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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

Allen: A High-Level Trigger on GPUs for LHCb

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Computing and Software for Big science 2020年第1期4卷 1-11页

作者： Aaij, R. Albrecht, J. Belous, M. Billoir, P. Boettcher, T. Brea Rodríguez, A. vom Bruch, D. Cámpora Pérez, D.H. Casais Vidal, A. Craik, D.C. Fernandez Declara, P. Funke, L. Gligorov, V.V. Jashal, B. Kazeev, N. Martínez Santos, D. Pisani, F. Pliushchenko, D. Popov, S. Quagliani, R. Rangel, M. Reiss, F. Sánchez Mayordomo, C. Schwemmer, R. Sokoloff, M. Stevens, H. Ustyuzhanin, A. Vilasís Cardona, X. Williams, M. Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Fakultät Physik Technische Universität Dortmund Dortmund Germany National Research University Higher School of Economics Moscow Russian Federation Yandex School of Data Analysis Moscow Russian Federation LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Massachusetts Institute of Technology Cambridge United States Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain Faculty of Science and Engineering Maastricht University Maastricht Netherlands European Organization for Nuclear Research (CERN) Geneva Switzerland Department of Computer Science and Engineering University Carlos III of Madrid Madrid Spain Instituto de Física Corpuscular Centro Mixto Universidad de Valencia CSIC Valencia Spain INFN Sezione di Bologna Bologna Italy Università di Bologna Bologna Italy National Research University Higher School of Economics Saint Petersburg Russian Federation National University of Science and Technology MISIS Moscow Russian Federation Instituto de Física Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil University of Cincinnati Cincinnati OH United States DS4DS la Salle Universitat Ramon Llull Barcelona Spain

We describe a fully GPU-based implementation of the first level trigger for the upgrade of the LHCb detector, due to start data taking in 2021. We demonstrate that our implementation, named Allen, can process the 40 Tbit/s data rate of the upgraded LHCb detector and perform a wide variety of pattern recognition tasks. These include finding the trajectories of charged particles, finding proton–proton collision points, identifying particles as hadrons or muons, and finding the displaced decay vertices of long-lived particles. We further demonstrate that Allen can be implemented in around 500 scientific or consumer GPU cards, that it is not I/O bound, and can be operated at the full LHC collision rate of 30 MHz. Allen is the first complete high-throughput GPU trigger proposed for a HEP experiment. © 2020, The Author(s).

关键词： GPU LHCb Real-time data selection Trigger

External exposome and incident asthma across the life course in 14 European cohorts: a prospective analysis within the EXPANSE project

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The Lancet Regional Health - Europe

The Lancet Regional Health - Europe 2025年 54卷

作者： Yu, Zhebin Kress, Sara Blay, Natalia Gregor, Petr Kukk, Hanna-Maria Leskien, Miriam Majewska, Renata Oosterwegel, Max J. Szabó, Daniel ten Have, Margreet Klánová, Jana Mikeš, Ondřej Bergström, Anna Bussalleu, Alonso de Cid, Rafael Dalecka, Andrea Dadvand, Payam van Dorsselaer, Saskia Fischer, Krista de Hoogh, Kees Koppelman, Gerard H. Kronberg, Jaanika Metspalu, Andres Milani, Lili Esko, Tõnu Metspalu, Mait Lakerveld, Jeroen Ljungman, Petter Merid, Simon Kebede Macek, Pawel Manczuk, Marta Merritt, Anne-Sophie Pac, Agnieszka Palta, Priit Pershagen, Göran Peters, Annette Pikhart, Hynek Saucy, Apolline Schikowski, Tamara Shen, Youchen Standl, Marie Tonne, Cathryn Vermeulen, Roel Vlaanderen, Jelle Vonk, Judith M. Wolf, Kathrin Ek, Carl Henrik Gruzieva, Olena Gehring, Ulrike Melén, Erik Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden IUF – Leibniz Research Institute for Environmental Medicine Düsseldorf Germany Genomes for Life-GCAT Lab CORE Program Germans Trias I Pujol Research Institute (IGTP) Badalona Spain Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP) Badalona Spain RECETOX Faculty of Science Masaryk University Brno Czech Republic Institute of Genomics University of Tartu Estonia Institute of Mathematics and Statistics University of Tartu Estonia Institute of Epidemiology Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg Germany Institute for Medical Information Processing Biometry and Epidemiology Medical Faculty Ludwig-Maximilians-Universität München Munich Germany Chair of Epidemiology and Preventive Medicine Jagiellonian University Medical College Kraków Poland Institute for Risk Assessment Sciences Utrecht University Utrecht Netherlands Netherlands Institute of Mental Health and Addiction Utrecht Netherlands Centre for Occupational and Environmental Medicine Region Stockholm Stockholm Sweden Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland ISGlobal Barcelona Spain Universitat Pompeu Fabra (UPF) Barcelona Spain CIBER Epidemiología y Salud Pública Spain University of Groningen University Medical Center Groningen Beatrix Children's Hospital Department of Pediatric Pulmonology and Pediatric Allergology Groningen Netherlands University of Groningen University Medical Center Groningen Groningen Research Institute for Asthma and COPD (GRIAC) Groningen Netherlands Department of Epidemiology and Data Science Amsterdam UMC Location Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Cardiology Danderyd Hospital Stockholm Sweden Department of Clinical Sciences and Education Södersjukhuset Karolinska Institutet Stockholm Sweden Collegium Medicum Jan Koc

