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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Huang%2C+J&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Huang%2C+J&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Huang%2C+J&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <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/2503.17738">arXiv:2503.17738</a> <span> [<a href="https://arxiv.org/pdf/2503.17738">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> </div> </div> <p class="title is-5 mathjax"> Tumor-associated CD19$^+$ macrophages induce immunosuppressive microenvironment in hepatocellular carcinoma </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Junli Wang</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+W">Wanyue Cao</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jinyan Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+Y">Yu Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+R">Rujia Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Lou%2C+Y">Yu Lou</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Jiaqi Yang</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+J">Jianghui Tang</a>, <a href="/search/q-bio?searchtype=author&query=Ye%2C+M">Mao Ye</a>, <a href="/search/q-bio?searchtype=author&query=Hong%2C+Z">Zhengtao Hong</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jiangchao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+H">Haonan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yuquan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Sheng%2C+J">Jianpeng Sheng</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+X">Xinjiang Lu</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+P">Pinglong Xu</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+X">Xiongbin Lu</a>, <a href="/search/q-bio?searchtype=author&query=Bai%2C+X">Xueli Bai</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+T">Tingbo Liang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qi Zhang</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="2503.17738v1-abstract-short" style="display: inline;"> Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enric… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.17738v1-abstract-full').style.display = 'inline'; document.getElementById('2503.17738v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.17738v1-abstract-full" style="display: none;"> Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enriched in many types of cancer, particularly in hepatocellular carcinoma (HCC). The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions. Targeting CD19$^+$ macrophages with anti-CD19 chimeric antigen receptor T (CAR-T) cells inhibited HCC tumor growth. We identify PAX5 as a primary driver of up-regulated mitochondrial biogenesis in CD19$^+$ macrophages, which depletes cytoplasmic Ca$^{2+}$, leading to lysosomal deficiency and consequent accumulation of CD73 and PD-L1. Inhibiting CD73 or mitochondrial oxidation enhanced the efficacy of immune checkpoint blockade therapy in treating HCC, suggesting great promise for CD19$^+$ macrophage-targeting therapeutics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.17738v1-abstract-full').style.display = 'none'; document.getElementById('2503.17738v1-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> 22 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </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">7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.15867">arXiv:2502.15867</a> <span> [<a href="https://arxiv.org/pdf/2502.15867">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Other Quantitative Biology">q-bio.OT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yingying Sun</a>, <a href="/search/q-bio?searchtype=author&query=A%2C+J">Jun A</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Zhiwei Liu</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+R">Rui Sun</a>, <a href="/search/q-bio?searchtype=author&query=Qian%2C+L">Liujia Qian</a>, <a href="/search/q-bio?searchtype=author&query=Payne%2C+S+H">Samuel H. Payne</a>, <a href="/search/q-bio?searchtype=author&query=Bittremieux%2C+W">Wout Bittremieux</a>, <a href="/search/q-bio?searchtype=author&query=Ralser%2C+M">Markus Ralser</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+C">Chen Li</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Dong%2C+Z">Zhen Dong</a>, <a href="/search/q-bio?searchtype=author&query=Perez-Riverol%2C+Y">Yasset Perez-Riverol</a>, <a href="/search/q-bio?searchtype=author&query=Khan%2C+A">Asif Khan</a>, <a href="/search/q-bio?searchtype=author&query=Sander%2C+C">Chris Sander</a>, <a href="/search/q-bio?searchtype=author&query=Aebersold%2C+R">Ruedi Aebersold</a>, <a href="/search/q-bio?searchtype=author&query=Vizca%C3%ADno%2C+J+A">Juan Antonio Vizca铆no</a>, <a href="/search/q-bio?searchtype=author&query=Krieger%2C+J+R">Jonathan R Krieger</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+J">Jianhua Yao</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+H">Han Wen</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Linfeng Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+Y">Yunping Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Xuan%2C+Y">Yue Xuan</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+B+B">Benjamin Boyang Sun</a>, <a href="/search/q-bio?searchtype=author&query=Qiao%2C+L">Liang Qiao</a>, <a href="/search/q-bio?searchtype=author&query=Hermjakob%2C+H">Henning Hermjakob</a> , et al. (37 additional authors not shown) </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="2502.15867v1-abstract-short" style="display: inline;"> Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15867v1-abstract-full').style.display = 'inline'; document.getElementById('2502.15867v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.15867v1-abstract-full" style="display: none;"> Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15867v1-abstract-full').style.display = 'none'; document.getElementById('2502.15867v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </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">28 pages, 2 figures, perspective in AI proteomics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.01768">arXiv:2501.01768</a> <span> [<a href="https://arxiv.org/pdf/2501.01768">pdf</a>, <a href="https://arxiv.org/format/2501.01768">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"> Remodeling Peptide-MHC-TCR Triad Binding as Sequence Fusion for Immunogenicity Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+J">Jiahao Ma</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+H">Hongzong Li</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jian-Dong Huang</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+Y">Ye-Fan Hu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yifan Chen</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="2501.01768v1-abstract-short" style="display: inline;"> The complex nature of tripartite peptide-MHC-TCR interactions is a critical yet underexplored area in immunogenicity prediction. Traditional studies on TCR-antigen binding have not fully addressed the complex dependencies in triad binding. In this paper, we propose new modeling approaches for these tripartite interactions, utilizing sequence information from MHCs, peptides, and TCRs. Our methods a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01768v1-abstract-full').style.display = 'inline'; document.getElementById('2501.01768v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.01768v1-abstract-full" style="display: none;"> The complex nature of tripartite peptide-MHC-TCR interactions is a critical yet underexplored area in immunogenicity prediction. Traditional studies on TCR-antigen binding have not fully addressed the complex dependencies in triad binding. In this paper, we propose new modeling approaches for these tripartite interactions, utilizing sequence information from MHCs, peptides, and TCRs. Our methods adhere to native sequence forms and align with biological processes to enhance prediction accuracy. By incorporating representation learning techniques, we introduce a fusion mechanism to integrate the three sequences effectively. Empirical experiments show that our models outperform traditional methods, achieving a 2.8 to 13.3 percent improvement in prediction accuracy across existing benchmarks. We further validate our approach with extensive ablation studies, demonstrating the effectiveness of the proposed model components. The model implementation, code, and supplementary materials, including a manuscript with colored hyperlinks and a technical appendix for digital viewing, will be open-sourced upon publication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01768v1-abstract-full').style.display = 'none'; document.getElementById('2501.01768v1-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> 3 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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">27 Pages 5 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.08239">arXiv:2412.08239</a> <span> [<a href="https://arxiv.org/pdf/2412.08239">pdf</a>, <a href="https://arxiv.org/ps/2412.08239">ps</a>, <a href="https://arxiv.org/format/2412.08239">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"> Deep learning assisted SERS detection of prolines and hydroxylated prolines using nitrilotriacetic acid functionalized gold nanopillars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yuan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Zhan%2C+K">Kuo Zhan</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+P">Peilin Xin</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yingqi Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+S">Shubo Wang</a>, <a href="/search/q-bio?searchtype=author&query=Hubarevich%2C+A">Aliaksandr Hubarevich</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xuejin Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jianan Huang</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="2412.08239v1-abstract-short" style="display: inline;"> Proline (Pro) is one kind of proteinogenic amino acid and an important signaling molecule in the process of metabolism. Hydroxyproline (Hyp) is a product on Pro oxygen sensing post-translational modification (PTM), which is efficiently modulated tumor cells for angiogenesis. Distinguishing between Pro and Hyp is crucial for diagnosing connective tissue disorders, as elevated levels of Hyp can indi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.08239v1-abstract-full').style.display = 'inline'; document.getElementById('2412.08239v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.08239v1-abstract-full" style="display: none;"> Proline (Pro) is one kind of proteinogenic amino acid and an important signaling molecule in the process of metabolism. Hydroxyproline (Hyp) is a product on Pro oxygen sensing post-translational modification (PTM), which is efficiently modulated tumor cells for angiogenesis. Distinguishing between Pro and Hyp is crucial for diagnosing connective tissue disorders, as elevated levels of Hyp can indicate abnormal collagen metabolism, often associated with diseases like osteogenesis imperfecta or fibrosis. However, there is a very small difference between molecular structures of Pro and Hyp, which is a big challenge for current detection technologies to distinguish them. For surface-enhanced Raman scattering (SERS) sensors, the similar molecule structure leads to similar Raman spectra that are difficult to distinguish. Furthermore, another problem is the weak affinity between amino acids sample and SERS-active substrates by physical adsorption. The selecting capturing of Pro and Hyp in the mixture of amino acids is not easy to achieve. In this work, we designed a new method for Pro and Hyp specifical detection and recognition by using gold nanopillars as the SERS substrate and combing nitrilotriacetic acid (NTA) with nickel (Ni) to form NTA-Ni structure as a specifical affinity agent. One side of NTA-Ni was attached to gold nanopillars through thiol binding. Another side captured the amino acids using reversible binding by receptor-ligand interaction between Ni and amino acids. Because of the different binding time with NTA-Ni and amino acids, the sensor can recognize Pro and Hyp from amino acids mixture. Then we used automatic peak assignment program for data analysis and machine learning model to distinguish between Pro and Hyp. The label-free SERS detection of amino acids PTM using gold nanopillars provides a potential method to further biomolecule detection and specifical capture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.08239v1-abstract-full').style.display = 'none'; document.getElementById('2412.08239v1-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> 11 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03111">arXiv:2411.03111</a> <span> [<a href="https://arxiv.org/pdf/2411.03111">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Radical-mediated Electrical Enzyme Assay For At-home Clinical Test </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jang%2C+H">Hyun-June Jang</a>, <a href="/search/q-bio?searchtype=author&query=Joung%2C+H">Hyou-Arm Joung</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+X">Xiaoao Shi</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+R">Rui Ding</a>, <a href="/search/q-bio?searchtype=author&query=Wagner%2C+J">Justine Wagner</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+E">Erting Tang</a>, <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+W">Wen Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Ryu%2C+B">Byunghoon Ryu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guanmin Chen</a>, <a href="/search/q-bio?searchtype=author&query=Yeo%2C+K+J">Kiang-Teck Jerry Yeo</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jun Huang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junhong Chen</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="2411.03111v1-abstract-short" style="display: inline;"> To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detecte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03111v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03111v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03111v1-abstract-full" style="display: none;"> To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detected by an ion-sensitive FET for biomarker quantification. Through screening 14 phenolic compounds, halogenated phenols emerged as optimal substrates for the REEA. Encased in an affordable cartridge ($0.55 per test), the system achieved a detection limit of 146 fg/mL for estradiol (E2), with a coefficient of variation (CV) below 9.2% in E2-spiked samples and an r2 of 0.963 across a measuring range of 19 to 4,551 pg/mL in clinical plasma samples, providing results in under 10 minutes. This adaptable system not only promises to offer a fast and reliable platform, but also holds significant potential for expansion to a wide array of biomarkers, paving the way for broader clinical and home-based applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03111v1-abstract-full').style.display = 'none'; document.getElementById('2411.03111v1-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> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.21848">arXiv:2410.21848</a> <span> [<a href="https://arxiv.org/pdf/2410.21848">pdf</a>, <a href="https://arxiv.org/ps/2410.21848">ps</a>, <a href="https://arxiv.org/format/2410.21848">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Classical Analysis and ODEs">math.CA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> On the study of the limit cycles for a class of population models with time-varying factors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tian%2C+R">Renhao Tian</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jianfeng Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yulin Zhao</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="2410.21848v2-abstract-short" style="display: inline;"> In this paper, we study a class of population models with time-varying factors, represented by one-dimensional piecewise smooth autonomous differential equations. We