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class='morefewer'>Showing up to 2000 entries per page: <a href=/list/q-bio/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>New submissions (showing 10 of 10 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2503.12234" title="Abstract" id="2503.12234"> arXiv:2503.12234 </a> [<a href="/pdf/2503.12234" title="Download PDF" id="pdf-2503.12234" aria-labelledby="pdf-2503.12234">pdf</a>, <a href="/format/2503.12234" title="Other formats" id="oth-2503.12234" aria-labelledby="oth-2503.12234">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Tumor microenvironment (Part I): Tissue integrity in a rat model of peripheral neural cancer </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Maqboul,+A">Ahmad Maqboul</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Elsadek,+B">Bakheet Elsadek</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 pages, 7 figures </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Heliyon, Volume 10, Issue 13, e33932, July 15, 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Tissues and Organs (q-bio.TO)</span> </div> <p class='mathjax'> ICAM-1 (intercellular adhesion molecule 1) and MPZ (myelin protein zero) are thought to be a factor in the integrity of nerve tissues. In this report, we attempted to trace the expression of ICAM-1, responsible for cell-to-cell adhesion, and of MPZ, the main constituent of myelin sheath, in malignant tissues of the sciatic nerve (SN) in inbred male Copenhagen rats. AT-1 Cells (anaplastic tumor 1) were injected in the perineurial sheath, and tissues of the SNs were collected after 7, 14 and 21 days and compared to a sham-operated group of rats (n = 6 each). Tissues were sectioned and histologically examined, under light microscope, and stained for measuring the immunoreactivity of ICAM-1 and MPZ under laser scanning microscope. The cancer model was established, and the tumor growth was confirmed. ICAM-1 showed severe decreases, proportional to the growing anaplastic cells, as compared to the sham group. MPZ revealed, however, a distinct defensive pattern before substantially decreasing in a comparison with sham. These results support the notion that malignancies damage peripheral nerves and cause severe axonal injury and loss of neuronal integrity, and clearly define the role of ICAM-1 and MPZ in safeguarding the nerve tissues. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2503.12262" title="Abstract" id="2503.12262"> arXiv:2503.12262 </a> [<a href="/pdf/2503.12262" title="Download PDF" id="pdf-2503.12262" aria-labelledby="pdf-2503.12262">pdf</a>, <a href="https://arxiv.org/html/2503.12262v1" title="View HTML" id="html-2503.12262" aria-labelledby="html-2503.12262" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12262" title="Other formats" id="oth-2503.12262" aria-labelledby="oth-2503.12262">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Florez,+D">Daniela Florez</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cortez,+R">Ricardo Cortez</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Hyman,+J+M">James M. Hyman</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Qu,+Z">Zhuolin Qu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 26 pages, 9 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Populations and Evolution (q-bio.PE)</span>; Numerical Analysis (math.NA) </div> <p class='mathjax'> Arboviral diseases remain a major public health concern, particularly in tropical and subtropical regions where mosquito populations thrive. One promising strategy to curb transmission is the release of Aedes aegypti mosquitoes infected with Wolbachia, a bacterium that reduces their ability to spread viruses. However, past large-scale releases have not always been successful, especially in complex urban settings, where restricted access to certain areas often leads to infection establishment failures and wasted resources. To address this, we developed and analyzed a partial differential equation model that simulates how Wolbachia-infected mosquitoes are established in different urban environments. We also explored strategies to improve their success under constraints on release size and the efficacy level of insecticide used for pre-release interventions. Our findings suggest that targeted releases are most effective in areas with limited mosquito movement without additional insecticide use. In higher mosquito dispersal areas, reducing at least 35% of wild mosquitoes before release significantly improves establishment within nine months. Additionally, distributing releases over 2-5 weekly batches enhances success more than a single large release, even without other interventions. These findings offer practical insights for designing cost-effective and efficient Wolbachia-based mosquito control programs, reducing the burden of mosquito-borne diseases on vulnerable communities. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2503.12330" title="Abstract" id="2503.12330"> arXiv:2503.12330 </a> [<a href="/pdf/2503.12330" title="Download PDF" id="pdf-2503.12330" aria-labelledby="pdf-2503.12330">pdf</a>, <a href="/format/2503.12330" title="Other formats" id="oth-2503.12330" aria-labelledby="oth-2503.12330">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Huang,+H">Hao Huang</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Xu,+K">Kaijing Xu</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Lardelli,+M">Michael Lardelli</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Quantitative Methods (q-bio.QM)</span>; Genomics (q-bio.GN); Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> Metabolism plays a crucial role in sleep regulation, yet its effects are challenging to track in real time. This study introduces a machine learning-based framework to analyze sleep patterns and identify how metabolic changes influence sleep at specific time points. We first established that sleep periods in Drosophila melanogaster function independently, with no causal relationship between different sleep episodes. Using gradient boosting models and explainable artificial intelligence techniques, we quantified the influence of time-dependent sleep features. Causal inference and autocorrelation analyses further confirmed that sleep states at different times are statistically independent, providing a robust foundation for exploring metabolic effects on sleep. Applying this framework to flies with altered monocarboxylate transporter 2 expression, we found that changes in ketone transport modified sleep stability and disrupted transitions between day and night sleep. In an Alzheimers disease model, metabolic interventions such as beta hydroxybutyrate supplementation and intermittent fasting selectively influenced the timing of day to night transitions rather than uniformly altering sleep duration. Autoencoder based similarity scoring and wavelet analysis reinforced that metabolic effects on sleep were highly time dependent. This study presents a novel approach to studying sleep-metabolism interactions, revealing that metabolic states exert their strongest influence at distinct time points, shaping sleep stability and circadian transitions. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2503.12334" title="Abstract" id="2503.12334"> arXiv:2503.12334 </a> [<a href="/pdf/2503.12334" title="Download PDF" id="pdf-2503.12334" aria-labelledby="pdf-2503.12334">pdf</a>, <a href="https://arxiv.org/html/2503.12334v1" title="View HTML" id="html-2503.12334" aria-labelledby="html-2503.12334" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12334" title="Other formats" id="oth-2503.12334" aria-labelledby="oth-2503.12334">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wang,+E+H">Edward Hong Wang</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wen,+C+X">Cynthia Xin Wen</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span>; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) </div> <p class='mathjax'> We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wearable devices for treating post-traumatic stress disorder (PTSD) and enabling naturalistic brain research. In PTSD Therapy Mode, an implanted closed-loop neural device monitors amygdala activity and provides on-demand stimulation upon detecting pathological theta oscillations, while an ensemble of wearables (smart glasses, smartwatches, smartphones) uses multimodal large language model (LLM) analysis of sensory data to detect environmental or physiological PTSD triggers and deliver timely audiovisual interventions. Logged events from both the neural and wearable loops are analyzed to personalize trigger detection and progressively transition patients to non-invasive interventions. In Neuroscience Research Mode, the same platform is adapted for real-world brain activity capture. Wearable-LLM systems recognize naturalistic events (social interactions, emotional situations, compulsive behaviors, decision making) and signal implanted RNS devices (via wireless triggers) to record synchronized intracranial data during these moments. This approach builds on recent advances in mobile intracranial EEG recording and closed-loop neuromodulation in humans (BRAIN Initiative, 2023) (Mobbs et al., 2021). We discuss how our interdisciplinary system could revolutionize PTSD therapy and cognitive neuroscience by enabling 24/7 monitoring, context-aware intervention, and rich data collection outside traditional labs. The vision is a future where AI-enhanced devices continuously collaborate with the human brain, offering therapeutic support and deep insights into neural function, with the resulting real-world context rich neural data, in turn, accelerating the development of more biologically-grounded and human-centric AI. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2503.12392" title="Abstract" id="2503.12392"> arXiv:2503.12392 </a> [<a href="/pdf/2503.12392" title="Download PDF" id="pdf-2503.12392" aria-labelledby="pdf-2503.12392">pdf</a>, <a href="https://arxiv.org/html/2503.12392v1" title="View HTML" id="html-2503.12392" aria-labelledby="html-2503.12392" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12392" title="Other formats" id="oth-2503.12392" aria-labelledby="oth-2503.12392">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Central and Central-Parietal EEG Signatures of Parkinson's Disease </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Lensky,+A">Artem Lensky</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15 with PD and 16 healthy controls). EEG signals were rigorously preprocessed to remove tremor artifacts, then converted to wavelet-based images by grouping spatially adjacent electrodes into triplets for convolutional neural network (CNN) classification. Our analysis across different brain regions and frequency bands showed distinct spatial-spectral patterns of PD-related neural oscillations. We identified high classification accuracy (74%) in the gamma band (40-62.4 Hz) for central-parietal electrodes (CP1, Pz, CP2), and 76% accuracy using central electrodes (C3, Cz, C4) with full-spectrum 0.4-62.4 Hz. In particular, we observed pronounced right-hemisphere involvement, specifically in parieto-occipital regions. Unlike previous studies that achieved higher accuracies by potentially including tremor artifacts, our approach isolates genuine neurophysiological alterations in cortical activity. These findings suggest that specific EEG-based oscillatory patterns, especially central-parietal gamma activity, may provide diagnostic information for PD, potentially before the onset of motor symptoms. