Professional Training Courses
Professional training courses are coordinated by the Education and Short Courses Committee based on feedback from previous participants, input from the SETAC membership community, and discussion with the local program committee for the annual meeting. The focus is on selecting cutting-edge and general scientific topics of interest. In addition, non-scientific courses that support skills scientists might need to succeed, for example communication or presentation skills, are offered. The courses are taught by experts in the field. Reserve your spot in a professional training course when you register for the meeting.
|All prices in US$||Full-day||Half-day|
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Sunday Full-Day Courses
8:00 a.m.–5:00 p.m. | 4 November
Room: Hyatt – Ballroom A
Instructors: Ben Young Landis, Creative Externalities
As scientists, we inherently understand the importance of our work — focusing on the experimental and analytical methods to explain the natural world or to answer policy questions. However, focusing on the finer details of research is often not the best way to draw interest from public stakeholders. Instead, discussing the big-picture implications of your findings is a preferable way to connect with non-technical audiences — especially if you can relate your work to their personal interests or policy goals. In this science communication workshop, we will help you find ways to explain the implications of your research in a relaxed, relevant manner that connects with your audience. Through introductory lectures and an interactive worksheet, participants will refine their talking points into clear, concise “elevator pitch” briefings tailored for specific audiences. We will then apply these prepared messages to a mock interview exercise, where participants will practice communicating their science in various media and networking scenarios. When you successfully connect with your audience, you can help inform their decisions, inspire their curiosity and broaden their understanding of the natural world around them. Begin exploring your scicomm practice today, and help amplify the impact of your research tomorrow.
Room: Hyatt – Ballroom B
Instructors: Steven Bay, Southern California Coastal Water Research Project (SCCWRP); Chris Beegan, California State Water Resources Control Board; David Gillett, Southern California Coastal Water Research Project; and Shelly Anghera, Latitude Environmental
This full-day course will provide environmental scientists and managers with the latest information and tools to work with California’s sediment quality objectives policy. Since 2009, California has implemented a multiple-lines-of-evidence approach using the sediment quality triad to evaluate sediment quality for protection of benthic macrofauna. Adoption of an additional framework to assess human health impacts from seafood contamination is expected in 2018. This course will include a description of the conceptual approach for both the benthic macrofauna and human health assessment frameworks. Essential technical concepts underlying data analysis and integration will be described. Data analysis tools and hands-on data analysis experience will be provided to illustrate key steps in data interpretation and site assessment. Case studies that illustrate challenges and solutions to implementing sediment quality assessment within regulatory programs will be presented. The course presenters are highly experienced in sediment quality assessment and played key roles in developing California’s assessment frameworks and implementation guidance. Most of course content is applicable to other locations in the United States and internationally, wherever sediment quality is of concern. This course has previously been presented at SETAC meetings and proven to be highly popular.
Transcriptomic analysis is valuable for understanding underlying molecular mechanisms of responses to environmental stressors. However, computational analysis of mRNA sequencing data continues to be a barrier for discoveries. This hands-on workshop offers an opportunity for participants to overcome this barrier. With this course, we aim to provide participants with the framework and tools for a differential expression analysis of transcriptomic data. We will cover experimental design considerations, transcriptome assembly and differential expression data analysis with a sample dataset. This workshop will be relevant for participants who have or anticipate having Illumina RNA sequencing data from a non-model organism with no closely related reference genome. We will provide scripts, a small example set of data to work with and cloud computing resources in addition to a discussion on experimental design. We will provide participants with a workshop-specific website containing step-by-step instructions and access to the sample data. Attendees may already have some familiarity with using command line software tools, but novices are also welcome. Equipped with workshop materials, participants will be able to conduct an analysis with mRNA seq data from raw data to a list of differentially expressed candidate genes.
In response to concerns that certain environmental chemicals might interfere with the endocrine system of humans and wildlife, regulations have been promulgated in various regulatory bodies around the world targeting the evaluation of these types of effects. The purpose of this short course is to address key topics related to endocrine system evaluation and regulatory requirements around the world. The course will provide basic information on the vertebrate endocrine system, mechanisms of control and adverse effects. The focus will be the estrogen, androgen and thyroid systems, although new endocrine system targets will be discussed. The requirements of the USEPA’s Endocrine Disruptor Screening Program as well as those for REACH and other regulatory initiatives around the world, including the development of definitions and application of the criteria in the EU, will be reviewed. Specific screens and tests used in these programs will be reviewed, including the “pivot” of the USEPA program towards high-throughput and computational methods, such as EDSP21, and the development of adverse outcome pathways. Use of weight of evidence evaluations in interpreting the data will be covered. Finally, an interactive simulation will be staged where small groups of participants can engage in a transparent and quantitative weight of evidence evaluation of data.
