Hongzhuo Chen, Zhanliang Wang, Florent Pollet, Mian Umair Ahsan, Joshua Bie, Tzung-Chien Hsieh, Peter Krawitz, Cong Liu, Wendy K Chung, Chunhua Weng, Gamze Gürsoy, Kai Wang
{"title":"FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases.","authors":"Hongzhuo Chen, Zhanliang Wang, Florent Pollet, Mian Umair Ahsan, Joshua Bie, Tzung-Chien Hsieh, Peter Krawitz, Cong Liu, Wendy K Chung, Chunhua Weng, Gamze Gürsoy, Kai Wang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology rely largely on qualitative clinical observation and free-text descriptions, which are often subjective, non-standardized, and difficult to reproduce across observers and institutions. Although the Human Phenotype Ontology (HPO) provides controlled terms for describing facial features, these terms are typically categorical rather than quantitative and may vary depending on examiner experience and interpretation. Here, we present FaceKit, a computational framework for quantitative facial phenotyping from frontal facial photographs. FaceKit extracts standardized measurements of facial landmarks and derived 120 morphological features, then reports feature-level z-scores representing deviation from population reference distributions. The reference distributions are built from the FairFace dataset spanning diverse ancestral groups. We evaluated FaceKit on a curated subset of the GestaltMatcher Database covering 50 rare-disease cohorts. In addition to quantitative facial analysis, FaceKit includes synthetic facial image generation to support rare disease model development and data augmentation. We also performed privacy evaluation to assess whether synthetic images reveal identifiable information from real patient photographs and could compromise patient privacy. Across disease case studies, FaceKit-derived quantitative measurements captured known facial features associated with rare genetic disorders and provided objective support for clinical phenotyping. Together, these results establish FaceKit as a useful tool for quantitative phenotyping, and has the potential to improve rare disease diagnosis, support genotype-phenotype studies, and enable more reproducible clinical characterization across diverse patient populations.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Shortcut to Structure in AlphaFold 3.","authors":"Jonathan Feldman, Jeffrey Skolnick","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>AlphaFold 3 predicts protein structures with remarkable accuracy, yet how structural information emerges within the model remains poorly understood. Here, through causal interventions on internal representations and direct probing of every Pairformer block, we trace the formation of global protein geometry and identify the multiple sequence alignment (MSA) as a structural shortcut to the fold. Removing the MSA largely preserves local secondary structure while disrupting the long-range relationships that define global topology. Restoring the MSA-enriched pair representation at only forty residues recovers most of this lost organization, including at pairs never directly modified. This contribution depends on the detailed direction of the MSA module's output rather than its magnitude. The Pairformer rapidly converts this signal into global geometry: the final fold becomes recoverable by approximately block 9 of 48 for a majority of proteins, roughly twenty-seven blocks before the model's decoder can render it, whereas without the MSA it remains inaccessible for most proteins throughout the pass. Which homologs are supplied shapes this trajectory more strongly than which query is supplied; it persists for a designed query that never evolved but collapses for a shuffled sequence. Most importantly, an alignment built for a different protein that shares the fold, supplied only at the structurally corresponding columns, raises the median TM-score against experiment from 0.44 to 0.72, while the same alignment shifted a few residues along the chain performs worse than supplying no alignment at all. What AlphaFold 3 reads from an alignment is therefore a description of the fold itself, transferable between proteins that share one, rather than the query's own evolutionary history. This explains both its accuracy and the limits of what it has solved.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem.","authors":"Rui-Xi Wang, Runzhong Wang, Connor W Coley","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment-based models follow a two-stage paradigm -- first generating candidate fragments and then scoring them -- analogous to two-stage R-CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single-stage transformer-based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state-of-the-art by a significant margin, achieving 70.0% and 69.7% Top-1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from the previous SOTA of 64.0%) and 52.5% and 38.5% respectively on the NIST'20 dataset (from 33.2%). Furthermore, GLACIER provides nearly 8-fold inference speedup over our prior two-stage model. Code is available at https://github.com/coleygroup/ms-pred.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix Nieto-Del-Amor, Jingru Fu, J-Sebastian Muehlboeck, Eric Westman, Daniel Ferreira, Rodrigo Moreno
