[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127298-en":3,"doc-seo-127298-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127298,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","Enhancing Fluorescence Correlation Spectroscopy with Machine Learning for Advanced Analysis of Anomalous Diffusion","Random molecular motion in living cells often deviates from standard Brownian behavior, a phenomenon known as anomalous diffusion. Fluorescence Correlation Spectroscopy (FCS) quantifies molecular dynamics, yet conventional analysis is restricted to limited motion classes and long acquisition times. A new machine-learning-driven framework infers an underlying motion model and estimates motion parameters from FCS data. Simulations show an expanded range of accessible anomalous motions, and experiments with glycerol-calibrated fluorescent beads validate the approach, boosting FCS analysis power toward best-in-class single-particle tracking performance.","arXiv :2407 . 12382v1 [ q-bio .QM] 17 Jul 2024  \nEnhancing Fluorescence Correlation Spectroscopy with Machine Learning for Advanced Analysis of Anomalous  \nDiffusion  \nNathan Quiblier 1 , Jan-Michael Rye 1 , Pierre Leclerc2 , Henri Truong2 , Abdelkrim  \nHannou2 , Laurent Héliot2 , and Hugues Berry 1,*  \n1 AIstroSight, Inria, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, F-69603 Villeurbanne, France  \n2 Univ. Lille, CNRS, UMR 8523, PhLAM Laboratoire de Physique des Lasers, Atomeset Molécules, F-59658, Lille, France  \n* [hugues.berry@inria.fr](hugues.berry@inria.fr)  \n18 juillet 2024  \n1 Abstract  \nThe random motion of molecules in living cells has consistently been reported to deviate from standard Brownian motion, a behavior coined as “anomalous diffusion”. Fluorescence Correlation Spectroscopy (FCS) is a powerful method to quantify molecular motions in living cells but its application is limited to a subset of random motions and to long acquisition times. Here, we propose a new analysis approach that frees FCS of these limitations by using machine learning to infer the underlying model of motion and estimate the motion parameters. Using simulated FCS recordings, we show that this approach enlarges the range of anomalous motions available in FCS. We further validate our approach via experimental FCS recordings of calibrated fluorescent beads in increasing concentrations of glycerol in water. Taken together, our approach significantly augments the analysis power of FCS to capacities that are similar to the best-in-class state-of-the-art algorithms for single-particle-tracking experiments.  \n2 Introduction  \nDeviation of random motion from standard Brownian motion (BM) has received considerable attention in the literature to describe diverse physical situations [1, 2 , 3] . For instance, anomalous diffusion, where the mean-squared displacement scales non-linearly with time, 􀀊r2 (t)􀀋 = Dtα , has been reported to describe the motion of several proteins or particles in living cells [4, 5 , 6 , 7 , 8] . In this case, the exponent α is usually referred to as the anomalous exponent, and D is the diffusion coefficient. All anomalous subdiffusion motion models exhibit α \u003C 1, whereas α = 1 for standard Brownian motion. However, anomalous subdiffusion is a characteristic shared by several unrelated types of motion. For instance, continuous-time random walk (CTRW), fractional Brownian motion (fBM) or random walk on a fractal support (RWf), all exhibit anomalous subdiffusion while the physical processes they describe are very different : heavy-tailed residence time distribution for CTRW, correlation between successive jumps for fBM or the fractal geometry of the object on which RWf takes place [9, 10] . Therefore, the complete characterization of the motion of a biomolecule in a live cell requires the completion of two tasks : (i) a classification or selection task to decide what model is the best at explaining the observations (e.g., BM, fBM, RWf or CTRW) and (ii) an inference or calibration task, to estimate the parameter values of the selected model given an experimental observation.  \nIn recent years, the advent of single-particle tracking supra-resolution microscopy [11, 12] has generalized the use of individual trajectories to quantify the motion of biomolecules or particles in living cells. A range of methods have been proposed for the classification and inference tasks based  \non individual trajectories [13], from simple (non-)linear regression [5, 14], statistical tests [15, 16] or Bayesian inference [17, 18], to machine- [19, 20] and deep-learning [21, 22] . A key factor here is the length of the observed individual trajectories, since for all the methods, the longer the individual trajectories, the better the performance. Experimentally, though, technical limits strongly constraint the typical time of a trajectory, which can be as large as several seconds for membrane proteins [23, 24] but is usually closer to millis","cbCaikyGDZgsCpPK","https://ap.wps.com/l/cbCaikyGDZgsCpPK","pdf",1386007,1,17,"English","en",105,"# Abstract\n# Introduction\n## Anomalous diffusion and its models\n## Limits of single-particle tracking versus FCS\n## FCS correlation analysis and parameter inference\n## Motivation for a machine-learning-based framework","[{\"question\":\"What problem does the proposed method address in fluorescence correlation spectroscopy (FCS)?\",\"answer\":\"It removes FCS limitations tied to restricted motion classes and long acquisition times by using machine learning to infer the motion model and estimate motion parameters from FCS measurements.\"},{\"question\":\"How is the approach validated in the paper?\",\"answer\":\"First, simulated FCS recordings demonstrate improved coverage of anomalous motions. Then, experimental FCS data from calibrated fluorescent beads in glycerol-water mixtures validate the method experimentally.\"},{\"question\":\"What kinds of motion parameters does the machine-learning approach estimate?\",\"answer\":\"It estimates the underlying motion-model parameters associated with anomalous diffusion, enabling both model inference (classification/selection) and parameter calibration from observed fluorescence correlations.\"}]","Enhancing Fluorescence Correlation Spectroscopy with Machine Learning for Advanced Analysis of Anomalous Diffusion | PDF",1785938179,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-fluorescence-correlation-spectroscopy-with-machine-learning-for-advanced-analysis-of-anomalous-diffusion","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-fluorescence-correlation-spectroscopy-with-machine-learning-for-advanced-analysis-of-anomalous-diffusion/127298/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the proposed method address in fluorescence correlation spectroscopy (FCS)?","Question",{"text":76,"@type":77},"It removes FCS limitations tied to restricted motion classes and long acquisition times by using machine learning to infer the motion model and estimate motion parameters from FCS measurements.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the approach validated in the paper?",{"text":81,"@type":77},"First, simulated FCS recordings demonstrate improved coverage of anomalous motions. Then, experimental FCS data from calibrated fluorescent beads in glycerol-water mixtures validate the method experimentally.",{"name":83,"@type":74,"acceptedAnswer":84},"What kinds of motion parameters does the machine-learning approach estimate?",{"text":85,"@type":77},"It estimates the underlying motion-model parameters associated with anomalous diffusion, enabling both model inference (classification/selection) and parameter calibration from observed fluorescence correlations.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]