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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11169">arXiv:2411.11169</a> <span> [<a href="https://arxiv.org/pdf/2411.11169">pdf</a>, <a href="https://arxiv.org/format/2411.11169">other</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="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Validating GWAS Findings through Reverse Engineering of Contingency Tables </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuzhou Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Ayday%2C+E">Erman Ayday</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="2411.11169v1-abstract-short" style="display: inline;"> Reproducibility in genome-wide association studies (GWAS) is crucial for ensuring reliable genomic research outcomes. However, limited access to original genomic datasets (mainly due to privacy concerns) prevents researchers from reproducing experiments to validate results. In this paper, we propose a novel method for GWAS reproducibility validation that detects unintentional errors without the ne… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11169v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11169v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11169v1-abstract-full" style="display: none;"> Reproducibility in genome-wide association studies (GWAS) is crucial for ensuring reliable genomic research outcomes. However, limited access to original genomic datasets (mainly due to privacy concerns) prevents researchers from reproducing experiments to validate results. In this paper, we propose a novel method for GWAS reproducibility validation that detects unintentional errors without the need for dataset sharing. Our approach leverages p-values from GWAS outcome reports to estimate contingency tables for each single nucleotide polymorphism (SNP) and calculates the Hamming distance between the minor allele frequencies (MAFs) derived from these contingency tables and publicly available phenotype-specific MAF data. By comparing the average Hamming distance, we validate results that fall within a trusted threshold as reliable, while flagging those that exceed the threshold for further inspection. This approach not only allows researchers to validate the correctness of GWAS findings of other researchers, but it also provides a self-check step for the researchers before they publish their findings. We evaluate our approach using three real-life SNP datasets from OpenSNP, showing its ability to detect unintentional errors effectively, even when small errors occur, such as 1\% of SNPs being reported incorrectly. This novel validation technique offers a promising solution to the GWAS reproducibility challenge, balancing the need for rigorous validation with the imperative of protecting sensitive genomic data, thereby enhancing trust and accuracy in genetic research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11169v1-abstract-full').style.display = 'none'; document.getElementById('2411.11169v1-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 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/2411.09766">arXiv:2411.09766</a> <span> [<a href="https://arxiv.org/pdf/2411.09766">pdf</a>, <a href="https://arxiv.org/format/2411.09766">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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+Q">Qiang Li</a>, <a href="/search/q-bio?searchtype=author&query=Teodoro%2C+G">George Teodoro</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Kong%2C+J">Jun Kong</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="2411.09766v2-abstract-short" style="display: inline;"> Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learni… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09766v2-abstract-full').style.display = 'inline'; document.getElementById('2411.09766v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09766v2-abstract-full" style="display: none;"> Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet). Our deep learning method identifies the histopathological labels on individual image tiles from WSIs, constructs a spatial TME graph, and represents each node with features derived from tissue texture and social network analysis. It predicts NAC response using a transformer graph convolution network model enhanced with graph isomorphism network layers. We evaluate our method with WSIs of a cohort of TNBC patient (N=105) and compared its performance with multiple state-of-the-art machine learning and deep learning models, including both graph and non-graph approaches. Our NACNet achieves 90.0% accuracy, 96.0% sensitivity, 88.0% specificity, and an AUC of 0.82, through eight-fold cross-validation, outperforming baseline models. These comprehensive experimental results suggest that NACNet holds strong potential for stratifying TNBC patients by NAC response, thereby helping to prevent overtreatment, improve patient quality of life, reduce treatment cost, and enhance clinical outcomes, marking an important advancement toward personalized breast cancer treatment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09766v2-abstract-full').style.display = 'none'; document.getElementById('2411.09766v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">This paper is accepted by Computerized Medical Imaging and Graphics (Nov 07 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.24022">arXiv:2410.24022</a> <span> [<a href="https://arxiv.org/pdf/2410.24022">pdf</a>, <a href="https://arxiv.org/format/2410.24022">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"> SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=He%2C+L">Liang He</a>, <a href="/search/q-bio?searchtype=author&query=Jin%2C+P">Peiran Jin</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+Y">Yaosen Min</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+S">Shufang Xie</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+L">Lijun Wu</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+T">Tao Qin</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+X">Xiaozhuan Liang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+K">Kaiyuan Gao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuliang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+T">Tie-Yan 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="2410.24022v1-abstract-short" style="display: inline;"> Proteins, essential to biological systems, perform functions intricately linked to their three-dimensional structures. Understanding the relationship between protein structures and their amino acid sequences remains a core challenge in protein modeling. While traditional protein foundation models benefit from pre-training on vast unlabeled datasets, they often struggle to capture critical co-evolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.24022v1-abstract-full').style.display = 'inline'; document.getElementById('2410.24022v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.24022v1-abstract-full" style="display: none;"> Proteins, essential to biological systems, perform functions intricately linked to their three-dimensional structures. Understanding the relationship between protein structures and their amino acid sequences remains a core challenge in protein modeling. While traditional protein foundation models benefit from pre-training on vast unlabeled datasets, they often struggle to capture critical co-evolutionary information, which evolutionary-based methods excel at. In this study, we introduce a novel pre-training strategy for protein foundation models that emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features from sequence data. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability, outperforming established baselines of similar size, including the ESM model, across diverse downstream tasks. Experimental results confirm the model's effectiveness in integrating co-evolutionary information, marking a significant step forward in protein sequence-based modeling. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.24022v1-abstract-full').style.display = 'none'; document.getElementById('2410.24022v1-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> 31 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/2410.07919">arXiv:2410.07919</a> <span> [<a href="https://arxiv.org/pdf/2410.07919">pdf</a>, <a href="https://arxiv.org/format/2410.07919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhuang%2C+X">Xiang Zhuang</a>, <a href="/search/q-bio?searchtype=author&query=Ding%2C+K">Keyan Ding</a>, <a href="/search/q-bio?searchtype=author&query=Lyu%2C+T">Tianwen Lyu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yinuo Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiaotong Li</a>, <a href="/search/q-bio?searchtype=author&query=Xiang%2C+Z">Zhuoyi Xiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zeyuan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Qin%2C+M">Ming Qin</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+K">Kehua Feng</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jike Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qiang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+H">Huajun 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="2410.07919v1-abstract-short" style="display: inline;"> Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology, and enzyme engineering. Recent breakthroughs in Artificial Intelligence (AI) have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between AI's computational power and res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07919v1-abstract-full').style.display = 'inline'; document.getElementById('2410.07919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07919v1-abstract-full" style="display: none;"> Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology, and enzyme engineering. Recent breakthroughs in Artificial Intelligence (AI) have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between AI's computational power and researchers' intuition, using natural language to align molecular complexity with human intentions. Large Language Models (LLMs) have shown potential to interpret human intentions, yet their application to biomolecular research remains nascent due to challenges including specialized knowledge requirements, multimodal data integration, and semantic alignment between natural language and biomolecules. To address these limitations, we present InstructBioMol, a novel LLM designed to bridge natural language and biomolecules through a comprehensive any-to-any alignment of natural language, molecules, and proteins. This model can integrate multimodal biomolecules as input, and enable researchers to articulate design goals in natural language, providing biomolecular outputs that meet precise biological needs. Experimental results demonstrate InstructBioMol can understand and design biomolecules following human instructions. Notably, it can generate drug molecules with a 10% improvement in binding affinity and design enzymes that achieve an ESP Score of 70.4, making it the only method to surpass the enzyme-substrate interaction threshold of 60.0 recommended by the ESP developer. This highlights its potential to transform real-world biomolecular research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07919v1-abstract-full').style.display = 'none'; document.getElementById('2410.07919v1-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 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/2405.03931">arXiv:2405.03931</a> <span> [<a href="https://arxiv.org/pdf/2405.03931">pdf</a>, <a href="https://arxiv.org/ps/2405.03931">ps</a>, <a href="https://arxiv.org/format/2405.03931">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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"> Incorporating changeable attitudes toward vaccination into an SIR infectious disease model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Kurianski%2C+K+M">Kristin M. Kurianski</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J+H">Jane HyoJin Lee</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+Y">Yanping Ma</a>, <a href="/search/q-bio?searchtype=author&query=Cicala%2C+D">Daniel Cicala</a>, <a href="/search/q-bio?searchtype=author&query=Ledder%2C+G">Glenn Ledder</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="2405.03931v2-abstract-short" style="display: inline;"> We develop a mechanistic model that classifies individuals both in terms of epidemiological status (SIR) and vaccination attitude (willing or unwilling), with the goal of discovering how disease spread is influenced by changing opinions about vaccination. Analysis of the model identifies existence and stability criteria for both disease-free and endemic disease equilibria. The analytical results,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.03931v2-abstract-full').style.display = 'inline'; document.getElementById('2405.03931v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.03931v2-abstract-full" style="display: none;"> We develop a mechanistic model that classifies individuals both in terms of epidemiological status (SIR) and vaccination attitude (willing or unwilling), with the goal of discovering how disease spread is influenced by changing opinions about vaccination. Analysis of the model identifies existence and stability criteria for both disease-free and endemic disease equilibria. The analytical results, supported by numerical simulations, show that attitude changes induced by disease prevalence can destabilize endemic disease equilibria, resulting in limit cycles. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.03931v2-abstract-full').style.display = 'none'; document.getElementById('2405.03931v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">30 pages, 3 tables, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 37N25 (Primary) 92D30 (Secondary) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.05781">arXiv:2404.05781</a> <span> [<a href="https://arxiv.org/pdf/2404.05781">pdf</a>, <a href="https://arxiv.org/format/2404.05781">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> </div> </div> <p class="title is-5 mathjax"> Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhou%2C+S">Shuo Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+J">Junhao Luo</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yaya Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+H">Haolin Wang</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+H">Haiping Lu</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+G">Gaolang Gong</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="2404.05781v1-abstract-short" style="display: inline;"> Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralizat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.05781v1-abstract-full').style.display = 'inline'; document.getElementById('2404.05781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.05781v1-abstract-full" style="display: none;"> Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models. To capture sex-specific patterns, we develop the Group-Specific Discriminant Analysis (GSDA) for first-order classification. The evaluation on two public neuroimaging datasets demonstrates the efficacy of GSDA in learning sex-specific models from functional networks, achieving a significant improvement in group specificity over baseline methods. The major sex differences are in the strength of lateralization and the interactions within and between lobes. The GSDA-based method is generic in nature and can be adapted to other group-specific analyses such as handedness-specific or disease-specific analyses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.05781v1-abstract-full').style.display = 'none'; document.getElementById('2404.05781v1-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.07791">arXiv:2311.07791</a> <span> [<a href="https://arxiv.org/pdf/2311.07791">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"> Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuming Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Rex%2C+D+A+B">Devasahayam Arokia Balaya Rex</a>, <a href="/search/q-bio?searchtype=author&query=Schuster%2C+D">Dina Schuster</a>, <a href="/search/q-bio?searchtype=author&query=Neely%2C+B+A">Benjamin A. Neely</a>, <a href="/search/q-bio?searchtype=author&query=Rosano%2C+G+L">Germ谩n L. Rosano</a>, <a href="/search/q-bio?searchtype=author&query=Volkmar%2C+N">Norbert Volkmar</a>, <a href="/search/q-bio?searchtype=author&query=Momenzadeh%2C+A">Amanda Momenzadeh</a>, <a href="/search/q-bio?searchtype=author&query=Peters-Clarke%2C+T+M">Trenton M. Peters-Clarke</a>, <a href="/search/q-bio?searchtype=author&query=Egbert%2C+S+B">Susan B. Egbert</a>, <a href="/search/q-bio?searchtype=author&query=Kreimer%2C+S">Simion Kreimer</a>, <a href="/search/q-bio?searchtype=author&query=Doud%2C+E+H">Emma H. Doud</a>, <a href="/search/q-bio?searchtype=author&query=Crook%2C+O+M">Oliver M. Crook</a>, <a href="/search/q-bio?searchtype=author&query=Yadav%2C+A+K">Amit Kumar Yadav</a>, <a href="/search/q-bio?searchtype=author&query=Vanuopadath%2C+M">Muralidharan Vanuopadath</a>, <a href="/search/q-bio?searchtype=author&query=Mayta%2C+M+L">Mart铆n L. Mayta</a>, <a href="/search/q-bio?searchtype=author&query=Duboff%2C+A+G">Anna G. Duboff</a>, <a href="/search/q-bio?searchtype=author&query=Riley%2C+N+M">Nicholas M. Riley</a>, <a href="/search/q-bio?searchtype=author&query=Moritz%2C+R+L">Robert L. Moritz</a>, <a href="/search/q-bio?searchtype=author&query=Meyer%2C+J+G">Jesse G. Meyer</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="2311.07791v1-abstract-short" style="display: inline;"> Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identificati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07791v1-abstract-full').style.display = 'inline'; document.getElementById('2311.07791v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.07791v1-abstract-full" style="display: none;"> Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07791v1-abstract-full').style.display = 'none'; document.getElementById('2311.07791v1-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.19624">arXiv:2310.19624</a> <span> [<a href="https://arxiv.org/pdf/2310.19624">pdf</a>, <a href="https://arxiv.org/format/2310.19624">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"> Exploring Post-Training Quantization of Protein Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Peng%2C+S">Shuang Peng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+F">Fei Yang</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+N">Ning Sun</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+S">Sheng Chen</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yanfeng Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Pan%2C+A">Aimin Pan</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="2310.19624v1-abstract-short" style="display: inline;"> Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited resources. To tackle this, we explore post-training quantization (PTQ) for Prot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19624v1-abstract-full').style.display = 'inline'; document.getElementById('2310.19624v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.19624v1-abstract-full" style="display: none;"> Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited resources. To tackle this, we explore post-training quantization (PTQ) for ProteinLMs, focusing on ESMFold, a simplified version of AlphaFold based on ESM-2 ProteinLM. Our study is the first attempt to quantize all weights and activations of ProteinLMs. We observed that the typical uniform quantization method performs poorly on ESMFold, causing a significant drop in TM-Score when using 8-bit quantization. We conducted extensive quantization experiments, uncovering unique challenges associated with ESMFold, particularly highly asymmetric activation ranges before Layer Normalization, making representation difficult using low-bit fixed-point formats. To address these challenges, we propose a new PTQ method for ProteinLMs, utilizing piecewise linear quantization for asymmetric activation values to ensure accurate approximation. We demonstrated the effectiveness of our method in protein structure prediction tasks, demonstrating that ESMFold can be accurately quantized to low-bit widths without compromising accuracy. Additionally, we applied our method to the contact prediction task, showcasing its versatility. In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19624v1-abstract-full').style.display = 'none'; document.getElementById('2310.19624v1-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> 30 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">8 pages, 4 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/2310.13913">arXiv:2310.13913</a> <span> [<a href="https://arxiv.org/pdf/2310.13913">pdf</a>, <a href="https://arxiv.org/format/2310.13913">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="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+L">Lihang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+S">Shanzhuo Zhang</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+D">Donglong He</a>, <a href="/search/q-bio?searchtype=author&query=Ye%2C+X">Xianbin Ye</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+J">Jingbo Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xiaonan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yaoyao Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Diao%2C+W">Weiming Diao</a>, <a href="/search/q-bio?searchtype=author&query=Yin%2C+H">Hang Yin</a>, <a href="/search/q-bio?searchtype=author&query=Chai%2C+H">Hua Chai</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+F">Fan Wang</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+J">Jingzhou He</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+L">Liang Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yonghui Li</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+X">Xiaomin Fang</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="2310.13913v4-abstract-short" style="display: inline;"> Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Recent advances have incorporated deep learning techniques to improve the accuracy of protein-ligand structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises conce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13913v4-abstract-full').style.display = 'inline'; document.getElementById('2310.13913v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.13913v4-abstract-full" style="display: none;"> Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Recent advances have incorporated deep learning techniques to improve the accuracy of protein-ligand structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can obtain a protein-ligand structure prediction model with outstanding performance. Specifically, this process involved the generation of 100 million docking conformations for protein-ligand pairings, an endeavor consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been rigorously benchmarked against both physics-based and deep learning-based baselines, demonstrating its exceptional precision and robust transferability in predicting binding confirmation. In addition, our investigation reveals the scaling laws governing pre-trained protein-ligand structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and the volume of pre-training data. Moreover, we applied HelixDock to several drug discovery-related tasks to validate its practical utility. HelixDock demonstrates outstanding capabilities on both cross-docking and structure-based virtual screening benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13913v4-abstract-full').style.display = 'none'; document.getElementById('2310.13913v4-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.16498">arXiv:2309.16498</a> <span> [<a href="https://arxiv.org/pdf/2309.16498">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=He%2C+X">Xiuxiu He</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+K">Kuangcai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+N">Ning Fang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</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="2309.16498v1-abstract-short" style="display: inline;"> Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16498v1-abstract-full').style.display = 'inline'; document.getElementById('2309.16498v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.16498v1-abstract-full" style="display: none;"> Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16498v1-abstract-full').style.display = 'none'; document.getElementById('2309.16498v1-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, 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">29 pages, 9 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/2308.14774">arXiv:2308.14774</a> <span> [<a href="https://arxiv.org/pdf/2308.14774">pdf</a>, <a href="https://arxiv.org/format/2308.14774">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</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"> EEG-Derived Voice Signature for Attended Speaker Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhu%2C+H">Hongxu Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Cai%2C+S">Siqi Cai</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yidi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Qiquan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+H">Haizhou Li</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="2308.14774v1-abstract-short" style="display: inline;"> \textit{Objective:} Conventional EEG-based auditory attention detection (AAD) is achieved by comparing the time-varying speech stimuli and the elicited EEG signals. However, in order to obtain reliable correlation values, these methods necessitate a long decision window, resulting in a long detection latency. Humans have a remarkable ability to recognize and follow a known speaker, regardless of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.14774v1-abstract-full').style.display = 'inline'; document.getElementById('2308.14774v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.14774v1-abstract-full" style="display: none;"> \textit{Objective:} Conventional EEG-based auditory attention detection (AAD) is achieved by comparing the time-varying speech stimuli and the elicited EEG signals. However, in order to obtain reliable correlation values, these methods necessitate a long decision window, resulting in a long detection latency. Humans have a remarkable ability to recognize and follow a known speaker, regardless of the spoken content. In this paper, we seek to detect the attended speaker among the pre-enrolled speakers from the elicited EEG signals. In this manner, we avoid relying on the speech stimuli for AAD at run-time. In doing so, we propose a novel EEG-based attended speaker detection (E-ASD) task. \textit{Methods:} We encode a speaker's voice with a fixed dimensional vector, known as speaker embedding, and project it to an audio-derived voice signature, which characterizes the speaker's unique voice regardless of the spoken content. We hypothesize that such a voice signature also exists in the listener's brain that can be decoded from the elicited EEG signals, referred to as EEG-derived voice signature. By comparing the audio-derived voice signature and the EEG-derived voice signature, we are able to effectively detect the attended speaker in the listening brain. \textit{Results:} Experiments show that E-ASD can effectively detect the attended speaker from the 0.5s EEG decision windows, achieving 99.78\% AAD accuracy, 99.94\% AUC, and 0.27\% EER. \textit{Conclusion:} We conclude that it is possible to derive the attended speaker's voice signature from the EEG signals so as to detect the attended speaker in a listening brain. \textit{Significance:} We present the first proof of concept for detecting the attended speaker from the elicited EEG signals in a cocktail party environment. The successful implementation of E-ASD marks a non-trivial, but crucial step towards smart hearing aids. