检索结果-内蒙古大学图书馆

Data annotation system for intelligent energy conservator in smart building

IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Bens Pardamean Tjeng Wawan Cenggoro Bloomest Jansen Chandra Reza Rahutomo Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and 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 College of Letters and Science University of Wisconsin 53706 Madison WI USA

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation (vol 21, pg 432, 2018)

引用

NATURE NEUROSCIENCE 2018年第7期21卷 1015-1015页

作者： Preissl, Sebastian Fang, Rongxin Huang, Hui Zhao, Yuan Raviram, Ramya Gorkin, David U. Zhang, Yanxiao Sos, Brandon C. Afzal, Veena Dickel, Diane E. Kuan, Samantha Visel, Axel Pennacchio, Len A. Zhang, Kun Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA US Department of Energy Joint Genome Institute Walnut Creek CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Institute of Genomic Medicine Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

关键词： Cell type diversity Chromatin Chromatin analysis Epigenomics

来源：评论

学校读者我要写书评

暂无评论

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

引用

BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

来源：评论

学校读者我要写书评

暂无评论

Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

arXiv

引用

arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data

引用

Procedia Computer Science 2018年 135卷 400-407页

作者： Bens Pardamean Tjeng Wawan Cenggoro Reza Rahutomo Arif Budiarto Ettikan Kandasamy Karuppiah Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified.

关键词： Transfer learning Convolutional neural network Medical image analysis Chest X-ray Mammogram

来源：评论

学校读者我要写书评

暂无评论

Tegavivint triggers TECR-dependent nonapoptotic cancer cell death

引用

Nature chemical biology 2025年 2025 May 26页

作者： Logan Leak Ziwei Wang Alby J Joseph Brianna Johnson Alyssa A Chan Cassandra M Decosto Leslie Magtanong Pin-Joe Ko Weaverly Colleen Lee Joan Ritho Sophia Manukian Alec Millner Shweta Chitkara Jennifer J Salinas Rachid Skouta Matthew G Rees Melissa M Ronan Jennifer A Roth Chad L Myers Jason Moffat Charles Boone Steven J Bensinger David A Nathanson G Ekin Atilla-Gokcumen Everett J Moding Scott J Dixon Department of Biology Stanford University Stanford CA USA. Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA. Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA. Department of Molecular and Medical Pharmacology University of California Los Angeles Los Angeles CA USA. Department of Chemistry University of Massachusetts Amherst Amherst MA USA. Department of Biology University of Massachusetts Amherst Amherst MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin Cities Minneapolis MN USA. Program in Genetics & Genome Biology The Hospital for Sick Children Toronto Ontario Canada. Department of Molecular Genetics University of Toronto Toronto Ontario Canada. Donnelly Centre University of Toronto Toronto Ontario Canada. RIKEN Center for Sustainable Resource Science Saitama Japan. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA USA. UCLA Lipidomics Laboratory University of California Los Angeles Los Angeles CA USA. Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Biology Stanford University Stanford CA USA. sjdixon@stanford.edu.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A shared disease-associated oligodendrocyte signature among multiple CNS pathologies

引用

Nature neuroscience 2023年 2023 Jan 31页

作者： Mor Kenigsbuch Pierre Bost Shahar Halevi Yuzhou Chang Shuo Chen Qin Ma Renana Hajbi Benno Schwikowski Bernd Bodenmiller Hongjun Fu Michal Schwartz Ido Amit Department of Neurobiology Weizmann Institute of Science Rehovot Israel. Department of Immunology Weizmann Institute of Science Rehovot Israel. Systems Biology Group Center for Bioinformatics Biostatistics and Integrative Biology Institut Pasteur CNRS Paris France. Sorbonne Université Complexité du vivant Paris France. University of Zurich Zurich Switzerland. Department of Biomedical Informatics College of Medicine Ohio State University Columbus OH USA. Biomedical Sciences Graduate Program Ohio State University Columbus OH USA. Department of Neuroscience College of Medicine Ohio State University Columbus OH USA. Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel. Department of Neurobiology Weizmann Institute of Science Rehovot Israel. michal.schwartz@weizmann.ac.il. Department of Immunology Weizmann Institute of Science Rehovot Israel. ido.amit@weizmann.ac.il.

