Optical transduction of E. Coli O157:H7 concentration by using the enhanced Goos-Hanchen shift


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Optical transduction of E. Coli O157:H7 concentration by using the enhanced Goos-Hanchen shift

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Human Brain Proteome
2009-06-18I. Introduction........................................................................ 3 II. Transcriptomics.................................................................... 4 III. Proteomics Methodologies for Biomarker Discovery......................... 7 IV. Principles of SELDI-TOF-MS..................................................... 12 V. SELDI-TOF-MS in Clinical Proteomics Research............................. 17 VI. Biomarkers in Specific Diseases................................................. 18 VII. Metabolomics....................................................................... 25 VIII. Conclusions......................................................................... 26 References.......................................................................... 27 Proteomic Analysis of Mitochondrial Proteins Mary F. Lopez,Simon Melov,Felicity Johnson, Nicole Nagulko,Eva Golenko,Scott Kuzdzal, Suzanne Ackloo, and Alvydas Mikulskis I. Introduction........................................................................ 31 II. Model Systems...................................................................... 32 III. Technological Approaches....................................................... 33 IV. Differential Expression of Proteins in Mouse Brain Mitochondria from Cortex and Synaptosomes........................................................ 38 V. Conclusions......................................................................... 43 References.......................................................................... 44 v vi CONTENTS SECTION II PROTEOMIC APPLICATIONS NMDA Receptors, Neural Pathways, and Protein Interaction Databases Holger Husi I. Introduction ........................................................................ 51 II. NMDA Receptor.................................................................... 52 III. Molecular Clustering .............................................................. 55 IV. Signal Transduction Pathways.................................................... 61 V. Bioinformatic Analysis............................................................. 63 VI. Proteomic Databases............................................................... 67 VII. Future Directions .................................................................. 73 References........................................................................... 74 Dopamine Transporter Network and Pathways Rajani Maiya and R. Dayne Mayfield I. Introduction ........................................................................ 79 II. Structure and Function of DAT ................................................. 80 III. Cellular Localization of DAT..................................................... 81 IV. Regulation of DAT................................................................. 81 V. Mass Spectrometry-Based Proteomics........................................... 83 VI. Isolation and Validation of the DAT Proteome............................... 84 VII. Mass Spectrometry................................................................. 85 VIII. Confirmation of Members of the DAT Proteome............................. 85 IX. In Silico Analysis of DAT Proteome.............................................. 90 References........................................................................... 93 Proteomic Approaches in Drug Discovery and Development Holly D. Soares,Stephen A. Williams,Peter J. Snyder,Feng Gao, Tom Stiger,Christian Rohlff,Athula Herath,Trey Sunderland, Karen Putnam, and W. Frost White I. Proteomics in Drug Discovery and Development............................. 97 II. Using Proteomics to Identify Biomarkers of Alzheimer’s Disease: A Case Study........................................................................ 104 III. Future Challenges and Conclusions............................................. 120 References........................................................................... 121 CONTENTS vii SECTION III INFORMATICS Proteomic Informatics Steven A. Russell,William Old,Katheryn A. Resing, and Lawrence Hunter I. What is Proteomics?............................................................... 129 II. Proteomic Informatics............................................................ 131 III. Mass Spectrometry and Shotgun Proteomics.................................. 131 IV. Identifying Proteins ............................................................... 134 V. Post-hoc Validation of Protein Identification Program Output............ 139 VI. Quantification...................................................................... 141 VII. Detection of Protein Isoforms................................................... 142 VIII. Systems and Workflow Issues .................................................... 146 IX. Conclusion.......................................................................... 150 X. Appendix: List of Mentioned Algorithms by Topic.......................... 150 References.......................................................................... 154 SECTION IV CHANGES IN THE PROTEOME BY DISEASE Proteomics Analysis in Alzheimer’s Disease: New Insights into Mechanisms of Neurodegeneration D. Allan Butterfield and Debra Boyd-Kimball I. Introduction........................................................................ 