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Rôle des mastocytes dans l’asthme sévère : analyse de données transcriptomiques dans la cohorte U-BIOPRED

Revue des Maladies Respiratoires Actualités 2021年第1期13卷 68-68页

作者： A. Tiotiu Y. Badi N. Kermani P. Hansbro C. Wheelock S.E. Dahlen Y.K. Guo P. Sterk R. Djukanovic I. Adcock K.F. Chung Département de pneumologie CHRU Nancy France Data Science Institute Department of Computing Imperial College London Londres Royaume-Uni Faculty of Science University of Technology Sydney Australie The Centre for Allergy Research the Institute of Environmental Medicine Karolinska Institute Stockholm Suède Department of pulmonary diseases Faculty of Medicine University of Amsterdam Amsterdam Pays-Bas NIHR Southampton Respiratory Biomedical Research Unit University Hospital Southampton Southampton Royaume-Uni National Heart & Lung Institute Imperial College London Londres Royaume-Uni

Introduction Reconnus pour leur rôle dans la réaction allergique, la contribution des mastocytes (MCs) dans la pathogenèse de l’asthme sévère (AS) n’est pas bien établie pour le moment....

Introduction Reconnus pour leur rôle dans la réaction allergique, la contribution des mastocytes (MCs) dans la pathogenèse de l’asthme sévère (AS) n’est pas bien établie pour le moment. Objectif Étudier le rôle des MCs dans l’AS comparativement à un groupe contrôle (C) et un groupe avec asthme modéré (AM), en utilisant les données transcriptomiques des expectorations de la cohorte U-BIOPRED. Méthodes Nous avons analysé les données transcriptomiques de 61 patients non-fumeurs avec AS (ASnf), 23 patients fumeurs/ex-fumeurs avec AS (ASf), 20 patients avec AM et 16 C. Neuf listes de gènes des MCs ont été utilisées pour étudier leurs réponses aux différents stimuli (IgE, IL-33, LPS et IFNγ) et leur état d’activation. En appliquant la méthode gene set variation analysis (GSVA), un score d’enrichissement a été calculé pour chaque liste de gènes pour chaque patient, avec une moyenne pour le groupe. Les analyses ont été réalisées pour comparer les groupes selon la sévérité de l’asthme et les phénoytpes inflammatoires (TAC1 inflammation prédominante éosinophilique, TAC2 inflammation prédominante neutrophilique et TAC3 inflammation paucigranulocytique) en utilisant la méthode ANOVA et le t test. Résultats Malgré leur nombre quasiment nul dans l’expectoration, les signatures de MCs stimulés par IgE, IL-33 et LPS étaient enrichies dans l’AS comparativement à l’AM et le groupe C ( p < 0,03), sans différences significatives entre l’ASnf vs ASf ou AM vs C. Les MCs inactifs, originaires du thymus et IgE-stimulés étaient significativement enrichis dans le groupe TAC1 par rapport aux groupes TAC2 et TAC3 ( p < 0,01) tandis que les MCs stimulés par IL-33, LPS, IFNg, IgE de manière répétitive, étaient enrichis dans le groupe TAC2 comparativement aux autres TACs ( p < 0,001). Les patients ayant un score d’enrichissement élevé pour la signature MC-activé par IgE avaient des caractéristiques distinctes et les 32 gènes uprégulés dans ce groupe, comparativement au groupe avec u

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Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and markov random field

arXiv

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arXiv 2020年

作者： Bi, Haixia Xu, Lin Cao, Xiangyong Xue, Yong Xu, Zongben Faculty of Engineering University of Bristol BristolBS8 1UB United Kingdom Institute of Artificial Intelligence Shanghai Em-Data Technology Co. Ltd. Shanghai200000 China School of Environment Science and Spatial Informatics China University of Mining and Technology XuzhouJiangsu221116 China School of Electronics Computing and Mathematics University of Derby DerbyDE22 1GB United Kingdom Institute for Information and System Sciences Xi’an Jiaotong University Xi’an China

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Educational video classification by using a transcript to image transform and supervised learning

Educational video classification by using a transcript to im...

