Note: Front Cover -- Immune Surveillance -- Copyright Page -- Table of Contents -- Conferees -- Preface -- Introductory Note -- Chapter I. Organization and Modulation of Cell Membrane Receptors -- PHENOTYPIC DIVERSITY OF SURFACE STRUCTURE AMONG DIFFERENTIATED CELL POPULATIONS: -- CELL SURFACE INDIVIDUALITY CONFERRED BY CODED ARRANGEMENTS OF MEMBRANE UNITS -- APPLICATION OF THE CONCEPT OF CONFIGURATIONAL SPECIFICITY TO CANCER AND SURVEILLANCE -- CONCLUSIONS -- Chapter II. Triggering Mechanisms for Cellular Recognition -- Chapter III. Effector Mechanisms Activated by Cellular Recognition. -- Chapter IV. Routes of Escape from Surveillance -- Do tumor specific transplantation antigens exist? -- Does immunologic surveillance against tumors exist? -- Chapter V. Generation of Antibody Diversity and Self Tolerance - A New Theory -- antigens of our own cells -- Chapter VI. Evaluation of the Evidence for Immune Surveillance -- IMPRESSIONS AND COMMENTS -- ABBREVIATIONS -- AUTHOR INDEX -- SUBJECT INDEX.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- 1: Introduction -- I: Methodology for Statistical Analysis of Environmental Processes -- 2: Modeling for environmental and ecological processes -- 2.1 Introduction -- 2.2 Stochastic modeling -- 2.3 Basics of Bayesian inference -- 2.3.1 Priors -- 2.3.2 Posterior inference -- 2.3.3 Bayesian computation -- 2.4 Hierarchical modeling -- 2.4.1 Introducing uncertainty -- 2.4.2 Random effects and missing data -- 2.5 Latent variables -- 2.6 Mixture models -- 2.7 Random effects -- 2.8 Dynamic models -- 2.9 Model adequacy -- 2.10 Model comparison -- 2.10.1 Bayesian model comparison -- 2.10.2 Model comparison in predictive space -- 2.11 Summary -- 3: Time series methodology -- 3.1 Introduction -- 3.2 Time series processes -- 3.3 Stationary processes -- 3.3.1 Filtering preserves stationarity -- 3.3.2 Classes of stationary processes -- 3.3.2.1 IID noise and white noise -- 3.3.2.2 Linear processes -- 3.3.2.3 Autoregressive moving average processes -- 3.4 Statistical inference for stationary series -- 3.4.1 Estimating the process mean -- 3.4.2 Estimating the ACVF and ACF -- 3.4.3 Prediction and forecasting -- 3.4.4 Using measures of correlation for ARMA model identification -- 3.4.5 Parameter estimation -- 3.4.6 Model assessment and comparison -- 3.4.7 Statistical inference for the Canadian lynx series -- 3.5 Nonstationary time series -- 3.5.1 A classical decomposition for nonstationary processes -- 3.5.2 Stochastic representations of nonstationarity -- 3.6 Long memory processes -- 3.7 Changepoint methods -- 3.8 Discussion and conclusions -- 4: Dynamic models -- 4.1 Introduction -- 4.2 Univariate Normal Dynamic Linear Models (NDLM) -- 4.2.1 Forward learning: the Kalman filter -- 4.2.2 Backward learning: the Kalman smoother -- 4.2.3 Integrated likelihood. , 4.2.4 Some properties of NDLMs -- 4.2.5 Dynamic generalized linear models (DGLM) -- 4.3 Multivariate Dynamic Linear Models -- 4.3.1 Multivariate NDLMs -- 4.3.2 Multivariate common-component NDLMs -- 4.3.3 Matrix-variate NDLMs -- 4.3.4 Hierarchical dynamic linear models (HDLM) -- 4.3.5 Spatio-temporal models -- 4.4 Further aspects of spatio-temporal modeling -- 4.4.1 Process convolution based approaches -- 4.4.2 Models based on stochastic