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2503.12509" title="Abstract" id="2503.12509"> arXiv:2503.12509 </a> [<a href="/pdf/2503.12509" title="Download PDF" id="pdf-2503.12509" aria-labelledby="pdf-2503.12509">pdf</a>, <a href="https://arxiv.org/html/2503.12509v1" title="View HTML" id="html-2503.12509" aria-labelledby="html-2503.12509" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12509" title="Other formats" id="oth-2503.12509" aria-labelledby="oth-2503.12509">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Yuan,+Z">Zhongju Yuan</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wiggins,+G">Geraint Wiggins</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Botteldooren,+D">Dick Botteldooren</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Rhythm is a fundamental aspect of human behaviour, present from infancy and deeply embedded in cultural practices. Rhythm anticipation is a spontaneous cognitive process that typically occurs before the onset of actual beats. While most research in both neuroscience and artificial intelligence has focused on metronome-based rhythm tasks, studies investigating the perception of complex musical rhythm patterns remain limited. To address this gap, we propose a hierarchical oscillator-based model to better understand the perception of complex musical rhythms in biological systems. The model consists of two types of coupled neurons that generate oscillations, with different layers tuned to respond to distinct perception levels. We evaluate the model using several representative rhythm patterns spanning the upper, middle, and lower bounds of human musical perception. Our findings demonstrate that, while maintaining a high degree of synchronization accuracy, the model exhibits human-like rhythmic behaviours. Additionally, the beta band neuronal activity in the model mirrors patterns observed in the human brain, further validating the biological plausibility of the approach. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2503.12647" title="Abstract" id="2503.12647"> arXiv:2503.12647 </a> [<a href="/pdf/2503.12647" title="Download PDF" id="pdf-2503.12647" aria-labelledby="pdf-2503.12647">pdf</a>, <a href="https://arxiv.org/html/2503.12647v1" title="View HTML" id="html-2503.12647" aria-labelledby="html-2503.12647" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12647" title="Other formats" id="oth-2503.12647" aria-labelledby="oth-2503.12647">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Transmission Sequence Design with ISI Matching in Molecular Communication </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Muraleedharan,+A">Aravind Muraleedharan</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Gupta,+A+K">Abhishek K. Gupta</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Molecular Networks (q-bio.MN)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> Molecular communication (MC) offers a groundbreaking approach to communication inspired by biological signaling. It is particularly suited for environments where traditional electromagnetic methods fail, such as fluid mediums or within the human body. This study focuses on addressing a major challenge in MC systems: inter symbol interference (ISI), which arises due to the random, diffusive propagation of molecules. We propose a novel technique that leverages transmission shaping to mitigate ISI effectively by designing optimal transmission pulse (or sequence) for symbols. Our approach centers on solving a multi-objective optimization problem that aims to maximize the separability of individual symbol's responses within the symbol duration while matching the interference caused by molecular spillover for all symbols. By making ISI of each symbol similar, the approach reduces the effect of previous symbols and thus not require any adaptive computations. We introduce a geometric analogy involving two families of ellipses to derive the optimal solution. Analytical insights are supported by numerical simulations to design optimized transmission profiles to enhance the resilience toward ISI. The proposed transmission shaping method is evaluated through symbol error rate (SER). These results mark a significant step forward in developing robust and efficient MC systems, opening doors to advanced applications in bio-inspired and nano-scale communication technologies. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2503.13097" title="Abstract" id="2503.13097"> arXiv:2503.13097 </a> [<a href="/pdf/2503.13097" title="Download PDF" id="pdf-2503.13097" aria-labelledby="pdf-2503.13097">pdf</a>, <a href="https://arxiv.org/html/2503.13097v1" title="View HTML" id="html-2503.13097" aria-labelledby="html-2503.13097" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13097" title="Other formats" id="oth-2503.13097" aria-labelledby="oth-2503.13097">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Combined impact of grey and superficial white matter abnormalities: implications for epilepsy surgery </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Kozma,+C">Csaba Kozma</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Horsley,+J">Jonathan Horsley</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Hall,+G">Gerard Hall</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Simpson,+C">Callum Simpson</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=de+Tisi,+J">Jane de Tisi</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Miserocchi,+A">Anna Miserocchi</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=McEvoy,+A+W">Andrew W. McEvoy</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Vos,+S+B">Sjoerd B. Vos</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Winston,+G+P">Gavin P. Winston</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wang,+Y">Yujiang Wang</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Duncan,+J+S">John S. Duncan</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Taylor,+P+N">Peter N. Taylor</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span> </div> <p class='mathjax'> Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post-surgical seizure outcome in people with temporal lobe epilepsy (TLE). <br>We analyzed structural imaging data from 143 TLE patients (pre-op dMRI and pre-op T1-weighted MRI) and 97 healthy controls. We calculated GM volume abnormalities and SWM mean diffusivity abnormalities and evaluated if their surgical removal distinguished seizure outcome groups post-surgically. <br>At a group level, GM and SWM abnormalities were most common in the ipsilateral temporal lobe and hippocampus in people with TLE. Analyzing both modalities together, compared to in isolation, improved surgical outcome discrimination (GM AUC = 0.68, p < 0.01, WM AUC = 0.65, p < 0.01; Union AUC = 0.72, p < 0.01, Concordance AUC = 0.64, p = 0.04). Additionally, 100% of people who had all concordant abnormal regions resected had ILAE$_{1,2}$ outcomes. <br>These findings suggest that regions identified as abnormal from both diffusion-weighted and T1-weighted MRIs are involved in the epileptogenic network and that resection of both types of abnormalities may enhance the chances of living without disabling seizures. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2503.13189" title="Abstract" id="2503.13189"> arXiv:2503.13189 </a> [<a href="/pdf/2503.13189" title="Download PDF" id="pdf-2503.13189" aria-labelledby="pdf-2503.13189">pdf</a>, <a href="/format/2503.13189" title="Other formats" id="oth-2503.13189" aria-labelledby="oth-2503.13189">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Causes of evolutionary divergence in prostate cancer </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Esenturk,+E">Emre Esenturk</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Sahli,+A">Atef Sahli</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Haberland,+V">Valeriia Haberland</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Ziuboniewicz,+A">Aleksandra Ziuboniewicz</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wirth,+C">Christopher Wirth</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Bova,+G+S">G. Steven Bova</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Bristow,+R+G">Robert G Bristow</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Brook,+M+N">Mark N Brook</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Brors,+B">Benedikt Brors</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Butler,+A">Adam Butler</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cancel-Tassin,+G">G茅raldine Cancel-Tassin</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cheng,+K+C">Kevin CL Cheng</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cooper,+C+S">Colin S Cooper</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Corcoran,+N+M">Niall M Corcoran</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cussenot,+O">Olivier Cussenot</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Eeles,+R+A">Ros A Eeles</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Favero,+F">Francesco Favero</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Gerhauser,+C">Clarissa Gerhauser</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Gihawi,+A">Abraham Gihawi</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Girma,+E+G">Etsehiwot G Girma</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Gnanapragasam,+V+J">Vincent J Gnanapragasam</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Gruber,+A+J">Andreas J Gruber</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Hamid,+A">Anis Hamid</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Hayes,+V+M">Vanessa M Hayes</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=He,+H+H">Housheng Hansen He</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Hovens,+C+M">Christopher M Hovens</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Imada,+E+L">Eddie Luidy Imada</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Jakobsdottir,+G+M">G. Maria Jakobsdottir</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Jung,+C">Chol-hee Jung</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Khani,+F">Francesca Khani</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Kote-Jarai,+Z">Zsofia Kote-Jarai</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Lamy,+P">Philippe Lamy</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Leeman,+G">Gregory Leeman</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Loda,+M">Massimo Loda</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Lutsik,+P">Pavlo Lutsik</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Marchionni,+L">Luigi Marchionni</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Molania,+R">Ramyar Molania</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Papenfuss,+A+T">Anthony T Papenfuss</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Pellegrina,+D">Diogo Pellegrina</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Pope,+B">Bernard Pope</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Queiroz,+L+R">Lucio R Queiroz</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Rausch,+T">Tobias Rausch</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Reimand,+J">J眉ri Reimand</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Robinson,+B">Brian Robinson</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Schlomm,+T">Thorsten Schlomm</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=S%C3%B8rensen,+K+D">Karina D S酶rensen</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Uhrig,+S">Sebastian Uhrig</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Weischenfeldt,+J">Joachim Weischenfeldt</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Xu,+Y">Yaobo Xu</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Yamaguchi,+T+N">Takafumi N Yamaguchi</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Zanettini,+C">Claudio Zanettini</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Lynch,+A+G">Andy G Lynch</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wedge,+D+C">David C Wedge</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Brewer,+D+S">Daniel S Brewer</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Woodcock,+D+J">Dan J Woodcock</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Genomics (q-bio.GN)</span> </div> <p class='mathjax'> Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer. </p> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2503.13237" title="Abstract" id="2503.13237"> arXiv:2503.13237 </a> [<a href="/pdf/2503.13237" title="Download PDF" id="pdf-2503.13237" aria-labelledby="pdf-2503.13237">pdf</a>, <a href="/format/2503.13237" title="Other formats" id="oth-2503.13237" aria-labelledby="oth-2503.13237">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Biodiversity conservation and strategies of public awareness, case study: The natural landscape of central Tunisia </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Saadaoui,+I">Islem Saadaoui</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Bryant,+C+R">Christopher Robin Bryant</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Rejeb,+H">Hichem Rejeb</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Petri%C5%9For,+A">Alexandru-Ionu牛 Petri艧or</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> PESD, VOL. 12, no. 2, 2018 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Populations and Evolution (q-bio.PE)</span> </div> <p class='mathjax'> This research examines global issues concerning the development of mountain areas considered as territories difficult to manage. The case study area is part of the sub-region of High Alpine Steppes belonging to the Tunisian Ridge and reaching Tebessa Mountains in Algeria. The central question of this article is based on the analysis of the links between the representations produced by mountain landscapes and the construction of a border line that must meet the requirements of sustainable development. Eco-landscape determinants and the role of public authorities and population must be better defined so that the products of this space provide a better quality of life endowed with the alternatives of local and sustainable development. Our hypothesis is that the mountain areas of West Central Tunisia still have a real ecological potential little disturbed by a chimerical development, and can constitute assets for the territorial development of the area. The approach adopted by this work is a scoping audit based on the floristic richness and the monitoring of its spatiotemporal dynamics. The results of this research allowed us to draw rich conclusions; the phyto-ecology approach has shown a relative floristic richness that remains highly dependent on the climatic cycles and intervention of human action; this area must be considered as a priority of the public planning policies aimed at improving the quality of lives in these fragile zones in the context of sustainable development. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 12 of 12 entries)</h3> <dt> <a name='item11'>[11]</a> <a href ="/abs/2503.11747" title="Abstract" id="2503.11747"> arXiv:2503.11747 </a> (cross-list from physics.bio-ph) [<a href="/pdf/2503.11747" title="Download PDF" id="pdf-2503.11747" aria-labelledby="pdf-2503.11747">pdf</a>, <a href="/format/2503.11747" title="Other formats" id="oth-2503.11747" aria-labelledby="oth-2503.11747">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Physical Principles of Quantum Biology </div> <div class='list-authors'><a href="https://arxiv.org/search/physics?searchtype=author&query=Babcock,+N+S">Nathan S. Babcock</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Babcock,+B+N">Brandy N. Babcock</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Technical monograph, 161 pages, 1754 citations </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Biological Physics (physics.bio-ph)</span>; Biomolecules (q-bio.BM); Cell Behavior (q-bio.CB); Subcellular Processes (q-bio.SC); Tissues and Organs (q-bio.TO); Quantum Physics (quant-ph) </div> <p class='mathjax'> This technical monograph provides a comprehensive overview of the field of quantum biology. It approaches quantum biology from a physical perspective with core quantum mechanical concepts presented foremost to provide a theoretical foundation for the field. An extensive body of research is covered to clarify the significance of quantum biology as a scientific field, outlining the field's long-standing importance in the historical development of quantum theory. This lays the essential groundwork to enable further advances in nanomedicine and biotechnology. Written for academics, biological science researchers, physicists, biochemists, medical technologists, and students of quantum mechanics, this text brings clarity to fundamental advances being made in the emerging science of quantum biology. </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2503.11900" title="Abstract" id="2503.11900"> arXiv:2503.11900 </a> (cross-list from cs.LG) [<a href="/pdf/2503.11900" title="Download PDF" id="pdf-2503.11900" aria-labelledby="pdf-2503.11900">pdf</a>, <a href="https://arxiv.org/html/2503.11900v1" title="View HTML" id="html-2503.11900" aria-labelledby="html-2503.11900" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.11900" title="Other formats" id="oth-2503.11900" aria-labelledby="oth-2503.11900">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Heterogenous graph neural networks for species distribution modeling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Harrell,+L">Lauren Harrell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaeser-Chen,+C">Christine Kaeser-Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ayan,+B+K">Burcu Karagol Ayan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anderson,+K">Keith Anderson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Conserva,+M">Michelangelo Conserva</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kleeman,+E">Elise Kleeman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Neumann,+M">Maxim Neumann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Overlan,+M">Matt Overlan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chapman,+M">Melissa Chapman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Purves,+D">Drew Purves</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 3 figures, </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Populations and Evolution (q-bio.PE); Machine Learning (stat.ML) </div> <p class='mathjax'> Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2503.12066" title="Abstract" id="2503.12066"> arXiv:2503.12066 </a> (cross-list from cs.LG) [<a href="/pdf/2503.12066" title="Download PDF" id="pdf-2503.12066" aria-labelledby="pdf-2503.12066">pdf</a>, <a href="/format/2503.12066" title="Other formats" id="oth-2503.12066" aria-labelledby="oth-2503.12066">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Impact of Data Patterns on Biotype identification Using Machine Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+Y">Yuetong Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ge,+R">Ruiyang Ge</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hacihaliloglu,+I">Ilker Hacihaliloglu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rauscher,+A">Alexander Rauscher</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tam,+R">Roger Tam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Frangou,+S">Sophia Frangou</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neurons and Cognition (q-bio.NC); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> Background: Patient stratification in brain disorders remains a significant challenge, despite advances in machine learning and multimodal neuroimaging. Automated machine learning algorithms have been widely applied for identifying patient subtypes (biotypes), but results have been inconsistent across studies. These inconsistencies are often attributed to algorithmic limitations, yet an overlooked factor may be the statistical properties of the input data. This study investigates the contribution of data patterns on algorithm performance by leveraging synthetic brain morphometry data as an exemplar. <br>Methods: Four widely used algorithms-SuStaIn, HYDRA, SmileGAN, and SurrealGAN were evaluated using multiple synthetic pseudo-patient datasets designed to include varying numbers and sizes of clusters and degrees of complexity of morphometric changes. Ground truth, representing predefined clusters, allowed for the evaluation of performance accuracy across algorithms and datasets. <br>Results: SuStaIn failed to process datasets with more than 17 variables, highlighting computational inefficiencies. HYDRA was able to perform individual-level classification in multiple datasets with no clear pattern explaining failures. SmileGAN and SurrealGAN outperformed other algorithms in identifying variable-based disease patterns, but these patterns were not able to provide individual-level classification. <br>Conclusions: Dataset characteristics significantly influence algorithm performance, often more than algorithmic design. The findings emphasize the need for rigorous validation using synthetic data before real-world application and highlight the limitations of current clustering approaches in capturing the heterogeneity of brain disorders. These insights extend beyond neuroimaging and have implications for machine learning applications in biomedical research. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2503.12286" title="Abstract" id="2503.12286"> arXiv:2503.12286 </a> (cross-list from cs.CL) [<a href="/pdf/2503.12286" title="Download PDF" id="pdf-2503.12286" aria-labelledby="pdf-2503.12286">pdf</a>, <a href="/format/2503.12286" title="Other formats" id="oth-2503.12286" aria-labelledby="oth-2503.12286">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+D">Da Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zhanliang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+Q">Quan Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+K">Kai Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 31 pages, 3 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation and Language (cs.CL)</span>; Artificial Intelligence (cs.AI); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Childrens Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2503.12331" title="Abstract" id="2503.12331"> arXiv:2503.12331 </a> (cross-list from physics.comp-ph) [<a href="/pdf/2503.12331" title="Download PDF" id="pdf-2503.12331" aria-labelledby="pdf-2503.12331">pdf</a>, <a href="/format/2503.12331" title="Other formats" id="oth-2503.12331" aria-labelledby="oth-2503.12331">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Carbon capture capacity estimation of taiga reforestation and afforestation at the western boreal edge using spatially explicit carbon budget modeling </div> <div class='list-authors'><a href="https://arxiv.org/search/physics?searchtype=author&query=Dsouza,+K+B">Kevin Bradley Dsouza</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Ofosu,+E">Enoch Ofosu</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Boudreault,+R">Richard Boudreault</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Moreno-Cruz,+J">Juan Moreno-Cruz</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Leonenko,+Y">Yuri Leonenko</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computational Physics (physics.comp-ph)</span>; Geophysics (physics.geo-ph); Populations and Evolution (q-bio.PE) </div> <p class='mathjax'> Canada's northern boreal has considerable potential for tree planting related climate change mitigation solutions, considering the sparsity of trees and large portions of non-forested land at the northern forest edge. Moreover, afforestation at the northern boreal edge would enable further the observed gradual tree-line advancement of the taiga into the southern arctic, assisting forests in their migration northward while capitalizing on their carbon capture capacity. However, significant uncertainties remain about the carbon capture capacity of large-scale tree planting in the northern boreal ecozones under changing climatic conditions due to lack of spatially explicit ecozone specific modeling. In this paper, we provide monte carlo estimates of carbon capture capacity of taiga reforestation and afforestation at the north-western boreal edge using spatially explicit carbon budget modeling. We combine satellite-based forest inventory data and probabilistic fire regime representations to simulate how total