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Inference and prediction of malaria transmission dynamics using time series data

Infectious Diseases of Poverty 2020年第4期9卷 84-96页

作者： Benyun Shi Shan Lin Qi Tan Jie Cao Xiaohong Zhou Shang Xia Xiao-Nong Zhou Jiming Liu School of Computer Science and Technology Nanjing Tech UniversityNanjing 211800JiangsuChina College of Information Engineering Nanjing University of Finance&EconomicsNanjing 210003JiangsuChina Department of Computer Science Hong Kong Baptist UniversityKowloonHong Kong Department of Pathogen Biology School of Public HealthSouthern Medical UniversityGuangzhou 510515GuangdongChina National Institute of Parasitic Diseases Chinese Center for Diseases Control and PreventionShanghai 200025China Key Laboratory of Parasite and Vector Biology National Health Commission of the People Republic of ChinaShanghai 200025China Chinese Center for Tropical Disease Research Shanghai 200025China World Health Organization Collaborating Centre for Tropical Diseases Shanghai 200025China

Background:Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time *** evaluate the severity of an epidemic,statistical methods are widely used to forecast the trend,seasonality,and the possible number of infections of a ***,most statistical methods are limited in revealing the underlying dynamics of disease transmission,which may be affected by various impact factors,such as environmental,meteorological,and physiological *** this study,we focus on investigating malaria transmission dynamics based on time series ***:A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence ***,the dynamics of malaria transmission is modeled based on the notion of vectorial capacity(VCAP)and entomological inoculation rate(EIR).A particle Markov chain Monte Carlo(PMCMC)method is employed to estimate the model ***,a one-step-ahead prediction method is proposed to project the number of future malaria ***,two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling,Yunnan province,***:The results show that the trained data-driven stochastic model can well fit the historical time series of *** prevalence data in both counties from 2007 to ***,with well-trained model parameters,the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria ***:By involving dynamically changing impact factors,the proposed data-driven model together with the PMCMC method can successfully(i)depict the dynamics of malaria transmission,and(ii)achieve accurate one-step-ahead prediction about malaria *** a data-

关键词： malaria prediction dynamics

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On the distribution of the ratio of products of fisher-snedecor f random variables and its applications

arXiv

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arXiv 2019年

作者： Du, Hongyang Zhang, Jiayi Peppas, Kostas P. Zhao, Hui Ai, Bo Zhang, Xiaodan School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Informatics and Telecommunications University of Peloponnese Tripoli22131 Greece Communication Systems Department EURECOM Sophia Antipolis06410 France State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Management Shenzhen Institute of Information Technology Shenzhen518172 China

The Fisher-Snedecor F distribution has been recently proposed as a more accurate and mathematically tractable composite fading model than traditional established models in some practical cases. In this paper, we firstly derive exact closed-form expressions for the main statistical characterizations of the ratio of products of F-distributed random variables, including the probability density function, the cumulative distribution function and the moment generating function. Secondly, simple and tight approximations to the distribution of products and ratio of products of F-distributed random variables are presented. These analytical results can be readily employed to evaluate the performance of several emerging system configurations, including full-duplex relaying systems operating in the presence of co-channel interference and wireless communication systems enhanced with physical-layer security. The proposed mathematical analysis is substantiated by numerically evaluated results, accompanied by equivalent ones obtained using Monte Carlo simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Random variables

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Correction: West Nile Virus Outbreak in Israel 2024 Compared with Previous Seasons: A Retrospective Study

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Infectious diseases and therapy 2025年 2025 May 24页

