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  • 1
    Keywords: Environmental toxicology. ; Electronic books.
    Description / Table of Contents: The fifth edition includes new sections on the use of adverse outcome pathways, how climate change changes how we think about toxicology, and a new chapter on contaminants of emerging concern. Additional information is provided on the derivation of exposure-response curves to describe toxicity and they are compared to the use of hypothesis testing.
    Type of Medium: Online Resource
    Pages: 1 online resource (491 pages)
    Edition: 5th ed.
    ISBN: 9781498750448
    DDC: 615.9/02
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgments -- About the Authors -- Chapter 1: Introduction to Environmental Toxicology -- 1.1 Environmental Toxicology as an Interdisciplinary Science -- 1.2 A Brief History and Organizations in Environmental Toxicology -- 1.3 Interactions and Connections of Environmental Toxicology to the Management of Ecological Systems -- 1.3.1 Research Programs -- 1.3.2 Scientific Community -- 1.3.3 Risk Assessment -- 1.3.4 Governmental and Regulatory Agencies -- 1.3.5 Industry -- 1.3.6 General Public and Nongovernmental Organizations -- 1.4 Legislation -- 1.5 Introduction to this Textbook -- Study Questions -- Chapter 2: Frameworks and Paradigms for Environmental Toxicology -- 2.1 The Fundamentals -- 2.2 Models -- 2.3 Fundamental Models for Environmental Toxicology -- 2.3.1 The Classical Viewpoint for Classifying Toxicological Effects -- 2.3.2 Chemical Physical-Chemical Characteristics -- 2.3.3 Bioaccumulation/Biotransformation/Biodegradation -- 2.3.4 Receptor and the Mode of Action -- 2.3.5 Biochemical and Molecular Effects -- 2.3.6 Physiological and Behavioral Effects -- 2.3.7 Population Parameters -- 2.3.8 Community Effects -- 2.3.9 Ecosystem Effects -- 2.3.10 The Adverse Outcome Pathway -- 2.4 An Alter Native Framework Incorporating Complexity Theory -- 2.5 Spatial and Temporal Scales -- 2.6 Combining Scale and Ecological Dynamics: The Hierarchical Patch Dynamic Paradigm -- 2.7 Language as a Model -- 2.7.1 The Importance of Terminology and Language -- 2.7.2 The Language of Normative Science -- 2.7.3 Bending Science -- Study Questions -- References and Suggested Readings -- Chapter 3: Overview of Toxicity-Testing Methods -- 3.1 Introduction -- 3.1.1 Animal Care and Use Considerations -- 3.2 Standard Methods -- 3.2.1 Advantages of Standard Methods. , 3.2.2 Disadvantages of Standard Methods -- 3.3 Classification of Toxicity Tests -- 3.3.1 The Design of Toxicity Tests -- 3.3.1.1 Design Parameters for Single-Species Toxicity Tests -- 3.3.1.2 Exposure Scenarios -- 3.3.1.3 Test Organisms -- 3.3.1.4 Comparison of Test Species -- 3.4 Multispecies Toxicity Tests -- 3.4.1 The Design of Multispecies Toxicity Tests -- 3.4.2 The Nature of Multispecies Toxicity Tests -- 3.4.3 Summary of Design Guidelines for Multispecies Toxicity Tests -- 3.5 Overview of Specific Toxicity Tests -- 3.5.1 Daphnia 48 h Acute Toxicity Test -- 3.5.2 Algal 96 h Growth Toxicity Test -- 3.5.3 Acute Toxicity Tests with Aquatic Vertebrates and Macroinvertebrates -- 3.5.4 Terrestrial Vertebrate Toxicity Tests -- 3.5.5 Frog Embryo Teratogenesis Assay -- 3.6 Multispecies Toxicity Tests -- 3.6.1 Standardized Aquatic Microcosm -- 3.6.2 Mixed-Flask Culture -- 3.6.3 FIFRA Microcosm -- 3.6.4 Soil-Core Microcosm -- 3.7 Summary -- Study Questions -- Appendix: The Natural History and Utilization of Selected Test Species -- Aquatic Vertebrates -- Coho Salmon (Oncorhynchus kisutch) -- Rainbow Trout (Oncorhynchus mykiss) -- Brook Trout (Salvelinus fontinalis) -- Goldfish (Carassius auratus) -- Fathead Minnow (Pimephales promelas) -- Channel Catfish (Ictalurus punctatus) -- Bluegill (Lepomis macrochirus) -- Green Sunfish (Lepomis cyanellus) -- Invertebrates-Freshwater -- Daphnids (Daphnia magna, D. pulex, D. pulicaria, Ceriodaphnia