引用

7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017

作者： Chatbri, Houssem Oliveira, Marlon McGuinness, Kevin Little, Suzanne Kameyama, Keisuke Kwan, Paul Sutherland, Alistair O'Connor, Noel E. Insight Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland Faculty of Engineering Information and Systems University of Tsukuba Japan School of Science and Technology University of New England NSW Australia

ISBN: (纸本)9781538618417

In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods. © 2017 IEEE.

关键词： Principal component analysis

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Statistical downscaling to predict drought events using high resolution satelite based geopotential data

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IOP Conference Series: Materials science and Engineering 2019年第5期546卷

作者： H Kuswanto I L Yuliatin H A Khoiri Departement of Statistics Faculty of Mathematics Computing and Data Science-Institut Teknologi Sepuluh Nopember (ITS) Indonesia

Drought prediction is a very challenging work due to high degree of uncertainty in the climate system. Geopotential height has been investigated as one of the dominant variables that can be used to predict drought events. This paper discussed the use of high resolution satelite based (reanalysis) data as the predictor of drought events, resulting on a high dimensional dataset. To deal with this, dimension reduction has been carried out by using Principle Component Analysis (PCA), prior to the development of the downscaling models which incorporate the past SPI (Standardized Precipitation Index) combined with the geopotential height at some specific atmosperic levels i.e. 500hPa, 850hPa, 900hPa, 975 hPa and 1000hPa. The SPI, as the drought risk measure is derived from the reduced dimension of precipitation data observed from the corresponding meteorological stations, while the geopotential height is reduced from gridded high resolution data. The downscaling process found the best model to predict the drought risk with various degree of R-squares. The outsample validation showed that predicting drought using SPI3 (three month period SPI) with geopotential at the 900hPa level as the predictor outperforms the others with R-square reaching 77%.

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In Silico Analysis Using Hybrid Support Vector Machine and Second Order of Markov Chain for Multiple Sequence Alignment to Identify the Types of Leukaemia

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Journal of Physics: Conference Series 2019年第1期1366卷 012052-012052页

作者： Mohammad Isa Irawan Mohammad Hamim Zajuli Al Faroby Awik Pudji Dyah Nurhayati Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Department of Biology Faculty of Science Institut Teknologi Sepuluh Nopember

Cancer is a disease that causes an abnormal growth of cells and can attack every part of the body, which is occurred because of a damage in deoxyribonucleic acid (DNA) that leads to a mutation in a vital gene that controls cell division. The biomarker technology that used in clinical practice still used a high cost and need a long time to detect the cancer signs. As the former studies about cancer, the biomarker has been detected in the microarray data. In this paper, we used a support vector machine (SVM) to classify 4 type of leukaemia. Begin with extracting the data feature of sequence DNA from a string into numeric using Second order of Markov chain, SVM classified DNA using 40 data for the training step and 25 data for testing step. In this paper, SVM used 3 types of the kernel, which are linear, Gaussian radial basis function, and polynomial. The results showed that the Gaussian kernel has the best accuracy then other kernel.

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Planar median graphs and cubesquare-graphs

arXiv

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arXiv 2021年

作者： Seemann, Carsten R. Moulton, Vincent Stadler, Peter F. 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 University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Germany Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Median graphs are connected graphs in which for all three vertices there is a unique vertex that belongs to shortest paths between each pair of these three vertices. In this paper we provide several novel characterizations of planar median graphs. More specifically, we characterize when a planar graph G is a median graph in terms of forbidden subgraphs and the structure of isometric cycles in G, and also in terms of subgraphs of G that are contained inside and outside of 4-cycles with respect to an arbitrary planar embedding of G. These results lead us to a new characterization of planar median graphs in terms of cubesquare-graphs that is, graphs that can be obtained by starting with cubes and square graphs, and iteratively replacing 4-cycle boundaries (relative to some embedding) by cubes or square-graphs. As a corollary we also show that a graph is planar median if and only if it can be obtained from cubes and square-graphs by a sequence of "square-boundary" amalgamations. These considerations also lead to an O(n log n)-time recognition algorithm to compute a decomposition of a planar median graph with n vertices into cubes and square-graphs. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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The inner kernel theorem for a certain Segal algebra

