolution in absorption and Zernike phase contrast, enabling in situ experiments at the beamline." addthis:title="Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments" addthis:url="https://doi.org/10.1107/S1600577520007407"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#mo5217pr').css('display','none')" /> </a> </div> <div id="mo5217pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5217cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?mo5217" title="Statistics"> <img id="mo5217_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mo5217_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mo5217">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5217cl" class="addthis_button_compact" href="#" addthis:description="Hard X-ray nanotomography enables 3D investigations of a wide range of samples with high resolution (<100 nm) with both synchrotron-based and laboratory-based setups. However, the advantage of synchrotron-based setups is the high flux, enabling time resolution, which cannot be achieved at laboratory sources. Here, the nanotomography setup at the imaging beamline P05 at PETRA III is presented, which offers high time resolution not only in absorption but for the first time also in Zernike phase contrast. Two test samples are used to evaluate the image quality in both contrast modalities based on the quantitative analysis of contrast-to-noise ratio (CNR) and spatial resolution. High-quality scans can be recorded in 15 min and fast scans down to 3 min are also possible without significant loss of image quality. At scan times well below 3 min, the CNR values decrease significantly and classical image-filtering techniques reach their limitation. A machine-learning approach shows promising results, enabling acquisition of a full tomography in only 6 s. Overall, the transmission X-ray microscopy instrument offers high temporal resolution in absorption and Zernike phase contrast, enabling in situ experiments at the beamline." addthis:title="Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments" addthis:url="https://doi.org/10.1107/S1600577520007407"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#mo5217pl').css('display','none')" /> </a> </div> <div id="mo5217pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5217cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2020/05/00/mo5217/index.html"><img src="/s/issues/2020/05/00/mo5217/mo5217thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2020/05/00/mo5217/index.html">Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast <span class="it"><i>in situ</i></span> experiments</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Flenner%2C%20S%2E"><span class="au">S. Flenner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Storm%2C%20M%2E"><span class="au">M. Storm</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kubec%2C%20A%2E"><span class="au">A. Kubec</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Longo%2C%20E%2E"><span class="au">E. Longo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Doring%2C%20F%2E"><span class="au">F. Döring</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pelt%2C%20D%2EM%2E"><span class="au">D. M. Pelt</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=David%2C%20C%2E"><span class="au">C. David</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Muller%2C%20M%2E"><span class="au">M. Müller</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Greving%2C%20I%2E"><span class="au">I. Greving</span></a></div> <div class="ica_synopsis">Unique transmission X-ray microscopy geometry allows high temporal resolution in absorption as well as phase contrast nanotomography. The evaluation of fast scan times versus image quality is presented.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2020/05/00/mo5217/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2021). </span><b><a href="/s/services/archive.html" style="">28</a></b>, <span><a href="/s/issues/2021/02/00/tv5012/index.html" style="">566-575</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577521001314" style="">https://doi.org/10.1107/S1600577521001314</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5012cr" class="addthis_button_compact" href="#" addthis:description="In recent years, major capability improvements at synchrotron beamlines have given researchers the ability to capture more complex structures at a higher resolution within a very short time. This opens up the possibility of studying dynamic processes and observing resulting structural changes over time. However, such studies can create a huge quantity of 3D image data, which presents a major challenge for segmentation and analysis. Here tomography experiments at the Australian synchrotron source are examined, which were used to study bread dough formulations during rising and baking, resulting in over 460 individual 3D datasets. The current pipeline for segmentation and analysis involves semi-automated methods using commercial software that require a large amount of user input. This paper focuses on exploring machine learning methods to automate this process. The main challenge to be faced is in generating adequate training datasets to train the machine learning model. Creating training data by manually segmenting real images is very labour-intensive, so instead methods of automatically creating synthetic training datasets which have the same attributes of the original images have been tested. The generated synthetic images are used to train a U-Net model, which is then used to segment the original bread dough images. The trained U-Net outperformed the previously used segmentation techniques while taking less manual effort. This automated model for data segmentation would alleviate the time-consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps." addthis:title="Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach" addthis:url="https://doi.org/10.1107/S1600577521001314"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#tv5012pr').css('display','none')" /> </a> </div> <div id="tv5012pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5012cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?tv5012" title="Statistics"> <img id="tv5012_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="tv5012_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?tv5012">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5012cl" class="addthis_button_compact" href="#" addthis:description="In recent years, major capability improvements at synchrotron beamlines have given researchers the ability to capture more complex structures at a higher resolution within a very short time. This opens up the possibility of studying dynamic processes and observing resulting structural changes over time. However, such studies can create a huge quantity of 3D image data, which presents a major challenge for segmentation and analysis. Here tomography experiments at the Australian synchrotron source are examined, which were used to study bread dough formulations during rising and baking, resulting in over 460 individual 3D datasets. The current pipeline for segmentation and analysis involves semi-automated methods using commercial software that require a large amount of user input. This paper focuses on exploring machine learning methods to automate this process. The main challenge to be faced is in generating adequate training datasets to train the machine learning model. Creating training data by manually segmenting real images is very labour-intensive, so instead methods of automatically creating synthetic training datasets which have the same attributes of the original images have been tested. The generated synthetic images are used to train a U-Net model, which is then used to segment the original bread dough images. The trained U-Net outperformed the previously used segmentation techniques while taking less manual effort. This automated model for data segmentation would alleviate the time-consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps." addthis:title="Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach" addthis:url="https://doi.org/10.1107/S1600577521001314"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#tv5012pl').css('display','none')" /> </a> </div> <div id="tv5012pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5012cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2021/02/00/tv5012/index.html"><img src="/s/issues/2021/02/00/tv5012/tv5012thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2021/02/00/tv5012/index.html">Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ali%2C%20S%2E"><span class="au">S. Ali</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mayo%2C%20S%2E"><span class="au">S. Mayo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gostar%2C%20A%2EK%2E"><span class="au">A. K. Gostar</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tennakoon%2C%20R%2E"><span class="au">R. Tennakoon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bab%2DHadiashar%2C%20A%2E"><span class="au">A. Bab-Hadiashar</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=MCann%2C%20T%2E"><span class="au">T. MCann</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tuhumury%2C%20H%2E"><span class="au">H. Tuhumury</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Favaro%2C%20J%2E"><span class="au">J. Favaro</span></a></div> <div class="ica_synopsis">This paper aims to develop a new method for training a deep neural network using synthetic data. The trained model will be used to automatically segment micro-CT images of bread dough collected at the Australian Synchrotron.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2021/02/00/tv5012/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2021). </span><b><a href="/s/services/archive.html" style="">28</a></b>, <span><a href="/s/issues/2021/03/00/tv5009/index.html" style="">910-918</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577521003441" style="">https://doi.org/10.1107/S1600577521003441</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5009cr" class="addthis_button_compact" href="#" addthis:description="The Long Short-Term Memory neural network (LSTM) has excellent learning ability for the time series of the nuclear pulse signal. It can accurately estimate the parameters (such as amplitude, time constant, etc.) of the digitally shaped nuclear pulse signal (especially the overlapping pulse signal). However, due to the large number of pulse sequences, the direct use of these sequences as samples to train the LSTM increases the complexity of the network, resulting in a lower training efficiency of the model. The convolution neural network (CNN) can effectively extract the sequence samples by using its unique convolution kernel structure, thus greatly reducing the number of sequence samples. Therefore, the CNN-LSTM deep neural network is used to estimate the parameters of overlapping pulse signals after digital trapezoidal shaping of exponential signals. Firstly, the estimation of the trapezoidal overlapping nuclear pulse is considered to be obtained after the superposition of multiple exponential nuclear pulses followed by trapezoidal shaping. Then, a data set containing multiple samples is set up; each sample is composed of the sequence of sampling values of the trapezoidal overlapping nuclear pulse and the set of shaping parameters of the exponential pulse before digital shaping. Secondly, the CNN is used to extract the abstract features of the training set in these samples, and then these abstract features are applied to the training of the LSTM model. In the training process, the pulse parameter set estimated by the present neural network is calculated by forward propagation. Thirdly, the loss function is used to calculate the loss value between the estimated pulse parameter set and the actual pulse parameter set. Finally, a gradient-based optimization algorithm is applied to update the weight by getting back the loss value together with the gradient of the loss function to the network, so as to realize the purpose of training the network. After model training was completed, the sampled values of the trapezoidal overlapping nuclear pulse were used as input to the CNN-LSTM model to obtain the required parameter set from the output of the CNN-LSTM model. The experimental results show that this method can effectively overcome the shortcomings of local convergence of traditional methods and greatly save the time of model training. At the same time, it can accurately estimate multiple trapezoidal overlapping pulses due to the wide width of the flat top, thus realizing the optimal estimation of nuclear pulse parameters in a global sense, which is a good pulse parameter estimation method." addthis:title="Estimation of trapezoidal-shaped overlapping nuclear pulse parameters based on a deep learning CNN-LSTM model" addthis:url="https://doi.org/10.1107/S1600577521003441"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#tv5009pr').css('display','none')" /> </a> </div> <div id="tv5009pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5009cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?tv5009" title="Statistics"> <img id="tv5009_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="tv5009_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?tv5009">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5009cl" class="addthis_button_compact" href="#" addthis:description="The Long Short-Term Memory neural network (LSTM) has excellent learning ability for the time series of the nuclear pulse signal. It can accurately estimate the parameters (such as amplitude, time constant, etc.) of the digitally shaped nuclear pulse signal (especially the overlapping pulse signal). However, due to the large number of pulse sequences, the direct use of these sequences as samples to train the LSTM increases the complexity of the network, resulting in a lower training efficiency of the model. The convolution neural network (CNN) can effectively extract the sequence samples by using its unique convolution kernel structure, thus greatly reducing the number of sequence samples. Therefore, the CNN-LSTM deep neural network is used to estimate the parameters of overlapping pulse signals after digital trapezoidal shaping of exponential signals. Firstly, the estimation of the trapezoidal overlapping nuclear pulse is considered to be obtained after the superposition of multiple exponential nuclear pulses followed by trapezoidal shaping. Then, a data set containing multiple samples is set up; each sample is composed of the sequence of sampling values of the trapezoidal overlapping nuclear pulse and the set of shaping parameters of the exponential pulse before digital shaping. Secondly, the CNN is used to extract the abstract features of the training set in these samples, and then these abstract features are applied to the training of the LSTM model. In the training process, the pulse parameter set estimated by the present neural network is calculated by forward propagation. Thirdly, the loss function is used to calculate the loss value between the estimated pulse parameter set and the actual pulse parameter set. Finally, a gradient-based optimization algorithm is applied to update the weight by getting back the loss value together with the gradient of the loss function to the network, so as to realize the purpose of training the network. After model training was completed, the sampled values of the trapezoidal overlapping nuclear pulse were used as input to the CNN-LSTM model to obtain the required parameter set from the output of the CNN-LSTM model. The experimental results show that this method can effectively overcome the shortcomings of local convergence of traditional methods and greatly save the time of model training. At the same time, it can accurately estimate multiple trapezoidal overlapping pulses due to the wide width of the flat top, thus realizing the optimal estimation of nuclear pulse parameters in a global sense, which is a good pulse parameter estimation method." addthis:title="Estimation of trapezoidal-shaped overlapping nuclear pulse parameters based on a deep learning CNN-LSTM model" addthis:url="https://doi.org/10.1107/S1600577521003441"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#tv5009pl').css('display','none')" /> </a> </div> <div id="tv5009pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5009cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2021/03/00/tv5009/index.html"><img src="/s/issues/2021/03/00/tv5009/tv5009thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2021/03/00/tv5009/index.html">Estimation of trapezoidal-shaped overlapping nuclear pulse parameters based on a deep learning CNN-LSTM model</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ma%2C%20X%2E%2DK%2E"><span class="au">X.