provide several derivative formulas in "discrete" form for the Poincar茅 map of such equations, and establish a criterion for the existence of limit cycles. These two tools, together with the known ones, are then combined in a prel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21848v2-abstract-full').style.display = 'inline'; document.getElementById('2410.21848v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21848v2-abstract-full" style="display: none;"> In this paper, we study a class of population models with time-varying factors, represented by one-dimensional piecewise smooth autonomous differential equations. We provide several derivative formulas in "discrete" form for the Poincar茅 map of such equations, and establish a criterion for the existence of limit cycles. These two tools, together with the known ones, are then combined in a preliminary procedure that can provide a simple and unified way to analyze the equations. As an application, we prove that a general model of single species with seasonal constant-yield harvesting can only possess at most two limit cycles, which improves the work of Xiao in 2016. We also apply our results to a general model described by the Abel equations with periodic step function coefficients, showing that its maximum number of limit cycles, is three. Finally, a population suppression model for mosquitos considered by Yu and Li in 2020 and Zheng et al. in 2021 is studied using our approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21848v2-abstract-full').style.display = 'none'; document.getElementById('2410.21848v2-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> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.00993">arXiv:2406.00993</a> <span> [<a href="https://arxiv.org/pdf/2406.00993">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Quantitative Biology">q-bio.OT</span> </div> </div> <p class="title is-5 mathjax"> Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wei%2C+J">Jiaming Wei</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+T">Tong Liu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jipeng Huang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiaowei Li</a>, <a href="/search/q-bio?searchtype=author&query=Qi%2C+Y">Yurui Qi</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+G">Gangyin Luo</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="2406.00993v1-abstract-short" style="display: inline;"> With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath analysis, characterized by its speed, convenience, and non-invasive nature, is leading the trend in diagnostic development. Studies have shown that the acetone levels in the breath of diabetes patients are higher than normal, making acetone a basis for dia… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00993v1-abstract-full').style.display = 'inline'; document.getElementById('2406.00993v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.00993v1-abstract-full" style="display: none;"> With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath analysis, characterized by its speed, convenience, and non-invasive nature, is leading the trend in diagnostic development. Studies have shown that the acetone levels in the breath of diabetes patients are higher than normal, making acetone a basis for diabetes breath analysis. This provides a more readily accepted method for early diabetes prevention and monitoring. Addressing issues such as the invasive nature, disease transmission risks, and complexity of diabetes testing, this study aims to design a diabetes gas biomarker acetone detection system centered around a sensor array using gas sensors and pattern recognition algorithms. The research covers sensor selection, sensor preparation, circuit design, data acquisition and processing, and detection model establishment to accurately identify acetone. Titanium dioxide was chosen as the nano gas-sensitive material to prepare the acetone gas sensor, with data collection conducted using STM32. Filtering was applied to process the raw sensor data, followed by feature extraction using principal component analysis. A recognition model based on support vector machine algorithm was used for qualitative identification of gas samples, while a recognition model based on backpropagation neural network was employed for quantitative detection of gas sample concentrations. Experimental results demonstrated recognition accuracies of 96% and 97.5% for acetone-ethanol and acetone-methanol mixed gases, and 90% for ternary acetone, ethanol, and methanol mixed gases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00993v1-abstract-full').style.display = 'none'; document.getElementById('2406.00993v1-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> 3 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </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">9 pages, 14 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.11759">arXiv:2404.11759</a> <span> [<a href="https://arxiv.org/pdf/2404.11759">pdf</a>, <a href="https://arxiv.org/format/2404.11759">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Modelling infectious disease transmission dynamics in conference environments: An individual-based approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xue Liu</a>, <a href="/search/q-bio?searchtype=author&query=Deng%2C+Y">Yue Deng</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jingying Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yuhong Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Lei%2C+J">Jinzhi Lei</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="2404.11759v1-abstract-short" style="display: inline;"> The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11759v1-abstract-full').style.display = 'inline'; document.getElementById('2404.11759v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.11759v1-abstract-full" style="display: none;"> The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simulate transmission processes within a condensed timeframe (one day), beginning with sporadic cases. Our model intricately captures the activities of individual attendees within the conference venue, encompassing meetings, rest intervals, and meal breaks. While meetings entail proximity seating, rest and lunch periods allow attendees to interact with diverse individuals. Moreover, the restroom environment poses an additional avenue for potential infection transmission. Employing an individual-based model, we meticulously replicated the transmission dynamics of infectious diseases, with a specific emphasis on close-contact interactions between infected and susceptible individuals. Through comprehensive analysis of model simulations, we elucidated the intricacies of disease transmission dynamics within conference settings and assessed the efficacy of control strategies to curb disease dissemination. Ultimately, our study proffers a numerical framework for assessing the risk of infectious disease transmission during short-duration conferences, furnishing conference organizers with valuable insights to inform the implementation of targeted prevention and control measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11759v1-abstract-full').style.display = 'none'; document.getElementById('2404.11759v1-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> 17 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </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">25 pages; 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19149">arXiv:2403.19149</a> <span> [<a href="https://arxiv.org/pdf/2403.19149">pdf</a>, <a href="https://arxiv.org/format/2403.19149">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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Topological Cycle Graph Attention Network for Brain Functional Connectivity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jinghan Huang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+N">Nanguang Chen</a>, <a href="/search/q-bio?searchtype=author&query=Qiu%2C+A">Anqi Qiu</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="2403.19149v1-abstract-short" style="display: inline;"> This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections that form cycles around this core structure. We first introduce a cycle incidence matrix that establishes an independent cycle basis within a graph, ma… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19149v1-abstract-full').style.display = 'inline'; document.getElementById('2403.19149v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19149v1-abstract-full" style="display: none;"> This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections that form cycles around this core structure. We first introduce a cycle incidence matrix that establishes an independent cycle basis within a graph, mapping its relationship with edges. We propose a cycle graph convolution that leverages a cycle adjacency matrix, derived from the cycle incidence matrix, to specifically filter edge signals in a domain of cycles. Additionally, we strengthen the representation power of the cycle graph convolution by adding an attention mechanism, which is further augmented by the introduction of edge positional encodings in cycles, to enhance the topological awareness of CycGAT. We demonstrate CycGAT's localization through simulation and its efficacy on an ABCD study's fMRI data (n=8765), comparing it with baseline models. CycGAT outperforms these models, identifying a functional backbone with significantly fewer cycles, crucial for understanding neural circuits related to general intelligence. Our code will be released once accepted. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19149v1-abstract-full').style.display = 'none'; document.getElementById('2403.19149v1-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> 28 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.14104">arXiv:2401.14104</a> <span> [<a href="https://arxiv.org/pdf/2401.14104">pdf</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"> Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/q-bio?searchtype=author&query=Lyu%2C+X">Xiaoming Lyu</a>, <a href="/search/q-bio?searchtype=author&query=Zhan%2C+K">Kuo Zhan</a>, <a href="/search/q-bio?searchtype=author&query=Ji%2C+H">Haoyu Ji</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+L">Lei Qin</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">JianAn Huang</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="2401.14104v2-abstract-short" style="display: inline;"> Exosomes are significant facilitators of inter-cellular communication that can unveil cell-cell interactions, signaling pathways, regulatory mechanisms and disease diagnostics. Nonetheless, current analysis required large amount of data for exosome identification that it hampers efficient and timely mechanism study and diagnostics. Here, we used a machine-learning assisted Surface-enhanced Raman s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.14104v2-abstract-full').style.display = 'inline'; document.getElementById('2401.14104v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.14104v2-abstract-full" style="display: none;"> Exosomes are significant facilitators of inter-cellular communication that can unveil cell-cell interactions, signaling pathways, regulatory mechanisms and disease diagnostics. Nonetheless, current analysis required large amount of data for exosome identification that it hampers efficient and timely mechanism study and diagnostics. Here, we used a machine-learning assisted Surface-enhanced Raman spectroscopy (SERS) method to detect exosomes derived from six distinct cell lines (HepG2, Hela, 143B, LO-2, BMSC, and H8) with small amount of data. By employing sodium borohydride-reduced silver nanoparticles and sodium borohydride solution as an aggregating agent, 100 SERS spectra of the each types of exosomes were collected and then subjected to multivariate and machine learning analysis. By integrating Principal Component Analysis with Support Vector Machine (PCA-SVM) models, our analysis achieved a high accuracy rate of 94.4% in predicting exosomes originating from various cellular sources. In comparison to other machine learning analysis, our method used small amount of SERS data to allow a simple and rapid exosome detection, which enables a timely subsequent study of cell-cell interactions, communication mechanisms, and disease mechanisms in life sciences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.14104v2-abstract-full').style.display = 'none'; document.getElementById('2401.14104v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </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">5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.10334">arXiv:2401.10334</a> <span> [<a href="https://arxiv.org/pdf/2401.10334">pdf</a>, <a href="https://arxiv.org/format/2401.10334">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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="Computation and Language">cs.CL</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"> DrugAssist: A Large Language Model for Molecule Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ye%2C+G">Geyan Ye</a>, <a href="/search/q-bio?searchtype=author&query=Cai%2C+X">Xibao Cai</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+H">Houtim Lai</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xing Wang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junhong Huang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+L">Longyue Wang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+W">Wei Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zeng%2C+X">Xiangxiang Zeng</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="2401.10334v1-abstract-short" style="display: inline;"> Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.10334v1-abstract-full').style.display = 'inline'; document.getElementById('2401.10334v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.10334v1-abstract-full" style="display: none;"> Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback. These non-interactive approaches overlook the fact that the drug discovery process is actually one that requires the integration of expert experience and iterative refinement. To address this gap, we propose DrugAssist, an interactive molecule optimization model which performs optimization through human-machine dialogue by leveraging LLM's strong interactivity and generalizability. DrugAssist has achieved leading results in both single and multiple property optimization, simultaneously showcasing immense potential in transferability and iterative optimization. In addition, we publicly release a large instruction-based dataset called MolOpt-Instructions for fine-tuning language models on molecule optimization tasks. We have made our code and data publicly available at https://github.com/blazerye/DrugAssist, which we hope to pave the way for future research in LLMs' application for drug discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.10334v1-abstract-full').style.display = 'none'; document.getElementById('2401.10334v1-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> 28 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </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">Geyan Ye and Xibao Cai are equal contributors; Longyue Wang is corresponding author</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.03004">arXiv:2401.03004</a> <span> [<a href="https://arxiv.org/pdf/2401.03004">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> SAPNet: a deep learning model for identification of single-molecule peptide post-translational modifications with surface enhanced Raman spectroscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yaltaye%2C+M+W">Mulusew W. Yaltaye</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yingqi Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Bozo%2C+E">Eva Bozo</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+P">Pei-Lin Xin</a>, <a href="/search/q-bio?searchtype=author&query=Farrah%2C+V">Vahid Farrah</a>, <a href="/search/q-bio?searchtype=author&query=De+Angelis%2C+F">Francesco De Angelis</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jian-An