检索结果-内蒙古大学图书馆

第五届中美生物医学工程暨海内外生物力学学术研讨会

作者： Jihye Seong Arash Tajik Jie Sun Jun-Lin Guan Martin J.Humphries Susan E.Craig Asha Shekaran Andres J.Garcia Ning Wang Yingxiao Wang Neuroscience Program University of IllinoisUrbana-ChampaignUrbanaIL61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL61801USA Department of Bioengineering & Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL61801USA Division of Molecular Medicine and Genetics Departments of Internal Medicine and Cell & Developmental BiologyUniversity of Michigan Medical SchoolAnn ArborMI48109USA Wellcome Trust Centre for Cell-Matrix Research Faculty of Life SciencesUniversity of ManchesterManchesterUK Woodruff School of Mechanical Engineering and Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA Department of Biomedical Engineering School of Life Science and TechnologyHuazhong University of Science and Technology Department of Integrative and Molecular Physiology Center for Biophysics and Computational BiologyInstitute for Genomic BiologyUniversity of IllinoisUrbana-ChampaignUrbanaIL61801USA Department of Bioengineering University of CaliforniaSan DiegoCA92093USA

Introduction Cells can sense and respond to the mechanical microenvironment by converting forces into biochemical signals inside the cells,*** adhesions are the major sites of interaction between a cell and its extracellular matrix(ECM) microenvironment, thus outside mechanical signals can be sensed at focal adhesions through transmembrane receptor *** particular,it has been shown that matrix elasticity can control the cell fate

关键词： FAK Distinct mechanisms of FAK mechanoactivation by different extracellular matrix proteins ECM

来源：评论

学校读者我要写书评

暂无评论

computational analysis of spatiotemporal molecular hierarchy in single live cells

引用

医用生物力学 2013年第S1期28卷 55-55页

作者： Shaoying Lu Jihye Seong Yi Wang Shiou-chi Chang John Paul Eichorst Mingxing Ouyang Yingxiao Wang Department of Bioengineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of California San DiegoLa JollaCA 92093USA Neuroscience Program Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Chemical Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Center of Biophysics and Computational Biology Beckman Institute for Advanced Science and TechnologyDepartment of Molecular and Integrative PhysiologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Genetically encoded biosensors based on fluorescence resonance energy transfer(FRET)have been widely applied to visualize the molecular activity in live cells with high spatiotemporal *** enormous amount of video images and the complex dynamics of signaling events present tremendous challenges for data analysis and demand the development of intelligent and automated imaging analysis methods specifically envisioned for the studies of live cell *** have developed a general correlative FRET imaging method(CFIM)to quantify the subcellular coupling between an enzymatic activity and a phenotypic response in live cells,*** focal adhesions(FAs).CFIM quantitatively evaluated the cause-effect relation-

关键词： visualize quantify FRET biosensors correlative intelligent quantitatively enormous automated tremendous

来源：评论

学校读者我要写书评

暂无评论

Zernike Phase Contrast Cryo-Electron Microscopy of Virus Particles

引用

Microscopy and Microanalysis 2012年第S2期18卷 462-463页

作者： Chiu, W. Rochat, R.H. Liu, X. Murata, K. Khant, H. Nagayama, K. Rixon, Frazer National Center for Macromolecular Imaging Baylor College of Medicine Houston TX 77030 United States Graduate Program in Structural Computational Biology Molecular Biophysics Baylor College of Medicine Houston TX 77030 United States Okazaki Institute for Integrative Bioscience National Institute of Natural Sciences Okazaki Aichi 444-8787 5-1 Higashiyama Myodaiji-cho Japan MRC - University of Glasgow Centre for Virus Research Glasgow G11 5JR United Kingdom

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015) : Jeddah, Kingdom of Saudi Arabia. 30 November - 3 December 2015