-K. Ma</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Huang%2C%20H%2E%2DQ%2E"><span class="au">H.-Q. Huang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ji%2C%20X%2E"><span class="au">X. Ji</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Dai%2C%20H%2E%2DY%2E"><span class="au">H.-Y. Dai</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wu%2C%20J%2E%2DH%2E"><span class="au">J.-H. Wu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhao%2C%20J%2E"><span class="au">J. Zhao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yang%2C%20F%2E"><span class="au">F. Yang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tang%2C%20L%2E"><span class="au">L. Tang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jiang%2C%20K%2E%2DM%2E"><span class="au">K.-M. Jiang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ding%2C%20W%2E%2DC%2E"><span class="au">W.-C. Ding</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhou%2C%20W%2E"><span class="au">W. Zhou</span></a></div> <div class="ica_synopsis">A parameter estimation method based on the deep learning CNN-LSTM model is proposed for overlapping nuclear pulses shaped by several exponential decay nuclear pulses.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2021/03/00/tv5009/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2021). </span><b><a href="/s/services/archive.html" style="">28</a></b>, <span><a href="/s/issues/2021/06/00/mo5245/index.html" style="">1909-1915</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577521008481" style="">https://doi.org/10.1107/S1600577521008481</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5245cr" class="addthis_button_compact" href="#" addthis:description="Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed." addthis:title="Deep-learning-based image registration for nano-resolution tomographic reconstruction" addthis:url="https://doi.org/10.1107/S1600577521008481"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#mo5245pr').css('display','none')" /> </a> </div> <div id="mo5245pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5245cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?mo5245" title="Statistics"> <img id="mo5245_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mo5245_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mo5245">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?mo5245" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mo5245_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mo5245">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5245cl" class="addthis_button_compact" href="#" addthis:description="Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed." addthis:title="Deep-learning-based image registration for nano-resolution tomographic reconstruction" addthis:url="https://doi.org/10.1107/S1600577521008481"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#mo5245pl').css('display','none')" /> </a> </div> <div id="mo5245pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5245cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2021/06/00/mo5245/index.html"><img src="/s/issues/2021/06/00/mo5245/mo5245thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2021/06/00/mo5245/index.html">Deep-learning-based image registration for nano-resolution tomographic reconstruction</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fu%2C%20T%2E"><span class="au">T. Fu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20K%2E"><span class="au">K. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20Y%2E"><span class="au">Y. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Li%2C%20J%2E"><span class="au">J. Li</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20J%2E"><span class="au">J. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yao%2C%20C%2E"><span class="au">C. Yao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=He%2C%20Q%2E"><span class="au">Q. He</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20S%2E"><span class="au">S. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Huang%2C%20W%2E"><span class="au">W. Huang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yuan%2C%20Q%2E"><span class="au">Q. Yuan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pianetta%2C%20P%2E"><span class="au">P. Pianetta</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20Y%2E"><span class="au">Y. Liu</span></a></div> <div class="ica_synopsis">A deep-learning-based image jitter correction method for synchrotron nano-resolution tomographic reconstruction with superior efficiency and accuracy is presented.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2021/06/00/mo5245/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2022). </span><b><a href="/s/services/archive.html" style="">29</a></b>, <span><a href="/s/issues/2022/01/00/tv5025/index.html" style="">230-238</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577521011139" style="">https://doi.org/10.1107/S1600577521011139</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5025cr" class="addthis_button_compact" href="#" addthis:description="High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields." addthis:title="Machine learning denoising of high-resolution X-ray nanotomography data" addthis:url="https://doi.org/10.1107/S1600577521011139"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#tv5025pr').css('display','none')" /> </a> </div> <div id="tv5025pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5025cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?tv5025" title="Statistics"> <img id="tv5025_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="tv5025_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?tv5025">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5025cl" class="addthis_button_compact" href="#" addthis:description="High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields." addthis:title="Machine learning denoising of high-resolution X-ray nanotomography data" addthis:url="https://doi.org/10.1107/S1600577521011139"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#tv5025pl').css('display','none')" /> </a> </div> <div id="tv5025pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5025cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2022/01/00/tv5025/index.html"><img src="/s/issues/2022/01/00/tv5025/tv5025thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2022/01/00/tv5025/index.html">Machine learning denoising of high-resolution X-ray nanotomography data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Flenner%2C%20S%2E"><span class="au">S. Flenner</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bruns%2C%20S%2E"><span class="au">S. Bruns</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Longo%2C%20E%2E"><span class="au">E. Longo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Parnell%2C%20A%2EJ%2E"><span class="au">A. J. Parnell</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Stockhausen%2C%20K%2EE%2E"><span class="au">K. E. Stockhausen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Muller%2C%20M%2E"><span class="au">M. Müller</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Greving%2C%20I%2E"><span class="au">I. Greving</span></a></div> <div class="ica_synopsis">A high-performance denoising filter based on machine learning for high-resolution synchrotron nanotomography data is analyzed and evaluated.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2022/01/00/tv5025/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2022). </span><b><a href="/s/services/archive.html" style="">29</a></b>, <span><a href="/s/issues/2022/05/00/gy5036/index.html" style="">1232-1240</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522006816" style="">https://doi.org/10.1107/S1600577522006816</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="gy5036cr" class="addthis_button_compact" href="#" addthis:description="New developments at synchrotron beamlines and the ongoing upgrades of synchrotron facilities allow the possibility to study complex structures with a much better spatial and temporal resolution than ever before. However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in synchrotron transmission X-ray micrograms. Transmission X-ray microscopy experiments were conducted on micropillars prepared from additively manufactured 316L steel to evaluate precipitate information. After training the 3D U-net DCNN model, it was used on unseen data and the prediction was compared with manual segmentation. A good agreement was found between both segmentations. An ablation study was performed and revealed that the proposed model showed better statistics than other models with lower numbers of layers and/or characteristic features. The proposed model is able to segment several hundreds of gigabytes of data in a few minutes and could be applied to other materials and tomography techniques. The code and the fitted weights are made available with this paper for any interested researcher to use for their needs (https://github.com/manasvupadhyay/erc-gamma-3D-DCNN)." addthis:title="3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms" addthis:url="https://doi.org/10.1107/S1600577522006816"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#gy5036pr').css('display','none')" /> </a> </div> <div id="gy5036pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gy5036cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?gy5036" title="Statistics"> <img id="gy5036_divwide" alt="" class="art_icon downloads" style="top: 0px; 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However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in synchrotron transmission X-ray micrograms. Transmission X-ray microscopy experiments were conducted on micropillars prepared from additively manufactured 316L steel to evaluate precipitate information. After training the 3D U-net DCNN model, it was used on unseen data and the prediction was compared with manual segmentation. A good agreement was found between both segmentations. An ablation study was performed and revealed that the proposed model showed better statistics than other models with lower numbers of layers and/or characteristic features. The proposed model is able to segment several hundreds of gigabytes of data in a few minutes and could be applied to other materials and tomography techniques. The code and the fitted weights are made available with this paper for any interested researcher to use for their needs (https://github.com/manasvupadhyay/erc-gamma-3D-DCNN)." addthis:title="3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms" addthis:url="https://doi.org/10.1107/S1600577522006816"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#gy5036pl').css('display','none')" /> </a> </div> <div id="gy5036pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gy5036cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2022/05/00/gy5036/index.html"><img src="/s/issues/2022/05/00/gy5036/gy5036thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2022/05/00/gy5036/index.html">3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gaudez%2C%20S%2E"><span class="au">S. Gaudez</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ben%20Haj%20Slama%2C%20M%2E"><span class="au">M. Ben Haj Slama</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kaestner%2C%20A%2E"><span class="au">A. Kaestner</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Upadhyay%2C%20M%2EV%2E"><span class="au">M. V. Upadhyay</span></a></div> <div class="ica_synopsis">A 3D U-net deep convolutional neural network has been developed and tested to segment precipitates in synchrotron-based X-ray tomography experiments. Comparison of predicted segmentation showed a good agreement with manual segmentation.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2022/05/00/gy5036/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2022). </span><b><a href="/s/services/archive.html" style="">29</a></b>, <span><a href="/s/issues/2022/05/00/ok5077/index.html" style="">1309-1317</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522006786" style="">https://doi.org/10.1107/S1600577522006786</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ok5077cr" class="addthis_button_compact" href="#" addthis:description="The Argonne X-ray Emission Analysis Package (AXEAP) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS." addthis:title="AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning" addthis:url="https://doi.org/10.1107/S1600577522006786"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ok5077pr').css('display','none')" /> </a> </div> <div id="ok5077pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ok5077cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ok5077" title="Statistics"> <img id="ok5077_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ok5077_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ok5077">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?ok5077" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS." addthis:title="AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning" addthis:url="https://doi.org/10.1107/S1600577522006786"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#ok5077pl').css('display','none')" /> </a> </div> <div id="ok5077pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ok5077cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2022/05/00/ok5077/index.html"><img src="/s/issues/2022/05/00/ok5077/ok5077thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2022/05/00/ok5077/index.html"><span class="it"><i>AXEAP</i></span>: a software package for X-ray emission data analysis using unsupervised machine learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hwang%2C%20I%2E%2DH%2E"><span class="au">I.-H. Hwang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Solovyev%2C%20M%2EA%2E"><span class="au">M. A. Solovyev</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Han%2C%20S%2E%2DW%2E"><span class="au">S.