161 II. Proteomics Tools .................................................................. 162 III. Proteomic Studies in AD......................................................... 170 IV. Proteomics Analysis of Transgenic Models of AD............................ 181 V. Future of Proteomics in AD ..................................................... 182 References.......................................................................... 182 Proteomics and Alcoholism Frank A. Witzmann and Wendy N. Strother I. Introduction........................................................................ 189 II. Proteomics of the Hippocampus and Nucleus Accumbens................. 192 III. Future Directions.................................................................. 203 IV. Conclusion.......................................................................... 211 References.......................................................................... 211 viii CONTENTS Proteomics Studies of Traumatic Brain Injury Kevin K. W. Wang,Andrew Ottens,William Haskins,Ming Cheng Liu, Firas Kobeissy,Nancy Denslow,SuShing Chen, and Ronald L. Hayes I. Introduction ........................................................................ 215 II. Traumatic Brain Injury............................................................ 216 III. Proteomics Analysis Overview.................................................... 221 IV. Protein Separation Methods...................................................... 221 V. Protein Identification and Quantification Methods.......................... 225 VI. TBI Proteomics Bioinformatics .................................................. 232 VII. Prospective Utilities of TBI Proteomics Data.................................. 237 References........................................................................... 237 Influence of Huntington’s Disease on the Human and Mouse Proteome Claus Zabel and Joachim Klose I. Introduction ........................................................................ 241 II. Neurodegenerative Disorders Caused by Elongated Poly-Glutamine Repeats .............................................................................. 242 III. Approach for an HD Proteomics Study ........................................ 248 IV. Materials and Methods............................................................ 248 V. Proteomics Study of HD .......................................................... 249 VI. Discussion........................................................................... 270 VII. Conclusion.......................................................................... 277 References........................................................................... 278 SECTION V OVERVIEW OF THE NEUROPROTEOME Proteomics—Application to the Brain Katrin Marcus,Oliver Schmidt,Heike Schaefer, Michael Hamacher,AndrO van Hall, and Helmut E. Meyer I. Introduction ........................................................................ 287 II. Potential of Proteomics ........................................................... 288 III. Methods in Proteome Analysis................................................... 289 IV. Proteome Analysis in Neurosciences............................................ 299 V. Administrative Realization of Neuroproteomics............................... 303 References........................................................................... 307 Index........................................................................................ 313 Contents of Recent Volumes..................................................... 325
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[book] Sensor Technology Handbook
2010-10-30Book (Wilson2005) Wilson, J. S. (ed.) Sensor Technology Handbook 2005 Abstract: CHAPTER 1: Sensor Fundamentals ............................................................................ 1 1.1 Basic Sensor Technology ................................................................................................ 1 1.2 Sensor Systems ............................................................................................................ 15 CHAPTER 2: Application Considerations ................................................................ 21 2.1 Sensor Characteristics .................................................................................................. 22 2.2 System Characteristics ................................................................................................. 22 2.3 Instrument Selection .................................................................................................... 23 2.4 Data Acquisition and Readout ..................................................................................... 26 2.5 Installation .................................................................................................................. 26 CHAPTER 3: Measurement Issues and Criteria ....................................................... 29 CHAPTER 4: Sensor Signal Conditioning ................................................................ 31 4.1 Conditioning Bridge Circuits ....................................................................................... 31 4.2 Amplifiers for Signal Conditioning ............................................................................... 45 4.3 Analog to Digital Converters for Signal Conditioning ................................................... 92 4.4 Signal Conditioning High Impedance Sensors ........................................................... 108 CHAPTER 5: Acceleration, Shock and Vibration Sensors ..................................... 137 5.1 Introduction .............