引用

BMC genomics 2016年第6期17 Suppl 6卷 487页

作者： Jerry W. Shay Noriko Homma Ruyun Zhou Muhammad Imran Naseer Adeel G. Chaudhary Mohammed Al-Qahtani Nobutaka Hirokawa Maryam Goudarzi Albert J. Fornace Saleh Baeesa Deema Hussain Mohammed Bangash Fahad Alghamdi Hans-Juergen Schulten Angel Carracedo Ishaq Khan Hanadi Qashqari Nawal Madkhali Mohamad Saka Kulvinder S. Saini Awatif Jamal Jaudah Al-Maghrabi Adel Abuzenadah Adeel Chaudhary Mohammed Al Qahtani Ghazi Damanhouri Heba Alkhatabi Anne Goodeve Laura Crookes Nikolas Niksic Nicholas Beauchamp Adel M. Abuzenadah Jim Vaught Bruce Budowle Mourad Assidi Abdelbaset Buhmeida Leena Merdad Sudhir Kumar Sayaka Miura Karen Gomez Mahmood Rasool Ahmed Rebai Sajjad Karim Hend F. Nour Eldin Heba Abusamra Elham M. Alhathli Nada Salem Mohammed H. Al-Qahtani Hossam Faheem Ashok Agarwa Eberhard Nieschlag Joachim Wistuba Oliver S. Damm Mohd A. Beg Taha A. Abdel-Meguid Hisham A. Mosli Osama S. Bajouh Serdar Coskun Muhammad Abu-Elmagd Ashraf Dallol Sahar Hakamy Wejdan Al-Qahtani Asia Al-Harbi Shireen Hussain Burak Ozkosem Rick DuBois Safia S. Messaoudi Maryam T. Dandana Touhami Mahjoub Wassim Y. Almawi S. Abdalla M. Nabil Al-Aama Asmaa Elzawahry Tsuyoshi Takahashi Sachiyo Mimaki Eisaku Furukawa Rie Nakatsuka Isao Kurosaka Takahiko Nishigaki Hiromi Nakamura Satoshi Serada Tetsuji Naka Seiichi Hirota Tatsuhiro Shibata Katsuya Tsuchihara Toshirou Nishida Mamoru Kato Sajid Mehmood Naeem Mahmood Ashraf Awais Asif Muhammad Bilal Malik Siddique Mehmood Aadil Hussain Qazi Mohammad Sajid Jamal Mughees Uddin Siddiqui Mohammad A. Alzohairy Mohammad A. Al Karaawi Taoufik Nedjadi Heba Al-Khattabi Adel Al-Ammari Ahmed Al-Sayyad Hédia Zitouni Nozha Raguema Marwa Ben Ali Wided Malah Raja Lfalah Wassim Almawi Mohammed Elanbari Andrey Ptitsyn Sana Mahjoub Rabeb El Ghali Bechir Achour Nidhal Ben Amor Brahim N’siri Hamid Morjani Esam Azhar Vera Chayeb Maryam Dendena Hedia Zitouni Khedija Zouari-Limayem Bassem Refaat Ahmed M. Ashshi Sarah A. Batwa Hazem Ramadan Amal Awad Ahmed Ateya Adel Galal Ahmed El-Shemi Ahmad Ashshi Mohammed Basalamah Youjin Na Cha University of Texas Southwestern Medical Center Dallas USA Department of Cell Biology and Anatomy Graduate School of Medicine the University of Tokyo Tokyo Japan Center of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Department of Biochemistry and Molecular & Cellular Biology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Oncology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Radiation Medicine Lombardi Comprehensive Cancer Center Georgetown University Washington USA Center of Excellence in Genomic Medicine Research (CEGMR) King Abdulaziz University Jeddah Kingdom of Saudi Arabia King Fahd Medical Research Centre King Abdulaziz University Jeddah Saudi Arabia Centre of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Sheffield Diagnostic Genetic Service Sheffield Children’s NHS Foundation Trust Western Bank Sheffield UK Center of Innovations in Personalized Medicine King Abdulaziz University Jeddah Saudi Arabia President ISBER Editor-in-Chief Biopreservation & Biobanking ᅟ USA Institute of Applied Genetics Department of Molecular and Medical Genetics University of North Texas Health Science Center Fort Worth USA Department of Dental Public Health Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Institute for Genomics and Evolutionary Medicine Temple University Philadelphia USA Center of Excellence in Genomic Medicine King Abdulaziz University Jeddah Kingdom of Saudi Arabia Laboratory of Molecular and Cellular Screening Processes (Bioinformatics Group) Centre of Biotechnology of Sfax Sfax Tunisia Fujitsu Technology Solutions International ᅟ ᅟ Case Western Reserve University School of Medicine and Lerner College of Medicine Cleveland Clinic Foundation Cleveland USA American Center for Reproductive Medicine Andrology Center Cleveland USA Center for Reproductive Medicine and