-W. Han</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chan%2C%20M%2EK%2EY%2E"><span class="au">M. K. Y. Chan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hammonds%2C%20J%2EP%2E"><span class="au">J. P. Hammonds</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Heald%2C%20S%2EM%2E"><span class="au">S. M. Heald</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kelly%2C%20S%2ED%2E"><span class="au">S. D. Kelly</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schwarz%2C%20N%2E"><span class="au">N. Schwarz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20X%2E"><span class="au">X. Zhang</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sun%2C%20C%2E%2DJ%2E"><span class="au">C.-J. Sun</span></a></div> <div class="ica_synopsis"><span class="it"><i>AXEAP</i></span>, a program that can process high-resolution emission spectrum data quickly, has been developed based on machine-learning algorithms.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2022/05/00/ok5077/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2022). </span><b><a href="/s/services/archive.html" style="">29</a></b>, <span><a href="/s/issues/2022/06/00/yi5121/index.html" style="">1368-1375</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522008554" style="">https://doi.org/10.1107/S1600577522008554</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yi5121cr" class="addthis_button_compact" href="#" addthis:description="Machine learning has recently been applied and deployed at several light source facilities in the domain of accelerator physics. Here, an approach based on machine learning to produce a fast-executing model is introduced that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning. This result will enable users to efficiently determine the insertion device settings for achieving arbitrary beam characteristics." addthis:title="A neural network model of a quasiperiodic elliptically polarizing undulator in universal mode" addthis:url="https://doi.org/10.1107/S1600577522008554"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#yi5121pr').css('display','none')" /> </a> </div> <div id="yi5121pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yi5121cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?yi5121" title="Statistics"> <img id="yi5121_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; 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Here, an approach based on machine learning to produce a fast-executing model is introduced that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning. This result will enable users to efficiently determine the insertion device settings for achieving arbitrary beam characteristics." addthis:title="A neural network model of a quasiperiodic elliptically polarizing undulator in universal mode" addthis:url="https://doi.org/10.1107/S1600577522008554"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#yi5121pl').css('display','none')" /> </a> </div> <div id="yi5121pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yi5121cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2022/06/00/yi5121/index.html"><img src="/s/issues/2022/06/00/yi5121/yi5121thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2022/06/00/yi5121/index.html">A neural network model of a quasiperiodic elliptically polarizing undulator in universal mode</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sheppard%2C%20R%2E"><span class="au">R. Sheppard</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Baribeau%2C%20C%2E"><span class="au">C. Baribeau</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pedersen%2C%20T%2E"><span class="au">T. Pedersen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Boland%2C%20M%2E"><span class="au">M. Boland</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bertwistle%2C%20D%2E"><span class="au">D. Bertwistle</span></a></div> <div class="ica_synopsis">An approach based on machine learning to produce a fast-executing model is introduced that predicts the polarization and energy of the radiated light produced at an insertion device.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2022/06/00/yi5121/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2022). </span><b><a href="/s/services/archive.html" style="">29</a></b>, <span><a href="/s/issues/2022/06/00/rv5165/index.html" style="">1495-1503</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522008876" style="">https://doi.org/10.1107/S1600577522008876</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rv5165cr" class="addthis_button_compact" href="#" addthis:description="The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. Coli), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed." addthis:title="ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy" addthis:url="https://doi.org/10.1107/S1600577522008876"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rv5165pr').css('display','none')" /> </a> </div> <div id="rv5165pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rv5165cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rv5165" title="Statistics"> <img id="rv5165_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rv5165_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rv5165">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?rv5165" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. Coli), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed." addthis:title="ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy" addthis:url="https://doi.org/10.1107/S1600577522008876"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#rv5165pl').css('display','none')" /> </a> </div> <div id="rv5165pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rv5165cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2022/06/00/rv5165/index.html"><img src="/s/issues/2022/06/00/rv5165/rv5165thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2022/06/00/rv5165/index.html"><span class="it"><i>ROI-Finder</i></span>: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chowdhury%2C%20M%2EA%2EZ%2E"><span class="au">M. A. Z. Chowdhury</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ok%2C%20K%2E"><span class="au">K. Ok</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Luo%2C%20Y%2E"><span class="au">Y. Luo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20Z%2E"><span class="au">Z. Liu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chen%2C%20S%2E"><span class="au">S. Chen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=O%27Halloran%2C%20T%2EV%2E"><span class="au">T. V. O'Halloran</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kettimuthu%2C%20R%2E"><span class="au">R. Kettimuthu</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tekawade%2C%20A%2E"><span class="au">A. Tekawade</span></a></div> <div class="ica_synopsis">The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2022/06/00/rv5165/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/01/00/tv5041/index.html" style="">57-64</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522011080" style="">https://doi.org/10.1107/S1600577522011080</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5041cr" class="addthis_button_compact" href="#" addthis:description="Adaptive X-ray mirrors are being adopted on high-coherent-flux synchrotron and X-ray free-electron laser beamlines where dynamic phase control and aberration compensation are necessary to preserve wavefront quality from source to sample, yet challenging to achieve. Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2 nm RMS. Using a prototype mirror and ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance." addthis:title="Data-driven modeling and control of an X-ray bimorph adaptive mirror" addthis:url="https://doi.org/10.1107/S1600577522011080"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#tv5041pr').css('display','none')" /> </a> </div> <div id="tv5041pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5041cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?tv5041" title="Statistics"> <img id="tv5041_divwide" alt="" class="art_icon downloads" style="top: 0px; 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Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2 nm RMS. Using a prototype mirror and ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance." addthis:title="Data-driven modeling and control of an X-ray bimorph adaptive mirror" addthis:url="https://doi.org/10.1107/S1600577522011080"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#tv5041pl').css('display','none')" /> </a> </div> <div id="tv5041pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5041cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/01/00/tv5041/index.html"><img src="/s/issues/2023/01/00/tv5041/tv5041thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/01/00/tv5041/index.html">Data-driven modeling and control of an X-ray bimorph adaptive mirror</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gunjala%2C%20G%2E"><span class="au">G. Gunjala</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wojdyla%2C%20A%2E"><span class="au">A. Wojdyla</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Goldberg%2C%20K%2EA%2E"><span class="au">K. A. Goldberg</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Qiao%2C%20Z%2E"><span class="au">Z. Qiao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shi%2C%20X%2E"><span class="au">X. Shi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Assoufid%2C%20L%2E"><span class="au">L. Assoufid</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Waller%2C%20L%2E"><span class="au">L. Waller</span></a></div> <div class="ica_synopsis">A framework for data-driven characterization of the nonlinear dynamics of a piezo-bimorph adaptive X-ray mirror has been developed. Rapid surface shape control and stability to within 2 nm RMS have been demonstrated.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/01/00/tv5041/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/01/00/vy5004/index.html" style="">137-146</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577522011274" style="">https://doi.org/10.1107/S1600577522011274</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vy5004cr" class="addthis_button_compact" href="#" addthis:description="In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions (e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye-Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 microm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method." addthis:title="Artifact identification in X-ray diffraction data using machine learning methods" addthis:url="https://doi.org/10.1107/S1600577522011274"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#vy5004pr').css('display','none')" /> </a> </div> <div id="vy5004pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vy5004cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?vy5004" title="Statistics"> <img id="vy5004_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="vy5004_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?vy5004">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="vy5004cl" class="addthis_button_compact" href="#" addthis:description="In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions (e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye-Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 microm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method." addthis:title="Artifact identification in X-ray diffraction data using machine learning methods" addthis:url="https://doi.org/10.1107/S1600577522011274"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#vy5004pl').css('display','none')" /> </a> </div> <div id="vy5004pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#vy5004cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/01/00/vy5004/index.html"><img src="/s/issues/2023/01/00/vy5004/vy5004thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/01/00/vy5004/index.html">Artifact identification in X-ray diffraction data using machine learning methods</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yanxon%2C%20H%2E"><span class="au">H. Yanxon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Weng%2C%20J%2E"><span class="au">J. Weng</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Parraga%2C%20H%2E"><span class="au">H. Parraga</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Xu%2C%20W%2E"><span class="au">W. Xu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ruett%2C%20U%2E"><span class="au">U. Ruett</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schwarz%2C%20N%2E"><span class="au">N. Schwarz</span></a></div> <div class="ica_synopsis">The capability of machine learning methods for identifying and separating artifacts that appear in a typical X-ray diffraction image is demonstrated.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/01/00/vy5004/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/03/00/mo5263/index.html" style="">620-626</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577523000917" style="">https://doi.org/10.1107/S1600577523000917</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5263cr" class="addthis_button_compact" href="#" addthis:description="X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost." addthis:title="Deep-learning-based ring artifact correction for tomographic reconstruction" addthis:url="https://doi.org/10.1107/S1600577523000917"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#mo5263pr').css('display','none')" /> </a> </div> <div id="mo5263pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5263cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?mo5263" title="Statistics"> <img id="mo5263_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mo5263_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mo5263">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mo5263cl" class="addthis_button_compact" href="#" addthis:description="X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost." addthis:title="Deep-learning-based ring artifact correction for tomographic reconstruction" addthis:url="https://doi.org/10.1107/S1600577523000917"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#mo5263pl').css('display','none')" /> </a> </div> <div id="mo5263pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mo5263cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/03/00/mo5263/index.html"><img src="/s/issues/2023/03/00/mo5263/mo5263thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/03/00/mo5263/index.html">Deep-learning-based ring artifact correction for tomographic reconstruction</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fu%2C%20T%2E"><span class="au">T. Fu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20Y%2E"><span class="au">Y. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20K%2E"><span class="au">K. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20J%2E"><span class="au">J. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20S%2E"><span class="au">S. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Huang%2C%20W%2E"><span class="au">W. Huang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wang%2C%20Y%2E"><span class="au">Y. Wang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yao%2C%20C%2E"><span class="au">C. Yao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhou%2C%20C%2E"><span class="au">C. Zhou</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yuan%2C%20Q%2E"><span class="au">Q. Yuan</span></a></div> <div class="ica_synopsis">A new ring artifact correction method based on a residual neural network for tomographic reconstruction with superior efficiency and accuracy is presented.