THE FALSE PROMISE of the GENOMICS REVOLUTION for ENVIRONMENTAL LAW David E
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Chemical, Biological, and Environmental Factors Responsible for The
CHEMICAL, BIOLOGICAL, AND ENVIRONMENTAL FACTORS RESPONSIBLE FOR THE - -v - - - IN THE AUBURN CITY WATER SUPPLY AGRICULTURAL EXPERIMENT STATION/AUBURN UNIVERSITY R. DENNIS ROUSE, DIRECTOR AUBURN, ALABAMA BULLETIN 490 JULY 1977 CONTENTS Page INTRODUCTION ........ .. .. ............... 3 Background on Odors of Biological Origin in Water .......................... 3 Odor in the Auburn Water ........................... 5 PROCEDURES ............................ 5 RESULTS AND DISCUSSION ........... ...... .......... 7 Biological Factors................................ 7 Chemical Factors ............................... 22 Environmental Factors Related to the Growth and Production of Odorous Substances by Actinomycetes ....... 24 Correlation of Rainfall, Temperature, and Threshold Odor Number (TON) ................... 39 SUMMARY ... ... ...................... ........... 41 REFERENCES ................... ....... ... .......... 45 FIRST PRINTING 3M, JULY 1977 ACKNOWLEDGEMENTS The authors wish to thank the Auburn Water Board for the financial support of this project, the Auburn Water Treatment Plant for supplying TON values, Dr. John Lawrence for use of the carbon analyzer, and Sandy Williams, Frank Seesock, and Susan Brownlee for technical assistance. Information contained herein is available to all without regard to race, color, or nationalorigin. CHEMICAL, BIOLOGICAL, and ENVIRONMENTAL FACTORS RESPONSIBLE for the EARTHY ODOR in the AUBURN CITY WATER SUPPLY J. D. WEETE, W. T. BLEVINS, G. R.WILT, and D. DURHAM' INTRODUCTION Background On Odors Of Biological Origin in Water HE OCCURRENCE of objectionable odors in water used for drinking and commercial purposes is well documented in certain regions of the United States (1, 4, 12, 15) and various other parts of the world. (1, 8, 9, 11). Odors in sur- face waters may originate from industrial and municipal sewage effluents or from biological activities of algae or heterotrophic microorganisms. Odors of biological origin may be due to decomposition of organisms or to the produc- tion of microbial metabolites. -
The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-Rnns and the Protein Structure Prediction Problem
Journal of Machine Learning Research 4 (2003) 575-602 Submitted 2/02; Revised 4/03; Published 9/03 The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-RNNs and the Protein Structure Prediction Problem Pierre Baldi [email protected] Gianluca Pollastri [email protected] School of Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine Irvine, CA 92697-3425, USA Editor: Michael I. Jordan Abstract We describe a general methodology for the design of large-scale recursive neural network architec- tures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connec- tions to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dy- namics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. -
Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level. -
Commentary: Facing the Challenge of Gene-Environment Interaction: the Two-By-Four Table and Beyond
American Journal of Epidemiology Vol. 153, No. 10 Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health Printed in U.S.A. All rights reserved The Challenge of Gene-Environment Interaction Botto and Khoury PRACTICE OF EPIDEMIOLOGY Commentary: Facing the Challenge of Gene-Environment Interaction: The Two-by-Four Table and Beyond Lorenzo D. Botto1 and Muin J. Khoury2 As a result of the Human Genome Project, epidemiologists can study thousands of genes and their interaction with the environment. The challenge is how to best present and analyze such studies of multiple genetic and environmental factors. The authors suggest emphasizing the fundamental core of gene-environment interaction—the separate assessment of the effects of individual and joint risk factors. In the simple analysis of one genotype and an exposure (both dichotomous), such study can be summarized in a two-by-four table. The advantages of such a table for data presentation and analysis are many: The table displays the data efficiently and highlights sample size issues; it allows for evaluation of the independent and joint roles of genotype and exposure on disease risk; and it emphasizes effect estimation over model testing. Researchers can easily estimate relative risks and attributable fractions and test different models of interaction. The two-by-four table is a useful tool for presenting, analyzing, and synthesizing data on gene-environment interaction. To highlight the role of gene-environment interaction in disease causation, the authors propose that the two-by-four table is the fundamental unit of epidemiologic analysis. Am J Epidemiol 2001;153:1016–20. -