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SVM分类器—基于SVM方法的分类器
2011-03-22用SVM方法制作的分类器1 Training Click “Learning “ from Toolbar or Menu, a dialog will appears like following : You can browse and choose the training sample data file(*.trn), or write the data file’s name into the text editor directly. And write down the training result data file(*.mdl). Then click “OK” to begin training .If you want to see training result after computation ,check the “Open model when finish” CheckBox. Testing Click “Classify “ from Toolbar or Menu, a dialog will appears like following: You can browse and choose the testing sample data file (*.tst), or write the data file’s name into the text editor directly. And write down the training result data file(*.mdl),test result file (*.rsl). Then click “OK” to begin training .If you want to see testing result after computation ,check the “Open result when finish” CheckBox. File Format The input file example_file contains the training examples. The first lines may contain comments and are ignored if they start with #. Each of the following lines represents one training example and is of the following format: <class> .=. +1 | -1 | 0 <feature> .=. integer <value> .=. real <line> .=. <class> <feature>:<value> <feature>:<value> ... <feature>:<value> The class label and each of the feature/value pairs are separated by a space character. Feature/value pairs MUST be ordered by increasing feature number. Features with value zero can be skipped. The +1 as class label marks a positive example, -1 a negative example respectively. A class label of 0 indicates that this example should be classified using transduction. The predictions for the examples classified by transduction are written to the file specified through the -l option. The order of the predictions is the same as in the training data. Options There are two types of options. One is for learning, such as kernel types, kernel parameters, etc; the other is for prompt information, such as show optimize information or not. Learning Options: To configure Learning Options, click “Learning Options” from toolbar /menu, a dialog will appear like following: You can set the learning parameters at this dialog. Particularly, you can choose kernel type at the dialog page following: Prompt Options To configure Learning Options, click “Prompt Options” from toolbar /menu, a dialog will appear like following: A little ugly? //sigh. I will improve it in next version. You can select the information you want to see when computing .You can modify this option even when computing. It is lucky that I didn’t write a single code for synchronization (Incredible?) More Details This SVM program is modified from SVM-light
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Gene regulation and signal transduction in the ICE-CBF-COR signaling pathway during cold stress in plants
2020-03-09植物冷胁迫下ICE-CBF-COR通路中的基因调控和信号转导,王大志,靳亚楠,低温是限制植物生长和生产的关键非生物胁迫因素。植物应答低温依赖于分子调控网络通路及其中的耐冷相关基因。ICE-CBF-COR通路是植物�
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SProtP: A Web Server to Recognize Those Short-Lived Proteins Based on Sequence-Derived Features in Human Cells
2021-02-09Protein turnover metabolism plays important roles in cell cycle progression, signal transduction, and differentiation. Those proteins with short half-lives are involved in various regulatory processes. To better understand the regulation of cell process, it is important to study the key sequence-derived factors affecting short-lived protein degradation. Until now, most of protein half-lives are still unknown due to the difficulties of traditional experimental methods in measuring protein halfliv
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Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning
2018-06-25Recurrent neural networks, particularly the long short-term memory networks, are extremely appealing for sequence-tosequence learning tasks. Despite their great success, they typically suffer from a fundamental shortcoming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus performance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional LSTMs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of sequence-level losses. Extensive experiments were performed on two standard sequence-to-sequence transduction tasks: machine transliteration and grapheme-to-phoneme transformation. The results show that the proposed approach achieves consistent and substantial improvements, compared to six state-of-the-art systems. In particular, our approach outperforms the best reported error rates by a margin (up to 9% relative gains) on the grapheme-to-phoneme task. Our toolkit is publicly available on https://github.com/lemaoliu/Agtarbidir.
