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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Song, Y"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Song%2C+Y&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Song%2C+Y&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Song%2C+Y&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14833">arXiv:2411.14833</a> <span> [<a href="https://arxiv.org/pdf/2411.14833">pdf</a>, <a href="https://arxiv.org/format/2411.14833">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="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"> Cell as Point: One-Stage Framework for Efficient Cell Tracking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yaxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Fan%2C+J">Jianan Fan</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+H">Heng Huang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+M">Mei Chen</a>, <a href="/search/q-bio?searchtype=author&query=Cai%2C+W">Weidong Cai</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="2411.14833v1-abstract-short" style="display: inline;"> Cellular activities are dynamic and intricate, playing a crucial role in advancing diagnostic and therapeutic techniques, yet they often require substantial resources for accurate tracking. Despite recent progress, the conventional multi-stage cell tracking approaches not only heavily rely on detection or segmentation results as a prerequisite for the tracking stage, demanding plenty of refined se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14833v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14833v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14833v1-abstract-full" style="display: none;"> Cellular activities are dynamic and intricate, playing a crucial role in advancing diagnostic and therapeutic techniques, yet they often require substantial resources for accurate tracking. Despite recent progress, the conventional multi-stage cell tracking approaches not only heavily rely on detection or segmentation results as a prerequisite for the tracking stage, demanding plenty of refined segmentation masks, but are also deteriorated by imbalanced and long sequence data, leading to under-learning in training and missing cells in inference procedures. To alleviate the above issues, this paper proposes the novel end-to-end CAP framework, which leverages the idea of regarding Cell as Point to achieve efficient and stable cell tracking in one stage. CAP abandons detection or segmentation stages and simplifies the process by exploiting the correlation among the trajectories of cell points to track cells jointly, thus reducing the label demand and complexity of the pipeline. With cell point trajectory and visibility to represent cell locations and lineage relationships, CAP leverages the key innovations of adaptive event-guided (AEG) sampling for addressing data imbalance in cell division events and the rolling-as-window (RAW) inference method to ensure continuous tracking of new cells in the long term. Eliminating the need for a prerequisite detection or segmentation stage, CAP demonstrates strong cell tracking performance while also being 10 to 55 times more efficient than existing methods. The code and models will be released. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14833v1-abstract-full').style.display = 'none'; document.getElementById('2411.14833v1-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 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">17 pages, 8 figures, 8 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/2411.13280">arXiv:2411.13280</a> <span> [<a href="https://arxiv.org/pdf/2411.13280">pdf</a>, <a href="https://arxiv.org/format/2411.13280">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Structure-Based Molecule Optimization via Gradient-Guided Bayesian Update </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qiu%2C+K">Keyue Qiu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+J">Jie Yu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Hongbo Ma</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+Z">Ziyao Cao</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+Z">Zhilong Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+Y">Yushuai Wu</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying 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="2411.13280v2-abstract-short" style="display: inline;"> Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and diffe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13280v2-abstract-full').style.display = 'inline'; document.getElementById('2411.13280v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13280v2-abstract-full" style="display: none;"> Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the first gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. Our proposed MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3% , Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility and potential. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13280v2-abstract-full').style.display = 'none'; document.getElementById('2411.13280v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 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">27 pages, 17 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/2410.03533">arXiv:2410.03533</a> <span> [<a href="https://arxiv.org/pdf/2410.03533">pdf</a>, <a href="https://arxiv.org/format/2410.03533">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yu Song</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+L">Liyuan Han</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+B">Bo Xu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+T">Tielin 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="2410.03533v1-abstract-short" style="display: inline;"> Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MF… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03533v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03533v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03533v1-abstract-full" style="display: none;"> Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03533v1-abstract-full').style.display = 'none'; document.getElementById('2410.03533v1-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 September, 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/2407.15132">arXiv:2407.15132</a> <span> [<a href="https://arxiv.org/pdf/2407.15132">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> <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"> Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Tchetchenian%2C+A">Ari Tchetchenian</a>, <a href="/search/q-bio?searchtype=author&query=Zekelman%2C+L">Leo Zekelman</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yuqian Chen</a>, <a href="/search/q-bio?searchtype=author&query=Rushmore%2C+J">Jarrett Rushmore</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+F">Fan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Yeterian%2C+E+H">Edward H. Yeterian</a>, <a href="/search/q-bio?searchtype=author&query=Makris%2C+N">Nikos Makris</a>, <a href="/search/q-bio?searchtype=author&query=Rathi%2C+Y">Yogesh Rathi</a>, <a href="/search/q-bio?searchtype=author&query=Meijering%2C+E">Erik Meijering</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yang Song</a>, <a href="/search/q-bio?searchtype=author&query=O%27Donnell%2C+L+J">Lauren J. O'Donnell</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="2407.15132v1-abstract-short" style="display: inline;"> Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15132v1-abstract-full').style.display = 'inline'; document.getElementById('2407.15132v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.15132v1-abstract-full" style="display: none;"> Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15132v1-abstract-full').style.display = 'none'; document.getElementById('2407.15132v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11435">arXiv:2407.11435</a> <span> [<a href="https://arxiv.org/pdf/2407.11435">pdf</a>, <a href="https://arxiv.org/format/2407.11435">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Genomic Language Models: Opportunities and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Benegas%2C+G">Gonzalo Benegas</a>, <a href="/search/q-bio?searchtype=author&query=Ye%2C+C">Chengzhong Ye</a>, <a href="/search/q-bio?searchtype=author&query=Albors%2C+C">Carlos Albors</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J+C">Jianan Canal Li</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="2407.11435v2-abstract-short" style="display: inline;"> Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to signif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11435v2-abstract-full').style.display = 'inline'; document.getElementById('2407.11435v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11435v2-abstract-full" style="display: none;"> Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating gLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11435v2-abstract-full').style.display = 'none'; document.getElementById('2407.11435v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">Review article; 26 pages, 3 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 92-08; 92B20; 68T50; 68T07 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.06654">arXiv:2405.06654</a> <span> [<a href="https://arxiv.org/pdf/2405.06654">pdf</a>, <a href="https://arxiv.org/format/2405.06654">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> PROflow: An iterative refinement model for PROTAC-induced structure prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qiang%2C+B">Bo Qiang</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+W">Wenxian Shi</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+M">Menghua 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="2405.06654v1-abstract-short" style="display: inline;"> Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), existing PROTAC docking methods have been… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06654v1-abstract-full').style.display = 'inline'; document.getElementById('2405.06654v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.06654v1-abstract-full" style="display: none;"> Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), existing PROTAC docking methods have been forced to simplify the problem into a distance-constrained protein-protein docking task. To address the data issue, we develop a novel pseudo-data generation scheme that requires only binary protein-protein complexes. This new dataset enables PROflow, an iterative refinement model for PROTAC-induced structure prediction that models the full PROTAC flexibility during constrained protein-protein docking. PROflow outperforms the state-of-the-art across docking metrics and runtime. Its inference speed enables the large-scale screening of PROTAC designs, and computed properties of predicted structures achieve statistically significant correlations with published degradation activities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06654v1-abstract-full').style.display = 'none'; document.getElementById('2405.06654v1-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 April, 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">Published at the GEM workshop, ICLR 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/2404.12141">arXiv:2404.12141</a> <span> [<a href="https://arxiv.org/pdf/2404.12141">pdf</a>, <a href="https://arxiv.org/format/2404.12141">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qu%2C+Y">Yanru Qu</a>, <a href="/search/q-bio?searchtype=author&query=Qiu%2C+K">Keyue Qiu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+J">Jiawei Han</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying 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="2404.12141v4-abstract-short" style="display: inline;"> Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12141v4-abstract-full').style.display = 'inline'; document.getElementById('2404.12141v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.12141v4-abstract-full" style="display: none;"> Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12141v4-abstract-full').style.display = 'none'; document.getElementById('2404.12141v4-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ICML 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/2404.11068">arXiv:2404.11068</a> <span> [<a href="https://arxiv.org/pdf/2404.11068">pdf</a>, <a href="https://arxiv.org/format/2404.11068">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="Distributed, Parallel, and Cluster Computing">cs.DC</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"> ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhu%2C+F">Feiwen Zhu</a>, <a href="/search/q-bio?searchtype=author&query=Nowaczynski%2C+A">Arkadiusz Nowaczynski</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+R">Rundong Li</a>, <a href="/search/q-bio?searchtype=author&query=Xin%2C+J">Jie Xin</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yifei Song</a>, <a href="/search/q-bio?searchtype=author&query=Marcinkiewicz%2C+M">Michal Marcinkiewicz</a>, <a href="/search/q-bio?searchtype=author&query=Eryilmaz%2C+S+B">Sukru Burc Eryilmaz</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+J">Jun Yang</a>, <a href="/search/q-bio?searchtype=author&query=Andersch%2C+M">Michael Andersch</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="2404.11068v1-abstract-short" style="display: inline;"> AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimplementation of AlphaFold. AlphaFold training procedure is prohibitively time-consuming, and gets diminishing benefits from scaling to more compute res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11068v1-abstract-full').style.display = 'inline'; document.getElementById('2404.11068v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.11068v1-abstract-full" style="display: none;"> AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimplementation of AlphaFold. AlphaFold training procedure is prohibitively time-consuming, and gets diminishing benefits from scaling to more compute resources. In this work, we conducted a comprehensive analysis on the AlphaFold training procedure based on Openfold, identified that inefficient communications and overhead-dominated computations were the key factors that prevented the AlphaFold training from effective scaling. We introduced ScaleFold, a systematic training method that incorporated optimizations specifically for these factors. ScaleFold successfully scaled the AlphaFold training to 2080 NVIDIA H100 GPUs with high resource utilization. In the MLPerf HPC v3.0 benchmark, ScaleFold finished the OpenFold benchmark in 7.51 minutes, shown over $6\times$ speedup than the baseline. For training the AlphaFold model from scratch, ScaleFold completed the pretraining in 10 hours, a significant improvement over the seven days required by the original AlphaFold pretraining baseline. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11068v1-abstract-full').style.display = 'none'; document.getElementById('2404.11068v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.08027">arXiv:2404.08027</a> <span> [<a href="https://arxiv.org/pdf/2404.08027">pdf</a>, <a href="https://arxiv.org/format/2404.08027">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Ying Chen</a>, <a href="/search/q-bio?searchtype=author&query=Xie%2C+J">Jiajing Xie</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+Y">Yuxiang Lin</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuhang Song</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+W">Wenxian Yang</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+R">Rongshan Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.08027v1-abstract-short" style="display: inline;"> Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integration could be derived. Moreover, many ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08027v1-abstract-full').style.display = 'inline'; document.getElementById('2404.08027v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.08027v1-abstract-full" style="display: none;"> Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integration could be derived. Moreover, many existing studies attempt to improve multi-modal representations through attention mechanisms, which inevitably lead to high complexity when processing high-dimensional WSIs and transcriptomic data. Recently, a structured state space model named Mamba emerged as a promising approach for its superior performance in modeling long sequences with low complexity. In this study, we propose Mamba with multi-grained multi-modal interaction (SurvMamba) for survival prediction. SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities, thereby capturing more detailed local features as well as rich global representations. In addition, an Interaction Fusion Mamba (IFM) module is used for cascaded inter-modal interactive fusion, yielding more comprehensive features for survival prediction. Comprehensive evaluations on five TCGA datasets demonstrate that SurvMamba outperforms other existing methods in terms of performance and computational cost. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08027v1-abstract-full').style.display = 'none'; document.getElementById('2404.08027v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 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/2403.15441">arXiv:2403.15441</a> <span> [<a href="https://arxiv.org/pdf/2403.15441">pdf</a>, <a href="https://arxiv.org/format/2403.15441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Qu%2C+Y">Yanru Qu</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Zheng%2C+M">Mingyue Zheng</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jingjing Liu</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Wei-Ying 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="2403.15441v1-abstract-short" style="display: inline;"> Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geom… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15441v1-abstract-full').style.display = 'inline'; document.getElementById('2403.15441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.15441v1-abstract-full" style="display: none;"> Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87% molecule stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (e.g., 20-times speedup without sacrificing performance). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15441v1-abstract-full').style.display = 'none'; document.getElementById('2403.15441v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">ICLR 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/2402.17153">arXiv:2402.17153</a> <span> [<a href="https://arxiv.org/pdf/2402.17153">pdf</a>, <a href="https://arxiv.org/format/2402.17153">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Exact and efficient phylodynamic simulation from arbitrarily large populations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Celentano%2C+M">Michael Celentano</a>, <a href="/search/q-bio?searchtype=author&query=DeWitt%2C+W+S">William S. DeWitt</a>, <a href="/search/q-bio?searchtype=author&query=Prillo%2C+S">Sebastian Prillo</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.17153v2-abstract-short" style="display: inline;"> Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the unde… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17153v2-abstract-full').style.display = 'inline'; document.getElementById('2402.17153v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.17153v2-abstract-full" style="display: none;"> Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling model, there exists an equivalent process with complete sampling and no death, a property which we leverage to develop a highly efficient algorithm for simulating trees. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17153v2-abstract-full').style.display = 'none'; document.getElementById('2402.17153v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">37 pages, 7 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 92-10; 92D15; 60J80 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.17433">arXiv:2401.17433</a> <span> [<a href="https://arxiv.org/pdf/2401.17433">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"> Coronary CTA and Quantitative Cardiac CT Perfusion (CCTP) in Coronary Artery Disease </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Hao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yingnan Song</a>, <a href="/search/q-bio?searchtype=author&query=Hoori%2C+A">Ammar Hoori</a>, <a href="/search/q-bio?searchtype=author&query=Subramaniam%2C+A">Ananya Subramaniam</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J">Juhwan Lee</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+J">Justin Kim</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+T">Tao Hu</a>, <a href="/search/q-bio?searchtype=author&query=Al-Kindi%2C+S">Sadeer Al-Kindi</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+W">Wei-Ming Huang</a>, <a href="/search/q-bio?searchtype=author&query=Yun%2C+C">Chun-Ho Yun</a>, <a href="/search/q-bio?searchtype=author&query=Hung%2C+C">Chung-Lieh Hung</a>, <a href="/search/q-bio?searchtype=author&query=Rajagopalan%2C+S">Sanjay Rajagopalan</a>, <a href="/search/q-bio?searchtype=author&query=Wilson%2C+D+L">David L. Wilson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.17433v1-abstract-short" style="display: inline;"> We assessed the benefit of combining stress cardiac CT perfusion (CCTP) myocardial blood flow (MBF) with coronary CT angiography (CCTA) using our innovative CCTP software. By combining CCTA and CCTP, one can uniquely identify a flow limiting stenosis (obstructive-lesion + low-MBF) versus MVD (no-obstructive-lesion + low-MBF. We retrospectively evaluated 104 patients with suspected CAD, including 1… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17433v1-abstract-full').style.display = 'inline'; document.getElementById('2401.17433v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.17433v1-abstract-full" style="display: none;"> We assessed the benefit of combining stress cardiac CT perfusion (CCTP) myocardial blood flow (MBF) with coronary CT angiography (CCTA) using our innovative CCTP software. By combining CCTA and CCTP, one can uniquely identify a flow limiting stenosis (obstructive-lesion + low-MBF) versus MVD (no-obstructive-lesion + low-MBF. We retrospectively evaluated 104 patients with suspected CAD, including 18 with diabetes, who underwent CCTA+CCTP. Whole heart and territorial MBF was assessed using our automated pipeline for CCTP analysis that included beam hardening correction; temporal scan registration; automated segmentation; fast, accurate, robust MBF estimation; and visualization. Stenosis severity was scored using the CCTA coronary-artery-disease-reporting-and-data-system (CAD-RADS), with obstructive stenosis deemed as CAD-RADS>=3. We established a threshold MBF (MBF=199-mL/min-100g) for normal perfusion. In patients with CAD-RADS>=3, 28/37(76%) patients showed ischemia in the corresponding territory. Two patients with obstructive disease had normal perfusion, suggesting collaterals and/or a hemodynamically insignificant stenosis. Among diabetics, 10 of 18 (56%) demonstrated diffuse ischemia consistent with MVD. Among non-diabetics, only 6% had MVD. Sex-specific prevalence of MVD was 21%/24% (M/F). On a per-vessel basis (n=256), MBF showed a significant difference between territories with and without obstructive stenosis (165 +/- 61 mL/min-100g vs. 274 +/- 62 mL/min-100g, p <0.05). A significant and negative rank correlation (rho=-0.53, p<0.05) between territory MBF and CAD-RADS was seen. CCTA in conjunction with a new automated quantitative CCTP approach can augment the interpretation of CAD, enabling the distinction of ischemia due to obstructive lesions and MVD. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17433v1-abstract-full').style.display = 'none'; document.getElementById('2401.17433v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.16190">arXiv:2401.16190</a> <span> [<a href="https://arxiv.org/pdf/2401.16190">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hu%2C+T">Tao Hu</a>, <a href="/search/q-bio?searchtype=author&query=Freeze%2C+J">Joshua Freeze</a>, <a href="/search/q-bio?searchtype=author&query=Singh%2C+P">Prerna Singh</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+J">Justin Kim</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yingnan Song</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Hao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J">Juhwan Lee</a>, <a href="/search/q-bio?searchtype=author&query=Al-Kindi%2C+S">Sadeer Al-Kindi</a>, <a href="/search/q-bio?searchtype=author&query=Rajagopalan%2C+S">Sanjay Rajagopalan</a>, <a href="/search/q-bio?searchtype=author&query=Wilson%2C+D+L">David L. Wilson</a>, <a href="/search/q-bio?searchtype=author&query=Hoori%2C+A">Ammar Hoori</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.16190v1-abstract-short" style="display: inline;"> Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE). Objectives: Create novel, hand-crafted EAT features, 'fat-omics', to capture the pathophysiology of EAT and improve MACE prediction. Methods: We segmented EAT using a previously-validated deep learn… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16190v1-abstract-full').style.display = 'inline'; document.getElementById('2401.16190v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.16190v1-abstract-full" style="display: none;"> Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE). Objectives: Create novel, hand-crafted EAT features, 'fat-omics', to capture the pathophysiology of EAT and improve MACE prediction. Methods: We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE. Results: Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/ BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001). Conclusion: Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16190v1-abstract-full').style.display = 'none'; document.getElementById('2401.16190v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 1 central illustration, 6 figures, 5 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/2311.14077">arXiv:2311.14077</a> <span> [<a href="https://arxiv.org/pdf/2311.14077">pdf</a>, <a href="https://arxiv.org/format/2311.14077">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"> RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yiming Wang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Minkai Xu</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+R">Rui Wang</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying 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="2311.14077v1-abstract-short" style="display: inline;"> Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task. Inspired by the recent advancements in discrete diffusion models for graph gene… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14077v1-abstract-full').style.display = 'inline'; document.getElementById('2311.14077v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14077v1-abstract-full" style="display: none;"> Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task. Inspired by the recent advancements in discrete diffusion models for graph generation, we introduce Retrosynthesis Diffusion (RetroDiff), a novel diffusion-based method designed to address this problem. However, integrating a diffusion-based graph-to-graph framework while retaining essential chemical reaction template information presents a notable challenge. Our key innovation is to develop a multi-stage diffusion process. In this method, we decompose the retrosynthesis procedure to first sample external groups from the dummy distribution given products and then generate the external bonds to connect the products and generated groups. Interestingly, such a generation process is exactly the reverse of the widely adapted semi-template retrosynthesis procedure, i.e. from reaction center identification to synthon completion, which significantly reduces the error accumulation. Experimental results on the benchmark have demonstrated the superiority of our method over all other semi-template methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14077v1-abstract-full').style.display = 'none'; document.getElementById('2311.14077v1-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> 23 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/2311.11596">arXiv:2311.11596</a> <span> [<a