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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations

Bridging Accuracy and Efficiency: The Role of PINNs in Immun...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fernandes, Thiago E. Dos Santos, Rodrigo W. Lobosco, Marcelo Federal University of Juiz de Fora Graduate Program on Computational Modelling Juiz de Fora Brazil Federal University of Juiz de Fora Institute of Exact Sciences Department of Computer Science Juiz de Fora Brazil

ISBN: (纸本)9798350386226

Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system's solutions, offering substantial computational efficiency for this application. © 2024 IEEE.

关键词： Pathogens

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Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands 2

Industrial revolution 4.0 on big data adoption factors on so...

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2nd International Conference on Lesson Study of Science Technology Engineering and Mathematics 2020, ICOLSSTEM 2020

作者： Sardjono, W. Rama Putra, G. Selviyanti, E. Cholidin, A. Salim, G. Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia Health Department Politeknik Negeri Jember Jember Jawa Timur68124 Indonesia Law Studi Program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jakarta Indonesia

The purpose of writing this paper is to look at how factors of big data adoption affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data adoption factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Big data

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Towards an applied multicriteria model to the diagnosis of Alzheimer's disease: A neuroimaging study case

Towards an applied multicriteria model to the diagnosis of A...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Isabelle Tamanini Ana Karoline de Castro Plácido Rogério Pinheiro Mirian Calíope Dantas Pinheiro University of Fortaleza (UNIFOR) - Graduate Program in Applied Computer Sciences Fortaleza Brazil University of Fortaleza (UNIFOR) - Graduate Program in Applied Computer Sciences Av. Washington Soares 1321 - Bl J Sl 30 - 60.811-905 - Brazil

The Alzheimer's disease has become the most frequent cause of dementia in the last few years, and it is responsible, alone or in association with other diseases, for 50% of the cases in western countries. The main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer's disease by using the Aranau Tool, structured on the Verbal Decision Analysis. In this work, the modeling and evaluation processes were conducted with the aid of a medical expert, bibliographic sources and questionnaires. The questionnaires taken into account were based mainly on patients' neuroimaging tests, and we analyzed wheter or not there were problems in the patients' brain that could be relevant to the diagnosis of Alzheimer's disease.

关键词： Alzheimer's disease Neuroimaging Dementia Testing Batteries Senior citizens Decision making Medical diagnostic imaging Demography Hippocampus

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A bee-inspired algorithm for optimal data clustering

A bee-inspired algorithm for optimal data clustering

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2013 IEEE Congress on Evolutionary Computation, CEC 2013

作者： Cruz, Davila Patricia Ferreira Maia, Renato Dourado Szabo, Alexandre De Castro, Leandro Nunes Educational Foundation of Montes Claros Montes Claros MG Brazil Computer Sciences Department State University of Montes Claros Montes Claros MG Brazil Graduate Program in Electrical Engineering Mackenzie University São Paulo Brazil

ISBN: (纸本)9781479904549

The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose an adaptation of a bee-inspired optimization algorithm so that it is able to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined. © 2013 IEEE.

关键词： Swarm intelligence

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The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

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Neuron 2025年

作者： Wolcott, Nora S. Redman, William T. Karpinska, Marie Jacobs, Emily G. Goard, Michael J. Department of Molecular Cellular and Developmental Biology University of California Santa Barbara Santa Barbara 93106 CA United States Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara Santa Barbara 93106 CA United States Intelligent Systems Center Johns Hopkins University Applied Physics Laboratory Laurel 20723 MD United States Department of Computer Science University of California Santa Barbara Santa Barbara 93106 CA United States Department of Psychological & Brain Sciences University of California Santa Barbara Santa Barbara 93106 CA United States Ann S. Bowers Women's Brain Health Initiative University of California Santa Barbara Santa Barbara 93106 CA United States Neuroscience Research Institute University of California Santa Barbara Santa Barbara 93106 CA United States

Histological evidence suggests that the estrous cycle exerts a powerful influence on CA1 neurons in the mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice across multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with spine density peaking during proestrus when estradiol levels are highest. These morphological changes coincided with greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed greater place field stability during proestrus, evident at both the single-cell and population levels. These findings demonstrate that the estrous cycle drives large-scale structural and functional plasticity in hippocampal neurons essential for learning and memory. © 2025 The Author(s)

关键词： dendritic integration estradiol estrous cycle hippocampus multiphoton imaging neuroendocrine place cells spine morphology

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Patient-specific pipeline to create virtual endoscopic third ventriculostomy scenarios

Patient-specific pipeline to create virtual endoscopic third...