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Proteomics in Drug Research
2009-06-18I Introduction 1 1 Administrative Optimization of Proteomics Networks for Drug Development 3 André van Hall and Michael Hamacher 1.1 Introduction 3 1.2 Tasks and Aims of Administration 4 1.3 Networking 6 1.4 Evaluation of Biomarkers 7 1.5 A Network for Proteomics in Drug Development 9 1.6 Realization of Administrative Networking: the Brain Proteome Projects 10 1.6.1 National Genome Research Network: the Human Brain Proteome Project 11 1.6.2 Human Proteome Organisation: the Brain Proteome Project 14 1.6.2.1 The Pilot Phase 15 References 17 2 Proteomic Data Standardization, Deposition and Exchange 19 Sandra Orchard, Henning Hermjakob, Manuela Pruess, and Rolf Apweiler 2.1 Introduction 19 2.2 Protein Analysis Tools 21 2.2.1 UniProt 21 2.2.2 InterPro 22 2.2.3 Proteome Analysis 22 2.2.4 International Protein Index (IPI) 23 Proteomics in Drug Research Edited by M. Hamacher, K. Marcus, K. Stühler, A. van Hall, B. Warscheid, H. E. Meyer Copyright (C) 2006 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 3-527-31226-9 Contents VI 2.2.5 Reactome 23 2.3 Data Storage and Retrieval 23 2.4 The Proteome Standards Initiative 24 2.5 General Proteomics Standards (GPS) 24 2.6 Mass Spectrometry 25 2.7 Molecular Interactions 27 2.8 Summary 28 References 28 II Proteomic Technologies 31 3 Difference Gel Electrophoresis (DIGE): the Next Generation of Two-Dimensional Gel Electrophoresis for Clinical Research 33 Barbara Sitek, Burghardt Scheibe, Klaus Jung, Alexander Schramm and Kai Stühler 3.1 Introduction 34 3.2 Difference Gel Electrophoresis: Next Generation of Protein Detection in 2-DE 36 3.2.1 Application of CyDye DIGE Minimal Fluors (Minimal Labeling with CyDye DIGE Minimal Fluors) 38 3.2.1.1 General Procedure 38 3.2.1.2 Example of Use: Identification of Kinetic Proteome Changes upon Ligand Activation of Trk-Receptors 39 3.2.2 Application of Saturation Labeling with CyDye DIGE Saturation Fluors 44 3.2.2.1 General Procedure 44 3.2.2.2 Example of Use: Analysis of 1000 Microdissected Cells from PanIN Grades for the Identification of a New Molecular Tumor Marker Using CyDye DIGE Saturation Fluors 45 3.2.3 Statistical Aspects of Applying DIGE Proteome Analysis 47 3.2.3.1 Calibration and Normalization of Protein Expression Data 48 3.2.3.2 Detection of Differentially Expressed Proteins 50 3.2.3.3 Sample Size Determination 51 3.2.3.4 Further Applications 52 References 52 4 Biological Mass Spectrometry: Basics and Drug Discovery Related Approaches 57 Bettina Warscheid 4.1 Introduction 57 4.2 Ionization Principles 58 4.2.1 Matrix-Assisted Laser Desorption/Ionization (MALDI) 58 4.2.2 Electrospray Ionization 60 4.3 Mass Spectrometric Instrumentation 62 Contents VII 4.4 Protein Identification Strategies 65 4.5 Quantitative Mass Spectrometry for Comparative and Functional Proteomics 67 4.6 Metabolic Labeling Approaches 69 15 N Labeling 70 4.6.1 4.6.2 Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) 71 4.7 Chemical Labeling Approaches 73 4.7.1 Chemical Isotope Labeling at the Protein Level 73 4.7.2 Stable Isotope Labeling at the Peptide Level 75 4.8 Quantitative MS for Deciphering Protein–Protein Interactions 78 4.9 Conclusions 80 References 81 5 Multidimensional Column Liquid Chromatography (LC) in Proteomics – Where Are We Now? 89 Egidijus Machtejevas, Klaus K. Unger and Reinhard Ditz 5.1 Introduction 90 5.2 Why Do We Need MD-LC/MS Methods? 