partial differential equations -- 4.4.3 Models based on integro-difference equations -- 5: Geostatistical Modeling for Environmental Processes -- 5.1 Introduction -- 5.2 Elements of point-referenced modeling -- 5.2.1 Spatial processes, covariance functions, stationarity and isotropy -- 5.2.2 Anisotropy and nonstationarity -- 5.2.3 Variograms -- 5.3 Spatial interpolation and kriging -- 5.4 Summary -- 6: Spatial and spatio-temporal point processes in ecological applications -- 6.1 Introduction - relevance of spatial point processes to ecology -- 6.2 Point processes as mathematical objects -- 6.3 Basic definitions -- 6.4 Exploratory analysis - summary characteristics -- 6.4.1 The Poisson process-a null model -- 6.4.2 Descriptive methods -- 6.4.3 Usage in ecology -- 6.5 Point process models -- 6.5.1 Modelling environmental heterogeneity - inhomogeneous Poisson processes and Cox processes -- 6.5.2 Modelling clustering - Neyman Scott processes -- 6.5.3 Modelling inter-individual interaction - Gibbs processes -- 6.5.4 Model fitting - approaches and software -- 6.5.4.1 Approaches -- 6.5.4.2 Relevant software packages -- 6.6 Point processes in ecological applications -- 6.7 Marked point processes - complex data structures -- 6.7.1 Different roles of marks in point patterns -- 6.7.2 Complex models - dependence between marks and patterns -- 6.7.3 Marked point pattern models reflecting the sampling process. , 6.8 Modelling partially observed point patterns -- 6.8.1 Point patterns observed in small subareas -- 6.8.2 Distance sampling -- 6.9 Discussion -- 6.9.1 Spatial point processes and geo-referenced data -- 6.9.2 Spatial point process modeling and statistical ecology -- 6.9.3 Other data structures -- 6.9.3.1 Telemetry data -- 6.9.3.2 Spatio-temporal patterns -- 6.9.4 Conclusion -- 6.10 Acknowledgments -- 7: Data assimilation -- 7.1 Introduction -- 7.2 Algorithms for data assimilation -- 7.2.1 Optimal interpolation -- 7.2.2 Variational approaches -- 7.2.3 Sequential approaches: the Kalman filter -- 7.3 Statistical approaches to data assimilation -- 7.3.1 Joint modeling approaches -- 7.3.2 Regression-based approaches -- 8: Univariate and Multivariate Extremes for the Environmental Sciences -- 8.1 Extremes and Environmental Studies -- 8.2 Univariate Extremes -- 8.2.1 Theoretical underpinnings -- 8.2.2 Modeling Block Maxima -- 8.2.3 Threshold exceedances -- 8.2.4 Regression models for extremes -- 8.2.5 Application: Fitting a time-varying GEV model to climate model output -- 8.2.5.1 Analysis of individual ensembles and all data -- 8.2.5.2 Borrowing strength across locations -- 8.3 Multivariate Extremes -- 8.3.1 Multivariate EVDs and componentwise block maxima -- 8.3.2 Multivariate threshold exceedances -- 8.3.3 Application: Santa Ana winds and dryness -- 8.3.3.1 Assessing tail dependence -- 8.3.3.2 Risk region occurrence probability estimation -- 8.4 Conclusions -- 9: Environmental Sampling Design -- 9.1 Introduction -- 9.2 Sampling Design for Environmental Monitoring -- 9.2.1 Design framework -- 9.2.2 Model-based design -- 9.2.2.1 Covariance estimation-based criteria -- 9.2.2.2 Prediction-based criteria -- 9.2.2.3 Mean estimation-based criteria -- 9.2.2.4 Multi-objective and entropy-based criteria -- 9.2.3 Probability-based