O1 Regulation of genes by telomere length over long distances Jerry W. Shay O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa O3 Integration of metagenomics and metabolomics in gut microbiome research Maryam Goudarzi, Albert J. Fornace Jr. O4 A unique integrated system to discern pathogenesis of central nervous system tumors Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis Heba Alkhatabi O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp O7 Targeted sequencing panels and their utilization in personalized medicine Adel M. Abuzenadah O8 International biobanking in the era of precision medicine Jim Vaught O9 Biobank and biodata for clinical and forensic applications Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors Jaudah Al-Maghrabi O11 The CEGMR biobanking unit: achievements, challenges and future plans Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad O12 Phylomedicine of tumors Sudhir Kumar, Sayaka Miura, Karen Gomez O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment Angel Carracedo, Mahmood Rasool O14 From association to causality: translation of GWAS findings for genomic me

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Bayesian model for SNP discovery based on next-generation sequencing data

A Bayesian model for SNP discovery based on next-generation ...

引用

IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Yanxun Xu Xiaofeng Zheng Yuan Yuan Marcos R Estecio Jean-Pierre Issa Yuan Ji Shoudan Liang Department of Statistics Rice University Houston TX USA Department of Bioinformatics and Computational Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX USA Department of Biochemistry and Molecular Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Fels Institute for Cancer Research and Molecular Biology Temple University Philadelphia PA USA CCRI NorthShore University HealthSystem Chicago IL USA

ISBN: (纸本)9781467352345

A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (≥95 %). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.

关键词： comparison candidates generation Single nucleotide polymorphism sequence number protection a posteriori probability DNA Sequence Bayesian inference Disease Susceptibility Analyses, Sequence

来源：评论

学校读者我要写书评

暂无评论

Human gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes

引用

BMC Research Notes 2012年第1期5卷 1-11页

作者： Michalopoulos, Ioannis Pavlopoulos, Georgios A. Malatras, Apostolos Karelas, Alexandros Kostadima, Myrto-Areti Schneider, Reinhard Kossida, Sophia Cryobiology of Stem Cells Centre of Immunology and Transplantation Academy of Athens Athens 11527 Soranou Efessiou 4 Greece ESAT-SCD/IBBT-K.U. Leuven Future Health Department Katholieke Universiteit Leuven Heverlee-Leuven 3001 Kasteelpark Arenberg 10 Belgium Department of Cell Biology and Biophysics Faculty of Biology University of Athens Panepistimiopolis Athens 15701 Greece Wellcome Trust Genome Campus European Bioinformatics Institute Cambridge CB10 1SD United Kingdom Bioinformatics and Medical Informatics Team Biomedical Research Foundation Academy of Athens Athens 11527 Soranou Efessiou 4 Greece Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg 69117 Meyerhofstrasse 1 Germany Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Campus Belval Esch sur Alzette L-4362 Avenue des Hauts-Fourneaux 7 Luxembourg

Background: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell. Findings: We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster. Conclusion: Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://***/coexpression/. © 2012 Michalopoulos et al.: licensee BioMed Central Ltd.

关键词： Functional annotation Gene annotation Gene coexpression Microarray analysis

来源：评论

学校读者我要写书评

暂无评论

A Bayesian graphical model for integrative analysis of TCGA data

A Bayesian graphical model for integrative analysis of TCGA ...

引用

IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Yanxun Xu Jie Zhang Yuan Yuan Riten Mitra Peter Müller Yuan Ji Department of Statistics Rice University Houston TX USA Department of Statistics University of Wisconsin Madison Madison WI USA Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX USA ICES University of Texas Austin Austin TX USA Department of Mathematics University of Texas Austin Austin TX USA CCRI NorthShore University HealthSystem Chicago IL USA

ISBN: (纸本)9781467352345

We integrate three TCGA data sets including measurements on matched DNA copy numbers (C), DNA methylation (M), and mRNA expression (E) over 500+ ovarian cancer samples. The integrative analysis is based on a Bayesian graphical model treating the three types of measurements as three vertices in a network. The graph is used as a convenient way to parameterize and display the dependence structure. Edges connecting vertices infer specific types of regulatory relationships. For example, an edge between M and E and a lack of edge between C and E implies methylation-controlled transcription, which is robust to copy number changes. In other words, the mRNA expression is sensitive to methylational variation but not copy number variation. We apply the graphical model to each of the genes in the TCGA data independently and provide a comprehensive list of inferred profiles. Examples are provided based on simulated data as well.

