ArXiv最新文献 - Book学术

FILM: Mapping organellar metabolism by mid-infrared photothermal modulated fluorescence. 胶片：利用中红外光热调制荧光绘制细胞器代谢图谱。

ArXiv Pub Date : 2026-05-04

Jianpeng Ao, Jiaze Yin, Haonan Lin, Guangrui Ding, Youchen Guan, Bethany Weinberg, Dashan Dong, Qing Xia, Zhongyue Guo, Marzia Savini, Biwen Gao, Ji-Xin Cheng, Meng C Wang

{"title":"FILM: Mapping organellar metabolism by mid-infrared photothermal modulated fluorescence.","authors":"Jianpeng Ao, Jiaze Yin, Haonan Lin, Guangrui Ding, Youchen Guan, Bethany Weinberg, Dashan Dong, Qing Xia, Zhongyue Guo, Marzia Savini, Biwen Gao, Ji-Xin Cheng, Meng C Wang","doi":"","DOIUrl":"","url":null,"abstract":"Metabolism unfolds within specific organelles in eukaryotic cells. Lysosomes are highly metabolically active organelles, and their metabolic states dynamically influence signal transduction, cellular homeostasis, and organismal physiopathology. Despite the significance of lysosomal metabolism, a method for its in vivo measurement is currently lacking. Here, we report optical boxcar-enhanced, fluorescence-detected mid-infrared photothermal microscopy, together with AI-assisted data denoising and spectral deconvolution, to map metabolic activity and composition of individual lysosomes in living cells and organisms. Using this method, we uncovered lipolysis and proteolysis heterogeneity across lysosomes within the same cell, as well as early-onset lysosomal dysfunction during organismal aging. Additionally, we discovered organelle-level metabolic changes associated with diverse lysosomal storage diseases. This method holds the broad potential to profile metabolic fingerprints of individual organelles within their native context and quantitatively assess their dynamic changes under different physiological and pathological conditions, providing a high-resolution chemical cellular atlas.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847326","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}

引用次数: 0

On a Keller-Segel type equation to model Brain Microvascular Endothelial Cells growth's patterns. 用Keller-Segel型方程模拟脑微血管内皮细胞的生长模式。

ArXiv Pub Date : 2026-05-01

B Ambrosio, A Garroudji, S Fitzsimons, H Zaag, F M Elahi

引用次数: 0

Pre-CAT: A web-based, graphical user-interface toolbox for preclinical CEST-MRI data processing and analysis. Pre-CAT：一个基于网络的图形用户界面工具箱，用于临床前CEST-MRI数据处理和分析。

ArXiv Pub Date : 2026-05-01

Jonah Weigand-Whittier, Samuel Rubin, Cindy Ayala, Mark Velasquez, Nikita Vladimirov, Hadas Avraham, Or Perlman, M Roselle Abraham, Moriel H Vandsburger

{"title":"Pre-CAT: A web-based, graphical user-interface toolbox for preclinical CEST-MRI data processing and analysis.","authors":"Jonah Weigand-Whittier, Samuel Rubin, Cindy Ayala, Mark Velasquez, Nikita Vladimirov, Hadas Avraham, Or Perlman, M Roselle Abraham, Moriel H Vandsburger","doi":"","DOIUrl":"","url":null,"abstract":"Purpose: As interest in CEST-MRI grows, particularly in the preclinical setting, the necessity for standardized and easy-to-use acquisition and data analysis pipelines has become apparent. While vendors have increasingly introduced support for CEST acquisitions on both clinical and preclinical hardware, image post-processing and analysis pipelines remain siloed based on privately developed code. We aim to develop an easy-to-use, open-source graphical user interface toolbox for preclinical CEST-MRI data analysis (Preclinical CEST-MRI Analysis Tool; Pre-CAT), supporting multiple acquisition types, organ systems, and CEST contrast mechanisms.Methods: Pre-CAT was developed in Python and utilizes the Numpy, Scipy, and Matplotlib libraries for data analysis and plotting. Inbuilt data processing steps include image loading, reconstruction, post-processing, and segmentation. Pre-CAT also supports data analysis for QUESP, CEST-MRF, and field mapping experiments using consensus protocols and methods. Pre-CAT's web interface and GUI were developed using Streamlit, an open-source Python framework. Pre-CAT is hosted and accessible online and can be downloaded for local installation to complete the data analysis pipeline in roughly one minute using modern hardware.Results: Pre-CAT analysis pipelines for Z-spectroscopy, CEST-MRF, and quantitative CEST (QUESP/QUEST) are demonstrated.Conclusion: With the introduction of Pre-CAT, we aim to standardize the preclinical CEST-MRI data analysis pipeline, fostering collaboration across research sites and reducing methodological redundancy. Pre-CAT is open-source and relatively modular, encouraging the addition of new methods and protocols.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847311","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}

引用次数: 0

Reconstruction of glymphatic transport fields from subject-specific imaging data, with particular emphasis on cerebrospinal fluid flow and tracer conservation. 从受试者特定的成像数据重建淋巴转运场，特别强调脑脊液流动和示踪剂保护。

