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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

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Journal of Biomedical science and engineering 2012年第12期5卷 720-728页

作者： Melaku A. Bogale Huiying Yu Thompson Sarkodie-Gyan Amr Abdelgawad Texas Tech University Health Science Center El Paso USA University of Texas at El Paso Computational Science Program El Paso USA University of Texas at El Paso Department of Electrical and Computer Engineering El Paso USA

People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process.

关键词： Fuzzy-Granular Algorithm Gait Phases Fuzzy Similarity Cerebral Palsy Multiple Sclerosis

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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

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Physical Review B 2020年第21期102卷 214113-214113页

作者： Jianhang Xu Chunyi Zhang Linfeng Zhang Mohan Chen Biswajit Santra Xifan Wu Department of Physics Temple University Philadelphia Pennsylvania 19122 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA CAPT HEDPS College of Engineering Peking University Beijing 100871 China Institute for Computational Molecular Science Temple University Philadelphia Pennsylvania 19122 USA

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

关键词： Atomic & molecular structure Electronic structure Isotope effect Water Ab initio calculations Density functional theory Machine learning Molecular dynamics Path-integral methods

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computational fluid dynamics applied to the study of falling liquid films in a wave film generation facility 6

Computational fluid dynamics applied to the study of falling...

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6th ECCOMAS European Conference on computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on computational Fluid Dynamics, ECFD 2018

作者： Iglesias, Susana M. Dominguez, Dany S. Escrivá, Alberto Muñoz-Cobo, José L. Berna, César Cuadros, José L. Rivera, Yago Universidade Estadual de Santa Cruz Postgraduate Program of Computational Modeling in Science and Technology Rodovia Jorge Amado km 16 Salobrinho Ilhéus BA45662-900 Brazil Universitat Politècnica de València Institute for Energy Engineering Camino de Vera 14 Valencia46022 Spain

ISBN: (纸本)9788494731167

Two phase gas-liquid flow appears in several engineering applications, such as boiling nuclear reactors, oil and gas industry, chemical plants, among others. For this phenomenon's study the experimental facility Wave Film Generator (GEPELON, Generador de Película Ondulatoria) was constructed. It uses conductivity sensors for the multi-phase flow analysis in vertical pipes. In this work, we propose the use of computational Fluid Dynamics for modelling the falling liquid films regime in this facility. Specifically, this air-water flow regime consists of a water film that falls down the pipe wall and a central region with stagnant air. We build up a simplified installation's geometrical model, considering a mass flow boundary condition in the inlet and constant pressure in the outlet. To capture the water-air interface a non-uniform structured mesh with fine elements close to the wall was constructed. Using a Volume of Fluid (VOF) model and the FLUENT solver, we simulate different flow rates and from the water volumetric fraction we estimate the film thickness in all the domain. The simulations are stationary and due to the hydrodynamic complexity, a pseudo-transient approach was used to stabilize the iterative process. The simulation values were compared with the experimental results obtained in the GEPELON facility. A mesh independence analysis was performed and several mass flows were simulated, showed good agreement with the experimental values for lower flows and reasonable for higher flows. copyright © Crown copyright (2018).All right reserved.

关键词： computational fluid dynamics

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Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

arXiv

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arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

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Computers, Materials & Continua 2022年第2期70卷 2971-2989页

作者： Jose Escorcia-Gutierrez Jordina Torrents-Barrena Margarita Gamarra Natasha Madera Pedro Romero-Aroca Aida Valls Domenec Puig Electronic and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Ophthalmology Service Universitari Hospital Sant JoanInstitut de Investigacio Sanitaria Pere VirgiliReus43201Spain Departament d’Enginyeria Informàtica i Matemàtiques Escola Tècnica Superior d’EnginyeriaUniversitat Rovira i VirgiliTarragona43007Spain

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational *** method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

关键词： Diabetic retinopathy artificial neural networks decision trees support vector machines feature selection retinal vasculature segmentation

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Distinct mechanisms of FAK mechanoactivation by different extracellular matrix proteins

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医用生物力学 2013年第S1期28卷 69-71页

作者： Jihye Seong Arash Tajik Jie Sun Jun-Lin Guan Martin J.Humphries Susan E.Craig Asha Shekaran Andrs J.García Ning Wang Yingxiao Wang Neuroscience Program University of Illinois Department of Mechanical Science and Engineering University of Illinois Department of Bioengineering & Beckman Institute for Advanced Science and Technology University of Illinois Division of Molecular Medicine and Genetics Departments of Internal Medicine and Cell & DevelopmentalBiologyUniversity of Michigan Medical School Wellcome Trust Centre for Cell-Matrix Research Faculty of Life SciencesUniversity of Manchester Woodruff School of Mechanical Engineering and Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology Department of Biomedical Engineering School of Life Science and TechnologyHuazhong University of Science and Technology Department of Integrative and Molecular Physiology Center for Biophysics and Computational BiologyInstitute for Genomic BiologyUniversity of Illinois Department of Bioengineering University of California