来源：评论

学校读者我要写书评

暂无评论

Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

引用

Journal of Medical Virology 2024年第5期96卷 e29648-e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

来源：评论

学校读者我要写书评

暂无评论

CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

引用

1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "Cross-sectional analysis of students and school workers reveals a high number of asymptomatic SARS-CoV-2 infections during school reopening in Brazilian cities" [Heliyon 8 (11) (November 2022) Article e11368]

引用

Heliyon 2023年第3期9卷 e14398页

作者： Lysandro P Borges Adriana G Guimarães Dennyson Leandro M Fonseca Paula P Freire Íkaro D C Barreto Daniela R V Souza Ricardo Q Gurgel Aline S A Lopes José Melquiades de Rezende Neto Kezia A Dos Santos Igor L S Matos Grazielly B da Invenção Brenda M Oliveira Aryanne A Santos Daniele Almeida Soares Pamela C de Jesus Cliomar A Dos Santos Marco A O Goes Desirée Rodrigues Plaça Igor Salerno Filgueiras Alexandre H C Marques Gabriela Crispim Baiocchi William CabralMiranda Gustavo Cabral de Miranda Niels Olsen Saraiva Camara Vera Lúcia Garcia Calich Rodrigo Nalio Ramos Helder I Nakaya Vanderson Rocha Lasse M Giil Hans D Ochs Lena F Schimke Mércia S F de Souza Luis E Cuevas Aline F Martins Otavio Cabral-Marques Department of Pharmacy Federal University of Sergipe São Cristóvão Sergipe Brazil. Interunit Postgraduate Program on Bioinformatics Institute of Mathematics and Statistics (IME) University of Sao Paulo (USP) Sao Paulo SP Brazil. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo SP Brazil. Nuclear and Energy Technology Graduate Program (UFPE) Recife Pernambuco Brazil. Department of Education in Health Lagarto and Post-graduate Program in Health Sciences Federal University of Sergipe Sergipe Brazil. Department of Medicine and Post-Graduate Programs in Parasitic Biology and Health Sciences Federal University of Sergipe Aracaju Sergipe Brazil. Sergipe Central Public Health Laboratory Aracaju Sergipe Brazil. State Health Department Sergipe Brazil. Department of Clinical and Toxicological Analyses School of Pharmaceutical Sciences University of São Paulo São Paulo SP Brazil. The Sabará Children's Hospital and PENSI Institute São Paulo SP Brazil. Laboratory of Medical Investigation in Pathogenesis and Directed Therapy in Onco-ImmunoHematology (LIM-31) Department of Hematology and Cell Therapy Hospital das Clínicas HCFMUSP Faculdade de Medicina University of São Paulo São Paulo Brazil. Instituto D'Or de Ensino e Pesquisa São Paulo Brazil. Hospital Israelita Albert Einstein São Paulo 05652-900 Brazil. Scientific Platform Pasteur University of São Paulo São Paulo 05508-020 Brazil. Fundação Pró-Sangue-Hemocentro de São Paulo São Paulo Brazil. Churchill Hospital Department of Hematology University of Oxford Oxford United Kingdom. Department of Internal Medicine Haraldsplass Deaconess Hospital Bergen Norway. Department of Pediatrics University of Washington School of Medicine And Seattle Children's Research Institute Seattle WA USA. Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool United Kingdom. Laboratory of Medical Investigation 29 University of São Paulo School of Medicine Brazil. D

[This corrects the article DOI: 10.1016/***.2022.e11368.].

关键词：

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：