href="https://arxiv.org/pdf/2311.11596">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="Information Theory">cs.IT</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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> High-performance cVEP-BCI under minimal calibration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Miao%2C+Y">Yining Miao</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+N">Nanlin Shi</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+C">Changxing Huang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yonghao Song</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xiaogang Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yijun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+X">Xiaorong Gao</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="2311.11596v1-abstract-short" style="display: inline;"> The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.11596v1-abstract-full').style.display = 'inline'; document.getElementById('2311.11596v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.11596v1-abstract-full" style="display: none;"> The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.11596v1-abstract-full').style.display = 'none'; document.getElementById('2311.11596v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">35 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/2310.02546">arXiv:2310.02546</a> <span> [<a href="https://arxiv.org/pdf/2310.02546">pdf</a>, <a href="https://arxiv.org/format/2310.02546">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="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Joint Design of Protein Sequence and Structure based on Motifs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Z">Zhenqiao Song</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+Y">Yunlong Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yufei Song</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+W">Wenxian Shi</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+Y">Yang Yang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+L">Lei 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="2310.02546v1-abstract-short" style="display: inline;"> Designing novel proteins with desired functions is crucial in biology and chemistry. However, most existing work focus on protein sequence design, leaving protein sequence and structure co-design underexplored. In this paper, we propose GeoPro, a method to design protein backbone structure and sequence jointly. Our motivation is that protein sequence and its backbone structure constrain each other… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.02546v1-abstract-full').style.display = 'inline'; document.getElementById('2310.02546v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.02546v1-abstract-full" style="display: none;"> Designing novel proteins with desired functions is crucial in biology and chemistry. However, most existing work focus on protein sequence design, leaving protein sequence and structure co-design underexplored. In this paper, we propose GeoPro, a method to design protein backbone structure and sequence jointly. Our motivation is that protein sequence and its backbone structure constrain each other, and thus joint design of both can not only avoid nonfolding and misfolding but also produce more diverse candidates with desired functions. To this end, GeoPro is powered by an equivariant encoder for three-dimensional (3D) backbone structure and a protein sequence decoder guided by 3D geometry. Experimental results on two biologically significant metalloprotein datasets, including $尾$-lactamases and myoglobins, show that our proposed GeoPro outperforms several strong baselines on most metrics. Remarkably, our method discovers novel $尾$-lactamases and myoglobins which are not present in protein data bank (PDB) and UniProt. These proteins exhibit stable folding and active site environments reminiscent of those of natural proteins, demonstrating their excellent potential to be biologically functional. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.02546v1-abstract-full').style.display = 'none'; document.getElementById('2310.02546v1-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 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/2308.13234">arXiv:2308.13234</a> <span> [<a href="https://arxiv.org/pdf/2308.13234">pdf</a>, <a href="https://arxiv.org/format/2308.13234">other</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="Artificial Intelligence">cs.AI</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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Decoding Natural Images from EEG for Object Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yonghao Song</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+B">Bingchuan Liu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiang Li</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+N">Nanlin Shi</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yijun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+X">Xiaorong Gao</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="2308.13234v3-abstract-short" style="display: inline;"> Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13234v3-abstract-full').style.display = 'inline'; document.getElementById('2308.13234v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.13234v3-abstract-full" style="display: none;"> Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes image and EEG encoders to extract features from paired image stimuli and EEG responses. Contrastive learning aligns these two modalities by constraining their similarity. With the framework, we attain significantly above-chance results on a comprehensive EEG-image dataset, achieving a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. Moreover, we perform extensive experiments to explore the biological plausibility by resolving the temporal, spatial, spectral, and semantic aspects of EEG signals. Besides, we introduce attention modules to capture spatial correlations, providing implicit evidence of the brain activity perceived from EEG data. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios. The code will be released on https://github.com/eeyhsong/NICE-EEG. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13234v3-abstract-full').style.display = 'none'; document.getElementById('2308.13234v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 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">ICLR, 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/2308.13232">arXiv:2308.13232</a> <span> [<a href="https://arxiv.org/pdf/2308.13232">pdf</a>, <a href="https://arxiv.org/format/2308.13232">other</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="Information Theory">cs.IT</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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Estimating and approaching maximum information rate of noninvasive visual brain-computer interface </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Shi%2C+N">Nanlin Shi</a>, <a href="/search/q-bio?searchtype=author&query=Miao%2C+Y">Yining Miao</a>, <a href="/search/q-bio?searchtype=author&query=Huang%2C+C">Changxing Huang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+X">Xiang Li</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yonghao Song</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xiaogang Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yijun Wang</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+X">Xiaorong Gao</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="2308.13232v1-abstract-short" style="display: inline;"> The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we investigate the information rate limits of the primary visual channel to explore whet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13232v1-abstract-full').style.display = 'inline'; document.getElementById('2308.13232v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.13232v1-abstract-full" style="display: none;"> The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we investigate the information rate limits of the primary visual channel to explore whether we can and how we should build visual BCI with higher information rate. Using information theory, we estimate a maximum achievable ITR of approximately 63 bits per second (bps) with a uniformly-distributed White Noise (WN) stimulus. Based on this discovery, we propose a broadband WN BCI approach that expands the utilization of stimulus bandwidth, in contrast to the current state-of-the-art visual BCI methods based on steady-state visual evoked potentials (SSVEPs). Through experimental validation, our broadband BCI outperforms the SSVEP BCI by an impressive margin of 7 bps, setting a new record of 50 bps. This achievement demonstrates the possibility of decoding 40 classes of noninvasive neural responses within a short duration of only 0.1 seconds. The information-theoretical framework introduced in this study provides valuable insights applicable to all sensory-evoked BCIs, making a significant step towards the development of next-generation human-machine interaction systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13232v1-abstract-full').style.display = 'none'; document.getElementById('2308.13232v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.12224">arXiv:2308.12224</a> <span> [<a href="https://arxiv.org/pdf/2308.12224">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Enhancing cardiovascular risk prediction through AI-enabled calcium-omics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hoori%2C+A">Ammar Hoori</a>, <a href="/search/q-bio?searchtype=author&query=Al-Kindi%2C+S">Sadeer Al-Kindi</a>, <a href="/search/q-bio?searchtype=author&query=Hu%2C+T">Tao Hu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yingnan Song</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+H">Hao Wu</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+J">Juhwan Lee</a>, <a href="/search/q-bio?searchtype=author&query=Tashtish%2C+N">Nour Tashtish</a>, <a href="/search/q-bio?searchtype=author&query=Fu%2C+P">Pingfu Fu</a>, <a href="/search/q-bio?searchtype=author&query=Gilkeson%2C+R">Robert Gilkeson</a>, <a href="/search/q-bio?searchtype=author&query=Rajagopalan%2C+S">Sanjay Rajagopalan</a>, <a href="/search/q-bio?searchtype=author&query=Wilson%2C+D+L">David L. Wilson</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="2308.12224v1-abstract-short" style="display: inline;"> Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.12224v1-abstract-full').style.display = 'inline'; document.getElementById('2308.12224v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.12224v1-abstract-full" style="display: none;"> Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction. Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics. Results. Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153. Conclusions. AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.12224v1-abstract-full').style.display = 'none'; document.getElementById('2308.12224v1-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> 23 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">12 pages, 8 figures, 2 tables, 4 pages supplemental, journal paper format (under review)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.13266">arXiv:2305.13266</a> <span> [<a href="https://arxiv.org/pdf/2305.13266">pdf</a>, <a href="https://arxiv.org/format/2305.13266">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Qiang%2C+B">Bo Qiang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuxuan Song</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Minkai Xu</a>, <a href="/search/q-bio?searchtype=author&query=Gong%2C+J">Jingjing Gong</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+B">Bowen Gao</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+W">Weiying Ma</a>, <a href="/search/q-bio?searchtype=author&query=Lan%2C+Y">Yanyan Lan</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="2305.13266v2-abstract-short" style="display: inline;"> Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.13266v2-abstract-full').style.display = 'inline'; document.getElementById('2305.13266v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.13266v2-abstract-full" style="display: none;"> Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a promising strategy, however, it is nontrivial to be adapted for 3D non-autoregressive generations because of the combinational optimization problems. In this paper, we utilize a coarse-to-fine strategy to tackle this problem, in which a Hierarchical Diffusion-based model (i.e.