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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.08703">arXiv:2402.08703</a> <span> [<a href="https://arxiv.org/pdf/2402.08703">pdf</a>, <a href="https://arxiv.org/format/2402.08703">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tang%2C+X">Xiangru Tang</a>, <a href="/search/q-bio?searchtype=author&query=Dai%2C+H">Howard Dai</a>, <a href="/search/q-bio?searchtype=author&query=Knight%2C+E">Elizabeth Knight</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+F">Fang Wu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yunyang Li</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+T">Tianxiao Li</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M">Mark Gerstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.08703v2-abstract-short" style="display: inline;"> Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08703v2-abstract-full').style.display = 'inline'; document.getElementById('2402.08703v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.08703v2-abstract-full" style="display: none;"> Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08703v2-abstract-full').style.display = 'none'; document.getElementById('2402.08703v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03946">arXiv:2310.03946</a> <span> [<a href="https://arxiv.org/pdf/2310.03946">pdf</a>, <a href="https://arxiv.org/format/2310.03946">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Improved prediction of ligand-protein binding affinities by meta-modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lee%2C+H">Ho-Joon Lee</a>, <a href="/search/q-bio?searchtype=author&query=Emani%2C+P+S">Prashant S. Emani</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M+B">Mark B. Gerstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03946v5-abstract-short" style="display: inline;"> The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03946v5-abstract-full').style.display = 'inline'; document.getElementById('2310.03946v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03946v5-abstract-full" style="display: none;"> The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03946v5-abstract-full').style.display = 'none'; document.getElementById('2310.03946v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">54 pages, 6 main tables, 6 main figures, 8 supplementary figures, and supporting information. For 11 supplementary tables and code, see https://github.com/Lee1701/Lee2023a</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.08171">arXiv:2210.08171</a> <span> [<a href="https://arxiv.org/pdf/2210.08171">pdf</a>, <a href="https://arxiv.org/format/2210.08171">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+T">Tianxiao Li</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongyu Guo</a>, <a href="/search/q-bio?searchtype=author&query=Grazioli%2C+F">Filippo Grazioli</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M">Mark Gerstein</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+M+R">Martin Renqiang Min</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.08171v2-abstract-short" style="display: inline;"> In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08171v2-abstract-full').style.display = 'inline'; document.getElementById('2210.08171v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.08171v2-abstract-full" style="display: none;"> In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actions involved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alter the function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08171v2-abstract-full').style.display = 'none'; document.getElementById('2210.08171v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.07127">arXiv:1911.07127</a> <span> [<a href="https://arxiv.org/pdf/1911.07127">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41592-020-01004-3">10.1038/s41592-020-01004-3 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantum Computing at the Frontiers of Biological Sciences </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Emani%2C+P+S">Prashant S. Emani</a>, <a href="/search/q-bio?searchtype=author&query=Warrell%2C+J">Jonathan Warrell</a>, <a href="/search/q-bio?searchtype=author&query=Anticevic%2C+A">Alan Anticevic</a>, <a href="/search/q-bio?searchtype=author&query=Bekiranov%2C+S">Stefan Bekiranov</a>, <a href="/search/q-bio?searchtype=author&query=Gandal%2C+M">Michael Gandal</a>, <a href="/search/q-bio?searchtype=author&query=McConnell%2C+M+J">Michael J. McConnell</a>, <a href="/search/q-bio?searchtype=author&query=Sapiro%2C+G">Guillermo Sapiro</a>, <a href="/search/q-bio?searchtype=author&query=Aspuru-Guzik%2C+A">Al谩n Aspuru-Guzik</a>, <a href="/search/q-bio?searchtype=author&query=Baker%2C+J">Justin Baker</a>, <a href="/search/q-bio?searchtype=author&query=Bastiani%2C+M">Matteo Bastiani</a>, <a href="/search/q-bio?searchtype=author&query=McClure%2C+P">Patrick McClure</a>, <a href="/search/q-bio?searchtype=author&query=Murray%2C+J">John Murray</a>, <a href="/search/q-bio?searchtype=author&query=Sotiropoulos%2C+S+N">Stamatios N Sotiropoulos</a>, <a href="/search/q-bio?searchtype=author&query=Taylor%2C+J">Jacob Taylor</a>, <a href="/search/q-bio?searchtype=author&query=Senthil%2C+G">Geetha Senthil</a>, <a href="/search/q-bio?searchtype=author&query=Lehner%2C+T">Thomas Lehner</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M+B">Mark B. Gerstein</a>, <a href="/search/q-bio?searchtype=author&query=Harrow%2C+A+W">Aram W. Harrow</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.07127v1-abstract-short" style="display: inline;"> The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07127v1-abstract-full').style.display = 'inline'; document.getElementById('1911.07127v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.07127v1-abstract-full" style="display: none;"> The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07127v1-abstract-full').style.display = 'none'; document.getElementById('1911.07127v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 3 figures, Perspective</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature Methods (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0709.4200">arXiv:0709.4200</a> <span> [<a