ArXiv Pub Date : 2026-05-01

A Derya Bakiler, Michael J Johnson, Michael R A Abdelmalik, Frimpong A Baidoo, Andrew Badachhape, Ananth V Annapragada, Thomas J R Hughes, Shaolie S Hossain

{"title":"Reconstruction of glymphatic transport fields from subject-specific imaging data, with particular emphasis on cerebrospinal fluid flow and tracer conservation.","authors":"A Derya Bakiler, Michael J Johnson, Michael R A Abdelmalik, Frimpong A Baidoo, Andrew Badachhape, Ananth V Annapragada, Thomas J R Hughes, Shaolie S Hossain","doi":"","DOIUrl":"","url":null,"abstract":"The reconstruction of physically valid transport fields from subject-specific imaging data is a fundamental challenge in image-based computational modeling due to measurement noise, modeling uncertainties and discretization errors. Without a methodology to construct models that faithfully reflect the underlying physics, mechanistic understanding of complex biological systems is inherently limited. In this work, we address this challenge in the glymphatic system, the brain's waste-clearance network, where cerebrospinal fluid (CSF) is transported through perivascular spaces into the brain parenchyma to facilitate metabolic waste removal. We introduce a computational framework for the high-fidelity reconstruction of subject-specific glymphatic transport fields from spatiotemporal imaging data. The formulation utilizes an advection-diffusion model with a velocity decomposition that imposes mass conservation, enabling the recovery of solenoidal (divergence-free) velocity fields through the solution of a constrained inverse problem. The system is discretized using immersed isogeometric analysis with quadratic B-spline basis functions, providing smooth, high-continuity solutions and inherent regularization of imaging noise. We demonstrate the framework's utility by using contrast-enhanced magnetic resonance imaging of tracer transport in a mouse brain, obtaining spatially varying estimates of CSF velocity, diffusivity, and clearance parameters. Forward simulations using the recovered fields show close agreement with experimental observations, validating the framework's ability to characterize complex transport dynamics while preserving physical integrity. This approach provides a generalizable methodology for the robust inference of physically consistent transport fields from imperfect imaging data, with broad applicability to the image-guided modeling of biological and engineering systems.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847314","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}

引用次数: 0

Effect of swimming mode on shielding of odor traces in turbulence. 游泳方式对湍流中气味痕迹屏蔽的影响。

ArXiv Pub Date : 2026-05-01

Martin James, Francesco Viola, Agnese Seminara

{"title":"Effect of swimming mode on shielding of odor traces in turbulence.","authors":"Martin James, Francesco Viola, Agnese Seminara","doi":"","DOIUrl":"","url":null,"abstract":"Marine organisms manipulate their surrounding flow through their swimming dynamics, which affects the transport of their own odor cues. We demonstrate by direct numerical simulations how a group of swimmers, moving at intermediate Reynolds numbers, immersed in a turbulent flow, alter the shape of the odor plume they release in the water. Odor mixing is enhanced by increased velocity fluctuations and a swimmer-induced flow circulation that widens the odor plume at close range while speeding up dilution of the chemical trace. Beyond a short-range increase in the likelihood of being detected, swimming considerably reduces detections with effects that can persist at distances on the order of ten times the size of the group or more. We find that pullerlike swimmers are more effective at olfactory shielding than pusherlike swimmers. We trace this difference back to the dynamics at the swimmer location, which tends to trap odor at the source for pushers and to dilute it for pullers. Olfactory shielding is robust to changes in the conditions, and is more pronounced for weak turbulent Reynolds numbers and large swimmer Reynolds numbers. Our results suggest that olfactory shielding may play a role in the emergence of different swimming modalities by marine organisms.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847256","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}

引用次数: 0

LNODE: latent dynamics reveal the shared spatiotemporal structure of amyloid-$β$ progression. LNODE：潜在动力学揭示了淀粉样蛋白- β$进展的共同时空结构。