ecosystem carbon (TEC) might evolve from 2025 until 2100 under different scenarios composed of fire return intervals (FRI), historical land classes, planting mortality, and climatic variables. Our findings suggest that afforestation at the north-western boreal edge could provide meaningful carbon sequestration toward Canada's climate targets, potentially storing approximately 3.88G Tonnes of $CO_{2}$e over the next 75 years in the average case resulting from afforestation on approximately 6.4M hectares, with the Northwest Territories (NT)-Taiga Shield West (TSW) zone showing the most potential. Further research is needed to refine these estimates using improved modeling, study economic viability of such a project, and investigate the impact on other regional processes such as permafrost thaw, energy fluxes, and albedo feedbacks. </p> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2503.12593" title="Abstract" id="2503.12593"> arXiv:2503.12593 </a> (cross-list from eess.IV) [<a href="/pdf/2503.12593" title="Download PDF" id="pdf-2503.12593" aria-labelledby="pdf-2503.12593">pdf</a>, <a href="https://arxiv.org/html/2503.12593v1" title="View HTML" id="html-2503.12593" aria-labelledby="html-2503.12593" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12593" title="Other formats" id="oth-2503.12593" aria-labelledby="oth-2503.12593">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fourier-Based 3D Multistage Transformer for Aberration Correction in Multicellular Specimens </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Alshaabi,+T">Thayer Alshaabi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Milkie,+D+E">Daniel E. Milkie</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+G">Gaoxiang Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Shirazinejad,+C">Cyna Shirazinejad</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hong,+J+L">Jason L. Hong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Achour,+K">Kemal Achour</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=G%C3%B6rlitz,+F">Frederik G枚rlitz</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Milunovic-Jevtic,+A">Ana Milunovic-Jevtic</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Simmons,+C">Cat Simmons</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Abuzahriyeh,+I+S">Ibrahim S. Abuzahriyeh</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hong,+E">Erin Hong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Williams,+S+E">Samara Erin Williams</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Harrison,+N">Nathanael Harrison</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Huang,+E">Evan Huang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bae,+E+S">Eun Seok Bae</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Killilea,+A+N">Alison N. Killilea</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Drubin,+D+G">David G. Drubin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Swinburne,+I+A">Ian A. Swinburne</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Upadhyayula,+S">Srigokul Upadhyayula</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Betzig,+E">Eric Betzig</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 52 pages, 6 figures, 23 si figures, 8 si tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Biological Physics (physics.bio-ph); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer) -- a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples. </p> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2503.12992" title="Abstract" id="2503.12992"> arXiv:2503.12992 </a> (cross-list from cs.AI) [<a href="/pdf/2503.12992" title="Download PDF" id="pdf-2503.12992" aria-labelledby="pdf-2503.12992">pdf</a>, <a href="https://arxiv.org/html/2503.12992v1" title="View HTML" id="html-2503.12992" aria-labelledby="html-2503.12992" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.12992" title="Other formats" id="oth-2503.12992" aria-labelledby="oth-2503.12992">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Intra-neuronal attention within language models Relationships between activation and semantics </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Pichat,+M">Michael Pichat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pogrund,+W">William Pogrund</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pichat,+P">Paloma Pichat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gasparian,+A">Armanouche Gasparian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Demarchi,+S">Samuel Demarchi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Georgeon,+C+A">Corbet Alois Georgeon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Veillet-Guillem,+M">Michael Veillet-Guillem</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention; that is, to identify different homogeneous categorical segments within the synthetic thought category they encode, based on a segmentation of specific activation zones for the tokens to which they are particularly responsive. The objective of this work is therefore to determine to what extent formal neurons can establish a homomorphic relationship between activation-based and categorical segmentations. The results suggest the existence of such a relationship, albeit tenuous, only at the level of tokens with very high activation levels. This intra-neuronal attention subsequently enables categorical restructuring processes at the level of neurons in the following layer, thereby contributing to the progressive formation of high-level categorical abstractions. </p> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2503.13078" title="Abstract" id="2503.13078"> arXiv:2503.13078 </a> (cross-list from stat.ME) [<a href="/pdf/2503.13078" title="Download PDF" id="pdf-2503.13078" aria-labelledby="pdf-2503.13078">pdf</a>, <a href="https://arxiv.org/html/2503.13078v1" title="View HTML" id="html-2503.13078" aria-labelledby="html-2503.13078" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13078" title="Other formats" id="oth-2503.13078" aria-labelledby="oth-2503.13078">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Hermansen,+T+%C3%98">Tobias 脴stmo Hermansen</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Zucknick,+M">Manuela Zucknick</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Zhao,+Z">Zhi Zhao</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Genomics (q-bio.GN) </div> <p class='mathjax'> An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project. </p> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2503.13154" title="Abstract" id="2503.13154"> arXiv:2503.13154 </a> (cross-list from math.PR) [<a href="/pdf/2503.13154" title="Download PDF" id="pdf-2503.13154" aria-labelledby="pdf-2503.13154">pdf</a>, <a href="https://arxiv.org/html/2503.13154v1" title="View HTML" id="html-2503.13154" aria-labelledby="html-2503.13154" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13154" title="Other formats" id="oth-2503.13154" aria-labelledby="oth-2503.13154">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Evolution of a trait distributed over a large fragmented population: Propagation of chaos meets adaptive dynamics </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Lambert,+A">Amaury Lambert</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Leman,+H">H茅l猫ne Leman</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Morlon,+H">H茅l猫ne Morlon</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Tchouanti,+J">Josu茅 Tchouanti</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 20 pages, 4 figures, prepublication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Probability (math.PR)</span>; Populations and Evolution (q-bio.PE) </div> <p class='mathjax'> We consider a metapopulation made up of $K$ demes, each containing $N$ individuals bearing a heritable quantitative trait. Demes are connected by migration and undergo independent Moran processes with mutation and selection based on trait values. Mutation and migration rates are tuned so that each deme receives a migrant or a mutant in the same slow timescale and is thus essentially monomorphic at all times for the trait (adaptive dynamics). In the timescale of mutation/migration, the metapopulation can then be seen as a giant spatial Moran model with size $K$ that we characterize. As $K\to \infty$ and physical space becomes continuous, the empirical distribution of the trait (over the physical and trait spaces) evolves deterministically according to an integro-differential evolution equation. In this limit, the trait of every migrant is drawn from this global distribution, so that conditional on its initial state, traits from finitely many demes evolve independently (propagation of chaos). Under mean-field dispersal, the value $X_t$ of the trait at time $t$ and at any given location has a law denoted $\mu_t$ and a jump kernel with two terms: a mutation-fixation term and a migration-fixation term involving $\mu_{t-}$ (McKean-Vlasov equation). In the limit where mutations have small effects and migration is further slowed down accordingly, we obtain the convergence of $X$, in the new migration timescale, to the solution of a stochastic differential equation which can be referred to as a new canonical equation of adaptive dynamics. This equation includes an advection term representing selection, a diffusive term due to genetic drift, and a jump term, representing the effect of migration, to a state distributed according to its own law. </p> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2503.13329" title="Abstract" id="2503.13329"> arXiv:2503.13329 </a> (cross-list from cs.LG) [<a href="/pdf/2503.13329" title="Download PDF" id="pdf-2503.13329" aria-labelledby="pdf-2503.13329">pdf</a>, <a href="/format/2503.13329" title="Other formats" id="oth-2503.13329" aria-labelledby="oth-2503.13329">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modelling and machine learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Costa-Gomes,+B">Beatriz Costa-Gomes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Greer,+J">Joel Greer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Juraschko,+N">Nikolai Juraschko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Parkhurst,+J">James Parkhurst</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mirecka,+J">Jola Mirecka</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Famili,+M">Marjan Famili</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rangel-Smith,+C">Camila Rangel-Smith</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Strickson,+O">Oliver Strickson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lowe,+A">Alan Lowe</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Basham,+M">Mark Basham</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Burnley,+T">Tom Burnley</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 22 pages, 4 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computational Engineering, Finance, and Science (cs.CE); Biomolecules (q-bio.BM) </div> <p class='mathjax'> Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated datasets. Being able to easily access and utilise these is crucial for allowing researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM datasets and/or creating new synthetic cryoEM datasets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning based algorithms for aiding numerous steps in the processing and reconstruction of experimental datasets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large datasets which can be cumbersome to curate and unwieldy to make use of. In this paper we present a suite of Python software packages which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or Alphafold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive datasets in a machine-learning compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labelling. These packages may be utilised independently or as building blocks in workflows. All are available in open source repositories and designed to be easily extensible to facilitate more advanced workflows if