Background: The joint impact of exposure to multiple urban environmental factors on asthma remains unclear. Methods: We analysed data from 14 European cohorts to assess the impact of the urban exposome on asthma incidence across the life course. We linked three external exposome domains (air pollution, built environment, ambient temperature) to the participants’ home addresses at baseline. We performed k-means clustering within each domain and assessed associations of clusters with asthma adjusting for potentially relevant covariates in cohort-specific analyses, with subsequent separate meta-analyses for birth and adult cohorts. An environmental risk score using a coefficient-weighted sum approach was used to assess the impact of combining the three domains. Findings: A total of 7428 incident asthma cases were identified among 349,037 participants (from birth up to age 70+). Overall, we observed higher risks of asthma for clusters characterized by high particulate matter and nitrogen dioxide exposure in adults (OR_meta = 1.13, 95%CI:1.01–1.25), and clusters characterized by high built-up area and low levels of greenness in both children and adults (OR_meta = 1.36, 95%CI: 1.14–1.64 for birth cohorts and OR_meta = 1.15, 95%CI: 1.03–1.28 for adult cohorts, respectively). The joint exposure using the environment risk score combining the three domains was consistently associated with higher risks of incident asthma (OR_meta = 1.13, 95%CI: 1.07–1.20 for birth cohorts, OR_meta = 1.15, 95%CI: 1.10–1.20 for adult cohorts per 20% increase). On average 11.6% of the incident asthma cases could be attributed to environmental risk score above cohort-specific median levels. Interpretation: Multiple environmental exposures jointly contribute to incident asthma risk across the life course. Urban planning accounting for these factors may help mitigate asthma development. Funding: This study was funded by the European Union's Horizon 2

关键词： Asthma Cohort Exposome Life course

Person re-identification in aerial imagery

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arXiv 2019年

作者： Zhang, Shizhou Zhang, Qi Yang, Yifei Wei, Xing Wang, Peng Jiao, Bingliang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via https://***/stormyoung/PRAI-1581. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Bio-supported of gold nanoparticles over magnetic nanoparticles: Synthesis, characterization and evaluation its catalytic activity for one-pot preparation of pyrano[2,3-d]pyrimidines

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Journal of Organometallic Chemistry 2025年