spatial design. , 9.2.3.1 Simple random sampling -- 9.2.3.2 Systematic random sampling -- 9.2.3.3 Stratified random sampling -- 9.2.3.4 Variable probability sampling -- 9.2.4 Space-filling designs -- 9.2.5 Design for multivariate data and stream networks -- 9.2.6 Space-time designs -- 9.2.7 Discussion -- 9.3 Sampling for Estimation of Abundance -- 9.3.1 Distance sampling -- 9.3.1.1 Standard probability-based designs -- 9.3.1.2 Adaptive distance sampling designs -- 9.3.1.3 Designed distance sampling experiments -- 9.3.2 Capture-recapture -- 9.3.2.1 Standard capture-recapture -- 9.3.2.2 Spatial capture-recapture -- 9.3.3 Discussion -- 10: Accommodating so many zeros: univariate and multivariate data -- 10.1 Introduction -- 10.2 Basic univariate modeling ideas -- 10.2.1 Zeros and ones -- 10.2.2 Zero-inflated count data -- 10.2.2.1 The k-ZIG -- 10.2.2.2 Properties of the k-ZIG model -- 10.2.2.3 Incorporating the covariates -- 10.2.2.4 Model fitting and inference -- 10.2.2.5 Hurdle models -- 10.2.3 Zeros with continuous density G(y) -- 10.3 Multinomial trials -- 10.3.1 Ordinal categorical data -- 10.3.2 Nominal categorical data -- 10.4 Spatial and spatio-temporal versions -- 10.5 Multivariate models with zeros -- 10.5.1 Multivariate Gaussian models -- 10.5.2 Joint species distribution models -- 10.5.3 A general framework for zero-dominated multivariate data -- 10.5.3.1 Model elements -- 10.5.3.2 Specific data types -- 10.6 Joint Attribute Modeling Application -- 10.6.1 Host state and its microbiome composition -- 10.6.2 Forest traits -- 10.7 Summary and Challenges -- 11: Gradient Analysis of Ecological Communities (Ordination) -- 11.1 Introduction -- 11.2 History of ordination methods -- 11.3 Theory and background -- 11.3.1 Properties of community data -- 11.3.2 Coenospace -- 11.3.3 Alpha, beta, gamma diversity -- 11.3.4 Ecological similarity and distance. , 11.4 Why ordination? -- 11.5 Exploratory analysis and hypothesis testing -- 11.6 Ordination vs. Factor Analysis -- 11.7 A classification of ordination -- 11.8 Informal techniques -- 11.9 Distance-based techniques -- 11.9.1 Polar ordination -- 11.9.1.1 Interpretation of ordination scatter plots -- 11.9.2 Principal coordinates analysis -- 11.9.3 Nonmetric Multidimensional Scaling -- 11.10 Eigenanalysis-based indirect gradient analysis -- 11.10.1 Principal Components Analysis -- 11.10.2 Correspondence Analysis -- 11.10.3 Detrended Correspondence Analysis -- 11.10.4 Contrast between DCA and NMDS -- 11.11 Direct gradient analysis -- 11.11.1 Canonical Correspondence Analysis -- 11.11.2 Environmental variables in CCA -- 11.11.3 Hypothesis testing -- 11.11.4 Redundancy Analysis -- 11.12 Extensions of direct ordination -- 11.13 Conclusions -- II: Topics in Ecological Processes -- 12: Species distribution models -- 12.1 Aims of species distribution modelling -- 12.2 Example data used in this chapter -- 12.3 Single species distribution models -- 12.4 Joint species distribution models -- 12.4.1 Shared responses to environmental covariates -- 12.4.2 Statistical co-occurrence -- 12.5 Prior distributions -- 12.6 Acknowledgments -- 13: Capture-Recapture and distance sampling to estimate population sizes -- 13.1 Basic ideas -- 13.2 Inference for closed populations -- 13.2.1 