91 5.3 Basic Aspects of Developing a MD-LC/MS Method 92 5.3.1 General 92 5.3.2 Issues to be Considered 93 5.3.3 Sample Clean-up 94 5.3.4 Choice of Phase Systems in MD-LC 94 5.3.5 Operational Aspects 97 5.3.6 State-of-the-Art – Digestion Strategy Included 98 5.3.6.1 Multidimensional LC MS Approaches 98 5.4 Applications of MD-LC Separation in Proteomics – a Brief Survey 100 5.5 Sample Clean-Up: Ways to Overcome the “Bottleneck” in Proteome Analysis 104 5.6 Summary 109 References 110 6 Peptidomics Technologies and Applications in Drug Research 113 Michael Schrader, Petra Budde, Horst Rose, Norbert Lamping, PeterSchulz-Knappe and Hans-Dieter Zucht 6.1 Introduction 114 6.2 Peptides in Drug Research 114 6.2.1 History of Peptide Research 114 6.2.2 Brief Biochemistry of Peptides 116 6.2.3 Peptides as Drugs 117 6.2.4 Peptides as Biomarkers 118 6.2.5 Clinical Peptidomics 118 6.3 Development of Peptidomics Technologies 120 6.3.1 Evolution of Peptide Analytical Methods 120 Contents VIII 6.3.2 Peptidomic Profiling 121 6.3.3 Top-Down Identification of Endogenous Peptides 123 6.4 Applications of Differential Display Peptidomics 124 6.4.1 Peptidomics in Drug Development 124 6.4.2 Peptidomics Applied to in vivo Models 127 6.5 Outlook 129 References 130 7 Protein Biochips in the Proteomic Field 137 Angelika Lücking and Dolores J. Cahill 7.1 Introduction 137 7.2 Technological Aspects 139 7.2.1 Protein Immobilization and Surface Chemistry 139 7.2.2 Transfer and Detection of Proteins 141 7.2.3 Chip Content 142 7.3 Applications of Protein Biochips 144 7.4 Contribution to Pharmaceutical Research and Development 150 References 151 8 Current Developments for the In Vitro Characterization of Protein Interactions 159 Daniela Moll, Bastian Zimmermann, Frank Gesellchen and Friedrich W.Herberg 8.1 Introduction 160 8.2 The Model System: cAMP-Dependent Protein Kinase 161 8.3 Real-time Monitoring of Interactions Using SPR Biosensors 161 8.4 ITC in Drug Design 163 8.5 Fluorescence Polarization, a Tool for High-Throughput Screening 165 8.6 AlphaScreen as a Pharmaceutical Screening Tool 167 8.7 Conclusions 170 References 171 9 Molecular Networks in Morphologically Intact Cells and Tissue–Challenge for Biology and Drug Development 173 Walter Schubert, Manuela Friedenberger and Marcus Bode 9.1 Introduction 173 9.2 A Metaphor of the Cell 174 9.3 Mapping Molecular Networks as Patterns: Theoretical Considerations 176 9.4 Imaging Cycler Robots 177 9.5 Formalization of Network Motifs as Geometric Objects 179 9.6 Gain of Functional Information: Perspectives for Drug Development 182 References 182 Contents IX III Applications 185 10 From Target to Lead Synthesis 187 Stefan Müllner, Holger Stark, Paivi Niskanen, Erich Eigenbrodt, SybilleMazurek and Hugo Fasold 10.1 Introduction 187 10.2 Materials and Methods 190 10.2.1 Cells and Culture Conditions 190 10.2.2 In Vitro Activity Testing 190 10.2.3 Affinity Chromatography 190 10.2.4 Electrophoresis and Protein Identification 191 10.2.5 BIAcore Analysis 191 10.2.6 Synthesis of Acyl Cyanides 192 10.2.6.1 Methyl 5-cyano-5-oxopentanoate 192 10.2.6.2 Methyl 6-cyano-6-oxohexanoate 193 10.2.6.3 Methyl-5-cyano-3-methyl-5-oxopentanoate 193 10.3 Results 193 10.4 Discussion 201 References 203 11 Differential Phosphoproteome Analysis in Medical Research 209 Elke Butt and Katrin Marcus 11.1 Introduction 210 11.2 Phosphoproteomics of Human Platelets 211 11.2.1 Cortactin 213 