Huang</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="2401.03004v1-abstract-short" style="display: inline;"> Nanopore resistive pulse sensors are emerging technologies for single-molecule protein sequencing. But they can hardly detect small post-translational modifications (PTMs) such as hydroxylation in single-molecule level. While a combination of surface enhanced Raman spectroscopy (SERS) with plasmonic nanopores can detect the small PTMs, the blinking Raman peaks in the single-molecule SERS spectra l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03004v1-abstract-full').style.display = 'inline'; document.getElementById('2401.03004v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03004v1-abstract-full" style="display: none;"> Nanopore resistive pulse sensors are emerging technologies for single-molecule protein sequencing. But they can hardly detect small post-translational modifications (PTMs) such as hydroxylation in single-molecule level. While a combination of surface enhanced Raman spectroscopy (SERS) with plasmonic nanopores can detect the small PTMs, the blinking Raman peaks in the single-molecule SERS spectra leads to a big challenge in data analysis and PTM identification. Herein, we developed and validated a one-dimensional convolutional neural network (1D-CNN) for amino acids and peptides identification from their PTMs including hydroxylation and phosphorylation by their single-molecule SERS spectra, named Single Amino acid and Peptide Network (SAPNet). Our work combines cutting-edge plasmonic nanopore technology for SERS signal acquisition and deep learning for fully automated extraction of information from the SERS signals. The SAPNet model achieved an overall accuracy of 99.66% for the identification of amino acids from their modification, and 98.38% for the identification of peptides from their PTM translation. We also evaluated the model with out-of-sample examples with good performance. Our work can be beneficial for early detection of diseases such as cancers and Alzheimer's disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03004v1-abstract-full').style.display = 'none'; document.getElementById('2401.03004v1-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> 5 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </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">20 pages, 5 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.05033">arXiv:2312.05033</a> <span> [<a href="https://arxiv.org/pdf/2312.05033">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Insomnia impairs muscle function via regulating protein degradation and muscle clock </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ouyang%2C+H">Hui Ouyang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+H">Hong Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jin Huang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Z">Zunjing Liu</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="2312.05033v1-abstract-short" style="display: inline;"> Background: Insomnia makes people more physically unable of doing daily duties, which results in a lack of strength, leads to lacking in strength. However, the effects of insomnia on muscle function have not yet been thoroughly investigated. So, the objectives of this study were to clarify how insomnia contributes to the decrease of muscular function and to investigate the mechanisms behind this p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05033v1-abstract-full').style.display = 'inline'; document.getElementById('2312.05033v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.05033v1-abstract-full" style="display: none;"> Background: Insomnia makes people more physically unable of doing daily duties, which results in a lack of strength, leads to lacking in strength. However, the effects of insomnia on muscle function have not yet been thoroughly investigated. So, the objectives of this study were to clarify how insomnia contributes to the decrease of muscular function and to investigate the mechanisms behind this phenomenon. Methods: To understand how insomnia influence muscle function, we analyzed the expression level of factors associated with muscle protein degradation, muscle protein synthesis , protein synthesis and degradation pathways and muscle clock. Results: The results showed that lower BMI and grip strength were observed in insomnia patients. The mice in the sleep deprivation(SD) group saw a 7.01 g loss in body mass. The SD group's tibialis anterior and gastrocnemius muscle mass decreased after 96 h of SD). The grip strength reduced in SD group. Using the RT-PCR approaches, we found a significant increase in muscle degradation factors expression in SD group versus normal control group. Conclusions: Insomnia can impair muscle function. The mechanism may be associated with the increased expression of muscle degradation related factors , as well as the abnormal expression of Clock gene. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05033v1-abstract-full').style.display = 'none'; document.getElementById('2312.05033v1-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> 8 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.19202">arXiv:2310.19202</a> <span> [<a href="https://arxiv.org/pdf/2310.19202">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xiong%2C+X">Xiong Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Ying Wang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+T">Tianyuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jinguo Huang</a>, <a href="/search/q-bio?searchtype=author&query=Kang%2C+G">Guixia Kang</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="2310.19202v1-abstract-short" style="display: inline;"> As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19202v1-abstract-full').style.display = 'inline'; document.getElementById('2310.19202v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.19202v1-abstract-full" style="display: none;"> As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. Besides, CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. To obtain more effective spatial filters for better extraction of spatial features that can improve classification to MI-EEG, this paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO). A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBCSP-ASP. The proposed method has achieved significant performance improvement on MI-BCI. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm (FBCSP), the proposed algorithm improves 11.44% and 7.11% on two datasets respectively. Furthermore, the analysis based on mutual information, t-SNE and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals, and explains the improvement of classification performance by the introduction of ASP features. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19202v1-abstract-full').style.display = 'none'; document.getElementById('2310.19202v1-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> 29 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">25 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.19198">arXiv:2310.19198</a> <span> [<a href="https://arxiv.org/pdf/2310.19198">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xiong%2C+X">Xiong Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+L">Li Su</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jinguo Huang</a>, <a href="/search/q-bio?searchtype=author&query=Kang%2C+G">Guixia Kang</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="2310.19198v1-abstract-short" style="display: inline;"> Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space Mapping using Dichotomous Filter Bank wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19198v1-abstract-full').style.display = 'inline'; document.getElementById('2310.19198v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.19198v1-abstract-full" style="display: none;"> Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space Mapping using Dichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhance the representation quality and decoding capability pertaining to MI features. DFBRTS first initiates the process by meticulously filtering EEG signals through a Dichotomous Filter Bank, structured in the fashion of a complete binary tree. Subsequently, it employs Riemann Tangent Space Mapping to extract salient EEG signal features within each sub-band. Finally, a lightweight convolutional neural network is employed for further feature extraction and classification, operating under the joint supervision of cross-entropy and center loss. To validate the efficacy, extensive experiments were conducted using DFBRTS on two well-established benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS was benchmarked against several state-of-the-art MI decoding methods, alongside other Riemannian geometry-based MI decoding approaches. Results: DFBRTS significantly outperforms other MI decoding algorithms on both datasets, achieving a remarkable classification accuracy of 78.16% for four-class and 71.58% for two-class hold-out classification, as compared to the existing benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19198v1-abstract-full').style.display = 'none'; document.getElementById('2310.19198v1-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> 29 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">22 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.14404">arXiv:2309.14404</a> <span> [<a href="https://arxiv.org/pdf/2309.14404">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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"> pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+Z">Zebin Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zou%2C+Y">Yonglin Zou</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+X">Xiaobin Huang</a>, <a href="/search/q-bio?searchtype=author&query=Yan%2C+W">Wenjin Yan</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Hao Xu</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Jiexin Yang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Ying Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jinqi Huang</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="2309.14404v1-abstract-short" style="display: inline;"> Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we deve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14404v1-abstract-full').style.display = 'inline'; document.getElementById('2309.14404v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.14404v1-abstract-full" style="display: none;"> Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we developed a tool called pLMFPPred (Protein Language Model-based Functional Peptide Predictor) for predicting functional peptides and identifying toxic peptides. We also introduced SMOTE-TOMEK data synthesis sampling and Shapley value-based feature selection techniques to relieve data imbalance issues and reduce computational costs. On a validated independent test set, pLMFPPred achieved accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score values of 0.974, 0.99, and 0.974, respectively. Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides.The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods. pLMFPPred has achieved good performance in predicting functional peptides and represents a new computational method for predicting functional peptides. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14404v1-abstract-full').style.display = 'none'; document.getElementById('2309.14404v1-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> 25 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">20 pages, 5 figures,under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.12682">arXiv:2307.12682</a> <span> [<a href="https://arxiv.org/pdf/2307.12682">pdf</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"> Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+F">Fan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Mingchen Li</a>, <a href="/search/q-bio?searchtype=author&query=Dong%2C+J">Jiajun Dong</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+Y">Yuanxi Yu</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+X">Xinyu Sun</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+B">Banghao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jin Huang</a>, <a href="/search/q-bio?searchtype=author&query=Kang%2C+L">Liqi Kang</a>, <a href="/search/q-bio?searchtype=author&query=Pei%2C+Y">Yufeng Pei</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Liang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+S">Shaojie Wang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+W">Wenxue Xu</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+J">Jingyao Xin</a>, <a href="/search/q-bio?searchtype=author&query=Ouyang%2C+W">Wanli Ouyang</a>, <a href="/search/q-bio?searchtype=author&query=Fan%2C+G">Guisheng Fan</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+L">Lirong Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Tan%2C+Y">Yang Tan</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+Z">Zhiqiang Hu</a>, <a href="/search/q-bio?searchtype=author&query=Xiong%2C+Y">Yi Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+Y">Yan Feng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+G">Guangyu Yang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+Q">Qian Liu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+J">Jie Song</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jia Liu</a>, <a href="/search/q-bio?searchtype=author&query=Hong%2C+L">Liang Hong</a> , et al. (1 additional authors not shown) </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="2307.12682v7-abstract-short" style="display: inline;"> Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants of improved stability and activity without any prior experimental mutagenesis data of the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.12682v7-abstract-full').style.display = 'inline'; document.getElementById('2307.12682v7-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.12682v7-abstract-full" style="display: none;"> Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants of improved stability and activity without any prior experimental mutagenesis data of the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive power compared to current state-of-the-art models on the public mutagenesis dataset over 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the top 30-45 single-site mutations' impact on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Remarkably, over 30% of the AI-recommended mutants exhibited superior performance compared to their pre-mutation counterparts across all proteins and desired properties. Moreover, we have developed an efficient, and successful method based on PRIME to rapidly obtain multi-site mutants with enhanced activity and stability. Hence, PRIME demonstrates the general applicability in protein engineering. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.12682v7-abstract-full').style.display = 'none'; document.getElementById('2307.12682v7-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> 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">arXiv admin note: text overlap with arXiv:2304.03780</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.03590">arXiv:2302.03590</a> <span> [<a href="https://arxiv.org/pdf/2302.03590">pdf</a>, <a href="https://arxiv.org/format/2302.03590">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Abdollahi%2C+N">Nasim Abdollahi</a>, <a href="/search/q-bio?searchtype=author&query=Tonekaboni%2C+S+A+M">Seyed Ali Madani Tonekaboni</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jay Huang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+B">Bo Wang</a>, <a href="/search/q-bio?searchtype=author&query=MacKinnon%2C+S">Stephen MacKinnon</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="2302.03590v1-abstract-short" style="display: inline;"> While accurate protein structure predictions are now available for nearly every observed protein sequence, predicted structures lack much of the functional context offered by experimental structure determination. We address this gap with NodeCoder, a task-independent platform that maps residue-based datasets onto 3D protein structures, embeds the resulting structural feature into a contact network… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03590v1-abstract-full').style.display = 'inline'; document.getElementById('2302.03590v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.03590v1-abstract-full" style="display: none;"> While accurate protein structure predictions are now available for nearly every observed protein sequence, predicted structures lack much of the functional context offered by experimental structure determination. We address this gap with NodeCoder, a task-independent platform that maps residue-based datasets onto 3D protein structures, embeds the resulting structural feature into a contact network, and models residue classification tasks with a Graph Convolutional Network (GCN). We demonstrate the versatility of this strategy by modeling six separate tasks, with some labels derived from other experimental structure studies (ligand, peptide, ion, and nucleic acid binding sites) and other labels derived from annotation databases (post-translational modification and transmembrane regions). Moreover, A NodeCoder model trained to identify ligand binding site residues was able to outperform P2Rank, a widely-used software developed specifically for ligand binding site detection. NodeCoder is available as an open-source python package at https://pypi.org/project/NodeCoder/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03590v1-abstract-full').style.display = 'none'; document.getElementById('2302.03590v1-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> 7 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">including supplementary materials 22 pages, 6 figures, 4 tables, presented at NeurIPS 2021 and ACS 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.12421">arXiv:2211.12421</a> <span> [<a href="https://arxiv.org/pdf/2211.12421">pdf</a>, <a href="https://arxiv.org/format/2211.12421">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Data-Driven Network Neuroscience: On Data Collection and Benchmark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+J">Jiaxing Xu</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Y">Yunhan Yang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+D+T+J">David Tse Jung Huang</a>, <a href="/search/q-bio?searchtype=author&query=Gururajapathy%2C+S+S">Sophi Shilpa Gururajapathy</a>, <a href="/search/q-bio?searchtype=author&query=Ke%2C+Y">Yiping Ke</a>, <a href="/search/q-bio?searchtype=author&query=Qiao%2C+M">Miao Qiao</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+A">Alan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Kumar%2C+H">Haribalan Kumar</a>, <a href="/search/q-bio?searchtype=author&query=McGeown%2C+J">Josh McGeown</a>, <a href="/search/q-bio?searchtype=author&query=Kwon%2C+E">Eryn Kwon</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="2211.12421v6-abstract-short" style="display: inline;"> This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.12421v6-abstract-full').style.display = 'inline'; document.getElementById('2211.12421v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.12421v6-abstract-full" style="display: none;"> This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains rich structural and positional information that traditional examination methods are unable to capture. However, the lack of publicly accessible brain network data prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert the data from MRI images into brain networks. We bridge this gap by collecting a large amount of MRI images from public databases and a private source, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 brain conditions, and consist of a total of 2,702 subjects. We test our graph datasets on 12 machine learning models to provide baselines and validate the data quality on a recent graph analysis model. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our brain network data and complete preprocessing details including codes at https://doi.org/10.17608/k6.auckland.21397377 and https://github.com/brainnetuoa/data_driven_network_neuroscience. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.12421v6-abstract-full').style.display = 'none'; document.getElementById('2211.12421v6-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> 29 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Advances in Neural Information Processing Systems, 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.10871">arXiv:2210.10871</a> <span> [<a href="https://arxiv.org/pdf/2210.10871">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Stable ion-tunable antiambipolarity in mixed ion-electron conducting polymers enables biorealistic artificial neurons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Harikesh%2C+P+C">Padinhare Cholakkal Harikesh</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+C">Chi-Yuan Yang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Han-Yan Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+S">Silan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jun-Da Huang</a>, <a href="/search/q-bio?searchtype=author&query=Berggren%2C+M">Magnus Berggren</a>, <a href="/search/q-bio?searchtype=author&query=Tu%2C+D">Deyu Tu</a>, <a href="/search/q-bio?searchtype=author&query=Fabiano%2C+S">Simone Fabiano</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="2210.10871v1-abstract-short" style="display: inline;"> Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silicon or organic semiconductor neuromorphic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.10871v1-abstract-full').style.display = 'inline'; document.getElementById('2210.10871v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.10871v1-abstract-full" style="display: none;"> Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silicon or organic semiconductor neuromorphic systems, can emulate only a few neural features. More functional neuron models, based on traditional complex Si-based complementary-metal-oxide-semiconductor (CMOS) or negative differential resistance (NDR) device circuits, are complicated to fabricate, not biocompatible, and lack ion- and chemical-based modulation features. Here we report a biorealistic conductance-based organic electrochemical neuron (c-OECN) using a mixed ion-electron conducting ladder-type polymer with reliable ion-tunable antiambipolarity. The latter is used to emulate the activation/inactivation of Na channels and delayed activation of K channels of biological neurons. These c-OECNs can then spike at bioplausible frequencies nearing 100 Hz, emulate most critical biological neural features, demonstrate stochastic spiking, and enable neurotransmitter and Ca2+-based spiking modulation. These combined features are impossible to achieve using previous technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.10871v1-abstract-full').style.display = 'none'; document.getElementById('2210.10871v1-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> 19 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/2201.09637">arXiv:2201.09637</a> <span> [<a href="https://arxiv.org/pdf/2201.09637">pdf</a>, <a href="https://arxiv.org/format/2201.09637">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="Artificial Intelligence">cs.AI</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"> DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ji%2C+Y">Yuanfeng Ji</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+L">Lu Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jiaxiang Wu</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+B">Bingzhe Wu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+L">Long-Kai Huang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+T">Tingyang Xu</a>, <a href="/search/q-bio?searchtype=author&query=Rong%2C+Y">Yu Rong</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+L">Lanqing Li</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+J">Jie Ren</a>, <a href="/search/q-bio?searchtype=author&query=Xue%2C+D">Ding Xue</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+H">Houtim Lai</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+S">Shaoyong Xu</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+J">Jing Feng</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+W">Wei Liu</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+P">Ping Luo</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+S">Shuigeng Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+P">Peilin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Bian%2C+Y">Yatao Bian</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="2201.09637v1-abstract-short" style="display: inline;"> AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its extensive use in many fields, such as ADMET prediction, virtual screening, protein folding and generative chemistry, little has been explored in terms of the out-of-distribution (OOD) learning problem with \emph{noise},… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.09637v1-abstract-full').style.display = 'inline'; document.getElementById('2201.09637v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.09637v1-abstract-full" style="display: none;"> AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its extensive use in many fields, such as ADMET prediction, virtual screening, protein folding and generative chemistry, little has been explored in terms of the out-of-distribution (OOD) learning problem with \emph{noise}, which is inevitable in real world AIDD applications. In this work, we present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery, which comes with an open-source Python package that fully automates the data curation and OOD benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction, which involves both macromolecule (protein target) and small-molecule (drug compound). In contrast to only providing fixed datasets, DrugOOD offers automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise annotations and rigorous benchmarking of state-of-the-art OOD algorithms. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for \emph{graph OOD learning} problems. Extensive empirical studies have shown a significant performance gap between in-distribution and out-of-distribution experiments, which highlights the need to develop better schemes that can allow for OOD generalization under noise for AIDD. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.09637v1-abstract-full').style.display = 'none'; document.getElementById('2201.09637v1-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> 24 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </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, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.04065">arXiv:2201.04065</a> <span> [<a href="https://arxiv.org/pdf/2201.04065">pdf</a>, <a href="https://arxiv.org/format/2201.04065">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y">Ya-Lin Huang</a>, <a href="/search/q-bio?searchtype=author&query=Hsieh%2C+C">Chia-Ying Hsieh</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jian-Xue Huang</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+C">Chun-Shu Wei</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="2201.04065v1-abstract-short" style="display: inline;"> We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-ima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04065v1-abstract-full').style.display = 'inline'; document.getElementById('2201.04065v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.04065v1-abstract-full" style="display: none;"> We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04065v1-abstract-full').style.display = 'none'; document.getElementById('2201.04065v1-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> 10 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.00622">arXiv:2201.00622</a> <span> [<a href="https://arxiv.org/pdf/2201.00622">pdf</a>, <a href="https://arxiv.org/format/2201.00622">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Learning shared neural manifolds from multi-subject FMRI data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jessie Huang</a>, <a href="/search/q-bio?searchtype=author&query=Busch%2C+E+L">Erica L. Busch</a>, <a href="/search/q-bio?searchtype=author&query=Wallenstein%2C+T">Tom Wallenstein</a>, <a href="/search/q-bio?searchtype=author&query=Gerasimiuk%2C+M">Michal Gerasimiuk</a>, <a href="/search/q-bio?searchtype=author&query=Benz%2C+A">Andrew Benz</a>, <a href="/search/q-bio?searchtype=author&query=Lajoie%2C+G">Guillaume Lajoie</a>, <a href="/search/q-bio?searchtype=author&query=Wolf%2C+G">Guy Wolf</a>, <a href="/search/q-bio?searchtype=author&query=Turk-Browne%2C+N+B">Nicholas B. Turk-Browne</a>, <a href="/search/q-bio?searchtype=author&query=Krishnaswamy%2C+S">Smita Krishnaswamy</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="2201.00622v1-abstract-short" style="display: inline;"> Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.00622v1-abstract-full').style.display = 'inline'; document.getElementById('2201.00622v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.00622v1-abstract-full" style="display: none;"> Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.00622v1-abstract-full').style.display = 'none'; document.getElementById('2201.00622v1-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> 22 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.11225">arXiv:2112.11225</a> <span> [<a href="https://arxiv.org/pdf/2112.11225">pdf</a>, <a href="https://arxiv.org/format/2112.11225">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey 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 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/10.3390/biom12091325">10.3390/biom12091325 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yan%2C+C">Chaochao Yan</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+P">Peilin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+C">Chan Lu</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+Y">Yang Yu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</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="2112.11225v2-abstract-short" style="display: inline;"> The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.11225v2-abstract-full').style.display = 'inline'; document.getElementById('2112.11225v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.11225v2-abstract-full" style="display: none;"> The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.11225v2-abstract-full').style.display = 'none'; document.getElementById('2112.11225v2-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> 22 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </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">15 pages; Accepted by the journal of Biomolecules</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.10374">arXiv:2109.10374</a> <span> [<a href="https://arxiv.org/pdf/2109.10374">pdf</a>, <a href="https://arxiv.org/format/2109.10374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Disordered Systems and Neural Networks">cond-mat.dis-nn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</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/10.1103/PhysRevLett.128.178001">10.1103/PhysRevLett.128.178001 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Shear-driven solidification and nonlinear elasticity in epithelial tissues </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junxiang Huang</a>, <a href="/search/q-bio?searchtype=author&query=Cochran%2C+J+O">James O. Cochran</a>, <a href="/search/q-bio?searchtype=author&query=Fielding%2C+S+M">Suzanne M. Fielding</a>, <a href="/search/q-bio?searchtype=author&query=Marchetti%2C+M+C">M. Cristina Marchetti</a>, <a href="/search/q-bio?searchtype=author&query=Bi%2C+D">Dapeng Bi</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="2109.10374v2-abstract-short" style="display: inline;"> Biological processes, from morphogenesis to tumor invasion, spontaneously generate shear stresses inside living tissue. The mechanisms that govern the transmission of mechanical forces in epithelia and the collective response of the tissue to bulk shear deformations remain, however, poorly understood. Using a minimal cell-based computational model, we investigate the constitutive relation of confl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.10374v2-abstract-full').style.display = 'inline'; document.getElementById('2109.10374v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.10374v2-abstract-full" style="display: none;"> Biological processes, from morphogenesis to tumor invasion, spontaneously generate shear stresses inside living tissue. The mechanisms that govern the transmission of mechanical forces in epithelia and the collective response of the tissue to bulk shear deformations remain, however, poorly understood. Using a minimal cell-based computational model, we investigate the constitutive relation of confluent tissues under simple shear deformation. We show that an initially undeformed fluidlike tissue acquires finite rigidity above a critical applied strain. This is akin to the shear-driven rigidity observed in other soft matter systems. Interestingly, shear-driven rigidity can be understood by a critical scaling analysis in the vicinity of the second order critical point that governs the liquid-solid transition of the undeformed system. We further show that a solidlike tissue responds linearly only to small strains and but then switches to a nonlinear response at larger stains, with substantial stiffening. Finally, we propose a mean-field formulation for cells under shear that offers a simple physical explanation of shear-driven rigidity and nonlinear response in a tissue. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.10374v2-abstract-full').style.display = 'none'; document.getElementById('2109.10374v2-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> 27 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Lett. 128, 178001 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.02893">arXiv:2011.02893</a> <span> [<a href="https://arxiv.org/pdf/2011.02893">pdf</a>, <a href="https://arxiv.org/format/2011.02893">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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"> RetroXpert: Decompose Retrosynthesis Prediction like a Chemist </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yan%2C+C">Chaochao Yan</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+Q">Qianggang Ding</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+P">Peilin Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+S">Shuangjia Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Jinyu Yang</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+Y">Yang Yu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</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="2011.02893v1-abstract-short" style="display: inline;"> Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.02893v1-abstract-full').style.display = 'inline'; document.getElementById('2011.02893v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.02893v1-abstract-full" style="display: none;"> Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis, various retrosynthesis prediction algorithms have been proposed. However, most of them are cumbersome and lack interpretability about their predictions. In this paper, we devise a novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach retrosynthesis prediction. Our method disassembles retrosynthesis into two steps: i) identify the potential reaction center of the target molecule through a novel graph neural network and generate intermediate synthons, and ii) generate the reactants associated with synthons via a robust reactant generation model. While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.02893v1-abstract-full').style.display = 'none'; document.getElementById('2011.02893v1-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> 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </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">17 pages, to appear in NeurIPS 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.02835">arXiv:2007.02835</a> <span> [<a href="https://arxiv.org/pdf/2007.02835">pdf</a>, <a href="https://arxiv.org/format/2007.02835">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Self-Supervised Graph Transformer on Large-Scale Molecular Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Rong%2C+Y">Yu Rong</a>, <a href="/search/q-bio?searchtype=author&query=Bian%2C+Y">Yatao Bian</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+T">Tingyang Xu</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+W">Weiyang Xie</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+Y">Ying Wei</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+W">Wenbing Huang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</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="2007.02835v2-abstract-short" style="display: inline;"> How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ Graph Neural Networks (GNNs) for molecular representation learning. Nevertheless, two issues impede the usage of GNNs in real scenarios: (1) insufficient labeled molecules for supervised training; (2) poor generalization… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.02835v2-abstract-full').style.display = 'inline'; document.getElementById('2007.02835v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.02835v2-abstract-full" style="display: none;"> How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ Graph Neural Networks (GNNs) for molecular representation learning. Nevertheless, two issues impede the usage of GNNs in real scenarios: (1) insufficient labeled molecules for supervised training; (2) poor generalization capability to new-synthesized molecules. To address them both, we propose a novel framework, GROVER, which stands for Graph Representation frOm self-superVised mEssage passing tRansformer. With carefully designed self-supervised tasks in node-, edge- and graph-level, GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. Rather, to encode such complex information, GROVER integrates Message Passing Networks into the Transformer-style architecture to deliver a class of more expressive encoders of molecules. The flexibility of GROVER allows it to be trained efficiently on large-scale molecular dataset without requiring any supervision, thus being immunized to the two issues mentioned above. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning. We then leverage the pre-trained GROVER for molecular property prediction followed by task-specific fine-tuning, where we observe a huge improvement (more than 6% on average) from current state-of-the-art methods on 11 challenging benchmarks. The insights we gained are that well-designed self-supervision losses and largely-expressive pre-trained models enjoy the significant potential on performance boosting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.02835v2-abstract-full').style.display = 'none'; document.getElementById('2007.02835v2-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> 28 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </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">17 pages, 7 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.10376">arXiv:2006.10376</a> <span> [<a href="https://arxiv.org/pdf/2006.10376">pdf</a>, <a href="https://arxiv.org/format/2006.10376">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> The Weather Impacts the Outbreak of COVID-19 in Mainland China </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+S">Siyu Huang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Ji Liu</a>, <a href="/search/q-bio?searchtype=author&query=Xiong%2C+H">Haoyi Xiong</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jizhou Huang</a>, <a href="/search/q-bio?searchtype=author&query=An%2C+H">Haozhe An</a>, <a href="/search/q-bio?searchtype=author&query=Dou%2C+D">Dejing Dou</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="2006.10376v1-abstract-short" style="display: inline;"> Recent literature has suggested that climate conditions have considerably significant influences on the transmission of coronavirus COVID-19. However, there is a lack of comprehensive study that investigates the relationships between multiple weather factors and the development of COVID-19 pandemic while excluding the impact of social factors. In this paper, we study the relationships between six… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.10376v1-abstract-full').style.display = 'inline'; document.getElementById('2006.10376v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.10376v1-abstract-full" style="display: none;"> Recent literature has suggested that climate conditions have considerably significant influences on the transmission of coronavirus COVID-19. However, there is a lack of comprehensive study that investigates the relationships between multiple weather factors and the development of COVID-19 pandemic while excluding the impact of social factors. In this paper, we study the relationships between six main weather factors and the infection statistics of COVID-19 on 250 cities in Mainland China. Our correlation analysis using weather and infection statistics indicates that all the studied weather factors are correlated with the spread of COVID-19, where precipitation shows the strongest correlation. We also build a weather-aware predictive model that forecasts the number of infected cases should there be a second wave of the outbreak in Mainland China. Our predicted results show that cities located in different geographical areas are likely to be challenged with the second wave of COVID-19 at very different time periods and the severity of the outbreak varies to a large degree, in correspondence with the varying weather conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.10376v1-abstract-full').style.display = 'none'; document.getElementById('2006.10376v1-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> 18 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </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">18 pages, 12 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.13607">arXiv:2005.13607</a> <span> [<a href="https://arxiv.org/pdf/2005.13607">pdf</a>, <a href="https://arxiv.org/format/2005.13607">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Multi-View Graph Neural Networks for Molecular Property Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hehuan Ma</a>, <a href="/search/q-bio?searchtype=author&query=Bian%2C+Y">Yatao Bian</a>, <a href="/search/q-bio?searchtype=author&query=Rong%2C+Y">Yu Rong</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+W">Wenbing Huang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+T">Tingyang Xu</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+W">Weiyang Xie</a>, <a href="/search/q-bio?searchtype=author&query=Ye%2C+G">Geyan Ye</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</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="2005.13607v3-abstract-short" style="display: inline;"> The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through Graph Neural Networks (GNNs). It is well known that both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model shall be able to exploit both node (atom) and edge (bond) information… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13607v3-abstract-full').style.display = 'inline'; document.getElementById('2005.13607v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.13607v3-abstract-full" style="display: none;"> The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through Graph Neural Networks (GNNs). It is well known that both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model shall be able to exploit both node (atom) and edge (bond) information simultaneously. Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. This readout component also renders the whole architecture interpretable. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme that enhances information communication of the two views, which results in the MV-GNN^cross variant. Lastly, we theoretically justify the expressiveness of the two proposed models in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that MV-GNN models achieve remarkably superior performance over the state-of-the-art models on a variety of challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of MV-GNN models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13607v3-abstract-full').style.display = 'none'; document.getElementById('2005.13607v3-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> 12 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.01668">arXiv:2005.01668</a> <span> [<a href="https://arxiv.org/pdf/2005.01668">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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/s41550-020-1069-4">10.1038/s41550-020-1069-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Laboratory studies on the viability of life in H$_2$-dominated exoplanet atmospheres </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Seager%2C+S">S. Seager</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">J. Huang</a>, <a href="/search/q-bio?searchtype=author&query=Petkowski%2C+J+J">J. J. Petkowski</a>, <a href="/search/q-bio?searchtype=author&query=Pajusalu%2C+M">M. Pajusalu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.01668v2-abstract-short" style="display: inline;"> Theory and observation for the search for life on exoplanets via atmospheric "biosignature gases" is accelerating, motivated by the capabilities of the next generation of space- and ground-based telescopes. The most observationally accessible rocky planet atmospheres are those dominated by molecular hydrogen gas, because the low density of H$_2$-gas leads to an expansive atmosphere. The capability… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01668v2-abstract-full').style.display = 'inline'; document.getElementById('2005.01668v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.01668v2-abstract-full" style="display: none;"> Theory and observation for the search for life on exoplanets via atmospheric "biosignature gases" is accelerating, motivated by the capabilities of the next generation of space- and ground-based telescopes. The most observationally accessible rocky planet atmospheres are those dominated by molecular hydrogen gas, because the low density of H$_2$-gas leads to an expansive atmosphere. The capability of life to withstand such exotic environments, however, has not been tested in this context. We demonstrate that single-celled microorganisms ($\textit{E. coli}$ and yeast) that normally do not inhabit H$_2$-dominated environments can survive and grow in a 100% H$_2$ atmosphere. We also describe the astonishing diversity of dozens of different gases produced by $\textit{E. coli}$, including many already proposed as potential biosignature gases (e.g., nitrous oxide, ammonia, methanethiol, dimethylsulfide, carbonyl sulfide, and isoprene). This work demonstrates the utility of lab experiments to better identify which kinds of alien environments can host some form of possibly detectable life. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01668v2-abstract-full').style.display = 'none'; document.getElementById('2005.01668v2-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> 7 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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">Nature Astronomy https://doi.org/10.1038/s41550-020-1069-4. V2 has a typo correction</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature Astronomy, 4(8), 802-806 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.06846">arXiv:2003.06846</a> <span> [<a href="https://arxiv.org/pdf/2003.06846">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Propagation analysis and prediction of the COVID-19 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+L">Lixiang Li</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Z">Zihang Yang</a>, <a href="/search/q-bio?searchtype=author&query=Dang%2C+Z">Zhongkai Dang</a>, <a href="/search/q-bio?searchtype=author&query=Meng%2C+C">Cui Meng</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jingze Huang</a>, <a href="/search/q-bio?searchtype=author&query=Meng%2C+H+T">Hao Tian Meng</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+D">Deyu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guanhua Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jiaxuan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+H">Haipeng Peng</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="2003.06846v1-abstract-short" style="display: inline;"> Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is within 3%. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.06846v1-abstract-full" style="display: none;"> Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is within 3%. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.06846v1-abstract-full').style.display = 'none'; document.getElementById('2003.06846v1-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> 15 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.04461">arXiv:2002.04461</a> <span> [<a href="https://arxiv.org/pdf/2002.04461">pdf</a>, <a href="https://arxiv.org/format/2002.04461">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">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey 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"> TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tong%2C+A">Alexander Tong</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jessie Huang</a>, <a href="/search/q-bio?searchtype=author&query=Wolf%2C+G">Guy Wolf</a>, <a href="/search/q-bio?searchtype=author&query=van+Dijk%2C+D">David van Dijk</a>, <a href="/search/q-bio?searchtype=author&query=Krishnaswamy%2C+S">Smita Krishnaswamy</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="2002.04461v2-abstract-short" style="display: inline;"> It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.04461v2-abstract-full').style.display = 'inline'; document.getElementById('2002.04461v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.04461v2-abstract-full" style="display: none;"> It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.04461v2-abstract-full').style.display = 'none'; document.getElementById('2002.04461v2-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </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">Presented at ICML 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.03538">arXiv:1912.03538</a> <span> [<a href="https://arxiv.org/pdf/1912.03538">pdf</a>, <a href="https://arxiv.org/format/1912.03538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Beery%2C+S">Sara Beery</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+G">Guanhang Wu</a>, <a href="/search/q-bio?searchtype=author&query=Rathod%2C+V">Vivek Rathod</a>, <a href="/search/q-bio?searchtype=author&query=Votel%2C+R">Ronny Votel</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jonathan Huang</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="1912.03538v3-abstract-short" style="display: inline;"> In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavior over multiple days, and background objects remain static. Due to power and storage constraints, sampling frequencies are low, often no faster than one frame per second, and sometimes are irregular due to the use of a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.03538v3-abstract-full').style.display = 'inline'; document.getElementById('1912.03538v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.03538v3-abstract-full" style="display: none;"> In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavior over multiple days, and background objects remain static. Due to power and storage constraints, sampling frequencies are low, often no faster than one frame per second, and sometimes are irregular due to the use of a motion trigger. In order to perform well in this setting, models must be robust to irregular sampling rates. In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera. Specifically, we propose an attention-based approach that allows our model, Context R-CNN, to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame. We apply Context R-CNN to two settings: (1) species detection using camera traps, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines. Moreover, we show that increasing the contextual time horizon leads to improved results. When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from up to a month of images outperforms a single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution based baseline) by 11.2% mAP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.03538v3-abstract-full').style.display = 'none'; document.getElementById('1912.03538v3-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> 22 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">CVPR 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.05567">arXiv:1911.05567</a> <span> [<a href="https://arxiv.org/pdf/1911.05567">pdf</a>, <a href="https://arxiv.org/format/1911.05567">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kaku%2C+A">Aakash Kaku</a>, <a href="/search/q-bio?searchtype=author&query=Hegde%2C+C+V">Chaitra V. Hegde</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jeffrey Huang</a>, <a href="/search/q-bio?searchtype=author&query=Chung%2C+S">Sohae Chung</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xiuyuan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Young%2C+M">Matthew Young</a>, <a href="/search/q-bio?searchtype=author&query=Radmanesh%2C+A">Alireza Radmanesh</a>, <a href="/search/q-bio?searchtype=author&query=Lui%2C+Y+W">Yvonne W. Lui</a>, <a href="/search/q-bio?searchtype=author&query=Razavian%2C+N">Narges Razavian</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="1911.05567v2-abstract-short" style="display: inline;"> Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05567v2-abstract-full').style.display = 'inline'; document.getElementById('1911.05567v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.05567v2-abstract-full" style="display: none;"> Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation). Our tool and trained models are available at https://github.com/NYUMedML/DARTS <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05567v2-abstract-full').style.display = 'none'; document.getElementById('1911.05567v2-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> 14 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.03083">arXiv:1909.03083</a> <span> [<a href="https://arxiv.org/pdf/1909.03083">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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> A community-based transcriptomics classification and nomenclature of neocortical cell types </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yuste%2C+R">Rafael Yuste</a>, <a href="/search/q-bio?searchtype=author&query=Hawrylycz%2C+M">Michael Hawrylycz</a>, <a href="/search/q-bio?searchtype=author&query=Aalling%2C+N">Nadia Aalling</a>, <a href="/search/q-bio?searchtype=author&query=Arendt%2C+D">Detlev Arendt</a>, <a href="/search/q-bio?searchtype=author&query=Armananzas%2C+R">Ruben Armananzas</a>, <a href="/search/q-bio?searchtype=author&query=Ascoli%2C+G">Giorgio Ascoli</a>, <a href="/search/q-bio?searchtype=author&query=Bielza%2C+C">Concha Bielza</a>, <a href="/search/q-bio?searchtype=author&query=Bokharaie%2C+V">Vahid Bokharaie</a>, <a href="/search/q-bio?searchtype=author&query=Bergmann%2C+T">Tobias Bergmann</a>, <a href="/search/q-bio?searchtype=author&query=Bystron%2C+I">Irina Bystron</a>, <a href="/search/q-bio?searchtype=author&query=Capogna%2C+M">Marco Capogna</a>, <a href="/search/q-bio?searchtype=author&query=Chang%2C+Y">Yoonjeung Chang</a>, <a href="/search/q-bio?searchtype=author&query=Clemens%2C+A">Ann Clemens</a>, <a href="/search/q-bio?searchtype=author&query=de+Kock%2C+C">Christiaan de Kock</a>, <a href="/search/q-bio?searchtype=author&query=DeFelipe%2C+J">Javier DeFelipe</a>, <a href="/search/q-bio?searchtype=author&query=Santos%2C+S+D">Sandra Dos Santos</a>, <a href="/search/q-bio?searchtype=author&query=Dunville%2C+K">Keagan Dunville</a>, <a href="/search/q-bio?searchtype=author&query=Feldmeyer%2C+D">Dirk Feldmeyer</a>, <a href="/search/q-bio?searchtype=author&query=Fiath%2C+R">Richard Fiath</a>, <a href="/search/q-bio?searchtype=author&query=Fishell%2C+G">Gordon Fishell</a>, <a href="/search/q-bio?searchtype=author&query=Foggetti%2C+A">Angelica Foggetti</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+X">Xuefan Gao</a>, <a href="/search/q-bio?searchtype=author&query=Ghaderi%2C+P">Parviz Ghaderi</a>, <a href="/search/q-bio?searchtype=author&query=Gunturkun%2C+O">Onur Gunturkun</a>, <a href="/search/q-bio?searchtype=author&query=Hall%2C+V+J">Vanessa Jane Hall</a> , et al. (46 additional authors not shown) </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="1909.03083v1-abstract-short" style="display: inline;"> To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throug… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.03083v1-abstract-full').style.display = 'inline'; document.getElementById('1909.03083v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.03083v1-abstract-full" style="display: none;"> To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.03083v1-abstract-full').style.display = 'none'; document.getElementById('1909.03083v1-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> 6 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">21 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.04206">arXiv:1908.04206</a> <span> [<a href="https://arxiv.org/pdf/1908.04206">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</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/10.1002/anie.202000489">10.1002/anie.202000489 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> SERS discrimination of single amino acid residue in single peptide by plasmonic nanocavities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jian-An Huang</a>, <a href="/search/q-bio?searchtype=author&query=Mousavi%2C+M+Z">Mansoureh Z. Mousavi</a>, <a href="/search/q-bio?searchtype=author&query=Giovannini%2C+G">Giorgia Giovannini</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yingqi Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Hubarevich%2C+A">Aliaksandr Hubarevich</a>, <a href="/search/q-bio?searchtype=author&query=Garoli%2C+D">Denis Garoli</a>, <a href="/search/q-bio?searchtype=author&query=De+Angelis%2C+F">Francesco De Angelis</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="1908.04206v2-abstract-short" style="display: inline;"> Surface-enhanced Raman spectroscopy (SERS) is a sensitive label-free optical method that can provide fingerprint Raman spectra of biomolecules such as DNA, amino acids and proteins. While SERS of single DNA molecule has been recently demonstrated, Raman analysis of single protein sequence was not possible because the SERS spectra of proteins are usually dominated by signals of aromatic amino acid… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.04206v2-abstract-full').style.display = 'inline'; document.getElementById('1908.04206v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.04206v2-abstract-full" style="display: none;"> Surface-enhanced Raman spectroscopy (SERS) is a sensitive label-free optical method that can provide fingerprint Raman spectra of biomolecules such as DNA, amino acids and proteins. While SERS of single DNA molecule has been recently demonstrated, Raman analysis of single protein sequence was not possible because the SERS spectra of proteins are usually dominated by signals of aromatic amino acid residues. Here, we used electroplasmonic approach to trap single gold nanoparticle in a nanohole for generating a plasmonic nanocavity between the trapped nanoparticle and the nanopore wall. The giant field generated in the nanocavity was so sensitive and localized that it enables SERS discrimination of 10 distinct amino acids at single-molecule level. The obtained spectra are used to analyze the spectra of 2 biomarkers (Vasopressin and Oxytocin) made of a short sequence of 9 amino-acids. Significantly, we demonstrated identification of single non-aromatic amino acid residues in a single short peptide chain as well as discrimination between two peptides with sequences distinguishable in 2 specific amino-acids. Our result demonstrate the high sensitivity of our method to identify single amino acid residue in a protein chain and a potential for further applications in proteomics and single-protein sequencing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.04206v2-abstract-full').style.display = 'none'; document.getElementById('1908.04206v2-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> 13 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">Totally 22 pages, 12 figures and 3 tables including supporting information. arXiv admin note: text overlap with arXiv:1905.01856</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Angewandte Chemie International Edition, 59, 11423-11431 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.10315">arXiv:1808.10315</a> <span> [<a href="https://arxiv.org/pdf/1808.10315">pdf</a>, <a href="https://arxiv.org/format/1808.10315">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning for Quality Control of Subcortical Brain 3D Shape Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Petrov%2C+D">Dmitry Petrov</a>, <a href="/search/q-bio?searchtype=author&query=Kuznetsov%2C+B+A+G+E">Boris A. Gutman Egor Kuznetsov</a>, <a href="/search/q-bio?searchtype=author&query=van+Erp%2C+T+G+M">Theo G. M. van Erp</a>, <a href="/search/q-bio?searchtype=author&query=Turner%2C+J+A">Jessica A. Turner</a>, <a href="/search/q-bio?searchtype=author&query=Schmaal%2C+L">Lianne Schmaal</a>, <a href="/search/q-bio?searchtype=author&query=Veltman%2C+D">Dick Veltman</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+L">Lei Wang</a>, <a href="/search/q-bio?searchtype=author&query=Alpert%2C+K">Kathryn Alpert</a>, <a href="/search/q-bio?searchtype=author&query=Isaev%2C+D">Dmitry Isaev</a>, <a href="/search/q-bio?searchtype=author&query=Zavaliangos-Petropulu%2C+A">Artemis Zavaliangos-Petropulu</a>, <a href="/search/q-bio?searchtype=author&query=Ching%2C+C+R+K">Christopher R. K. Ching</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+V">Vince Calhoun</a>, <a href="/search/q-bio?searchtype=author&query=Glahn%2C+D">David Glahn</a>, <a href="/search/q-bio?searchtype=author&query=Satterthwaite%2C+T+D">Theodore D. Satterthwaite</a>, <a href="/search/q-bio?searchtype=author&query=Andreassen%2C+O+A">Ole Andreas Andreassen</a>, <a href="/search/q-bio?searchtype=author&query=Borgwardt%2C+S">Stefan Borgwardt</a>, <a href="/search/q-bio?searchtype=author&query=Howells%2C+F">Fleur Howells</a>, <a href="/search/q-bio?searchtype=author&query=Groenewold%2C+N">Nynke Groenewold</a>, <a href="/search/q-bio?searchtype=author&query=Voineskos%2C+A">Aristotle Voineskos</a>, <a href="/search/q-bio?searchtype=author&query=Radua%2C+J">Joaquim Radua</a>, <a href="/search/q-bio?searchtype=author&query=Potkin%2C+S+G">Steven G. Potkin</a>, <a href="/search/q-bio?searchtype=author&query=Crespo-Facorro%2C+B">Benedicto Crespo-Facorro</a>, <a href="/search/q-bio?searchtype=author&query=Tordesillas-Gutierrez%2C+D">Diana Tordesillas-Gutierrez</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+L">Li Shen</a>, <a href="/search/q-bio?searchtype=author&query=Lebedeva%2C+I">Irina Lebedeva</a> , et al. (48 additional authors not shown) </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="1808.10315v2-abstract-short" style="display: inline;"> We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.10315v2-abstract-full').style.display = 'inline'; document.getElementById('1808.10315v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.10315v2-abstract-full" style="display: none;"> We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.10315v2-abstract-full').style.display = 'none'; document.getElementById('1808.10315v2-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> 4 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </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">Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.06867">arXiv:1710.06867</a> <span> [<a href="https://arxiv.org/pdf/1710.06867">pdf</a>, <a href="https://arxiv.org/format/1710.06867">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Adaptation and Self-Organizing Systems">nlin.AO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</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/10.1073/pnas.1717729115">10.1073/pnas.1717729115 