ArXiv最新文献

{"title":"Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.","authors":"Mojtaba Safari, Zach Eidex, Richard L J Qiu, Matthew Goette, Tonghe Wang, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions.</p><p><strong>Methods: </strong>A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approaches, particularly generative models, for the detection and correction of MRI motion artifacts. Quantitative data were extracted regarding utilized datasets, DL architectures, and performance metrics.</p><p><strong>Results: </strong>DL, particularly generative models, shows promise for reducing motion artifacts and improving image quality; however, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting.</p><p><strong>Conclusions: </strong>AI-driven methods, particularly DL generative models, show significant potential for improving MRI image quality by effectively addressing motion artifacts. However, critical challenges must be addressed, including the need for comprehensive public datasets, standardized reporting protocols for artifact levels, and more advanced, adaptable DL techniques to reduce reliance on extensive paired datasets. Addressing these aspects could substantially enhance MRI diagnostic accuracy, reduce healthcare costs, and improve patient care outcomes.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066708","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":"Accelerated Ostwald ripening by chemical activity.","authors":"Benjamin Sorkin, Ned S Wingreen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Phase separation of biomolecular condensates promotes membrane-free compartmentalization in cells. The dynamics of these biocondensates is routinely regulated by energy-consuming processes. Here, we devise a theory pinpointing how active chemical reactions, interconverting molecules between phase-separating and inert forms, can drive faster condensate coarsening. We find that mass conservation limits droplet volume growth to being linear in time regardless of activity, resembling the passive Lifshitz-Slyozov law. However, if reactions are restricted to occur only outside droplets, the rate of Ostwald ripening can be increased by an arbitrarily large factor. Our theory is quantitatively supported by recent experiments on ripening in the presence of fueled interconversion reactions, under precisely the predicted conditions. We posit that the ability to induce rapid biocondensate coarsening can be advantageous in synthetic-biological contexts, e.g., as a regulator of metabolic channeling.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12155969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144277019","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":"Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data.","authors":"Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M Brown, Mahmoud Bukar Maina, James J Chambers, Beth A Cimini, Kevin Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault, Ronald N Germain, Wojtek Goscinski, David Grunwald, Michael Halter, Dorit Hanein, John W Hickey, Judith Lacoste, Alex Laude, Emma Lundberg, Jian Ma, Leonel Malacrida, Josh Moore, Glyn Nelson, Elizabeth Kathleen Neumann, Roland Nitschke, Shuichi Onami, Jaime A Pimentel, Anne L Plant, Andrea J Radtke, Bikash Sabata, Denis Schapiro, Johannes Schöneberg, Jeffrey M Spraggins, Damir Sudar, Wouter-Michiel Adrien Maria Vierdag, Niels Volkmann, Carolina Wählby, Siyuan, Wang, Ziv Yaniv, Caterina Strambio-De-Castillia","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Alongside molecular insights into genes and proteins, biological imaging holds great promise for deepening scientific understanding of complex cellular systems and advancing predictive, personalized therapies for human health. To realize this potential, quality-assured image data must be shared globally across laboratories to enable comparison, pooling, and reanalysis-unlocking value far beyond the original purpose of data collection. Two broad sets of requirements are essential to enable image data sharing in the life sciences. The companion article Enabling Global Image Data Sharing in the Life Sciences outlines the need to develop cyberinfrastructure for sharing bioimage data. In this manuscript, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse bioimage data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10862930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731191","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":"Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI.","authors":"Mojtaba Safari, Shansong Wang, Qiang Li, Zach Eidex, Richard L J Qiu, Chih-Wei Chang, Hui Mao, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correction.</p><p><strong>Approach: </strong>Res-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with attention layers replaced by Swin Transformer blocks, to enhance robustness across resolutions. Furthermore, the training process integrates a combined l1+l2 loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as PSNR, SSIM, and NMSE.</p><p><strong>Main results: </strong>The proposed method demonstrated superior performance in removing motion artifacts across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91+-2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095829","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":"The dynamic interplay between in-context and in-weight learning in humans and neural networks.","authors":"Jacob Russin, Ellie Pavlick, Michael J Frank","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Human learning embodies a striking duality: sometimes, we appear capable of following logical, compositional rules and benefit from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved. Influential psychological theories explain this seemingly disparate behavioral evidence by positing two qualitatively different learning systems-one for rapid, rule-based inferences and another for slow, incremental adaptation. It remains unclear how to reconcile such theories with neural networks, which learn via incremental weight updates and are thus a natural model for the latter type of learning, but are not obviously compatible with the former. However, recent evidence suggests that metalearning neural networks and large language models are capable of \"in-context learning\" (ICL)-the ability to flexibly grasp the structure of a new task from a few examples. Here, we show that the dynamic interplay between ICL and default in-weight learning (IWL) naturally captures a broad range of learning phenomena observed in humans, reproducing curriculum effects on category-learning and compositional tasks, and recapitulating a tradeoff between flexibility and retention. Our work shows how emergent ICL can equip neural networks with fundamentally different learning properties that can coexist with their native IWL, thus offering a novel perspective on dual-process theories and human cognitive flexibility.