11.2.2 Myosin Regulatory Light Chain 213 11.2.3 Protein Disulfide Isomerase 214 11.3 Identification of cAMP- and cGMP-Dependent Protein Kinase Substrates in Human Platelets 216 11.4 Identification of a New Therapeutic Target for Anti-Inflammatory Therapy byAnalyzing Differences in the Phosphoproteome of Wild Type and Knock Out Mice 218 11.5 Concluding Remarks and Outlook 219 References 220 12 Biomarker Discovery in Renal Cell Carcinoma Applying Proteome-Based Studies in Combination with Serology 223 Barbara Seliger and Roland Kellner 12.1 Introduction 224 12.1.1 Renal Cell Carcinoma 224 12.2 Rational Approaches Used for Biomarker Discovery 225 12.3 Advantages of Different Proteome-Based Technologies for the Identification ofBiomarkers 226 Contents X 12.4 Type of Biomarker 228 12.5 Proteome Analysis of Renal Cell Carcinoma Cell Lines and Biopsies 229 12.6 Validation of Differentially Expressed Proteins 234 12.7 Conclusions 235 References 235 13 Studies of Drug Resistance Using Organelle Proteomics 241 Catherine Fenselau and Zongming Fu 13.1 Introduction 242 13.1.1 The Clinical Problem and the Proteomics Response 242 13.2 Objectives and Experimental Design 243 13.2.1 The Cell Lines 243 13.2.2 Organelle Isolation 244 13.2.2.1 Criteria for Isolation 244 13.2.2.2 Plasma Membrane Isolation 245 13.2.3 Protein Fractionation and Identification 247 13.2.4 Quantitative Comparisons of Protein Abundances 249 13.3 Changes in Plasma Membrane and Nuclear Proteins in MCF-7 Cells Resistant toMitoxantrone 252 References 254 14 Clinical Neuroproteomics of Human Body Fluids: CSF and Blood Assays forEarly and Differential Diagnosis of Dementia 259 Jens Wiltfang and Piotr Lewczuk 14.1 Introduction 259 14.2 Neurochemical Markers of Alzheimer’s Disease 260 14.2.1 β-Amyloid Precursor Protein (β-APP): Metabolismand ImpactonADDiagnosis 261 14.2.2 Tau Protein and its Phosphorylated Forms 263 14.2.2.1 Hyperphosphorylation of Tau as a Pathological Event 264 14.2.2.2 Phosphorylated Tau in CSF as a Biomarker of Alzheimer’s Disease 265 14.2.3 Apolipoprotein E (ApoE) Genotype 266 14.2.4 Other Possible Factors 267 14.2.5 Combined Analysis of CSF Parameters 267 14.2.6 Perspectives: Novel Techniques to Search for AD Biomarkers – Mass Spectrometry (MS), Differential Gel Electrophoresis (DIGE), and Multiplexing 270 14.3 Conclusions 271 References 272 15 Proteomics in Alzheimer’s Disease 279 Michael Fountoulakis, Sophia Kossida and Gert Lubec 15.1 Introduction 279 Contents XI 15.2 Proteomic Analysis 280 15.2.1 Sample Preparation 280 15.2.2 Two-Dimensional Electrophoresis 282 15.2.3 Protein Quantification 282 15.2.4 Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectroscopy 283 15.3 Proteins with Deranged Levels and Modifications in AD 284 15.3.1 Synaptosomal Proteins 290 15.3.2 Guidance Proteins 291 15.3.3 Signal Transduction Proteins 291 15.3.4 Oxidized Proteins 292 15.3.5 Heat Shock Proteins 293 15.3.6 Proteins Enriched in Amyloid Plaques 293 15.4 Limitations 294 References 294 16 Cardiac Proteomics 299 Emma McGregor and Michael J. Dunn 16.1 Heart Proteomics 300 16.1.1 Heart 2-D Protein Databases 300 16.1.2 Dilated Cardiomyopathy 300 16.1.3 Animal Models of Heart Disease 301 16.1.4 Subproteomics of the Heart 302 16.1.4.1 Mitochondria 302 16.1.4.2 PKC Signal Transduction Pathways 304 16.1.5 Proteomics of Cultured Cardiac Myocytes 305 16.1.6 Proteomic Characterization of Cardiac Antigens in Heart Disease and Transplantation 