检索结果-内蒙古大学图书馆

Metaproteomics in the One Health framework for unraveling microbial effectors in microbiomes

学校读者我要写书评

暂无评论

Microbiome 2025年第1期13卷 134页

作者： Robert Heyer Maximilian Wolf Dirk Benndorf Sergio Uzzau Jana Seifert Lucia Grenga Martin Pabst Heike Schmitt Bart Mesuere Tim Van Den Bossche Sven-Bastiaan Haange Nico Jehmlich Mariagrazia Di Luca Manuel Ferrer Sergio Serrano-Villar Jean Armengaud Helge B Bode Patrick Hellwig Catherine Robbe Masselot Renaud Léonard Paul Wilmes Multidimensional Omics Analyses Group Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V. Bunsen-Kirchhoff-Straße 11 44139 Dortmund Germany. robert.heyer@ISAS.de. Multidimensional Omics Analyses Group Faculty of Technology Bielefeld University Universitätsstraße 25 33615 Bielefeld Germany. robert.heyer@ISAS.de. Multidimensional Omics Analyses Group Faculty of Technology Bielefeld University Universitätsstraße 25 33615 Bielefeld Germany. Bioprocess Engineering Otto-Von-Guericke University Magdeburg Universitätsplatz 2 39106 Magdeburg Germany. Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Sandtorstraße 1 39106 Magdeburg Germany. Applied Biosciences and Process Engineering Anhalt University of Applied Sciences Köthen Germany. Department of Biomedical Sciences University of Sassari 07100 Sassari Italy. Institute of Animal Science University of Hohenheim Emil-Wolff-Str Stuttgart Germany. HoLMiR - Hohenheim Center for Livestock Microbiome Research University of Hohenheim Leonore-Blosser-Reisen Weg Stuttgart Germany. Département Médicaments Et Technologies Pour La Santé (DMTS) Université Paris-Saclay CEA INRAE SPI Bagnols-Sur-Cèze France. Department of Biotechnology Delft University of Technology Delft The Netherlands. Institute for Infectious Disease Control National Institute for Public Health and the Environment (RIVM) Bilthoven The Netherlands. Department of Applied Mathematics Computer Science and Statistics Ghent University 9000 Ghent Belgium. VIB - UGent Center for Medical Biotechnology VIB 9052 Ghent Belgium. Department of Biomolecular Medicine Faculty of Medicine and Health Sciences Ghent University 9052 Ghent Belgium. Department of Molecular Toxicology Helmholtz-Centre for Environmental Research - UFZ GmbH Permoserstrasse 15 04318 Leipzig Germany. Department of Biology University of Pisa 56127 Pisa Italy. Instituto de Catalisis y Petroleoquimica (ICP) CSIC 28049 Madrid Sp

One Health seeks to integrate and balance the health of humans, animals, and environmental systems, which are intricately linked through microbiomes. These microbial communities exchange microbes and genes, influencing not only human and animal health but also key environmental, agricultural, and biotechnological processes. Preventing the emergence of pathogens as well as monitoring and controlling the composition of microbiomes through microbial effectors including virulence factors, toxins, antibiotics, non-ribosomal peptides, and viruses holds transformative potential. However, the mechanisms by which these microbial effectors shape microbiomes and their broader functional consequences for host and ecosystem health remain poorly understood. Metaproteomics offers a novel methodological framework as it provides insights into microbial dynamics by quantifying microbial biomass composition, metabolic functions, and detecting effectors like viruses, antimicrobial resistance proteins, and non-ribosomal peptides. Here, we highlight the potential of metaproteomics in elucidating microbial effectors and their impact on microbiomes and discuss their potential for modulating microbiomes to foster desired functions.

关键词： Metaproteomics Microbial community Bacteriophages Microbiome Microbial effectors

The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

学校读者我要写书评

暂无评论

Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

关键词： Biogeography Epidemiology Infectious diseases Malaria

Transfer Learning for EEG-Based Brain-computer Interfaces: A Review of Progress Made Since 2016

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Tang, Song Yang, Yan Ma, Zhiyuan Hendrich, Norman Zeng, Fanyu Ge, Shuzhi Sam Zhang, Changshui Zhang, Jianwei The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Group Department of Informatics Universität Hamburg Hamburg Germany The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Lab. for Novel Software Technology Nanjing University Nanjing China The Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore The Department of Automation Tsinghua University Beijing China

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is available at https://***/tntek/N2DCX. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral control

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liang, Chen Wang, Weihong Zhenghua, L.I.U. Chao, L.A.I. Zhou, Benchun School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Navigation and Control Technology Research Institute of China North Industries Group Corporation Beijing100089 China