required. </p> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2503.13336" title="Abstract" id="2503.13336"> arXiv:2503.13336 </a> (cross-list from eess.SY) [<a href="/pdf/2503.13336" title="Download PDF" id="pdf-2503.13336" aria-labelledby="pdf-2503.13336">pdf</a>, <a href="https://arxiv.org/html/2503.13336v1" title="View HTML" id="html-2503.13336" aria-labelledby="html-2503.13336" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13336" title="Other formats" id="oth-2503.13336" aria-labelledby="oth-2503.13336">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Parameter Invariance Analysis of Moment Equations Using Dulmage-Mendelsohn Decomposition </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Igarashi,+A">Akito Igarashi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hori,+Y">Yutaka Hori</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Systems and Control (eess.SY)</span>; Molecular Networks (q-bio.MN) </div> <p class='mathjax'> Living organisms maintain stable functioning amid environmental fluctuations through homeostasis, a mechanism that preserves a system's behavior despite changes in environmental conditions. To elucidate homeostasis in stochastic biochemical reactions, theoretical tools for assessing population-level invariance under parameter perturbations are crucial. In this paper, we propose a systematic method for identifying the stationary moments that remain invariant under parameter perturbations by leveraging the structural properties of the stationary moment equations. A key step in this development is addressing the underdetermined nature of moment equations, which has traditionally made it difficult to characterize how stationary moments depend on system parameters. To overcome this, we utilize the Dulmage-Mendelsohn (DM) decomposition of the coefficient matrix to extract welldetermined subequations and reveal their hierarchical structure. Leveraging this structure, we identify stationary moments whose partial derivatives with respect to parameters are structurally zero, facilitating the exploration of fundamental constraints that govern homeostatic behavior in stochastic biochemical systems. </p> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2503.13399" title="Abstract" id="2503.13399"> arXiv:2503.13399 </a> (cross-list from cs.CV) [<a href="/pdf/2503.13399" title="Download PDF" id="pdf-2503.13399" aria-labelledby="pdf-2503.13399">pdf</a>, <a href="https://arxiv.org/html/2503.13399v1" title="View HTML" id="html-2503.13399" aria-labelledby="html-2503.13399" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.13399" title="Other formats" id="oth-2503.13399" aria-labelledby="oth-2503.13399">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Burgess,+J">James Burgess</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nirschl,+J+J">Jeffrey J Nirschl</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bravo-S%C3%A1nchez,+L">Laura Bravo-S谩nchez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lozano,+A">Alejandro Lozano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gupte,+S+R">Sanket Rajan Gupte</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Galaz-Montoya,+J+G">Jesus G. Galaz-Montoya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yuhui Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Su,+Y">Yuchang Su</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bhowmik,+D">Disha Bhowmik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Coman,+Z">Zachary Coman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hasan,+S+M">Sarina M. Hasan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Johannesson,+A">Alexandra Johannesson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Leineweber,+W+D">William D. Leineweber</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nair,+M+G">Malvika G Nair</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yarlagadda,+R">Ridhi Yarlagadda</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zuraski,+C">Connor Zuraski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chiu,+W">Wah Chiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cohen,+S">Sarah Cohen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hansen,+J+N">Jan N. Hansen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Leonetti,+M+D">Manuel D Leonetti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+C">Chad Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lundberg,+E">Emma Lundberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yeung-Levy,+S">Serena Yeung-Levy</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> CVPR 2025 (Conference on Computer Vision and Pattern Recognition) Project page at <a href="https://jmhb0.github.io/microvqa" rel="external noopener nofollow" class="link-external link-https">this https URL</a> Benchmark at <a href="https://huggingface.co/datasets/jmhb/microvqa" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Cell Behavior (q-bio.CB) </div> <p class='mathjax'> Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based `RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53\%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at <a href="https://huggingface.co/datasets/jmhb/microvqa" rel="external noopener nofollow" class="link-external link-https">this https URL</a>, and project page at <a href="https://jmhb0.github.io/microvqa" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 17 of 17 entries)</h3> <dt> <a name='item23'>[23]</a> <a href ="/abs/2404.11143" title="Abstract" id="2404.11143"> arXiv:2404.11143 </a> (replaced) [<a href="/pdf/2404.11143" title="Download PDF" id="pdf-2404.11143" aria-labelledby="pdf-2404.11143">pdf</a>, <a href="https://arxiv.org/html/2404.11143v2" title="View HTML" id="html-2404.11143" aria-labelledby="html-2404.11143" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2404.11143" title="Other formats" id="oth-2404.11143" aria-labelledby="oth-2404.11143">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Functional Brain-to-Brain Transformation with No Shared Data </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wasserman,+N">Navve Wasserman</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Beliy,+R">Roman Beliy</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Urbach,+R">Roy Urbach</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Irani,+M">Michal Irani</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 16 pages, 8 figures, 1 table. In review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span> </div> <p class='mathjax'> Combining Functional MRI (fMRI) data across different subjects and datasets is crucial for many neuroscience tasks. Relying solely on shared anatomy for brain-to-brain mapping is inadequate. Existing functional transformation methods thus depend on shared stimuli across subjects and fMRI datasets, which are often unavailable. In this paper, we propose an approach for computing functional brain-to-brain transformations without any shared data, a feat not previously achieved in functional transformations. This presents exciting research prospects for merging and enriching diverse datasets, even when they involve distinct stimuli that were collected using different fMRI machines of varying resolutions (e.g., 3-Tesla and 7-Tesla). Our approach combines brain-to-brain transformation with image-to-fMRI encoders, thus enabling to learn functional transformations on visual stimuli to which subjects were never exposed. Furthermore, we demonstrate the applicability of our method for improving image-to-fMRI encoding of subjects scanned on older low-resolution 3T fMRI datasets, by using a new high-resolution 7T fMRI dataset (scanned on different subjects and different stimuli). </p> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2411.04130" title="Abstract" id="2411.04130"> arXiv:2411.04130 </a> (replaced) [<a href="/pdf/2411.04130" title="Download PDF" id="pdf-2411.04130" aria-labelledby="pdf-2411.04130">pdf</a>, <a href="https://arxiv.org/html/2411.04130v2" title="View HTML" id="html-2411.04130" aria-labelledby="html-2411.04130" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.04130" title="Other formats" id="oth-2411.04130" aria-labelledby="oth-2411.04130">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Adams,+K">Keir Adams</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Abeywardane,+K">Kento Abeywardane</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Fromer,+J">Jenna Fromer</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Coley,+C+W">Connor W. Coley</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Biomolecules (q-bio.BM)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD's ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD's potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging. </p> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2411.06802" title="Abstract" id="2411.06802"> arXiv:2411.06802 </a> (replaced) [<a href="/pdf/2411.06802" title="Download PDF" id="pdf-2411.06802" aria-labelledby="pdf-2411.06802">pdf</a>, <a href="https://arxiv.org/html/2411.06802v3" title="View HTML" id="html-2411.06802" aria-labelledby="html-2411.06802" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.06802" title="Other formats" id="oth-2411.06802" aria-labelledby="oth-2411.06802">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Shao,+Y">Yuxiu Shao</a> (1 and 2), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Dahmen,+D">David Dahmen</a> (3), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Recanatesi,+S">Stefano Recanatesi</a> (4), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Shea-Brown,+E">Eric Shea-Brown</a> (5 and 6), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Ostojic,+S">Srdjan Ostojic</a> (2) ((1) School of Systems Science, Beijing Normal University, China, (2) Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, France, (3) Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience, J眉lich Research Center, Germany, (4) Technion, Israel Institute of Technology, Israel, (5) Department of Applied Mathematics and Computational Neuroscience Center, University of Washington, USA, (6) Allen Institute for Brain Science, USA)</div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 30 pages, 17 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span>; Disordered Systems and Neural Networks (cond-mat.dis-nn); Neural and Evolutionary Computing (cs.NE) </div> <p class='mathjax'> Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of specific connectivity patterns that go well beyond what is expected from independent connections. While decades of influential research have demonstrated the strong role of the basic EI cell type structure, to which extent additional connectivity features influence dynamics remains to be fully determined. Here we examine the effects of pairwise connectivity motifs on the linear dynamics in EI networks using an analytical framework that approximates the connectivity in terms of low-rank structures. This low-rank approximation is based on a mathematical derivation of the dominant eigenvalues of the connectivity matrix and predicts the impact on responses to external inputs of connectivity motifs and their interactions with cell-type structure. Our results reveal that a particular pattern of connectivity, chain motifs, have a much stronger impact on dominant eigenmodes than other pairwise motifs. An overrepresentation of chain motifs induces a strong positive eigenvalue in inhibition-dominated networks and generates a potential instability that requires revisiting the classical excitation-inhibition balance criteria. Examining effects of external inputs, we show that chain motifs can on their own induce paradoxical responses where an increased input to inhibitory neurons leads to a decrease in their activity due to the recurrent feedback. These findings have direct implications for the interpretation of experiments in which responses to optogenetic perturbations are measured and used to infer the dynamical regime of cortical