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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="Ito, T"> <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" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.02685">arXiv:2411.02685</a> <span> [<a href="https://arxiv.org/pdf/2411.02685">pdf</a>, <a href="https://arxiv.org/format/2411.02685">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Geometry of naturalistic object representations in recurrent neural network models of working memory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lei%2C+X">Xiaoxuan Lei</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Takuya Ito</a>, <a href="/search/q-bio?searchtype=author&query=Bashivan%2C+P">Pouya Bashivan</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="2411.02685v1-abstract-short" style="display: inline;"> Working memory is a central cognitive ability crucial for intelligent decision-making. Recent experimental and computational work studying working memory has primarily used categorical (i.e., one-hot) inputs, rather than ecologically relevant, multidimensional naturalistic ones. Moreover, studies have primarily investigated working memory during single or few cognitive tasks. As a result, an under… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02685v1-abstract-full').style.display = 'inline'; document.getElementById('2411.02685v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.02685v1-abstract-full" style="display: none;"> Working memory is a central cognitive ability crucial for intelligent decision-making. Recent experimental and computational work studying working memory has primarily used categorical (i.e., one-hot) inputs, rather than ecologically relevant, multidimensional naturalistic ones. Moreover, studies have primarily investigated working memory during single or few cognitive tasks. As a result, an understanding of how naturalistic object information is maintained in working memory in neural networks is still lacking. To bridge this gap, we developed sensory-cognitive models, comprising a convolutional neural network (CNN) coupled with a recurrent neural network (RNN), and trained them on nine distinct N-back tasks using naturalistic stimuli. By examining the RNN's latent space, we found that: (1) Multi-task RNNs represent both task-relevant and irrelevant information simultaneously while performing tasks; (2) The latent subspaces used to maintain specific object properties in vanilla RNNs are largely shared across tasks, but highly task-specific in gated RNNs such as GRU and LSTM; (3) Surprisingly, RNNs embed objects in new representational spaces in which individual object features are less orthogonalized relative to the perceptual space; (4) The transformation of working memory encodings (i.e., embedding of visual inputs in the RNN latent space) into memory was shared across stimuli, yet the transformations governing the retention of a memory in the face of incoming distractor stimuli were distinct across time. Our findings indicate that goal-driven RNNs employ chronological memory subspaces to track information over short time spans, enabling testable predictions with neural data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02685v1-abstract-full').style.display = 'none'; document.getElementById('2411.02685v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.07431">arXiv:2209.07431</a> <span> [<a href="https://arxiv.org/pdf/2209.07431">pdf</a>, <a href="https://arxiv.org/format/2209.07431">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"> Compositional generalization through abstract representations in human and artificial neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Takuya Ito</a>, <a href="/search/q-bio?searchtype=author&query=Klinger%2C+T">Tim Klinger</a>, <a href="/search/q-bio?searchtype=author&query=Schultz%2C+D+H">Douglas H. Schultz</a>, <a href="/search/q-bio?searchtype=author&query=Murray%2C+J+D">John D. Murray</a>, <a href="/search/q-bio?searchtype=author&query=Cole%2C+M+W">Michael W. Cole</a>, <a href="/search/q-bio?searchtype=author&query=Rigotti%2C+M">Mattia Rigotti</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="2209.07431v3-abstract-short" style="display: inline;"> Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.07431v3-abstract-full').style.display = 