{"title":"NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI.","authors":"Felix Nieto-Del-Amor, Jingru Fu, J-Sebastian Muehlboeck, Eric Westman, Daniel Ferreira, Rodrigo Moreno","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, and treatment planning; however, existing methods are often limited to small datasets, single brain regions, or coarse class labels, thereby restricting their clinical utility and generalizability. Here, we present NeuroCBIR, a framework for fast and flexible retrieval of both whole-brain and region-specific 3D T1w MRI scans. A total of 103 cortical and subcortical regions are extracted to enable both whole-brain and region-level queries. NeuroCBIR leverages latent representations learned by a variational autoencoder (VAE) combined with contrastive learning, producing scan-specific embeddings that capture anatomical patterns. These embeddings were evaluated for subject re-identification, zero-shot age prediction, and zero-shot multi-class pathology stratification. Re-identification performance was high across both whole-brain and brain-region levels (mean average precision across the top-5 retrieved images (mAP@5) >= 98.4%), with robust generalization across datasets and acquisition conditions. While NeuroCBIR is not trained for age prediction or pathology stratification, zero-shot evaluations for these two tasks demonstrate that the embeddings encode meaningful information for downstream tasks. Embedding extraction on a 4-core CPU required approximately 18.7 s per scan, whereas similarity search was effectively instantaneous (less than 0.01 s). NeuroCBIR is publicly available for brain MRI with more than 26,000 precomputed T1w MRI embeddings. It supports reproducible research, region-specific flexibility, and clinically meaningful personalized diagnostic support. The software is available at https://github.com/minnelab/NeuroCBIR.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Broken scale symmetries in undercomplete linear autoencoders.","authors":"Farhad Pashakhanloo, Jacob A Zavatone-Veth","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Neural network loss landscapes have many symmetries, which are preserved by gradient flow but broken by finite-stepsize stochastic gradient descent (SGD). A canonical example of such a symmetry is scale in homogeneous networks: one can scale up the parameters in one layer and down in the next without changing the network output. Previous work has documented cases in which SGD breaks this symmetry in favor of balancing gradient noise or minimizing fluctuations. Here, we show that the solution geometry of undercomplete linear autoencoders instead selects a preferred sign for scale drift: on the PCA solution manifold, SGD favors large decoder weights. This directed scale drift occurs on a slow timescale, and its dynamics admit an analytically-tractable effective description. However, it cannot continue indefinitely: increasing scale eventually drives the dynamics towards a finite-stepsize stability boundary. The resulting solutions are sharper than a balanced baseline in the sense of the maximum eigenvalue of the loss Hessian, but different sharpness measures can move in opposing directions. Thus, undercomplete autoencoders give a concrete illustration of how loss geometry can convert residual gradient noise into directed motion along a manifold of functionally-equivalent solutions.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kerstin Howe, Dolores R Agius, Tyler Alioto, Jean-Marc Aury, Roberto Biello, Mark Blaxter, Matthew W Brown, Tom Brown, Bernat Burriel-Carranza, Haoyu Cheng, Dario Copetti, Solenne Correard, Fernando Cruz, Paul Davis, Aleksandra Denisova, Giulio Formenti, Pedro A F Galante, Caroline Howard, Kamil S Jaron, Erich D Jarvis, Sergei Kliver, Sergey Koren, Ilia J Leitch, Harris Lewin, Heng Li, Kerstin Lindblad-Toh, Jack A Medico, Matthieu Muffato, Zemin Ning, Javan Okendo, Shujun Ou, Marc Palmada-Flores, Brendan J Pinto, Aurora Ruiz-Herrera, Abdoallah Sharaf, Ying Sims, Jeramiah J Smith, Marco Sollitto, Cibele G Sotero-Caio, Marcela Uliano-Silva, Sibelle T Vilaça, Robert M Waterhouse, Melissa A Wilson, Chentao Yang, Guojie Zhang, Chenxi Zhou, Yang Zhou, Richard Durbin