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Individuals, Institutions, and Innovation in the Debates of the French Revolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Barron%2C+A+T+J">Alexander T. J. Barron</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jenny Huang</a>, <a href="/search/q-bio?searchtype=author&query=Spang%2C+R+L">Rebecca L. Spang</a>, <a href="/search/q-bio?searchtype=author&query=DeDeo%2C+S">Simon DeDeo</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="1710.06867v1-abstract-short" style="display: inline;"> The French Revolution brought principles of "liberty, equality, and brotherhood" to bear on the day-to-day challenges of governing what was then the largest country in Europe. Its experiments provided a model for future revolutions and democracies across the globe, but this first modern revolution had no model to follow. Using reconstructed transcripts of debates held in the Revolution's first par… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.06867v1-abstract-full').style.display = 'inline'; document.getElementById('1710.06867v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.06867v1-abstract-full" style="display: none;"> The French Revolution brought principles of "liberty, equality, and brotherhood" to bear on the day-to-day challenges of governing what was then the largest country in Europe. Its experiments provided a model for future revolutions and democracies across the globe, but this first modern revolution had no model to follow. Using reconstructed transcripts of debates held in the Revolution's first parliament, we present a quantitative analysis of how this system managed innovation. We use information theory to track the creation, transmission, and destruction of patterns of word-use across over 40,000 speeches and more than one thousand speakers. The parliament as a whole was biased toward the adoption of new patterns, but speakers' individual qualities could break these overall trends. Speakers on the left innovated at higher rates while speakers on the right acted, often successfully, to preserve prior patterns. Key players such as Robespierre (on the left) and Abb茅 Maury (on the right) played information-processing roles emblematic of their politics. Newly-created organizational functions---such as the Assembly's President and committee chairs---had significant effects on debate outcomes, and a distinct transition appears mid-way through the parliament when committees, external to the debate process, gain new powers to "propose and dispose" to the body as a whole. Taken together, these quantitative results align with existing qualitative interpretations but also reveal crucial information-processing dynamics that have hitherto been overlooked. Great orators had the public's attention, but deputies (mostly on the political left) who mastered the committee system gained new powers to shape revolutionary legislation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.06867v1-abstract-full').style.display = 'none'; document.getElementById('1710.06867v1-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> 18 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </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">8 pages, 3 figures, 1 table. Comments solicited</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the National Academy of Sciences, 201717729 (2018) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1707.06353">arXiv:1707.06353</a> <span> [<a href="https://arxiv.org/pdf/1707.06353">pdf</a>, <a href="https://arxiv.org/format/1707.06353">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Petrov%2C+D">Dmitry Petrov</a>, <a href="/search/q-bio?searchtype=author&query=Gutman%2C+B+A">Boris A. Gutman</a>, <a href="/search/q-bio?searchtype=author&query=Shih-Hua"> Shih-Hua</a>, <a href="/search/q-bio?searchtype=author&query=Yu"> Yu</a>, <a href="/search/q-bio?searchtype=author&query=van+Erp%2C+T+G+M">Theo G. M. van Erp</a>, <a href="/search/q-bio?searchtype=author&query=Turner%2C+J+A">Jessica A. Turner</a>, <a href="/search/q-bio?searchtype=author&query=Schmaal%2C+L">Lianne Schmaal</a>, <a href="/search/q-bio?searchtype=author&query=Veltman%2C+D">Dick Veltman</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+L">Lei Wang</a>, <a href="/search/q-bio?searchtype=author&query=Alpert%2C+K">Kathryn Alpert</a>, <a href="/search/q-bio?searchtype=author&query=Isaev%2C+D">Dmitry Isaev</a>, <a href="/search/q-bio?searchtype=author&query=Zavaliangos-Petropulu%2C+A">Artemis Zavaliangos-Petropulu</a>, <a href="/search/q-bio?searchtype=author&query=Ching%2C+C+R+K">Christopher R. K. Ching</a>, <a href="/search/q-bio?searchtype=author&query=Calhoun%2C+V">Vince Calhoun</a>, <a href="/search/q-bio?searchtype=author&query=Glahn%2C+D">David Glahn</a>, <a href="/search/q-bio?searchtype=author&query=Satterthwaite%2C+T+D">Theodore D. Satterthwaite</a>, <a href="/search/q-bio?searchtype=author&query=Andreasen%2C+O+A">Ole Andreas Andreasen</a>, <a href="/search/q-bio?searchtype=author&query=Borgwardt%2C+S">Stefan Borgwardt</a>, <a href="/search/q-bio?searchtype=author&query=Howells%2C+F">Fleur Howells</a>, <a href="/search/q-bio?searchtype=author&query=Groenewold%2C+N">Nynke Groenewold</a>, <a href="/search/q-bio?searchtype=author&query=Voineskos%2C+A">Aristotle Voineskos</a>, <a href="/search/q-bio?searchtype=author&query=Radua%2C+J">Joaquim Radua</a>, <a href="/search/q-bio?searchtype=author&query=Potkin%2C+S+G">Steven G. Potkin</a>, <a href="/search/q-bio?searchtype=author&query=Crespo-Facorro%2C+B">Benedicto Crespo-Facorro</a>, <a href="/search/q-bio?searchtype=author&query=Tordesillas-Gutierrez%2C+D">Diana Tordesillas-Gutierrez</a> , et al. (50 additional authors not shown) </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="1707.06353v4-abstract-short" style="display: inline;"> As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape fe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.06353v4-abstract-full').style.display = 'inline'; document.getElementById('1707.06353v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1707.06353v4-abstract-full" style="display: none;"> As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.06353v4-abstract-full').style.display = 'none'; document.getElementById('1707.06353v4-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> 7 August, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2017. </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">Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1704.06086">arXiv:1704.06086</a> <span> [<a href="https://arxiv.org/pdf/1704.06086">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> </div> </div> <p class="title is-5 mathjax"> Do ROS really slow down aging in C. elegans? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ren%2C+Y">Yaguang Ren</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+S">Sixi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+M">Mengmeng Ma</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+C">Congjie Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+K">Kejie Wang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+F">Feng Li</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+W">Wenxuan Guo</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jiatao Huang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+C">Chao Zhang</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="1704.06086v2-abstract-short" style="display: inline;"> The view that ROS slow down aging is getting popular. We here proposed an idea that aging is slowed down by secondary responses rather than ROS. </span> <span class="abstract-full has-text-grey-dark mathjax" id="1704.06086v2-abstract-full" style="display: none;"> The view that ROS slow down aging is getting popular. We here proposed an idea that aging is slowed down by secondary responses rather than ROS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.06086v2-abstract-full').style.display = 'none'; document.getElementById('1704.06086v2-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 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1702.08078">arXiv:1702.08078</a> <span> [<a href="https://arxiv.org/pdf/1702.08078">pdf</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"> Intra-protein binding peptide fragments have specific and intrinsic sequence patterns </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yuhong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junzhou Huang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+W">Wei Li</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+S">Sheng Wang</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+C">Chuanfan Ding</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="1702.08078v1-abstract-short" style="display: inline;"> The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that similar rules and intrinsic sequence patterns between intra-protein binding peptide fragments do exist, and they can be extracted using a deep… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1702.08078v1-abstract-full').style.display = 'inline'; document.getElementById('1702.08078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1702.08078v1-abstract-full" style="display: none;"> The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that similar rules and intrinsic sequence patterns between intra-protein binding peptide fragments do exist, and they can be extracted using a deep learning algorithm. Multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. This discovery has the potential in helping solve protein folding and protein-protein interaction problems, two open and fundamental problems in molecular biology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1702.08078v1-abstract-full').style.display = 'none'; document.getElementById('1702.08078v1-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 February, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2017. </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">6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1609.01569">arXiv:1609.01569</a> <span> [<a href="https://arxiv.org/pdf/1609.01569">pdf</a>, <a href="https://arxiv.org/format/1609.01569">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 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/10.1142/9789813202382_0004">10.1142/9789813202382_0004 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Statistical Mechanics and Kinetics of Amyloid Fibrillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hong%2C+L">Liu Hong</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+C+F">Chiu Fan Lee</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Y+J">Ya Jing Huang</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="1609.01569v1-abstract-short" style="display: inline;"> Amyloid fibrillation is a protein self-assembly phenomenon that is intimately related to well-known human neurodegenerative diseases. During the past few decades, striking advances have been achieved in our understanding of the physical origin of this phenomenon and they constitute the contents of this review. Starting from a minimal model of amyloid fibrils, we explore systematically the equilibr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01569v1-abstract-full').style.display = 'inline'; document.getElementById('1609.01569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.01569v1-abstract-full" style="display: none;"> Amyloid fibrillation is a protein self-assembly phenomenon that is intimately related to well-known human neurodegenerative diseases. During the past few decades, striking advances have been achieved in our understanding of the physical origin of this phenomenon and they constitute the contents of this review. Starting from a minimal model of amyloid fibrils, we explore systematically the equilibrium and kinetic aspects of amyloid fibrillation in both dilute and semi-dilute limits. We then incorporate further molecular mechanisms into the analyses. We also discuss the mathematical foundation of kinetic modeling based on chemical mass-action equations, the quantitative linkage with experimental measurements, as well as the procedure to perform global fitting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01569v1-abstract-full').style.display = 'none'; document.getElementById('1609.01569v1-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> 6 September, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2016. </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">68 pages, 18 figures, 201 references</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In Biophysics and Biochemistry of Protein Aggregation, edited by J.-M. Yuan and H.-X. Zhou (World Scienti?c, 2017), Chapter 4, pp. 113-186 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.07497">arXiv:1606.07497</a> <span> [<a href="https://arxiv.org/pdf/1606.07497">pdf</a>, <a href="https://arxiv.org/ps/1606.07497">ps</a>, <a href="https://arxiv.org/format/1606.07497">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</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/srep34598">10.1038/srep34598 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Testing Modeling Assumptions in the West Africa Ebola Outbreak </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Burghardt%2C+K">Keith Burghardt</a>, <a href="/search/q-bio?searchtype=author&query=Verzijl%2C+C">Christopher Verzijl</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Junming Huang</a>, <a href="/search/q-bio?searchtype=author&query=Ingram%2C+M">Matthew Ingram</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+B">Binyang Song</a>, <a href="/search/q-bio?searchtype=author&query=Hasne%2C+M">Marie-Pierre Hasne</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="1606.07497v2-abstract-short" style="display: inline;"> The Ebola virus in West Africa has infected almost 30,000 and killed over 11,000 people. Recent models of Ebola Virus Disease (EVD) have often made assumptions about how the disease spreads, such as uniform transmissibility and homogeneous mixing within a population. In this paper, we test whether these assumptions are necessarily correct, and offer simple solutions that may improve disease model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.07497v2-abstract-full').style.display = 'inline'; document.getElementById('1606.07497v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.07497v2-abstract-full" style="display: none;"> The Ebola virus in West Africa has infected almost 30,000 and killed over 11,000 people. Recent models of Ebola Virus Disease (EVD) have often made assumptions about how the disease spreads, such as uniform transmissibility and homogeneous mixing within a population. In this paper, we test whether these assumptions are necessarily correct, and offer simple solutions that may improve disease model accuracy. First, we use data and models of West African migration to show that EVD does not homogeneously mix, but spreads in a predictable manner. Next, we estimate the initial growth rate of EVD within country administrative divisions and find that it significantly decreases with population density. Finally, we test whether EVD strains have uniform transmissibility through a novel statistical test, and find that certain strains appear more often than expected by chance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.07497v2-abstract-full').style.display = 'none'; document.getElementById('1606.07497v2-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> 12 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </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">16 pages, 14 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sci. Rep., 6: 34598 (2016) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1204.0615">arXiv:1204.0615</a> <span> [<a href="https://arxiv.org/pdf/1204.0615">pdf</a>, <a href="https://arxiv.org/ps/1204.0615">ps</a>, <a href="https://arxiv.org/format/1204.0615">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</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/10.1103/PhysRevLett.108.198102">10.1103/PhysRevLett.108.198102 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Stripe