~HierDiff) is proposed to preserve the validity of local segments without relying on autoregressive modeling. Specifically, HierDiff first generates coarse-grained molecule geometries via an equivariant diffusion process, where each coarse-grained node reflects a fragment in a molecule. Then the coarse-grained nodes are decoded into fine-grained fragments by a message-passing process and a newly designed iterative refined sampling module. Lastly, the fine-grained fragments are then assembled to derive a complete atomic molecular structure. Extensive experiments demonstrate that HierDiff consistently improves the quality of molecule generation over existing methods <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.13266v2-abstract-full').style.display = 'none'; document.getElementById('2305.13266v2-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">ICML 2023 poster</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.08975">arXiv:2207.08975</a> <span> [<a href="https://arxiv.org/pdf/2207.08975">pdf</a>, <a href="https://arxiv.org/format/2207.08975">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div 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.media.2023.102759">10.1016/j.media.2023.102759 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xue%2C+T">Tengfei Xue</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+F">Fan Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+C">Chaoyi Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yuqian Chen</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yang Song</a>, <a href="/search/q-bio?searchtype=author&query=Golby%2C+A+J">Alexandra J. Golby</a>, <a href="/search/q-bio?searchtype=author&query=Makris%2C+N">Nikos Makris</a>, <a href="/search/q-bio?searchtype=author&query=Rathi%2C+Y">Yogesh Rathi</a>, <a href="/search/q-bio?searchtype=author&query=Cai%2C+W">Weidong Cai</a>, <a href="/search/q-bio?searchtype=author&query=O%27Donnell%2C+L+J">Lauren J. O'Donnell</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="2207.08975v3-abstract-short" style="display: inline;"> Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas few… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.08975v3-abstract-full').style.display = 'inline'; document.getElementById('2207.08975v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.08975v3-abstract-full" style="display: none;"> Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.08975v3-abstract-full').style.display = 'none'; document.getElementById('2207.08975v3-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> 23 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">Accepted by Medical Image Analysis</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.02629">arXiv:2206.02629</a> <span> [<a href="https://arxiv.org/pdf/2206.02629">pdf</a>, <a href="https://arxiv.org/format/2206.02629">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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Millidge%2C+B">Beren Millidge</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yuhang Song</a>, <a href="/search/q-bio?searchtype=author&query=Salvatori%2C+T">Tommaso Salvatori</a>, <a href="/search/q-bio?searchtype=author&query=Lukasiewicz%2C+T">Thomas Lukasiewicz</a>, <a href="/search/q-bio?searchtype=author&query=Bogacz%2C+R">Rafal Bogacz</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="2206.02629v3-abstract-short" style="display: inline;"> How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EB… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02629v3-abstract-full').style.display = 'inline'; document.getElementById('2206.02629v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.02629v3-abstract-full" style="display: none;"> How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EBMs), in which all free variables in the model are optimized to minimize a global energy function. However, in the literature, these algorithms exist in isolation and no unified theory exists linking them together. Here, we provide a comprehensive theory of the conditions under which EBMs can approximate BP, which lets us unify many of the BP approximation results in the literature (namely, predictive coding, equilibrium propagation, and contrastive Hebbian learning) and demonstrate that their approximation to BP arises from a simple and general mathematical property of EBMs at free-phase equilibrium. This property can then be exploited in different ways with different energy functions, and these specific choices yield a family of BP-approximating algorithms, which both includes the known results in the literature and can be used to derive new ones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02629v3-abstract-full').style.display = 'none'; document.getElementById('2206.02629v3-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 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">31/05/22 initial upload; 22/06/22 change corresponding author; 03/08/22 revisions</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.01411">arXiv:2204.01411</a> <span> [<a href="https://arxiv.org/pdf/2204.01411">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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"> Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jiang%2C+J">Jiyang Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+D">Dadong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yang Song</a>, <a href="/search/q-bio?searchtype=author&query=Sachdev%2C+P+S">Perminder S. Sachdev</a>, <a href="/search/q-bio?searchtype=author&query=Wen%2C+W">Wei Wen</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="2204.01411v1-abstract-short" style="display: inline;"> Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.01411v1-abstract-full').style.display = 'inline'; document.getElementById('2204.01411v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.01411v1-abstract-full" style="display: none;"> Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy-one classical image processing, classical machine learning, and deep learning studies were identified. CMB and PVS have been better studied, compared to lacunes. While good performance metrics have been achieved in local test datasets, there have not been generalisable pipelines validated in different research or clinical cohorts. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in training data. Future studies could consider pooling data from multiple sources to increase diversity, and validating the performance of the methods using both image processing metrics and associations with clinical measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.01411v1-abstract-full').style.display = 'none'; document.getElementById('2204.01411v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 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/2203.02923">arXiv:2203.02923</a> <span> [<a href="https://arxiv.org/pdf/2203.02923">pdf</a>, <a href="https://arxiv.org/format/2203.02923">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"> GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+M">Minkai Xu</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+L">Lantao Yu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yang Song</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+C">Chence Shi</a>, <a href="/search/q-bio?searchtype=author&query=Ermon%2C+S">Stefano Ermon</a>, <a href="/search/q-bio?searchtype=author&query=Tang%2C+J">Jian Tang</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="2203.02923v1-abstract-short" style="display: inline;"> Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamics where heated particles will diffuse from original states to a noise distributi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.02923v1-abstract-full').style.display = 'inline'; document.getElementById('2203.02923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.02923v1-abstract-full" style="display: none;"> Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamics where heated particles will diffuse from original states to a noise distribution, in this paper, we propose a novel generative model named GeoDiff for molecular conformation prediction. GeoDiff treats each atom as a particle and learns to directly reverse the diffusion process (i.e., transforming from a noise distribution to stable conformations) as a Markov chain. Modeling such a generation process is however very challenging as the likelihood of conformations should be roto-translational invariant. We theoretically show that Markov chains evolving with equivariant Markov kernels can induce an invariant distribution by design, and further propose building blocks for the Markov kernels to preserve the desirable equivariance property. The whole framework can be efficiently trained in an end-to-end fashion by optimizing a weighted variational lower bound to the (conditional) likelihood. Experiments on multiple benchmarks show that GeoDiff is superior or comparable to existing state-of-the-art approaches, especially on large molecules. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.02923v1-abstract-full').style.display = 'none'; document.getElementById('2203.02923v1-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 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Published as a conference paper at ICLR 2022 (https://openreview.net/forum?id=PzcvxEMzvQC)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.15261">arXiv:2109.15261</a> <span> [<a href="https://arxiv.org/pdf/2109.15261">pdf</a>, <a href="https://arxiv.org/format/2109.15261">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="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</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"> A simple and flexible test of sample exchangeability with applications to statistical genomics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Aw%2C+A+J">Alan J. Aw</a>, <a href="/search/q-bio?searchtype=author&query=Spence%2C+J+P">Jeffrey P. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.15261v3-abstract-short" style="display: inline;"> In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.15261v3-abstract-full').style.display = 'inline'; document.getElementById('2109.15261v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.15261v3-abstract-full" style="display: none;"> In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these considerations are fundamental to downstream tasks such as demographic inference and the construction of polygenic risk scores. We propose a non-parametric approach, which we call the V test, to address these two questions, namely, a test of sample exchangeability given dependency structure of features, and a test of feature independence given sample exchangeability. Our test is conceptually simple, yet fast and flexible. It controls the Type I error across realistic scenarios, and handles data of arbitrary dimensions by leveraging large-sample asymptotics. Through extensive simulations and a comparison against unsupervised tests of stratification based on random matrix theory, we find that our test compares favorably in various scenarios of interest. We apply the test to data from the 1000 Genomes Project, demonstrating how it can be employed to assess exchangeability of the genetic sample, or find optimal linkage disequilibrium (LD) splits for downstream analysis. For exchangeability assessment, we find that removing rare variants can substantially increase the p-value of the test statistic. For optimal LD splitting, the V test reports different optimal splits than previous approaches not relying on hypothesis testing. Software for our methods is available in R (CRAN: flintyR) and Python (PyPI: flintyPy). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.15261v3-abstract-full').style.display = 'none'; document.getElementById('2109.15261v3-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages. Supplementary Information file (38 pages, contains mathematical proofs) is available at https://github.com/songlab-cal/flinty/</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 62G10; 62H15; 62P10 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> G.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.10590">arXiv:2105.10590</a> <span> [<a href="https://arxiv.org/pdf/2105.10590">pdf</a>, <a href="https://arxiv.org/format/2105.10590">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</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"> Parallelizing Contextual Bandits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chan%2C+J">Jeffrey Chan</a>, <a href="/search/q-bio?searchtype=author&query=Pacchiano%2C+A">Aldo Pacchiano</a>, <a href="/search/q-bio?searchtype=author&query=Tripuraneni%2C+N">Nilesh Tripuraneni</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</a>, <a href="/search/q-bio?searchtype=author&query=Bartlett%2C+P">Peter Bartlett</a>, <a href="/search/q-bio?searchtype=author&query=Jordan%2C+M+I">Michael I. Jordan</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="2105.10590v2-abstract-short" style="display: inline;"> Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions, which leverages available resources for parallel experimentation, has the potential to rapidly accelerate exploration. We present a family of (parallel) contextual bandit algorithms applicable to problems with bounded e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.10590v2-abstract-full').style.display = 'inline'; document.getElementById('2105.10590v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.10590v2-abstract-full" style="display: none;"> Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions, which leverages available resources for parallel experimentation, has the potential to rapidly accelerate exploration. We present a family of (parallel) contextual bandit algorithms applicable to problems with bounded eluder dimension whose regret is nearly identical to their perfectly sequential counterparts -- given access to the same total number of oracle queries -- up to a lower-order ``burn-in" term. We further show these algorithms can be specialized to the class of linear reward functions where we introduce and analyze several new linear bandit algorithms which explicitly introduce diversity into their action selection. Finally, we also present an empirical evaluation of these parallel algorithms in several domains, including materials discovery and biological sequence design problems, to demonstrate the utility of parallelized bandits in practical settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.10590v2-abstract-full').style.display = 'none'; document.getElementById('2105.10590v2-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.07511">arXiv:2011.07511</a> <span> [<a href="https://arxiv.org/pdf/2011.07511">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</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"> Wide-field Decodable Orthogonal Fingerprints of Single Nanoparticles Unlock Multiplexed Digital Assays </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liao%2C+J">Jiayan Liao</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+J">Jiajia Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yiliao Song</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+B">Baolei Liu</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Y">Yinghui Chen</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+F">Fan Wang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+C">Chaohao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+J">Jun Lin</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xueyuan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+J">Jie Lu</a>, <a href="/search/q-bio?searchtype=author&query=Jin%2C+D">Dayong Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.07511v1-abstract-short" style="display: inline;"> The control in optical uniformity of single nanoparticles and tuning their diversity in orthogonal dimensions, dot to dot, holds the key to unlock nanoscience and