306 16.1.7 Markers of Acute Allograft Rejection 307 16.2 Vessel Proteomics 307 16.2.1 Proteomics of Intact Vessels 307 16.2.2 Proteomics of Isolated Vessel Cells 308 16.2.3 Laser Capture Microdissection 311 16.3 Concluding Remarks 312 References 312 IV To the Market 319 17 Innovation Processes 321 Sven Rüger 17.1 Introduction 321 17.2 Innovation Process Criteria 322 17.3 The Concept 322 17.4 Market Attractiveness 323 Contents XII 17.5 Competitive Market Position 323 17.6 Competitive Technology Position 324 17.7 Strengthen the Fit 325 17.8 Reward 325 17.9 Risk 325 17.10 Innovation Process Deliverables for each Stage 326 17.11 Stage Gate-Like Process 326 17.11.1 Designation as an Evaluation Project (EvP) 327 17.11.2 Advancement to Exploratory Project (EP) 329 17.11.3 For Advancement to Progressed Project (PP) 331 17.11.4 Advancement to Market Preparation 334 17.12 Conclusion 335 Subject Index 337
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Wiley Biochemistry of Signal Transduction and Regulation 2nd
2009-01-08Wiley Biochemistry of Signal Transduction and Regulation 2nd
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Identification of microRNAs related to the genetic male sterility of Chinese cabbage using high-throughput sequencing
2020-03-13Identification of microRNAs related to the genetic male sterility of Chinese cabbage using high-throughput sequencing,LIU Chang,LIU Zhiyong,MicroRNAs (miRNAs) negatively regulate gene expression, and play important roles in growth and development, cell proliferation, apoptosis, signal transduction, and in biotic and ab
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英文原版-Cancer Signaling From Molecular Biology to Targeted Therapy 1st Edition
2019-09-23Cancer, which has become the second-most prevalent health issue globally, is essentially a malfunction of cell signaling. Understanding how the intricate signaling networks of cells and tissues allow cancer to thrive - and how they can be turned into potent weapons against it - is the key to managing cancer in the clinic and improving the outcome of cancer therapies. In their ground-breaking textbook, the authors provide a compelling story of how cancer works on the molecular level, and how targeted therapies using kinase inhibitors and other modulators of signaling pathways can contain and eventually cure it.The first part of the book gives an introduction into the cell and molecular biology of cancer, focusing on the key mechanisms of cancer formation. The second part of the book introduces the main signaling transduction mechanisms responsible for carcinogenesis and compares their function in healthy versus cancer cells. In contrast to the complexity of its topic, the text is easy to read. 32 specially prepared teaching videos on key concepts and pathways in cancer signaling are available online.,解压密码 share.weimo.info
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stog:AMR解析为序列图转换-源码
2021-02-03AMR解析为序列图转换 我们的ACL 2019论文“ ”中的AMR解析器代码。 如果您发现我们的代码很有用,请引用: @inproceedings{zhang-etal-2018-stog, title = "{AMR Parsing as Sequence-to-Graph Transduction}", author = "Zhang, Sheng and Ma, Xutai and Duh, Kevin and Van Durme, Benjamin", booktitle = "Proceedings of the 57t
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Oxford NLP lecture
2017-11-17This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed. This course is organised by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.