arXiv

引用

arXiv 2018年

作者： Feichtinger, Hans G. Jakobsen, Mads Sielemann University of Vienna Faculty of Mathematics Vienna Austria Center for Data and Computing in Natural Science CDCS Deutsches Elektronen-Synchrotron DESY Germany

MSC Codes 41A65, 43A15, 43A25, 44A05, 46A11, 46F05, 46F10, 47B10, 47B34The Segal algebra S0(G) is well defined for arbitrary locally compact Abelian Hausdorff (LCA) groups G. It is a Banach space that exhibits a kernel theorem similar to the well-known Schwartz kernel theorem. Specifically, we call this characterization of the continuous linear operators from S0(G1) to S'0(G2) by generalized functions in S'0(G1 × G2) the "outer kernel theorem". The main subject of this paper is to formulate what we call the "inner kernel theorem". This is the characterization of those linear operators that have kernels in S0(G1 × G2). Such operators are regularizing - in the sense that they map S'0(G1) into S0(G2) in a w∗ to norm continuous manner. A detailed functional analytic treatment of these operators is given and applied to the case of general LCA groups. This is done without the use of Wilson bases, which have previously been employed for the case of elementary LCA groups. We apply our approach to describe natural laws of composition for operators that imitate those of linear mappings via matrix multiplications. Furthermore, we detail how these operators approximate general operators (in a weak form). As a concrete example, we derive the widespread statement of engineers and physicists that pure frequencies "integrate" to a Dirac delta distribution in a mathematically justifiable way. Copyright © 2018, The Authors. All rights reserved.

关键词： Mathematical operators

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Method and systemfor image analysis to detect cancer

arXiv

引用

arXiv 2019年

作者： Yousef, Waleed A. Abouelkahire, Ahmed A. Almahallawi, Deyaaeldeen Smarzouk, Omar Kmohamed, Sameh Amustafa, Waleed Osamag, Omarm Saleh, Ali A. Abdelrazek, Naglaam Computer Science Department Faculty of Computers and Information Helwan University Egypt Egypt Senior Data Scientist TeraData Egypt School of Informatics and Computing Indiana University Bloomington United States Sweden Insight Center for Data Analytics National University of Ireland Ireland Department of Informatics University of Hamburg Germany Mesc for Research and Development Egypt Hamburg University Germany Faculty OfMedicine Alfa Scan Radiology Center Cairo University Quality ControlManager Quality ControlManager at National Screening Campaign Egypt

Breast cancer is the most common cancer and is the leading cause of cancer death among women worldwide. Detection of breast cancer, while it is still small and confined to the breast, provides the best chance of effective treatment. Computer Aided Detection (CAD) systems that detect cancer from mammograms will help in reducing the human errors that lead to missing breast carcinoma. Literature is rich of scientific papers for methods of CAD design, yet with no complete system architecture to deploy those methods. On the other hand, commercial CADs are developed and deployed only to vendors' mammography machines with no availability to public access. This paper presents a complete CAD;it is complete since it combines, on a hand, the rigor of algorithm design and assessment (method), and, on the other hand, the implementation and deployment of a system architecture for public accessibility (system). (1) We develop a novel algorithm for image enhancement so that mammograms acquired from any digital mammography machine look qualitatively of the same clarity to radiologists' inspection;and is quantitatively standardized for the detection algorithms. (2) We develop novel algorithms for masses and microcalcifications detection with accuracy superior to both literature results and the majority of approved commercial systems. (3) We design, implement, and deploy a system architecture that is computationally effective to allow for deploying these algorithms to cloud for public access. Copyright © 2019, The Authors. All rights reserved.

关键词： Mammography

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