检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

来源：评论

学校读者我要写书评

暂无评论

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

引用

ACS nano 2025年第23期19卷 2025 Jun 3页

作者： Joseph Cave Anne Christiono Carmine Schiavone Henry J Pownall Vittorio Cristini Daniela I Staquicini Renata Pasqualini Wadih Arap C Jeffrey Brinker Matthew Campen Zhihui Wang Hien Van Nguyen Achraf Noureddine Prashant Dogra Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston Texas 77030 United States. Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York New York 10065 United States. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 United States. Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy. Department of Medicine Houston Methodist Houston Texas 77030 United States. Department of Medicine Weill Cornell Medicine New York New York 10065 United States. Neal Cancer Center Houston Methodist Research Institute Houston Texas 77030 United States. Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston Texas 77030 United States. Rutgers Cancer Institute Newark New Jersey 08901 United States. Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Department of Chemical and Biological Engineering University of New Mexico Albuquerque New Mexico 87106 United States. College of Pharmacy University of New Mexico Albuquerque New Mexico 87106 United States. Department of Physiology and Biophysics Weill Cornell Medical College New York New York 10065 United States. Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 United States.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for settings. To enable organ-specific toxicity predictions , we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

关键词： PBPK artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity

来源：评论

学校读者我要写书评

暂无评论

Building a quantum engineering undergraduate program

arXiv

引用

arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： engineering education

来源：评论

学校读者我要写书评

暂无评论

Citizen science and the United Nations Sustainable Development Goals (vol 2, pg 922, 2019)

引用

NATURE SUSTAINABILITY 2019年第11期2卷 1063-1063页

作者： Fritz, Steffen See, Linda Carlson, Tyler Haklay, Mordechai (Muki) Oliver, Jessie L. Fraisl, Dilek Mondardini, Rosy Brocklehurst, Martin Shanley, Lea A. Schade, Sven Wehn, Uta Abrate, Tommaso Anstee, Janet Arnold, Stephan Billot, Matthew Campbell, Jillian Espey, Jessica Gold, Margaret Hager, Gerid He, Shan Hepburn, Libby Hsu, Angel Long, Deborah Maso, Joan McCallum, Ian Muniafu, Maina Moorthy, Inian Obersteiner, Michael Parker, Alison J. Weisspflug, Maike West, Sarah Ecosystem Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria School of Resource and Environmental Management Simon Fraser University Burnaby Canada University College London London UK School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Australian Citizen Science Association c/o Faculty of Science Partner Engagement and Outreach University of Sydney Sydney Australia University of Natural Resources and Life Sciences Vienna Austria Competence Center Citizen Science ETH Zurich and University of Zurich Zurich Switzerland Citizen Science Global Partnership Washington DC USA European Citizen Science Association Museum für Naturkunde Berlin Berlin Germany Nelson Institute University of Wisconsin at Madison Madison USA European Commission Joint Research Center Ispra Italy IHE Delft Institute for Water Education Delft The Netherlands World Meteorological Organization Geneva Switzerland Commonwealth Scientific and Industrial Research Organisation Canberra Australia Federal Statistical Office – Destatis Wiesbaden Germany UN Environment Programme Nairobi Kenya United Nations Sustainable Development Solutions Network New York USA CitizenScience.Asia Hong Kong China Yale-NUS College Singapore Singapore University of Dundee Dundee UK Scottish Environment LINK Perth UK Grumets Research Group CREAF Universitat Autònoma de Barcelona Barcelona Spain African Citizen Science Association Nairobi Kenya Woodrow Wilson Center Washington DC USA Museum für Naturkunde Berlin Berlin Germany Stockholm Environment Institute University of York York UK

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

关键词： Planning

来源：评论

学校读者我要写书评

暂无评论

Stakeholder Act as an IT and General Compliance Auditor

Stakeholder Act as an IT and General Compliance Auditor

引用

International Conference on Sustainable Information engineering and Technology (SIET)

作者： Surjandy Erick Fernando Meyliana Raymond Kosala Suhono Harso Supangkat Diani Dwiningrum Merry Marshela Angelica Renita Amalia Information Systems Audit Program Information Systems Department Bina Nusantara University Jakarta Indonesia Information Systems Departement Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674086;9781538674079

Recently, the Indonesian government requires auditors, the need for auditors around 40,000 auditors in 2016, currently has only about 10,800 auditors, however, the lack of the auditors impacting the risk of financial loss for the state. Referring to the problem, this experimental research was conducted. The experiment of the research was conducted two times and the audit topic is a compliance audit in the year 2017 and year 2018. The goal of this research is to know, can the stakeholder act as an IT and general compliance auditor? The experiment was conducted by 8 university students in 2017 and 19 university students in 2018, and the supporting tools of audit activities the university student use the smartphone to capture the object as evidence. The experiment results with descriptive method show university students can act as an auditor and found several pieces of evidence that might cause future impact to the institution.