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.14774v1-abstract-full').style.display = 'none'; document.getElementById('2308.14774v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">8 pages, 2 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/2306.04899">arXiv:2306.04899</a> <span> [<a href="https://arxiv.org/pdf/2306.04899">pdf</a>, <a href="https://arxiv.org/format/2306.04899">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> </div> </div> <p class="title is-5 mathjax"> Multi-level Protein Representation Learning for Blind Mutational Effect Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tan%2C+Y">Yang Tan</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+B">Bingxin Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuanhong Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y+G">Yu Guang Wang</a>, <a href="/search/q-bio?searchtype=author&query=Hong%2C+L">Liang Hong</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="2306.04899v1-abstract-short" style="display: inline;"> Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects of protein variants necessitates an in-depth understanding of protein structure and function. Although large self-supervised language models have demonstrated remarkable performance in zero-shot inference using only pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04899v1-abstract-full').style.display = 'inline'; document.getElementById('2306.04899v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04899v1-abstract-full" style="display: none;"> Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects of protein variants necessitates an in-depth understanding of protein structure and function. Although large self-supervised language models have demonstrated remarkable performance in zero-shot inference using only protein sequences, these models inherently do not interpret the spatial characteristics of protein structures, which are crucial for comprehending protein folding stability and internal molecular interactions. This paper introduces a novel pre-training framework that cascades sequential and geometric analyzers for protein primary and tertiary structures. It guides mutational directions toward desired traits by simulating natural selection on wild-type proteins and evaluates the effects of variants based on their fitness to perform the function. We assess the proposed approach using a public database and two new databases for a variety of variant effect prediction tasks, which encompass a diverse set of proteins and assays from different taxa. The prediction results achieve state-of-the-art performance over other zero-shot learning methods for both single-site mutations and deep mutations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04899v1-abstract-full').style.display = 'none'; document.getElementById('2306.04899v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.03731">arXiv:2302.03731</a> <span> [<a href="https://arxiv.org/pdf/2302.03731">pdf</a>, <a href="https://arxiv.org/format/2302.03731">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+J">Jingyan Shen</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yunfan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+Z">Zhaohui Huang</a>, <a href="/search/q-bio?searchtype=author&query=Hao%2C+M">Minsheng Hao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xuegong 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="2302.03731v2-abstract-short" style="display: inline;"> The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrill… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03731v2-abstract-full').style.display = 'inline'; document.getElementById('2302.03731v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.03731v2-abstract-full" style="display: none;"> The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inherent from stage-by-stage approaches. This paper proposes the Multi-level Multi-task Attention-based Recurrent Neural Network for three-class discrimination on patients and localization of the exact timing of AF episodes. Our model captures three-level sequential features based on a hierarchical architecture utilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) and attention layers, and accomplishes the two tasks simultaneously with a multi-head classifier. The model is designed as an end-to-end framework to enhance information interaction and reduce error accumulation. Finally, we conduct experiments on CPSC 2021 dataset and the result demonstrates the superior performance of our method, indicating the potential application of MMA-RNN to wearable mobile devices for routine AF monitoring and early diagnosis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03731v2-abstract-full').style.display = 'none'; document.getElementById('2302.03731v2-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 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">9 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/2301.11846">arXiv:2301.11846</a> <span> [<a href="https://arxiv.org/pdf/2301.11846">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"> Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=He%2C+X">Xiaofu He</a>, <a href="/search/q-bio?searchtype=author&query=Moreno%2C+D+R">Diana Rodriguez Moreno</a>, <a href="/search/q-bio?searchtype=author&query=Hou%2C+Z">Zhenghua Hou</a>, <a href="/search/q-bio?searchtype=author&query=Cheslack-Postava%2C+K">Keely Cheslack-Postava</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yanni Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+T">Tong Li</a>, <a href="/search/q-bio?searchtype=author&query=Kishon%2C+R">Ronit Kishon</a>, <a href="/search/q-bio?searchtype=author&query=Amsel%2C+L">Larry Amsel</a>, <a href="/search/q-bio?searchtype=author&query=Musa%2C+G">George Musa</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zhishun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Hoven%2C+C+W">Christina W. Hoven</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="2301.11846v1-abstract-short" style="display: inline;"> Background: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes be… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.11846v1-abstract-full').style.display = 'inline'; document.getElementById('2301.11846v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.11846v1-abstract-full" style="display: none;"> Background: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. Method: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. Results: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.11846v1-abstract-full').style.display = 'none'; document.getElementById('2301.11846v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.03659">arXiv:2301.03659</a> <span> [<a href="https://arxiv.org/pdf/2301.03659">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"> Multifunctional fiber-based optoacoustic emitter for non-genetic bidirectional neural communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zheng%2C+N">Nan Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Ying Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+S">Shan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+J">Jongwoon Kim</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yueming Li</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Ji-Xin Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Jia%2C+X">Xiaoting Jia</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+C">Chen Yang</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="2301.03659v1-abstract-short" style="display: inline;"> A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Here we report a miniaturized multifunctional fiber based optoacoustic emitter (mFOE) that first integrates simultaneous non-genetic optoacoustic stimulation for "write" and electrophysiology recording of neural circuit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03659v1-abstract-full').style.display = 'inline'; document.getElementById('2301.03659v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.03659v1-abstract-full" style="display: none;"> A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Here we report a miniaturized multifunctional fiber based optoacoustic emitter (mFOE) that first integrates simultaneous non-genetic optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". The non-genetic feature addresses the challenges of the viral transfection required by optogenetics in primates and human. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. We first validated the non-genetic stimulation function of the mFOE in rat cultured neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities was confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minimal brain tissue damage has been confirmed after these applications. The capability of non-genetic neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03659v1-abstract-full').style.display = 'none'; document.getElementById('2301.03659v1-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> 9 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.01575">arXiv:2212.01575</a> <span> [<a href="https://arxiv.org/pdf/2212.01575">pdf</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="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Pang%2C+C">Chao Pang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+R">Ruheng Wang</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+R">Ran Su</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+L">Leyi 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="2212.01575v1-abstract-short" style="display: inline;"> In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01575v1-abstract-full').style.display = 'inline'; document.getElementById('2212.01575v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.01575v1-abstract-full" style="display: none;"> In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. In particular, we showcase the multi-view deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties, demonstrating the strong capability of our model in exploring the chemical space deeply. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs. Further, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01575v1-abstract-full').style.display = 'none'; document.getElementById('2212.01575v1-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.01535">arXiv:2212.01535</a> <span> [<a href="https://arxiv.org/pdf/2212.01535">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"> Prototype matching: children's preference for forming scientific concepts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zhong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yi Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</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="2212.01535v1-abstract-short" style="display: inline;"> Inspired by a sample lesson, this paper studies and discusses children's preferences in learning scientific concepts. In a "Dissolution" lesson, one of the students took the demonstration experiment of "carmine dissolves in Water" demonstrated by the teacher as the prototype to judge whether a new phenomenon belongs to dissolution, instead of analyzing and judging the phenomenon by using the disso… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01535v1-abstract-full').style.display = 'inline'; document.getElementById('2212.01535v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.01535v1-abstract-full" style="display: none;"> Inspired by a sample lesson, this paper studies and discusses children's preferences in learning scientific concepts. In a "Dissolution" lesson, one of the students took the demonstration experiment of "carmine dissolves in Water" demonstrated by the teacher as the prototype to judge whether a new phenomenon belongs to dissolution, instead of analyzing and judging the phenomenon by using the dissolution definition. Therefore, we propose a conjecture that "prototype matching" may be a more preferred way for children to learn concepts than thinking through inquiry experiment, analysis, deduction, etc. To this end, we conducted a targeted test on 160 fifth grade students (all of whom had learned this lesson) from a primary school in Beijing, and used goodness of fit test to statistically analyze the results. The results showed that: 1. the Chi square of the general result is 73.865, P<0.001, indicating that children did have obvious prototype preference; 2. We "tampered" some of the prototypes, that is, they looked like the prototypes that the teacher had told students, but they were actually wrong. However, the results showed that children still preferred these so-called "prototypes" (chi square is 21.823, P<0.001). Conclusion: 1. Children have an obvious preference for "prototype matching" in scientific concept learning, which is not only obviously deviated from the current general understanding of science education that emphasizes discovery/inquiry construction, but also points out that there may be a priority relationship among various ways of concept organization (such as definition theory, prototype theory, schema theory, etc.). 2. Children's preference for prototypes seems to be unthinking, and they will not identify the authenticity of prototypes, which is particularly noteworthy in front-line teaching. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01535v1-abstract-full').style.display = 'none'; document.getElementById('2212.01535v1-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.17205">arXiv:2211.17205</a> <span> [<a href="https://arxiv.org/pdf/2211.17205">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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/10.1177/0962280219859026">10.1177/0962280219859026 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An integrative sparse boosting analysis of cancer genomic commonality and difference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+Z">Zhengyang Sun</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+S">Shuangge Ma</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="2211.17205v1-abstract-short" style="display: inline;"> In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.17205v1-abstract-full').style.display = 'inline'; document.getElementById('2211.17205v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.17205v1-abstract-full" style="display: none;"> In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonality and difference are very limited. In this study, a novel estimation and marker selection method based on the sparse boosting technique is developed to address the commonality/difference problem. In terms of technical innovation, a new penalty and computation of increments are introduced. The proposed method can also effectively accommodate the grouping structure of covariates. Simulation shows that it can outperform direct competitors under a wide spectrum of settings. The analysis of two TCGA (The Cancer Genome Atlas) datasets is conducted, showing that the proposed analysis can identify markers with important biological implications and have satisfactory prediction and stability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.17205v1-abstract-full').style.display = 'none'; document.getElementById('2211.17205v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Statistical Methods in Medical Research, 5: 1325-1337, 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.17203">arXiv:2211.17203</a> <span> [<a href="https://arxiv.org/pdf/2211.17203">pdf</a>, <a href="https://arxiv.org/format/2211.17203">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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/sim.8027">10.1002/sim.8027 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Identification of cancer omics commonality and difference via community fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+S">Shuangge Ma</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="2211.17203v1-abstract-short" style="display: inline;"> The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datase… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.17203v1-abstract-full').style.display = 'inline'; document.getElementById('2211.17203v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.17203v1-abstract-full" style="display: none;"> The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.17203v1-abstract-full').style.display = 'none'; document.getElementById('2211.17203v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 11 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Statistics in Medicine 38: 1200-1212, 2019 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.16681">arXiv:2211.16681</a> <span> [<a