The Frank R. Lautenberg Chemical Safety for the 21st Century Act was enacted in 2016, which amended the Toxic Substances Control Act (TSCA), the US’s primary chemicals management law. Implementation of the amended legislation is carried out by USEPA’s Office of Pollution Prevention and Toxics (OPPT) and includes assessments of both new and existing chemical substances. For the vast majority of new chemical submissions, little or no ecotoxicological information is provided to the USEPA, which presents challenges to the ecological risk assessment process. USEPA relies on screening-level tools to identify chemicals that may pose unreasonable risks before their entry into commerce. The purpose of the course is to describe ecological risk assessment tools and approaches currently used within OPPT’s New Chemicals Program with a focus on the assessment of risk to the aquatic compartment. Instructors will present OPPT’s ecological risk assessment process, provide hands on risk screening examples, and discuss the challenges and opportunities regarding ecological risk assessment under the amended TSCA.
Room: CC – 301
Instructors: Richard Erickson, USGS
Environmental scientists must increasingly deal with large, messy data to address their questions. For example, a scientist might need to clean and merge data from multiple sources prior to statistical analysis. Furthermore, these datasets might not exactly match (e.g., observation times might be on different scales) and might be large (e.g., GBs in size). Data science combines statistics, data management and computer programing to address these challenges. Topics covered during this course will include:
- Working with and cleaning large datasets in R using the data.table package
- Exploring and visualizing the data with the ggplot2 package
- Creating reproducible results using RMarkdown files
Prior experience with R will be helpful but is not required. Participants will be expected to bring their own laptop with R and RStudio installed so that they can follow along and work on exercises during the course.
This course covers statistical considerations of experimental design and analysis used to evaluate toxicity of chemicals in the environment. Both hypothesis testing to determine a NOEC and regression modeling to determine an ECx or benchmark dose are developed in detail. Discussion includes advantages and disadvantages of both approaches, their use in risk assessment, differences in experimental design and the implication of basing one type of analysis on a design intended for another. The instructors work closely with OECD and USEPA, are active members of the OECD Validation Management Group for Ecotoxicity and several other multi-displanary teams, and were instrumental in developing several OECD Test Guidelines, guidance documents and methodology. Continuous, quantal and severity score (histopath) data and both normal and Poisson models are explored. The instructors have decades of practical experience designing and analyzing ecotoxicity experiments, performing risk assessments and dealing with related regulatory issues, and they drew on that experience in developing this class. Underlying principles will be discussed, but the focus will be on practical issues. All topics include illustration by real laboratory ecotoxicity data examples illustrating the relevant points and techniques. Logical flow-charts and discussion of software for NOEC determination and for regression model fitting will be presented.
Sunday Morning Half-Day Courses
8:00 a.m.–12:00 p.m. | 4 November
It is recognized that limited empirical data is available for a majority of chemicals in commerce for the evaluation of chemical toxicity. Further, data describing the potential adverse effects of chemicals across species is even more sparse. Therefore, novel strategies are needed to make the greatest use of existing data to understand chemical effects across species. Aligning with the vision and strategy for toxicity testing in the 21st century reported by the National Research Council in 2007, in silico approaches to species extrapolation are being explored to address the question of how broadly toxicity data and pathway knowledge can be extrapolated across taxa. An underutilized data source for purposes of species extrapolation is protein sequence and structural information. With sequencing and annotation techniques becoming more cost-effective and streamlined, this growing source of data served as the motivation for developing the sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, which utilizes this data to predict chemical susceptibility across species based on concepts derived from evolutionary biology. The assumption underlying the SeqAPASS tool is that the greater the similarity between the protein target in a sensitive or model organism to other species, the more likely the protein in the other species functions similarly, either binding to a chemical or performing a similar role in a pathway. This knowledge of conservation across species from SeqAPASS provides a rapid mechanism for understanding how well model organisms serve as surrogates for other untested species and provides a line of evidence for extrapolation of toxicity or pathway data to other species based on chemical molecular target conservation.
