Note: Front Cover -- Title Page -- Copyright -- Contents -- Contributors -- Chapter One: Proteomic applications in identifying protein-protein interactionsProteomic applications in identifying protein-protein interactions -- 1 Introduction -- 2 Isolation of protein complexes -- 2.1 Immunoprecipitation -- 2.2 Pull-down assays -- 2.3 Validation -- 3 Proteomics enters the PPI field -- 3.1 Technology developments enabling proteome-wide identification of PPIs -- 3.2 From hypothesis to discovery-driven interactomics -- 3.3 Using discovery-driven results to generate hypotheses -- 4 Whole proteome screening for PPIs -- 4.1 High-throughput mass spectrometric protein complex identification -- 4.2 Co-fractionation with mass spectrometry -- 4.3 Yeast two-hybrid assays -- 4.4 Chemical cross-linking combined with mass spectrometry -- 4.4.1 Choosing the cross-linking agent -- 4.4.1.1 Amine-reactive cross-linkers -- 4.4.1.2 Thiol-reactive cross-linkers -- 4.4.1.3 Photoreactive cross-linkers -- 4.4.1.4 Carboxyl-reactive cross-linkers -- 4.4.1.5 Formaldehyde -- 4.4.1.6 Cleavable cross-linkers -- 4.4.2 Analysis of yeast mitochondria interactions using CL-MS -- 4.5 Proximity labeling methods -- 4.5.1 Biotin ligase proximity labeling -- 4.5.2 Horseradish peroxidase proximity labeling -- 4.5.3 Ascorbate peroxidase proximity labeling -- 4.5.4 Limits of proximity labeling methods -- 5 Protein-protein interaction databases -- 6 Challenges in generating global PPI maps -- 7 Conclusions -- Acknowledgements -- Conflict of interest -- References -- Chapter Two: Functional proteomics based on protein microarray technology for biomedical researchFunctional proteomics based on protein microarray technology for biomedical research -- 1 Introduction -- 2 Proteomic technologies -- 3 Microarrays applications -- 3.1 Analytical microarrays -- 3.2 Functional microarrays -- 3.3 RPPA. , 4 Conclusions and future perspectives -- Acknowledgements -- Conflict of interest -- Funding and additional information -- Authors contributions -- References -- Chapter Three: Understanding functions of eEF1 translation elongation factors beyond translation. A proteomic approach -- 1 Human translation elongation factors -- 2 eEF1A1 and eEF1A2 -- 3 Difference in physical properties of eEF1A1 and eEF1A2 -- 4 Principles of research -- 5 Common moonlighting functions of eEF1A1 and eEF1A2 -- 6 Potential novel functions of the eEF1A1 isoform -- 6.1 Arp2/3 complex-mediated actin nucleation -- 6.2 Mitochondrial transcription initiation -- 6.3 The ribonuclease H2 complex and Aicardi-Goutieres syndrome -- 6.4 Components of Cul3-RING ubiquitin ligase complex -- 7 Potential novel functions of eEF1A2 isoform -- 7.1 Interaction with adenylyl cyclase-associated proteins -- 7.2 Focal adhesion -- 7.3 Spinocerebellar ataxias -- 7.4 Protein complexes related to chromatin remodeling -- 7.4.1 The NURF complex -- 7.4.2 The SWI/SNF complex -- 7.4.3 The NURD complex -- 8 Concluding remarks -- Acknowledgments -- References -- Chapter Four: Proteomics provides insights into the theranostic potential of extracellular vesicles -- 1 Introduction -- 2 Advances in proteomics: Unveiling biological complexity, disease mechanisms, and personalized medicine -- 2.1 The pathogenesis mechanism of EVs has been analyzed by proteomics -- 2.2 Proteomics can be used to identify candidate new biomarkers -- 2.3 Monitoring of therapy -- 2.4 Searching for disease-specific proteins in EVs for proposing new therapy -- 2.5 Therapeutic potency of EVs linked to their protein cargoes (Discovering the therapeutic cargo) -- 3 The protein cargoes of EVs are influenced by different factors -- 4 More studies are needed -- Available databases -- 5 Conclusion -- References. , Chapter Five: Towards a structural and functional analysis of the immunoglobulin-fold proteome -- 1 Introduction to the immunoglobulin fold -- 1.1 What is the Ig fold? -- 1.2 Evolution of the Ig fold -- 1.2.1 Common ancestry -- 1.2.2 Gene duplication and divergence -- 1.2.3 Horizontal gene transfer -- 1.2.4 Co-option of existing domains -- 1.3 Diversity of the Ig fold -- 1.4 Structural and functional analysis -- 1.4.1 IgStRAnD universal residue numbering scheme -- 1.4.2 iCn3D -- 2 Ig fold proteome in physiology and disease -- 2.1 Ig interactions in protein function -- 2.1.1 Ig/Ig interactions -- 2.1.2 Ig/non-Ig interactions -- 2.2 Diversity of Ig functions by cellular location -- 2.2.1 Extracellular Igs -- 2.2.2 Membrane bound Igs -- 2.2.3 Cytoplasmic Igs -- 2.2.4 Nuclear Igs -- 2.3 Ig dysfunction in disease -- 3 Applications of the Ig fold domains -- 3.1 Antibodies in therapy -- 3.2 Single-domain antibodies or nanobodies -- 3.2.1 Generation of nanobody libraries -- 3.2.2 Nanobodies in molecular and structural biology -- 3.2.3 Nanobodies in therapy -- 3.3 De novo design of Ig fold domains -- Acknowledgments -- References -- Chapter Six: Functional unfoldomics: Roles of intrinsic disorder in protein (multi)functionality -- 1 Most of the textbook knowledge about proteins is incomplete: introducing the protein intrinsic disorder phenomenon -- 1.1 Natural abundance of IDPs/IDRs: unfoldome and unfoldomics -- 2 Structural complexity of intrinsic disorder -- 3 Complexity of the disorder-based functional code and protein structure-function continuum -- 4 Major disorder-based biological functions -- 5 Interaction specialists: intrinsic disorder is advantageous for interactions -- 6 Disorder-based foldable binding sites of proteins: static disorder-based interactions -- 7 Fuzzy or dynamic complexes: binding with minimal folding or with no folding at all. , 8 Liquid-liquid phase separation, membrane-less organelles, and intrinsic disorder -- 9 Concluding remarks -- References -- Chapter Seven: Analysis of endoglucanases production using metatranscriptomics and proteomics approachAnalysis of endoglucanases production using metatranscriptomics and proteomics approach -- 1 Introduction -- 2 Application of endoglucanases -- 3 Metatranscriptomic approach -- 4 Proteomics tool for analyzing endoglucanase production -- 4.1 Comparative proteomic analysis -- 4.2 Quantitative proteomics for the analysis -- 5 Conclusion -- Acknowledgement -- References -- Chapter Eight: In silico network pharmacology study on Glycyrrhiza glabra: Analyzing the immune-boosting phytochemical properties of Siddha medicinal plant against COVID-19In silico network pharmacology study on Glycyrrhiza glabra -- 1 Introduction -- 2 Materials and methods -- 2.1 Mining of phytoconstituents and its target -- 2.2 Enrichment and network analysis -- 2.3 Druglikeness and ADMET profiling -- 2.4 Molecular docking -- 2.5 Molecular dynamics simulation -- 3 Results -- 3.1 Phytoconstituents and their target -- 3.2 Gene-set enrichment analysis and network analysis -- 3.3 Druglikeness and ADMET profiling -- 3.4 Molecular docking -- 3.5 Molecular dynamics simulation -- 4 Discussion -- 5 Conclusion -- CRediT authorship contribution statement -- Data availability statement -- Declaration of competing interest -- Appendix A Supporting information -- References -- Chapter Nine: In silico network pharmacology analysis and molecular docking validation of Swasa Kudori tablet for screening druggable phytoconstituents of asthma -- 1 Introduction -- 2 Materials and methods -- 2.1 In silico assessment of Swasa Kudori to understand the therapeutic activity against asthma -- 2.2 Comparison of efficacy between Swasa Kudori tablet and co-drug -- 3 Results. , 3.1 Screening the active components -- 3.2 Gene marker identification -- 3.3 In silico pharmacokinetics (drug-likeness) assessment -- 3.4 Protein-protein interaction network construction, hub gene selection, and pathway enrichment analysis -- 3.5 Molecular docking of HO-1 with pinocembrin -- 3.6 Molecular dynamics simulation -- 4 Discussion -- Funding -- CRediT authorship contribution statement -- Data availability statement -- Declaration of competing interest -- Appendix A Supporting information -- References -- Chapter Ten: Proteomics and genomics insights on malignant osteosarcoma -- 1 Introduction -- 2 Types of osteosarcoma -- 2.1 Central-Conventional osteosarcoma -- 2.2 Central-Telangiectatic osteosarcoma -- 2.3 Central-Small-graded osteosarcoma -- 2.4 Central-Low-graded osteosarcoma -- 2.5 Surface-Parosteal osteosarcoma (PAOS) -- 2.6 Surface-Peristeal osteosarcoma -- 2.7 Surface-High-graded surface osteosarcoma -- 3 Bone metastasis -- 4 Etiology of osteosarcoma -- 5 Epidemiology of osteosarcoma -- 6 Epigenetic of osteosarcoma -- 6.1 Methylation of DNA -- 6.2 Modification of histones -- 6.3 Nucleosome remodelling -- 7 Gene mutation -- 8 Molecular biology and pathogenesis of osteosarcoma -- 9 Pathway involved in osteosarcoma -- 9.1 PI3K/Akt/mTOR pathway -- 9.2 Receptor tyrosine kinases and downstream pathways -- 9.3 Notch pathway -- 9.4 Ezrin pathway -- 10 Biomarker of osteosarcoma -- 10.1 Protein serum biomarker -- 10.2 Nucleic acids -- 11 Proteomics in osteosarcoma -- 12 Diagnosis and staging -- 13 Cancer treatment caused bone loss -- 14 Future perspective -- Acknowledgements -- Declaration of interest -- References -- Chapter Eleven: Application of functional proteomics in understanding RNA virus-mediated infectionApplication of functional proteomics in understanding RNA virus-mediated infection -- 1 Introduction. , 2 Functional proteomics-overview.