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/03/00/mo5263/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/05/00/gy5046/index.html" style="">978-988</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577523005635" style="">https://doi.org/10.1107/S1600577523005635</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="gy5046cr" class="addthis_button_compact" href="#" addthis:description="Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7degreesC temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated." addthis:title="A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging" addthis:url="https://doi.org/10.1107/S1600577523005635"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#gy5046pr').css('display','none')" /> </a> </div> <div id="gy5046pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gy5046cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?gy5046" title="Statistics"> <img id="gy5046_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="gy5046_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?gy5046">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="gy5046cl" class="addthis_button_compact" href="#" addthis:description="Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7degreesC temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated." addthis:title="A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging" addthis:url="https://doi.org/10.1107/S1600577523005635"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#gy5046pl').css('display','none')" /> </a> </div> <div id="gy5046pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#gy5046cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/05/00/gy5046/index.html"><img src="/s/issues/2023/05/00/gy5046/gy5046thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/05/00/gy5046/index.html">A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fokin%2C%20M%2EI%2E"><span class="au">M. I. Fokin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nikitin%2C%20V%2EV%2E"><span class="au">V. V. Nikitin</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Duchkov%2C%20A%2EA%2E"><span class="au">A. A. Duchkov</span></a></div> <div class="ica_synopsis">A new hybrid machine-learning approach for the automatic segmentation of dynamic computed tomography images during methane hydrate formation in sandy samples is presented. The algorithm allows for accurate and fast segmentation of gas hydrate changes and fluid flow in the low-contrast environment that is the main step to perform automatic quantitative analysis of processes in hydrate-bearing samples.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/05/00/gy5046/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/06/00/ju5054/index.html" style="">1064-1075</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S160057752300749X" style="">https://doi.org/10.1107/S160057752300749X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ju5054cr" class="addthis_button_compact" href="#" addthis:description="Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup." addthis:title="Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments" addthis:url="https://doi.org/10.1107/S160057752300749X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ju5054pr').css('display','none')" /> </a> </div> <div id="ju5054pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ju5054cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ju5054" title="Statistics"> <img id="ju5054_divwide" alt="" class="art_icon downloads" style="top: 0px; 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ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup." addthis:title="Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments" addthis:url="https://doi.org/10.1107/S160057752300749X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ju5054pl').css('display','none')" /> </a> </div> <div id="ju5054pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ju5054cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/06/00/ju5054/index.html"><img src="/s/issues/2023/06/00/ju5054/ju5054thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/06/00/ju5054/index.html">Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pithan%2C%20L%2E"><span class="au">L. Pithan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Starostin%2C%20V%2E"><span class="au">V. Starostin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Marecek%2C%20D%2E"><span class="au">D. Mareček</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Petersdorf%2C%20L%2E"><span class="au">L. Petersdorf</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Volter%2C%20C%2E"><span class="au">C. Völter</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Munteanu%2C%20V%2E"><span class="au">V. Munteanu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jankowski%2C%20M%2E"><span class="au">M. Jankowski</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Konovalov%2C%20O%2E"><span class="au">O. Konovalov</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gerlach%2C%20A%2E"><span class="au">A. Gerlach</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hinderhofer%2C%20A%2E"><span class="au">A. Hinderhofer</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Murphy%2C%20B%2E"><span class="au">B. Murphy</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kowarik%2C%20S%2E"><span class="au">S. Kowarik</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schreiber%2C%20F%2E"><span class="au">F. Schreiber</span></a></div> <div class="ica_synopsis">A machine-learning-based closed-loop solution for reflectometry analysis in synchrotron beamline operation utilizing online data analysis is presented. This work focuses on the perspective of visiting facility users and strategies to provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/06/00/ju5054/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span style=""><span style="font-style:italic;">J. Synchrotron Rad.</span></span><span style="color:#000;"> (2023). </span><b><a href="/s/services/archive.html" style="">30</a></b>, <span><a href="/s/issues/2023/06/00/tv5044/index.html" style="">1135-1142</a></span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S1600577523008032" style="">https://doi.org/10.1107/S1600577523008032</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5044cr" class="addthis_button_compact" href="#" addthis:description="Synchrotron radiation can be used as a light source in X-ray microscopy to acquire a high-resolution image of a microscale object for tomography. However, numerous projections must be captured for a high-quality tomographic image to be reconstructed; thus, image acquisition is time consuming. Such dense imaging is not only expensive and time consuming but also results in the target receiving a large dose of radiation. To resolve these problems, sparse acquisition techniques have been proposed; however, the generated images often have many artefacts and are noisy. In this study, a deep-learning-based approach is proposed for the tomographic reconstruction of sparse-view projections that are acquired with a synchrotron light source; this approach proceeds as follows. A convolutional neural network (CNN) is used to first interpolate sparse X-ray projections and then synthesize a sufficiently large set of images to produce a sinogram. After the sinogram is constructed, a second CNN is used for error correction. In experiments, this method successfully produced high-quality tomography images from sparse-view projections for two data sets comprising Drosophila and mouse tomography images. However, the initial results for the smaller mouse data set were poor; therefore, transfer learning was used to apply the Drosophila model to the mouse data set, greatly improving the quality of the reconstructed sinogram. The method could be used to achieve high-quality tomography while reducing the radiation dose to imaging subjects and the imaging time and cost." addthis:title="Sparse-view synchrotron X-ray tomographic reconstruction with learning-based sinogram synthesis" addthis:url="https://doi.org/10.1107/S1600577523008032"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#tv5044pr').css('display','none')" /> </a> </div> <div id="tv5044pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5044cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?tv5044" title="Statistics"> <img id="tv5044_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="tv5044_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?tv5044">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="tv5044cl" class="addthis_button_compact" href="#" addthis:description="Synchrotron radiation can be used as a light source in X-ray microscopy to acquire a high-resolution image of a microscale object for tomography. However, numerous projections must be captured for a high-quality tomographic image to be reconstructed; thus, image acquisition is time consuming. Such dense imaging is not only expensive and time consuming but also results in the target receiving a large dose of radiation. To resolve these problems, sparse acquisition techniques have been proposed; however, the generated images often have many artefacts and are noisy. In this study, a deep-learning-based approach is proposed for the tomographic reconstruction of sparse-view projections that are acquired with a synchrotron light source; this approach proceeds as follows. A convolutional neural network (CNN) is used to first interpolate sparse X-ray projections and then synthesize a sufficiently large set of images to produce a sinogram. After the sinogram is constructed, a second CNN is used for error correction. In experiments, this method successfully produced high-quality tomography images from sparse-view projections for two data sets comprising Drosophila and mouse tomography images. However, the initial results for the smaller mouse data set were poor; therefore, transfer learning was used to apply the Drosophila model to the mouse data set, greatly improving the quality of the reconstructed sinogram. The method could be used to achieve high-quality tomography while reducing the radiation dose to imaging subjects and the imaging time and cost." addthis:title="Sparse-view synchrotron X-ray tomographic reconstruction with learning-based sinogram synthesis" addthis:url="https://doi.org/10.1107/S1600577523008032"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#tv5044pl').css('display','none')" /> </a> </div> <div id="tv5044pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#tv5044cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""><div class="openaccesslogo"><div class="oalogotext" style="" title="This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.">Open <img alt="Open Access" class="oalogo" src="/logos/open.png" /> access</div></div></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/s/issues/2023/06/00/tv5044/index.html"><img src="/s/issues/2023/06/00/tv5044/tv5044thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/s/issues/2023/06/00/tv5044/index.html">Sparse-view synchrotron X-ray tomographic reconstruction with learning-based sinogram synthesis</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cheng%2C%20C%2E%2DC%2E"><span class="au">C.-C. Cheng</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chiang%2C%20M%2E%2DH%2E"><span class="au">M.-H. Chiang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yeh%2C%20C%2E%2DH%2E"><span class="au">C.-H. Yeh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lee%2C%20T%2E%2DT%2E"><span class="au">T.-T. Lee</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ching%2C%20Y%2E%2DT%2E"><span class="au">Y.-T. Ching</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hwu%2C%20Y%2E"><span class="au">Y. Hwu</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chiang%2C%20A%2E%2DS%2E"><span class="au">A.-S. Chiang</span></a></div> <div class="ica_synopsis">This article proposes a deep-learning-based approach for synchrotron X-ray computed tomography with sparse-view projections. The experimental results indicate that tomographic images can be reconstructed by 75 X-ray projections without obvious streak artefacts and noise.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/s/issues/2023/06/00/tv5044/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2017) </span><b><a href="/m/services/archive.html" style="">4</a></b>, <span class="ica_pages" style="color: #000;">486-494</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S205225251700714X" style="">https://doi.org/10.1107/S205225251700714X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fc5018cr" class="addthis_button_compact" href="#" addthis:description="A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds." addthis:title="Classification of crystal structure using a convolutional neural network" addthis:url="https://doi.org/10.1107/S205225251700714X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fc5018pr').css('display','none')" /> </a> </div> <div id="fc5018pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fc5018cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fc5018" title="Statistics"> <img id="fc5018_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fc5018_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fc5018">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?fc5018" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fc5018_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fc5018">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fc5018cl" class="addthis_button_compact" href="#" addthis:description="A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds." addthis:title="Classification of crystal structure using a convolutional neural network" addthis:url="https://doi.org/10.1107/S205225251700714X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#fc5018pl').css('display','none')" /> </a> </div> <div id="fc5018pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fc5018cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2017/04/00/fc5018/index.html"><img src="/m/issues/2017/04/00/fc5018/fc5018thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2017/04/00/fc5018/index.html">Classification of crystal structure using a convolutional neural network</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Park%2C%20W%2EB%2E"><span class="au">W. B. Park</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chung%2C%20J%2E"><span class="au">J. Chung</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jung%2C%20J%2E"><span class="au">J. Jung</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sohn%2C%20K%2E"><span class="au">K. Sohn</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Singh%2C%20S%2EP%2E"><span class="au">S. P. Singh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pyo%2C%20M%2E"><span class="au">M. Pyo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shin%2C%20N%2E"><span class="au">N. Shin</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sohn%2C%20K%2E%2DS%2E"><span class="au">K.-S. Sohn</span></a></div> <div class="ica_synopsis">A deep-machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been employed for the classification of crystal system, extinction group and space group for given powder X-ray diffraction patterns of inorganic materials.