作者： Ali B.M. Ali Abdulsalam Abdulsattar Abdulazez Elyor Berdimurodov Narinderjit Singh Sawaran Singh Maher Ali Rusho Aseel Smerat M.A. Diab Heba A. El-Sabban Amir Ibrahim Ali Arabi Saiful Islam Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Medical Laboratory Techniques department College of Health and Medical Technology University of Al-maarif Anbar Iraq Faculty of Chemistry National University of Uzbekistan Tashkent 100034 Uzbekistan School of Medicine Central Asian University Tashkent 111221 Uzbekistan Faculty of Chemistry and Biology Karshi State University Karshi Uzbekistan Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder CO 80309 USA Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Department of Biotechnology Yeungnam University Gyeongsan Gyeongbuk 38541 Republic of Korea Water Analysis Department Central Public Health Laboratories Ministry of Health and Population Cairo 11613 Egypt Integrated Materials Chemistry Laboratory School of Materials Science and Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Analysis and Evaluation Department Egyptian Petroleum Research Institute (EPRI) 1 Ahmed El Zomor St. Nasr Cairo 11727 Egypt Mechanical Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia

In this research, we introduced the synthesis of bio-induced gold nanoparticles (Au NPs) utilizing Achillea millefolium extract to functionalize magnetic Fe 3 O 4 NPs. The extract of Achillea millefolium was used as a sustainable reducing agent and excels in preserving the stability of the synthesized NPs. The resulting Fe 3 O 4 @AMextract/Au nanocomposite underwent a thorough physicochemical characterization employing techniques such as FE-SEM, TEM, EDX, VSM, and ICP-OES. The heterogeneous Fe 3 O 4 @AMextract/Au catalyst exhibited remarkable catalytic efficiency in the production of pyrano[2,3-d]pyrimidines via a three-component reaction that included barbituric acid, aryl aldehydes, and malononitrile. Furthermore, the Fe 3 O 4 @AMextract/Au catalyst demonstrated commendable recyclability, sustaining its catalytic efficacy after being utilized seven times with minimal reduction in performance.

关键词：

Stock market analysis using social networks 18

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Stock market analysis using social networks

2018 Australasian computer science Week Multiconference, ACSW 2018

作者： Li, Man Puthal, Deepak Yang, Chi Luo, Yun Zhang, Jin Li, Jianxin Business School University of Western Australia Perth Australia School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Unitec Institution of Technology New Zealand University of Western Australia Perth Australia Faculty of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Software Engineering University of Western Australia Perth Australia

ISBN: (纸本)9781450354363

Nowadays, the use of social media has reached unprecedented levels. Among all social media, with its popular micro-blogging service, Twitter enables users to share short messages in real time about events or express their own opinions. In this paper, we examine the effectiveness of various machine learning techniques on retrieved tweet corpus. A machine learning model is deployed to predict tweet sentiment, as well as gain an insight into the correlation between twitter sentiment and stock prices. Specifically, that correlation is acquired by mining tweets using Twitter's search API and process it for further analysis. To determine tweet sentiment, two types of machine learning techniques are adopted including Naïve Bayes classification and Support vector machines. By evaluating each model, we discover that support vector machine gives higher accuracy through cross validation. After predicting tweet sentiment, we mine historical stock data using Yahoo finance API, while the designed feature matrix for stock market prediction includes positive, negative, neutral and total sentiment score and stock price for each day. In order to capturing the correlation situation between tweet opinions and stock market prices, hence, evaluating the direct correlation between tweet sentiments and stock market prices, the same machine learning algorithm is implemented for conducting our empirical study. © 2018 ACM.

关键词： Sentiment analysis

IoT Based Smart Agriculture Monitoring, Automation and Intrusion Detection System

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Journal of Physics: Conference Series 2021年第1期1962卷

作者： Ahmad Faisol Suhaimi Naimah Yaakob Sawsan Ali Saad Khairul Azami Sidek Mohamed Elobaid Elshaikh Alaa K.Y. Dafhalla Ong Bi Lynn Mahathir Almashor Faculty of Electronic Engineering technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Advanced Computing Centre of Excellence (CoE) Universiti malaysia Perlis (UniMAP) Perlis Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Kingdom of Saudi Arabia Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia (IIUM) Selangor Malaysia Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia

Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.

关键词：

Indirect identification of horizontal gene transfer

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arXiv 2020年

作者： Schaller, David Lafond, Manuel Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Department of Computer Science Université de Sherbrooke 2500 boul. de l’Université SherbrookeQCJ1K 2R1 Canada Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs. © 2020, CC BY.

关键词： Phylogenetic