dubia) -- Amphipods (Gammarus lacustris, G. fasciatus, G. pseudolimnaeus, Hyalella azteca) -- Crayfish (Orconectes sp., Combarus sp., Procambarus sp., Pacifastacus leniusculus) -- Stoneflies (Pteronarcys sp.) -- Mayflies (Baetis sp., Ephemerella sp., Hexagenia limbata, H. bilineata) -- Midges (Chironomus sp.) -- Snails (Physa integra, P. heterostropha, Amnicola limosa): (Mollusca, Gastropoda). , Planaria (Dugesia tigrina): (Platyhelminthes, Turbellaria) -- Invertebrates: Saltwater -- Copepods (Acartia clausi, A. tonsa) -- Algae -- Chlamydomonas reinhardi -- Ulothrix sp. -- Microcystis aeruginosa -- Anabaena flos-aquae -- Avian Species -- Mallard (Anas platyrhynchos) -- Northern Bobwhite (Colinus virginianus) -- Ring-Necked Pheasant (Phasianus colchicus) -- References -- Chapter 4: Analysis of Exposure-Response -- 4.1 Introduction -- 4.2 The Exposure-Response Curve -- 4.3 Thresholds and Hormesis -- 4.4 Terminologies from Hypothesis Testing -- 4.5 Overview of the Tools for the Analysis of Exposure-Response Relationships -- 4.5.1 Limitations and Alternatives to Hypothesis Testing -- 4.5.2 Comparison of Calculations of Several Programs for Calculating Probit Analysis -- 4.5.3 Modeling Using R and the DRC Package -- 4.5.4 Hypothesis Testing -- 4.6 Curve Fitting and Regression Modeling versus Hypothesis Testing -- 4.7 The Debate: Regression Model versus the Hypothesis-Testing Debate -- 4.8 Data Analysis and Interpretation of Multispecies Toxicity Tests -- 4.8.1 Univariate Methods -- 4.8.2 Multivariate Methods -- 4.8.3 Visualization -- 4.9 Summary of Design Guidelines for Multispecies Toxicity Tests -- Study Questions -- References and Suggested Readings -- Chapter 5: The Fate and Transport of Contaminants -- 5.1 Introduction to the Fate and Transport of Contaminants -- 5.2 Transport Mechanisms -- 5.2.1 Advection, Diffusion, and Dispersion -- 5.2.2 Long-Range Atmospheric Transport -- 5.3 Persistence -- 5.4 Biotransport -- 5.5 Abiotic Degradation/Transformation -- 5.6 Multimedia Box Models -- 5.7 Equilibrium -- 5.8 The Fugacity Approach -- 5.9 Bioconcentration versus Biomagnification -- 5.10 Bioavailability -- 5.10.1 Measures of Bioavailability -- 5.10.2 Metal Speciation and the Biotic Ligand Model. , 5.10.3 Acid Volatile Sulfide/Simultaneously Extracted Metals in Anoxic Sediment -- 5.11 Summary -- Study Questions -- References and Suggested Readings -- Chapter 6: Uptake and Modes of Action -- 6.1 Introduction -- 6.2 The Damage Process -- 6.2.1 Atmospheric Pollutants and Plants -- 6.2.2 Plant Injury -- 6.2.3 Vertebrates -- 6.2.3.1 Exposure -- 6.2.3.2 Uptake -- 6.2.3.3 Transport -- 6.2.3.4 Storage -- 6.2.3.5 Metabolism -- 6.2.3.6 Excretion -- 6.3 Mechanisms of Action -- 6.3.1 Disruption or Destruction of Cellular Structure -- 6.3.2 Direct Chemical Combination with a Cellular Constituent -- 6.3.3 Effect on Enzymes -- 6.3.4 Secondary Action as a Result of the Presence of a Pollutant -- 6.3.5 Metal Shift -- 6.4 Details of Specific Modes of Action -- 6.4.1 Narcosis -- 6.4.2 Organophosphates -- 6.4.3 Modes of Action of Chemical Warfare Agents -- 6.4.4 Monohaloacetic Acids -- 6.5 Receptor-Mediated Toxicity and Endocrine Disruption -- 6.5.1 Specificity of the Hormone-Receptor Interaction -- 6.5.2 The Receptor Pathway for TCDD -- 6.5.3 The Structure-Activity Relationships of PCB and Related Compounds -- 6.5.4 Polybrominated Diphenylethers -- 6.6 The Multiple Modes of Action of Atrazine -- 6.7 Adverse Outcome Pathway Models -- 6.8 Introduction to Quantitative Structure-Activity Relationships -- 6.8.1 Construction of Quantitative Structure-Activity Relationships Models -- 6.8.2 Typical Quantitative Structure-Activity Relationships Model Development -- 6.8.3 Estimation of Toxicity Using Quantitative Structure-Activity Relationships -- Study Questions -- References and Suggested Readings -- Chapter 7: Modification in Toxic Responses, Mixtures, and Climate Change -- 