检索结果-内蒙古大学图书馆

Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

来源：评论

学校读者我要写书评

暂无评论

Near field communication (NFC) model for arduino uno based security systems office system

引用

IOP Conference Series: Materials science and Engineering 2018年第1期332卷

作者： A Chairunnas I Abdurrasyid Computer Science Study Program Faculty of Mathematics and Natural Sciences University of Pakuan Indonesia

Currently, many offices or companies that start growing rapidly in a company or office should have a very limited room to enter only people entitled to enter the room and use the facilities contained in it, for example, Files in it must have many files and documents very important because to reduce the abuse of files and irresponsible person. Because it will be made room door security system by using Near Field Communication on android smartphone. Software used is Arduino IDE. The tools used in this system are Arduino Uno R3, NFC shield, pear sensor, bell, led, servo, 16 × 2 LCD, and Near Field Communication (NFC) in android smartphone. This system runs based on 2 inputs of a new technology that is Near Field Communication (NFC) in android smartphone. And also use pear sensor to detect unauthorized person entering the room. If the correct password is entered then the door will open and the pear sensor will light off if wrong then the bell will light up.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Beyond trees: Classification with sparse pairwise dependencies

arXiv

引用

arXiv 2018年

作者： Tenzer, Yaniv Moscovich, Amit Dorn, Mary Frances Nadler, Boaz Spiegelman, Clifford Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Los Alamos National Laboratory P.O. Box 1663 MS F600 Los AlamosNM87545 United States Department of Statistics Texas A&M University College StationTX77843 United States

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criteria, and finally construct a linear SVM on the retained log-transformed densities. We demonstrate using both synthetic and real data that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is competitive with popular classification *** Codes 62H30 Copyright © 2018, The Authors. All rights reserved.

关键词： Binary trees

来源：评论

学校读者我要写书评

暂无评论

Live chat alternative security protocol

引用

Journal of Physics: Conference Series 2018年第1期1013卷

作者： J P R Rahman E Nugraha A Febriany Computer Science Study Program Department of Computer Science Education Faculty of Mathematics and Natural Sciences Education Indonesia University of Education.

Indonesia is one of the largest e-commerce markets in Southeast Asia, as many as 5 million people do transactions in e-commerce, therefore more and more people use live chat service to communicate with customer service. In live chat, the customer service often asks customers' data such as, full name, address, e-mail, transaction id, which aims to verify the purchase of the product. One of the risks that will happen is sniffing which will lead to the theft of confidential information that will cause huge losses to the customer. The anticipation that will be done is build an alternative security protocol for user interaction in live chat by using a cryptographic algorithm that is useful for protecting confidential messages. Live chat requires confidentiality and data integration with encryption and hash functions. The used algorithm are Rijndael 256 bits, RSA, and SHA256. To increase the complexity, the Rijndael algorithm will be modified in the S-box and ShiftRow sections based on the shannon principle rule, the results show that all pass the Randomness test, but the modification in Shiftrow indicates a better avalanche effect. Therefore the message will be difficult to be stolen or changed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Designing the Application of Security Text Messages into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End of File (EOF) Method 1

Designing the Application of Security Text Messages into Aud...

引用

1st Workshop on Environmental science, Society, and Technology, WESTECH 2018

作者： Pardede, A.M.H. Pramana, Anggi Zarlis, Muhammad Iskandar, Akbar Manurung, Rosida Tiurma Sriadhi, S. Kurniawan, Citra Hasibuan, Abdurrozzaq Siregar, Nurintan Asyiah Sari, Ayu Esteka Mulyaningsih, Indrya Iskandar Alam, Teguh Hidayat Ibrahim Listyorini, Tri Winarno, Edy Tulus STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Informatics Engineering Universitas Muhammadiyah Sorong Sorong Indonesia Department of English Education Universitas Muhammadiyah Sorong Sorong Indonesia Department of Informatics Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data. © Published under licence by IOP Publishing Ltd.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Modeling on clinical group decision support system for screening and working diagnosis acute respiratory infections

引用

IOP Conference Series: Materials science and Engineering 2019年第1期508卷

作者： Muhammad Syaukani Sri Hartati Hari Kusnanto Suryo Guritno Study Program of Information Technology STMIK Indonesia Banjarmasin Indonesia Dept. of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Study Program of Publich Health Interests Health Management Information System Faculty of Medicine Universitas Gadjah Mada Yogyakarta Indonesia

There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support Systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support System (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning binary latent variable models: A tensor eigenpair approach

arXiv

引用

arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

引用

arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

arXiv

引用

arXiv 2023年

作者： Adewole, Maruf Rudie, Jeffrey D. Gbdamosi, Anu Toyobo, Oluyemisi Raymond, Confidence Zhang, Dong Omidiji, Olubukola Akinola, Rachel Suwaid, Mohammad Abba Emegoakor, Adaobi Ojo, Nancy Aguh, Kenneth Kalaiwo, Chinasa Babatunde, Gabriel Ogunleye, Afolabi Gbadamosi, Yewande Iorpagher, Kator Calabrese, Evan Aboian, Mariam Linguraru, Marius Albrecht, Jake Wiestler, Benedikt Kofler, Florian Janas, Anastasia LaBella, Dominic Kzerooni, Anahita Fathi Li, Hongwei Bran Iglesias, Juan Eugenio Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Van Leemput, Koen Bukas, Christina Piraud, Maire Conte, Gian-Marco Johansson, Elaine Meier, Zeke Menze, Bjoern H. Baid, Ujjwal Bakas, Spyridon Dako, Farouk Fatade, Abiodun Anazodo, Udunna C. Lagos Nigeria Department of Radiation Biology Radiotherapy and Radiodiagnosis University of Lagos Lagos Nigeria Department of Radiology University of California San Diego United States Crestview Radiology Limited Lagos Nigeria Lagos University Teaching Hospital Lagos Nigeria Lagos State University Teaching Hospital Lagos Ikeja Nigeria NSIA-Kano Diagnostic Center Kano Nigeria Nnamdi Azikiwe University Teaching Hospital Anambra State Nnewi Nigeria Federal Medical Centre Ogun State Abeokuta Nigeria Federal Medical Centre Abia State Umuahia Nigeria National Hospital Abuja FCT Nigeria Benue State University Teaching Hospital Benue State Makurdi Nigeria Duke University Medical Center Department of Radiology United States University of California San FranciscoCA United States Yale University New HavenCT United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States Sage Bionetworks United States Department of Neuroradiology Technical University of Munich Munich Germany Helmholtz Research Center Munich Germany Duke University Medical Center Department of Radiation Oncology United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States University of Zurich Switzerland Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Mayo Clinic MN United States Precision FDA U.S. Food and Drug Administration Silver SpringMD United States Booz Allen Hamilton McLeanVA United States Department of Radiology Perelman School of Medicine U

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state- of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely. © 2023, CC BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Diversity of Artists in Major U.S. Museums

arXiv

引用

arXiv 2018年

作者： Topaz, Chad M. Klingenberg, Bernhard Turek, Daniel Heggeseth, Brianna Harris, Pamela E. Blackwood, Julie C. Ondine Chavoya, C. Nelson, Steven Murphy, Kevin M. Department of Mathematics and Statistics Williams College WilliamstownMA United States Graduate Program in Data Science New College of Florida SarasotaFL United States Department of Mathematics Statistics and Computer Science Macalester College St. PaulMN United States Department of Art Williams College WilliamstownMA United States Department of Art History University of California Los AngelesCA United States Williams College Museum of Art WilliamstownMA United States

The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields. Copyright © 2018, The Authors. All rights reserved.

关键词： Population statistics

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：