Tabla de Contenidos: “…8.2.1 Installation of Workflow -- 8.2.2 Importing the Raw Data for Processing -- 8.2.3 Retrieving Sample Annotation of the Data -- 8.2.4 Quality Control -- 8.3 Normalization of Microarray Data Using the RMA Method -- 8.3.1 Background Correction -- 8.3.2 Normalization -- 8.3.3 Summarization -- 8.4 Statistical Analysis for Differential Gene Expression -- 8.5 Conclusion -- References -- 9 Machine Learning in Bioinformatics -- 9.1 Introduction and Background -- 9.1.1 Bioinformatics -- 9.1.2 Text Mining -- 9.1.3 IoT Devices -- 9.2 Machine Learning Applications in Bioinformatics -- 9.3 Machine Learning Approaches -- 9.4 Conclusion and Closing Remarks -- References -- 10 DNA-RNA Barcoding and Gene Sequencing -- 10.1 Introduction -- 10.2 RNA -- 10.3 DNA Barcoding -- 10.3.1 Introduction -- 10.3.2 DNA Barcoding and Molecular Phylogeny -- 10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)-ITS -- 10.3.4 Chloroplast DNA -- 10.3.5 Mitochondrial DNA -- 10.3.6 Molecular Phylogenetic Analysis -- 10.3.7 Metabarcoding -- 10.3.8 Materials for DNA Barcoding -- 10.4 Main Reasons of DNA Barcoding -- 10.5 Limitations/Restrictions of DNA Barcoding -- 10.6 RNA Barcoding -- 10.6.1 Overview of the Method -- 10.7 Methodology -- 10.7.1 Materials Required -- 10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections -- 10.7.3 Using RNA to Trace Neurons -- 10.7.4 A Life Conservation Barcoder -- 10.7.5 Gene Sequencing -- 10.7.5.1 DNA Sequencing Methods -- 10.7.5.2 First-Generation Sequencing Techniques -- 10.7.5.3 Maxam's and Gilbert's Chemical Method -- 10.7.5.4 Sanger Sequencing -- 10.7.5.5 Automation in DNA Sequencing -- 10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs -- 10.7.5.7 Dye Terminator Sequencing -- 10.7.5.8 Using Capillary Electrophoresis -- 10.7.6 Developments and High-Throughput Methods in DNA Sequencing -- 10.7.7 Pyrosequencing Method…”

Tabla de Contenidos: “…5.A.6 Proof of Theorem 5.22 -- References -- 6 Distributed State Estimation in an LSS -- 6.1 Introduction -- 6.2 Predictor Design With Local Measurements -- 6.2.1 Derivation of the Optimal Gain Matrix -- 6.2.2 Relations With the Kalman Filter -- 6.2.3 Robusti cation of the Distributed Predictor -- 6.3 Distributed State Filtering -- 6.4 Asymptotic Property of the Distributed Observers -- 6.5 Distributed State Estimation Through Neighbor Information Exchanges -- 6.6 Bibliographic Notes -- Appendix 6.A -- 6.A.1 Proof of Theorem 6.1 -- 6.A.2 Proof of Theorem 6.2 -- 6.A.3 Proof of Theorem 6.3 -- 6.A.4 Proof of Theorem 6.4 -- 6.A.5 Derivation of Eqs. (6.46) and (6.47) -- 6.A.6 Proof of Theorem 6.7 -- 6.A.7 Proof of Theorem 6.8 -- References -- 7 Stability and Robust Stability of a Large-Scale NCS -- 7.1 Introduction -- 7.2 A Networked System With Discrete-Time Subsystems -- 7.2.1 System Description -- 7.2.2 Stability of a Networked System -- 7.2.3 Robust Stability of a Networked System -- 7.3 A Networked System With Continuous-Time Subsystems -- 7.3.1 Modeling Errors Described by IQCs -- 7.3.2 Robust Stability With IQC-Described Modeling Errors -- 7.4 Concluding Remarks -- 7.5 Bibliographic Notes -- Appendix 7.A -- 7.A.1 Proof of Theorem 7.3 -- 7.A.2 Proof of Theorem 7.4 -- References -- 8 Control With Communication Constraints -- 8.1 Introduction -- 8.2 Entropies and Capacities of a Communication Channel -- 8.2.1 Entropy in Information Theory -- 8.2.2 Topological Entropy in Feedback Theory -- 8.2.3 Channel Capacities -- 8.3 Stabilization Over Communication Channel -- 8.3.1 Classical Approach for Quantized Control -- 8.4 Universal Lower Bound -- 8.5 Coder-Decoder Design -- 8.6 Extension to Lossy Channels -- 8.6.1 Erasure Channels -- 8.6.2 Gilbert-Elliott Channels -- 8.7 Bibliographic Notes -- References -- 9 Distributed Control for Large-Scale NCSs…”