Introduction Cells can sense and respond to the mechanical microenvironment by converting forces into biochemical signals inside the cells,***[1-3].Focal adhesions are the major sites of interaction between a cell and... 详细信息

Introduction Cells can sense and respond to the mechanical microenvironment by converting forces into biochemical signals inside the cells,***^[1-3].Focal adhesions are the major sites of interaction between a cell and its extracellular matrix（ECM）microenvironment,thus outside mechanical signals can be sensed at focal adhesions through transmembrane receptor *** particular,it has been shown that matrix elasticity can control the cell fate^[4]by modulating the interactions between ECM proteins and their receptor integrins^[5,6].For example,different rigidity of polyacrylamide（PA）gels can lead to different density of ECM ancho-

关键词： integrin converting transmembrane biochemical modulating elasticity rigidity polyacrylamide outside purchased

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Machine learning based non-Newtonian fluid model with molecular fidelity

arXiv

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arXiv 2020年

作者： Lei, Huan Wu, Lei Weinan, E. Department of Computational Mathematics Science & Engineering Department of Statistics & Probability Michigan State University MI48824 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Polymer solution is used as an example to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the micro-scale polymer configurations and their macro-scale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the micro-scale model and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a new form of the objective tensor derivative. Numerical results demonstrate the accuracy of DeePN2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscous flow

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Pretreatment Chemometrics in Holistic Biogas Life Cycle Assessment: Framing Case Study with Carica papaya

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Waste and Biomass Valorization 2018年 1-14页

作者： Jensen, Cory D. Olugbemide, A.D. Akpa, F.A.O. Oladipo, Ajayi College of Engineering and Computational Sciences Colorado School of Mines GoldenCO United States Division of Liberal Arts and International Studies Joint Policy Program with the University of Colorado Colorado School of Mines GoldenCO United States Auchi Polytechnic AuchiEdo State Nigeria Department of Basic Sciences Auchi Polytechnic AuchiEdo State Nigeria Department of Science Laboratory Technology Auchi Polytechnic AuchiEdo State Nigeria Department of Agricultural and Bio-Environmental Engineering Auchi Polytechnic AuchiEdo State Nigeria

This research explores synergistic pre-treatment parameters and chemometrics as applicable to systems level biogas related research frameworks (such as the sustainable development goals) for anaerobic digestion biomass processes-policy. Specifically, the hypothesis that simple feedstock pretreatment-digestion methods can be applied to rapidly evaluate biochemical-biogas potential of anaerobically digested Carica papaya petioles was tested. Pretreatment parameters explored include hydration, i.e. 1:4, 1:6, 1:8 and 1:10 substrate mass to water weight ratios and hydrochloric acid pretreatment chemistry, i.e. 1%—0.3 M, 3%—0.7 M, 5%—1.0 M, in batch digestions experiments with non-inoculated C. papaya petioles. Biomass feedstock was obtained from Edo State, Nigeria. Digestions were performed at ambient temperature, i.e. 27 °C, for 3 weeks. Pretreated C. papaya petiole samples were analyzed with Fourier transform infrared spectroscopy to characterize spectra with characteristic absorbance ratios indicative of physicochemical changes in biomass that include;cross-linked lignin ratio, the lateral order index, and hydrogen bond intensity. Cumulative gaseous yields from C. papaya petiole digestions were fit to the modified Gompertz form and optimized with non-linear regression in Matlab® to derive mathematical parameters, i.e. daily maximum production rate of biogas, a lag phase prior to biogas production, and biogas potential. An optimum C. papaya petiole pretreatment digestion scenario, i.e. 1:4 substrate-to-water ratio and 3%, i.e. 0.7 M, HCl pretreatment resulted in a 150% increase in yield, (10.9 mL gas/g VS) compared to non-pretreated C. papaya petiole digestion. Analysis with the modified Gompertz form also suggests rapid biogas potential assessment can be performed in a few days-time, i.e. approximately 10 days with distinct visual characterization of individual digestion performance. It was concluded that the applied digestion procedures with the use of C. papaya peti

关键词： Anaerobic digestion

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Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

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aBIOTECH 2024年第3期5卷 281-297页

作者： Sishi Chen Xuqi Lu Hongda Fang Anand Babu Perumal Ruyue Li Lei Feng Mengcen Wang Yufei Liu College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China State Key Laboratory of Rice Biology and Breeding Zhejiang UniversityHangzhou 310058China Computational Modeling and Nanoscale Processing Unit National Institute of Food TechnologyEntrepreneurship and Management-ThanjavurMinistry of Food Processing IndustriesThanjavur 613005India College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Global Education Program for AgriScience Frontiers Graduate School of AgricultureHokkaido UniversitySapporo 060-8589Japan

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing *** disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as *** disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of ***,early disease surveillance plays a crucial role in securing rice *** monitoring methods are both time-consuming and labor-intensive and cannot be broadly *** this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae *** data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease *** data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning *** on this model,an average accuracy of 92.2%was achieved on the 21st day of *** also achieved an accuracy of 79.4%as early as the 9th *** characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field ***,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

关键词： Bakanae disease Hyperspectral imaging Deep learning Early surveillance Disease monitoring

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