circuits. </p> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2412.06847" title="Abstract" id="2412.06847"> arXiv:2412.06847 </a> (replaced) [<a href="/pdf/2412.06847" title="Download PDF" id="pdf-2412.06847" aria-labelledby="pdf-2412.06847">pdf</a>, <a href="https://arxiv.org/html/2412.06847v2" title="View HTML" id="html-2412.06847" aria-labelledby="html-2412.06847" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2412.06847" title="Other formats" id="oth-2412.06847" aria-labelledby="oth-2412.06847">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Guo,+S">Siyuan Guo</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wang,+L">Lexuan Wang</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Jin,+C">Chang Jin</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Wang,+J">Jinxian Wang</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Peng,+H">Han Peng</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Shi,+H">Huayang Shi</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Li,+W">Wengen Li</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Guan,+J">Jihong Guan</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Zhou,+S">Shuigeng Zhou</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Quantitative Methods (q-bio.QM)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> This paper introduces M$^{3}$-20M, a large-scale Multi-Modal Molecule dataset that contains over 20 million molecules, with the data mainly being integrated from existing databases and partially generated by large language models. Designed to support AI-driven drug design and discovery, M$^{3}$-20M is 71 times more in the number of molecules than the largest existing dataset, providing an unprecedented scale that can highly benefit the training or fine-tuning of models, including large language models for drug design and discovery tasks. This dataset integrates one-dimensional SMILES, two-dimensional molecular graphs, three-dimensional molecular structures, physicochemical properties, and textual descriptions collected through web crawling and generated using GPT-3.5, offering a comprehensive view of each molecule. To demonstrate the power of M$^{3}$-20M in drug design and discovery, we conduct extensive experiments on two key tasks: molecule generation and molecular property prediction, using large language models including GLM4, GPT-3.5, GPT-4, and Llama3-8b. Our experimental results show that M$^{3}$-20M can significantly boost model performance in both tasks. Specifically, it enables the models to generate more diverse and valid molecular structures and achieve higher property prediction accuracy than existing single-modal datasets, which validates the value and potential of M$^{3}$-20M in supporting AI-driven drug design and discovery. The dataset is available at <a href="https://github.com/bz99bz/M-3" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2501.01966" title="Abstract" id="2501.01966"> arXiv:2501.01966 </a> (replaced) [<a href="/pdf/2501.01966" title="Download PDF" id="pdf-2501.01966" aria-labelledby="pdf-2501.01966">pdf</a>, <a href="/format/2501.01966" title="Other formats" id="oth-2501.01966" aria-labelledby="oth-2501.01966">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The impact of motor and non-motor symptoms fluctuations on health-related quality of life in people with functional motor disorder </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Jir%C3%A1sek,+M">Martin Jir谩sek</a> (1,2), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Sieger,+T">Tom谩拧 Sieger</a> (1,3), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Chaloupkov%C3%A1,+G">Gabriela Chaloupkov谩</a> (1), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Nov%C3%A1kov%C3%A1,+L">Lucia Nov谩kov谩</a> (1), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Sojka,+P">Petr Sojka</a> (1), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Edwards,+M+J">Mark J Edwards</a> (4), <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Serranov%C3%A1,+T">Tereza Serranov谩</a> (1) ((1) Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague/Czech Republic, (2) Department of Rehabilitation and Sports Medicine, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague/Czech Republic, (3) Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague/Czech Republic, (4) King's College London, Institute of Psychiatry, Psychology &amp; Neuroscience, Department of Basic &amp; Clinical Neuroscience, London/United Kingdom)</div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 21 pages, 3 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span> </div> <p class='mathjax'> Objective: To assess the effect of overall, between- and within-day subjectively rated fluctuations in motor and non-motor symptoms in people with functional motor disorder (FMD) on the health-related quality of life (HRQoL). Background: FMD is a complex condition characterized by fluctuating motor and non-motor symptoms that may negatively impact HRQoL. Methods: Seventy-seven patients (54 females, mean age 45.4 (SD 10.4) years) with a clinically established diagnosis of FMD, including weakness, completed symptom diaries, rating the severity of motor and non-motor symptoms (i.e., pain, fatigue, mood, cognitive difficulties) on a 10-point numerical scale three times daily for seven consecutive days. HRQoL was assessed using the SF-36 questionnaire. For the analysis, fluctuation magnitude was defined in terms of the variability in self-reported symptom scores. Results: The mental component of SF-36 was jointly predicted by the overall severity scores (t(74) = -3.61, P < 0.001) and overall general fluctuations (t(74) = -2.98, P = 0.004). The physical SF-36 was found to be related only to the overall symptom severity scores (t(74) = -7.09, P < 0.001), but not to the overall fluctuations. The assessment of the impact of different components showed that the mental component of SF-36 was significantly influenced by the combined effect of average fatigue (t(73) = -3.86, P < 0.001), between-day cognitive symptoms fluctuations (t(73) = -3.22, P = 0.002), and within-day mood fluctuations (t(73) = -2.48, P = 0.015). Conclusions: This study demonstrated the impact of self-reported symptom fluctuations across multiple motor and non-motor domains on mental but not physical HRQoL in FMD and highlighted the importance of assessing and managing fluctuations in clinical practice. </p> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2501.02634" title="Abstract" id="2501.02634"> arXiv:2501.02634 </a> (replaced) [<a href="/pdf/2501.02634" title="Download PDF" id="pdf-2501.02634" aria-labelledby="pdf-2501.02634">pdf</a>, <a href="https://arxiv.org/html/2501.02634v4" title="View HTML" id="html-2501.02634" aria-labelledby="html-2501.02634" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2501.02634" title="Other formats" id="oth-2501.02634" aria-labelledby="oth-2501.02634">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Inference of Asynchronous Boolean Network Models </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Karlebach,+G">Guy Karlebach</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Molecular Networks (q-bio.MN)</span> </div> <p class='mathjax'> Associations between phenotype and genomic and epigenomic markers are often derived by correlation. Systems Biology aims to make more robust connections and uncover broader insights by modeling the cellular mechanisms that produce a phenotype. The question of choosing the modeling methodology is of central importance. A model that does not capture biological reality closely enough will not explain the system's behavior. At the same time, highly detailed models suffer from computational limitations and are likely to overfit the data. Boolean networks strike a balance between complexity and descriptiveness and thus have received increasing interest. We previously described an algorithm for fitting Boolean networks to high-throughout experimental data that finds the optimal network with respect to the information in a given dataset. In this work, we describe a simple extension that enables the modeling of asynchronous dynamics, i.e. different reaction times for different network nodes. In addition, we present a new method for pseudo-time assignment for single-cell RNA sequencing data that is derived from the modeling procedure. Our approach greatly simplifies the construction of Boolean network models for time-series datasets, where asynchronicity often occurs. We demonstrate our methodology by integrating real data from transcriptomics experiments. These results significantly expand the applicability of the Boolean network model to experimental data. </p> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2503.07837" title="Abstract" id="2503.07837"> arXiv:2503.07837 </a> (replaced) [<a href="/pdf/2503.07837" title="Download PDF" id="pdf-2503.07837" aria-labelledby="pdf-2503.07837">pdf</a>, <a href="https://arxiv.org/html/2503.07837v2" title="View HTML" id="html-2503.07837" aria-labelledby="html-2503.07837" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.07837" title="Other formats" id="oth-2503.07837" aria-labelledby="oth-2503.07837">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Slowing translation to avoid ribosome population extinction and maintain stable allocation at slow growth rates </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Goberman,+D">Dotan Goberman</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Roy,+A">Anjan Roy</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Pugatch,+R">Rami Pugatch</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 3 figures (a few typos corrected) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Other Quantitative Biology (q-bio.OT)</span>; Biological Physics (physics.bio-ph) </div> <p class='mathjax'> To double the cellular population of ribosomes, a fraction of the active ribosomes is allocated to synthesize ribosomal proteins. Subsequently, these ribosomal proteins enter the ribosome self-assembly process, synthesizing new ribosomes and forming the well-known ribosome autocatalytic subcycle. Neglecting ribosome lifetime and the duration of the self-assembly process, the doubling rate of all cellular biomass can be equated with the fraction of ribosomes allocated to synthesize an essential ribosomal protein times its synthesis rate. However, ribosomes have a finite lifetime, and the assembly process has a finite duration. Furthermore, the number of ribosomes is known to decrease with slow growth rates. The finite lifetime of ribosomes and the decline in their numbers present a challenge in sustaining slow growth solely through controlling the allocation of ribosomes to synthesize more ribosomal proteins. When the number of ribosomes allocated per mRNA of an essential ribosomal protein is approximately one, the resulting fluctuations in the production rate of new ribosomes increase, causing a potential risk that the actual production rate will fall below the ribosome death rate. Thus, in this regime, a significant risk of extinction of the ribosome population emerges. To mitigate this risk, we suggest that the ribosome translation speed is used as an alternative control parameter, which facilitates the maintenance of slow growth rates with a larger ribosome pool. We clarify the observed reduction in translation speed at harsh environments in E. coli and C. Glutamicum, explore other mitigation strategies, and suggest additional falsifiable predictions of our model. </p> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2309.07261" title="Abstract" id="2309.07261"> arXiv:2309.07261 </a> (replaced) [<a href="/pdf/2309.07261" title="Download PDF" id="pdf-2309.07261" aria-labelledby="pdf-2309.07261">pdf</a>, <a href="https://arxiv.org/html/2309.07261v5" title="View HTML" id="html-2309.07261" aria-labelledby="html-2309.07261" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2309.07261" title="Other formats" id="oth-2309.07261" aria-labelledby="oth-2309.07261">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Simultaneous inference for generalized linear models with unmeasured confounders </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Du,+J">Jin-Hong Du</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Wasserman,+L">Larry Wasserman</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Roeder,+K">Kathryn Roeder</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Main text: 23 pages and 6 figures; appendix: 50 pages and 12 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Machine Learning (cs.LG); Genomics (q-bio.GN); Machine Learning (stat.ML) </div> <p class='mathjax'> Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It begins by disentangling marginal and uncorrelated confounding effects to recover the latent coefficients. Subsequently, latent factors and primary effects are jointly estimated through lasso-type optimization. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish the identification conditions of various effects and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model. </p> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2309.15604" title="Abstract" id="2309.15604"> arXiv:2309.15604 </a> (replaced) [<a href="/pdf/2309.15604" title="Download PDF" id="pdf-2309.15604" aria-labelledby="pdf-2309.15604">pdf</a>, <a href="https://arxiv.org/html/2309.15604v2" title="View HTML" id="html-2309.15604" aria-labelledby="html-2309.15604" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2309.15604" title="Other formats" id="oth-2309.15604" aria-labelledby="oth-2309.15604">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Entropic Matching for Expectation Propagation of Markov Jump Processes </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Eich,+Y">Yannick Eich</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alt,+B">Bastian Alt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koeppl,+H">Heinz Koeppl</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> AISTATS 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Molecular Networks (q-bio.MN); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML) </div> <p class='mathjax'> We propose a novel, tractable latent state inference scheme for Markov jump processes, for which exact inference is often intractable. Our approach is based on an entropic matching framework that can be embedded into the well-known expectation propagation algorithm. We demonstrate the effectiveness of our method by providing closed-form results for a simple family of approximate distributions and apply it to the general class of chemical reaction networks, which are a crucial tool for modeling in systems biology. Moreover, we derive closed-form expressions for point estimation of the underlying parameters using an approximate expectation maximization procedure. We evaluate our method across various chemical reaction networks and compare it to multiple baseline approaches, demonstrating superior performance in approximating the mean of the posterior process. Finally, we discuss the limitations of our method and potential avenues for future improvement, highlighting its promising direction for addressing complex continuous-time Bayesian inference problems. </p> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2406.03044" title="Abstract" id="2406.03044"> arXiv:2406.03044 </a> (replaced) [<a href="/pdf/2406.03044" title="Download PDF" id="pdf-2406.03044" aria-labelledby="pdf-2406.03044">pdf</a>, <a href="https://arxiv.org/html/2406.03044v3" title="View HTML" id="html-2406.03044" aria-labelledby="html-2406.03044" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2406.03044" title="Other formats" id="oth-2406.03044" aria-labelledby="oth-2406.03044">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Population Transformer: Learning Population-level Representations of Neural Activity </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chau,+G">Geeling Chau</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+C">Christopher Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Talukder,+S">Sabera Talukder</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Subramaniam,+V">Vighnesh Subramaniam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soedarmadji,+S">Saraswati Soedarmadji</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yue,+Y">Yisong Yue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Katz,+B">Boris Katz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barbu,+A">Andrei Barbu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 22 pages, 17 figures, ICLR 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with neural time-series data, namely, sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) stacks on top of pretrained temporal embeddings and enhances downstream decoding by enabling learned aggregation of multiple spatially-sparse data channels. The pretrained PopT lowers the amount of data required for downstream decoding experiments, while increasing accuracy, even on held-out subjects and tasks. Compared to end-to-end methods, this approach is computationally lightweight, while achieving similar or better decoding performance. We further show how our framework is generalizable to multiple time-series embeddings and neural data modalities. Beyond decoding, we interpret the pretrained and fine-tuned PopT models to show how they can be used to extract neuroscience insights from large amounts of data. We release our code as well as a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability. Code is available at <a href="https://github.com/czlwang/PopulationTransformer" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2409.02476" title="Abstract" id="2409.02476"> arXiv:2409.02476 </a> (replaced) [<a href="/pdf/2409.02476" title="Download PDF" id="pdf-2409.02476" aria-labelledby="pdf-2409.02476">pdf</a>, <a href="https://arxiv.org/html/2409.02476v2" title="View HTML" id="html-2409.02476" aria-labelledby="html-2409.02476" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.02476" title="Other formats" id="oth-2409.02476" aria-labelledby="oth-2409.02476">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Phase changes of the flow rate in the vertebral artery caused by debranching thoracic endovascular aortic repair: effects of flow path and local vessel stiffness on vertebral arterial pulsation </div> <div class='list-authors'><a href="https://arxiv.org/search/physics?searchtype=author&query=Takeishia,+N">Naoki Takeishia</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Jialongb,+L">Li Jialongb</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Yokoyamac,+N">Naoto Yokoyamac</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Gotoe,+T">Takasumi Gotoe</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Tanakad,+H">Hisashi Tanakad</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Miyagawa,+S">Shigeru Miyagawa</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Wada,+S">Shigeo Wada</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Biological Physics (physics.bio-ph)</span>; Tissues and Organs (q-bio.TO) </div> <p class='mathjax'> Despite numerous studies on cerebral arterial blood flow, there has not yet been a comprehensive description of hemodynamics in patients undergoing debranching thoracic endovascular aortic repair (dTEVAR), a promising surgical option for aortic arch aneurysms. A phase delay of the flow rate in the left vertebral artery (LVA) in patients after dTEVAR compared to those before was experimentally observed, while the phase in the right vertebral artery (RVA) remained almost the same before and after surgery. Since this surgical intervention included stent graft implantation and extra-anatomical bypass, it was expected that the intracranial hemodynamic changes due to dTEVAR were coupled with fluid flow and pulse waves in cerebral arteries. To clarify this issue, A one-dimensional model (1D) was used to numerically investigate the relative contribution (i.e., local vessel stiffness and flow path changes) of the VA flow rate to the phase difference. The numerical results demonstrated a phase delay of flow rate in the LVA but not the RVA in postoperative patients undergoing dTEVAR relative to preoperative patients. The results further showed that the primary factor affecting the phase delay of the flow rate in the LVA after surgery compared to that before was the bypass, i.e., alteration of flow path, rather than stent grafting, i.e., the change in local vessel stiffness. The numerical results provide insights into hemodynamics in postoperative patients undergoing dTEVAR, as well as knowledge about therapeutic decisions. </p> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2409.19583" title="Abstract" id="2409.19583"> arXiv:2409.19583 </a> (replaced) [<a href="/pdf/2409.19583" title="Download PDF" id="pdf-2409.19583" aria-labelledby="pdf-2409.19583">pdf</a>, <a href="https://arxiv.org/html/2409.19583v3" title="View HTML" id="html-2409.19583" aria-labelledby="html-2409.19583" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.19583" title="Other formats" id="oth-2409.19583" aria-labelledby="oth-2409.19583">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Brain Tumor Classification on MRI in Light of Molecular Markers </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+J">Jun Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yuan,+G">Geng Yuan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zeng,+W">Weihao Zeng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tang,+H">Hao Tang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+W">Wenbin Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lin,+X">Xue Lin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+X">XiaoLin Xu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Huang,+D">Dong Huang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+Y">Yanzhi Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICAI'22 - The 24th International Conference on Artificial Intelligence, The 2022 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'22), Las Vegas, USA. The paper acceptance rate 17% for regular papers. The publication of the CSCE 2022 conference proceedings has been delayed due to the pandemic </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Springer Nature - Book Series: Transactions on Computational Science & Computational Intelligence, 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planning and patient follow-up. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection. Although public networks such as RestNet and AlexNet can effectively diagnose brain cancers using transfer learning, the model includes quite a few weights that have nothing to do with medical images. As a result, the diagnostic results are unreliable by the transfer learning model. To deal with the problem of trustworthiness, we create the model from the ground up, rather than depending on a pre-trained model. To enable flexibility, we combined convolution stacking with a dropout and full connect operation, it improved performance by reducing overfitting. During model training, we also supplement the given dataset and inject Gaussian noise. We use three--fold cross-validation to train the best selection model. Comparing InceptionV3, VGG16, and MobileNetV2 fine-tuned with pre-trained models, our model produces better results. On an validation set of 125 codeletion vs. 31 not codeletion images, the proposed network achieves 96.37\% percent F1-score, 97.46\% percent precision, and 96.34\% percent recall when classifying 1p/19q codeletion and not codeletion images. </p> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2411.06518" title="Abstract" id="2411.06518"> arXiv:2411.06518 </a> (replaced) [<a href="/pdf/2411.06518" title="Download PDF" id="pdf-2411.06518" aria-labelledby="pdf-2411.06518">pdf</a>, <a href="https://arxiv.org/html/2411.06518v3" title="View HTML" id="html-2411.06518" aria-labelledby="html-2411.06518" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.06518" title="Other formats" id="oth-2411.06518" aria-labelledby="oth-2411.06518">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Causal Representation Learning from Multimodal Biomedical Observations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Y">Yuewen Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kong,+L">Lingjing Kong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+G">Guangyi Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+L">Loka Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luo,+G">Gongxu Luo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Z">Zijian Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yixuan Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+Y">Yujia Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+M">Mengyue Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stojanov,+P">Petar Stojanov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Segal,+E">Eran Segal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xing,+E+P">Eric P. Xing</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+K">Kun Zhang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Quantitative Methods (q-bio.QM); Methodology (stat.ME) </div> <p class='mathjax'> Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, current machine learning (ML) models designed to analyze these datasets often lack interpretability and identifiability guarantees, which are essential for biomedical research. Recent advances in causal representation learning have shown promise in identifying interpretable latent causal variables with formal theoretical guarantees. Unfortunately, most current work on multimodal distributions either relies on restrictive parametric assumptions or yields only coarse identification results, limiting their applicability to biomedical research that favors a detailed understanding of the mechanisms. <br>In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets. Theoretically, we consider a nonparametric latent distribution (c.f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities. We establish identifiability guarantees for each latent component, extending the subspace identification results from previous work. Our key theoretical contribution is the structural sparsity of causal connections between modalities, which, as we will discuss, is natural for a large collection of biomedical systems. Empirically, we present a practical framework to instantiate our theoretical insights. We demonstrate the effectiveness of our approach through extensive experiments on both numerical and synthetic datasets. Results on a real-world human phenotype dataset are consistent with established biomedical research, validating our theoretical and methodological framework. </p> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2411.06913" title="Abstract" id="2411.06913"> arXiv:2411.06913 </a> (replaced) [<a href="/pdf/2411.06913" title="Download PDF" id="pdf-2411.06913" aria-labelledby="pdf-2411.06913">pdf</a>, <a href="/format/2411.06913" title="Other formats" id="oth-2411.06913" aria-labelledby="oth-2411.06913">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Penn,+J">Jordan Penn</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Gunderson,+L+M">Lee M. Gunderson</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Bravo-Hermsdorff,+G">Gecia Bravo-Hermsdorff</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Silva,+R">Ricardo Silva</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Watson,+D+S">David S. Watson</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 4 figures. Substantial revision following acceptance to AISTATS 2025, including new experiments </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Statistics Theory (math.ST); Quantitative Methods (q-bio.QM) </div> <p class='mathjax'> Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclusively through the exposure and be unconfounded with the outcome. We present a framework for relaxing either or both of these strong assumptions with tuneable and interpretable budget constraints. Our algorithm returns a feasible set of causal effects that can be identified exactly given relevant covariance parameters. The feasible set may be disconnected but is a finite union of convex subsets. We discuss conditions under which this set is sharp, i.e., contains all and only effects consistent with the background assumptions and the joint distribution of observable variables. Our method applies to a wide class of semiparametric models, and we demonstrate how its ability to select specific subsets of instruments confers an advantage over convex relaxations in both linear and nonlinear settings. We also adapt our algorithm to form confidence sets that are asymptotically valid under a common statistical assumption from the Mendelian randomization literature. </p> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2412.12134" title="Abstract" id="2412.12134"> arXiv:2412.12134 </a> (replaced) [<a href="/pdf/2412.12134" title="Download PDF" id="pdf-2412.12134" aria-labelledby="pdf-2412.12134">pdf</a>, <a href="https://arxiv.org/html/2412.12134v2" title="View HTML" id="html-2412.12134" aria-labelledby="html-2412.12134" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2412.12134" title="Other formats" id="oth-2412.12134" aria-labelledby="oth-2412.12134">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Chaotic dynamics and fractal geometry in ring lattice systems of nonchaotic Rulkov neurons </div> <div class='list-authors'><a href="https://arxiv.org/search/nlin?searchtype=author&query=Le,+B+B">Brandon B. Le</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 15 pages, 7 figures. v2: more details, added appendix </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Chaotic Dynamics (nlin.CD)</span>; Dynamical Systems (math.DS); Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> This paper investigates the complex dynamics and fractal attractors that emerge from a 60-dimensional ring lattice system of electrically coupled nonchaotic Rulkov neurons. Although networks of chaotic Rulkov neurons are well studied, systems of nonchaotic Rulkov neurons have not been extensively explored due to the piecewise complexity of the nonchaotic Rulkov map. We find rich dynamics emerge from the electrical coupling of regular spiking Rulkov neurons, including chaotic spiking, synchronized chaotic bursting, and complete chaos. By varying the electrical coupling strength between the neurons, we also discover general trends in the maximal Lyapunov exponent across different regimes of the ring lattice system. By means of the Kaplan-Yorke conjecture, we also examine the fractal geometry of the ring system's high-dimensional chaotic attractors and find various correlations and differences between the fractal dimensions of the attractors and the chaotic dynamics on them. </p> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2502.02713" title="Abstract" id="2502.02713"> arXiv:2502.02713 </a> (replaced) [<a href="/pdf/2502.02713" title="Download PDF" id="pdf-2502.02713" aria-labelledby="pdf-2502.02713">pdf</a>, <a href="https://arxiv.org/html/2502.02713v2" title="View HTML" id="html-2502.02713" aria-labelledby="html-2502.02713" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.02713" title="Other formats" id="oth-2502.02713" aria-labelledby="oth-2502.02713">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> When Less is More: Evolutionary Dynamics of Deception in a Sender-Receiver Game </div> <div class='list-authors'><a href="https://arxiv.org/search/physics?searchtype=author&query=Vieira,+E+V+M">Eduardo V. M. Vieira</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Fontanari,+J+F">Jos茅 F. Fontanari</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Physics and Society (physics.soc-ph)</span>; Adaptation and Self-Organizing Systems (nlin.AO); Populations and Evolution (q-bio.PE) </div> <p class='mathjax'> The spread of disinformation poses a significant threat to societal well-being. We analyze this phenomenon using an evolutionary game theory model of the sender-receiver game, where senders aim to mislead receivers and receivers aim to discern the truth. Using a combination of replicator equations, finite-size scaling analysis, and extensive Monte Carlo simulations, we investigate the long-term evolutionary dynamics of this game. Our central finding is a counterintuitive threshold phenomenon: the role (sender or receiver) with the larger difference in payoffs between successful and unsuccessful interactions is surprisingly more likely to lose in the long run. We show that this effect is robust across different parameter values and arises from the interplay between the relative speeds of evolution of the two roles and the ability of the slower evolving role to exploit the fixed strategy of the faster evolving role. Moreover, for finite populations we find that the initially less frequent strategy of the slower role is more likely to fixate in the population. The initially rarer strategy in the less-rewarded role is, paradoxically, more likely to prevail. </p> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2502.20580" title="Abstract" id="2502.20580"> arXiv:2502.20580 </a> (replaced) [<a href="/pdf/2502.20580" title="Download PDF" id="pdf-2502.20580" aria-labelledby="pdf-2502.20580">pdf</a>, <a href="https://arxiv.org/html/2502.20580v2" title="View HTML" id="html-2502.20580" aria-labelledby="html-2502.20580" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.20580" title="Other formats" id="oth-2502.20580" aria-labelledby="oth-2502.20580">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Training Large Neural Networks With Low-Dimensional Error Feedback </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hanut,+M">Maher Hanut</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kadmon,+J">Jonathan Kadmon</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> Training deep neural networks typically relies on backpropagating high dimensional error signals a computationally intensive process with little evidence supporting its implementation in the brain. However, since most tasks involve low-dimensional outputs, we propose that low-dimensional error signals may suffice for effective learning. To test this hypothesis, we introduce a novel local learning rule based on Feedback Alignment that leverages indirect, low-dimensional error feedback to train large networks. Our method decouples the backward pass from the forward pass, enabling precise control over error signal dimensionality while maintaining high-dimensional representations. We begin with a detailed theoretical derivation for linear networks, which forms the foundation of our learning framework, and extend our approach to nonlinear, convolutional, and transformer architectures. Remarkably, we demonstrate that even minimal error dimensionality on the order of the task dimensionality can achieve performance matching that of traditional backpropagation. Furthermore, our rule enables efficient training of convolutional networks, which have previously been resistant to Feedback Alignment methods, with minimal error. This breakthrough not only paves the way toward more biologically accurate models of learning but also challenges the conventional reliance on high-dimensional gradient signals in neural network training. Our findings suggest that low-dimensional error signals can be as effective as high-dimensional ones, prompting a reevaluation of gradient-based learning in high-dimensional systems. Ultimately, our work offers a fresh perspective on neural network optimization and contributes to understanding learning mechanisms in both artificial and biological systems. </p> </div> </dd> </dl> <div class='paging'>Total of 39 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/q-bio/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em"> <!-- Macro-Column 1 --> <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div> <!-- End Macro-Column 1 --> <!-- Macro-Column 2 --> <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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