Note: Front cover -- Half title -- Title -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1 Machine learning methods -- 1.1 Introduction -- 1.2 Holistic view of learning models -- 1.2.1 Supervised learning -- 1.2.2 Unsupervised learning -- 1.2.3 Hybrid learning -- 1.2.4 Reinforcement learning -- 1.3 Classification of learning techniques -- 1.3.1 Data perspective -- 1.3.2 Algorithmic perspective -- 1.4 Machine learning methods -- 1.4.1 Dimensionality reduction -- 1.4.2 Neural networks and deep learning -- 1.4.3 Natural language processing -- 1.4.4 Machine learning as an interpolation function -- 1.5 Conclusion -- Acknowledgment -- References -- Chapter 2 Learning first-principles knowledge from data -- 2.1 Background -- 2.2 Approaches to analyze manufacturing data -- 2.2.1 Static data approaches -- 2.2.2 Dynamic data approaches -- 2.3 Automation of model selection and hyperparameter search -- 2.3.1 Automation for general data: AutoML -- 2.3.2 Automation for manufacturing data: smart process data analytics -- 2.4 Conclusion -- References -- Chapter 3 Convolutional neural networks: Basic concepts and applications in manufacturing -- 3.1 Introduction -- 3.2 Data objects and mathematical representations -- 3.2.1 Tensor representations -- 3.2.2 Graph representations -- 3.2.3 Color representations -- 3.3 Convolutional neural network architectures -- 3.3.1 Convolution operations -- 3.3.2 Activation functions -- 3.3.3 Pooling -- 3.3.4 Convolution blocks -- 3.3.5 Feedforward neural networks -- 3.3.6 Data augmentation -- 3.3.7 Training and testing procedures -- 3.3.8 CNN architecture optimization -- 3.3.9 Transfer learning -- 3.4 Case studies -- 3.4.1 CNNs for sensor design -- 3.4.2 Molecule design -- 3.4.3 Decoding of spectra -- 3.4.4 CNNs for multivariate process monitoring -- 3.4.5 CNNs for image-based feedback control -- 3.5 Conclusion. , Acknowledgment -- References -- Chapter 4 Sparse mathematical programming for fundamental learning of governing equations -- 4.1 Introduction -- 4.2 Problem definitions -- 4.2.1 Distilling governing equations -- 4.2.2 Surrogate modeling and system identification -- 4.2.3 Parameter estimation -- 4.3 Physics-informed machine learning -- 4.3.1 General principle -- 4.3.2 Example application of PINN to the Burgers equation -- 4.4 Regression-based approaches -- 4.4.1 General principle -- 4.4.2 Expansive strategies -- 4.4.3 Contractive strategies -- 4.5 Techniques based on mathematical programming -- 4.5.1 General principle -- 4.5.2 Moving horizon formulations -- 4.6 Demonstration of moving horizon discovery for a batch chemical process -- 4.7 Conclusion -- References -- Chapter 5 Data-driven optimization algorithms -- 5.1 Introduction -- 5.2 Algorithmic approaches for data-driven optimization -- 5.2.1 Direct search algorithms -- 5.2.2 Model-based algorithms -- 5.3 Applications to large-scale manufacturing systems -- 5.4 Extensions to other classes of problems -- 5.4.1 Data-driven multi-objective optimization with & -- #x03F5 -- -constraint -- 5.4.2 Data-driven mixed-integer nonlinear bi-level optimization with DOMINO -- 5.5 Remarks -- 5.6 Conclusion -- References -- Chapter 6 Machine learning for control of (bio)chemical manufacturing systems -- 6.1 Introduction -- 6.2 (Bio)chemical processes -- 6.2.1 Plant modeling -- 6.2.2 Processes operating modes -- 6.2.3 Control architecture -- 6.2.4 Monitoring -- 6.3 The machine learning Oracle and machine learning approaches in a nutshell -- 6.3.1 The machine learning-Oracle -- 6.3.2 Abstraction of the machine learning-Oracle -- 6.3.3 Machine learning Oracle examples -- 6.4 Machine learning-supported modeling for monitoring and control -- 6.4.1 Learning system models for process monitoring. , 6.4.2 Learning models for process control -- 6.5 Control via machine learning -- 6.5.1 Learning uncertainties for safe control -- 6.5.2 Substitute conventional controllers with machine learning -- 6.5.3 Outlook -- 6.6 Conclusion -- References -- Chapter 7 Learning first principles systems knowledge from data: Stability and safety with applications to learning from demonstration -- 7.1 Introduction -- 7.1.1 What to imitate? -- 7.1.2 How to imitate? -- 7.1.3 What are the user interfaces for demonstrations? -- 7.1.4 Low-level learning of motions using movement primitives -- 7.1.5 High-level composition of complex tasks -- 7.1.6 Combining imitation learning with reinforcement learning and meta-learning -- 7.1.7 Contracting and safe movement primitives -- 7.2 Learning robot motions using dynamical systems primitive -- 7.2.1 System model -- 7.2.2 Function approximation -- 7.2.3 Learning GMM parameters with contraction constraints -- 7.2.4 Learning ELM model parameters with Lyapunov and barrier constraints -- 7.2.5 Simulations -- 7.3 Conclusion -- 7.3.1 Discussion on the constrained model learning methods -- 7.3.2 Who to imitate? -- 7.3.3 Using imitation learning when a robot collaborates with a human -- 7.3.4 Imitation across a large number of robots -- Acknowledgment -- References -- Chapter 8 Artificial intelligence for materials damage diagnostics and prognostics -- 8.1 Introduction -- 8.1.1 Background on materials damage -- 8.1.2 Need for AI in materials damage diagnostics and prognostics -- 8.2 AI methods for materials diagnostics and prognostics -- 8.2.1 Support vector machines -- 8.2.2 Tree-based classifiers -- 8.2.3 Bayesian classifiers -- 8.2.4 Unsupervised machine learning -- 8.2.5 Deep learning methods -- 8.2.6 Hidden Markov models -- 8.2.7 Filtering-based approaches. , 8.3 Challenges and opportunities for AI methods for damage diagnostics and prognostics -- 8.4 Conclusion -- References -- Chapter 9 Artificial intelligence for machining process monitoring -- 9.1 Introduction -- 9.2 Data acquisition systems -- 9.3 Feature engineering and machine learning -- 9.3.1 Feature extraction -- 9.3.2 Feature selection -- 9.3.3 Machine learning models -- 9.3.4 Open-source datasets used in machining process monitoring -- 9.4 Signal decomposition methods -- 9.5 Deep learning -- 9.6 Transfer learning -- 9.6.1 Cross-domain transfer learning -- 9.6.2 Physics-guided transfer learning -- 9.7 Conclusion -- Acknowledgment -- References -- Index -- Back cover.