ArXiv Pub Date : 2026-04-30

Zheyu Wen, George Biros

{"title":"LNODE: latent dynamics reveal the shared spatiotemporal structure of amyloid-$β$ progression.","authors":"Zheyu Wen, George Biros","doi":"","DOIUrl":"","url":null,"abstract":"We introduce LNODE, a mechanism-based phenomenological model for amyloid beta (A$β$) dynamics, calibrated using positron emission tomography (PET) imaging. A$β$ is a key biomarker of Alzheimer's disease. LNODE is designed to support the fusion, harmonization, quantitative analysis, and interpretation of Abeta PET scans. We evaluate LNODE on 1461 subjects in the ADNI cohort and 1070 subjects in the A4 Study, using MUSE and DKT anatomical atlases. LNODE is formulated as a regional neural ordinary differential equation (ODE) model that is jointly calibrated on all available scans within a cohort. The model captures the spatial propagation, proliferation, and clearance of A$β$ and incorporates a latent-state representation that modulates A$β$ dynamics. The temporal evolution of these latent states is governed by cohort-shared parameters, enabling LNODE to represent both population-level trajectories and subject-specific deviations. The proposed model demonstrates strong parameter identifiability and stability properties, supported by synthetic experiments and analytical analysis of the Hessian condition number. To mitigate overfitting and reduce spurious correlations, LNODE is intentionally underparameterized, employing approximately five to ten parameters per subject. Despite this parsimonious parameterization, LNODE achieves $R^2 > 0.99$ in both the ADNI and A4 datasets. LNODE exhibits strong predictive performance: in the A4 cohort, it accurately forecasts the A$β$ PET signal in previously unseen follow-up scans, including cases with inter-scan intervals exceeding four years. Clustering in the learned latent-state space reveals distinct subgroups, consistent with the existence of different subtypes of Alzheimer's disease progression.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847268","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}

引用次数: 0

Complex Effects of Salt on Small-Angle X-ray Scattering of BSA Originate From the Interplay of Ions and Hydration Water. 盐对牛血清白蛋白小角x射线散射的复杂影响源于离子与水合水的相互作用。

ArXiv Pub Date : 2026-04-30

Anshika Dhiman, Sanbo Qin, Huan-Xiang Zhou

引用次数: 0

The Genetic and Environmental Architecture of the Human Functional Connectome. 人类功能连接体的遗传和环境结构。

ArXiv Pub Date : 2026-04-27

Tanu Raghav, Daniel Guerrero, Uttara Tipnis, Julie Sara Benny, Mintao Liu, Mario Dzemidzic, Arian Ashourvan, Alex P Miller, Beau Ances, Jaroslaw Harezlak, Joaquín Goñi

{"title":"The Genetic and Environmental Architecture of the Human Functional Connectome.","authors":"Tanu Raghav, Daniel Guerrero, Uttara Tipnis, Julie Sara Benny, Mintao Liu, Mario Dzemidzic, Arian Ashourvan, Alex P Miller, Beau Ances, Jaroslaw Harezlak, Joaquín Goñi","doi":"","DOIUrl":"","url":null,"abstract":"Functional connectivity varies across individuals due to genetic and environmental factors, yet classical twin models typically confound non-shared environment with measurement error and are largely limited to resting-state analyses. We hypothesized that: i) explicitly modeling measurement error from repeated fMRI sessions enables more accurate application of classical twin models (ACE/ADE) to functional connectivity; ii) model applicability depends on scan-length and parcellation granularity; iii) genetic and environmental effects on functional connectomes show differentiated functional modules across conditions. We extended ACE/ADE models to include a repeated-scan derived error term by analyzing monozygotic and dizygotic twins from the Young-Adult Human Connectome Project dataset. Genetic and environment variance components were estimated for all functional couplings across resting-state and task conditions, integrated across conditions using a minimum-error criterion, and analyzed using multilayer community detection across resolution scales. Functional couplings segregated into distinct categories characterized by shared environmental, additive, dominant, or epistatic influences, with a substantial fraction not meeting twin-model assumptions. Integrating across conditions revealed hierarchical community structure in genetic and environmental components observed across community resolution scales. Incorporating measurement error into twin models improves interpretability and applicability at the functional connectome level, revealing that genetic and environmental influences are structured into coherent, multiscale brain networks.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847273","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}

引用次数: 0

Generative diffusion models for spatiotemporal influenza forecasting. 时空流感预测的生成扩散模型。

ArXiv Pub Date : 2026-04-27

Joseph Lemaitre, Justin Lessler

{"title":"Generative diffusion models for spatiotemporal influenza forecasting.","authors":"Joseph Lemaitre, Justin Lessler","doi":"","DOIUrl":"","url":null,"abstract":"Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture multimodal uncertainty or emergent trends. Influpaint adapts denoising diffusion probabilistic models to epidemic forecasting. By encoding influenza seasons as spatiotemporal images in which pixel intensity represents incidence, Influpaint learns a rich distribution of disease dynamics from a hybrid dataset of surveillance and simulated trajectories. Forecasting is formulated as a conditional generation (inpainting) task from partial observations. We show that Influpaint generates realistic, diverse epidemic trajectories and achieves forecast accuracy that is competitive with leading ensemble methods in retrospective evaluation. In real-time evaluation during the 2023--2025 U.S. CDC FluSight challenges, performance improved substantially across seasons, with highly accurate but somewhat overconfident projections in 2024--2025. The best performance was achieved with a training dataset containing 30% surveillance and 70% simulated trajectories. These results show that diffusion models can capture important spatiotemporal structure in influenza dynamics and provide a flexible framework for probabilistic infectious disease forecasting.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847332","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}

引用次数: 0

Solution of a large nonlinear recurrent neural network at fixed connectivity. 一类大型非线性递归神经网络在固定连通性下的解。

ArXiv Pub Date : 2026-04-27

Albert J Wakhloo

引用次数: 0