applications. Here we report that the time-domain emissive profile from single upconversion nanoparticle, including the rising, decay and peak moment of the excited state population (T2 profile), can be arbitrarily tuned by upconversion… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.07511v1-abstract-full').style.display = 'inline'; document.getElementById('2011.07511v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.07511v1-abstract-full" style="display: none;"> The control in optical uniformity of single nanoparticles and tuning their diversity in orthogonal dimensions, dot to dot, holds the key to unlock nanoscience and applications. Here we report that the time-domain emissive profile from single upconversion nanoparticle, including the rising, decay and peak moment of the excited state population (T2 profile), can be arbitrarily tuned by upconversion schemes, including interfacial energy migration, concentration dependency, energy transfer, and isolation of surface quenchers. This allows us to significantly increase the coding capacity at the nanoscale. We further implement both time-resolved wide-field imaging and deep-learning techniques to decode these fingerprints, showing high accuracies at high throughput. These high-dimensional optical fingerprints provide a new horizon for applications spanning from sub-diffraction-limit data storage, security inks, to high-throughput single-molecule digital assays and super-resolution imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.07511v1-abstract-full').style.display = 'none'; document.getElementById('2011.07511v1-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 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.13478">arXiv:2010.13478</a> <span> [<a href="https://arxiv.org/pdf/2010.13478">pdf</a>, <a href="https://arxiv.org/format/2010.13478">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="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+J">Yong Joon Song</a>, <a href="/search/q-bio?searchtype=author&query=Ji%2C+D+J">Dong Jin Ji</a>, <a href="/search/q-bio?searchtype=author&query=Seo%2C+H+I">Hye In Seo</a>, <a href="/search/q-bio?searchtype=author&query=Han%2C+G+B">Gyu Bum Han</a>, <a href="/search/q-bio?searchtype=author&query=Cho%2C+D+H">Dong Ho Cho</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="2010.13478v1-abstract-short" style="display: inline;"> Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.13478v1-abstract-full').style.display = 'inline'; document.getElementById('2010.13478v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.13478v1-abstract-full" style="display: none;"> Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.13478v1-abstract-full').style.display = 'none'; document.getElementById('2010.13478v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">20pages, 9figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.13034">arXiv:2010.13034</a> <span> [<a href="https://arxiv.org/pdf/2010.13034">pdf</a>, <a href="https://arxiv.org/format/2010.13034">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.bpj.2021.02.004">10.1016/j.bpj.2021.02.004 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> EGGTART: A computational tool to visualize the dynamics of biophysical transport processes under the inhomogeneous $\ell$-TASEP </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Erdmann-Pham%2C+D+D">Dan D. Erdmann-Pham</a>, <a href="/search/q-bio?searchtype=author&query=Son%2C+W">Wonjun Son</a>, <a href="/search/q-bio?searchtype=author&query=Duc%2C+K+D">Khanh Dao Duc</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="2010.13034v1-abstract-short" style="display: inline;"> The totally asymmetric simple exclusion process (TASEP), which describes the stochastic dynamics of interacting particles on a lattice, has been actively studied over the past several decades and applied to model important biological transport processes. Here we present a software package, called EGGTART (Extensive GUI gives TASEP-realization in real time), which quantifies and visualizes the dyna… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.13034v1-abstract-full').style.display = 'inline'; document.getElementById('2010.13034v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.13034v1-abstract-full" style="display: none;"> The totally asymmetric simple exclusion process (TASEP), which describes the stochastic dynamics of interacting particles on a lattice, has been actively studied over the past several decades and applied to model important biological transport processes. Here we present a software package, called EGGTART (Extensive GUI gives TASEP-realization in real time), which quantifies and visualizes the dynamics associated with a generalized version of the TASEP with an extended particle size and heterogeneous jump rates. This computational tool is based on analytic formulas obtained from deriving and solving the hydrodynamic limit of the process. It allows an immediate quantification of the particle density, flux, and phase diagram, as a function of a few key parameters associated with the system, which would be difficult to achieve via conventional stochastic simulations. Our software should therefore be of interest to biophysicists studying general transport processes, and can in particular be used in the context of gene expression to model and quantify mRNA translation of different coding sequences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.13034v1-abstract-full').style.display = 'none'; document.getElementById('2010.13034v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">12 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/2002.06391">arXiv:2002.06391</a> <span> [<a href="https://arxiv.org/pdf/2002.06391">pdf</a>, <a href="https://arxiv.org/format/2002.06391">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/10.1142/S1793048020500010">10.1142/S1793048020500010 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Single-cell entropy to quantify the cellular transcription from single-cell RNA-seq data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Liu%2C+J">Jingxin Liu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">You Song</a>, <a href="/search/q-bio?searchtype=author&query=Lei%2C+J">Jinzhi Lei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.06391v1-abstract-short" style="display: inline;"> We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06391v1-abstract-full').style.display = 'inline'; document.getElementById('2002.06391v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.06391v1-abstract-full" style="display: none;"> We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell type classification that includes no parameters and no clustering; however, it is comparable to existing machine learning-based methods in benchmarking studies and facilitates biological interpretation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06391v1-abstract-full').style.display = 'none'; document.getElementById('2002.06391v1-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 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">7 pages, 5 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Biophysical Reviews and Letters, 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.00492">arXiv:2001.00492</a> <span> [<a href="https://arxiv.org/pdf/2001.00492">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> The Effect of Treatment-Related Deaths and "Sticky" Diagnoses on Recorded Prostate Cancer Mortality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Welch%2C+H+G">H. Gilbert Welch</a>, <a href="/search/q-bio?searchtype=author&query=Barry%2C+M+J">Michael J. Barry</a>, <a href="/search/q-bio?searchtype=author&query=Black%2C+W+C">William C Black</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yunjie Song</a>, <a href="/search/q-bio?searchtype=author&query=Fisher%2C+E+S">Elliott S. Fisher</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="2001.00492v1-abstract-short" style="display: inline;"> Background: Although recorded cancer mortality should include both deaths from cancer and deaths from cancer treatment, there is evidence suggesting that the measure may be incomplete. To investigate the completeness of recorded prostate cancer mortality, we compared other-cause (non-prostate cancer) mortality in men found and not found to have prostate cancer following a needle biopsy. Methods:… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.00492v1-abstract-full').style.display = 'inline'; document.getElementById('2001.00492v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.00492v1-abstract-full" style="display: none;"> Background: Although recorded cancer mortality should include both deaths from cancer and deaths from cancer treatment, there is evidence suggesting that the measure may be incomplete. To investigate the completeness of recorded prostate cancer mortality, we compared other-cause (non-prostate cancer) mortality in men found and not found to have prostate cancer following a needle biopsy. Methods: We linked Medicare claims data to SEER data to analyze survival in the population of men aged 65+ enrolled in Medicare who resided in a SEER area and received a needle biopsy in 1993-2001. We compared other-cause mortality in men found to have prostate cancer (n=53,462) to that in men not found to have prostate cancer (n=103,659). Results: The age-race adjusted other-cause mortality rate was 471 per 10,000 person-years in men found to have prostate cancer vs. 468 per 10,000 in men not found to have prostate cancer (RR = 1.01;95% CI:0.98-1.03). The effect was modified, however, by age. The RR declined in a stepwise fashion from 1.08 (95% CI:1.03-1.14) in men age 65-69 to 0.89 (95% CI:0.83-0.95) in men age 85 and older. If the excess (or deficit) in other-cause mortality were added to the recorded prostate cancer mortality, prostate cancer mortality would rise 23% in the youngest age group (from 90 to 111 per 10,000) and would fall 30% in the oldest age group (from 551 to 388 per 10,000). Conclusion: Although recorded prostate cancer mortality appears to be an accurate measure overall, it systematically underestimates the mortality associated with prostate cancer diagnosis and treatment in younger men and overestimates it in the very old. We surmise that in younger men treatment-related deaths are incompletely captured in recorded prostate cancer mortality, while in older men the diagnosis "sticks"-- once diagnosed, they are more likely to be said to have died from the disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.00492v1-abstract-full').style.display = 'none'; document.getElementById('2001.00492v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">20 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/1908.09653">arXiv:1908.09653</a> <span> [<a href="https://arxiv.org/pdf/1908.09653">pdf</a>, <a href="https://arxiv.org/format/1908.09653">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Fusing heterogeneous data sets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yipeng Song</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.09653v1-abstract-short" style="display: inline;"> In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomics, proteomics and RNAseq data in genomics. These different omics data reflect th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.09653v1-abstract-full').style.display = 'inline'; document.getElementById('1908.09653v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.09653v1-abstract-full" style="display: none;"> In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomics, proteomics and RNAseq data in genomics. These different omics data reflect the properties of the studied biological system from different perspectives. The second one is the type of scale, which indicates the measurements obtained at different scales, such as binary, ordinal, interval and ratio-scaled variables. In this thesis, we developed several statistical methods capable to fuse data sets of these two types of heterogeneity. The advantages of the proposed methods in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real biological data sets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.09653v1-abstract-full').style.display = 'none'; document.getElementById('1908.09653v1-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> 23 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">PhD thesis, 173 pages, 60 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/1906.08230">arXiv:1906.08230</a> <span> [<a href="https://arxiv.org/pdf/1906.08230">pdf</a>, <a href="https://arxiv.org/format/1906.08230">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="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Evaluating Protein Transfer Learning with TAPE </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Rao%2C+R">Roshan Rao</a>, <a href="/search/q-bio?searchtype=author&query=Bhattacharya%2C+N">Nicholas Bhattacharya</a>, <a href="/search/q-bio?searchtype=author&query=Thomas%2C+N">Neil Thomas</a>, <a href="/search/q-bio?searchtype=author&query=Duan%2C+Y">Yan Duan</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+X">Xi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Canny%2C+J">John Canny</a>, <a href="/search/q-bio?searchtype=author&query=Abbeel%2C+P">Pieter Abbeel</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1906.08230v1-abstract-short" style="display: inline;"> Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.08230v1-abstract-full').style.display = 'inline'; document.getElementById('1906.08230v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.08230v1-abstract-full" style="display: none;"> Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We benchmark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems. Toward this end, all data and code used to run these experiments are available at https://github.com/songlab-cal/tape. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.08230v1-abstract-full').style.display = 'none'; document.getElementById('1906.08230v1-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 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 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/1901.09975">arXiv:1901.09975</a> <span> [<a href="https://arxiv.org/pdf/1901.09975">pdf</a>, <a href="https://arxiv.org/format/1901.09975">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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Spectral Dynamic Causal Modelling of Resting-State fMRI: Relating Effective Brain Connectivity in the Default Mode Network to Genetics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Nie%2C+Y">Yunlong Nie</a>, <a href="/search/q-bio?searchtype=author&query=Opoku%2C+E">Eugene Opoku</a>, <a href="/search/q-bio?searchtype=author&query=Yasmin%2C+L">Laila Yasmin</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yin Song</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J">Jie Wang</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+S">Sidi Wu</a>, <a href="/search/q-bio?searchtype=author&query=Scarapicchia%2C+V">Vanessa Scarapicchia</a>, <a href="/search/q-bio?searchtype=author&query=Gawryluk%2C+J">Jodie Gawryluk</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+L">Liangliang Wang</a>, <a href="/search/q-bio?searchtype=author&query=Cao%2C+J">Jiguo Cao</a>, <a href="/search/q-bio?searchtype=author&query=Nathoo%2C+F+S">Farouk S. Nathoo</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="1901.09975v8-abstract-short" style="display: inline;"> We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.09975v8-abstract-full').style.display = 'inline'; document.getElementById('1901.09975v8-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.09975v8-abstract-full" style="display: none;"> We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We examine longitudinal data in both a 4-region and an 6-region network and relate longitudinal effective brain connectivity networks estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In the former case we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from the chi-squared null distribution. We also implement a parametric bootstrap approach for testing regression functions in FSR and we make comparisons between p-values obtained from the parametric bootstrap to p-values obtained using the F-distribution with degrees-of-freedom based on Satterthwaite's approximation. In both networks we report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.09975v8-abstract-full').style.display = 'none'; document.getElementById('1901.09975v8-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 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.02763">arXiv:1807.02763</a> <span> [<a href="https://arxiv.org/pdf/1807.02763">pdf</a>, <a href="https://arxiv.org/format/1807.02763">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Inference of Population History using Coalescent HMMs: Review and Outlook </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Spence%2C+J+P">Jeffrey P. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Steinr%C3%BCcken%2C+M">Matthias Steinr眉cken</a>, <a href="/search/q-bio?searchtype=author&query=Terhorst%2C+J">Jonathan Terhorst</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1807.02763v1-abstract-short" style="display: inline;"> Studying how diverse human populations are related is of historical and anthropological interest, in addition to providing a realistic null model for testing for signatures of natural selection or disease associations. Furthermore, understanding the demographic histories of other species is playing an increasingly important role in conservation genetics. A number of statistical methods have been d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.02763v1-abstract-full').style.display = 'inline'; document.getElementById('1807.02763v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.02763v1-abstract-full" style="display: none;"> Studying how diverse human populations are related is of historical and anthropological interest, in addition to providing a realistic null model for testing for signatures of natural selection or disease associations. Furthermore, understanding the demographic histories of other species is playing an increasingly important role in conservation genetics. A number of statistical methods have been developed to infer population demographic histories using whole-genome sequence data, with recent advances focusing on allowing for more flexible modeling choices, scaling to larger data sets, and increasing statistical power. Here we review coalescent hidden Markov models, a powerful class of population genetic inference methods that can effectively utilize linkage disequilibrium information. We highlight recent advances, give advice for practitioners, point out potential pitfalls, and present possible future research directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.02763v1-abstract-full').style.display = 'none'; document.getElementById('1807.02763v1-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 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">12 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/1803.05609">arXiv:1803.05609</a> <span> [<a href="https://arxiv.org/pdf/1803.05609">pdf</a>, <a href="https://arxiv.org/format/1803.05609">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</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.cels.2019.12.003">10.1016/j.cels.2019.12.003 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The key parameters that govern translation efficiency </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Erdmann-Pham%2C+D+D">Dan D. Erdmann-Pham</a>, <a href="/search/q-bio?searchtype=author&query=Duc%2C+K+D">Khanh Dao Duc</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1803.05609v2-abstract-short" style="display: inline;"> Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA (namely, the inhomogeneous $\ell$-TASEP), and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.05609v2-abstract-full').style.display = 'inline'; document.getElementById('1803.05609v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.05609v2-abstract-full" style="display: none;"> Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA (namely, the inhomogeneous $\ell$-TASEP), and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we shows that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as synthetic biology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.05609v2-abstract-full').style.display = 'none'; document.getElementById('1803.05609v2-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 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">To appear in Cell Systems. 32 pages, 10 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.09197">arXiv:1802.09197</a> <span> [<a href="https://arxiv.org/pdf/1802.09197">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Choi%2C+W+Y">Woo Yong Choi</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+K+Y">Kyu Ye Song</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+C+W">Chan Woo Lee</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="1802.09197v1-abstract-short" style="display: inline;"> Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requires laboratory tests that require resources typically lacking in a breakout emer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.09197v1-abstract-full').style.display = 'inline'; document.getElementById('1802.09197v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.09197v1-abstract-full" style="display: none;"> Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requires laboratory tests that require resources typically lacking in a breakout emergency. In this study, we propose new quantitative methods utilizing machine learning and deep learning to correctly classify host species given raw DNA sequence data of the influenza virus, and provide probabilities for each classification. The best deep learning models achieve top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%, on a dataset of 11 host species classes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.09197v1-abstract-full').style.display = 'none'; document.getElementById('1802.09197v1-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, 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/1802.06153">arXiv:1802.06153</a> <span> [<a href="https://arxiv.org/pdf/1802.06153">pdf</a>, <a href="https://arxiv.org/format/1802.06153">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="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chan%2C+J">Jeffrey Chan</a>, <a href="/search/q-bio?searchtype=author&query=Perrone%2C+V">Valerio Perrone</a>, <a href="/search/q-bio?searchtype=author&query=Spence%2C+J+P">Jeffrey P. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Jenkins%2C+P+A">Paul A. Jenkins</a>, <a href="/search/q-bio?searchtype=author&query=Mathieson%2C+S">Sara Mathieson</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1802.06153v2-abstract-short" style="display: inline;"> An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential chal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.06153v2-abstract-full').style.display = 'inline'; document.getElementById('1802.06153v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.06153v2-abstract-full" style="display: none;"> An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our framework can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.06153v2-abstract-full').style.display = 'none'; document.getElementById('1802.06153v2-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 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">9 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.05035">arXiv:1712.05035</a> <span> [<a href="https://arxiv.org/pdf/1712.05035">pdf</a>, <a href="https://arxiv.org/format/1712.05035">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="Algebraic Geometry">math.AG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> </div> </div> <p class="title is-5 mathjax"> Geometry of the sample frequency spectrum and the perils of demographic inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Rosen%2C+Z">Zvi Rosen</a>, <a href="/search/q-bio?searchtype=author&query=Bhaskar%2C+A">Anand Bhaskar</a>, <a href="/search/q-bio?searchtype=author&query=Roch%2C+S">Sebastien Roch</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1712.05035v1-abstract-short" style="display: inline;"> The sample frequency spectrum (SFS), which describes the distribution of mutant alleles in a sample of DNA sequences, is a widely used summary statistic in population genetics. The expected SFS has a strong dependence on the historical population demography and this property is exploited by popular statistical methods to infer complex demographic histories from DNA sequence data. Most, if not all,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05035v1-abstract-full').style.display = 'inline'; document.getElementById('1712.05035v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.05035v1-abstract-full" style="display: none;"> The sample frequency spectrum (SFS), which describes the distribution of mutant alleles in a sample of DNA sequences, is a widely used summary statistic in population genetics. The expected SFS has a strong dependence on the historical population demography and this property is exploited by popular statistical methods to infer complex demographic histories from DNA sequence data. Most, if not all, of these inference methods exhibit pathological behavior, however. Specifically, they often display runaway behavior in optimization, where the inferred population sizes and epoch durations can degenerate to 0 or diverge to infinity, and show undesirable sensitivity of the inferred demography to perturbations in the data. The goal of this paper is to provide theoretical insights into why such problems arise. To this end, we characterize the geometry of the expected SFS for piecewise-constant demographic histories and use our results to show that the aforementioned pathological behavior of popular inference methods is intrinsic to the geometry of the expected SFS. We provide explicit descriptions and visualizations for a toy model with sample size 4, and generalize our intuition to arbitrary sample sizes n using tools from convex and algebraic geometry. We also develop a universal characterization result which shows that the expected SFS of a sample of size n under an arbitrary population history can be recapitulated by a piecewise-constant demography with only k(n) epochs, where k(n) is between n/2 and 2n-1. The set of expected SFS for piecewise-constant demographies with fewer than k(n) epochs is open and non-convex, which causes the above phenomena for inference from data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05035v1-abstract-full').style.display = 'none'; document.getElementById('1712.05035v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 December, 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">21 pages, 5 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 92D10; 14P10; 52A20; 62P10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1706.00780">arXiv:1706.00780</a> <span> [<a href="https://arxiv.org/pdf/1706.00780">pdf</a>, <a href="https://arxiv.org/format/1706.00780">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (渭ECoG) Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yilin Song</a>, <a href="/search/q-bio?searchtype=author&query=Viventi%2C+J">Jonathan Viventi</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Y">Yao 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="1706.00780v1-abstract-short" style="display: inline;"> For the past few years, we have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices hav… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.00780v1-abstract-full').style.display = 'inline'; document.getElementById('1706.00780v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.00780v1-abstract-full" style="display: none;"> For the past few years, we have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices have not yet been developed. In this work we proposed an unsupervised learning framework for spike analysis, which by itself reveals spike pattern. By applying advanced video processing techniques for separating a multi-channel recording into individual spike segments, unfolding the spike segments manifold and identifying natural clusters for spike patterns, we are able to find the common spike motion patterns. And we further explored using these