Censuses and finite population sampling -- 13.2.2 The problem of imperfect detection -- 13.2.3 Capture-recapture on closed populations -- 13.2.4 Distance sampling methods on closed populations -- 13.2.5 N-mixture models for closed populations -- 13.2.6 Count regression -- 13.3 Inference for open populations -- 13.3.1 Crosbie-Manly-Schwarz-Arnason model -- 13.3.2 Cormack-Jolly-Seber model and tag-recovery models -- 13.3.3 Pollock's robust design. , 13.3.4 Capture recapture models for population growth rate.

Note: Intro -- Preface -- Acknowledgements -- Contents -- Contributors -- Editors' Biography -- Part I: Lignin and Its Production -- Chapter 1: Properties, Chemical Characteristics and Application of Lignin and Its Derivatives -- 1.1 Occurrence of Lignin in Biomass -- 1.1.1 Source, Monolignol Constituents and Sub-unit Structures -- 1.1.2 Distribution, Content and Chemical Structures of Lignin Sub-units -- 1.1.3 Biological Functions -- 1.1.4 Sources of Technical Lignin and Their Promise in Bio-­refining Process -- 1.2 Techniques for Determining Structural and Chemical Features of Lignin -- 1.2.1 Importance of Lignin Chemistry -- 1.2.2 Lignin Content -- 1.2.2.1 Wet Chemistry Methods -- 1.2.2.2 Spectroscopic Methods -- 1.2.3 Distribution of Lignin -- 1.2.3.1 Scanning Electron Microscopy and Atomic Force Microscopy Methods -- 1.2.3.2 Spectroscopy and Other Microscopy Methods -- 1.2.4 Molecular Weight and Polydispersity -- 1.2.5 Functional Side-Chain Groups -- 1.2.5.1 Nuclear Magnetic Resonance Methods -- 1.2.5.2 UV and GC-FID Methods -- 1.2.6 Content of Phenolic Units of Lignin -- 1.2.7 Content of Inter-molecular Linkages -- 1.2.7.1 13C- and 31P NMR Methods -- 1.2.7.2 FT-IR Spectroscopy Method -- 1.2.8 Lignin-Lignin Linkages and Macromolecular Assembly -- 1.2.8.1 Chemical Oxidation and GC-MS/FID Method -- 1.2.8.2 Pyrolysis Degradation and GC-MS/FID Method -- 1.2.8.3 Chemo-Thermo Degradation Method -- 1.2.8.4 Enzymatic Oxidization and Resonance Raman Spectroscopy Method -- 1.3 Derivatization and End-Use of Lignin and Lignin Derivatives -- 1.3.1 Sources of Lignocellulosic Biomass for Technical Lignin Derivatives -- 1.3.2 Application of Lignin and Lignin Derivatives -- 1.3.2.1 Energy -- 1.3.2.2 Renewable Chemicals -- 1.3.2.3 Materials and Additives -- 1.4 Conclusions and Future Outlook -- References. , Chapter 2: Extraction of Technical Lignins from Pulping Spent Liquors, Challenges and Opportunities -- 2.1 Introduction -- 2.2 Kraft Pulping Process -- 2.2.1 Properties of Black Liquor -- 2.2.2 Acidification -- 2.2.3 Membrane -- 2.2.4 Electrolysis -- 2.2.5 Solvent -- 2.3 Prehydrolysis Based Kraft Process -- 2.3.1 Properties of PHL -- 2.3.2 Acidification of PHL -- 2.3.3 Adsorption -- 2.3.4 Flocculation -- 2.3.5 In Situ Adsorption/Flocculation System -- 2.4 Spent Liquor of Sulfite Process -- 2.4.1 Properties of Spent Liquor -- 2.4.2 Membrane -- 2.4.3 Amine Extraction -- 2.4.4 Electrolysis -- 2.4.5 Ion Exchange Resin -- 2.5 Isolation of Lignosulfonate from Spent Liquor of NSSC Process -- 2.5.1 Properties of Spent Liquor in NSSC Process -- 2.5.2 Adsorption/Flocculation/Coagulation -- 2.5.3 Solvent Extraction -- 2.6 Conclusions and Future Outlook -- References -- Chapter 3: Recovery of Low-Ash and Ultrapure Lignins from