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2017/04/00/fc5018/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2018) </span><b><a href="/m/services/archive.html" style="">5</a></b>, <span class="ica_pages" style="color: #000;">854-865</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252518014392" style="">https://doi.org/10.1107/S2052252518014392</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rq5002cr" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different `cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%." addthis:title="Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy" addthis:url="https://doi.org/10.1107/S2052252518014392"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#rq5002pr').css('display','none')" /> </a> </div> <div id="rq5002pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rq5002cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?rq5002" title="Statistics"> <img id="rq5002_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="rq5002_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?rq5002">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="rq5002cl" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different `cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%." addthis:title="Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy" addthis:url="https://doi.org/10.1107/S2052252518014392"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#rq5002pl').css('display','none')" /> </a> </div> <div id="rq5002pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#rq5002cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2018/06/00/rq5002/index.html"><img src="/m/issues/2018/06/00/rq5002/rq5002thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2018/06/00/rq5002/index.html"><span class="it"><i>Deep Consensus</i></span>, a deep learning-based approach for particle pruning in cryo-electron microscopy</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sanchez%2DGarcia%2C%20R%2E"><span class="au">R. Sanchez-Garcia</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Segura%2C%20J%2E"><span class="au">J. Segura</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Maluenda%2C%20D%2E"><span class="au">D. Maluenda</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Carazo%2C%20J%2EM%2E"><span class="au">J. M. Carazo</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sorzano%2C%20C%2EO%2ES%2E"><span class="au">C. O. S. Sorzano</span></a></div> <div class="ica_synopsis"><span class="it"><i>Deep Consensus</i></span> performs particle pruning in cryo-EM image-processing workflows using a smart consensus.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2018/06/00/rq5002/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2019) </span><b><a href="/m/services/archive.html" style="">6</a></b>, <span class="ica_pages" style="color: #000;">331-340</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252519001854" style="">https://doi.org/10.1107/S2052252519001854</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="cw5020cr" class="addthis_button_compact" href="#" addthis:description="Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps." addthis:title="Evaluation of the performance of classification algorithms for XFEL single-particle imaging data" addthis:url="https://doi.org/10.1107/S2052252519001854"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#cw5020pr').css('display','none')" /> </a> </div> <div id="cw5020pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5020cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?cw5020" title="Statistics"> <img id="cw5020_divwide" alt="" class="art_icon downloads" style="top: 0px; 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Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps." addthis:title="Evaluation of the performance of classification algorithms for XFEL single-particle imaging data" addthis:url="https://doi.org/10.1107/S2052252519001854"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#cw5020pl').css('display','none')" /> </a> </div> <div id="cw5020pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5020cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2019/02/00/cw5020/index.html"><img src="/m/issues/2019/02/00/cw5020/cw5020thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2019/02/00/cw5020/index.html">Evaluation of the performance of classification algorithms for XFEL single-particle imaging data</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shi%2C%20Y%2E"><span class="au">Y. Shi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yin%2C%20K%2E"><span class="au">K. Yin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tai%2C%20X%2E"><span class="au">X. Tai</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=DeMirci%2C%20H%2E"><span class="au">H. DeMirci</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hosseinizadeh%2C%20A%2E"><span class="au">A. Hosseinizadeh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hogue%2C%20B%2EG%2E"><span class="au">B. G. Hogue</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Li%2C%20H%2E"><span class="au">H. Li</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ourmazd%2C%20A%2E"><span class="au">A. Ourmazd</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schwander%2C%20P%2E"><span class="au">P. Schwander</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Vartanyants%2C%20I%2EA%2E"><span class="au">I. A. Vartanyants</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yoon%2C%20C%2EH%2E"><span class="au">C. H. Yoon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Aquila%2C%20A%2E"><span class="au">A. Aquila</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20H%2E"><span class="au">H. Liu</span></a></div> <div class="ica_synopsis">The performances of three image-classification algorithms were evaluated. The three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2019/02/00/cw5020/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2019) </span><b><a href="/m/services/archive.html" style="">6</a></b>, <span class="ica_pages" style="color: #000;">1054-1063</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252519011692" style="">https://doi.org/10.1107/S2052252519011692</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5008cr" class="addthis_button_compact" href="#" addthis:description="In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed." addthis:title="DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps" addthis:url="https://doi.org/10.1107/S2052252519011692"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fq5008pr').css('display','none')" /> </a> </div> <div id="fq5008pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5008cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fq5008" title="Statistics"> <img id="fq5008_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fq5008_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fq5008">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?fq5008" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fq5008_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fq5008">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5008cl" class="addthis_button_compact" href="#" addthis:description="In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed." addthis:title="DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps" addthis:url="https://doi.org/10.1107/S2052252519011692"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#fq5008pl').css('display','none')" /> </a> </div> <div id="fq5008pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5008cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2019/06/00/fq5008/index.html"><img src="/m/issues/2019/06/00/fq5008/fq5008thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2019/06/00/fq5008/index.html"><span class="it"><i>DeepRes</i></span>: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ramirez%2DAportela%2C%20E%2E"><span class="au">E. Ramírez-Aportela</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mota%2C%20J%2E"><span class="au">J. Mota</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Conesa%2C%20P%2E"><span class="au">P. Conesa</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Carazo%2C%20J%2EM%2E"><span class="au">J. M. Carazo</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sorzano%2C%20C%2EO%2ES%2E"><span class="au">C. O. S. Sorzano</span></a></div> <div class="ica_synopsis">A method (<span class="it"><i>DeepRes</i></span>) is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. <span class="it"><i>DeepRes</i></span> is fully automatic and parameter-free and avoids the issues of most current methods, such as their insensitivity to enhancements owing to <span class="it"><i>B</i></span>-factor sharpening, among others.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2019/06/00/fq5008/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2020) </span><b><a href="/m/services/archive.html" style="">7</a></b>, <span class="ica_pages" style="color: #000;">719-727</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252520007241" style="">https://doi.org/10.1107/S2052252520007241</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="pw5014cr" class="addthis_button_compact" href="#" addthis:description="High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 A or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2." addthis:title="A self-supervised workflow for particle picking in cryo-EM" addthis:url="https://doi.org/10.1107/S2052252520007241"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#pw5014pr').css('display','none')" /> </a> </div> <div id="pw5014pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5014cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?pw5014" title="Statistics"> <img id="pw5014_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="pw5014_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?pw5014">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?pw5014" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="pw5014_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?pw5014">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="pw5014cl" class="addthis_button_compact" href="#" addthis:description="High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 A or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2." addthis:title="A self-supervised workflow for particle picking in cryo-EM" addthis:url="https://doi.org/10.1107/S2052252520007241"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#pw5014pl').css('display','none')" /> </a> </div> <div id="pw5014pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5014cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2020/04/00/pw5014/index.html"><img src="/m/issues/2020/04/00/pw5014/pw5014thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2020/04/00/pw5014/index.html">A self-supervised workflow for particle picking in cryo-EM</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McSweeney%2C%20D%2EM%2E"><span class="au">D. M. McSweeney</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=McSweeney%2C%20S%2EM%2E"><span class="au">S. M. McSweeney</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20Q%2E"><span class="au">Q. Liu</span></a></div> <div class="ica_synopsis">A self-supervised workflow uses a 2D class average to progressively train a convolutional neural network for automated particle picking in cryo-EM.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2020/04/00/pw5014/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2020) </span><b><a href="/m/services/archive.html" style="">7</a></b>, <span class="ica_pages" style="color: #000;">881-892</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252520009306" style="">https://doi.org/10.1107/S2052252520009306</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="eh5009cr" class="addthis_button_compact" href="#" addthis:description="Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 A), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 A resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure." addthis:title="Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM" addthis:url="https://doi.org/10.1107/S2052252520009306"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#eh5009pr').css('display','none')" /> </a> </div> <div id="eh5009pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#eh5009cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?eh5009" title="Statistics"> <img id="eh5009_divwide" alt="" class="art_icon downloads" style="top: 0px; 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In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 A resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure." addthis:title="Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM" addthis:url="https://doi.org/10.1107/S2052252520009306"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 115px; width: 20px;" onclick="$('#eh5009pl').css('display','none')" /> </a> </div> <div id="eh5009pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#eh5009cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2020/05/00/eh5009/index.html"><img src="/m/issues/2020/05/00/eh5009/eh5009thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2020/05/00/eh5009/index.html">Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Farrell%2C%20D%2EP%2E"><span class="au">D. P. Farrell</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Anishchenko%2C%20I%2E"><span class="au">I. Anishchenko</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shakeel%2C%20S%2E"><span class="au">S. Shakeel</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lauko%2C%20A%2E"><span class="au">A. Lauko</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Passmore%2C%20L%2EA%2E"><span class="au">L. A. Passmore</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Baker%2C%20D%2E"><span class="au">D. Baker</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=DiMaio%2C%20F%2E"><span class="au">F. DiMaio</span></a></div> <div class="ica_synopsis">This paper describes a method for determining an atomic model of a protein complex using moderate-resolution cryoEM data and distance predictions from deep learning.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2020/05/00/eh5009/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2020) </span><b><a href="/m/services/archive.html" style="">7</a></b>, <span class="ica_pages" style="color: #000;">1036-1047</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252520010088" style="">https://doi.org/10.1107/S2052252520010088</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yu5018cr" class="addthis_button_compact" href="#" addthis:description="New Nd-Fe-B crystal structures can be formed via the elemental substitution of LA-T-X host structures, including lanthanides (LA), transition metals (T) and light elements, X = B, C, N and O. The 5967 samples of ternary LA-T-X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the LA-T-X host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures." addthis:title="Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship" addthis:url="https://doi.org/10.1107/S2052252520010088"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#yu5018pr').css('display','none')" /> </a> </div> <div id="yu5018pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yu5018cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?yu5018" title="Statistics"> <img id="yu5018_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="yu5018_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?yu5018">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="yu5018cl" class="addthis_button_compact" href="#" addthis:description="New Nd-Fe-B crystal structures can be formed via the elemental substitution of LA-T-X host structures, including lanthanides (LA), transition metals (T) and light elements, X = B, C, N and O. The 5967 samples of ternary LA-T-X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the LA-T-X host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures." addthis:title="Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship" addthis:url="https://doi.org/10.1107/S2052252520010088"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#yu5018pl').css('display','none')" /> </a> </div> <div id="yu5018pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#yu5018cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2020/06/00/yu5018/index.html"><img src="/m/issues/2020/06/00/yu5018/yu5018thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2020/06/00/yu5018/index.html">Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pham%2C%20T%2E%2DL%2E"><span class="au">T.