Note: Front Cover -- Continental Northeast Asian Amphibians -- Continental Northeast Asian Amphibians Origins, Behavioural Ecology, and Conservation -- Copyright -- Contents -- 1 - Introduction to continental northeast Asian Amphibians -- 1.1 Taxonomic scope -- 1.2 Knowledge disclaimer -- References -- 2 - Bufonidae (Bufo & -- Strauchbufo) -- 2.1 Bufo -- 2.1.1 Bufo gargarizans -- 2.1.1.1 Origin and distribution -- 2.1.1.2 Habitat -- 2.1.1.3 Behavioural ecology -- 2.1.1.4 Threats and conservation -- 2.1.1.5 Identification -- 2.1.2 Bufo sachalinensis -- 2.1.2.1 Origin and distribution -- 2.1.2.2 Habitat -- 2.1.2.3 Behavioural ecology -- 2.1.2.4 Threats and conservation -- 2.1.2.5 Identification -- 2.1.3 Bufo stejnegeri -- 2.1.3.1 Origin and distribution -- 2.1.3.2 Habitat -- 2.1.3.3 Behavioural ecology -- 2.1.3.4 Threats and conservation -- 2.1.3.5 Identification -- 2.2 Strauchbufo -- 2.2.1 Strauchbufo raddei -- 2.2.1.1 Origin and distribution -- 2.2.1.2 Habitat -- 2.2.1.3 Behavioural ecology -- 2.2.1.4 Threats and conservation -- 2.2.1.5 Identification -- References -- 3 - Hylidae (Dryophytes) -- 3.1 Dryophytes -- 3.1.1 Dryophytes japonicus -- 3.1.1.1 Origin and distribution -- 3.1.1.2 Habitat -- 3.1.1.3 Behavioural ecology -- 3.1.1.4 Threats and conservation -- 3.1.1.5 Identification -- 3.1.2 Dryophytes immaculatus -- 3.1.2.1 Origin and distribution -- 3.1.2.2 Habitat -- 3.1.2.3 Behavioural ecology -- 3.1.2.4 Threats and conservation -- 3.1.2.5 Identification -- 3.1.3 Dryophytes suweonensis -- 3.1.3.1 Origin and distribution -- 3.1.3.2 Habitat -- 3.1.3.3 Behavioural ecology -- 3.1.3.4 Threats and conservation -- 3.1.3.5 Identification -- 3.1.4 Dryophytes flaviventris -- 3.1.4.1 Origin and distribution -- 3.1.4.2 Habitat -- 3.1.4.3 Behavioural ecology -- 3.1.4.4 Threats and conservation -- 3.1.4.5 Identification -- References. , 4 - Bombinatoridae (Bombina) -- 4.1 Bombina -- 4.1.1 Bombina orientalis -- 4.1.1.1 Origin and distribution -- 4.1.1.2 Habitat -- 4.1.1.3 Behavioural ecology -- 4.1.1.4 Threats and conservation -- 4.1.1.5 Identification -- References -- 5 - Microhylidae (Kaloula) -- 5.1 Kaloula -- 5.1.1 Kaloula borealis -- 5.1.1.1 Origin and distribution -- 5.1.1.2 Habitat -- 5.1.1.3 Behavioural ecology -- 5.1.1.4 Threats and conservation -- 5.1.1.5 Identification -- References -- 6 - Ranidae (Rana, Glandirana, and Pelophylax) -- 6.1 Rana -- 6.1.1 Rana amurensis -- 6.1.1.1 Origin and distribution -- 6.1.1.2 Habitat -- 6.1.1.3 Behavioural ecology -- 6.1.1.4 Threats and conservation -- 6.1.1.5 Identification -- 6.1.2 Rana coreana -- 6.1.2.1 Origin and distribution -- 6.1.2.2 Habitat -- 6.1.2.3 Behavioural ecology -- 6.1.2.4 Threats and conservation -- 6.1.2.5 Identification -- 6.1.3 Rana dybowskii -- 6.1.3.1 Origin and distribution -- 6.1.3.2 Habitat -- 6.1.3.3 Behavioural ecology -- 6.1.3.4 Threats and conservation -- 6.1.3.5 Identification -- 6.1.4 Rana uenoi -- 6.1.4.1 Origin and distribution -- 6.1.4.2 Habitat -- 6.1.4.3 Behavioural ecology -- 6.1.4.4 Threats and conservation -- 6.1.4.5 Identification -- 6.1.5 Rana chensinensis -- 6.1.5.1 Origin and distribution -- 6.1.5.2 Habitat -- 6.1.5.3 Behavioural ecology -- 6.1.5.4 Threats and conservation -- 6.1.5.5 Identification -- 6.1.6 Rana taihangensis -- 6.1.6.1 Origin and distribution -- 6.1.6.2 Habitat -- 6.1.6.3 Behavioural ecology -- 6.1.6.4 Threats and conservation -- 6.1.6.5 Identification -- 6.1.7 Rana huanrenensis -- 6.1.7.1 Origin and distribution -- 6.1.7.2 Habitat -- 6.1.7.3 Behavioural ecology -- 6.1.7.4 Threats and conservation -- 6.1.7.5 Identification -- 6.2 Glandirana -- 6.2.1 Glandirana emeljanovi -- 6.2.1.1 Origin and distribution -- 6.2.1.2 Habitat -- 6.2.1.3 Behavioural ecology. , 6.2.1.4 Threats and conservation -- 6.2.1.5 Identification -- 6.3 Pelophylax -- 6.3.1 Pelophylax nigromaculatus -- 6.3.1.1 Origin and distribution -- 6.3.1.2 Habitat -- 6.3.1.3 Behavioural ecology -- 6.3.1.4 Threats and conservation -- 6.3.1.5 Identification -- 6.3.2 Pelophylax mongolius -- 6.3.2.1 Origin and distribution -- 6.3.2.2 Habitat -- 6.3.2.3 Behavioural ecology -- 6.3.2.4 Threats and conservation -- 6.3.2.5 Identification -- 6.3.3 Pelophylax chosenicus -- 6.3.3.1 Origin and distribution -- 6.3.3.2 Habitat -- 6.3.3.3 Behavioural ecology -- 6.3.3.4 Threats and conservation -- 6.3.3.5 Identification -- 6.3.4 Pelophylax plancyi -- 6.3.4.1 Origin and distribution -- 6.3.4.2 Habitat -- 6.3.4.3 Behavioural ecology -- 6.3.4.4 Threats and conservation -- 6.3.4.5 Identification -- 6.4 Lithobates -- 6.4.1 Lithobates catesbeianus -- 6.4.1.1 Origin and distribution -- 6.4.1.2 Habitat -- 6.4.1.3 Behavioural ecology -- 6.4.1.4 Threats and conservation -- 6.4.1.5 Identification -- References -- 7 - Dicroglossidae (Fejervarya) -- 7.1 Fejervarya -- 7.1.1 Fejervarya kawamurai -- 7.1.1.1 Origin and distribution -- 7.1.1.2 Habitat -- 7.1.1.3 Behavioural ecology -- 7.1.1.4 Threats and conservation -- 7.1.1.5 Identification -- References -- 8 - Hynobiidae (Onychodactylus, Salamandrella and Hynobius) -- 8.1 Onychodactylus -- 8.1.1 Onychodactylus fischeri -- 8.1.1.1 Origin and distribution -- 8.1.1.2 Habitat -- 8.1.1.3 Behavioural ecology -- 8.1.1.4 Threats and conservation -- 8.1.1.5 Identification -- 8.1.2 Onychodactylus zhangyapingi -- 8.1.2.1 Origin and distribution -- 8.1.2.2 Habitat -- 8.1.2.3 Behavioural ecology -- 8.1.2.4 Threats and conservation -- 8.1.2.5 Identification -- 8.1.3 Onychodactylus zhaoermii -- 8.1.3.1 Origin and distribution -- 8.1.3.2 Habitat -- 8.1.3.3 Behavioural ecology -- 8.1.3.4 Threats and conservation -- 8.1.3.5 Identification. , 8.1.4 Onychodactylus sillanus -- 8.1.4.1 Origin and distribution -- 8.1.4.2 Habitat -- 8.1.4.3 Behavioural ecology -- 8.1.4.4 Threats and conservation -- 8.1.4.5 Identification -- 8.1.5 Onychodactylus koreanus -- 8.1.5.1 Origin and distribution -- 8.1.5.2 Habitat -- 8.1.5.3 Behavioural ecology -- 8.1.5.4 Threats and conservation -- 8.1.5.5 Identification -- 8.2 Hynobius -- 8.2.1 Hynobius leechii -- 8.2.1.1 Origin and distribution -- 8.2.1.2 Habitat -- 8.2.1.3 Behavioural ecology -- 8.2.1.4 Threats and conservation -- 8.2.1.5 Identification -- 8.2.2 Hynobius yangi -- 8.2.2.1 Origin and distribution -- 8.2.2.2 Habitat -- 8.2.2.3 Behavioural ecology -- 8.2.2.4 Threats and conservation -- 8.2.2.5 Identification -- 8.2.3 Hynobius geojeensis -- 8.2.3.1 Origin and distribution -- 8.2.3.2 Habitat -- 8.2.3.3 Behavioural ecology -- 8.2.3.4 Threats and conservation -- 8.2.3.5 Identification -- 8.2.4 Hynobius perplicatus -- 8.2.4.1 Origin and distribution -- 8.2.4.2 Habitat -- 8.2.4.3 Behavioural ecology -- 8.2.4.4 Threats and conservation -- 8.2.4.5 Identification -- 8.2.5 Hynobius quelpaertensis -- 8.2.5.1 Origin and distribution -- 8.2.5.2 Habitat -- 8.2.5.3 Behavioural ecology -- 8.2.5.4 Threats and conservation -- 8.2.5.5 Identification -- 8.2.6 Hynobius unisacculus -- 8.2.6.1 Origin and distribution -- 8.2.6.2 Habitat -- 8.2.6.3 Behavioural ecology -- 8.2.6.4 Threats and conservation -- 8.2.6.5 Identification -- 8.2.7 Hynobius notialis -- 8.2.7.1 Origin and distribution -- 8.2.7.2 Habitat -- 8.2.7.3 Behavioural ecology -- 8.2.7.4 Threats and conservation -- 8.2.7.5 Identification -- 8.3 Salamandrella -- 8.3.1 Salamandrella keyserlingii -- 8.3.1.1 Origin and distribution -- 8.3.1.2 Habitat -- 8.3.1.3 Behavioural ecology -- 8.3.1.4 Threats and conservation -- 8.3.1.5 Identification -- 8.3.2 Salamandrella tridactyla -- 8.3.2.1 Origin and distribution. , 8.3.2.2 Habitat -- 8.3.2.3 Behavioural ecology -- 8.3.2.4 Threats and conservation -- 8.3.2.5 Identification -- References -- 9 - Plethodontidae (Karsenia) -- 9.1 Karsenia -- 9.1.1 Karsenia koreana -- 9.1.1.1 Origin and distribution -- 9.1.1.2 Habitat -- 9.1.1.3 Behavioural ecology -- 9.1.1.4 Threats and conservation -- 9.1.1.5 Identification -- References -- 10 - Conclusions on continental northeast Asian amphibians -- 10.1 Conclusion -- 10.2 Conservation considerations -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- W -- Y -- Z -- Back Cover.