Environmental Factors Affecting the Composition and Diversity of The
Imai et al. Journal of Ecology and Environment (2017) 41:8 Journal of Ecology DOI 10.1186/s41610-017-0027-2 and Environment RESEARCH Open Access Environmental factors affecting the composition and diversity of the avian community in igune, a traditional agricultural landscape in northern Japan Haruka Imai1*, Tohru Nakashizuka1,2 and Michio Oguro1,3 Abstract Background: “Igune,” a traditional agricultural landscape in the Tohoku region of Japan, is characterized by small-scale artificial woodlots surrounding a farmer’s house that are interspersed with paddy fields. During the rapid economic growth of Japan over recent decades, some igune woodlots have been abandoned or logged. Biodiversity conservation is an important issue worldwide, and traditional agricultural landscapes are of particular interest. To elucidate the role of igune landscapes in conserving biodiversity, we examined the effects of environmental factors on avian communities. Results: The study was conducted in the suburban areas of Oshu and Hanamaki cities, Iwate Prefecture, Japan, at eight sites that varied in the density and area of igune woodlots within the landscape. Bird surveys were conducted from the middle to late breeding season, and several environmental factors of the igune landscape were also measured. The results of canonical correspondence analysis indicated that the characteristics of avian communities were mainly determined by the total forested area in the landscape. Increased total forested area and shrubs layer of igune woodlots did not cause a reduction in number of bird species of any habitat and foraging types, while increased both in species number and abundance of insectivores and forest species. The number of raptor species increased in igune sites without shrubs. -
How Much Global Ill Health Is Attributable to Environmental Factors?
ORIGINAL ARTICLES How Much Global Ill Health Is Attributable to Environmental Factors? Kirk R. Smith,1 Carlos F. Corvalán2, and Tord Kjellström3 Over the years, estimates have been made of the portions of attributed to environmental risk factors. Children under 5 human mortality and morbidity that can be attributed to years of age seem to bear the largest environmental burden, environmental factors. Frustratingly, however, even for a and the portion of disease due to environmental risks seems single category of disease such as cancer, these estimates to decrease with economic development. A summary of have often varied widely. Here we attempt to explain why these estimates first appeared in the 1997 report, "Health such efforts have come to such different results in the past and Environment in Sustainable Development," which was and to provide guidance for doing such estimates more the World Health Organization's contribution to the 5-year consistently in the future to avoid the most important pitfalls. anniversary of the Rio Earth Summit. A full explanation of We do so by carefully defining what we mean by the terms how these estimates were made is first presented here. We "environmental," "ill health," and "attributable." Finally, end with a call for a program of "strategic epidemiology," based on these recommendations, we attempt our own which would be designed to fill important gaps in the estimate, appropriately qualified according to the many understanding of major environmental health risks in remaining uncertainties. Our estimate is that 25-33% of the important population groups worldwide. (Epidemiology global burden of disease can be 1999;10:573-584) Keywords: environmental exposures, morbidity, mortality, children, environmental health risks, populations, global factors, public health. -
Toxicogenomics Applications of New Functional Genomics Technologies in Toxicology
\-\w j Toxicogenomics Applications of new functional genomics technologies in toxicology Wilbert H.M. Heijne Proefschrift ter verkrijging vand egraa dva n doctor opgeza gva nd e rector magnificus vanWageninge n Universiteit, Prof.dr.ir. L. Speelman, in netopenbaa r te verdedigen op maandag6 decembe r200 4 des namiddagst e half twee ind eAul a - Table of contents Abstract Chapter I. page 1 General introduction [1] Chapter II page 21 Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach[2] Chapter III page 48 Bromobenzene-induced hepatotoxicity atth etranscriptom e level PI Chapter IV page 67 Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis[4] Chapter V page 88 Liver gene expression profiles in relation to subacute toxicity in rats exposed to benzene[5] Chapter VI page 115 Toxicogenomics analysis of liver gene expression in relation to subacute toxicity in rats exposed totrichloroethylen e [6] Chapter VII page 135 Toxicogenomics analysis ofjoin t effects of benzene and trichloroethylene mixtures in rats m Chapter VII page 159 Discussion and conclusions References page 171 Appendices page 187 Samenvatting page 199 Dankwoord About the author Glossary Abbreviations List of genes Chapter I General introduction Parts of this introduction were publishedin : Molecular Biology in Medicinal Chemistry, Heijne etal., 2003 m NATO Advanced Research Workshop proceedings, Heijne eral., 2003 81 Chapter I 1. General introduction 1.1 Background /.1.1 Toxicologicalrisk -