7.1 Introduction -- 7.2 Physicochemical Properties of Pollutants -- 7.3 Time and Mode of Exposure -- 7.4 Environmental Factors -- 7.4.1 Temperature -- 7.4.2 Humidity -- 7.4.3 Light Intensity. , 7.5 Biological Factors Affecting Toxicity -- 7.5.1 Diseases -- 7.5.2 Behavioral Factors -- 7.5.3 Sex Variation -- 7.5.4 Nutritional Factors -- 7.5.5 Fasting or Starvation -- 7.5.6 Proteins -- 7.5.7 Carbohydrates -- 7.5.8 Lipids -- 7.5.9 Vitamin A -- 7.5.10 Vitamin D -- 7.5.11 Vitamin E -- 7.5.12 Vitamin C -- 7.5.13 Minerals -- 7.6 Mixtures and the Effects on Toxicity -- 7.6.1 Synergism and Potentiation -- 7.6.2 Antagonism -- 7.6.3 Estimating the Toxicity of Mixtures -- 7.6.4 Simple Models of Mixture Toxicity -- 7.6.5 Mixture Estimation System -- 7.6.6 Estimating the Toxicity of Mixtures of Polynuclear Aromatic Hydrocarbons -- 7.7 Carbamate and Organophosphate Synergistic Toxicity -- 7.8 Climate Change and Toxicology -- Study Questions -- References and Suggested Readings -- Chapter 8: Inorganic Gaseous Pollutants -- 8.1 Sulfur Oxides -- 8.1.1 Sources of SO2 -- 8.1.2 Characteristics of SO2 -- 8.1.3 Effects on Plants -- 8.1.4 Effect on Animals -- 8.1.5 Effect on Humans -- 8.2 Nitrogen Oxides -- 8.2.1 Forms and Formation of Nitrogen Oxides -- 8.2.2 Major Reactive N Species in the Troposphere -- 8.2.3 Effects on Plants -- 8.2.4 Effects on Humans and Animals -- 8.2.5 Physiological Effects -- 8.2.6 Biochemical Effects -- 8.3 Ozone -- 8.3.1 Sources -- 8.3.2 Photochemical Smog -- 8.3.3 Effects on Plants -- 8.3.4 Effects on Humans and Animals -- 8.3.5 Biochemical Effects -- 8.4 Carbon Monoxide -- 8.4.1 Formation of CO -- 8.4.2 Human Exposure to CO -- 8.4.3 Toxicological Effects -- 8.4.4 Mechanism of Action -- Study Questions -- Refernces and Suggested Readings -- Chapter 9: Fluoride as a Contaminant of Developing Economies -- 9.1 Environmental Sources and Forms of Fluoride -- 9.1.1 Minerals and Soils -- 9.1.2 Natural Waters -- 9.1.3 Foods -- 9.1.4 Air -- 9.2 Industrial Sources of Fluoride Pollution -- 9.3 Effects on Plants -- 9.3.1 Injuries to Leaf Tissues. , 9.3.2 Effect on Germination.
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  • 2
    Publication Date: 2015-06-20
    Description: Background: Gossypium raimondii is a Verticillium wilt-resistant cotton species whose genome encodes numerous disease resistance genes that play important roles in the defence against pathogens. However, the characteristics of resistance gene analogues (RGAs) and Verticillium dahliae response loci (VdRLs) have not been investigated on a global scale. In this study, the characteristics of RGA genes were systematically analysed using bioinformatics-driven methods. Moreover, the potential VdRLs involved in the defence response to Verticillium wilt were identified by RNA-seq and correlations with known resistance QTLs. Results: The G. raimondii genome encodes 1004 RGA genes, and most of these genes cluster in homology groups based on high levels of similarity. Interestingly, nearly half of the RGA genes occurred in 26 RGA-gene-rich clusters (Rgrcs). The homology analysis showed that sequence exchanges and tandem duplications frequently occurred within Rgrcs, and segmental duplications took place among the different Rgrcs. An RNA-seq analysis showed that the RGA genes play roles in cotton defence responses, forming 26 VdRLs inside in the Rgrcs after being inoculated with V. dahliae. A correlation analysis found that 12 VdRLs were adjacent to the known Verticillium wilt resistance QTLs, and that 5 were rich in NB-ARC domain-containing disease resistance genes. Conclusions: The cotton genome contains numerous RGA genes, and nearly half of them are located in clusters, which evolved by sequence exchanges, tandem duplications and segmental duplications. In the Rgrcs, 26 loci were induced by the V. dahliae inoculation, and 12 are in the vicinity of known Verticillium wilt resistance QTLs.