Note: Intro -- Microbes at Bio/Nano Interfaces -- Copyright -- Contents -- Contributors -- Preface -- Chapter One: Advanced hydrogel for management of bacterial wound infections -- 1. Introduction -- 1.1. Wound healing phases -- 1.1.1. Types of wounds -- 1.1.2. Bacterial chronic wound infection -- 1.2. The challenges of antimicrobial resistance -- 1.3. Wound practice and management -- 1.4. An ideal hydrogel for clinical wound management -- 1.4.1. Classification of hydrogels -- 1.4.2. Hydrogels for wound infection application -- 1.5. Silver-impregnated antibacterial hydrogel -- 1.5.1. Delivery of silver nanoparticles using hydrogel -- 1.6. Smart hydrogels -- 1.6.1. Temperature-responsive antibacterial hydrogels -- 1.6.2. pH-responsive antibacterial hydrogels -- 1.6.3. Photothermal-responsive antibacterial hydrogels -- 1.6.4. Biochemical-responsive antibacterial hydrogels -- 1.6.5. Multi responsive hydrogel as antibacterial hydrogel -- 2. Conclusion -- Glossary -- Reference -- Chapter Two: Biofilm characterization: Imaging, analysis and considerations -- 1. Introduction -- 1.1. Biofilm formation -- 1.2. Phenotypic variations -- 1.3. Environmental effects -- 1.4. Medical relevance -- 1.5. Importance of characterization techniques -- 2. Microscopy techniques -- 2.1. Light microscopy -- 2.2. Confocal laser scanning microscopy -- 2.3. Electron microscopy -- 2.4. Scanning electron microscopy -- 2.4.1. Preparation of biofilm samples for SEM -- 2.5. Cryo-SEM -- 2.6. Transmission electron microscopy -- 2.7. Atomic force microscopy -- 3. Infrared spectroscopy -- 4. Raman spectroscopy -- 5. Microbial and molecular techniques -- 5.1. Colony-forming unit enumeration -- 5.2. Flow-based cell counting -- 5.3. Quantitative polymerase chain reaction -- 5.4. Crystal violet assays -- 6. Methods for biofilm removal for testing -- 7. Sensors -- 7.1. Optical sensors. , 7.2. Electrochemical sensors -- 7.3. Mechanical sensors -- 7.4. Lab-on-a-chip sensors -- 8. Conclusions -- References -- Chapter Three: Functional genomics methods to target the interface between schistosomes and the host immune system -- 1. Gene technology in parasitic helminths -- 2. Brief history of approaches to schistosome transgenesis -- 2.1. Schistosomiasis -- 2.2. Schistosome functional genomics -- 3. Overview of immune responses to schistosome infection -- 4. Lentiviral system for the delivery of silencing constructs -- 5. Latest advancements in schistosome transgenesis using CRISPR/Cas9 -- 5.1. Concluding remarks -- 6. Methodological considerations for the pseudotyped Lentivirus method -- References -- Chapter Four: Investigation of microbes and surface carbohydrates using atomic force microscopy -- 1. Introduction -- 1.1. Bacterial surface scanning -- 1.2. AFM imaging of proteins -- 1.3. Bacterial glycosylation -- 1.4. Using AFM to probe viruses and virus-like particles -- 1.5. Immobilising bacterial cells for AFM imaging -- 1.6. Use of immunoassays and binding assays in conjunction with AFM to investigate microorganisms and virus-like particles -- 2. Materials and methods -- 2.1. Immunofluorescence methods -- 2.2. Immunofluorescence materials -- 2.3. Direct enzyme linked-immunosorbent assay (ELISA) and enzyme linked-lectin assay (ELLA) materials -- 2.4. Atomic force microscopy methods -- 2.5. Atomic force microscopy materials -- 3. Protocols -- 3.1. Immunofluorescence and labelled lectin staining protocol -- 3.2. Direct ELISA and ELLA protocol -- 3.3. Atomic force microscopy protocol -- 4. Analysis and troubleshooting -- 4.1. Immunofluorescence -- 4.2. Direct ELISA and ELLA -- 4.3. Atomic force microscopy -- 5. Conclusions -- 6. Notes -- References -- Chapter Five: Interactions between microbial cells and titanium implant surfaces. , 1. Introduction -- 2. Antimicrobial resistance -- 3. How do infection causing microbes infiltrate a surface? -- 3.1. Bacterial and fungal attachment and biofilm formation on surfaces -- 3.2. Mechanism and stages of formation -- 3.2.1. Stages of cell adhesion to surfaces -- 3.2.2. Two stages of cell adhesion -- 3.3. Methods of surface attachment -- 3.3.1. Cellular appendages -- 3.3.2. Chemical methods of attachment -- 3.3.3. Cell-to-cell communication -- 3.3.4. Quorum sensing in gram-negative bacteria -- 3.3.5. Quorum sensing in gram-positive bacteria -- 3.3.6. Quorum sensing in fungal cells -- 3.3.7. Adhesion molecules in fungal cells -- 4. Impacts of surface properties on cellular adhesion -- 4.1. Influence of surface wettability -- 4.2. Influence of surface roughness and topography -- 4.3. Influence of surface charge -- 5. Common implant surfaces -- 6. Properties of titanium implants -- 6.1. Osseointegration-Implants interfacing with the body -- 7. Antimicrobial surfaces -- 7.1. Bactericidal or antifouling surface? -- 8. Titanium surface modification to combat adhesion and proliferation -- 8.1. Nanoparticles -- 8.1.1. Silver -- 8.1.2. Copper -- 8.1.3. Zinc oxide -- 8.1.4. Selenium -- 8.1.5. Titanium dioxide -- 8.2. Physical modification of titanium surfaces -- 8.2.1. Roughness -- 8.2.2. Nanostructures -- 8.3. Coatings -- 8.3.1. Antibiotic drug coatings -- 9. Additive manufacturing of titanium implant materials -- 10. Conclusion -- References -- Chapter Six: Targeting bacterial polysaccharides with antibodies and vaccines -- 1. Introduction to bacterial polysaccharides -- 1.1. Bacterial polysaccharides: Structure and function -- 1.1.1. Definition of bacterial polysaccharides -- 1.1.2. Overview of polysaccharide structures -- 1.1.3. Importance of bacterial polysaccharides in pathogenesis -- 1.2. Antibodies and vaccines: An overview. , 1.2.1. Introduction to antibodies and their role in the immune system -- 1.2.2. Overview of vaccines and their purpose -- 1.2.3. Importance of targeting bacterial polysaccharides with antibodies and vaccines -- 2. Antibodies as tools for targeting bacterial polysaccharides -- 2.1. Antibodies and their specificity -- 2.1.1. Antibody structure and function -- 2.1.2. Antibody-antigen interactions -- 2.1.3. Specificity of antibodies for bacterial polysaccharides -- 2.2. Mechanisms of antibody-mediated bacterial clearance -- 2.3. Monoclonal antibodies and their applications -- 2.3.1. Introduction to monoclonal antibodies -- 2.3.2. Production and characterization of monoclonal antibodies -- 2.3.3. Therapeutic and diagnostic applications of monoclonal antibodies targeting bacterial polysaccharides -- 3. Vaccines targeting bacterial polysaccharides -- 3.1. Polysaccharide vaccines -- 3.1.1. Introduction to polysaccharide vaccines -- 3.1.2. Examples of polysaccharide vaccines and their targets -- 3.1.3. Mechanisms of protection provided by polysaccharide vaccines -- 3.2. Conjugate vaccines -- 3.2.1. Rationale behind conjugate vaccines -- 3.2.2. Conjugation methods for bacterial polysaccharides -- 3.2.3. Clinical success and impact of conjugate vaccines -- 3.3. Challenges and advances in polysaccharide vaccinology -- 3.3.1. Immunological challenges of targeting bacterial polysaccharides -- 3.3.2. Novel approaches for polysaccharide vaccine design -- 3.3.3. Adjuvants and their role in enhancing vaccine efficacy -- 4. Case studies and success stories -- 4.1. Haemophilus influenzae type b (Hib) -- 4.1.1. Background and clinical significance -- 4.1.2. Development and impact of Hib conjugate vaccines -- 4.2. Streptococcus pneumoniae -- 4.2.1. Overview of pneumococcal disease -- 4.2.2. Pneumococcal polysaccharide and conjugate vaccines -- 4.3. Neisseria meningitidis. , 4.3.1. Meningococcal disease and its impact -- 4.3.2. Meningococcal polysaccharide and conjugate vaccines -- 5. Future directions and conclusion -- 5.1. Advances in bacterial polysaccharide research -- 5.1.1. Glycoengineering and synthetic glycobiology -- 5.1.2. Structural and functional characterization techniques -- 5.1.3. Novel targets and strategies for antibody and vaccine development -- 5.2. Implications for public health -- 5.2.1. The role of bacterial polysaccharide-targeting antibodies and vaccines in disease prevention -- 5.2.2. Challenges and opportunities in global vaccine implementation -- 6. PNAG polysaccharide: Structure, genetics, immunity, and clinical prospects -- 6.1. General structure of PNAG -- 6.2. Genetics and biosynthesis of PNAG -- 6.2.1. The ica and pga loci -- 6.2.2. The hms locus of Y. pestis and eps locus of Bacillus subtilis -- 6.3. Detection of PNAG expression by a broad range of microbial pathogens -- 6.4. Functional properties of PNAG -- 6.5. Naturally-occurring antibodies to PNAG -- 6.6. Discovery of the means to successfully induce functional immunity to PNAG: PNAG vs dPNAG and production of synthetic ... -- 6.7. In vitro correlates of PNAG-mediated immunity -- 6.7.1. ELISA antibody titers -- 6.7.2. Complement-mediated opsonic and bactericidal killing -- 6.7.3. Complement deposition assays -- 6.8. Development of human monoclonal antibody F598 against PNAG -- 6.9. Path to clinical testing -- 6.9.1. Phase 1 clinical trial of monoclonal antibody F598 -- 6.9.2. Phase 1 clinical trial of vaccine AV0328 -- 7. Conclusion -- References -- Chapter Seven: Using next generation sequencing to study host-pathogen interactions -- 1. Introduction -- 2. Methods -- 2.1. Considerations for sampling and sample storage -- 2.2. DNA extraction -- 2.3. Protocol: Bead-beating of samples -- 2.4. PCR for sequencing. , 2.4.1. Round 1 PCR: Amplification of 16S and/or ITS-1.

