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Single-molecule tracking of myelin basic protein during oligodendrocyte differentiation

Biological imaging 2023年 3卷 e24页

作者： Sayed M Rassul Masahiro Otsu Iain B Styles Robert K Neely Daniel Fulton Neuroscience and Ophthalmology Research Group Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham Birmingham UK. Physical Sciences of Imaging in the Biomedical Sciences Training Programme University of Birmingham Birmingham UK. Braizon Therapeutics Inc. Kanagawa Japan. School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK. School of Chemistry University of Birmingham Birmingham UK.

This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.

关键词： TIRF imaging myelin myelin basic protein oligodendrocyte single-molecule tracking

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Combining machine learning and computational chemistry for predictive insights into chemical systems

arXiv

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arXiv 2021年

作者： Keith, John A. Vassilev-Galindo, Valentin Cheng, Bingqing Chmiela, Stefan Gastegger, Michael Müller, Klaus-Robert Tkatchenko, Alexandre Department of Chemical and Petroleum Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh United States Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Accelerate Programme for Scientific Discovery Department of Computer Science and Technology 15 J. J. Thomson Avenue CambridgeCB3 0FD United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Avenue CambridgeCB3 0HE United Kingdom Department of Software Engineering and Theoretical Computer Science Technische Universität Berlin Berlin10587 Germany Machine Learning Group Technische Universität Berlin Berlin10587 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max-Planck-Institut für Informatik Saarbrücken Germany Google Research Brain team Berlin Germany

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design. © 2021, CC BY-SA.

关键词： Computational chemistry

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Rationalising Development of Classification Systems Describing Livestock Production Systems for Disease Burden Analysis within the Global Burden of Animal Diseases programme

SSRN

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SSRN 2023年

作者： Li, Yin McIntyre, K. Marie Rasmussen, Philip Gilbert, William Chaters, Gemma Raymond, Kassy Jemberu, Wudu T. Larkins, Andrew Patterson, Grace T. Kwok, Stephen Kappes, Alexander James Mayberry, Dianne Schrobback, Peggy Acosta, Mario Herrero Stacey, Deborah A. Kallon, Edna Massay Jeannin, Morgan Huntington, Benjamin Bruce, Mieghan Knight-Jones, Theodore Rushton, Jonathan Programme Commonwealth Scientific and Industrial Research Organization Agriculture and Food Brisbane4067 Australia School of Veterinary Medicine Centre for Biosecurity and One Health Harry Butler Institute Murdoch University Australia School of Natural and Environmental Sciences Newcastle University United Kingdom Institute of Infection and Global Health University of Liverpool IC2 Building 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Department of Veterinary and Animal Sciences Section for Animal Welfare and Disease Control University of Copenhagen Copenhagen Denmark Section for Epidemiology University of Zurich Zurich Switzerland School of Computer Science University of Guelph Canada International Livestock Research Institute P O Box 5689 Addis Ababa Ethiopia University of Gondar P. O. Box 196 Gondar Ethiopia College of Agriculture and Life Sciences Cornell University 250C Warren Hall IthacaNY14853 United States School of Economic Sciences & Paul G. Allen School for Global Health Washington State University United States Science Department World Organisation for Animal Health

Estimates of the burden of livestock ill health and expenditure on averting ill health are needed to identify where the largest losses are occurring and who across society is impacted, which is the aim of the Global Burden of Animal Diseases programme. The heterogeneity that exists across the global spectrum of livestock production and per capita wealth means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. Livestock populations must, therefore, be systematically classified into different types of production system, reflective of the heterogeneity across production systems, so that burden of disease estimates can be made specific to the represented livestock population and people reliant upon *** paper explores the data currently available of livestock production system classifications and animal health through a scoping review in order to work towards the development of a framework that facilitates more refined and specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global *** proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems and outline the need for finer-level data to inform locally relevant classifications that will benefit the development of evidence-based animal health policy in the future. The classification system for GBADs will evolve and strengthen through the capture of local and national classification

关键词： Classification (of information)

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Rationalising Development of Classification Systems Describing Livestock Production Systems for Disease Burden Analysis within the Global Burden of Animal Diseases programme

SSRN

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SSRN 2023年

作者： Li, Yin McIntyre, K. Marie Rasmussen, Philip Gilbert, William Chaters, Gemma Jemberu, Wudu T. Raymond, Kassy Larkins, Andrew Patterson, Grace T. Kwok, Stephen Kappes, Alexander James Mayberry, Dianne Schrobback, Peggy Acosta, Mario Herrero Stacey, Deborah A. Kallon, Edna Massay Jeannin, Morgan Huntington, Benjamin Bruce, Mieghan Knight-Jones, Theodore Rushton, Jonathan Programme Commonwealth Scientific and Industrial Research Organization Agriculture and Food Brisbane4067 Australia School of Veterinary Medicine Centre for Biosecurity and One Health Harry Butler Institute Murdoch University Australia School of Natural and Environmental Sciences Newcastle University United Kingdom Institute of Infection and Global Health University of Liverpool IC2 Building 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Department of Veterinary and Animal Sciences Section for Animal Welfare and Disease Control University of Copenhagen Copenhagen Denmark Section for Epidemiology University of Zurich Zurich Switzerland School of Computer Science University of Guelph Canada International Livestock Research Institute P O Box 5689 Addis Ababa Ethiopia University of Gondar P. O. Box 196 Gondar Ethiopia College of Agriculture and Life Sciences Cornell University 250C Warren Hall IthacaNY14853 United States School of Economic Sciences & Paul G. Allen School for Global Health Washington State University United States Science Department World Organisation for Animal Health