    Electronic ISSN: 1471-2229
    Topics: Biology
    Published by BioMed Central
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  • 3
    Publication Date: 2015-10-17
    Description: Journal of the American Chemical Society DOI: 10.1021/jacs.5b09111
    Print ISSN: 0002-7863
    Electronic ISSN: 1520-5126
    Topics: Chemistry and Pharmacology
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  • 4
    Publication Date: 2015-08-08
    Description: Background: Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators. Results: In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387–404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783–802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real data application, the confident difference criterion methods successfully identified more clinically important DE genes than the other methods. Conclusion: The confident difference criterion method proposed in this paper provides a new efficient approach for both microarray studies and sequence-based high-throughput studies to identify differentially expressed genes.
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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  • 5
    Publication Date: 2015-07-17
    Description: Article The typical small sizes of silver microcrystals prevent the full characterization of fundamental plasmonic properties and limits applications. Here, Wang et al . report the synthesis of large colloidal silver crystals with superior nonlinear properties and surface plasmon polariton propagation lengths beyond 100 μm. Nature Communications doi: 10.1038/ncomms8734 Authors: Chun-Yuan Wang, Hung-Ying Chen, Liuyang Sun, Wei-Liang Chen, Yu-Ming Chang, Hyeyoung Ahn, Xiaoqin Li, Shangjr Gwo
    Electronic ISSN: 2041-1723
    Topics: Biology , Chemistry and Pharmacology , Natural Sciences in General , Physics
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  • 6
    Publication Date: 2015-07-31
    Description: Organic Letters DOI: 10.1021/acs.orglett.5b01933
    Print ISSN: 1523-7060
    Electronic ISSN: 1523-7052
    Topics: Chemistry and Pharmacology
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  • 7
    Publication Date: 2015-10-07
    Description: Analytical Chemistry DOI: 10.1021/acs.analchem.5b02728
    Print ISSN: 0003-2700
    Electronic ISSN: 1520-6882
    Topics: Chemistry and Pharmacology
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  • 8
    Publication Date: 2015-12-24
    Description: We examined the effects of co-worker and supervisor support on job stress and presenteeism in an aging workforce. Structural equation modelling was used to evaluate data from the 2010 wave of the Health and Retirement Survey in the United States (n = 1649). The level of presenteeism was low and the level of job stress was moderate among aging US workers. SEM revealed that co-worker support and supervisor support were strongly correlated (β = 0.67; p 〈 0.001). Job stress had a significant direct positive effect on presenteeism (β = 0.30; p 〈 0.001). Co-worker support had a significant direct negative effect on job stress (β = −0.10; p 〈 0.001) and presenteeism (β = −0.11; p 〈 0.001). Supervisor support had a significant direct negative effect on job stress (β = −0.40; p 〈 0.001) but not presenteeism. The findings suggest that presenteeism is reduced by increased respect and concern for employee stress at the workplace, by necessary support at work from colleagues and employers, and by the presence of comfortable interpersonal relationships among colleagues and between employers and employees.
    Print ISSN: 1661-7827
    Electronic ISSN: 1660-4601
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Medicine
    Published by MDPI Publishing
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  • 9
    Publication Date: 2015-12-31
    Description: In this paper we present some inequalities of Hermite-Hadamard type for functions whose third derivative absolute values are quasi-convex. Moreover, an application to special means of real numbers is also considered.Mathematics Subject Classification: 26D10, 26A51, 26E60.
    Electronic ISSN: 2193-1801
    Topics: Natural Sciences in General
    Published by SpringerOpen
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  • 10
    Publication Date: 2015-11-28
    Description: Article Lithium-sulfur batteries are a promising candidate for next-generation battery technologies. Here, the authors report a pie-like structured electrode in which sulfur is confined in multichannel carbon nanofibers which is then coated by amino-functionalized graphene, leading to good balance between electrochemical performance and cell energy density. Nature Communications doi: 10.1038/ncomms9850 Authors: Zhen Li, Jin Tao Zhang, Yu Ming Chen, Ju Li, Xiong Wen (David) Lou
    Electronic ISSN: 2041-1723
    Topics: Biology , Chemistry and Pharmacology , Natural Sciences in General , Physics
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