Note: Front Cover -- Scale-up and Chemical Process for Microbial Production of Plant-Derived Bioactive Compounds -- Copyright Page -- Contents -- List of contributors -- 1 Advances and prospects for advanced biomanufacturing -- Introduction -- Optimization and scale-up of fermentation process based on process parameter correlation analysis -- Key technologies and applications of optimization and scale-up of biological fermentation process based on process model -- Rational scale-up methodology combining cellular physiology and flow field of biochemical reactor -- Intelligent biomanufacturing is a strong driving force for the transformation and upgrading of bioeconomy -- Intelligent sensing of cell metabolism based on advanced online sensing technology -- Intelligent analysis of biological processes based on data science -- Intelligent manufacturing of industrial-scale bioprocess -- Acknowledgment -- References -- 2 Different microbial strategies used for industrial-scale production of plant-derived bioactive compounds -- Metabolic engineering facilitates de novo synthesis -- Typical steps -- Gene identification and characterization -- Suitable microbial host -- Introduction of target gene -- Optimization of target gene expression level -- Metabolic network modification -- Positive strains selection -- Scale-up fermentation -- Enzyme engineering for continuous biotransformation -- Enzyme engineering -- Identification of target enzyme with desired properties -- Mutant library generation -- High-throughput screening -- Processing technology -- Whole-cell catalysis -- Cell-free catalysis -- References -- 3 Optimization parameters for efficient scale-up of fermentation process -- Introduction -- Fermentation process characterization -- Process data acquiring, fusing, and visualization -- Laboratory scale-down tools for efficient and rational scale-up. , Emerging trends and future directions -- References -- 4 Development of economic extraction and purification process for plant-derived bioactive compounds -- Introduction -- Separation and purification methods for bioactive compounds -- Solvent extraction method -- Microwave extraction method -- Adsorption separation technology -- Activated carbon adsorption separation technology -- Macroporous resin adsorption separation technology -- Foam separation technique -- Membrane separation technology -- Simulated moving bed chromatography -- Supercritical fluid extraction technology -- Existing problems and prospects -- Acknowledgment -- Declaration of interest -- References -- 5 Screening and characterization of probiotics for large-scale production of plant-derived prebiotics -- Methods for screening probiotics for plant fermentation -- Isolation of probiotics from plant-based foods and in vitro studies -- Whole-genome sequencing technologies -- Animal models -- Human clinical studies -- Generation of prebiotics from engineered microbes via plant biomass fermentation -- Grain fermentation to produce prebiotics -- Bean fermentation for prebiotic production -- Fruit and vegetable fermentation for prebiotic production -- Fermentation of other plants for prebiotic production -- Fermented tea -- Fermented feed -- Isolation of bioactive compounds -- Extraction of dietary fiber, polysaccharide, and oligosaccharide -- Physical methods -- Water extraction -- Microwave-assisted extraction -- Ultrasonic extraction -- Ultra-high-pressure extraction -- Chemical separation -- Enzyme separation -- Membrane separation -- Separation and extraction of functional peptides -- Macroporous resin adsorption chromatography -- Gel filtration chromatography -- Ion exchange chromatography -- Affinity chromatography -- Reversed-phase high-performance liquid chromatography (RP-HPLC). , Separation and extraction of phenols -- Commercial production of plant-derived bioactive compounds -- Cereal-based production -- Vegetable- and fruit-based production -- Soy-based production -- Other plant-based materials -- Perspective -- Acknowledgments -- Declaration of interest -- References -- 6 Designing and engineering synthetic microbiota to utilize plant lignin-based biomass for the synthesis of bioactive compounds -- Introduction -- Upstream processing of lignin -- Lignin fractionation -- Depolymerization of lignin -- Acid/base-catalyzed depolymerization -- Oxidative depolymerization -- Reductive depolymerization -- Thermochemical transformation -- Photocatalytic depolymerization -- Bioprocessing -- Downstream bioprocessing of lignin -- Efficient utilization of lignin hydrolysate by lignin-degrading bacteria -- Transformation of platform compounds by lignin-degrading bacteria -- Biosynthesis of value-added aromatic compounds from platform compounds -- Biosynthesis of amines from platform compounds -- Biosynthesis of other refined compounds from platform compounds -- Conclusion and outlook -- Acknowledgments -- Author contributions -- Competing financial interests -- References -- 7 The principles to design and optimization of industrial bioprocesses -- Introduction -- Typical modes of bioprocess used in production -- Batch fermentation -- Continuous fermentation -- Fed-batch fermentation -- Design principles of the fermentation process -- Each fermentation type has its optimal application scenarios -- Basic principles for bioprocess design -- Strategies to improve performance of bioprocess -- Optimization of fermentation process based on online/offline parameters -- Optimization of fermentation processes based on predictions from machine learning -- Flow field optimization within bioreactors -- Changing the stirring method. , Improving the flow field with baffles -- Simulation and optimization of the flow fluid within the fermenter -- Perspectives -- Acknowledgments -- Declaration of interests -- References -- 8 The synthetic probiotic microbiota and their potential applications in the production of plant-derived products -- Introduction -- Probiotics derived from fermented foods -- Synthetic probiotic microbiota -- Production of plant-derived products using synthetic probiotic microbiota -- Conclusions and perspectives -- Acknowledgments -- Declaration of interests -- References -- 9 Fermentation utilizing engineered microbes: revolutionizing the production of commercial products from plant-derived bioa... -- Abbreviations -- Introduction -- Commonly used engineered microorganisms for plant bioactive compound synthesis -- Escherichia coli -- Saccharomyces cerevisiae -- Advances in engineering metabolic pathways for plant bioactive compound synthesis -- Terpenoids -- Monoterpenoids -- Geraniol -- Linalool -- Sesquiterpenes -- Artemisinin -- Nerolidol -- Diterpenoids -- Triterpenoids -- Tetraterpenoids -- Alkaloids -- Monoterpene indole alkaloids -- Benzylisoquinoline alkaloids -- Flavonoids -- Flavanones -- Isoflavones -- Factors impacting the fermentation process of plant-derived bioactive compounds -- Strain engineering and optimization -- Carbon source -- Nitrogen source -- Incubation temperature -- Three main modes: batch, fed-batch, and continuous fermentation -- Batch mode -- Fed-batch mode -- Continuous mode -- Scaling up of industrial microbial fermentations -- Fermentation techniques: solid-state fermentation and submerged fermentation -- Solid-state fermentation -- Submerged fermentation -- Challenges ahead for the microbial synthesis of plant bioactive compounds -- Summary and future prospects -- References. , 10 Examples for successful commercial production of plant-derived bioactive compounds -- Introduction -- Ginsenoside -- Biological activity -- Preparation of ginsenoside -- Clinical application and products -- β-Nicotinamide mononucleotide -- Biological activity -- Preparation of β-nicotinamide mononucleotide -- Clinical application and products -- Artemisinin -- Biological activity -- Preparation of artemisinin -- Clinical application and products -- Conclusion -- Acknowledgments -- Conflict of interest -- References -- 11 Production of synthetic edible oils with engineered yeasts: from lab to commercialization -- Introduction -- Yeasts are potential edible oil producer -- The lipid biosynthetic pathway of yeasts -- Cocoa butter production in yeasts -- Omega-3 production in yeasts -- Several companies focusing on synthetic oil and other food products -- Perspective -- Acknowledgments -- Declaration of interests -- References -- Index -- Back Cover.

Note: Front Cover -- Safe Major Hepatectomy after Preoperative Liver Regeneration -- Safe Major Hepatectomy after Preoperative Liver Regeneration -- Copyright -- Contents -- Contributors -- Foreword -- Preface -- Reference -- 1 - Toward safe major hepatectomy after preoperative liver regeneration -- History and development of portal vein embolization -- Modifications of portal vein embolization and two-stage hepatectomy -- Development of ALPPS -- Modifications of ALPPS procedure -- Summary -- References -- 2 - Hepatic functional deterioration in chronic liver disease -- What is the liver function? -- Chronic liver disease and liver function impairment -- Chronic viral hepatitis -- Autoimmune liver diseases -- Alcoholic liver disease -- Nonalcoholic fatty liver disease -- Liver regeneration -- Liver cirrhosis -- Liver function tests -- Albumin -- Prothrombin time -- Bilirubin -- Platelet count -- Indocyanine green test -- Integrated systems for the evaluation of liver functional reserve -- Child-Turcotte-Pugh score -- Model for end-stage liver disease score -- Albumin-Bilirubin grade -- Summary -- References -- 3 - Evaluation of preoperative hepatic functional reserve before major hepatectomy -- Introduction -- Basic principles in major hepatectomy with a small FLR -- Currently used test for evaluating the hepatic functional reserve -- ICG test and conventional Makuuchi's criteria -- Evaluation of specific regional hepatic functional reserve by imaging modalities -- Scintigraphy using 99mTc-GSA -- Gd-EOB-DTPA-enhanced MRI -- Estimation of the hepatic