作者： Guy Katzenellenbogen Michal Canetti Ili Margalit Yonatan Shusterman Alon Simchovitz-Gesher Lior Naveh Nadav Baharav Miki Goldenfeld Ana Belkin Marina Brod Anat Wieder-Finesod Eyal Leshem Erez Magiel Itzchak Levy Yaniv Lustig Victoria Indenbaum Nicola Maggio Shahar Dekel Bella Mechnik Yovel Peretz Noam Barda Amir Tafesh Dafna Yahav Gili Regev-Yochay Pediatric Infectious Diseases Unit Safra Medical Center Ramat-Gan Israel. Faculty of Medical and Health Sciences Tel-Aviv University Ramat-Aviv Tel-Aviv Israel. michalcanetti@***. Internal Medicine E Sheba Medical Center 2 Sheba Road 52621 Ramat-Gan Israel. michalcanetti@***. Faculty of Medical and Health Sciences Tel-Aviv University Ramat-Aviv Tel-Aviv Israel. Infectious Diseases Unit Sheba Medical Center Ramat-Gan Israel. Department of Neurology Sheba Medical Center Ramat-Gan Israel. Internal Medicine T Sheba Medical Center Ramat-Gan Israel. Internal Medicine A Sheba Medical Center Ramat-Gan Israel. Internal Medicine D Sheba Medical Center Ramat-Gan Israel. Internal Medicine F Sheba Medical Center Ramat-Gan Israel. Central Virology Laboratory Public Health Services Ministry of Health Tel-Hashomer Ramat Gan Israel. Infectious Control Unit Sheba Medical Center Ramat-Gan Israel. The Sheba Pandemic Preparedness Research Institute (SPRI) Sheba Medical Center Ramat-Gan Israel. ARC Innovation Center Sheba Medical Center Tel Hashomer Ramat Gan Israel. Software and Information Systems Engineering Ben-Gurion University of the Negev Be'er Sheva Israel. Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Be'er Sheva Israel. Infectious Diseases Division Rambam Health Care Campus Haifa Israel. Leumit Health Care Services Tel-Aviv Israel. Faculty of Medicine Technion-Israel Institute of Technology Haifa Israel.

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Author Correction: Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

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Nature cell biology 2024年第5期26卷 839页

作者： Sanjay Jain Liming Pei Jeffrey M Spraggins Michael Angelo James P Carson Nils Gehlenborg Fiona Ginty Joana P Gonçalves James S Hagood John W Hickey Neil L Kelleher Louise C Laurent Shin Lin Yiing Lin Huiping Liu Alexandra Naba Ernesto S Nakayasu Wei-Jun Qian Andrea Radtke Paul Robson Brent R Stockwell Raf Van de Plas Ioannis S Vlachos Mowei Zhou Katy Börner Michael P Snyder Department of Medicine Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pediatrics Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. sanjayjain@wustl.edu. Center for Mitochondrial and Epigenomic Medicine Department of Pathology and Laboratory Medicine Children's Hospital of Philadelphia and Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. lpei@pennmedicine.upenn.edu. Department of Cell and Developmental Biology and the Mass Spectrometry Research Center Vanderbilt University School of Medicine Nashville TN USA. jeff.spraggins@vanderbilt.edu. Department of Pathology Stanford School of Medicine Stanford CA USA. Texas Advanced Computing Center University of Texas at Austin Austin TX USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. GE Research Center Niskayuna NY USA. Department of Intelligent Systems Delft University of Technology Delft Netherlands. Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Microbiology and Immunology Stanford University Stanford CA USA. Departments of Medicine Chemistry and Molecular Biosciences Northwestern University Evanston IL USA. Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA. Division of Cardiology University of Washington School of Medicine Seattle WA USA. Department of Surgery Washington University School of Medicine St Louis MO USA. Departments of Pharmacology Medicine (Hematology and Oncology) Lurie Cancer Center Northwestern University Feinberg School of Medicine Chicago IL USA. Department of Physiology and Biophysics University of Illinois at Chicago Chicago IL USA. Biological Sciences Division

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Axonal blocking and injuring responses caused by asymmetric high frequency electrical stimulations of frog's sciatic nerve 2

Axonal blocking and injuring responses caused by asymmetric ...

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2nd International Conference on Biological information and Biomedical engineering, BIBE 2018

作者： Zhou, Weishuai Sun, Lifei Zhang, Xin Chen, Weiwu Yang, Ming Liu, Hailong Li, Sen Guan, Shui Sun, Changkai Sun, Changsen School of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electronic Engineering Dalian Neusoft University of Information Dalian116032 China College of Information Science and Technology Dalian Maritime University Dalian116026 China School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China Research Center for the Control Engineering of Translational Precision Medicine Dalian University of Technology Dalian116024 China State Key Laboratory of Fine Chemicals Dalian R&D Center for Stem Cell and Tissue Engineering Dalian University of Technology Dalian116024 China Liaoning Provincial Key Laboratory of Cerebral Diseases Institute for Brain Disorders Dalian Medical University Dalian116044 China