关键词： expression transcription variation Meta-Analysis Graphical models Genetic Transcription edges mRNA Expression Bayesian inference simulated data

来源：评论

学校读者我要写书评

暂无评论

Statistical measures on residue-level protein structural properties

引用

Journal of structural and Functional Genomics 2011年第2期12卷 119-136页

作者： Huang, Yuanyuan Bonett, Stephen Kloczkowski, Andrzej Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50014 United States Summer REU Program on Computational Systems Biology Iowa State University Ames IA 50014 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50014 United States Department of Mathematics Iowa State University Ames IA 50014 United States

The atomic-level structural properties of proteins, such as bond lengths, bond angles, and torsion angles, have been well studied and understood based on either chemistry knowledge or statistical analysis. Similar properties on the residue-level, such as the distances between two residues and the angles formed by short sequences of residues, can be equally important for structural analysis and modeling, but these have not been examined and documented on a similar scale. While these properties are difficult to measure experimentally, they can be statistically estimated in meaningful ways based on their distributions in known proteins structures. Residue-level structural properties including various types of residue distances and angles are estimated statistically. A software package is built to provide direct access to the statistical data for the properties including some important correlations not previously investigated. The distributions of residue distances and angles may vary with varying sequences, but in most cases, are concentrated in some high probability ranges, corresponding to their frequent occurrences in either α-helices or β-sheets. Strong correlations among neighboring residue angles, similar to those between neighboring torsion angles at the atomic-level, are revealed based on their statistical measures. Residue-level statistical potentials can be defined using the statistical distributions and correlations of the residue distances and angles. Ramachandran-like plots for strongly correlated residue angles are plotted and analyzed. Their applications to structural evaluation and refinement are demonstrated. With the increase in both number and quality of known protein structures, many structural properties can be derived from sets of protein structures by statistical analysis and data mining, and these can even be used as a supplement to the experimental data for structure determinations. Indeed, the statistical measures on various types of residue d

关键词： Protein structural analysis Residue-level structural properties Statistical methods Statistical potentials structural bioinformatics structural correlation plots structural databases

来源：评论

学校读者我要写书评

暂无评论

P.R.E.S.S. — An R-package for exploring residual-level protein structural statistics

P.R.E.S.S. — An R-package for exploring residual-level prot...

引用

IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Yuanyuan Huang Steve Bonett Andrzej Kloczkowski Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Iowa State University Ames IA USA Summer REU Program on Computational Systems Biology Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Program on Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA

P.R.E.S.S. is an R package developed to allow researchers to get access to and manipulate on a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures are downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. will be released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment.

关键词： Correlation Proteins Graphical user interfaces Potential energy Protein engineering Software Graphics

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Solution NMR structures reveal unique homodimer formation by a winged helix-turn-helix motif and provide first structures for protein domain family PF10771

引用

Journal of structural and Functional Genomics 2012年第1期13卷 47-47页

作者： Eletsky, Alexander Petrey, Donald Zhang, Qiangfeng Cliff Lee, Hsiau-Wei Acton, Thomas B. Xiao, Rong Everett, John K. Prestegard, James H. Honig, Barry Montelione, Gaetano T. Szyperski, Thomas Department of Chemistry The State University of New York at Buffalo and Northeast Structural Genomics Consortium Buffalo USA Department of Biochemistry and Molecular Biophysics Howard Hughes Medical Institute Center for Computational Biology and Bioinformatics Columbia University New York USA Complex Carbohydrate Research Center University of Georgia and Northeast Structural Genomics Consortium Athens USA Department of Molecular Biology and Biochemistry Center of Advanced Biotechnology and Medicine Rutgers The State University of New Jersey Piscataway USA Department of Biochemistry Robert Wood Johnson Medical School UMDNJ and Northeast Structural Genomics Consortium Piscataway USA

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：