The heterogeneity that exists across the global spectrum of livestock production and per capita wealth means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. Livestock populations must, therefore, be systematically classified into different types of production system, reflective of the heterogeneity across production systems, so that burden of disease estimates can be made specific to the represented livestock population and people reliant upon *** paper explores the data currently available of livestock production system classifications and animal health through a scoping review in order to work towards the development of a framework that facilitates more refined and specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global *** proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems. This development will fill a major gap in how livestock production and health are currently approached and analyzed. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Consumer product preference prediction towards online shopping

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IOP Conference Series: Materials science and Engineering 2021年第1期1055卷

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

arXiv

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arXiv 2019年

作者： Palombo, Marco Ianus, Andrada Guerreri, Michele Nunes, Daniel Alexander, Daniel C. Shemesh, Noam Zhang, Hui Centre for Medical Image Computing Dept of Computer Science University College London London United Kingdom Champalimaud Neuroscience Programme Champalimaud Centre for the Unknown Lisbon Portugal

This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging trough DW-MRI presents water diffusion in white (WM) and grey (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b3 ms/m2, it has been also shown to fail in GM at high b values (b>>3 ms/m2). Here we hypothesize that the unmodelled soma compartment may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40 ms/m2) and human (bmax= 10 ms/m2)brain. We show that SANDI defines new contrasts representing new complementary information on the brain cyto- and myelo-architecture. Indeed, we show for the first-time maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience. Copyright © 2019, The Authors. All rights reserved.

关键词： Microstructure

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Clinicians risk becoming 'liability sinks' for artificial intelligence

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Future healthcare journal 2024年第1期11卷 100007页

作者： Tom Lawton Phillip Morgan Zoe Porter Shireen Hickey Alice Cunningham Nathan Hughes Ioanna Iacovides Yan Jia Vishal Sharma Ibrahim Habli Improvement Academy Bradford Institute for Health Research Bradford Royal Infirmary Duckworth Lane Bradford BD9 6RJ UK. Assuring Autonomy International Programme University of York Heslington York YO10 5DD UK. York Law School University of York Heslington York YO10 5DD UK. Department of Computer Science University of York Heslington York YO10 5DD UK.

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An Implementation of Backtracking Algorithm for Solving A Sudoku-Puzzle Based on Android

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Herimanto P Sitorus E M Zamzami Master Programme in Informatics Universitas Sumatera Utara. Faculty of Computer Science and Information Technology Universitas Sumatera Utara

Sudoku is a pretty popular number game. The goal of this game is to fill a 9x9 matrix with unique numbers, and there should not be repeated numbers in each row, column, or block. This paper proposed a solution to solve Sudoku using the Backtracking Algorithm. This algorithm is quite efficient because it does not have to check all the possibilities that exist, but which leads to only the solution that will be processed, namely by pruning every element that does not lead to the solution. Thus the time required is quite efficient and suitable for use in reasonably complex numbers games like sudoku. By implementing the backtracking algorithm in the sudoku game, the complexity of the algorithm can be as large θ (n3).

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Incomplete nutation diffusion imaging: An ultrafast, single-scan approach for diffusion mapping

arXiv

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arXiv 2019年

作者： Ianuş, Andrada Shemesh, Noam Champalimaud Neuroscience Programme Champalimaud Centre for the Unknown Lisbon Portugal Centre for Medical Image Computing Dept. of Computer Science University College London London United Kingdom

Purpose: Diffusion Magnetic Resonance Imaging (dMRI) is confounded by its long acquisition duration, thereby thwarting the detection of rapid microstructural changes, especially when diffusivity variations are accompanied by rapid changes in T2. The purpose of the present study is to accelerate dMRI to a single scan acquisition, and to enable a more accurate estimation of diffusivity as function of time. Methods: A general methodology termed Incomplete Initial Nutation Diffusion Imaging (INDI) capturing two diffusion contrasts in a single shot, is presented. INDI creates a longitudinal magnetization reservoir that facilitates the successive acquisition of two images separated by only a few milliseconds. INDI's theory is presented, followed by proof-of-concept ex- and in-vivo experiments at 16.4 T and 9.4 T. Results: Mean diffusivities (MDs) extracted from INDI were comparable with Diffusion Tensor Imaging (DTI) and the two-shot IDE in the water phantom. As expected in the brain tissues, DTI provided lower MD than UF-IDE and IDE, but IDE and UF-IDE were in excellent agreement. Simulations are presented for identifying the regimes where INDI is most beneficial. Conclusions: INDI accelerates dMRI acquisition to single-shot mode, which can be of great importance for mapping dynamic microstructural properties in-vivo without T2 bias. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Classification: using Back Propagation Neural Network Algorithm to Identify Cataract Disease

引用

Journal of Physics: Conference Series 2020年第1期1566卷

作者： W S Simamora R S Lubis E M Zamzami Master Programme in Informatics Universitas Sumatera Utara. Faculty of Computer Science and Information Technology Universitas Sumatera Utara.

Artificial Neural Networks are often used in the fields of pattern recognition, speech, and image recognition, where high levels of computation are needed. One method that can be used is the Back-propagation Neural Network. The method can be used to identify several diseases, one of which is a cataract. A cataract is one of the most significant diseases that can cause blindness. Cataracts consist of three levels, namely mature cataracts, immature cataracts, and hyper-mature cataracts. The testing method uses two parameter values, namely epoch with value 1000,5000,10000 and learning rate with value 0.01, 0.05, 0.1. From the test, it was found that the best parameters of the above trial results were epoch with a value of 10000 and learning rate with a value of 0.1 on 80 experimental data. In experiments conducted to identify normal, mature, immature, and hyper mature obtained an accuracy of 100%, 95%, 85%, and 90%. The percentage value of the accuracy of the test results using the BPN method is 92.5%.

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