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JAVA五子棋代码
2012-07-22自己写的五子棋代码,新手专用。。比较容易懂
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细胞信号通路大全_Signal_Transduction_(下).pdf
2021-01-23最全细胞信号通路图总结,医学的同仁们可以收藏了
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IMMUNOPROTECTION AND DIAGNOSTIC POTENTIAL OF SIGNALING PROTEIN 14-3-3 OF SCHISTOSOMA JAPONICUM×
2020-02-01IMMUNOPROTECTION AND DIAGNOSTIC POTENTIAL OF SIGNALING PROTEIN 14-3-3 OF SCHISTOSOMA JAPONICUM×,刘庆中,沈继龙,The 14-3-3 protein is a key player in signal transduction processes in various species of animals and plants. Here, we cloned and expressed the 14-3-3 of Schistosoma japonicum (Sj1
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Sequence Transduction with Recurrent Neural Networks
2019-10-03深度学习中,最初始的利用Recurrent Neural Networks 技术做语音识别的论文
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PIE:使用BERT的快速+非自回归语法错误校正。 论文《本地序列转导的并行迭代编辑模型》的代码和预训练模型:www.aclweb.organthologyD19-1435.pdf(EMNLP-IJCNLP 2019)-源码
2021-02-03PIE:用于局部序列转导的并行迭代编辑模型 使用BERT的快速语法错误校正 伴随我们的论文《用于局部序列转导的并行迭代编辑模型》(EMNLP-IJCNLP 2019)附带的代码和预训练模型 我们介绍了PIE,一种基于BERT的体系结构,用于本地序列转导任务,如语法错误校正。 与将GEC建模为从“不正确”到“正确”语言的翻译任务的标准方法不同,我们将GEC视为本地序列编辑任务。 我们进一步将局部序列编辑问题减少到序列标记设置中,其中我们利用BERT来非自回归地对输入标记进行编辑。 我们专门为序列编辑的任务重新连接了BERT架构(无需重新培训)。 我们发现,GEC的PIE模型比现有的最新体系结构快
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超牛逼的自然语言处理论文和代码
2012-01-30Research on Issues of Translation Selection for Phrase and Structure in Statistical Machine Translation_hezhongjun_phd thesis 2008.pdf Research on domain adaptation in Statistical Machine Translation_caojie_master thesis 2010.pdf Research on Statistical Machine Translation Based on Bracketing Transduction Grammar and Dependency Grammar_xiongdeyi_phd thesis 2007.pdf Research on Implementation Technology of Large-scale Statistical Language Model_huangyun_master thesis 2008.pdf The Research and System Implementation of Automatic Acquisition of Large-scale Bilingual Parallel Corpus from Web_yeshanni_master thesis 2008.pdf Research on Fault-tolerant Statistical Machine Translation_mihaitao_phd thesis 2009.pdf Research on Tree-to-String Statistical Translation Models_liuyang_phd thesis 2007.pdf Automatic Extraction and Application of Multiword Expression Translation Pairs_renzhixiang_master thesis 2009.pdf Research on Some Issues of Large-scale Data Precessing in Statistical Machine Translation_luoweihua_PhD thesis 2010.pdf 融合翻译模板的统计机器翻译技术研究.pdf 主题可定制的web双语平行语料库自动获取技术研究.pdf
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CobaltStrike4.0用户手册_中文翻译-带书签.pdf
CobaltStrike4.0用户手册_中文翻译-带书签.pdf
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华宇软件:2020年年度报告.PDF
华宇软件:2020年年度报告.PDF
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86-GMII接收接口设计.7z
86-GMII接收接口设计.7z
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IEEE Std 982.1 - 2005 可靠性软件方面的度量词典 - 完整英文电子版(44页)
IEEE Std 982.1 - 2005 可靠性软件方面的度量词典 - 完整英文电子版(44页)
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