href="https://arxiv.org/pdf/2211.16681">pdf</a>, <a href="https://arxiv.org/format/2211.16681">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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/sim.9006">10.1002/sim.9006 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+S">Sanguo Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+X">Xiaonan Hu</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+Z">Ziye Luo</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+S">Shuangge Ma</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="2211.16681v1-abstract-short" style="display: inline;"> Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene expressions) by CNVs (copy number variations) and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.16681v1-abstract-full').style.display = 'inline'; document.getElementById('2211.16681v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.16681v1-abstract-full" style="display: none;"> Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene expressions) by CNVs (copy number variations) and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation as well as sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is "guided" by a known biomarker. We develop an MSF (Multivariate Sparse Fusion) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA data leads to interesting findings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.16681v1-abstract-full').style.display = 'none'; document.getElementById('2211.16681v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 8 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Statistics in Medicine, 40: 3915-3936, 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.02630">arXiv:2210.02630</a> <span> [<a href="https://arxiv.org/pdf/2210.02630">pdf</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="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey 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/10.1038/s41467-023-41698-5">10.1038/s41467-023-41698-5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Pang%2C+C">Chao Pang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yuzhe Wang</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Jin%2C+J">Junru Jin</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+S">Sirui Liang</a>, <a href="/search/q-bio?searchtype=author&query=Zou%2C+Q">Quan Zou</a>, <a href="/search/q-bio?searchtype=author&query=Wei%2C+L">Leyi 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="2210.02630v1-abstract-short" style="display: inline;"> Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their applications in real retrosynthesis scenarios. Here, we propose MechRetro, a chemical-mechanism-driven graph learning framework for interpretable retrosynt… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.02630v1-abstract-full').style.display = 'inline'; document.getElementById('2210.02630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.02630v1-abstract-full" style="display: none;"> Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their applications in real retrosynthesis scenarios. Here, we propose MechRetro, a chemical-mechanism-driven graph learning framework for interpretable retrosynthetic prediction and pathway planning, which learns several retrosynthetic actions to simulate a reverse reaction via elaborate self-adaptive joint learning. By integrating chemical knowledge as prior information, we design a novel Graph Transformer architecture to adaptively learn discriminative and chemically meaningful molecule representations, highlighting the strong capacity in molecule feature representation learning. We demonstrate that MechRetro outperforms the state-of-the-art approaches for retrosynthetic prediction with a large margin on large-scale benchmark datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we identify efficient synthetic routes via an interpretable reasoning mechanism, leading to a better understanding in the realm of knowledgeable synthetic chemists. We also showcase that MechRetro discovers a novel pathway for protokylol, along with energy scores for uncertainty assessment, broadening the applicability for practical scenarios. Overall, we expect MechRetro to provide meaningful insights for high-throughput automated organic synthesis in drug discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.02630v1-abstract-full').style.display = 'none'; document.getElementById('2210.02630v1-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 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nat Commun 14, 6155 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.14639">arXiv:2207.14639</a> <span> [<a href="https://arxiv.org/pdf/2207.14639">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yang%2C+H">Hai Yang</a>, <a href="/search/q-bio?searchtype=author&query=Sheng%2C+Y">Yuhang Sheng</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Fang%2C+X">Xiaoyang Fang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+D">Dongdong Li</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jing Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zhe Wang</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="2207.14639v1-abstract-short" style="display: inline;"> Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtyp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.14639v1-abstract-full').style.display = 'inline'; document.getElementById('2207.14639v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.14639v1-abstract-full" style="display: none;"> Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve accurate subtyping results. We compared Subtype-Former with the other state-of-the-art subtyping methods across the TCGA 10 cancer types. We found that Subtype-Former can perform better on the benchmark datasets of more than 5000 tumors based on the survival analysis. In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level. Finally, we applied Subtype-Former to the TCGA 10 types of cancers. We identified 50 essential biomarkers, which can be used to study targeted cancer drugs and promote the development of cancer treatments in the era of precision medicine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.14639v1-abstract-full').style.display = 'none'; document.getElementById('2207.14639v1-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.02536">arXiv:2206.02536</a> <span> [<a href="https://arxiv.org/pdf/2206.02536">pdf</a>, <a href="https://arxiv.org/format/2206.02536">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey 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="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yukang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Tian%2C+T">Ting Tian</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+H">Huajun Xie</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hailiang Guo</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xueqin Wang</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="2206.02536v2-abstract-short" style="display: inline;"> Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02536v2-abstract-full').style.display = 'inline'; document.getElementById('2206.02536v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.02536v2-abstract-full" style="display: none;"> Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02536v2-abstract-full').style.display = 'none'; document.getElementById('2206.02536v2-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">18 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/2204.09119">arXiv:2204.09119</a> <span> [<a href="https://arxiv.org/pdf/2204.09119">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/10.1038/s41377-022-01004-2">10.1038/s41377-022-01004-2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yueming Li</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Ying Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+L">Lu Lan</a>, <a href="/search/q-bio?searchtype=author&query=Ge%2C+X">Xiaowei Ge</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+R">Ran Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhan%2C+Y">Yuewei Zhan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guo Chen</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+L">Linli Shi</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+R">Runyu Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+N">Nan Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+C">Chen Yang</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Ji-Xin Cheng</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="2204.09119v2-abstract-short" style="display: inline;"> High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited precision at the millimeter level. Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through emb… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.09119v2-abstract-full').style.display = 'inline'; document.getElementById('2204.09119v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.09119v2-abstract-full" style="display: none;"> High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited precision at the millimeter level. Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane film. SOAP generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83 um, which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound (tFUS). Here, we show effective OFUS neurostimulation in vitro with a single ultrasound cycle. We demonstrate submillimeter transcranial stimulation of the mouse motor cortex in vivo. An acoustic energy of 0.6 mJ/cm^2, four orders of magnitude less than that of tFUS, is sufficient for successful OFUS neurostimulation. OFUS offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision non-invasively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.09119v2-abstract-full').style.display = 'none'; document.getElementById('2204.09119v2-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">36 pages, 5 main figures, 13 supplementary figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Light Sci Appl 11, 321 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.02130">arXiv:2204.02130</a> <span> [<a href="https://arxiv.org/pdf/2204.02130">pdf</a>, <a href="https://arxiv.org/format/2204.02130">other</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yikang Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chu%2C+X">Xiaomin Chu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yelu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Hongjie Wu</a>, <a href="/search/q-bio?searchtype=author&query=Quan%2C+L">Lijun Quan</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="2204.02130v2-abstract-short" style="display: inline;"> A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug-DNA interactions, but also promote or inhibit the expression of critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.02130v2-abstract-full').style.display = 'inline'; document.getElementById('2204.02130v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.02130v2-abstract-full" style="display: none;"> A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug-DNA interactions, but also promote or inhibit the expression of critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, Biological experimental techniques for measuring it are expensive and time consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information of bases in gene sequences. To address these issues, we proposed a new solution named SemanticCAP. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of a certain site in gene sequences. Basically, we merge the features provided by the gene language model into our chromatin accessibility model. During the process, we designed some methods to make feature fusion smoother. Compared with other systems under public benchmarks, our model proved to have better performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.02130v2-abstract-full').style.display = 'none'; document.getElementById('2204.02130v2-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 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.00570">arXiv:2101.00570</a> <span> [<a href="https://arxiv.org/pdf/2101.00570">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jia%2C+S">Shen Jia</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yulin Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+Y">Yiming Mao</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+J">Jiawei Gao</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yixuan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuxuan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+H">Haochen Luo</a>, <a href="/search/q-bio?searchtype=author&query=Lv%2C+K">Kebo Lv</a>, <a href="/search/q-bio?searchtype=author&query=Su%2C+J">Jionglong Su</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="2101.00570v1-abstract-short" style="display: inline;"> DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.00570v1-abstract-full').style.display = 'inline'; document.getElementById('2101.00570v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.00570v1-abstract-full" style="display: none;"> DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene selection in high dimensional data(over 450 thousand). Variance filtering is first introduced for dimension reduction and Recursive feature elimination (RFE) is then used for feature selection. We address the problem of selecting a small subsets of genes from large number of methylated sites, and our parsimonious model is demonstrated to be efficient, achieving an accuracy over 91%, outperforming other studies which use DNA micro-arrays and RNA-seq Data . The performance of 20 models, which are based on 4 estimators (Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5 classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is examined. Results suggest that the combined model of extra tree plus catboost classifier offers the best performance in cancer identification, with an overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and 50 features respectively. The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies. Therefore, our method may be utilzed as an auxiliary diagnostic method to determine the actual clinicopathological status of a specific cancer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.00570v1-abstract-full').style.display = 'none'; document.getElementById('2101.00570v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">39 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/2012.09930">arXiv:2012.09930</a> <span> [<a href="https://arxiv.org/pdf/2012.09930">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"> Non-genetic acoustic stimulation of single neurons by a tapered fiber optoacoustic emitter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Shi%2C+L">Linli Shi</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Ying Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Fernandez%2C+F+R">Fernando R. Fernandez</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+L">Lu