{"title":"Generating eukaryotic reference genome assemblies: Earth BioGenome Project quality standards and recommendations.","authors":"Kerstin Howe, Dolores R Agius, Tyler Alioto, Jean-Marc Aury, Roberto Biello, Mark Blaxter, Matthew W Brown, Tom Brown, Bernat Burriel-Carranza, Haoyu Cheng, Dario Copetti, Solenne Correard, Fernando Cruz, Paul Davis, Aleksandra Denisova, Giulio Formenti, Pedro A F Galante, Caroline Howard, Kamil S Jaron, Erich D Jarvis, Sergei Kliver, Sergey Koren, Ilia J Leitch, Harris Lewin, Heng Li, Kerstin Lindblad-Toh, Jack A Medico, Matthieu Muffato, Zemin Ning, Javan Okendo, Shujun Ou, Marc Palmada-Flores, Brendan J Pinto, Aurora Ruiz-Herrera, Abdoallah Sharaf, Ying Sims, Jeramiah J Smith, Marco Sollitto, Cibele G Sotero-Caio, Marcela Uliano-Silva, Sibelle T Vilaça, Robert M Waterhouse, Melissa A Wilson, Chentao Yang, Guojie Zhang, Chenxi Zhou, Yang Zhou, Richard Durbin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Reference genome assemblies are foundational resources across the biological sciences as they begin to expose the fundamental building blocks of each species and the molecular toolkits vital for adaptation and survival. High-quality genomes enable insights into evolution, facilitate ecosystem monitoring and protection and conservation of species, access to new biomaterials and biomedicine, advances in agri- and aquaculture, and support planetary health. The amount of information gathered from a species' genome assembly is directly dependent on its quality. However, different sequencing technologies and software solutions generate a wide range of quality outcomes. Here, the Earth BioGenome Project's Sequencing and Assembly committee, together with a large community of researchers performing eukaryote sequencing and assembly, outlines quality standards for a \"reference\" assembly and formulates recommendations to ensure these standards are met.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"triangulax: Differentiable Simulations with Triangular Meshes across Soft Matter Physics.","authors":"Samuel Silliman, Qiwei Yu, Nikolas Claussen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many physical and biological systems -foams, membranes, elastic shells, or tissue sheets- can be mathematically described as 2D surfaces. These surfaces can host complex dynamics, like reaction-diffusion systems, and undergo drastic deformations. Here, we present triangulax, an open-source Python library for simulations and geometry processing that discretizes surfaces using triangular meshes available at https://github.com/nikolas-claussen/triangulax. It combines two features. First, discrete differential geometry provides coordinate-free discretization, which allows simulating strongly deforming surfaces. Second, the library is built on the machine-learning framework JAX, so mesh-based energies and entire simulations can be differentiated automatically (including simulations with stochastic forces or topological modifications). This simplifies multi-physics simulations by computing forces automatically and allows fitting model parameters to experimental data or design objectives. We implement an algorithm to preserve mesh quality in strongly deforming surfaces and apply it to a model of membrane mechanics coupled to an on-membrane concentration field. We validate our approach against exact solutions and existing software, and demonstrate triangulax across a range of problems, including membrane mechanics, vertex models, and inverse design of a shape-morphing elastomer sheet.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M Merz, Joseph A Morrone