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10896373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974957","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":"T-cell receptor specificity landscape revealed through de novo peptide design.","authors":"Gian Marco Visani, Michael N Pun, Anastasia A Minervina, Philip Bradley, Paul Thomas, Armita Nourmohammad","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs-with up to five substitutions from the native sequence-activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T-cell therapies and vaccines.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066751","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":"Network-driven discovery of repurposable drugs targeting hallmarks of aging.","authors":"Bnaya Gross, Joseph Ehlert, Vadim N Gladyshev, Joseph Loscalzo, Albert-László Barabási","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, a lack of advance rooted in the multifactorial nature of longevity and the functional interconnectedness of the molecular components implicated in aging. Here, we introduce a network medicine framework that integrates 2,358 longevity-associated genes onto the human interactome to identify existing drugs that can modulate aging processes. We find that genes associated with each hallmark of aging form a connected subgraph, or hallmark module, a discovery enabling us to measure the proximity of 6,442 clinically approved or experimental compounds to each hallmark. We then introduce a transcription-based metric, <i>pAGE</i>, which evaluates whether the drug-induced expression shifts reinforce or counteract known age-related expression changes. By integrating network proximity and <i>pAGE</i>, we identify multiple drug repurposing candidate that not only target specific hallmarks but act to reverse their aging-associated transcriptional changes. Our findings are interpretable, revealing for each drug the molecular mechanisms through which it modulates the hallmark, offering an experimentally falsifiable framework to leverage genomic discoveries to accelerate drug repurposing for longevity.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066756","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":"From Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural Dynamics.","authors":"Nimrod Sherf, Xaq Pitkow, Krešimir Josić, Kevin E Bassler","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic activities in cortex. However, most studies have focused on networks with random or simple connectivity structures. Experimental observations find that high-order cortical connectivity patterns affect the temporal patterns of network activity, but a theory that relates such complex structure to network dynamics has yet to be developed. Here, we show that third- and higher-order cyclic correlations in synaptic connectivities greatly impact neuronal dynamics. Specifically, strong cyclic correlations in a network suppress chaotic dynamics and promote oscillatory or fixed activity. This change in dynamics is related to the form of the unstable eigenvalues of the random connectivity matrix. A phase transition from chaotic to fixed or oscillatory activity coincides with the development of a cusp at the leading edge of the eigenvalue support. We also relate the dimensions of activity to the network structure.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066723","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":"Advancements in Monte Carlo simulations with gMicroMC: reactive species build-up promotes radical-radical reactions at Flash dose rates.","authors":"Miguel Molina-Hernández, Patrícia Gonçalves, Yujie Chi, João Seco","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Ultra-high dose rate irradiations to water indicate an enhancement of radical-radical reactions, which could potentially correlate with the Flash effect. The purpose of this work was to extend gMicroMC to support multiple pulse simulations and Flash dose rates, and to investigate, in a pure water model, the mechanisms underlying the enhancement of radical-radical reactions under Flash conditions. gMicroMC, a GPU-based Monte Carlo track-structure algorithm, was extended to simulate multiple pulses. Pure water was exposed to multiple 70 MeV protons pulses delivering up to 20 Gy. The pulse dose rate was set to 2 · 10⁵ and 10⁶ Gy/s, while the average dose rate ranged from 0.01 to 100000 Gy/s. The G-values of H₂O₂ were used to monitor the influence of dose rate on radical-radical reactions. The multiple pulse extension of gMicroMC was validated against Kinetiscope. Multiple pulse simulations indicated an average dose rate threshold. Below it, complete radical depletion occurred within the pulses, leading to constant G-values. Above it, reactive species accumulated throughout the irradiation, resulting in an increase of radical-radical reactions and thus the G-values of H₂O₂. The average dose rate thresholds were in the order of 10 and 100 Gy/s for pulse dose rates of 2 · 10⁵ and 10⁵ Gy/s, respectively. At ultra-high dose rates, the brief intervals between pulses led to a reactive species build-up, which enhanced radical-radical reactions. This build-up is more likely to promote radical-radical reactions than the inter-track mechanism. The advancements in gMicroMC provide a sophisticated tool to study chemical dose rate dependencies.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066621","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":"Randomness with constraints: constructing minimal models for high-dimensional biology.","authors":"Ilya Nemenman, Pankaj Mehta","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Biologists and physicists have a rich tradition of modeling living systems with simple models composed of a few interacting components. Despite the remarkable success of this approach, it remains unclear how to use such finely tuned models to study complex biological systems composed of numerous heterogeneous, interacting components. One possible strategy for taming this biological complexity is to embrace the idea that many biological behaviors we observe are \"typical\" and can be modeled using random systems that respect biologically-motivated constraints. Here, we review recent works showing how this approach can be used to make close connection with experiments in biological systems ranging from neuroscience to ecology and evolution and beyond. Collectively, these works suggest that the \"random-with-constraints\" paradigm represents a promising new modeling strategy for capturing experimentally observed dynamical and statistical features in high-dimensional biological data and provides a powerful minimal modeling philosophy for biology.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066674","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}