functional reserve considering veno-occlusive areas -- Summary -- References -- 4 - Major hepatectomy after preoperative liver regeneration-Experience in Tokyo University -- Introduction -- Indication and techniques -- The University of Tokyo experience -- Two-stage hepatectomy and ALPPS -- Modified ALPPS. , PVE in combination with transcatheter arterial chemoembolization -- PVE in combination with hepatic vein embolization -- References -- 5 - Major hepatectomy after preoperative liver regeneration-Experience in MDACC -- Introduction -- Evaluation of hepatic function -- sFLR versus FLR -- Cut-off of sFLR (%) value -- Proposal of new indices for evaluating FLR hypertrophy -- Strategies to obtain rapid FLR hypertrophy in two-stage hepatectomy instead of ALPPS -- Right PVE including segment 4 -- Liver venous deprivation -- Fast-track PVE in two-stage hepatectomy -- References -- 6 - Portal venous anatomy and percutaneous preoperative portal vein embolization -- Introduction -- Portal venous anatomy -- Impact on PVE -- Percutaneous preoperative portal vein embolization -- Choice of access -- Hepatic venous pressure gradient -- Choice of embolic agent -- Segment 4 (S4) embolization -- Complications -- Expected FLR hypertrophy -- Patient drop-out and oncologic effect of PVE on right-sided liver tumors -- Factors which may affect PVE outcomes -- Conclusions -- References -- 7 - Mechanism of liver segmental hypertrophy after preoperative portal vein embolization and its pathological, volu ... -- Introduction -- Mechanisms of liver regeneration following partial hepatectomy and segmental liver hypertrophy after portal vein ligation/e ... -- Hemodynamic events that occur immediately after partial hepatectomy and portal vein ligation/embolization -- Histological consequences of partial liver resection and portal vein ligation/embolization and the resulting atrophy-hypert ... -- Role of the ligated/embolized part of the liver in the atrophy/hypertrophy complex -- Degree of hypertrophy of the nonembolized hemiliver after portal vein embolization -- Functional aspects of liver regeneration after partial hepatectomy and portal vein ligation/embolization. , Several notes on the interpretation of ICG clearance and/or the elimination constant -- References -- 8 - Preoperative portal vein embolization and major hepatectomy for perihilar cancer -- Introduction -- Definition of perihilar cholangiocarcinoma -- History of surgical treatment for perihilar cholangiocarcinoma -- Portal vein embolization -- History -- Indication -- Embolic materials -- Liver regeneration after PVE -- Potential problem of PVE -- PVE for perihilar cholangiocarcinoma -- Associating liver partition and portal vein ligation for staged hepatectomy -- Nagoya experience -- Future plans -- References -- 9 - Laparoscopic major hepatectomy after liver regeneration -- Introduction -- Preoperative optimization -- Surgical planning and relevant anatomy -- Technique -- Positioning and port placement -- Initial evaluation -- Mobilization -- Glissoneal pedicle dissection -- Parenchymal transection -- Case completion -- Unplanned conversion -- Conclusion -- References -- 10 - ALPPS versus two-stage hepatectomy -- Introduction -- Classification of staged hepatectomy -- ALPPS versus TSH -- Increase of FLR -- Completion of two sequential surgeries -- Complications -- Oncological outcomes -- Paul Brousse experience -- Future perspective -- References -- 11 - Functional and volumetric regeneration following PVE and ALPPS -- Introduction -- Liver regeneration after PVE -- Liver regeneration after ALPPS -- Mechanisms of parenchymal regeneration after PVE or ALPPS -- Modalities to assess volumetric and functional liver regeneration -- Discrepancies of volume and function -- Quantitative tests of liver function -- Hepatobiliary scintigraphy -- 13C-methacetin breath test -- Functional imaging using MRI -- Assessment of liver regeneration after PVE -- Assessment of liver regeneration after stage-1 in ALPPS -- Discussion -- References. , 12 - ALPPS for cirrhotic liver -- Introduction -- Anterior approach versus conventional approach -- Complete versus partial split in chronic liver disease -- Laparoscopic ALPPS -- Oncological outcomes for HCC -- Conclusions -- References -- 13 - Mini-ALPPS -- Introduction -- Surgical technique -- Stage 1 -- Stage 2 -- Discussion -- Safety -- Technical considerations -- Increase in FLR volume and function -- Oncological results -- Conclusions -- References -- 14 - Modified ALPPS procedures -- Introduction -- Modified ALPPS to increase the safety and minimize the invasiveness -- Anterior approach for cirrhotic patients with HCC -- Partial ALPPS -- Partial TIPE ALPPS and Mini-ALPPS -- Case presentation: successful left trisectionectomy and biliary resection and reconstruction using partial TIPE ALPPS approach -- First stage of partial TIPE ALPPS -- Second stage of partial TIPE ALPPS -- Summary of initial experience of partial TIPE ALPPS -- Laparoscopic minimally invasive approach -- Other modification -- Modified ALPPS to expand the indication for further fewer FLRV -- Rescue ALPPS after failure of PVE -- Limitation and perspective of modified ALPPS -- Conclusion -- References -- 15 - Major hepatectomy following liver venous deprivation -- Introduction -- Indication for LVD -- Portal vein embolization with hepatic vein embolization -- Mechanism of HVE -- Technique of HVE -- Procedure-related morbidity -- Volumetric analysis -- Intra- and postoperative outcomes -- Conclusion -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- U -- V -- Back Cover.

Note: Front Cover -- Advances in the Study of Behavior -- Copyright -- Contents -- Contributors -- Preface -- Chapter One: Wall-following behavior: Its ultimate and proximate explanations, prevalence, and implications -- 1 Introduction -- 2 Anxiety, stress, and predation-avoidance behavior -- 3 Exploration -- 4 Favorable biotic and abiotic conditions -- 5 The test arena's size, shape, and structure -- 6 Development, aging, carryover effects, and parental effects -- 7 Sex, population, and other intraspecific and interspecific differences -- 8 Behavioral repeatability and correlations with other behaviors -- 9 Future research on wall-following behavior -- 10 Concluding comments -- Acknowledgments -- References -- Chapter Two: Quiet but not forgotten: Insights into adaptive evolution and behavior from 20 years of (mostly) silent Hawaiian crickets -- 1 Introduction -- 1.1 Hawaiian crickets as a microcosm of behavioral and evolutionary biology -- 1.2 Eavesdropping parasitoid flies impose strong selection on crickets -- 1.3 Adaptive breakage: An understudied mode of adaptation and diversification -- 2 Behavior's role in adaptive evolution -- 2.1 Current evidence: Support (or not) for general principles -- 2.2 Insights from Hawaiian crickets -- 3 Behavior links signal, form, and function -- 3.1 State of the field -- 3.2 Insights from anti-parasitoid cricket adaptations -- 4 Rapid convergent adaptation: Causes and consequences -- 4.1 Convergent evolution of behavior -- 4.2 Parallel evolution and adaptive breakage in Hawaiian crickets -- 4.3 Consequences of rapid cricket morph evolution -- 5 Synthesis: The value of long-term insect studies in nature -- Acknowledgements -- References -- Further readings -- Chapter Three: Patterns of host specificity in interactions involving behavioral manipulation of spiders by Darwin wasps -- 1 Introduction. , 1.1 General patterns of host specificity in parasitoid wasps -- 1.2 Host-parasitoid interactions in the Polysphincta group -- 1.3 A brief historical overview of spider-parasitoid interactions -- 1.4 Macroecological perspective of host specificity in polysphinctine wasps -- 2 Methods -- 2.1 Systematic review -- 2.2 Data analysis -- 3 State-of-art and trends in studies of polysphinctine-host interactions -- 4 Patterns of host specificity -- 5 Parasitoid-spider networks -- 6 Factors influencing specificity patterns -- 6.1 Phylogenetic constraints -- 6.2 Web structures, shelters, and other defenses -- 6.3 Morphological and chemical host traits -- 6.4 Variation in the attack behavior of polysphinctine wasps on spiders -- 7 Conclusions -- Acknowledgements -- Appendix A Supporting information -- References -- Chapter Four: Orb web construction in a new generation of behavioral analysis: A user's guide -- 1 Introduction -- 2 A brief introduction to orb construction behavior -- 2.1 Operations (tasks) -- 2.2 Detailed movements -- 2.3 Cues guiding decisions regarding sticky spiral spacing -- 2.3.1 "Reference point" variables -- 2.3.2 The first loop of sticky spiral -- 2.3.3 "Intermediate", gradually changing variables -- 2.3.4 "Prior settings" variables -- 2.3.5 Other likely cues -- 2.3.6 Independence of different cues -- 2.4 Potential cues that are not used -- 3 Additional decisions during sticky spiral construction -- 4 Uniformity of cues used in different families of orb weavers -- 4.1 The spider's agility and precision -- 4.2 "Rigid flexibility"? Pre-programmed versus flexible, open-ended responses to cues -- 4.2.1 Operations (tasks) -- 4.2.2 Detailed movements -- 5 Learning and maturation -- 6 Coordination and independence of flexible adjustments of web variables -- 6.1 Memory of preceding stages and insightful solutions to problems. , 7 Do spiders have expectations regarding the sites and orientations of web lines? -- 7.1 Modularity: Categories of behavior that are biologically real -- 7.2 Topics of general interest for future research -- 7.2.1 When and where do orb weavers make errors? -- 7.2.2 Senile behavior -- 7.2.3 Sustained attention-where orb weavers shine -- 7.2.4 Leg loss and flexibility -- 7.2.5 Evolutionary transitions and "task-specific" motivations -- 7.2.6 Translating variable cues and behaviors into consistent physical results -- 8 Conclusion: The promise of orbs for new directions of behavioral research -- Acknowledgments -- References -- Further readings -- Back Cover.