{"title":"Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion.","authors":"Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M Merz, Joseph A Morrone","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation. Prediction is challenging for machine learning models because it reflects the size, shape, and ionization state of a gas-phase molecular ion. Most predictors either ignore explicit 3D structure or treat adduct identity as a late categorical feature, which limits their ability to capture adduct-dependent geometric effects. We present GRACE (Geometric Residual Adduct Conditioning via Early-fusion), a 3D CCS predictor that adapts a pretrained molecular geometry encoder using geometric residual adduct conditioning via early fusion. GRACE combines two inductive biases: a residual objective relative to an adduct-aware physical descriptor baseline and adduct conditioning within the encoder via a learned adduct token and low-rank attention adapters. We evaluate the model on a curated set of over 9,000 experimental molecule-adduct CCS records with random, scaffold, and adduct-sensitive splits designed to separate interpolation, scaffold generalization, and adduct-driven generalization. GRACE achieves the best mean percentage difference among the evaluated learned models on all three splits: 1.67% on the random split, 2.11% on the scaffold split, and 2.36% on the adduct-sensitive split. Diagnostic analyses suggest that residual learning stabilizes training by removing the dominant mass-CCS trend, while early fusion improves adduct-sensitive prediction relative to late fusion. Across four independent external test sets, GRACE shows consistently lower error than the other evaluated models. On a held-out set, GRACE also attains the lowest mean percent difference when compared with four previously reported physics-based workflows. These results support residual learning and encoder-level adduct conditioning as practical inductive biases for fast, accurate CCS prediction.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles Ochieng' Olwal, Declan M Winters, Immy A Ashley, Valerie Vandersall, Amberin Rahman, Alexandra Lindsey Djomkam Zune, Kyla F Dangerfield, Yennifer Delgado, Sara K Makanani, Sophie F Blanc, Shipra Sharma, Mehdi Bouhaddou
{"title":"Protocol for Discovering Convergent Molecular Networks across Multi-Omics Datasets Using Network Propagation.","authors":"Charles Ochieng' Olwal, Declan M Winters, Immy A Ashley, Valerie Vandersall, Amberin Rahman, Alexandra Lindsey Djomkam Zune, Kyla F Dangerfield, Yennifer Delgado, Sara K Makanani, Sophie F Blanc, Shipra Sharma, Mehdi Bouhaddou","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Phenotypic outcomes arise from coordinated changes across molecular layers. Although multi-omics data capture complementary molecular signals, integrating them into coherent biological mechanisms remains challenging due to limited overlap at the gene level. Network propagation maps significant gene hits from each dataset onto a prior knowledge molecular interaction network and diffuses their signals to neighboring genes. Mathematical integration of propagated networks, followed by clustering and pathway enrichment, reveals shared molecular networks, pinpointing the core biology captured across orthogonal molecular layers.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Convex Optimization Methods for State-Space Heartbeat Dynamics Models with Gamma Generalized Linear Models.","authors":"Sabrina Liu, Andrew S Perley, Todd P Coleman","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>This work proposes a dynamic state-space statistical model of heartbeat dynamics whose optimal state estimate can be solved efficiently with convex optimization and used to assess autonomic and cardiovascular health.</p><p><strong>Methods: </strong>This state-space model combines a Gauss-Markov process prior with gamma emissions using a log-link function. Parameters are estimated using a static version of the model, and alternating direction method of multipliers is used to efficiently find the optimal maximum a priori latent state estimate. Model selection is performed by finding the tradeoff between model size and model fit using the partial autocorrelation function.</p><p><strong>Results: </strong>We demonstrate in synthetically generated heartbeats that this model is able to closely recover the underlying latent weights. Then, we show using a tilt table dataset that this statistical model is able to better capture sharp transients in heart rate patterns than traditional local averaging methods. In addition, combining heart beat timings with information from other organs such as the respiratory phase, we show that our statistical modeling framework enables the extraction of information-theoretic measures to quantify inter-organ coupling such as respiratory sinus arrythmia.</p><p><strong>Conclusion: </strong>This statistical framework for modeling heartbeat dynamics provides an efficient framework that can provide physiologically meaningful insights.</p><p><strong>Significance: </strong>This work broadens potential tools that can be used to assess heartbeat dynamics.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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