-L. Pham</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nguyen%2C%20D%2E%2DN%2E"><span class="au">D.-N. Nguyen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ha%2C%20M%2E%2DQ%2E"><span class="au">M.-Q. Ha</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kino%2C%20H%2E"><span class="au">H. Kino</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Miyake%2C%20T%2E"><span class="au">T. Miyake</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Dam%2C%20H%2E%2DC%2E"><span class="au">H.-C. Dam</span></a></div> <div class="ica_synopsis">Twenty new Nd–Fe–B crystal structures can be found via the elemental substitution of 5967 host structures of lanthanides–transition metals–light elements collected from the Open Quantum Materials Database. The average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the structure–stability relationship of the new substituted Nd–Fe–B crystal structures.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2020/06/00/yu5018/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2021) </span><b><a href="/m/services/archive.html" style="">8</a></b>, <span class="ica_pages" style="color: #000;">12-21</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252520013780" style="">https://doi.org/10.1107/S2052252520013780</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="cw5029cr" class="addthis_button_compact" href="#" addthis:description="The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data." addthis:title="Complex imaging of phase domains by deep neural networks" addthis:url="https://doi.org/10.1107/S2052252520013780"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#cw5029pr').css('display','none')" /> </a> </div> <div id="cw5029pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5029cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?cw5029" title="Statistics"> <img id="cw5029_divwide" alt="" class="art_icon downloads" style="top: 0px; left: -23px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="cw5029_stats"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?cw5029">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/biblio_page?cw5029" title="Download citation"> <img alt="" class="art_icon" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="cw5029_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?cw5029">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="cw5029cl" class="addthis_button_compact" href="#" addthis:description="The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data." addthis:title="Complex imaging of phase domains by deep neural networks" addthis:url="https://doi.org/10.1107/S2052252520013780"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#cw5029pl').css('display','none')" /> </a> </div> <div id="cw5029pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5029cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2021/01/00/cw5029/index.html"><img src="/m/issues/2021/01/00/cw5029/cw5029thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2021/01/00/cw5029/index.html">Complex imaging of phase domains by deep neural networks</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wu%2C%20L%2E"><span class="au">L. Wu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Juhas%2C%20P%2E"><span class="au">P. Juhas</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yoo%2C%20S%2E"><span class="au">S. Yoo</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Robinson%2C%20I%2E"><span class="au">I. Robinson</span></a></div> <div class="ica_synopsis">Machine-learning approaches can greatly facilitate single-particle-imaging experiments at X-ray free-electron-laser facilities by providing real-time images from the coherent X-ray diffraction data stream, using methods presented in this article.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2021/01/00/cw5029/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2021) </span><b><a href="/m/services/archive.html" style="">8</a></b>, <span class="ica_pages" style="color: #000;">60-75</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252520014384" style="">https://doi.org/10.1107/S2052252520014384</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5015cr" class="addthis_button_compact" href="#" addthis:description="Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed." addthis:title="Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination" addthis:url="https://doi.org/10.1107/S2052252520014384"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fq5015pr').css('display','none')" /> </a> </div> <div id="fq5015pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5015cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fq5015" title="Statistics"> <img id="fq5015_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fq5015_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fq5015">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5015cl" class="addthis_button_compact" href="#" addthis:description="Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed." addthis:title="Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination" addthis:url="https://doi.org/10.1107/S2052252520014384"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#fq5015pl').css('display','none')" /> </a> </div> <div id="fq5015pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5015cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2021/01/00/fq5015/index.html"><img src="/m/issues/2021/01/00/fq5015/fq5015thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2021/01/00/fq5015/index.html">Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kimanius%2C%20D%2E"><span class="au">D. Kimanius</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zickert%2C%20G%2E"><span class="au">G. Zickert</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nakane%2C%20T%2E"><span class="au">T. Nakane</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Adler%2C%20J%2E"><span class="au">J. Adler</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Lunz%2C%20S%2E"><span class="au">S. Lunz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schonlieb%2C%20C%2E%2DB%2E"><span class="au">C.-B. Schönlieb</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Oktem%2C%20O%2E"><span class="au">O. Öktem</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Scheres%2C%20S%2EH%2EW%2E"><span class="au">S. H. W. Scheres</span></a></div> <div class="ica_synopsis">The incorporation of prior knowledge about the structures of biological macromolecules into the reconstruction process of cryo-EM structure determination is proposed. Using a novel algorithm inspired by regularization by denoising, it is shown how convolutional neural networks can be used within this framework to improve reconstructions from simulated data.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2021/01/00/fq5015/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2021) </span><b><a href="/m/services/archive.html" style="">8</a></b>, <span class="ica_pages" style="color: #000;">408-420</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252521002402" style="">https://doi.org/10.1107/S2052252521002402</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fc5051cr" class="addthis_button_compact" href="#" addthis:description="Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans." addthis:title="Enhancing deep-learning training for phase identification in powder X-ray diffractograms" addthis:url="https://doi.org/10.1107/S2052252521002402"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fc5051pr').css('display','none')" /> </a> </div> <div id="fc5051pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fc5051cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fc5051" title="Statistics"> <img id="fc5051_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fc5051_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fc5051">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fc5051cl" class="addthis_button_compact" href="#" addthis:description="Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans." addthis:title="Enhancing deep-learning training for phase identification in powder X-ray diffractograms" addthis:url="https://doi.org/10.1107/S2052252521002402"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#fc5051pl').css('display','none')" /> </a> </div> <div id="fc5051pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fc5051cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2021/03/00/fc5051/index.html"><img src="/m/issues/2021/03/00/fc5051/fc5051thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2021/03/00/fc5051/index.html">Enhancing deep-learning training for phase identification in powder X-ray diffractograms</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Schuetzke%2C%20J%2E"><span class="au">J. Schuetzke</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Benedix%2C%20A%2E"><span class="au">A. Benedix</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mikut%2C%20R%2E"><span class="au">R. Mikut</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Reischl%2C%20M%2E"><span class="au">M. Reischl</span></a></div> <div class="ica_synopsis">A framework is described for the efficient and realistic simulation of X-ray diffraction scans to train machine- or deep-learning models like convolutional neural networks for the automatic phase-identification task in multiphase compounds.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2021/03/00/fc5051/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2021) </span><b><a href="/m/services/archive.html" style="">8</a></b>, <span class="ica_pages" style="color: #000;">719-731</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252521005480" style="">https://doi.org/10.1107/S2052252521005480</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ro5028cr" class="addthis_button_compact" href="#" addthis:description="Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping." addthis:title="Deep learning for improving non-destructive grain mapping in 3D" addthis:url="https://doi.org/10.1107/S2052252521005480"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ro5028pr').css('display','none')" /> </a> </div> <div id="ro5028pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ro5028cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ro5028" title="Statistics"> <img id="ro5028_divwide" alt="" class="art_icon downloads" style="top: 0px; 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An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping." addthis:title="Deep learning for improving non-destructive grain mapping in 3D" addthis:url="https://doi.org/10.1107/S2052252521005480"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ro5028pl').css('display','none')" /> </a> </div> <div id="ro5028pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ro5028cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2021/05/00/ro5028/index.html"><img src="/m/issues/2021/05/00/ro5028/ro5028thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2021/05/00/ro5028/index.html">Deep learning for improving non-destructive grain mapping in 3D</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fang%2C%20H%2E"><span class="au">H. Fang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Hovad%2C%20E%2E"><span class="au">E. Hovad</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20Y%2E"><span class="au">Y. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Clemmensen%2C%20L%2EK%2EH%2E"><span class="au">L. K. H. Clemmensen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ersboll%2C%20B%2EK%2E"><span class="au">B. K. Ersbøll</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Juul%20Jensen%2C%20D%2E"><span class="au">D. Juul Jensen</span></a></div> <div class="ica_synopsis">A deep learning neural network has been developed to efficiently and accurately clean the background noise in experimental lab-based X-ray diffraction images. A better spot segmentation is obtained and thus grain mapping in 3D is improved compared with the conventional method.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2021/05/00/ro5028/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2022) </span><b><a href="/m/services/archive.html" style="">9</a></b>, <span class="ica_pages" style="color: #000;">86-97</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252521011088" style="">https://doi.org/10.1107/S2052252521011088</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="pw5018cr" class="addthis_button_compact" href="#" addthis:description="Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures." addthis:title="findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM" addthis:url="https://doi.org/10.1107/S2052252521011088"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#pw5018pr').css('display','none')" /> </a> </div> <div id="pw5018pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5018cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?pw5018" title="Statistics"> <img id="pw5018_divwide" alt="" class="art_icon downloads" style="top: 0px; 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They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures." addthis:title="findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM" addthis:url="https://doi.org/10.1107/S2052252521011088"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 115px; width: 20px;" onclick="$('#pw5018pl').css('display','none')" /> </a> </div> <div id="pw5018pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5018cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2022/01/00/pw5018/index.html"><img src="/m/issues/2022/01/00/pw5018/pw5018thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2022/01/00/pw5018/index.html"><span class="it"><i>findMySequence</i></span>: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chojnowski%2C%20G%2E"><span class="au">G. Chojnowski</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Simpkin%2C%20A%2EJ%2E"><span class="au">A. J. Simpkin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Leonardo%2C%20D%2EA%2E"><span class="au">D. A. Leonardo</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Seifert%2DDavila%2C%20W%2E"><span class="au">W. Seifert-Davila</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Vivas%2DRuiz%2C%20D%2EE%2E"><span class="au">D. E. Vivas-Ruiz</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Keegan%2C%20R%2EM%2E"><span class="au">R. M. Keegan</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Rigden%2C%20D%2EJ%2E"><span class="au">D. J. Rigden</span></a></div> <div class="ica_synopsis"><span class="it"><i>findMySequence</i></span> is presented – a machine-learning method for the identification of unknown proteins and sequence-assignment validation in cryo-EM and X-ray crystallography.