Alkaline Liquor By-Product Streams -- 3.1 Introduction and Background -- 3.1.1 Low-Ash Lignins from Alkaline Liquors -- 3.1.2 From Low-Ash to Ultrapure Lignins -- 3.2 Low-Ash Lignins via the SLRP Process -- 3.2.1 Procedure -- 3.2.1.1 Carbonation -- 3.2.1.2 Acidification -- 3.2.1.3 Filtration -- 3.2.1.4 Vent-Gas Capture -- 3.2.2 Properties of Liquid-Lignin Phase -- 3.2.3 Fractionating the Liquid-Lignin Phase via SLRP for Control of the Bulk and Molecular Properties of Lignin -- 3.3 Ultrapure Lignins via the ALPHA Process -- 3.3.1 Liquid-Liquid Equilibrium Phase Behavior for the Acetic Acid-Water-Lignin System -- 3.3.2 ALPHA as a Single-Stage, Batch Process -- 3.3.3 Two-Stage Batch ALPHA for Generating Ultrapure Lignins -- 3.3.4 ALPHA as a Continuous Process: Minimizing Residence Times and Maximizing Throughputs for Ultrapure Lignins -- 3.4 Conclusions and Future Outlook -- References -- Part II: Biological Conversion. , Chapter 4: Lignin Degrading Fungal Enzymes -- 4.1 Introduction -- 4.2 Carbohydrate Active Enzyme Database (CAZy) -- 4.3 Fungal Oxidative Lignin Enzymes (FOLy) -- 4.4 Lignin Oxidizing Enzymes (LO) -- 4.4.1 Laccases (EC 1.10.3.2, Benzenediol: Oxygen Oxidoreductase) -- 4.4.2 Peroxidases (EC:1.11.1.x) -- 4.4.3 Lignin Peroxidases (E.C. 1.11.1.14) -- 4.4.4 Manganese Peroxidases (EC 1.11.1.13) -- 4.4.5 Versatile Peroxidases -- 4.5 Cellobiose Dehydrogenase -- 4.6 Lignin Degrading Auxiliary Enzymes (LDA) -- 4.6.1 Aryl Alcohol Oxidase -- 4.6.2 Vanillyl Alcohol Oxidase -- 4.6.3 Glyoxal Oxidase -- 4.6.4 Pyranose Oxidase -- 4.6.5 Galactose Oxidase -- 4.6.6 Glucose Oxidase -- 4.6.7 Benzoquinone Reductase -- 4.7 A Short Note on Genome Sequencing Studies of Lignin Degrading Fungi -- 4.8 Conclusion and Future Outlook -- References -- Chapter 5: Bacterial Enzymes for Lignin Oxidation and Conversion to Renewable Chemicals -- 5.1 Discovery of Lignin-Metabolising Bacteria -- 5.2 Bacterial Enzymes for Lignin Biotransformation -- 5.2.1 Dye-Decolorizing Peroxidases -- 5.2.2 Bacterial Laccases -- 5.2.3 Glutathione-Dependent β-Etherase Enzymes -- 5.2.4 Other Lignin-Metabolising Enzymes -- 5.3 Metabolic Pathways for Lignin Metabolism in Bacteria -- 5.4 Use of Metabolic Engineering for Generation of Renewable Chemicals from Lignin -- 5.5 Conclusions and Future Outlook -- References -- Chapter 6: Lignin Biodegradation with Fungi, Bacteria and Enzymes for Producing Chemicals and Increasing Process Efficiency -- 6.1 Introduction -- 6.2 Fungal Degradation -- 6.2.1 Delignification -- 6.2.2 Waste Treatment -- 6.2.3 Chemical Production -- 6.2.4 Perspectives -- 6.3 Bacterial Degradation -- 6.3.1 Delignification -- 6.3.2 Chemical Production -- 6.3.3 Perspectives -- 6.4 Enzymatic Degradation -- 6.4.1 Laccases -- 6.4.2 Peroxidases -- 6.4.3 Cocktails. , 6.4.4 Bioinspired Enzyme-Like Synthetic Compounds -- 6.4.5 Perspectives -- 6.5 Conclusion and Future Outlook -- References -- Part III: Chemical Conversion -- Chapter 7: Chemical Modification of Lignin for Renewable Polymers or Chemicals -- 7.1 Introduction -- 7.1.1 Lignin: An Important Renewable Resource -- 7.1.2 Possible Uses of Lignin -- 7.1.3 The Types of Chemical Modifications Carried Out on Lignin -- 7.1.3.1 Alkylation and Oxidation -- 7.1.3.2 Alkylation and Thioacidolysis -- 7.1.3.3 