Deep Learning in Chemoinformatics Using Tensor Flow
UC Irvine UC Irvine Electronic Theses and Dissertations Title Deep Learning in Chemoinformatics using Tensor Flow Permalink https://escholarship.org/uc/item/963505w5 Author Jain, Akshay Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Deep Learning in Chemoinformatics using Tensor Flow THESIS submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Computer Science by Akshay Jain Thesis Committee: Professor Pierre Baldi, Chair Professor Cristina Videira Lopes Professor Eric Mjolsness 2017 c 2017 Akshay Jain DEDICATION To my family and friends. ii TABLE OF CONTENTS Page LIST OF FIGURES v LIST OF TABLES vi ACKNOWLEDGMENTS vii ABSTRACT OF THE THESIS viii 1 Introduction 1 1.1 QSAR Prediction Methods . .2 1.2 Deep Learning . .4 2 Artificial Neural Networks(ANN) 5 2.1 Artificial Neuron . .5 2.2 Activation Function . .7 2.3 Loss function . .8 2.4 Optimization . .8 3 Deep Recursive Architectures 10 3.1 Recurrent Neural Networks (RNN) . 10 3.2 Recursive Neural Networks . 11 3.3 Directed Acyclic Graph Recursive Neural Networks (DAG-RNN) . 11 4 UG-RNN for small molecules 14 4.1 DAG Generation . 16 4.2 Local Information Vector . 16 4.3 Contextual Vectors . 17 4.4 Activity Prediction . 17 4.5 UG-RNN With Contracted Rings (UG-RNN-CR) . 18 4.6 Example: UG-RNN Model of Propionic Acid . 20 5 Implementation 24 6 Data & Results 26 6.1 Aqueous Solubility Prediction . 26 6.2 Melting Point Prediction . 28 iii 7 Conclusions 30 Bibliography 32 A Source Code 37 A.1 UGRNN . -
Genomic Dose Response
Genomic Dose Response: The Picture NTP Genomic Dose Response Modeling Expert Panel Meeting October 23, 2017 Russell Thomas Director National Center for Computational Toxicology The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA Scott May Want To Rethink Asking Me To Be The “Big Picture Guy”… National Center for Computational Toxicology It is a Well Known Fact that Toxicology Continues to Have a Data Problem US National Research Council, 1984 Size of Estimate Mean Percent Category Category In the Select Universe Pesticides and Inert Ingredients of Pesticides 3,350 Formulations 10 24 2 26 38 Cosmetic Ingredients 3,410 2 14 10 18 56 Drugs and Excipients Used in Drug Formulations 1,815 18 18 3 36 25 Food Additives 8,627 • Major challenge is too many 5 14 1 34 46 Chemicals in Commerce: chemicals and not enough At Least 1 Million 12,860 Pounds/Year data 11 11 78 Chemicals in Commerce: • Total # chemicals = 65,725 Less than 1 Million 13,911 Pounds/Year • Chemicals with no toxicity 12 12 76 data of any kind = ~46,000 Chemicals in Commerce: Production Unknown or 21,752 Inaccessible 10 8 82 Complete Partial Minimal Some No Toxicity Health Health Toxicity Toxicity Information Hazard Hazard Information Information Available Assessment Assessment Available Available 2 Possible Possible (But Below Minimal) National Center for Computational Toxicology It is a Well Known Fact that Toxicology Continues to Have a Data Problem 70 60 50 40 30 20 Percent of Chemicals <1% 10 0 Acute Cancer Gentox Dev Tox Repro Tox EDSP Tier 1 Modified from Judson et al., EHP 2009 3 National Center for Computational Toxicology For Those With Data, Have We Been Truly Predictive or Just Protective? …data compiled from 150 compounds with 221 human toxicity events reported. -
Course Outline
Department of Computer Science and Software Engineering COMP 6811 Bioinformatics Algorithms (Reading Course) Fall 2019 Section AA Instructor: Gregory Butler Curriculum Description COMP 6811 Bioinformatics Algorithms (4 credits) The principal objectives of the course are to cover the major algorithms used in bioinformatics; sequence alignment, multiple sequence alignment, phylogeny; classi- fying patterns in sequences; secondary structure prediction; 3D structure prediction; analysis of gene expression data. This includes dynamic programming, machine learning, simulated annealing, and clustering algorithms. Algorithmic principles will be emphasized. A project is required. Outline of Topics The course will focus on algorithms for protein sequence analysis. It