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DNAku Consumers Profile: One of The First Direct to Customer Genetics Testing in Indonesia

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Deby Erina Parung Kians Azizatikarna Dian Amirulloh Erlin Listiyaningsih Bharuno Mahesworo Arif Budiarto Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

DNAku test is one of the first direct to customer genetics testing in Indonesia. DNAku test came with a different approach as it gave their customer reports of their genetic predisposition to corresponding traits. Four months after launch, we create consumer report of this genetics testing based on DNAku questionnaire results. We applied three statistical methods, including descriptive statistics, multivariate normality test, and t-test to the demographic data of the DNAku customers. The result shows population clusters which may represent population groups that are interested in genetics testing. The questionnaire also has questions about the customer's lifestyle. The results of these questions were processed to find the lifestyle of DNAku customers.

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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science China(Life sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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ERGODIC THEOREMS FOR DYNAMIC IMPRECISE PROBABILITY KINEMATICS

arXiv

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arXiv 2020年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Dept. of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany The Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Dept. of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

We formulate an ergodic theory for the (almost sure) limit PE co Ε͂ of a sequence (PcoΕn) of successive dynamic imprecise probability kinematics (DIPK, introduced in [10]) updates of a set Pco Ε0 representing the initial beliefs of an agent. As a consequence, we formulate a strong law of large *** Codes 60A99, 37A50, 60F15, 60G05 © 2020, CC BY.

关键词： Kinematics

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Landscape of Immune Cell States and Ecosystems in Patients with Myelodysplastic Syndrome to Refine Prognostic Assessment and Predict Treatment Response. a Study By i4MDS Consortium

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Blood 2024年 144卷 665-665页

作者： Elena Riva Michela Calvi Matteo Zampini Lorenzo Dall'Olio Alessandra Merlotti Antonio Russo Giulia Maggioni Lara Orlandi Alessandro Frigo Francesca Ficara Laura Crisafulli Elisabetta Sauta Saverio D'Amico Enrico Lugli Alessia Campagna Marta Ubezio Cristina Astrid Tentori Gabriele Todisco Luca Lanino Alessandro Buizza Matteo Giovanni Della Porta IRCCS Humanitas Research Hospital Rozzano Italy Department of Medical Biotechnologies and Translational Medicine University of Milan Milan Italy Department of Biomedical Sciences Humanitas University Milan Italy Data Science and Bioinformatics Laboratory IRCCS Institute of Neurological Sciences of Bologna Bologna Italy Department of Physics and Astronomy DIFA University of Bologna Bologna Italy IRCCS Istituto delle Scienze Neurologiche di Bologna Bologna Italy Institute for Genetic and Biomedical Research Milan Unit CNR Milan Italy Department of Biomedical Sciences Humanitas University Pieve Emanuele Italy

BACKGROUND. Biological heterogeneity in Myelodysplastic Syndromes (MDS) is partly driven by tumor-associated genomic lesions, but increasing evidence suggests that immune tumor microenvironment plays a significant role. However, dissecting these cell states and understanding their clinical relevance on a large scale remains challenging. AIMS. In this study, conducted by i4MDS consortium, we characterized the immune ecosystems in a prospective cohort of MDS patients. Specific objectives were: 1) to analyze the contribution of immune ecosystems in refining patients' prognosis; 2) to define specific immune profiles to predict probability of response to hypomethylating agents (HMA); and 3) to develop a panel for immune monitoring in clinical practice. METHODS. We prospectively studied 286 MDS patients at Humanitas Cancer Center Milan, Italy (evaluated at diagnosis and at multiple time points throughout the disease's natural history). T lymphocytes, Natural Killer (NK) and myeloid cells were evaluated in bone marrow (BM) and peripheral blood (PB) by extensive multi-color flow cytometry (PMID:38756352). Phenograph was used to analyze immune cell subset distribution and phenotype, while HDBSCAN identified clusters of patients with homogeneous immune features (defined as ecosystems). Each ecosystem was further characterized by integrating RNA-seq data from CD34+ progenitors. A DURAClone dry pre-formulated antibody panel was designed for implementation in the clinical work-up of patients. RESULTS. We identified five immune ecosystems in the BM, each characterized by varying functionality and maturation stages of immune cells. Two clusters exhibited features of a healthy-like immune system: one with an expanded pool of immature and plastic Naïve T cells and CD56bright NK cells (referred to as “Naïve” ecosystem); and another enriched with memory T cells and functionally activated T and NK cells, termed “Memory, activated”. The other three clusters showed progressive levels of

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FAIR Principles:Interpretations and Implementation Considerations

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data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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FINITE ADMIXTURE MODELS: A BRIDGE WITH STOCHASTIC GEOMETRY AND CHOQUET THEORY

arXiv

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arXiv 2020年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Dept. of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany the Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Dept. of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

In the context of a finite admixture model whose components and weights are unknown, if the number of identifiable components is a function of the amount of data collected, we use techniques from stochastic convex geometry to find the growth rate of its expected value. In addition, when the components are known but the weights are not, we provide an application of the classic Glivenko-Cantelli’s theorem that allows us to retrieve the Choquet measure supported on the identifiable admixture components. In turn, this gives us the identifiable admixture weights. Finally, we propose a novel algorithm that estimates the model capturing the complexity of the data using only the strictly necessary number of *** Codes 52B11, 60D05, 62G20 © 2020, CC BY.

关键词： Stochastic systems

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Preface

Studies in Big Data

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Studies in Big data 2023年 94卷 v-vii页

作者： Mahalle, Parikshit Narendra Shinde, Gitanjali Rahul Pise, Priya Dudhale Deshmukh, Jyoti Yogesh Department of Artificial intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Engineering STES’s Smt. Kashibai Navale College of Engineering Maharashtra Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Dr. D. Y. Patil Biotechnology and Bioinformatics Institute Pune India Department of Computer Engineering JSPM’s Bhivarabai Sawant Institute of Technology and Research Pune India

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Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

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Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

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data Engineering Pipeline to Analyse Jakarta's Air Quality during COVID-19-Caused Lockdown Periods

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Reza Rahutomo Bens Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta's air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from ***, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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