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21st Medicine Meets Virtual Reality Conference, NextMed/MMVR 2014

作者： Armstrong, Ryan Eagleson, Roy De Ribaupierre, Sandrine Biomedical Engineering Graduate Program University of Western Ontario 1151 Richmond St. London ON Canada Department of Clinical Neurological Sciences University of Western Ontario Canada Department of Electrical and Computer Engineering University of Western Ontario Canada

ISBN: (纸本)9781614993742

Graphically realistic, haptics-enabled, virtual reality surgical simulations are becoming increasingly popular training tools for neurosurgical procedures and surgical skills. Compared with traditional training models, virtual simulations are safe, versatile, consistent, and are relatively cost effective. Most simulations are deficient, however, in representing the diverse anatomical variations that occur clinically. In this work, we describe the design and implementation of a pipeline to create patient-specific surgical scenarios for simulator-based training of the endoscopic third ventriculostomy: the procedure of choice for treating obstructive hydrocephalus. © 2014 The authors and IOS Press. All rights reserved.

关键词： Virtual reality

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Hybrid support vector regression in electric load during national holiday season

Hybrid support vector regression in electric load during nat...

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2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017

作者： Caraka, Rezzy Eko Bakar, Sakhinah Abu Pardamean, Bens Budiarto, Arif School of Mathematical Sciences Faculty of Science and Technology National University of Malaysia Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538640456

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation. © 2017 IEEE.

关键词： Time series

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Rich learning experiences for minority undergraduate students through inquiry-based project activities in the field and laboratory settings

Rich learning experiences for minority undergraduate student...

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114th Annual ASEE Conference and Exposition, 2007

作者： Nagchaudhuri, Abhijit Mitra, Madhumi Stoakley, Rose Turner, Tiara Cherinet, Suraf Ladd, Gabriel Chalyam, Harsha Hartman, Christopher Burrows-Mcelwain, James B. University of Maryland-Eastern Shore United States Department of Engineering and Aviation Sciences University of Maryland Eastern Shore United States ASME SME ASEE Department of Biology and Environmental Science University of Maryland Eastern Shore United States Biology Education Program UMES France Mathematics Program University of Maryland Eastern Shore Department of Mathemtics and Computer Sciences United States Aviation Science Program University of Maryland Eastern Shore United States Graduate Program UMES United States Department of Mathematics and Computer Science University of Maryland Eastern Shore United States Aviation Science Program Department of Engineering and Aviation Science University of Maryland Eastern Shore United States Aviation Program Department of Engineering and Aviation Sciences Aviation Program

With support from HBCU-UP (Historically Black College and University Undergraduate program) at National Science Foundation (NSF), the ACTION (Advanced Curriculum and Technology-Based Instructional Opportunities Network) program at University of Maryland Eastern Shore (UMES) is promoting inquiry based active learning and research projects among undergraduate STEM (Science, Technology, Engineering and Mathematics) majors. Field based experiential learning efforts titled (i) AIRSPACES: Aerial Imaging and Remote Sensing for Precision Agriculture and Environmental Stewardship and (ii) ECPA: Environmentally Conscious Precision Agriculture, provided a perfect setting for STEM undergraduates to interact with UMES farm personnel, graduate students, NASA and USDA researchers collaborating with the faculty members in the project. In the summer of 2006 ACTION program supported three undergraduate students to work with the principal author to work on a chosen aspect of the ongoing effort. For this ACTION project students decided to focus on aspects of yield monitoring and remote sensing for the precision agriculture project at UMES. This paper will highlight the student efforts related to the ACTION project for 2006 summer and discuss how the out of classroom, field, and laboratory based activities enhanced learning. © American Society for Engineering Education, 2007.

关键词： Learning systems

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Assessment of force control improvement induced by sinusoidal vibrotactile stimulation in dominant and non-dominant hands

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Research on Biomedical Engineering 2021年第1期37卷 95-103页

作者： Germer, Carina Marconi Moreira, Luciana Sobral Elias, Leonardo Abdala Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas CampinasSP Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas CampinasSP Brazil Cellular and Structural Biology Graduate Program Institute of Biology University of Campinas CampinasSP Brazil Faculty of Biological Sciences Center for Life Sciences Pontifical Catholic University of Campinas CampinasSP Brazil

Purpose: Vibrotactile stimulation has been shown to improve sensorimotor integration in the central nervous system, thereby enhancing motor performance in several conditions. Here we aim to address two questions: (1) Would lateral asymmetries yielding handedness influence the force control improvement induced by sinusoidal vibrotactile stimulation? (2) Does the force control improvement depend on finding an optimum stimulus intensity? Methods: Eleven participants performed a visually guided force-matching task, while different intensities of sinusoidal vibrations were applied to the index finger skin. Tasks consisted of abductions of the index fingers of dominant and non-dominant hands at 5% of their maximal voluntary contractions. The force coefficient of variation (CoV) in the condition without vibration was used as the reference (control) for later comparison with five vibration intensities. Also, force CoV in the control condition was compared with those obtained from the optimal vibration selected either from both hands or from a single hand (the one that started the experiment). Results: Despite the known sensory asymmetries in cortical and spinal circuits, handedness did not significantly influence the improvement of force control induced by sinusoidal vibrotactile stimulation during low-intensity contractions. Moreover, there was a significant reduction in force variability irrespective of the criteria used to select the vibration intensity. Conclusion: These findings add to the current knowledge on the effects of sinusoidal vibrotactile stimulation on force control and have potential implications for the development of wearable motor enhancer devices. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Force control

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