patterns for more interesting and practical problems as seizure prediction and spike wavefront prediction. These methods have been applied to in-vivo feline seizure recordings and yielded promising results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.00780v1-abstract-full').style.display = 'none'; document.getElementById('1706.00780v1-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 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1611.05403">arXiv:1611.05403</a> <span> [<a href="https://arxiv.org/pdf/1611.05403">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cell Behavior">q-bio.CB</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1002/chem.201505173">10.1002/chem.201505173 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Graphitic C3N4 Sensitized TiO2 Nanotube Layers: A Visible Light Activated Efficient Antimicrobial Platform </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Xu%2C+J">Jingwen Xu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yan Li</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+X">Xuemei Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yuzhen Li</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+Z">Zhi-Da Gao</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yan-Yan Song</a>, <a href="/search/q-bio?searchtype=author&query=Schmuki%2C+P">Patrik Schmuki</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="1611.05403v1-abstract-short" style="display: inline;"> In this work, we introduce a facile procedure to graft a thin graphitic C3N4 (g-C3N4) layer on aligned TiO2 nanotube arrays (TiNT) by one-step chemical vapor deposition (CVD) approach. This provides a platform to enhance the visible-light response of TiO2 nanotubes for antimicrobial applications. The formed g- C3N4/TiNT binary nanocomposite exhibits excellent bactericidal efficiency against E. col… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05403v1-abstract-full').style.display = 'inline'; document.getElementById('1611.05403v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1611.05403v1-abstract-full" style="display: none;"> In this work, we introduce a facile procedure to graft a thin graphitic C3N4 (g-C3N4) layer on aligned TiO2 nanotube arrays (TiNT) by one-step chemical vapor deposition (CVD) approach. This provides a platform to enhance the visible-light response of TiO2 nanotubes for antimicrobial applications. The formed g- C3N4/TiNT binary nanocomposite exhibits excellent bactericidal efficiency against E. coli as a visiblelight activated antibacterial coating. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05403v1-abstract-full').style.display = 'none'; document.getElementById('1611.05403v1-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 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Chemistry - A European Journal, Volume 22, Issue 12, pages 3947-3951, March 14, 2016 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1609.01987">arXiv:1609.01987</a> <span> [<a href="https://arxiv.org/pdf/1609.01987">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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"> CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hua%2C+L">Lei Hua</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yang Song</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+N">Namhee Kim</a>, <a href="/search/q-bio?searchtype=author&query=Laing%2C+C">Christian Laing</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+J+T+L">Jason T. L. Wang</a>, <a href="/search/q-bio?searchtype=author&query=Schlick%2C+T">Tamar Schlick</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="1609.01987v1-abstract-short" style="display: inline;"> RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Speci… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01987v1-abstract-full').style.display = 'inline'; document.getElementById('1609.01987v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.01987v1-abstract-full" style="display: none;"> RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01987v1-abstract-full').style.display = 'none'; document.getElementById('1609.01987v1-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 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">45 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/1603.02453">arXiv:1603.02453</a> <span> [<a href="https://arxiv.org/pdf/1603.02453">pdf</a>, <a href="https://arxiv.org/ps/1603.02453">ps</a>, <a href="https://arxiv.org/format/1603.02453">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Subcellular Processes">q-bio.SC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/0953-8984/29/2/025101">10.1088/0953-8984/29/2/025101 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Proofreading of DNA Polymerase: a new kinetic model with higher-order terminal effects </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yong-Shun Song</a>, <a href="/search/q-bio?searchtype=author&query=Shu%2C+Y">Yao-Gen Shu</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+X">Xin Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Ou-Yang%2C+Z">Zhong-Can Ou-Yang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Ming 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="1603.02453v2-abstract-short" style="display: inline;"> The fidelity of DNA replication by DNA polymerase (DNAP) has long been an important issue in biology. While numerous experiments have revealed details of the molecular structure and working mechanism of DNAP which consists of both a polymerase site and an exonuclease (proofreading) site, there were quite few theoretical studies on the fidelity issue. The first model which explicitly considered bot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1603.02453v2-abstract-full').style.display = 'inline'; document.getElementById('1603.02453v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1603.02453v2-abstract-full" style="display: none;"> The fidelity of DNA replication by DNA polymerase (DNAP) has long been an important issue in biology. While numerous experiments have revealed details of the molecular structure and working mechanism of DNAP which consists of both a polymerase site and an exonuclease (proofreading) site, there were quite few theoretical studies on the fidelity issue. The first model which explicitly considered both sites was proposed in 1970s' and the basic idea was widely accepted by later models. However, all these models did not systematically and rigorously investigate the dominant factor on DNAP fidelity, i.e, the higher-order terminal effects through which the polymerization pathway and the proofreading pathway coordinate to achieve high fidelity. In this paper, we propose a new and comprehensive kinetic model of DNAP based on some recent experimental observations, which includes previous models as special cases. We present a rigorous and unified treatment of the corresponding steady-state kinetic equations of any-order terminal effects, and derive analytical expressions for fidelity in terms of kinetic parameters under bio-relevant conditions. These expressions offer new insights on how the the higher-order terminal effects contribute substantially to the fidelity in an order-by-order way, and also show that the polymerization-and-proofreading mechanism is dominated only by very few key parameters. We then apply these results to calculate the fidelity of some real DNAPs, which are in good agreements with previous intuitive estimates given by experimentalists. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1603.02453v2-abstract-full').style.display = 'none'; document.getElementById('1603.02453v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 May, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 March, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">11 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Phys.: Condens. Matter 29 (2017) 025101 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1602.01056">arXiv:1602.01056</a> <span> [<a href="https://arxiv.org/pdf/1602.01056">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Atomic Physics">physics.atom-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1073/pnas.1601513113">10.1073/pnas.1601513113 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Optical magnetic detection of single-neuron action potentials using quantum defects in diamond </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Barry%2C+J+F">J. F. Barry</a>, <a href="/search/q-bio?searchtype=author&query=Turner%2C+M+J">M. J. Turner</a>, <a href="/search/q-bio?searchtype=author&query=Schloss%2C+J+M">J. M. Schloss</a>, <a href="/search/q-bio?searchtype=author&query=Glenn%2C+D+R">D. R. Glenn</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Y. Song</a>, <a href="/search/q-bio?searchtype=author&query=Lukin%2C+M+D">M. D. Lukin</a>, <a href="/search/q-bio?searchtype=author&query=Park%2C+H">H. Park</a>, <a href="/search/q-bio?searchtype=author&query=Walsworth%2C+R+L">R. L. Walsworth</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="1602.01056v1-abstract-short" style="display: inline;"> A key challenge for neuroscience is noninvasive, label-free sensing of action potential (AP) dynamics in whole organisms with single-neuron resolution. Here, we present a new approach to this problem: using nitrogen-vacancy (NV) quantum defects in diamond to measure the time-dependent magnetic fields produced by single-neuron APs. Our technique has a unique combination of features: (i) it is nonin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01056v1-abstract-full').style.display = 'inline'; document.getElementById('1602.01056v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1602.01056v1-abstract-full" style="display: none;"> A key challenge for neuroscience is noninvasive, label-free sensing of action potential (AP) dynamics in whole organisms with single-neuron resolution. Here, we present a new approach to this problem: using nitrogen-vacancy (NV) quantum defects in diamond to measure the time-dependent magnetic fields produced by single-neuron APs. Our technique has a unique combination of features: (i) it is noninvasive, as the light that probes the NV sensors stays within the biocompatible diamond chip and does not enter the organism, enabling activity monitoring over extended periods; (ii) it is label-free and should be widely applicable to most organisms; (iii) it provides high spatial and temporal resolution, allowing precise measurement of the AP waveforms and conduction velocities of individual neurons; (iv) it directly determines AP propagation direction through the inherent sensitivity of NVs to the associated AP magnetic field vector; (v) it is applicable to neurons located within optically opaque tissue or whole organisms, through which magnetic fields pass largely unperturbed; and (vi) it is easy-to-use, scalable, and can be integrated with existing techniques such as wide-field and superresolution imaging. We demonstrate our method using excised single neurons from two invertebrate species, marine worm and squid; and then by single-neuron AP magnetic sensing exterior to whole, live, opaque marine worms for extended periods with no adverse effect. The results lay the groundwork for real-time, noninvasive 3D magnetic mapping of functional neuronal networks, ultimately with synapse-scale (~10 nm) resolution and circuit-scale (~1 cm) field-of-view. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01056v1-abstract-full').style.display = 'none'; document.getElementById('1602.01056v1-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 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">23 pages, 12 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PNAS 2016 113 (49) 14133-14138; November 22, 2016 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1601.05113">arXiv:1601.05113</a> <span> [<a href="https://arxiv.org/pdf/1601.05113">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> <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"> Validating non-invasive EEG source imaging using optimal electrode configurations on a representative rat head model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Valdes-Hernandez%2C+P+A">Pedro A. Valdes-Hernandez</a>, <a href="/search/q-bio?searchtype=author&query=Bae%2C+J">Jihye Bae</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Yinchen Song</a>, <a href="/search/q-bio?searchtype=author&query=Sumiyoshi%2C+A">Akira Sumiyoshi</a>, <a href="/search/q-bio?searchtype=author&query=Aubert-Vazquez%2C+E">Eduardo Aubert-Vazquez</a>, <a href="/search/q-bio?searchtype=author&query=Riera%2C+J+J">Jorge J. Riera</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="1601.05113v2-abstract-short" style="display: inline;"> The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI temp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.05113v2-abstract-full').style.display = 'inline'; document.getElementById('1601.05113v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.05113v2-abstract-full" style="display: none;"> The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations. With the half and fifth sensitivity volumes we determine both the theoretical minimum electrode separation for non-redundant scalp EEG measurements and the electrode sensitivity resolution, which vary over the scalp because of the head geometry. According to our results, electrodes should be at least ~3-3.5 mm apart for an optimal configuration. The sensitivity resolution is generally worse for electrodes at the boundaries of the scalp measured region, though, by analogy with human montages, concentrates the sensitivity enough to localize sources. Cram茅r-Rao lower bounds of source localization errors indicate it is theoretically possible to achieve ESI accuracy at the level of anatomical structures, such as the stimulus-specific somatosensory areas, using the template. More validation for this approximation is provided through the comparison between the template and the individual lead field matrices, for several rats. Finally, using well-accepted inverse methods, we demonstrate that somatosensory ESI is not only expected but also allows exploring unknown phenomena related to global sensory integration. Inheriting the advantages and pitfalls of human ESI, rat ESI will boost the understanding of brain pathophysiological mechanisms and the evaluation of ESI methodologies, new pharmacological treatments and ESI-based biomarkers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.05113v2-abstract-full').style.display = 'none'; document.getElementById('1601.05113v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 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">27 pages, 2 tables and 14 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1510.06017">arXiv:1510.06017</a> <span> [<a href="https://arxiv.org/pdf/1510.06017">pdf</a>, <a href="https://arxiv.org/format/1510.06017">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="Probability">math.PR</span> </div> </div> <p class="title is-5 mathjax"> Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kamm%2C+J+A">John A. Kamm</a>, <a href="/search/q-bio?searchtype=author&query=Spence%2C+J+P">Jeffrey P. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Chan%2C+J">Jeffrey Chan</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1510.06017v2-abstract-short" style="display: inline;"> Two-locus sampling probabilities have played a central role in devising an efficient composite likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.06017v2-abstract-full').style.display = 'inline'; document.getElementById('1510.06017v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1510.06017v2-abstract-full" style="display: none;"> Two-locus sampling probabilities have played a central role in devising an efficient composite likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estimates can be severely biased in certain cases of historical population size changes. To alleviate this problem, we develop here new methods to compute the sampling probability for variable population size functions that are piecewise constant. Our main theoretical result, implemented in a new software package called LDpop, is a novel formula for the sampling probability that can be evaluated by numerically exponentiating a large but sparse matrix. This formula can handle moderate sample sizes ($n \leq 50$) and demographic size histories with a large number of epochs ($\mathcal{D} \geq 64$). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ($n \geq 256$). Finally, LDpop includes an importance sampler for the posterior distribution of two-locus genealogies, based on a new result for the optimal proposal distribution in the variable-size setting. Using our methods, we study how a sharp population bottleneck followed by rapid growth affects the correlation between partially linked sites. Then, through an extensive simulation study, we show that accounting for population size changes under such a demographic model leads to substantial improvements in fine-scale recombination rate estimation. LDpop is freely available for download at https://github.com/popgenmethods/ldpop <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.06017v2-abstract-full').style.display = 'none'; document.getElementById('1510.06017v2-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 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 October, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2015. </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">32 pages, 13 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/1510.05631">arXiv:1510.05631</a> <span> [<a href="https://arxiv.org/pdf/1510.05631">pdf</a>, <a href="https://arxiv.org/format/1510.05631">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="Probability">math.PR</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/10.1534/genetics.115.184101">10.1534/genetics.115.184101 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The site frequency spectrum for general coalescents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Spence%2C+J+P">Jeffrey P. Spence</a>, <a href="/search/q-bio?searchtype=author&query=Kamm%2C+J+A">John A. Kamm</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1510.05631v2-abstract-short" style="display: inline;"> General genealogical processes such as $螞$- and $螢$-coalescents, which respectively model multiple and simultaneous mergers, have important applications in studying marine species, strong positive selection, recurrent selective sweeps, strong bottlenecks, large sample sizes, and so on. Recently, there has been significant progress in developing useful inference tools for such general models. In pa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.05631v2-abstract-full').style.display = 'inline'; document.getElementById('1510.05631v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1510.05631v2-abstract-full" style="display: none;"> General genealogical processes such as $螞$- and $螢$-coalescents, which respectively model multiple and simultaneous mergers, have important applications in studying marine species, strong positive selection, recurrent selective sweeps, strong bottlenecks, large sample sizes, and so on. Recently, there has been significant progress in developing useful inference tools for such general models. In particular, inference methods based on the site frequency spectrum (SFS) have received noticeable attention. Here, we derive a new formula for the expected SFS for general $螞$- and $螢$-coalescents, which leads to an efficient algorithm. For time-homogeneous coalescents, the runtime of our algorithm for computing the expected SFS is $O(n^2)$, where $n$ is the sample size. This is a factor of $n^2$ faster than the state-of-the-art method. Furthermore, in contrast to existing methods, our method generalizes to time-inhomogeneous $螞$- and $螢$-coalescents with measures that factorize as $螞(dx)/味(t)$ and $螢(dx)/味(t)$, respectively, where $味$ denotes a strictly positive function of time. The runtime of our algorithm in this setting is $O(n^3)$. We also obtain general theoretical results for the identifiability of the $螞$ measure when $味$ is a constant function, as well as for the identifiability of the function $味$ under a fixed $螢$ measure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.05631v2-abstract-full').style.display = 'none'; document.getElementById('1510.05631v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 4 figure</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Genetics, Vol. 202 No. 4 (2016) 1549-1561 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1507.08008">arXiv:1507.08008</a> <span> [<a href="https://arxiv.org/pdf/1507.08008">pdf</a>, <a href="https://arxiv.org/ps/1507.08008">ps</a>, <a href="https://arxiv.org/format/1507.08008">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="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/10.1088/0256-307X/32/10/108701">10.1088/0256-307X/32/10/108701 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Temperature Effects on Information Capacity and Energy Efficiency of Hodgkin-Huxley Neuron </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wang%2C+L">Long-Fei Wang</a>, <a href="/search/q-bio?searchtype=author&query=Jia%2C+F">Fei Jia</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xiao-Zhi Liu</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y">Ya-lei Song</a>, <a href="/search/q-bio?searchtype=author&query=Yu%2C+L">Lian-Chun Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1507.08008v1-abstract-short" style="display: inline;"> Recent experimental and theoretical studies show that energy efficiency, which measures the amount of information processed by a neuron with per unit of energy consumption, plays an important role in the evolution of neural systems. Here, we calculated the information rates and energy efficiencies of the Hodgkin-Huxley (HH) neuron model at different temperatures in a noisy environment. We found th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.08008v1-abstract-full').style.display = 'inline'; document.getElementById('1507.08008v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1507.08008v1-abstract-full" style="display: none;"> Recent experimental and theoretical studies show that energy efficiency, which measures the amount of information processed by a neuron with per unit of energy consumption, plays an important role in the evolution of neural systems. Here, we calculated the information rates and energy efficiencies of the Hodgkin-Huxley (HH) neuron model at different temperatures in a noisy environment. We found that both the information rate and energy efficiency are maximized by certain temperatures. Though the information rate and energy efficiency cannot be maximized simultaneously, the neuron holds a high information processing capacity at the temperature corresponding to maximal energy efficiency. Our results support the idea that the energy efficiency is a selective pressure that influences the evolution of nervous systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.08008v1-abstract-full').style.display = 'none'; document.getElementById('1507.08008v1-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, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures, submitted to Chinese Physics Letters</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Chin. Phys. Lett. 32, 108701 (2015) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1505.04228">arXiv:1505.04228</a> <span> [<a href="https://arxiv.org/pdf/1505.04228">pdf</a>, <a href="https://arxiv.org/format/1505.04228">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="Statistics Theory">math.ST</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1073/pnas.1503717112">10.1073/pnas.1503717112 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Terhorst%2C+J">Jonathan Terhorst</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1505.04228v1-abstract-short" style="display: inline;"> The sample frequency spectrum (SFS) of DNA sequences from a collection of individuals is a summary statistic which is commonly used for parametric inference in population genetics. Despite the popularity of SFS-based inference methods, currently little is known about the information-theoretic limit on the estimation accuracy as a function of sample size. Here, we show that using the SFS to estimat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.04228v1-abstract-full').style.display = 'inline'; document.getElementById('1505.04228v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1505.04228v1-abstract-full" style="display: none;"> The sample frequency spectrum (SFS) of DNA sequences from a collection of individuals is a summary statistic which is commonly used for parametric inference in population genetics. Despite the popularity of SFS-based inference methods, currently little is known about the information-theoretic limit on the estimation accuracy as a function of sample size. Here, we show that using the SFS to estimate the size history of a population has a minimax error of at least $O(1/\log s)$, where $s$ is the number of independent segregating sites used in the analysis. This rate is exponentially worse than known convergence rates for many classical estimation problems in statistics. Another surprising aspect of our theoretical bound is that it does not depend on the dimension of the SFS, which is related to the number of sampled individuals. This means that, for a fixed number $s$ of segregating sites considered, using more individuals does not help to reduce the minimax error bound. Our result pertains to populations that have experienced a bottleneck, and we argue that it can be expected to apply to many populations in nature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.04228v1-abstract-full').style.display = 'none'; document.getElementById('1505.04228v1-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 May, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2015. </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, 1 figure</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proc. Natl. Acad. Sci. U.S.A., Vol. 112, No. 25 (2015) 7677-7682 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1503.01133">arXiv:1503.01133</a> <span> [<a href="https://arxiv.org/pdf/1503.01133">pdf</a>, <a href="https://arxiv.org/format/1503.01133">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</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/10.1080/10618600.2016.1159212">10.1080/10618600.2016.1159212 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Efficient computation of the joint sample frequency spectra for multiple populations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kamm%2C+J+A">John A. Kamm</a>, <a href="/search/q-bio?searchtype=author&query=Terhorst%2C+J">Jonathan Terhorst</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Y+S">Yun S. Song</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="1503.01133v1-abstract-short" style="display: inline;"> A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences. In particular, recently there has been growing interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including v… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.01133v1-abstract-full').style.display = 'inline'; document.getElementById('1503.01133v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1503.01133v1-abstract-full" style="display: none;"> A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences. In particular, recently there has been growing interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. Although much methodological progress has been made, existing SFS-based inference methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable efficient computation of the expected joint SFS for multiple populations related by a complex demographic model with arbitrary population size histories (including piecewise exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study involving tens of populations, we demonstrate our improvements to numerical stability and computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.01133v1-abstract-full').style.display = 'none'; document.getElementById('1503.01133v1-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 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2015. </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, 5 figures</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a 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