Halogenation -- 7.1.3.4 Nitration -- 7.1.3.5 Amination -- 7.1.3.6 Phosphitylation -- 7.1.3.7 Other Chemical Modifications of Lignin -- 7.2 Depolymerization of Modified Lignin -- 7.2.1 Sequential Lignin Modification Applied to Lignin Structural Analysis -- 7.2.2 Benzylic Oxidations Followed by Cleavage as a Route to Chemicals -- 7.3 Lignin Modification Leading to Novel Polymeric Materials -- 7.3.1 Overview -- 7.3.2 Reaction with Mono-functional Monomers -- 7.3.2.1 'Grafting Onto' Approach -- 7.3.2.2 'Grafting From' Approach -- 7.3.3 Reaction with Multi-functional Monomers -- 7.3.3.1 Phenol Formaldehyde Thermoset Materials -- 7.3.3.2 Polyurethanes -- 7.3.4 Polymer Blending -- 7.3.5 Smart Lignin Materials -- 7.4 Concluding Remarks & -- Future Outlook -- References -- Chapter 8: Carbon Materials from Lignin and Their Applications -- 8.1 Introduction -- 8.2 Activated Carbons from Lignin -- 8.2.1 Physical Activation -- 8.2.2 Chemical Activation -- 8.2.3 Applications of Lignin-Derived Activated Carbons -- 8.2.3.1 Applications in Adsorption -- 8.2.3.2 Applications in Catalysis -- 8.3 Lignin-Based Carbon Fibers -- 8.3.1 Lignin-Based CFs by Melt-Spinning Methods -- 8.3.2 Electrospinning -- 8.3.3 Oxidative Thermostabilization of Lignin Fibers -- 8.3.4 Potential Applications of Lignin-Based Carbon Fibers -- 8.3.4.1 CFs for Structural Applications. , 8.3.4.2 CFs for Functional Applications -- 8.4 Templated Carbons from Lignin -- 8.5 Lignin Graphitization -- 8.6 Conclusions and Future Outlook -- References -- Chapter 9: Biofuels and Chemicals from Lignin Based on Pyrolysis -- 9.1 Introduction -- 9.2 Fundamentals of Lignin Pyrolysis -- 9.2.1 Lignin Structures Related to Complexity of Pyrolysis -- 9.2.2 Pyrolysis Kinetics of Lignin -- 9.2.3 Py-GC/MS of Lignin -- 9.2.4 Factors Affecting Lignin Pyrolysis -- 9.3 Pyrolysis of Technical Lignin -- 9.3.1 Pyrolysis of Lignin in Lab-Scale Reactors -- 9.3.2 Properties of Lignin Pyrolysis Oil -- 9.4 Catalytic Upgrading of Lignin -- 9.4.1 Catalytic Upgrading of Pyrolysis Vapor of Lignin -- 9.4.2 Catalytic Upgrading of Phenolic Oil -- 9.5 Application of Lignin Pyrolysis Products -- 9.6 Conclusions and Future Outlook -- References -- Chapter 10: Lignin Depolymerization (LDP) with Solvolysis for Selective Production of Renewable Aromatic Chemicals -- 10.1 Introduction -- 10.2 Lignin in Conventional Heating -- 10.2.1 Hydrogenolysis -- 10.2.2 Hydrogen-Donor Solvent System -- 10.2.3 Hydrogen-Involved System -- 10.2.4 Oxidativelysis -- 10.2.5 Organometallic Catalysts -- 10.2.6 Metal-Free-Organic Catalysts -- 10.2.7 Acid/Base Catalysts -- 10.2.8 Metal Salt Catalysts -- 10.2.9 Two-Step LDP -- 10.3 LDP Assisted by microwave Heating -- 10.3.1 Hydrogenolysis -- 10.3.2 Oxidativelysis -- 10.4 Conclusions and Future Outlook -- References -- Chapter 11: Molecular Mechanisms in the Thermochemical Conversion of Lignins into Bio-Oil/Chemicals and Biofuels -- 11.1 Introduction -- 11.2 Lignin Devolatilization Temperature -- 11.3 Pyrolysis Products and Effects of Temperature -- 11.4 Primary Pyrolysis Reactions -- 11.4.1 Model Compound Reactivity -- 11.4.2 Ether Cleavage Mechanisms -- 11.4.3 Radical Chain Reactions -- 11.4.4 Re-polymerization and Side Chain Conversion. , 11.4.5 Side-Chain Conversion Mechanism.