will not cover genome assembly, genome mapping, or gene recognition. • Background in Biology and Genomics • Sequence Alignment: Pairwise and Multiple • Representation of Protein Amino Acid Composition • Profile Hidden Markov Models • Specificity Determining Sites • Curation, Annotation, and Ontologies • Machine Learning: Secondary Structure, Signals, Subcellular Location • Protein Families, Phylogenomics, and Orthologous Groups • Profile-Based Alignments • Algorithms Based on k-mers 1 Texts | in Library D. Higgins and W. Taylor (editors). Bioinformatics: Sequence, Structure and Databanks, Oxford University Press, 2000. A. D. Baxevanis and B. F. F. Ouelette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley, 1998. Richard Durbin, Sean R. Eddy, Anders Krogh, -
Public Health Classifications Project – Determinants of Health
Public Health Classifications Project – Determinants of Health Phase Two: Final Report Acknowledgements We acknowledge the National Public Health Partnership for leading the first phase of work on public health classification; the New South Wales Department of Health and the Public Health Information Development Unit, the University of Adelaide for sponsoring this Project; colleagues from both for their support of the Project Officer; the Sax Institute for providing meeting premises and equipment; and the National Centre for Classification in Health for access to ICD-10, the 11th coding conference and opportunities to meet and discuss issues with ICD coders. NSW DEPARTMENT OF HEALTH 73 Miller Street NORTH SYDNEY NSW 2060 Tel. (02) 9391 9000 Fax. (02) 9391 9101 TTY. (02) 9391 9900 www.health.nsw.gov.au Report prepared for the New South Wales Department of Health Report prepared by Su Gruszin, the Project Officer; Louisa Jorm, the Chair of the Project Working Group; and Working Group members: Tim Churches, Richard Madden, Sarah Thackway, and Ros Madden. This work is copyright. It may be reproduced in whole or in part for study training purposes subject to the inclusion of an acknowledgement of the source. It may not be reproduced for commercial usage or sale. Reproduction for purposes other than those indicated above requires written permission from the NSW Department of Health. © NSW Department of Health 2010 SHPN (CER) 100195 ISBN 978-1-74187-461-7 Further copies of this document can be downloaded from the NSW Health website www.health.nsw.gov.au December 2010 Contents Executive Summary ................................................................. 3 Section 1: Introduction ............................................................. -
Next-Generation Sequencing Data for Use in Risk Assessment Bruce Alexander Merrick
Available online at www.sciencedirect.com Current Opinion in ScienceDirect Toxicology Next-generation sequencing data for use in risk assessment Bruce Alexander Merrick Abstract fluorescently labeled nucleotides and capillary electro- Next-generation sequencing (NGS) represents several phoresis that gave rise to automated sequencing in- powerful platforms that have revolutionized RNA and DNA struments such as the Applied Biosystems, Inc. model analysis. The parallel sequencing of millions of DNA molecules 370A sequencers and others, on the basis of Sanger can provide mechanistic insights into toxicology and provide chemistries [2]. Read length per each sample was at 500e new avenues for biomarker discovery with growing relevance 800 nucleotides, and sample throughput was limited. for risk assessment. The evolution of NGS technologies has improved over the last decade with increased sensitivity and Sanger-based DNA sequencing instruments are accuracy to foster new biomarker assays from tissue, blood, considered first-generation platforms. Instruments that and other biofluids. NGS technologies can identify transcrip- perform multiple sequencing reactions simultaneously tional changes and genomic targets with base pair precision in in a ‘massively parallel’ fashion have been dubbed, response to chemical exposure. Furthermore, there are ‘NextGeneration’ or NextGen Sequencing [3]. How several exciting movements within the toxicology community NGS came about compared with other genomic plat- that incorporate NGS platforms into new strategies for more forms is interlinked with microarray technology rapid toxicological characterizations. These include the Tox21 (Figure 1). Both platforms can provide whole genomic in vitro high-throughput transcriptomic screening program, approaches to research problems. Microarrays are a development of organotypic spheroids, alternative animal fluorescent probe hybridization-based technology with models, mining archival tissues, liquid biopsy, and epige- origins in the mid-1990s that are now a mature genomic nomics.