In this paper, we present a novel guidance scheme based on model-based deep reinforcement learning (RL) technique. With model-based deep RL method, a deep neural network is trained as a predictive model of guidance dynamics which is incorporated into a model predictive path integral (MPPI) control framework. However the traditional MPPI framework assumes the actual environment similar to the training dataset for the deep neural network which is impractical in practice with different maneuvering of target, other perturbations and actuator failures. To address this problem, our method utilize meta-learning technique to make the deep neural dynamics model adapt to such changes online. With this approach we can alleviate the performance deterioration of standard MPPI control caused by the difference between actual environment and training data. Then, a novel guidance law for a varying velocity interceptor intercepting maneuvering target with desired terminal impact angle under actuator failure is constructed based on aforementioned techniques. Simulation and experiment results under different cases show the effectiveness and robustness of the proposed guidance law in achieving successful interceptions of maneuvering target. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

Towards better surgical instrument segmentation in endoscopic vision: multi-angle feature aggregation and contour supervision

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Qin, Fangbo Lin, Shan Li, Yangming Bly, Randall A. Moe, Kris S. Hannaford, Blake Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China SeattleWA98195-2500 United States Department of Otolaryngology-Head & Neck Surgery UW Seattle United States Department of Electrical Computer and Telecommunications Engineering Technology Rochester Institute of Technology United States

—Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-Angle Feature Aggregation (MAFA) method, leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes. Secondly, in the end-to-end training stage, the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness, so that the contour shape of segmentation mask is more precise. The effectiveness of the proposed methods is validated with ablation experiments conducted on novel Sinus-Surgery datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Fault Detection and Basic In-Flight Reconfiguration of a Small UAV Equipped with Elevons ⁎

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2018年第24期51卷 600-607页

作者： Bauer, Peter Venkataraman, Raghu Vanek, Balint Seiler, Peter J. Bokor, Jozsef Systems and Control Laboratory Institute for Computer Science and Control Hungarian Academy of Sciences H-1111 Budapest Kende utca 13-17 Hungary Department of Aerospace Engineering and Mechanics University of Minnesota 110 Union Street SE MinneapolisMN55455 United States MTA-BME Control Engineering Research Group United States

The paper introduces the in-flight fault detection and basic reconfiguration of a small unmanned aerial vehicle equipped with two elevons and an electric motor. The considered fault scenario is one control surface stuck at a given position during straight and level flight. The fault detection is solved with Multiple Model Adaptive Estimation considering non-faulty and faulty (left or right surface stuck) system models. Basic reconfiguration to stabilize the flight against atmospheric disturbances is done applying the remaining surface in the lateral channel and the total energy control concept to hold the airspeed and altitude between acceptable limits in the longitudinal channel. Promising results are achieved in software-in-the-loop simulation with the fault detection and reconfiguration. © 2018

关键词： Fault detection

Design of Experiments for Battery Aging Estimation

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2018年第28期51卷 386-391页

作者： Pózna, Anna I. Hangos, Katalin M. Magyar, Attila Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Egyetem str. 10. VeszprémH-8200 Hungary Process Control Research Group HAS Research Institute for Computer Science and Control Kende str. 13-17. BudapestH-1111 Hungary

Li-ion batteries are widely used in EV applications and are imposed to several aging effects during their lifetime. Since battery health cannot be measured directly, information about its health can be obtained by iteratively re-estimating the parameters of the model describing its dynamical behavior. The optimal design of experiments is investigated in this paper. The proposed method applies families of input signals (PRBS current and constant current-constant voltage (CC-CV) signals) to the batteries to estimate the key aging factors. Simulation experiments have been used to analyze the statistical properties of the estimators as a function of the design parameters of the input signal families. The results show that the CC-CV charging-discharging cycle has the possibility to gain the most information out of the battery model parameter estimation. © 2018

关键词： Parameter estimation Battery management systems Design of experiments Electric excitation Iterative methods Lithium ion batteries Planning Electric Experiment design Input Model formulation Storage operations

Fault-tolerant environmental perception architecture for robust automated driving

学校读者我要写书评

暂无评论

Fault-tolerant environmental perception architecture for rob...

International Conference on Connected Vehicles and Expo (ICCVE)

作者： Stephanie Grubmüller Georg Stettinger Miguel Ángel Sotelo Daniel Watzenig Virtual Vehicle Research Center Graz Austria Computer Engineering Department University of Alcalá Alcalá de Henares Spain Institute of Automation and Control University of Technology Graz Graz Austria

Autonomous vehicles gain more and more attention. Moving towards highly automated vehicles requires the implementation of fault-tolerant systems. In this paper we propose an architecture for a fault-tolerant environmental perception, where either one fault in the hardware or one in the software can be detected. The hardware fault detection relies on a Landmark (LM) tracking approach. The software fault detection is based on comparing the outputs of redundant programs. The faulty module is then excluded in the data fusion algorithm by a fault masking. The functionality of the proposed approach is tested in simulation via injecting one hardware and one software fault.

关键词：