ISBN: (纸本)9783800747276

High frequency (HF) electrical stimulations have proven to be able to block nerve conductions, which would have widespread potential clinical applications. However, some studies demonstrated that the HF blocking stimulus of short duration of 5 s could also cause long lasting injuries to neural conductions. Further revelations of the effects of various kinds of HF stimulus on nerve's conduction would provide more insights into the possible underlying mechanisms and directions towards designing more safe and efficacious HF waveforms. This study investigated the blocking and injur-ing effects of the asymmetric HF currents on neural conductions on bullfrog's sciatic nerves. The used stimulus includ-ed the 5 s low frequency (10 Hz) pulse train to generate control and test compound action potentials (CAPs) and the 5 s HF asymmetric currents to block the CAPs conduction. The asymmetric HF waveforms were characterized by various frequencies (F=1, 2, or 5 kHz) and anode-to-cathode pulse width ratios (r=1, 2, or 3), and the first cathode pulse had a constant width of 0.05 ms. The parameters of blocking rates (BR) and injuring rates (IR) were measured to identify the possible effects. The results demonstrated that the HF currents of relatively high F (5 kHz) and low r (1 and 2) might be useful for achieving nerve conduction blockades of relatively high blocking rates and low injury rates. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Cathodes

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

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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).

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Electrical bioimpedance spectroscopy as a tool for non-destructive monitoring nitrogen supply in two lettuce genotypes

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Scientia Horticulturae 2025年 349卷

作者： Flórián Kovács Ákos Odry Zoltán Vizvári Anita Szegő Haimei Chen Enikő Papdi Ingrid Melinda Gyalai Ágnes Kun Péter Odry Katalin Juhos Department of Agro-Environmental Studies Hungarian University of Agriculture and Life Sciences Villányi str. 29-43 Budapest H-1118 Hungary Faculty of Agriculture Institute of Plant Sciences and Environmental Protection University of Szeged Hódmezővásárhely 6800 Hungary Department of Mechatronics and Automation Faculty of Engineering University of Szeged Moszkvai krt. 9 Szeged 6725 Hungary Department of Control Engineering and Information Technology University of Dunaújváros Táncsics Mihály u. 1 Dunaújváros 2400 Hungary John Von Neumann Faculty of Informatics Biomatics and Applied Artificial Intelligence Institute Óbuda University Bécsi str. 96 Budapest H-1034 Hungary Bioimpedance Technologies Research Center University Research and Innovation Center Óbuda University Bécsi str. 96 Budapest H-1034 Hungary Multidisciplinary Medical and Engineering Cellular Bioimpedance Research Group Szentagothai Research Centre University of Pecs Ifjusag str. 20 Pecs H-7624 Hungary Department of Plant Physiology and Plant Ecology Institute of Agronomy Hungarian University of Agriculture and Life Sciences Ménesi Str. 44 Budapest H-1118 Hungary Department of Floriculture and Dendrology Urban Planning and Garden Art Institute of Landscapes Architecture Hungarian University of Agriculture and Life Sciences Villányi ut 29-43 Budapest 1118 Hungary Research Center for Irrigation and Water Management Institute of Environmental Sciences Hungarian University of Agriculture and Life Sciences Anna-Liget str. 35. Szarvas H-5540 Hungary

Accurate monitoring of nutrient supply is crucial for effective plant nutrition management. This study introduces a bioimpedance spectroscopy (BIS) approach to evaluate how lettuce genotypes respond to nitrogen (N) supply, illuminating the link between BIS and N metabolism. The research consisted of two parts: a preliminary experiment focusing on extracellular fluid resistance, followed by a second experiment involving five different N treatments applied to two lettuce genotypes. BIS spectra were recorded from 1 Hz to 100 kHz, with a total of 224 measurements. Plant physiological analyses assessed total and extracellular fluid nitrate (NO 3 − ) concentrations, pigment concentration, total N content, and cell membrane stability index. In the preliminary experiment, a significant negative correlation was observed between extracellular fluid NO 3 − concentration and extracellular fluid resistance (r = – 0.67). Furthermore, the frequency range around 63 Hz (R 2 = 0.97) appeared most sensitive to NO 3 − dynamics in the ‘Kobak’ lettuce genotype. In the two lettuce genotypes exposed to the five different N treatments, extracellular fluid resistance negatively correlated with total N and NO 3 − content ( r = – 0.76 and – 0.73), while vacuole fluid resistance showed a moderate correlation ( r = – 0.51). Cell membrane capacitance was also significantly correlated with membrane stability index ( r = 0.68), indicating that low-N conditions reduce membrane charge storage. However, these correlations appeared highly genotype dependent. Heatmap-based hierarchical clustering of Z-score standardized BIS parameters further confirmed these genotype-specific patterns. Collectively, these findings suggest that BIS captures key aspects of N uptake and storage, particularly in the extracellular fluid compartment. Overall, BIS offers a robust, non-destructive method for monitoring N status and may enhance precision in nutrient management for lettuce and other crops, although the genotype-s

关键词： Extracellular fluid resistance Vacuole fluid resistance Cell membrane capacitance Nitrogen uptake

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