</div><div class="ica_codelinks" style=""> <div class="ica_codelinks_pdb"><span class="ica_codelinks_head" style="">PDB reference</span>: <a href="https://scripts.iucr.org/cgi-bin/cr.cgi?rm=pdb&pdbId=7m6c">Crystal structure of PLA2 from snake venom of Peruvian <span class="it"><i>Bothrops atrox</i></span>, 7m6c</a> </div> </div> <div class="ica_readmore"> <a href="/m/issues/2022/01/00/pw5018/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2022) </span><b><a href="/m/services/archive.html" style="">9</a></b>, <span class="ica_pages" style="color: #000;">104-113</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252521011258" style="">https://doi.org/10.1107/S2052252521011258</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fs5198cr" class="addthis_button_compact" href="#" addthis:description="X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software." addthis:title="BraggNN: fast X-ray Bragg peak analysis using deep learning" addthis:url="https://doi.org/10.1107/S2052252521011258"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fs5198pr').css('display','none')" /> </a> </div> <div id="fs5198pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fs5198cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fs5198" title="Statistics"> <img id="fs5198_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 46px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fs5198_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fs5198">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fs5198cl" class="addthis_button_compact" href="#" addthis:description="X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software." addthis:title="BraggNN: fast X-ray Bragg peak analysis using deep learning" addthis:url="https://doi.org/10.1107/S2052252521011258"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 69px; width: 20px;" onclick="$('#fs5198pl').css('display','none')" /> </a> </div> <div id="fs5198pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fs5198cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2022/01/00/fs5198/index.html"><img src="/m/issues/2022/01/00/fs5198/fs5198thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2022/01/00/fs5198/index.html"><span class="it"><i>BraggNN</i></span>: fast X-ray Bragg peak analysis using deep learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Liu%2C%20Z%2E"><span class="au">Z. Liu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sharma%2C%20H%2E"><span class="au">H. Sharma</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Park%2C%20J%2E%2DS%2E"><span class="au">J.-S. Park</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kenesei%2C%20P%2E"><span class="au">P. Kenesei</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Miceli%2C%20A%2E"><span class="au">A. Miceli</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Almer%2C%20J%2E"><span class="au">J. Almer</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kettimuthu%2C%20R%2E"><span class="au">R. Kettimuthu</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Foster%2C%20I%2E"><span class="au">I. Foster</span></a></div> <div class="ica_synopsis">We propose <span class="it"><i>BraggNN</i></span>, a deep-learning based method, to accelerate the most computation-intensive part of polycrystal diffraction data analysis (diffraction signal characterization). The application of <span class="it"><i>BraggNN</i></span> for real experimental data demonstrates that it can deliver consistent (sometimes even slightly better) results compared with the conventional method while running hundreds of times faster.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2022/01/00/fs5198/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2022) </span><b><a href="/m/services/archive.html" style="">9</a></b>, <span class="ica_pages" style="color: #000;">223-230</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252521012963" style="">https://doi.org/10.1107/S2052252521012963</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="cw5032cr" class="addthis_button_compact" href="#" addthis:description="Radiation damage and a low signal-to-noise ratio are the primary factors that limit spatial resolution in coherent diffraction imaging (CDI) of biomaterials using X-ray sources. Introduced here is a clustering algorithm named ConvRe based on deep learning, and it is applied to obtain accurate and consistent image reconstruction from noisy diffraction patterns of weakly scattering biomaterials. To investigate the impact of X-ray radiation on soft biomaterials, CDI experiments were performed on mitochondria from human embryonic kidney cells using synchrotron radiation. Benefiting from the new algorithm, structural changes in the mitochondria induced by X-ray radiation damage were quantitatively characterized and analysed at the nanoscale with different radiation doses. This work also provides a promising approach for improving the imaging quality of biomaterials with XFEL-based plane-wave CDI." addthis:title="Quantitative analysis of the effect of radiation on mitochondria structure using coherent diffraction imaging with a clustering algorithm" addthis:url="https://doi.org/10.1107/S2052252521012963"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#cw5032pr').css('display','none')" /> </a> </div> <div id="cw5032pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5032cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?cw5032" title="Statistics"> <img id="cw5032_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="cw5032_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?cw5032">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="cw5032cl" class="addthis_button_compact" href="#" addthis:description="Radiation damage and a low signal-to-noise ratio are the primary factors that limit spatial resolution in coherent diffraction imaging (CDI) of biomaterials using X-ray sources. Introduced here is a clustering algorithm named ConvRe based on deep learning, and it is applied to obtain accurate and consistent image reconstruction from noisy diffraction patterns of weakly scattering biomaterials. To investigate the impact of X-ray radiation on soft biomaterials, CDI experiments were performed on mitochondria from human embryonic kidney cells using synchrotron radiation. Benefiting from the new algorithm, structural changes in the mitochondria induced by X-ray radiation damage were quantitatively characterized and analysed at the nanoscale with different radiation doses. This work also provides a promising approach for improving the imaging quality of biomaterials with XFEL-based plane-wave CDI." addthis:title="Quantitative analysis of the effect of radiation on mitochondria structure using coherent diffraction imaging with a clustering algorithm" addthis:url="https://doi.org/10.1107/S2052252521012963"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#cw5032pl').css('display','none')" /> </a> </div> <div id="cw5032pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#cw5032cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2022/02/00/cw5032/index.html"><img src="/m/issues/2022/02/00/cw5032/cw5032thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2022/02/00/cw5032/index.html">Quantitative analysis of the effect of radiation on mitochondria structure using coherent diffraction imaging with a clustering algorithm</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pan%2C%20D%2E"><span class="au">D. Pan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Fan%2C%20J%2E"><span class="au">J. Fan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Nie%2C%20Z%2E"><span class="au">Z. Nie</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sun%2C%20Z%2E"><span class="au">Z. Sun</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20J%2E"><span class="au">J. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Tong%2C%20Y%2E"><span class="au">Y. Tong</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=He%2C%20B%2E"><span class="au">B. He</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Song%2C%20C%2E"><span class="au">C. Song</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kohmura%2C%20Y%2E"><span class="au">Y. Kohmura</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yabashi%2C%20M%2E"><span class="au">M. Yabashi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ishikawa%2C%20T%2E"><span class="au">T. Ishikawa</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Shen%2C%20Y%2E"><span class="au">Y. Shen</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jiang%2C%20H%2E"><span class="au">H. Jiang</span></a></div> <div class="ica_synopsis">A clustering algorithm based on deep learning is proposed to perform accurate image reconstruction from noisy coherent diffraction patterns. Structural changes in mitochondria induced by X-ray radiation damage are quantitatively characterized and analysed at the nanoscale with different radiation doses.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2022/02/00/cw5032/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2022) </span><b><a href="/m/services/archive.html" style="">9</a></b>, <span class="ica_pages" style="color: #000;">632-638</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252522006959" style="">https://doi.org/10.1107/S2052252522006959</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5020cr" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy has become a powerful technique for the 3D structure determination of biological molecules. The last decade has seen an astonishing development of both hardware and software, and an exponential growth of new structures obtained at medium-high resolution. However, the knowledge accumulated in this field over the years has hardly been utilized as feedback in the reconstruction of new structures. In this context, this article explores the use of the deep-learning approach deepEMhancer as a regularizer in the RELION refinement process. deepEMhancer introduces prior information derived from macromolecular structures, and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy. These features have a direct effect on image alignment and reduction of overfitting during iterative refinement. The advantages of this combination are demonstrated for several membrane proteins, for which it is especially useful because of their high disorder and flexibility." addthis:title="Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools" addthis:url="https://doi.org/10.1107/S2052252522006959"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#fq5020pr').css('display','none')" /> </a> </div> <div id="fq5020pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5020cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?fq5020" title="Statistics"> <img id="fq5020_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="fq5020_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?fq5020">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="fq5020cl" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy has become a powerful technique for the 3D structure determination of biological molecules. The last decade has seen an astonishing development of both hardware and software, and an exponential growth of new structures obtained at medium-high resolution. However, the knowledge accumulated in this field over the years has hardly been utilized as feedback in the reconstruction of new structures. In this context, this article explores the use of the deep-learning approach deepEMhancer as a regularizer in the RELION refinement process. deepEMhancer introduces prior information derived from macromolecular structures, and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy. These features have a direct effect on image alignment and reduction of overfitting during iterative refinement. The advantages of this combination are demonstrated for several membrane proteins, for which it is especially useful because of their high disorder and flexibility." addthis:title="Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools" addthis:url="https://doi.org/10.1107/S2052252522006959"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#fq5020pl').css('display','none')" /> </a> </div> <div id="fq5020pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#fq5020cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2022/05/00/fq5020/index.html"><img src="/m/issues/2022/05/00/fq5020/fq5020thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2022/05/00/fq5020/index.html">Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Ramirez%2DAportela%2C%20E%2E"><span class="au">E. Ramírez-Aportela</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Carazo%2C%20J%2EM%2E"><span class="au">J. M. Carazo</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sorzano%2C%20C%2EO%2ES%2E"><span class="au">C. O. S. Sorzano</span></a></div> <div class="ica_synopsis">This article explores the use of the deep learning approach <span class="it"><i>deepEMhancer</i></span> as a regularizer in the <span class="it"><i>RELION</i></span> refinement process. <span class="it"><i>deepEMhancer</i></span> introduces prior information derived from macromolecular structures and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy, with a direct effect on image alignment and a reduction of overfitting during iterative refinement.