formation in bacterial systems with density-suppressed motility </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Fu%2C+X">Xiongfei Fu</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+L">Lei-Han Tang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+C">Chenli Liu</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jian-Dong Huang</a>, <a href="/search/q-bio?searchtype=author&query=Hwa%2C+T">Terence Hwa</a>, <a href="/search/q-bio?searchtype=author&query=Lenz%2C+P">Peter Lenz</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="1204.0615v1-abstract-short" style="display: inline;"> Engineered bacteria in which motility is reduced by local cell density generate periodic stripes of high and low density when spotted on agar plates. We study theoretically the origin and mechanism of this process in a kinetic model that includes growth and density-suppressed motility of the cells. The spreading of a region of immotile cells into an initially cell-free region is analyzed. From the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1204.0615v1-abstract-full').style.display = 'inline'; document.getElementById('1204.0615v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1204.0615v1-abstract-full" style="display: none;"> Engineered bacteria in which motility is reduced by local cell density generate periodic stripes of high and low density when spotted on agar plates. We study theoretically the origin and mechanism of this process in a kinetic model that includes growth and density-suppressed motility of the cells. The spreading of a region of immotile cells into an initially cell-free region is analyzed. From the calculated front profile we provide an analytic ansatz to determine the phase boundary between the stripe and the no-stripe phases. The influence of various parameters on the phase boundary is discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1204.0615v1-abstract-full').style.display = 'none'; document.getElementById('1204.0615v1-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> 3 April, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2012. </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">5 pages, 3 figures. Phys. Rev. Lett. in press (2012)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1104.0876">arXiv:1104.0876</a> <span> [<a href="https://arxiv.org/pdf/1104.0876">pdf</a>, <a href="https://arxiv.org/format/1104.0876">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Subcellular Processes">q-bio.SC</span> </div> </div> <p class="title is-5 mathjax"> Direct measurement of the correlated dynamics of the protein-backbone and proximal waters of hydration in mechanically strained elastin </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+C">Cheng Sun</a>, <a href="/search/q-bio?searchtype=author&query=Mitchell%2C+O">Odingo Mitchell</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J">Jiaxin Huang</a>, <a href="/search/q-bio?searchtype=author&query=Boutis%2C+G+S">Gregory S. Boutis</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="1104.0876v1-abstract-short" style="display: inline;"> We report on the direct measurement of the correlation times of the protein backbone carbons and proximal waters of hydration in mechanically strained elastin by nuclear magnetic resonance methods. The experimental data indicate a decrease in the correlation times of the carbonyl carbons as the strain on the biopolymer is increased. These observations are in good agreement with short 4ns molecular… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1104.0876v1-abstract-full').style.display = 'inline'; document.getElementById('1104.0876v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1104.0876v1-abstract-full" style="display: none;"> We report on the direct measurement of the correlation times of the protein backbone carbons and proximal waters of hydration in mechanically strained elastin by nuclear magnetic resonance methods. The experimental data indicate a decrease in the correlation times of the carbonyl carbons as the strain on the biopolymer is increased. These observations are in good agreement with short 4ns molecular dynamics simulations of (VPGVG)3, a well studied mimetic peptide of elastin. The experimental results also indicate a reduction in the correlation time of proximal waters of hydration with increasing strain applied to the elastomer. A simple model is suggested that correlates the increase in the motion of proximal waters of hydration to the increase in frequency of libration of the protein backbone that develops with increasing strain. Together, the reduction in the protein entropy accompanied with the increase in entropy of the proximal waters of hydration with increasing strain, support the notion that the source of elasticity is driven by an entropic mechanism arising from the change in entropy of the protein backbone. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1104.0876v1-abstract-full').style.display = 'none'; document.getElementById('1104.0876v1-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> 5 April, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2011. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1009.5750">arXiv:1009.5750</a> <span> [<a href="https://arxiv.org/pdf/1009.5750">pdf</a>, <a href="https://arxiv.org/ps/1009.5750">ps</a>, <a href="https://arxiv.org/format/1009.5750">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</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/10.1214/09-AOAS253">10.1214/09-AOAS253 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Martinez%2C+J+G">Josue G. Martinez</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+J+Z">Jianhua Z. Huang</a>, <a href="/search/q-bio?searchtype=author&query=Burghardt%2C+R+C">Robert C. Burghardt</a>, <a href="/search/q-bio?searchtype=author&query=Barhoumi%2C+R">Rola Barhoumi</a>, <a href="/search/q-bio?searchtype=author&query=Carroll%2C+R+J">Raymond J. Carroll</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="1009.5750v1-abstract-short" style="display: inline;"> We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1009.5750v1-abstract-full').style.display = 'inline'; document.getElementById('1009.5750v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1009.5750v1-abstract-full" style="display: none;"> We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100$+$ pixels, so that they form 100$+$ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably unbiased manner. Finally, the $\mathrm {Ca}^{2+}$ signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the $\mathrm {Ca}^{2+}$ signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare $\mathrm {Ca}^{2+}$ signals across experimental conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1009.5750v1-abstract-full').style.display = 'none'; document.getElementById('1009.5750v1-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> 28 September, 2010; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2010. </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">Published in at http://dx.doi.org/10.1214/09-AOAS253 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> IMS-AOAS-AOAS253 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Annals of Applied Statistics 2009, Vol. 3, No. 4, 1467-1492 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0901.4598">arXiv:0901.4598</a> <span> [<a href="https://arxiv.org/pdf/0901.4598">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</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/10.1371/journal.pcbi.1000334">10.1371/journal.pcbi.1000334 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bohland%2C+J+W">Jason W. Bohland</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+C">Caizhi Wu</a>, <a href="/search/q-bio?searchtype=author&query=Barbas%2C+H">Helen Barbas</a>, <a href="/search/q-bio?searchtype=author&query=Bokil%2C+H">Hemant Bokil</a>, <a href="/search/q-bio?searchtype=author&query=Bota%2C+M">Mihail Bota</a>, <a href="/search/q-bio?searchtype=author&query=Breiter%2C+H+C">Hans C. Breiter</a>, <a href="/search/q-bio?searchtype=author&query=Cline%2C+H+T">Hollis T. Cline</a>, <a href="/search/q-bio?searchtype=author&query=Doyle%2C+J+C">John C. Doyle</a>, <a href="/search/q-bio?searchtype=author&query=Freed%2C+P+J">Peter J. Freed</a>, <a href="/search/q-bio?searchtype=author&query=Greenspan%2C+R+J">Ralph J. Greenspan</a>, <a href="/search/q-bio?searchtype=author&query=Haber%2C+S+N">Suzanne N. Haber</a>, <a href="/search/q-bio?searchtype=author&query=Hawrylycz%2C+M">Michael Hawrylycz</a>, <a href="/search/q-bio?searchtype=author&query=Herrera%2C+D+G">Daniel G. Herrera</a>, <a href="/search/q-bio?searchtype=author&query=Hilgetag%2C+C+C">Claus C. Hilgetag</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Z+J">Z. Josh Huang</a>, <a href="/search/q-bio?searchtype=author&query=Jones%2C+A">Allan Jones</a>, <a href="/search/q-bio?searchtype=author&query=Jones%2C+E+G">Edward G. Jones</a>, <a href="/search/q-bio?searchtype=author&query=Karten%2C+H+J">Harvey J. Karten</a>, <a href="/search/q-bio?searchtype=author&query=Kleinfeld%2C+D">David Kleinfeld</a>, <a href="/search/q-bio?searchtype=author&query=Kotter%2C+R">Rolf Kotter</a>, <a href="/search/q-bio?searchtype=author&query=Lester%2C+H+A">Henry A. Lester</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+J+M">John M. Lin</a>, <a href="/search/q-bio?searchtype=author&query=Mensh%2C+B+D">Brett D. Mensh</a>, <a href="/search/q-bio?searchtype=author&query=Mikula%2C+S">Shawn Mikula</a>, <a href="/search/q-bio?searchtype=author&query=Panksepp%2C+J">Jaak Panksepp</a> , et al. (12 additional authors not shown) </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="0901.4598v1-abstract-short" style="display: inline;"> In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0901.4598v1-abstract-full').style.display = 'inline'; document.getElementById('0901.4598v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0901.4598v1-abstract-full" style="display: none;"> In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0901.4598v1-abstract-full').style.display = 'none'; document.getElementById('0901.4598v1-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> 28 January, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2009. </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">41 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> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cond-mat/0306271">arXiv:cond-mat/0306271</a> <span> [<a href="https://arxiv.org/pdf/cond-mat/0306271">pdf</a>, <a href="https://arxiv.org/ps/cond-mat/0306271">ps</a>, <a href="https://arxiv.org/format/cond-mat/0306271">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Biology">q-bio</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/10.1103/PhysRevE.69.051402">10.1103/PhysRevE.69.051402 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Electrokinetic behavior of two touching inhomogeneous biological cells and colloidal particles: Effects of multipolar interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J+P">J. P. Huang</a>, <a href="/search/q-bio?searchtype=author&query=Karttunen%2C+M">Mikko Karttunen</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+K+W">K. W. Yu</a>, <a href="/search/q-bio?searchtype=author&query=Dong%2C+L">L. Dong</a>, <a href="/search/q-bio?searchtype=author&query=Gu%2C+G+Q">G. Q. Gu</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="cond-mat/0306271v2-abstract-short" style="display: inline;"> We present a theory to investigate electro-kinetic behavior, namely, electrorotation and dielectrophoresis under alternating current (AC) applied fields for a pair of touching inhomogeneous colloidal particles and biological cells. These inhomogeneous particles are treated as graded ones with physically motivated model dielectric and conductivity profiles. The mutual polarization interaction bet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0306271v2-abstract-full').style.display = 'inline'; document.getElementById('cond-mat/0306271v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cond-mat/0306271v2-abstract-full" style="display: none;"> We present a theory to investigate electro-kinetic behavior, namely, electrorotation and dielectrophoresis under alternating current (AC) applied fields for a pair of touching inhomogeneous colloidal particles and biological cells. These inhomogeneous particles are treated as graded ones with physically motivated model dielectric and conductivity profiles. The mutual polarization interaction between the particles yields a change in their respective dipole moments, and hence in the AC electrokinetic spectra. The multipolar interactions between polarized particles are accurately captured by the multiple images method. In the point-dipole limit, our theory reproduces the known results. We find that the multipolar interactions as well as the spatial fluctuations inside the particles can affect the AC electrokinetic spectra significantly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0306271v2-abstract-full').style.display = 'none'; document.getElementById('cond-mat/0306271v2-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> 7 November, 2003; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2003; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2003. </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">Revised version with minor changes: References added and discussion extended</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. E 69, 051402 (2004) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cond-mat/0202458">arXiv:cond-mat/0202458</a> <span> [<a href="https://arxiv.org/pdf/cond-mat/0202458">pdf</a>, <a href="https://arxiv.org/ps/cond-mat/0202458">ps</a>, <a href="https://arxiv.org/format/cond-mat/0202458">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Biology">q-bio</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/10.1088/0253-6102/39/4/506">10.1088/0253-6102/39/4/506 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Dielectric behavior of oblate spheroidal particles: Application to erythrocytes suspensions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Huang%2C+J+P">J. P. Huang</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+K+W">K. W. Yu</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="cond-mat/0202458v1-abstract-short" style="display: inline;"> We have investigated the effect of particle shape on the eletrorotation (ER) spectrum of living cells suspensions. In particular, we consider coated oblate spheroidal particles and present a theoretical study of ER based on the spectral representation theory. Analytic expressions for the characteristic frequency as well as the dispersion strength can be obtained, thus simplifying the fitting of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0202458v1-abstract-full').style.display = 'inline'; document.getElementById('cond-mat/0202458v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cond-mat/0202458v1-abstract-full" style="display: none;"> We have investigated the effect of particle shape on the eletrorotation (ER) spectrum of living cells suspensions. In particular, we consider coated oblate spheroidal particles and present a theoretical study of ER based on the spectral representation theory. Analytic expressions for the characteristic frequency as well as the dispersion strength can be obtained, thus simplifying the fitting of experimental data on oblate spheroidal cells that abound in the literature. From the theoretical analysis, we find that the cell shape, coating as well as material parameters can change the ER spectrum. We demonstrate good agreement between our theoretical predictions and experimental data on human erthrocytes suspensions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0202458v1-abstract-full').style.display = 'none'; document.getElementById('cond-mat/0202458v1-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 February, 2002; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2002. </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">RevTex; 5 eps figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Commun. Theor. 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