Note: Front Cover -- Current Omics Advancement in Plant Abiotic Stress Biology -- Copyright Page -- Contents -- List of contributors -- About the editors -- Foreword -- Preface -- Acknowledgments -- 1 Advancement in the understanding of the different abiotic stresses using "omics" -- 1.1 Drought stress -- 1.2 Salt stress -- 1.3 Heat stress -- 1.4 Nutrient stress -- 1.5 Cold stress -- References -- 2 Omics advancements in plant abiotic stress -- 2.1 Introduction -- 2.2 Wild relatives of crops are the promising source to enhance abiotic stress tolerance -- 2.3 Understanding the genetics of plant abiotic stress tolerance -- 2.4 Transcriptomics and RNA-mediated silencing -- 2.5 Growing significance of noncoding RNAs -- 2.6 Metabolomics and proteomics -- 2.7 Phenomics -- 2.8 Pangenomics for harnessing novel genomic diversity -- 2.9 Genomic selection: relevance and prospects -- 2.10 Computational biology tools -- 2.11 Conclusion -- Author contributions -- Declaration of competing interests -- Data availability statement -- Acknowledgments -- References -- 3 Implication of integrated multiomics approaches to decode the molecular basis of drought stress response in plants: an Om... -- 3.1 Introduction -- 3.2 Cell signaling and molecular responses in plants during stress -- 3.3 Omics: an overview -- 3.4 Utilization of omics resources for exploring drought stress tolerance -- 3.5 Multiomics assisted approaches for crop improvement -- 3.6 Conclusion and future prospects -- Funding -- Acknowledgments -- Conflict of interest -- References -- 4 Advancement in understanding cold stress tolerance using "omics" tools -- 4.1 Introduction -- 4.2 Phenomics: phenotypic variations underlying cold stress in plants -- 4.3 Genomics: structural and functional changes in genome under cold stress response in plants. , 4.4 Transcriptomics: a tool to dissect genes responsible for cold stress tolerance -- 4.5 Understanding of protein regulation underlying cold stress -- 4.6 Metabolomics regulation for cold stress tolerance in plants -- 4.7 Interaction of cold with other abiotic stresses -- 4.8 Conclusion -- Author contributions -- Acknowledgments -- Conflicts of interest -- References -- 5 Omics-based strategies for improving salt tolerance in rice -- 5.1 Introduction -- 5.2 Omics-based approaches in the modern era -- 5.2.1 Genomics-based approach -- 5.2.2 Transcriptomics approach -- 5.2.3 Proteomics approach -- 5.2.4 Metabolomics approach -- 5.2.5 Phenomics approach -- 5.2.6 Integration of "omics" approach to improve the salt stress tolerance -- 5.3 Conclusion -- Acknowledgments -- Author contributions -- Declaration of competing interest -- References -- 6 Master players in the chase of establishing heat tolerance: a molecular perspective -- 6.1 Introduction -- 6.2 Effects of high-temperature stress on plants -- 6.2.1 Vegetative development -- 6.2.2 Reproductive development -- 6.3 Signaling of heat stress -- 6.4 Identification of heat shock factors -- 6.5 Expression and regulation of heat shock factors -- 6.5.1 Heat shock factor: functions -- 6.5.2 Heat shock proteins -- 6.5.3 HSP 70 family genes -- 6.5.4 HSP 90 family genes -- 6.5.5 HSP 100 family genes -- 6.6 Conclusions -- Acknowledgment -- Author contribution -- Declaration of competing interest -- References -- 7 Advancements in understanding molecular interlinkages to combat combinations of drought and salinity stresses in crops -- 7.1 Introduction -- 7.2 Plant response to combined salinity and drought stress -- 7.3 Genetic control to combat both salinity and drought stresses -- 7.4 Applications of different molecular techniques -- 7.4.1 Overcome salinity and drought stress through nanoparticles. , 7.4.2 Metabolism of plants in drought and salinity stress -- 7.4.3 Role of microbes in mitigating drought and salinity -- 7.4.4 Molecular approaches for the drought and salinity -- 7.5 Quantitative trait loci in salinity and dehydration stresses -- 7.6 Conclusions and future prospective -- References -- 8 Integrated omics approaches for nutrient stress management in plants -- 8.1 Introduction -- 8.2 Abiotic stresses affect plants -- 8.3 The development of nutrient-stress-resistant or nutrient-efficient cultivars -- 8.4 The molecular basis of plant resilience to nutrient stress -- 8.4.1 General considerations -- 8.4.2 Reducing the effects of biotic stress requires a multiomics approach -- 8.4.3 Genomics -- 8.5 Molecular mechanisms of gene expression in response to nutritional stress -- 8.6 Different omics approaches -- 8.6.1 Metagenomics -- 8.6.1.1 Osmolytes -- 8.6.1.2 Proline -- 8.6.1.3 Mannitol -- 8.6.1.4 Pinnitol/ononitol -- 8.6.1.5 Sorbitol -- 8.6.1.6 Polyamines -- 8.6.2 Transcriptomics -- 8.6.3 Metabolomics -- 8.6.4 Proteomics -- 8.6.5 Defense at large -- 8.6.6 A readjustment of osmotic pressure -- 8.7 Plant proteins that detect mineral deficiencies and trigger responses -- 8.7.1 Deficit in phosphorus -- 8.7.2 Nitrogen deficiency -- 8.7.3 Iron deficiency -- 8.7.4 Deficiency of other nutrients -- 8.8 The omics integration process -- 8.9 Analysis of the S-deficient transcriptome and metabolome together -- 8.10 Conclusion -- Acknowledgment -- Conflicts of interest -- References -- 9 Role of omics in understanding heavy metal responses and tolerance in plants -- 9.1 Introduction -- 9.2 Interconnection between plants and heavy metals -- 9.3 Omics approaches to investigate heavy metals tolerance -- 9.3.1 Genomics -- 9.3.2 Transcriptomics -- 9.3.3 Proteomics -- 9.3.4 Metabolomics -- 9.3.5 Ionomics -- 9.3.6 miRNAomics -- 9.4 Future prospectives. , References -- 10 Physiological and genomic approaches for improving tolerance of flooding during germination and seedling establishment i... -- 10.1 Introduction -- 10.2 Problems in the germination of rice under anoxia or hypoxia -- 10.3 Seedling establishment under flooding in a direct-seeded system -- 10.4 Physiological mechanism for anaerobic conditions tolerance in rice -- 10.5 Cloning of flood tolerance allele at seedling emergence stage -- 10.6 Role of anaerobic germination potential rice varieties in food securities -- 10.6.1 Benefits of anaerobic germination potential rice -- 10.6.2 Challenges of anaerobic germination potential rice varieties -- 10.7 Marker-assisted selection breeding for anaerobic condition tolerance in rice varieties -- 10.8 Molecular mechanisms for anaerobic condition tolerance in rice varieties -- 10.9 Conclusion and future outlook -- References -- 11 Advances in understanding and engineering plant root system architecture to alleviate abiotic stress -- Abbreviations -- 11.1 Introduction -- 11.2 Root system architecture -- 11.2.1 Root (primary and lateral) development -- 11.2.2 Diversity in the root system -- 11.2.3 Change in root system architecture under environmental stress -- 11.2.3.1 Drought stress -- 11.2.3.2 Waterlogging stress -- 11.2.3.3 Salinity stress -- 11.2.3.4 Heavy metal stress -- 11.2.3.5 Soil nutrient stress -- 11.2.3.6 Temperature stress -- 11.3 Root phenomics -- 11.3.1 Establishment of root phenotyping pipeline -- 11.3.2 High-throughput image-based root phenotyping pipeline for sugarcane -- 11.3.3 Imaging, image processing, and data extraction -- 11.3.4 Recent developments in higher-end automated/robotic-assisted imaging platform -- 11.4 Root transcriptomics -- 11.4.1 Drought stress -- 11.4.2 Osmotic stress -- 11.4.3 Salinity stress -- 11.4.4 Acidity. , 11.4.5 Heavy metal stress (arsenic, cadmium, chromium, etc.) -- 11.4.6 Soil nutrient stress (copper, aluminum, magnesium, nitrogen, sulfur, phosphorus, etc.) -- 11.4.7 Temperature stress -- 11.5 Root metabolomics -- 11.5.1 Methods and tools for metabolomic analysis -- 11.5.2 Metabolomic studies of candidate traits in roots for abiotic stresses -- 11.5.2.1 Drought stress -- 11.5.2.2 Salinity stress -- 11.5.2.3 Temperature stress -- 11.5.2.4 Heavy metal stress -- 11.6 Conclusions -- References -- 12 Role of omics in understanding signaling cascade of abiotic stress in plants -- 12.1 Introduction -- 12.2 Impact of abiotic stress at cellular level -- 12.3 Molecular mechanisms for abiotic stress signaling -- 12.3.1 MAPK-dependent signaling -- 12.3.2 Calcium ion-dependent signaling cascade for late embryogenesis abundant proteins -- 12.3.3 Calcium-dependent salt-overly-sensitive signaling pathway -- 12.4 Genomics -- 12.5 Epigenomics -- 12.6 Functional genomics for understanding abiotic stress responses -- 12.6.1 Map-based cloning of stress signaling genes -- 12.6.2 Genome editing -- 12.6.2.1 Mechanism -- 12.6.3 Transcriptomics in abiotic stress signaling -- 12.6.4 Proteomics for plant stress -- 12.6.4.1 Quantitative proteomics -- 12.6.4.2 Gel-based quantification methodology -- 12.6.4.2.1 Two-dimensional gel electrophoresis -- 12.6.4.2.2 Two-dimensional fluorescence difference gel electrophoresis -- 12.6.4.3 Mass spectrometry-based quantification methods -- 12.6.4.4 Peptide mass fingerprinting -- 12.6.4.5 Application of quantitative proteomics in plant abiotic stress research -- 12.6.4.6 Protein-protein interaction and protein interaction network -- 12.6.4.7 Peptide sequencing and mass spectrometry software for peptide identification -- 12.6.5 Metabolomics in abiotic stress signaling -- 12.6.5.1 Tools of metabolomics. , 12.6.5.1.1 Gas chromatography-mass spectrometry.