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2022/05/00/fq5020/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2023) </span><b><a href="/m/services/archive.html" style="">10</a></b>, <span class="ica_pages" style="color: #000;">77-89</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252522010624" style="">https://doi.org/10.1107/S2052252522010624</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="eh5015cr" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ~6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services." addthis:title="Fully automated multi-grid cryoEM screening using Smart Leginon" addthis:url="https://doi.org/10.1107/S2052252522010624"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#eh5015pr').css('display','none')" /> </a> </div> <div id="eh5015pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#eh5015cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?eh5015" title="Statistics"> <img id="eh5015_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="eh5015_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?eh5015">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="eh5015cl" class="addthis_button_compact" href="#" addthis:description="Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ~6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services." addthis:title="Fully automated multi-grid cryoEM screening using Smart Leginon" addthis:url="https://doi.org/10.1107/S2052252522010624"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#eh5015pl').css('display','none')" /> </a> </div> <div id="eh5015pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#eh5015cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2023/01/00/eh5015/index.html"><img src="/m/issues/2023/01/00/eh5015/eh5015thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2023/01/00/eh5015/index.html">Fully automated multi-grid cryoEM screening using <span class="it"><i>Smart Leginon</i></span></a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cheng%2C%20A%2E"><span class="au">A. Cheng</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kim%2C%20P%2ET%2E"><span class="au">P. T. Kim</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kuang%2C%20H%2E"><span class="au">H. Kuang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Mendez%2C%20J%2EH%2E"><span class="au">J. H. Mendez</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chua%2C%20E%2EY%2ED%2E"><span class="au">E. Y. D. Chua</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Maruthi%2C%20K%2E"><span class="au">K. Maruthi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wei%2C%20H%2E"><span class="au">H. Wei</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sawh%2C%20A%2E"><span class="au">A. Sawh</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Aragon%2C%20M%2EF%2E"><span class="au">M. F. Aragon</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Serbynovskyi%2C%20V%2E"><span class="au">V. Serbynovskyi</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Neselu%2C%20K%2E"><span class="au">K. Neselu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Eng%2C%20E%2ET%2E"><span class="au">E. T. Eng</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Potter%2C%20C%2ES%2E"><span class="au">C. S. Potter</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Carragher%2C%20B%2E"><span class="au">B. Carragher</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bepler%2C%20T%2E"><span class="au">T. Bepler</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Noble%2C%20A%2EJ%2E"><span class="au">A. J. Noble</span></a></div> <div class="ica_synopsis"><span class="it"><i>Smart Leginon</i></span> leverages new machine-learning algorithms together with new microscope and image-handling algorithms to enable unattended, multi-grid cryoEM screening. <span class="it"><i>Smart Leginon Autoscreen</i></span> reduces microscope operator time from ∼6 h to <10 min for a typical session and recovers substantial idle microscope time outside of normal working hours.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2023/01/00/eh5015/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2023) </span><b><a href="/m/services/archive.html" style="">10</a></b>, <span class="ica_pages" style="color: #000;">90-102</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252522010612" style="">https://doi.org/10.1107/S2052252522010612</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="pw5021cr" class="addthis_button_compact" href="#" addthis:description="Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software." addthis:title="Learning to automate cryo-electron microscopy data collection with Ptolemy" addthis:url="https://doi.org/10.1107/S2052252522010612"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#pw5021pr').css('display','none')" /> </a> </div> <div id="pw5021pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5021cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?pw5021" title="Statistics"> <img id="pw5021_divwide" alt="" class="art_icon downloads" style="top: 0px; 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To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software." addthis:title="Learning to automate cryo-electron microscopy data collection with Ptolemy" addthis:url="https://doi.org/10.1107/S2052252522010612"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#pw5021pl').css('display','none')" /> </a> </div> <div id="pw5021pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#pw5021cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2023/01/00/pw5021/index.html"><img src="/m/issues/2023/01/00/pw5021/pw5021thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2023/01/00/pw5021/index.html">Learning to automate cryo-electron microscopy data collection with <span class="it"><i>Ptolemy</i></span></a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kim%2C%20P%2ET%2E"><span class="au">P. T. Kim</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Noble%2C%20A%2EJ%2E"><span class="au">A. J. Noble</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Cheng%2C%20A%2E"><span class="au">A. Cheng</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Bepler%2C%20T%2E"><span class="au">T. Bepler</span></a></div> <div class="ica_synopsis"><span class="it"><i>Ptolemy</i></span> uses deep learning and computer vision to automate multi-scale targeting in cryoEM data collection. It accurately finds and ranks targets of interest (`squares' and `holes') at successive magnification levels and generalizes across samples and microscopes.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2023/01/00/pw5021/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2023) </span><b><a href="/m/services/archive.html" style="">10</a></b>, <span class="ica_pages" style="color: #000;">297-308</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S205225252300204X" style="">https://doi.org/10.1107/S205225252300204X</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ro5032cr" class="addthis_button_compact" href="#" addthis:description="Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials." addthis:title="Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning" addthis:url="https://doi.org/10.1107/S205225252300204X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ro5032pr').css('display','none')" /> </a> </div> <div id="ro5032pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ro5032cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ro5032" title="Statistics"> <img id="ro5032_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ro5032_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ro5032">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ro5032cl" class="addthis_button_compact" href="#" addthis:description="Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials." addthis:title="Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning" addthis:url="https://doi.org/10.1107/S205225252300204X"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ro5032pl').css('display','none')" /> </a> </div> <div id="ro5032pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ro5032cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2023/03/00/ro5032/index.html"><img src="/m/issues/2023/03/00/ro5032/ro5032thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2023/03/00/ro5032/index.html">Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Sun%2C%20M%2E"><span class="au">M. Sun</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Dong%2C%20Z%2E"><span class="au">Z. Dong</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Wu%2C%20L%2E"><span class="au">L. Wu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Yao%2C%20H%2E"><span class="au">H. Yao</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Niu%2C%20W%2E"><span class="au">W. Niu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Xu%2C%20D%2E"><span class="au">D. Xu</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chen%2C%20P%2E"><span class="au">P. Chen</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Gupta%2C%20H%2ES%2E"><span class="au">H. S. Gupta</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhang%2C%20Y%2E"><span class="au">Y. Zhang</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Dong%2C%20Y%2E"><span class="au">Y. Dong</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Chen%2C%20C%2E"><span class="au">C. Chen</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Zhao%2C%20L%2E"><span class="au">L. Zhao</span></a></div> <div class="ica_synopsis">A machine-learning (ML) based framework can help to unveil the nanofiber orientation in hierarchically structured biological materials with the advantages of fast speed and automation. It promises to meet the analysis demand of extensive amounts of related data online and in real time in near-future next-generation synchrotrons.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2023/03/00/ro5032/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2023) </span><b><a href="/m/services/archive.html" style="">10</a></b>, <span class="ica_pages" style="color: #000;">487-496</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252523004293" style="">https://doi.org/10.1107/S2052252523004293</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mf5063cr" class="addthis_button_compact" href="#" addthis:description="The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept." addthis:title="A deep learning solution for crystallographic structure determination" addthis:url="https://doi.org/10.1107/S2052252523004293"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#mf5063pr').css('display','none')" /> </a> </div> <div id="mf5063pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mf5063cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?mf5063" title="Statistics"> <img id="mf5063_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="mf5063_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?mf5063">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="mf5063cl" class="addthis_button_compact" href="#" addthis:description="The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept." addthis:title="A deep learning solution for crystallographic structure determination" addthis:url="https://doi.org/10.1107/S2052252523004293"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#mf5063pl').css('display','none')" /> </a> </div> <div id="mf5063pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#mf5063cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2023/04/00/mf5063/index.html"><img src="/m/issues/2023/04/00/mf5063/mf5063thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2023/04/00/mf5063/index.html">A deep learning solution for crystallographic structure determination</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Pan%2C%20T%2E"><span class="au">T. Pan</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Jin%2C%20S%2E"><span class="au">S. Jin</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Miller%2C%20M%2ED%2E"><span class="au">M. D. Miller</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Kyrillidis%2C%20A%2E"><span class="au">A. Kyrillidis</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Phillips%2C%20G%2EN%2E"><span class="au">G. N. Phillips Jr</span></a></div> <div class="ica_synopsis">Machine learning methods have been applied to solve simple Patterson maps. The results demonstrate the potential to use neural networks for solving the phase problem in crystallography.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2023/04/00/mf5063/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div><hr style="-moz-box-sizing: content-box; box-sizing: content-box; height:1px; border-width:0; color:#b0b0b0; background-color:#b0b0b0;" /> <div class="issuecontentsarticle"> <div class="ica_header"> <div class="ica_headerleft"> <div class="ica_header" style=""> <span class="ica_iucrjname iucrjname" style=""><a href="https://www.iucrj.org" style="">IUCrJ</a></span><span style="color:#000;"> (2023) </span><b><a href="/m/services/archive.html" style="">10</a></b>, <span class="ica_pages" style="color: #000;">610-623</span><br/><span class="ica_doi" style=""><a href="https://doi.org/10.1107/S2052252523006887" style="">https://doi.org/10.1107/S2052252523006887</a></span> </div> </div> <div class="ica_headerright" style=""> <div class="art_issuecontentsfunctions"> <div class="article_functions af_horizontal">  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ct5021cr" class="addthis_button_compact" href="#" addthis:description="The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%." addthis:title="Towards the extraction of the crystal cell parameters from pair distribution function profiles" addthis:url="https://doi.org/10.1107/S2052252523006887"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 0px; width: 20px;" onclick="$('#ct5021pr').css('display','none')" /> </a> </div> <div id="ct5021pr" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ct5021cr').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a href="https://scripts.iucr.org/cgi-bin/citedin?ct5021" title="Statistics"> <img id="ct5021_divwide" alt="" class="art_icon downloads" style="top: 0px; 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left: 69px; width: 20px;" title="Statistics" src="/logos/buttonlogos/pageviews.png"/> </a> </div> <div style="opacity: 0;" class="popup"> <img class="but_close" src="/logos/buttonlogos/cross.png" /> <div class="" id="ct5021_stnar"><span class="popuphd"><a title="Statistics" href="https://scripts.iucr.org/cgi-bin/citedin?ct5021">Article statistics</a></span></div> </div> </div>  <div class="bubbleInfo"> <div class="sidebutton"> <a id="ct5021cl" class="addthis_button_compact" href="#" addthis:description="The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%." addthis:title="Towards the extraction of the crystal cell parameters from pair distribution function profiles" addthis:url="https://doi.org/10.1107/S2052252523006887"> <img class="art_icon" src="/logos/buttonlogos/addthis.png" alt="share" style="top: 0px; left: 92px; width: 20px;" onclick="$('#ct5021pl').css('display','none')" /> </a> </div> <div id="ct5021pl" class="popup" style="opacity: 0;"> <span class="popuphd" onclick="$(this).parent().css('display','none');$('#ct5021cl').trigger('click');return false;"><a onclick="return false;" href="">Share</a></span><img class="but_close" alt="close" src="/logos/buttonlogos/cross.png" /> </div> </div> </div> </div> <div class="ica_openaccesslogo" style=""></div> </div> <div class="clear"></div> </div> <div class="ica_body"> <div class="ica_bodyleft"> <div class="ica_thumbnail"><a href="/m/issues/2023/05/00/ct5021/index.html"><img src="/m/issues/2023/05/00/ct5021/ct5021thumbnail.gif" alt="link to html" /></a></div> </div> <div class="ica_bodyright"> <div class="ica_title"><h3 class="atl"><a href="/m/issues/2023/05/00/ct5021/index.html">Towards the extraction of the crystal cell parameters from pair distribution function profiles</a></h3></div> <div class="ica_authors"><a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Guccione%2C%20P%2E"><span class="au">P. Guccione</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Diacono%2C%20D%2E"><span class="au">D. Diacono</span></a>, <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Toso%2C%20S%2E"><span class="au">S. Toso</span></a> and <a href="https://scripts.iucr.org/cgi-bin/citedin?search_on=name&author_name=Caliandro%2C%20R%2E"><span class="au">R. Caliandro</span></a></div> <div class="ica_synopsis">A method to estimate crystal cell parameters directly from a pair distribution function profile, which makes use of machine-learning approaches combined with multivariate analysis and vector superposition techniques, is presented.</div><div class="ica_codelinks" style=""> </div> <div class="ica_readmore"> <a href="/m/issues/2023/05/00/ct5021/index.html">Read article</a> </div> </div> </div> </div> <div class="clear"></div></div> </div> <div id="side"> <div class="side_box"> <div style="margin-top: 0.2em; margin-bottom: 0.5em; width: 100%; max-width: 300px; min-width: 75px; border-radius: 5px;"> <a target="_blank" href="https://www.iucr.org/people/associates" title="Associates Programme"><img style="margin-bottom: 0.5em; width: 100%; max-width: 300px; min-width: 75px;border-radius: 5px;" src="/logos/adverts/ap_banner.gif" alt="Associates Programme" /></a> </div> </div> <div class="side_box"> <div style="margin-top: 0.2em; 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