href="https://arxiv.org/pdf/0709.4200">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Copy Number Variants and Segmental Duplications Show Different Formation Signatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kim%2C+P+M">Philip M. Kim</a>, <a href="/search/q-bio?searchtype=author&query=Korbel%2C+J+O">Jan O. Korbel</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xueying Chen</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M+B">Mark B. Gerstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="0709.4200v1-abstract-short" style="display: inline;"> In addition to variation in terms of single nucleotide polymorphisms (SNPs), whole regions ranging from several kilobases up to a megabase in length differ in copy number among individuals. These differences are referred to as Copy Number Variants (CNVs) and extensive mapping of these is underway. Recent studies have highlighted their great prevalence in the human genome. Segmental Duplications… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0709.4200v1-abstract-full').style.display = 'inline'; document.getElementById('0709.4200v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0709.4200v1-abstract-full" style="display: none;"> In addition to variation in terms of single nucleotide polymorphisms (SNPs), whole regions ranging from several kilobases up to a megabase in length differ in copy number among individuals. These differences are referred to as Copy Number Variants (CNVs) and extensive mapping of these is underway. Recent studies have highlighted their great prevalence in the human genome. Segmental Duplications (SDs) are long (>1kb) stretches of duplicated DNA with high sequence identity. First, we analyzed the co-localization of SDs and find that SDs are significantly co-localized with each other, resulting in a power-law distribution, which suggests a preferential attachment mechanism, i.e. existing SDs are likely to be involved in creating new ones nearby. Second, we look at the relationship of CNVs/SDs with various types of repeats. We we find that the previously recognized association of SDs with Alu elements is significantly stronger for older SDs and is sharply decreasing for younger ones. While it might be expected that the patterns should be similar for SDs and CNVs, we find, surprisingly, no association of CNVs with Alu elements. This trend is consistent with the decreasing correlation between Alu elements and younger SDs, the activity of Alu elements has been decreasing and by now it they seem no longer active. Furthermore, we find a striking association of SDs with processed pseudogenes suggesting that they may also have mediated SD formation. Moreover, find strong association with microsatellites for both SDs and CNVs that suggests a role for satellites in the formation of both. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0709.4200v1-abstract-full').style.display = 'none'; document.getElementById('0709.4200v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2007; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2007. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0706.0194">arXiv:0706.0194</a> <span> [<a href="https://arxiv.org/pdf/0706.0194">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> </div> <p class="title is-5 mathjax"> Comparing Classical Pathways and Modern Networks: Towards the Development of an Edge Ontology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lu%2C+L+J">Long J. Lu</a>, <a href="/search/q-bio?searchtype=author&query=Sboner%2C+A">Andrea Sboner</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y+J">Yuanpeng J. Huang</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+H+X">Hao Xin Lu</a>, <a href="/search/q-bio?searchtype=author&query=Gianoulis%2C+T+A">Tara A. Gianoulis</a>, <a href="/search/q-bio?searchtype=author&query=Yip%2C+K+Y">Kevin Y. Yip</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+P+M">Philip M. Kim</a>, <a href="/search/q-bio?searchtype=author&query=Montelione%2C+G+T">Gaetano T. Montelione</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M+B">Mark B. Gerstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="0706.0194v1-abstract-short" style="display: inline;"> Pathways are integral to systems biology. Their classical representation has proven useful but is inconsistent in the meaning assigned to each arrow (or edge) and inadvertently implies the isolation of one pathway from another. Conversely, modern high-throughput experiments give rise to standardized networks facilitating topological calculations. Combining these perspectives, we can embed classi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0706.0194v1-abstract-full').style.display = 'inline'; document.getElementById('0706.0194v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0706.0194v1-abstract-full" style="display: none;"> Pathways are integral to systems biology. Their classical representation has proven useful but is inconsistent in the meaning assigned to each arrow (or edge) and inadvertently implies the isolation of one pathway from another. Conversely, modern high-throughput experiments give rise to standardized networks facilitating topological calculations. Combining these perspectives, we can embed classical pathways within large-scale networks and thus demonstrate the crosstalk between them. As more diverse types of high-throughput data become available, we can effectively merge both perspectives, embedding pathways simultaneously in multiple networks. However, the original problem still remains - the current edge representation is inadequate to accurately convey all the information in pathways. Therefore, we suggest that a standardized, well-defined, edge ontology is necessary and propose a prototype here, as a starting point for reaching this goal. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0706.0194v1-abstract-full').style.display = 'none'; document.getElementById('0706.0194v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2007; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2007. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages including 5 figures and supplemental material</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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