Lan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+G">Guo Chen</a>, <a href="/search/q-bio?searchtype=author&query=Man%2C+H">Heng-ye Man</a>, <a href="/search/q-bio?searchtype=author&query=White%2C+J+A">John A. White</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Ji-Xin Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+C">Chen Yang</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="2012.09930v1-abstract-short" style="display: inline;"> As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans. Yet, the spatial resolution of ultrasound does not allow for high-precision stimulation. Here, we developed a tapered fiber optoacoustic emitter (TFOE) for optoacoustic stimulation of neurons with an unprecedented spatial re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.09930v1-abstract-full').style.display = 'inline'; document.getElementById('2012.09930v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.09930v1-abstract-full" style="display: none;"> As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans. Yet, the spatial resolution of ultrasound does not allow for high-precision stimulation. Here, we developed a tapered fiber optoacoustic emitter (TFOE) for optoacoustic stimulation of neurons with an unprecedented spatial resolution of 20 microns, enabling selective activation of single neurons or subcellular structures, such as axons and dendrites. A single acoustic pulse of 1 microsecond converted by the TFOE from a single laser pulse of 3 nanoseconds is shown as the shortest acoustic stimuli so far for successful neuron activation. The highly localized ultrasound generated by the TFOE made it possible to integrate the optoacoustic stimulation and highly stable patch clamp recording on single neurons. Direct measurements of electrical response of single neurons to acoustic stimulation, which is difficult for conventional ultrasound stimulation, have been demonstrated for the first time. By coupling TFOE with ex vivo brain slice electrophysiology, we unveil cell-type-specific response of excitatory and inhibitory neurons to acoustic stimulation. These results demonstrate that TFOE is a non-genetic single-cell and sub-cellular modulation technology, which could shed new insights into the mechanism of neurostimulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.09930v1-abstract-full').style.display = 'none'; document.getElementById('2012.09930v1-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 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">25 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/2006.00523">arXiv:2006.00523</a> <span> [<a href="https://arxiv.org/pdf/2006.00523">pdf</a>, <a href="https://arxiv.org/format/2006.00523">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> The Effects of Stringent Interventions for Coronavirus Pandemic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tian%2C+T">Ting Tian</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+W">Wenxiang Luo</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yukang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+M">Minqiong Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+C">Canhong Wen</a>, <a href="/search/q-bio?searchtype=author&query=Pan%2C+W">Wenliang Pan</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xueqin Wang</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="2006.00523v1-abstract-short" style="display: inline;"> The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and societal importance to evaluate the effects of interventions in the pandemic of COVID-19. In this paper, with help of synthetic control method, regression discontinuity and a Susceptible-Infected and infectious without isolat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.00523v1-abstract-full').style.display = 'inline'; document.getElementById('2006.00523v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.00523v1-abstract-full" style="display: none;"> The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and societal importance to evaluate the effects of interventions in the pandemic of COVID-19. In this paper, with help of synthetic control method, regression discontinuity and a Susceptible-Infected and infectious without isolation-Hospitalized in isolation-Removed (SIHR) model, we evaluate the horizontal and longitudinal effects of stringent interventions implemented in Wenzhou, a representative urban city of China, where stringent interventions were enforced to curb its own epidemic situation with rapidly increasing newly confirmed cases. We found that there were statistically significant treatment effects of those stringent interventions which reduced the cumulative confirmed cases of COVID-19. Those reduction effects would increase over time. Also, if the stringent interventions were delayed by 2 days or mild interventions were implemented instead, the expected number of cumulative confirmed cases would have been nearly 2 times or 5 times of the actual number. The effects of stringent interventions are significant in mitigating the epidemic situation of COVID-19. The slower the interventions were implemented, the more severe the epidemic would have been, and the stronger the interventions would have been required. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.00523v1-abstract-full').style.display = 'none'; document.getElementById('2006.00523v1-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> 31 May, 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">29 pages, 6 figures, Ting Tian, Wenxiang Luo and Yukang Jiang contributed equally to this article</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.00908">arXiv:2004.00908</a> <span> [<a href="https://arxiv.org/pdf/2004.00908">pdf</a>, <a href="https://arxiv.org/format/2004.00908">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="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/10.4208/csiam-am.2020-0006">10.4208/csiam-am.2020-0006 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Detecting Suspected Epidemic Cases Using Trajectory Big Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhou%2C+C">Chuansai Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Yuan%2C+W">Wen Yuan</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Haiyong Xu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yong Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xinmin Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+Q+H">Qiuzi Han Wen</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+P">Pingwen 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="2004.00908v3-abstract-short" style="display: inline;"> Emerging infectious diseases are existential threats to human health and global stability. The recent outbreaks of the novel coronavirus COVID-19 have rapidly formed a global pandemic, causing hundreds of thousands of infections and huge economic loss. The WHO declares that more precise measures to track, detect and isolate infected people are among the most effective means to quickly contain the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00908v3-abstract-full').style.display = 'inline'; document.getElementById('2004.00908v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.00908v3-abstract-full" style="display: none;"> Emerging infectious diseases are existential threats to human health and global stability. The recent outbreaks of the novel coronavirus COVID-19 have rapidly formed a global pandemic, causing hundreds of thousands of infections and huge economic loss. The WHO declares that more precise measures to track, detect and isolate infected people are among the most effective means to quickly contain the outbreak. Based on trajectory provided by the big data and the mean field theory, we establish an aggregated risk mean field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map. It has dynamic fine spatial resolution and high computation efficiency enabling fast update. We then propose an objective individual epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference and machine learning methods for detecting suspected epidemic-infected individuals. We conduct numerical experiments by applying the proposed methods to study the early outbreak of COVID-19 in China. Results show that the HiRES risk map has strong ability in capturing global trend and local variability of the epidemic risk, thus can be applied to monitor epidemic risk at country, province, city and community levels, as well as at specific high-risk locations such as hospital and station. HiRES-p score seems to be an effective measurement of personal epidemic risk. The accuracy of both detecting methods are above 90\% when the population infection rate is under 20\%, which indicates great application potential in epidemic risk prevention and control practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.00908v3-abstract-full').style.display = 'none'; document.getElementById('2004.00908v3-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 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> CSIAM Transactions on Applied Mathematics. 1(2020).186-206 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.05447">arXiv:2003.05447</a> <span> [<a href="https://arxiv.org/pdf/2003.05447">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> </div> </div> <p class="title is-5 mathjax"> Prediction and analysis of Coronavirus Disease 2019 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jia%2C+L">Lin Jia</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+K">Kewen Li</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+X">Xin Guo</a>, <a href="/search/q-bio?searchtype=author&query=zhao%2C+T">Ting 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="2003.05447v2-abstract-short" style="display: inline;"> In December 2019, a novel coronavirus was found in a seafood wholesale market in Wuhan, China. WHO officially named this coronavirus as COVID-19. Since the first patient was hospitalized on December 12, 2019, China has reported a total of 78,824 confirmed CONID-19 cases and 2,788 deaths as of February 28, 2020. Wuhan's cumulative confirmed cases and deaths accounted for 61.1% and 76.5% of the whol… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.05447v2-abstract-full').style.display = 'inline'; document.getElementById('2003.05447v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.05447v2-abstract-full" style="display: none;"> In December 2019, a novel coronavirus was found in a seafood wholesale market in Wuhan, China. WHO officially named this coronavirus as COVID-19. Since the first patient was hospitalized on December 12, 2019, China has reported a total of 78,824 confirmed CONID-19 cases and 2,788 deaths as of February 28, 2020. Wuhan's cumulative confirmed cases and deaths accounted for 61.1% and 76.5% of the whole China mainland , making it the priority center for epidemic prevention and control. Meanwhile, 51 countries and regions outside China have reported 4,879 confirmed cases and 79 deaths as of February 28, 2020. COVID-19 epidemic does great harm to people's daily life and country's economic development. This paper adopts three kinds of mathematical models, i.e., Logistic model, Bertalanffy model and Gompertz model. The epidemic trends of SARS were first fitted and analyzed in order to prove the validity of the existing mathematical models. The results were then used to fit and analyze the situation of COVID-19. The prediction results of three different mathematical models are different for different parameters and in different regions. In general, the fitting effect of Logistic model may be the best among the three models studied in this paper, while the fitting effect of Gompertz model may be better than Bertalanffy model. According to the current trend, based on the three models, the total number of people expected to be infected is 49852-57447 in Wuhan,12972-13405 in non-Hubei areas and 80261-85140 in China respectively. The total death toll is 2502-5108 in Wuhan, 107-125 in Non-Hubei areas and 3150-6286 in China respetively. COVID-19 will be over p robably in late-April, 2020 in Wuhan and before late-March, 2020 in other areas respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.05447v2-abstract-full').style.display = 'none'; document.getElementById('2003.05447v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 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/2002.02590">arXiv:2002.02590</a> <span> [<a href="https://arxiv.org/pdf/2002.02590">pdf</a>, <a href="https://arxiv.org/format/2002.02590">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="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"> A Time Delay Dynamic System with External Source for the Local Outbreak of 2019-nCoV </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Jin Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+K">Keji 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="2002.02590v1-abstract-short" style="display: inline;"> How to model the 2019 CoronaVirus (2019-nCov) spread in China is one of the most urgent and interesting problems in applied mathematics. In this paper, we propose a novel time delay dynamic system with external source to describe the trend of local outbreak for the 2019-nCoV. The external source is introduced in the newly proposed dynamic system, which can be considered as the suspected people tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.02590v1-abstract-full').style.display = 'inline'; document.getElementById('2002.02590v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.02590v1-abstract-full" style="display: none;"> How to model the 2019 CoronaVirus (2019-nCov) spread in China is one of the most urgent and interesting problems in applied mathematics. In this paper, we propose a novel time delay dynamic system with external source to describe the trend of local outbreak for the 2019-nCoV. The external source is introduced in the newly proposed dynamic system, which can be considered as the suspected people travel to different areas. The numerical simulations exhibit the dynamic system with the external source is more reliable than the one without it, and the rate of isolation is extremely important for controlling the increase of cumulative confirmed people of 2019-nCoV. Based on our numerical simulation results with the public data, we suggest that the local government should have some more strict measures to maintain the rate of isolation. Otherwise the local cumulative confirmed people of 2019-nCoV might be out of control. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.02590v1-abstract-full').style.display = 'none'; document.getElementById('2002.02590v1-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 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">13 pages, 13figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 35R30; 65N21 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.00418">arXiv:2002.00418</a> <span> [<a href="https://arxiv.org/pdf/2002.00418">pdf</a>, <a href="https://arxiv.org/format/2002.00418">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> <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="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> A Time Delay Dynamical Model for Outbreak of 2019-nCoV and the Parameter Identification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Jin Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yu Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+K">Keji 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="2002.00418v3-abstract-short" style="display: inline;"> In this paper, we propose a novel dynamical system with time delay to describe the outbreak of 2019-nCoV in China. One typical feature of this epidemic is that it can spread in latent period, which is therefore described by the time delay process in the differential equations. The accumulated numbers of classified populations are employed as variables, which is consistent with the official data an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.00418v3-abstract-full').style.display = 'inline'; document.getElementById('2002.00418v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.00418v3-abstract-full" style="display: none;"> In this paper, we propose a novel dynamical system with time delay to describe the outbreak of 2019-nCoV in China. One typical feature of this epidemic is that it can spread in latent period, which is therefore described by the time delay process in the differential equations. The accumulated numbers of classified populations are employed as variables, which is consistent with the official data and facilitates the parameter identification. The numerical methods for the prediction of outbreak of 2019-nCoV and parameter identification are provided, and the numerical results show that the novel dynamic system can well predict the outbreak trend so far. Based on the numerical simulations, we suggest that the transmission of individuals should be greatly controlled with high isolation rate by the government. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.00418v3-abstract-full').style.display = 'none'; document.getElementById('2002.00418v3-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 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 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">11 pages, 7 figures. arXiv admin note: text overlap with arXiv:2002.02590</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 35R30; 65N21 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.05235">arXiv:1902.05235</a> <span> [<a href="https://arxiv.org/pdf/1902.05235">pdf</a>, <a href="https://arxiv.org/format/1902.05235">other</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> </div> </div> <p class="title is-5 mathjax"> BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Mao%2C+S">Shunfu Mao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yihan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Mathew%2C+E+B">Edwin Basil Mathew</a>, <a href="/search/q-bio?searchtype=author&query=Kannan%2C+S">Sreeram Kannan</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="1902.05235v1-abstract-short" style="display: inline;"> High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05235v1-abstract-full').style.display = 'inline'; document.getElementById('1902.05235v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.05235v1-abstract-full" style="display: none;"> High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets. Here we propose BOAssembler (https://github.com/olivomao/boassembler), a framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05235v1-abstract-full').style.display = 'none'; document.getElementById('1902.05235v1-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 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.03429">arXiv:1902.03429</a> <span> [<a href="https://arxiv.org/pdf/1902.03429">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> <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"> Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qin%2C+C">Chu Qin</a>, <a href="/search/q-bio?searchtype=author&query=Tan%2C+Y">Ying Tan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+S+Y">Shang Ying Chen</a>, <a href="/search/q-bio?searchtype=author&query=Zeng%2C+X">Xian Zeng</a>, <a href="/search/q-bio?searchtype=author&query=Qi%2C+X">Xingxing Qi</a>, <a href="/search/q-bio?searchtype=author&query=Jin%2C+T">Tian Jin</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+H">Huan Shi</a>, <a href="/search/q-bio?searchtype=author&query=Wan%2C+Y">Yiwei Wan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Jingfeng Li</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+W">Weidong He</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yali Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+P">Peng Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Zhu%2C+F">Feng Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+H">Hongping Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuyang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yuzong 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="1902.03429v1-abstract-short" style="display: inline;"> Unsupervised clustering has broad applications in data stratification, pattern investigation and new discovery beyond existing knowledge. In particular, clustering of bioactive molecules facilitates chemical space mapping, structure-activity studies, and drug discovery. These tasks, conventionally conducted by similarity-based methods, are complicated by data complexity and diversity. We ex-plored… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.03429v1-abstract-full').style.display = 'inline'; document.getElementById('1902.03429v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.03429v1-abstract-full" style="display: none;"> Unsupervised clustering has broad applications in data stratification, pattern investigation and new discovery beyond existing knowledge. In particular, clustering of bioactive molecules facilitates chemical space mapping, structure-activity studies, and drug discovery. These tasks, conventionally conducted by similarity-based methods, are complicated by data complexity and diversity. We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1.39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space. These band-clusters, displayed by a space-navigation simulation software, band molecules of selected bioactivity classes into individual band-clusters possessing unique sets of common sub-structural features beyond structural similarity. These sub-structural features form the frameworks of the literature-reported pharmacophores and privileged fragments. Within each band-cluster, molecules are further banded into selected sub-regions with respect to their bioactivity target, sub-structural features and molecular scaffolds. Our method is potentially applicable for big data clustering tasks of different fields. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.03429v1-abstract-full').style.display = 'none'; document.getElementById('1902.03429v1-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> 9 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.10458">arXiv:1809.10458</a> <span> [<a href="https://arxiv.org/pdf/1809.10458">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"> Protein token: a dynamic unit in protein interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Luo%2C+S">Si-Wei Luo</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi-Hua Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Liang%2C+Z">Zhi Liang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+J">Jia-Rui Wu</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="1809.10458v1-abstract-short" style="display: inline;"> In this study, we introduced a new unit, named "protein token", as a dynamic protein structural unit for protein-protein interactions. Unlike the conventional structural units, protein token is not based on the sequential or spatial arrangement of residues, but comprises remote residues involved in cooperative conformational changes during protein interactions. Application of protein token on Ras… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.10458v1-abstract-full').style.display = 'inline'; document.getElementById('1809.10458v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.10458v1-abstract-full" style="display: none;"> In this study, we introduced a new unit, named "protein token", as a dynamic protein structural unit for protein-protein interactions. Unlike the conventional structural units, protein token is not based on the sequential or spatial arrangement of residues, but comprises remote residues involved in cooperative conformational changes during protein interactions. Application of protein token on Ras GTPases revealed various tokens present in the superfamily. Distinct token combinations were found in H-Ras interacting with its various regulators and effectors, directing to a possible clue for the multiplexer property of Ras superfamily. Thus, this protein token theory may provide a new approach to study protein-protein interactions in broad applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.10458v1-abstract-full').style.display = 'none'; document.getElementById('1809.10458v1-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 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.06676">arXiv:1809.06676</a> <span> [<a href="https://arxiv.org/pdf/1809.06676">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="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/10.1109/TCDS.2020.2965135">10.1109/TCDS.2020.2965135 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Reconfiguration of Brain Network between Resting-state and Oddball Paradigm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+F">Fali Li</a>, <a href="/search/q-bio?searchtype=author&query=Yi%2C+C">Chanlin Yi</a>, <a href="/search/q-bio?searchtype=author&query=Liao%2C+Y">Yuanyuan Liao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuanling Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Si%2C+Y">Yajing Si</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+L">Limeng Song</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+T">Tao Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+D">Dezhong Yao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Y">Yangsong Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+Z">Zehong Cao</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+P">Peng Xu</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="1809.06676v1-abstract-short" style="display: inline;"> The oddball paradigm is widely applied to the investigation of multiple cognitive functions. Prior studies have explored the cortical oscillation and power spectral differing from the resting-state conduction to oddball paradigm, but whether brain networks existing the significant difference is still unclear. Our study addressed how the brain reconfigures its architecture from a resting-state cond… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.06676v1-abstract-full').style.display = 'inline'; document.getElementById('1809.06676v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.06676v1-abstract-full" style="display: none;"> The oddball paradigm is widely applied to the investigation of multiple cognitive functions. Prior studies have explored the cortical oscillation and power spectral differing from the resting-state conduction to oddball paradigm, but whether brain networks existing the significant difference is still unclear. Our study addressed how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to P300 stimulus task in the visual oddball paradigm. In this study, electroencephalogram (EEG) datasets were collected from 24 postgraduate students, who were required to only mentally count the number of target stimulus; afterwards the functional EEG networks constructed in different frequency bands were compared between baseline and oddball task conditions to evaluate the reconfiguration of functional network in the brain. Compared to the baseline, our results showed the significantly (p < 0.05) enhanced delta/theta EEG connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the P300 task. Furthermore, the reconfigured coupling strengths were demonstrated to relate to P300 amplitudes, which were then regarded as input features to train a classifier to differentiate the high and low P300 amplitudes groups with an accuracy of 77.78%. The findings of our study help us to understand the changes of functional brain connectivity from resting-state to oddball stimulus task, and the reconfigured network pattern has the potential for the selection of good subjects for P300-based brain- computer interface. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.06676v1-abstract-full').style.display = 'none'; document.getElementById('1809.06676v1-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 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">This manuscript is submitting to IEEE Transactions on Cognitive and Developmental Systems</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.05766">arXiv:1808.05766</a> <span> [<a href="https://arxiv.org/pdf/1808.05766">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="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> The Function Transformation Omics - Funomics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yongshuai Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+J">Jing Xu</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+S">Simeng Hu</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+D">Di Liu</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+L">Linna Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+X">Xu Zhou</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="1808.05766v1-abstract-short" style="display: inline;"> There are no two identical leaves in the world, so how to find effective markers or features to distinguish them is an important issue. Function transformation, such as f(x,y) and f(x,y,z), can transform two, three, or multiple input/observation variables (in biology, it generally refers to the observed/measured value of biomarkers, biological characteristics, or other indicators) into a new outpu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.05766v1-abstract-full').style.display = 'inline'; document.getElementById('1808.05766v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.05766v1-abstract-full" style="display: none;"> There are no two identical leaves in the world, so how to find effective markers or features to distinguish them is an important issue. Function transformation, such as f(x,y) and f(x,y,z), can transform two, three, or multiple input/observation variables (in biology, it generally refers to the observed/measured value of biomarkers, biological characteristics, or other indicators) into a new output variable (new characteristics or indicators). This provided us a chance to re-cognize objective things or relationships beyond the original measurements. For example, Body Mass Index, which transform weight and high into a new indicator BMI=x/y^2 (where x is weight and y is high), is commonly used in to gauge obesity. Here, we proposed a new system, Funomics (Function Transformation Omics), for understanding the world in a different perspective. Funome can be understood as a set of math functions consist of basic elementary functions (such as power functions and exponential functions) and basic mathematical operations (such as addition, subtraction). By scanning the whole Funome, researchers can identify some special functions (called handsome functions) which can generate the novel important output variable (characteristics or indicators). We also start "the Funome project" to develop novel methods, function library and analysis software for Funome studies. The Funome project will accelerate the discovery of new useful indicators or characteristics, will improve the utilization efficiency of directly measured data, and will enhance our ability to understand the world. The analysis tools and data resources about the Funome project can be found gradually at http://www.funome.com. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.05766v1-abstract-full').style.display = 'none'; document.getElementById('1808.05766v1-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 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.02425">arXiv:1802.02425</a> <span> [<a href="https://arxiv.org/pdf/1802.02425">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> </div> </div> <p class="title is-5 mathjax"> Impact of Land Use on the DOM Composition in Different Seasons in a Subtropical River Flowing through the Region Undergoing Rapid Urbanization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+Q">Qi Liu</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Hou%2C+Z">Zhaojiang Hou</a>, <a href="/search/q-bio?searchtype=author&query=Tian%2C+Y">Yulu Tian</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+K">Kejian He</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+L">Lan Fu</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Hui Xu</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="1802.02425v1-abstract-short" style="display: inline;"> The dissolved organic matter (DOM) composition in river ecosystems could reflect the human impacts on the river ecosystem, and plays an important role in the carbon cycling process. We collected water and phytoplankton samples at 107 sites in the Dongjiang River in two seasons to assess the impact of the sub-catchments land use structure on the DOM composition. The results showed that (1) the fore… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.02425v1-abstract-full').style.display = 'inline'; document.getElementById('1802.02425v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.02425v1-abstract-full" style="display: none;"> The dissolved organic matter (DOM) composition in river ecosystems could reflect the human impacts on the river ecosystem, and plays an important role in the carbon cycling process. We collected water and phytoplankton samples at 107 sites in the Dongjiang River in two seasons to assess the impact of the sub-catchments land use structure on the DOM composition. The results showed that (1) the forested sub-catchments had higher humic-like C1 (16.45%) and C2 (25.04%) and lower protein-like C3 (22.57%) and C4 (35.95%) than urbanized and mixed forest-agriculture sub-catchments, while the urbanized sub-catchments showed an inverse trend (4.54%, 15.51%, 33.97% and 45.98%, respectively). (2) The significant variation in the proportion of C1 and C4 between the dry and rainy seasons was recorded in both the forested and the mixed forest-agriculture sub-catchments (p<0.01), but only C4 showed an obvious seasonal variation in the urbanized sub-catchments (p<0.01). While the DOM composition was mainly related to the proportion of urbanized land and forested land year-round (p<0.01), it had stronger correlation with forested land in the dry season and urbanized land in the rainy season. (3) No significant correlation between the DOM composition and the agricultural land proportion was found in either season (p>0.05). Our findings indicated that the DOM composition was strongly dependent on the land use structure of the sub-catchments and varied seasonally, but the seasonal variation pattern could be disturbed by human activities in the extensively urbanized catchments. Notably, the higher C4 proportion compared with those in temperate rivers implied a larger amount of CO2 released from the subtropical rivers into the atmosphere when considering bioavailability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.02425v1-abstract-full').style.display = 'none'; document.getElementById('1802.02425v1-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, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.09679">arXiv:1712.09679</a> <span> [<a href="https://arxiv.org/pdf/1712.09679">pdf</a>, <a href="https://arxiv.org/format/1712.09679">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Enumerating consistent subgraphs of directed acyclic graphs: an insight into biomedical ontologies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Peng%2C+Y">Yisu Peng</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuxiang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Radivojac%2C+P">Predrag Radivojac</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="1712.09679v1-abstract-short" style="display: inline;"> Modern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontologies can be seen as directed acyclic graphs, where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.09679v1-abstract-full').style.display = 'inline'; document.getElementById('1712.09679v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.09679v1-abstract-full" style="display: none;"> Modern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontologies can be seen as directed acyclic graphs, where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent subgraph; that is, a subgraph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent subgraphs; however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology. In this work we propose an algorithm for enumerating consistent subgraphs of directed acyclic graphs. The algorithm recursively partitions the graph into strictly smaller graphs until the resulting graph becomes a rooted tree (forest), for which a linear-time solution is computed. It then combines the tallies from graphs created in the recursion to obtain the final count. We prove the correctness of this algorithm and then apply it to characterize four major biomedical ontologies. We believe this work provides valuable insights into concept annotation spaces and predictability of ontological annotation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.09679v1-abstract-full').style.display = 'none'; document.getElementById('1712.09679v1-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 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">18 pages, 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/1711.07462">arXiv:1711.07462</a> <span> [<a href="https://arxiv.org/pdf/1711.07462">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Systems and Control">eess.SY</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"> Real-time brain machine interaction via social robot gesture control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Abiri%2C+R">Reza Abiri</a>, <a href="/search/q-bio?searchtype=author&query=Borhani%2C+S">Soheil Borhani</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+X">Xiaopeng Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yang Jiang</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="1711.07462v1-abstract-short" style="display: inline;"> Brain-Machine Interaction (BMI) system motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic platform using a personalized social robot in order to assist patients having cognitive deficits throu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.07462v1-abstract-full').style.display = 'inline'; document.getElementById('1711.07462v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.07462v1-abstract-full" style="display: none;"> Brain-Machine Interaction (BMI) system motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic platform using a personalized social robot in order to assist patients having cognitive deficits through bilateral rehabilitation and mental training. For initial testing of the platform, electroencephalography (EEG) brainwaves of a human user were collected in real time during tasks of imaginary movements. First, the brainwaves associated with imagined body kinematics parameters were decoded to control a cursor on a computer screen in training protocol. Then, the experienced subject was able to interact with a social robot via our real-time BMI robotic platform. Corresponding to subject's imagery performance, he/she received specific gesture movements and eye color changes as neural-based feedback from the robot. This hands-free neurofeedback interaction not only can be used for mind control of a social robot's movements, but also sets the stage for application to enhancing and recovering mental abilities such as attention via training in humans by providing real-time neurofeedback from a social robot. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.07462v1-abstract-full').style.display = 'none'; document.getElementById('1711.07462v1-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> 20 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.05809">arXiv:1701.05809</a> <span> [<a href="https://arxiv.org/pdf/1701.05809">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"> Early monsoon drought and mid-summer vapor pressure deficit induce growth cessation of lower margin Picea crassifolia </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhao%2C+S">Shoudong Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Dong%2C+M">Manyu Dong</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+H">Hui Xu</a>, <a href="/search/q-bio?searchtype=author&query=Pederson%2C+N">Neil Pederson</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="1701.05809v2-abstract-short" style="display: inline;"> Extreme climatic events have been shown to be strong drivers of tree growth, forest dynamics, and range contraction. Here we study the climatic drivers of Picea crassifolia Kom., an endemic to northwest China where climate has significantly warmed. Picea crassifolia was sampled from its lower distributional margin to its upper distributional margin on the Helan Mountains to test the hypothesis tha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.05809v2-abstract-full').style.display = 'inline'; document.getElementById('1701.05809v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.05809v2-abstract-full" style="display: none;"> Extreme climatic events have been shown to be strong drivers of tree growth, forest dynamics, and range contraction. Here we study the climatic drivers of Picea crassifolia Kom., an endemic to northwest China where climate has significantly warmed. Picea crassifolia was sampled from its lower distributional margin to its upper distributional margin on the Helan Mountains to test the hypothesis that 1) growth at the upper limit is limited by cool temperatures and 2) is limited by drought at its lower limit. We found that trees at the lower distributional margin have experienced a higher rate of stem-growth cessation events since 2001 compared to trees at other elevations. While all populations have a similar climatic sensitivity, stem-growth cessation events in trees at lower distributional margin appear to be driven by low precipitation in June as the monsoon begins to deliver moisture to the region. Evidence indicates that mid-summer (July) vapor pressure deficit (VPD) exacerbates the frequency of these events. These data and our analysis makes it evident that an increase in severity and frequency of drought early in the monsoon season could increase the frequency and severity of stem-growth cessation in Picea crassifolia trees at lower elevations. Increases in VPD and warming would likely exacerbate the growth stress of this species on Helan Mountain. Hypothetically, if the combinations of low moisture and increased VPD stress becomes more common, the mortality rate of lower distributional margin trees could increase, especially of those that are already experiencing events of temporary growth cessation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.05809v2-abstract-full').style.display = 'none'; document.getElementById('1701.05809v2-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 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">33 pages, 8 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/1609.04496">arXiv:1609.04496</a> <span> [<a href="https://arxiv.org/pdf/1609.04496">pdf</a>, <a href="https://arxiv.org/ps/1609.04496">ps</a>, <a href="https://arxiv.org/format/1609.04496">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> </div> </div> <p class="title is-5 mathjax"> Druse-Induced Morphology Evolution in Retinal Pigment Epithelium </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Mazzitello%2C+K+I">K. I. Mazzitello</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Q">Q. Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chrenek%2C+M+A">M. A. Chrenek</a>, <a href="/search/q-bio?searchtype=author&query=Family%2C+F">F. Family</a>, <a href="/search/q-bio?searchtype=author&query=Grossniklaus%2C+H+E">H. E. Grossniklaus</a>, <a href="/search/q-bio?searchtype=author&query=Nickerson%2C+J+M">J. M. Nickerson</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Y. Jiang</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="1609.04496v2-abstract-short" style="display: inline;"> The retinal pigment epithelium (RPE) is a key site of pathogenesis for many retina diseases. The formation of drusen in the retina is characteristic of retinal degeneration. We investigate morphological changes in the RPE in the presence of soft drusen using an integrated experimental and modeling approach. We collect RPE flat mount images from donated human eyes and develop 1) statistical tools t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.04496v2-abstract-full').style.display = 'inline'; document.getElementById('1609.04496v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.04496v2-abstract-full" style="display: none;"> The retinal pigment epithelium (RPE) is a key site of pathogenesis for many retina diseases. The formation of drusen in the retina is characteristic of retinal degeneration. We investigate morphological changes in the RPE in the presence of soft drusen using an integrated experimental and modeling approach. We collect RPE flat mount images from donated human eyes and develop 1) statistical tools to quantify the images and 2) a cell-based model to simulate the morphology evolution. We compare three different mechanisms of RPE repair evolution, cell apoptosis, cell fusion, and expansion, and Simulations of our RPE morphogenesis model quantitatively reproduce deformations of human RPE morphology due to drusen, suggesting that a purse-string mechanism is sufficient to explain how RPE heals cell loss caused by drusen-damage. We found that drusen beneath tissue promote cell death in a number that far exceeds the cell numbers covering the drusen. Tissue deformations are studied using area distributions, Voronoi domains and a texture tensor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.04496v2-abstract-full').style.display = 'none'; document.getElementById('1609.04496v2-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> 2 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 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">10 pages, 9 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/1606.02167">arXiv:1606.02167</a> <span> [<a href="https://arxiv.org/pdf/1606.02167">pdf</a>, <a href="https://arxiv.org/format/1606.02167">other</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"> Integrative modeling of sprout formation in angiogenesis: coupling the VEGFA-Notch signaling in a dynamic stalk-tip cell selection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Prokopiou%2C+S+A">Sotiris A. Prokopiou</a>, <a href="/search/q-bio?searchtype=author&query=Owen%2C+M+R">Markus R. Owen</a>, <a href="/search/q-bio?searchtype=author&query=Byrne%2C+H+M">Helen M. Byrne</a>, <a href="/search/q-bio?searchtype=author&query=Ziyad%2C+S">Safiyyah Ziyad</a>, <a href="/search/q-bio?searchtype=author&query=Domigan%2C+C">Courtney Domigan</a>, <a href="/search/q-bio?searchtype=author&query=Iruela-Arispe%2C+M+L">M. Luisa Iruela-Arispe</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</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="1606.02167v1-abstract-short" style="display: inline;"> During angiogenesis, new blood vessels headed by a migrating endothelial tip cell sprout from pre-existing ones. This process is known to be regulated by two signaling pathways concurrently, vascular endothelial growth factor A (VEGFA) and Notch-Delta. Extracellular VEGFA activates the intracellular Notch-Delta pathway in nearby endothelial cells which results in endothelial (stalk, tip) different… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.02167v1-abstract-full').style.display = 'inline'; document.getElementById('1606.02167v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.02167v1-abstract-full" style="display: none;"> During angiogenesis, new blood vessels headed by a migrating endothelial tip cell sprout from pre-existing ones. This process is known to be regulated by two signaling pathways concurrently, vascular endothelial growth factor A (VEGFA) and Notch-Delta. Extracellular VEGFA activates the intracellular Notch-Delta pathway in nearby endothelial cells which results in endothelial (stalk, tip) differentiation. Retinal astrocytes appear to play a crucial role in polarizing new sprouts by secreting VEGFA. \emph{In vivo} retinal angiogenesis experiments in neonatal mouse generated quantitative data on daily cell counts and morphological data of vascular network expanding over fibronectin-rich matrix. Based on this set of data and other existing ones, we developed a cell-based, multiscale mathematical model using the cellular Potts model framework to investigate the sprout evolution by integrating the VEGFA and Notch-Delta signaling pathways. The model incorporates three levels of description: intracellular, intercellular, and extracellular. Starting with a single astrocyte embedded in a fibronectin-rich matrix, we use the model to assess different scenarios regarding VEGFA levels and its interaction with matrix proteins. Simulation results suggest that astrocyte-derived VEGFA gradients along with heterogeneous ECM reproduces sprouting morphology, and the extension speed is in agreement with experimental data in 7 days postnatal mouse retina. Results also reproduce empirical observations in sprouting angiogenesis, including anastomosis, dynamic tip cell competition, and sprout regression as a result of Notch blockade. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.02167v1-abstract-full').style.display = 'none'; document.getElementById('1606.02167v1-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 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">33 pages, 16 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/1601.00891">arXiv:1601.00891</a> <span> [<a href="https://arxiv.org/pdf/1601.00891">pdf</a>, <a href="https://arxiv.org/format/1601.00891">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 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/10.1186/s13059-016-1037-6">10.1186/s13059-016-1037-6 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An expanded evaluation of protein function prediction methods shows an improvement in accuracy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yuxiang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Oron%2C+T+R">Tal Ronnen Oron</a>, <a href="/search/q-bio?searchtype=author&query=Clark%2C+W+T">Wyatt T Clark</a>, <a href="/search/q-bio?searchtype=author&query=Bankapur%2C+A+R">Asma R Bankapur</a>, <a href="/search/q-bio?searchtype=author&query=D%27Andrea%2C+D">Daniel D'Andrea</a>, <a href="/search/q-bio?searchtype=author&query=Lepore%2C+R">Rosalba Lepore</a>, <a href="/search/q-bio?searchtype=author&query=Funk%2C+C+S">Christopher S Funk</a>, <a href="/search/q-bio?searchtype=author&query=Kahanda%2C+I">Indika Kahanda</a>, <a href="/search/q-bio?searchtype=author&query=Verspoor%2C+K+M">Karin M Verspoor</a>, <a href="/search/q-bio?searchtype=author&query=Ben-Hur%2C+A">Asa Ben-Hur</a>, <a href="/search/q-bio?searchtype=author&query=Koo%2C+E">Emily Koo</a>, <a href="/search/q-bio?searchtype=author&query=Penfold-Brown%2C+D">Duncan Penfold-Brown</a>, <a href="/search/q-bio?searchtype=author&query=Shasha%2C+D">Dennis Shasha</a>, <a href="/search/q-bio?searchtype=author&query=Youngs%2C+N">Noah Youngs</a>, <a href="/search/q-bio?searchtype=author&query=Bonneau%2C+R">Richard Bonneau</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+A">Alexandra Lin</a>, <a href="/search/q-bio?searchtype=author&query=Sahraeian%2C+S+M">Sayed ME Sahraeian</a>, <a href="/search/q-bio?searchtype=author&query=Martelli%2C+P+L">Pier Luigi Martelli</a>, <a href="/search/q-bio?searchtype=author&query=Profiti%2C+G">Giuseppe Profiti</a>, <a href="/search/q-bio?searchtype=author&query=Casadio%2C+R">Rita Casadio</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+R">Renzhi Cao</a>, <a href="/search/q-bio?searchtype=author&query=Zhong%2C+Z">Zhaolong Zhong</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+J">Jianlin Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Altenhoff%2C+A">Adrian Altenhoff</a>, <a href="/search/q-bio?searchtype=author&query=Skunca%2C+N">Nives Skunca</a> , et al. (122 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="1601.00891v1-abstract-short" style="display: inline;"> Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.00891v1-abstract-full').style.display = 'inline'; document.getElementById('1601.00891v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.00891v1-abstract-full" style="display: none;"> Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.00891v1-abstract-full').style.display = 'none'; document.getElementById('1601.00891v1-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> 2 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">Submitted to Genome Biology</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0802.3926">arXiv:0802.3926</a> <span> [<a href="https://arxiv.org/pdf/0802.3926">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> <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="Cellular Automata and Lattice Gases">nlin.CG</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"> Stochastic Network Model of Receptor Cross-Talk Predicts Anti-Angiogenic Effects </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bauer%2C+A+L">Amy L. Bauer</a>, <a href="/search/q-bio?searchtype=author&query=Jackson%2C+T+L">Trachette L. Jackson</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Rohlf%2C+T">Thimo Rohlf</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="0802.3926v1-abstract-short" style="display: inline;"> Cancer invasion and metastasis depend on angiogenesis. The cellular processes (growth, migration, and apoptosis) that occur during angiogenesis are tightly regulated by signaling molecules. Thus, understanding how cells synthesize multiple biochemical signals initiated by key external stimuli can lead to the development of novel therapeutic strategies to combat cancer. In the face of large amoun… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0802.3926v1-abstract-full').style.display = 'inline'; document.getElementById('0802.3926v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0802.3926v1-abstract-full" style="display: none;"> Cancer invasion and metastasis depend on angiogenesis. The cellular processes (growth, migration, and apoptosis) that occur during angiogenesis are tightly regulated by signaling molecules. Thus, understanding how cells synthesize multiple biochemical signals initiated by key external stimuli can lead to the development of novel therapeutic strategies to combat cancer. In the face of large amounts of disjoint experimental data generated from multitudes of laboratories using various assays, theoretical signal transduction models provide a framework to distill this vast amount of data. Such models offer an opportunity to formulate and test new hypotheses, and can be used to make experimentally verifiable predictions. This study is the first to propose a network model that highlights the cross-talk between the key receptors involved in angiogenesis, namely growth factor, integrin, and cadherin receptors. From available experimental data, we construct a stochastic Boolean network model of receptor cross-talk, and systematically analyze the dynamical stability of the network under continuous-time Boolean dynamics with a noisy production function. We find that the signal transduction network exhibits a robust and fast response to external signals, independent of the internal cell state. We derive an input-output table that maps external stimuli to cell phenotypes, which is extraordinarily stable against molecular noise with one important exception: an oscillatory feedback loop between the key signaling molecules RhoA and Rac1 is unstable under arbitrarily low noise, leading to erratic, dysfunctional cell motion. Finally, we show that the network exhibits an apoptotic response rate that increases with noise, suggesting that the probability of programmed cell death depends on cell health. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0802.3926v1-abstract-full').style.display = 'none'; document.getElementById('0802.3926v1-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, 2008; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2008. </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, 4 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LA-UR 08-0706 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/q-bio/0401014">arXiv:q-bio/0401014</a> <span> [<a href="https://arxiv.org/pdf/q-bio/0401014">pdf</a>, <a href="https://arxiv.org/ps/q-bio/0401014">ps</a>, <a href="https://arxiv.org/format/q-bio/0401014">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="Other Quantitative Biology">q-bio.OT</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/10.1016/j.physd.2003.11.012">10.1016/j.physd.2003.11.012 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Lattice gas cellular automata model for rippling and aggregation in myxobacteria </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Alber%2C+M+S">Mark S. Alber</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Y">Yi Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Kiskowski%2C+M+A">Maria A. Kiskowski</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="q-bio/0401014v1-abstract-short" style="display: inline;"> A lattice-gas cellular automaton (LGCA) model is used to simulate rippling and aggregation in myxobacteria. An efficient way of representing cells of different cell size, shape and orientation is presented that may be easily extended to model later stages of fruiting body formation. This LGCA model is designed to investigate whether a refractory period, a minimum response time, a maximum oscilla… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0401014v1-abstract-full').style.display = 'inline'; document.getElementById('q-bio/0401014v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="q-bio/0401014v1-abstract-full" style="display: none;"> A lattice-gas cellular automaton (LGCA) model is used to simulate rippling and aggregation in myxobacteria. An efficient way of representing cells of different cell size, shape and orientation is presented that may be easily extended to model later stages of fruiting body formation. This LGCA model is designed to investigate whether a refractory period, a minimum response time, a maximum oscillation period and non-linear dependence of reversals of cells on C-factor are necessary assumptions for rippling. It is shown that a refractory period of 2-3 minutes, a minimum response time of up to 1 minute and no maximum oscillation period best reproduce rippling in the experiments of {\it Myxoccoccus xanthus}. Non-linear dependence of reversals on C-factor is critical at high cell density. Quantitative simulations demonstrate that the increase in wavelength of ripples when a culture is diluted with non-signaling cells can be explained entirely by the decreased density of C-signaling cells. This result further supports the hypothesis that levels of C-signaling quantitatively depend on and modulate cell density. Analysis of the interpenetrating high density waves shows the presence of a phase shift analogous to the phase shift of interpenetrating solitons. Finally, a model for swarming, aggregation and early fruiting body formation is presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('q-bio/0401014v1-abstract-full').style.display = 'none'; document.getElementById('q-bio/0401014v1-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> 9 January, 2004; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2004. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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