Note: Front Cover -- Phylogenomics -- Copyright Page -- Contents -- List of contributors -- Preface -- I. General topics and foundations -- 1 Phylogenomic analysis and the origin and early evolution of viruses -- 1.1 Introduction -- 1.2 Retrodiction -- 1.3 The analytical basis of the phylogenetic framework -- 1.4 Rooting trees -- 1.5 Phylogenomic analysis -- 1.6 Deep evolutionary explorations with alignment-free methods -- 1.7 Untangling the origin and evolution of viruses with structural phylogenomics -- 1.8 Conclusions -- References -- 2 Application of next-generation sequencing for genetic and phenotypic studies of bacteria -- 2.1 Introduction -- 2.2 Whole genome sequence data -- 2.2.1 Read lengths and base accuracy -- 2.2.2 Data quality control -- 2.3 Reference genomes -- 2.4 Pangenome, core genome, and accessory genome -- 2.5 Principles of genotypic classification -- 2.6 Species classification and identification using whole genome sequencing data -- 2.7 Genotyping based on whole genome sequencing data -- 2.7.1 Single nucleotide variant-based genotyping -- 2.7.1.1 Criteria for genotypic classification of bacteria based on single nucleotide variant phylogeny -- 2.7.1.2 Genotyping barcodes -- 2.7.2 Genotyping by structural variants -- 2.7.2.1 Deletions -- 2.7.2.2 Insertions -- 2.7.2.3 Copy number variations -- 2.7.3 Congruence between single nucleotide variant-based genotyping and other classification methods -- 2.7.3.1 Other genotyping methods -- 2.7.3.2 Serotyping -- 2.7.4 Correlation of genotypes and phenotypes -- 2.7.4.1 Demographic information -- 2.7.4.2 Drug resistance -- 2.7.4.3 Disease severity and contagiousness -- 2.8 Genotyping and control of infectious diseases -- 2.9 Genotyping and human genetics of infectious diseases -- 2.10 Conclusion -- References -- 3 Genomic insights into deciphering bacterial outbreaks -- 3.1 Introduction. , 3.2 Genomes and variation -- 3.3 Sequencing whole genomes -- 3.4 Outbreak delimitation -- 3.5 Forensic analysis of outbreaks and transmissions -- 3.6 Conclusion -- References -- 4 Drug resistance in bacteria, molecular mechanisms, and evolution -- 4.1 Introduction -- 4.2 What are antibiotics -- 4.2.1 Historical perspective -- 4.3 Mechanisms of bacterial resistance to antibiotics -- 4.3.1 Countering presence/entry of antibiotics -- 4.3.2 Antibiotics destroying mechanisms in vivo -- 4.3.3 Antibiotic target alterations -- 4.3.4 Heteroresistance -- 4.4 Evolution of antibiotic resistance -- 4.4.1 Chromosomal mutations -- 4.4.2 Acquisition of genetic material -- 4.4.3 Ancient origins -- 4.4.4 Cost of resistance to bacteria: bacterial fitness -- 4.5 Classes of antibiotics, mechanisms of action and resistance -- 4.5.1 Disruptors of DNA synthesis -- 4.5.1.1 Sulfonamides -- 4.5.1.2 Quinolones -- 4.5.1.3 Nitroimidazoles -- 4.6 Disruptors of cell envelope -- 4.6.1 Beta-lactams -- 4.6.2 Glycopeptides and lipoglycopeptides -- 4.6.3 Lipopeptides (Daptomycin) -- 4.6.4 Polypeptides (Bacitracin) -- 4.7 Other antibiotics -- 4.7.1 Aminoglycosides (gentamicin, tobramycin, amikacin, netilmicin, plazomicin, kanamycin, streptomycin, and paromomycin) -- 4.7.2 Polymyxins -- 4.7.3 Bacteriostatic protein synthesis inhibitors -- 4.7.3.1 Tetracyclines, tigecycline, and eravacycline -- 4.7.3.2 Macrolides (erythromycin, clarithromycin, azithromycin, and fidaxomicin) -- 4.7.4 Lincosamides -- 4.7.5 Oxazolidinones -- 4.7.6 Pleuromutilins -- 4.7.6.1 Streptogramins (quinupristin: dalfopristin, 30:70) -- 4.7.7 Chloramphenicol -- 4.7.8 Mupirocin -- 4.8 Origin and evolution of bacterial resistance -- 4.8.1 Integrons -- 4.8.2 Phages -- 4.8.3 Resistance evolution mechanisms -- 4.8.3.1 Efflux pumps -- 4.8.3.2 Acquired efflux mechanisms -- 4.8.4 Beta-lactams -- 4.8.5 Ceftazidime. , 4.8.6 Quinolones -- 4.8.7 Colistin -- 4.8.8 New compounds -- 4.9 Newer approaches to deal with antimicrobial resistance -- 4.9.1 Integrons with dysfunctional integrase (antievolution drugs) -- 4.9.2 COM blockers -- 4.9.3 Potentiator genes/evolutionary catalysts -- 4.9.4 Evolvability factors -- 4.10 Conclusions -- References -- 5 Virulence evolution of bacterial species -- 5.1 Introduction -- 5.2 What is virulence? -- 5.3 Models for the evolution of virulence -- 5.3.1 Avirulence hypothesis or law of declining virulence -- 5.3.2 Trade-off hypothesis -- 5.3.2.1 "Short-sighted" evolution of virulence -- 5.3.3 Coincidental evolution of virulence -- 5.4 Molecular infection biology -- 5.5 Virulence factors and mechanisms in Gram-negative pathogens -- 5.5.1 Enterobacterales -- 5.5.2 Vibrio -- 5.5.3 Pseudomonas -- 5.5.4 Campylobacter -- 5.5.5 Helicobacter -- 5.5.6 Neisseria -- 5.5.7 Haemophilus -- 5.6 Virulence factors and mechanisms in Gram-positive bacteria -- 5.6.1 Staphylococcus -- 5.6.2 Streptococcus -- 5.6.3 Enterococcus -- 5.6.4 Bacillus -- 5.6.5 Listeria -- 5.6.6 Clostridium -- 5.7 Acid-fast bacteria -- 5.7.1 Mycobacterium -- 5.8 Final remarks -- References -- II. Methods in the phylogenomics -- 6 Modeling evolutionary changes of k-mer patterns of bacterial genomes -- 6.1 Introduction -- 6.2 Estimation of evolutionary distances by comparison of k-mer patterns -- 6.3 Driving forces on the k-mer pattern evolution -- 6.4 Using graph models in investigation of k-mer pattern evolution -- 6.5 Conclusion -- References -- 7 Clock rates and Bayesian evaluation of temporal signal -- 7.1 Introduction -- 7.2 TMRCAs and mutation rate -- 7.3 Tip-dating and tip randomization -- 7.4 Population changes through time -- 7.5 Case study: Is there a molecular clock in Mycobacterium tuberculosis? -- 7.6 Models limits and sensitivity -- 7.7 Fluctuating mutation rates. , 7.8 The rise of fine-tuned phyloepidemiology models -- 7.9 Conclusion and perspectives -- References -- 8 Microbial evolutionary reconstruction in the presence of mosaic sequences -- 8.1 Introduction -- 8.2 Biological processes giving rise to viral and bacterial mosaic sequences -- 8.2.1 Recombination in viruses -- 8.2.1.1 Recombination in RNA viruses -- 8.2.1.2 Recombination in DNA viruses -- 8.2.1.3 Reassortment in viruses with segmented genomes -- 8.2.2 Genetic recombination and horizontal gene transfer in prokaryotes -- 8.2.3 Long-distant horizontal gene transfer in viruses -- 8.2.4 Incomplete lineage sorting -- 8.3 Mosaic sequence and structure detection methods -- 8.3.1 Methods for detecting the presence of mosaic sequences in the dataset -- 8.3.2 Methods for detecting mosaic sequences and transferred/exchanged regions -- 8.3.2.1 Explicit phylogenetic methods -- 8.3.2.2 Implicit phylogenetic methods -- 8.3.2.3 Parametric methods -- 8.3.3 An illustrative example-detecting recombination regions within simian foamy viruses' env gene -- 8.4 Potential impacts of mixed evolutionary signals on traditional phylogenetic reconstruction -- 8.4.1 Potential impacts of mixed evolutionary signals on traditional phylogenetic reconstruction -- 8.4.2 An illustrative example-the phylogenetic reconstruction of simian foamy viruses' env gene with and without considerat... -- 8.5 Tree-based approaches to dealing with mosaic sequences and mixed evolutionary signals -- 8.5.1 Removing mosaic sequences/regions from the dataset -- 8.5.2 Supertree approach -- 8.5.3 Considering trees of genomic regions of different evolutionary pasts together -- 8.6 Network-based approaches to reconstructing an entangled evolutionary history -- 8.6.1 Phylogenetic network-how does it differ from a phylogenetic tree? -- 8.6.2 Various kinds of phylogenetic networks. , 8.6.3 Implicit phylogenetic networks -- 8.6.4 Explicit phylogenetic networks -- 8.6.5 An illustrative example-phylogenetic networks of simian foamy viruses' env gene -- 8.6.5.1 An implicit phylogenetic network of simian foamy viruses' env gene -- 8.6.5.2 An explicit phylogenetic network of simian foamy viruses' env gene -- 8.7 Final remarks -- References -- 9 Tools for short variant calling and the way to deal with big datasets -- 9.1 Introduction -- 9.2 Types of whole genome sequencing data -- 9.3 Pretreatment of data -- 9.3.1 Trimming and deduplication -- 9.3.2 Decontamination -- 9.3.2.1 Evaluating the amplitude of contamination -- 9.3.2.2 Alignment-based exclusion of reads from distant species -- 9.3.2.3 K-mer-based exclusion of reads from distant species -- 9.3.2.4 Taxonomic classifiers to detect and remove contaminants -- 9.3.2.5 Potential impact of contamination on variant calling -- 9.4 Calling of short variants -- 9.4.1 Alignment-based variant calling, single nucleotide variants, and short indels as compared to a reference -- 9.4.1.1 Procedure -- 9.4.1.2 Importance of the reference -- 9.4.1.3 Filtering parameters/thresholds -- 9.4.1.4 Interactions between aligners and variant callers -- 9.4.1.5 Alignment-based variant calling performance -- 9.4.1.6 Machine learning for variant discovery improvement -- 9.4.2 k-mer-based methods for variant calling -- 9.5 Postprocessing of variants -- 9.6 Large datasets and computational solutions to deal with them -- 9.6.1 Volume -- 9.6.2 Velocity -- 9.7 All-in-one pipelines -- 9.7.1 Diversity of all-in-one pipelines proposed for Mycobacterium tuberculosis -- 9.7.2 General assessment of all-in-one pipelines and perspectives for future tools -- 9.8 Conclusion -- Funding -- References -- III. Phylogenomics of specific pathogens -- 10 Phylogenomics of Yersinia pestis -- 10.1 Introduction. , 10.1.1 Introduction of Yersinia pestis.