'inline'; document.getElementById('2209.07431v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.07431v3-abstract-full" style="display: none;"> Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified behavioral signatures of compositional generalization in humans, along with their neural correlates using whole-cortex functional magnetic resonance imaging (fMRI) data. Next, we designed pretraining paradigms aided by a procedure we term {\em primitives pretraining} to endow compositional task elements into ANNs. We found that ANNs with this prior knowledge had greater correspondence with human behavior and neural compositional signatures. Importantly, primitives pretraining induced abstract internal representations, excellent zero-shot generalization, and sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract representations that matched human fMRI data, where sensory rule abstractions emerged in early sensory areas, and motor rule abstractions emerged in later motor areas. Our findings give empirical support to the role of compositional generalization in human behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.07431v3-abstract-full').style.display = 'none'; document.getElementById('2209.07431v3-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 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">NeurIPS 2022. 24 pages (10 pages main text), 17 figures (8 main text 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/2202.03132">arXiv:2202.03132</a> <span> [<a href="https://arxiv.org/pdf/2202.03132">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s43705-023-00233-9">10.1038/s43705-023-00233-9 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Agricultural quality matrix-based multiomics structural analysis of carrots in soils fertilized with thermophile-fermented compost </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Miyamoto%2C+H">Hirokuni Miyamoto</a>, <a href="/search/q-bio?searchtype=author&query=Shigeta%2C+K">Katsumi Shigeta</a>, <a href="/search/q-bio?searchtype=author&query=Suda%2C+W">Wataru Suda</a>, <a href="/search/q-bio?searchtype=author&query=Ichihashi%2C+Y">Yasunori Ichihashi</a>, <a href="/search/q-bio?searchtype=author&query=Nihei%2C+N">Naoto Nihei</a>, <a href="/search/q-bio?searchtype=author&query=Matsuura%2C+M">Makiko Matsuura</a>, <a href="/search/q-bio?searchtype=author&query=Tsuboi%2C+A">Arisa Tsuboi</a>, <a href="/search/q-bio?searchtype=author&query=Tominaga%2C+N">Naoki Tominaga</a>, <a href="/search/q-bio?searchtype=author&query=Aono%2C+M">Masahiko Aono</a>, <a href="/search/q-bio?searchtype=author&query=Sato%2C+M">Muneo Sato</a>, <a href="/search/q-bio?searchtype=author&query=Taguchi%2C+S">Shunya Taguchi</a>, <a href="/search/q-bio?searchtype=author&query=Nakaguma%2C+T">Teruno Nakaguma</a>, <a href="/search/q-bio?searchtype=author&query=Tsuji%2C+N">Naoko Tsuji</a>, <a href="/search/q-bio?searchtype=author&query=Ishii%2C+C">Chitose Ishii</a>, <a href="/search/q-bio?searchtype=author&query=Matsushita%2C+T">Teruo Matsushita</a>, <a href="/search/q-bio?searchtype=author&query=Shindo%2C+C">Chie Shindo</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Toshiaki Ito</a>, <a href="/search/q-bio?searchtype=author&query=Kato%2C+T">Tamotsu Kato</a>, <a href="/search/q-bio?searchtype=author&query=Ohno%2C+H">Hiroshi Ohno</a>, <a href="/search/q-bio?searchtype=author&query=Kurotani%2C+A">Atsushi Kurotani</a>, <a href="/search/q-bio?searchtype=author&query=Shima%2C+H">Hideaki Shima</a>, <a href="/search/q-bio?searchtype=author&query=Moriya%2C+S">Shigeharu Moriya</a>, <a href="/search/q-bio?searchtype=author&query=Horiuchi%2C+S">Sankichi Horiuchi</a>, <a href="/search/q-bio?searchtype=author&query=Satoh%2C+T">Takashi Satoh</a>, <a href="/search/q-bio?searchtype=author&query=Mori%2C+K">Kenichi Mori</a> , et al. (6 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.03132v5-abstract-short" style="display: inline;"> Compost is used worldwide as a soil conditioner for crops, but its functions have still been explored. Here, the omics profiles of carrots were investigated, as a root vegetable plant model, in a field amended with compost fermented with thermophilic Bacillaceae for growth and quality indices. Exposure to compost significantly increased the productivity, antioxidant activity, red color, and taste… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03132v5-abstract-full').style.display = 