关键词： Regulation Stakeholders Companies Libraries Finance

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Feasibility of peak temperature targets in light of institutional constraints

引用

Nature Climate Change 2025年 119页

作者： Christoph Bertram Ryna Cui Nate Hultman Gokul Iyer Gunnar Luderer Elmar Kriegler Rahel Mandaroux Elina Brutschin Bas van Ruijven Oliver Fricko Shinichiro Fujimori Volker Krey Joeri Rogelj Keywan Riahi Laurent Drouet Lara Aleluia Reis Luiz Bernardo Baptista Roberto Schaeffer Harmen-Sytze de Boer Vassilis Daioglou Isabela Tagomori Detlef van Vuuren Florian Fosse Kimon Keramidas Dimitris Fragkiadakis Zoi Vrontisi Diego Silva Robin D. Lamboll Pedro Rochedo Center for Global Sustainability (CGS) School of Public Policy University of Maryland College Park MD USA Potsdam Institute for Climate Impact Research (PIK) Member of the Leibniz Association Potsdam Germany Joint Global Change Research Institute Pacific Northwest National Laboratory and University of Maryland College Park MD USA Global Energy Systems Analysis Technische Universität Berlin Berlin Germany Faculty of Economics and Social Sciences University of Potsdam Potsdam Germany International Institute for Applied System Analysis (IIASA) Laxenburg Austria Department of Environmental Engineering Kyoto University Kyoto Japan Social Systems Division National Institute for Environmental Studies (NIES) Tsukuba Japan Centre for Environmental Policy and Grantham Institute–Climate Change and Environment Imperial College London London UK Graz University of Technology Graz Austria CMCC Foundation–Euro-Mediterraneaen Center on Climate Change Milan Italy RFF-CMCC European Institute of Economics and the Environment Milan Italy Centre for Energy and Environmental Economics (Cenergia) Energy Planning Program (PPE) COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Netherland’s Environmental Agency (PBL) The Hague The Netherlands Copernicus Institute for Sustainable Development Utrecht University Utrecht The Netherlands European Commission Joint Research Centre (JRC) Seville Spain Grenoble Applied Economics Lab Université Grenoble Alpes Grenoble France E3Modelling Athens Greece Research and Innovation Center on CO2 and Hydrogen (RICH Center) and Management Science and Engineering Department Khalifa University Abu Dhabi United Arab Emirates

来源：评论

学校读者我要写书评

暂无评论

Sherlock: Capturing Probe Requests for Automatic Presence Detection

Sherlock: Capturing Probe Requests for Automatic Presence De...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Luiz Oliveira João Henrique Daniel Schneider Jano de Souza Sérgio Rodriques Weiming Sherr IFRJ Federal Institute of Rio de Janeiro Brazil Systems Engineering and Computer Science Program/COPPE Federal University of Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil National Research Council Canada Constructicn Research Centre Canada

The ubiquity of smartphones creates great opportunities for participatory sensing, where people can implicitly contribute observations about their local environments through sensors such as cameras and accelerometers. The collected data can then be aggregated and used to benefit the crowd in some way. In this paper, we report on the current development of Sherlock, a device capable of automatically detecting the presence of people in localities through evidences left by smartphones called probe requests, without any user intervention. To validate the proposed mechanism implemented in the device, we performed an experiment with ten participants in six rounds where it was possible to automatically detect 41 presence events, of which 66% could be detected within less than 30s. The implicit crowdsourcing mechanism behind this approach may allow real-time monitoring of people flows in public environments, which can enable, among other things, energy systems to be automatically orchestrated according to demand, reducing associated costs.

关键词： Probes Wireless fidelity Smart phones Wireless networks Monitoring Prototypes

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Greengenes2 unifies microbial data in a single reference tree

引用

Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

来源：评论

学校读者我要写书评

暂无评论

Who is right? Evaluating empirical contradictions in the readability & comprehensibility of source code 20

Who is right? Evaluating empirical contradictions in the rea...

引用

20th Ibero-American Conference on Software engineering, CIbSE 2017

作者： Ribeiro, Talita Vieira Travassos, Guilherme Horta PESC/COPPE/UFRJ - Systems Engineering and Computer Science Program Centro de Tecnologia Federal University of Rio de Janeiro Cidade Universitária Bloco H Sala 319 Rio de Janeiro RJ Brazil

ISBN: (纸本)9789873806988

Context: Several code guidelines aim at providing a common perspective on the readability and comprehensibility of source code. However, in many cases, they are contradictory on how to improve these quality characteristics. Objective: To analyze the influence of three contradictory source code attributes - indentation spacing, identifier length and code size - on the source code reada-bility and comprehensibility, interpreting whether programming experience and domain knowledge can support explaining these contradictions. Method: To per-form an empirical study with software developers collecting quantitative (Likert scale) and qualitative data to assess the readability and comprehensibility of de-velopers regarding Python snippets. To observe eventual perceptions contradic-tions and whether their levels of experience and knowledge have something to do with such contrary results. Results: Regardless their programming experience, 4-spaces indentation dominated the readability preference of participants. While the readability and comprehensibility preferences towards long and complete-word identifiers were mostly true for both novice/experts, developers with more experience and low domain knowledge level seemed to be more affected by the length of identifiers. Furthermore, while all participants showed more positive comprehensibility perceptions for Pythin snippets with more lines of code, their readability perceptions regardnig code size were contradictory since the less experienced participants preferred more lines of code and the more experienced ones prefer fewer lines. Conclusion: The results presented in the technical literature seemed to be caused by the interchangeable use of the readability and comprehensibility concepts. Further investigation is still needed to observe whether other confounding factors might support the explanation of such contrary results.

关键词： Codes (symbols)

来源：评论

学校读者我要写书评

暂无评论

A Collection of Software engineering Challenges for Big Data System Development

A Collection of Software Engineering Challenges for Big Data...

引用

Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Oliver Hummel Holger Eichelberger Andreas Giloj Dominik Werle Klaus Schmid Faculty of Computer Science Mannheim University of Applied Sciences Software Systems Engineering University of Hildesheim Institute for Program Structures and Data Organization Karlsruhe Institute of Technology

ISBN: (纸本)9781538673843

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.

关键词： Big Data Software Software engineering computer architecture Data analysis Data privacy Systematics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：