'inline'; document.getElementById('2202.03132v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.03132v5-abstract-full" style="display: none;"> Compost is used worldwide as a soil conditioner for crops, but its functions have still been explored. Here, the omics profiles of carrots were investigated, as a root vegetable plant model, in a field amended with compost fermented with thermophilic Bacillaceae for growth and quality indices. Exposure to compost significantly increased the productivity, antioxidant activity, red color, and taste of the carrot root and altered the soil bacterial composition with the levels of characteristic metabolites of the leaf, root, and soil. Based on the data, structural equation modeling (SEM) estimated that L-2-aminoadipate, phenylalanine, flavonoids and / or carotenoids in plants were optimally linked by exposure to compost. The SEM of the soil estimated that the genus Paenibacillus, L-2-aminoadipate and nicotinamide, and S-methyl L-cysteine were optimally involved during exposure. These estimates did not show a contradiction between the whole genomic analysis of compost-derived Paenibacillus isolates and the bioactivity data, inferring the presence of a complex cascade of plant growth-promoting effects and modulation of the nitrogen cycle by compost itself. These observations have provided information on the qualitative indicators of compost in complex soil-plant interactions and offer a new perspective for chemically independent sustainable agriculture through the efficient use of natural nitrogen. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03132v5-abstract-full').style.display = 'none'; document.getElementById('2202.03132v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">6 figures, 1 Table, and support information</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.10895">arXiv:2201.10895</a> <span> [<a href="https://arxiv.org/pdf/2201.10895">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle, and its estimation by structural equation modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Miyamoto%2C+H">Hirokuni Miyamoto</a>, <a href="/search/q-bio?searchtype=author&query=Suda%2C+W">Wataru Suda</a>, <a href="/search/q-bio?searchtype=author&query=Kodama%2C+H">Hiroaki Kodama</a>, <a href="/search/q-bio?searchtype=author&query=Takahashi%2C+H">Hideyuki Takahashi</a>, <a href="/search/q-bio?searchtype=author&query=Nakanishi%2C+Y">Yumiko Nakanishi</a>, <a href="/search/q-bio?searchtype=author&query=Moriya%2C+S">Shigeharu Moriya</a>, <a href="/search/q-bio?searchtype=author&query=Adachi%2C+K">Kana Adachi</a>, <a href="/search/q-bio?searchtype=author&query=Kiriyama%2C+N">Nao Kiriyama</a>, <a href="/search/q-bio?searchtype=author&query=Wada%2C+M">Masaya Wada</a>, <a href="/search/q-bio?searchtype=author&query=Sudo%2C+D">Daisuke Sudo</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+S">Shunsuke Ito</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+S">Shunsuke Ito</a>, <a href="/search/q-bio?searchtype=author&query=Shibata%2C+M">Minami Shibata</a>, <a href="/search/q-bio?searchtype=author&query=Wada%2C+S">Shinji Wada</a>, <a href="/search/q-bio?searchtype=author&query=Murano%2C+T">Takako Murano</a>, <a href="/search/q-bio?searchtype=author&query=Taguchi%2C+H">Hitoshi Taguchi</a>, <a href="/search/q-bio?searchtype=author&query=Shindo%2C+C">Chie Shindo</a>, <a href="/search/q-bio?searchtype=author&query=Tsuboi%2C+A">Arisa Tsuboi</a>, <a href="/search/q-bio?searchtype=author&query=Tsuji%2C+N">Naoko Tsuji</a>, <a href="/search/q-bio?searchtype=author&query=Matsuura%2C+M">Makiko Matsuura</a>, <a href="/search/q-bio?searchtype=author&query=Ishii%2C+C">Chitose Ishii</a>, <a href="/search/q-bio?searchtype=author&query=Nakaguma%2C+T">Teruno Nakaguma</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Toshiyuki Ito</a>, <a href="/search/q-bio?searchtype=author&query=Okada%2C+T">Toru Okada</a>, <a href="/search/q-bio?searchtype=author&query=Matsushita%2C+T">Teruo Matsushita</a> , et al. (18 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.10895v4-abstract-short" style="display: inline;"> Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophyla… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10895v4-abstract-full').style.display = 'inline'; document.getElementById('2201.10895v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.10895v4-abstract-full" style="display: none;"> Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10895v4-abstract-full').style.display = 'none'; document.getElementById('2201.10895v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.07531">arXiv:2110.07531</a> <span> [<a href="https://arxiv.org/pdf/2110.07531">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">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="Biological Physics">physics.bio-ph</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"> Deep learning models for predicting RNA degradation via dual crowdsourcing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wayment-Steele%2C+H+K">Hannah K. Wayment-Steele</a>, <a href="/search/q-bio?searchtype=author&query=Kladwang%2C+W">Wipapat Kladwang</a>, <a href="/search/q-bio?searchtype=author&query=Watkins%2C+A+M">Andrew M. Watkins</a>, <a href="/search/q-bio?searchtype=author&query=Kim%2C+D+S">Do Soon Kim</a>, <a href="/search/q-bio?searchtype=author&query=Tunguz%2C+B">Bojan Tunguz</a>, <a href="/search/q-bio?searchtype=author&query=Reade%2C+W">Walter Reade</a>, <a href="/search/q-bio?searchtype=author&query=Demkin%2C+M">Maggie Demkin</a>, <a href="/search/q-bio?searchtype=author&query=Romano%2C+J">Jonathan Romano</a>, <a href="/search/q-bio?searchtype=author&query=Wellington-Oguri%2C+R">Roger Wellington-Oguri</a>, <a href="/search/q-bio?searchtype=author&query=Nicol%2C+J+J">John J. Nicol</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+J">Jiayang Gao</a>, <a href="/search/q-bio?searchtype=author&query=Onodera%2C+K">Kazuki Onodera</a>, <a href="/search/q-bio?searchtype=author&query=Fujikawa%2C+K">Kazuki Fujikawa</a>, <a href="/search/q-bio?searchtype=author&query=Mao%2C+H">Hanfei Mao</a>, <a href="/search/q-bio?searchtype=author&query=Vandewiele%2C+G">Gilles Vandewiele</a>, <a href="/search/q-bio?searchtype=author&query=Tinti%2C+M">Michele Tinti</a>, <a href="/search/q-bio?searchtype=author&query=Steenwinckel%2C+B">Bram Steenwinckel</a>, <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Takuya Ito</a>, <a href="/search/q-bio?searchtype=author&query=Noumi%2C+T">Taiga Noumi</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+S">Shujun He</a>, <a href="/search/q-bio?searchtype=author&query=Ishi%2C+K">Keiichiro Ishi</a>, <a href="/search/q-bio?searchtype=author&query=Lee%2C+Y">Youhan Lee</a>, <a href="/search/q-bio?searchtype=author&query=%C3%96zt%C3%BCrk%2C+F">Fatih 脰zt眉rk</a>, <a href="/search/q-bio?searchtype=author&query=Chiu%2C+A">Anthony Chiu</a>, <a href="/search/q-bio?searchtype=author&query=%C3%96zt%C3%BCrk%2C+E">Emin 脰zt眉rk</a> , et al. (4 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.07531v2-abstract-short" style="display: inline;"> Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a ke… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07531v2-abstract-full').style.display = 'inline'; document.getElementById('2110.07531v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.07531v2-abstract-full" style="display: none;"> Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07531v2-abstract-full').style.display = 'none'; document.getElementById('2110.07531v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.03612">arXiv:1907.03612</a> <span> [<a href="https://arxiv.org/pdf/1907.03612">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Discovering the Computational Relevance of Brain Network Organization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ito%2C+T">Takuya Ito</a>, <a href="/search/q-bio?searchtype=author&query=Hearne%2C+L">Luke Hearne</a>, <a href="/search/q-bio?searchtype=author&query=Mill%2C+R">Ravi Mill</a>, <a href="/search/q-bio?searchtype=author&query=Cocuzza%2C+C">Carrisa Cocuzza</a>, <a href="/search/q-bio?searchtype=author&query=Cole%2C+M+W">Michael W. Cole</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="1907.03612v2-abstract-short" style="display: inline;"> Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.03612v2-abstract-full').style.display = 'inline'; document.getElementById('1907.03612v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.03612v2-abstract-full" style="display: none;"> Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition - network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.03612v2-abstract-full').style.display = 'none'; document.getElementById('1907.03612v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> 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