The course will cover:
- Challenges in species extrapolation
- Introduction to the computational tools, with a focus on SeqAPASS – what is it, how has it been applied for species extrapolation
- Hands-on SeqAPASS training
- Highlight best practices and limitations
- Hands-on evaluation of defined case-studies
- Feedback and discussion
Sunday Afternoon Half-Day Courses
1:00 p.m.–5:00 p.m. | 4 November
Room: CC – 316
Lead Instructor: Vincent J. Kramer, Corteva Agriscience, the Agriculture Division of DowDupont; Houston Howerton, and Joe Wheelock, Syntech Research Inc.; and Bridget O’Neill, Corteva Agriscience, the Agriculture Division of DowDupont
Honeybees and other pollinators may be exposed to pesticide residues in nectar and pollen. A quantitative estimation of the risk posed by these residues requires the measurement of residue levels in those pollinator food items. Sampling nectar and pollen poses a number of challenges including:
- Obtaining adequate sample mass for the analytical method
- Achieving a representative, unbiased sample
- Avoiding contamination especially of nectar by pollen
- Optimizing plant growth conditions to achieve adequate blooming density
- All of the various uncontrollable challenges experienced in field studies
Participants in the course will understand the reasons for conducting nectar and pollen residue studies, appreciate the complexities of setting up studies in the field, experience the intricacies of the sampling methods themselves with hands-on demonstrations, and learn about the potential pitfalls awaiting the practitioner.
Lead Instructor: Nick Ralston, University of North Dakota
Biochemical mechanisms of mercury (Hg) toxicity involve irreversible inhibition of selenium (Se)-dependent enzymes (selenoenzymes). The 25 selenoenzymes and Se-transport proteins of the human genome are all vulnerable to inhibition by high mercury exposures, the most important targets being those that prevent and reverse oxidative damage in brain and endocrine tissues. With its low pKa (5.2), the Se of selenocysteine (Sec), the 21st genetically encoded amino acid, is the most powerful intracellular nucleophile, a factor which contributes to its vulnerability to binding by Hg and other soft electrophiles known to cause neurotoxic effects. Since methyl-Hg (MeHg) is not only capable of irreversibly inhibiting selenoenzymes, but also permanently sequestering tissue reserves of Se in the biologically insoluble HgSe form, the inverse relationship between environmental Hg and Se appears likely to reflect the effects of these physiological reactions. The purpose of this professional training course is to provide an overview of how Hg and Se interact at the molecular, cellular, tissue, organism and ecosystem levels. Topics to be discussed include the mechanisms of mercury toxicity, bioaccumulation dynamics, toxicity to aquatic life, health risk assessment and fish consumption guidelines, and the urgent need to identify populations at risk from MeHg exposure.
Thursday Morning Half-Day Courses
8:00 a.m.–12:00 p.m. | 8 November
Room: CC – 105
Lead Instructor: Qingyu Meng, Human and Ecological Risk Office, California Department of Toxic Substances Control, and Anne-Cooper Doherty, Safer Consumer Products Program, California Department of Toxic Substances Control
The overall goal of the course is to introduce the USEPA’s Positive Matrix Factorization (PMF) receptor model as a research and regulatory tool and demonstrate how this model can be appropriately used for decision-making purposes. Receptor models are a group of mathematical models used to map air/water pollutant concentrations to their sources. The USEPA’s PMF Model is a computer program that executes PMF analyses, visualizes model results and has been increasingly used to identify air or water pollution sources and to inform regulatory decisions. There is also potential for using the PMF model to inform the implementation of new regulatory approaches, such as California’s Safer Consumer Products regulations. Topics to be discussed in this training course include fundamental concepts of receptor modeling, data quality requirements for PMF input, PMF output interpretation, advantages and limitations of PMF modeling, as well as how to use PMF for decision making. The model will be illustrated with multiple environmental media examples and hands-on exercises. By the completion of this course, participants are expected to be able to conduct a basic source appointment analysis using the USEPA’s PMF Model, interpret PMF findings and understand how source apportionment can be used to support various regulatory programs.
Room: CC – 104
Lead Instructor: Karen R. Ryberg, U.S. Geological Survey
Good multivariate analysis starts with exploratory and graphical analyses to reveal potential relations in the data and to highlight potential outliers. This course will first present ways to extend univariate and bivariate methods for graphical analysis to multivariate data, as well as methods unique to multivariate data. The second part of the course will focus on multivariate outlier detection. The third and most extensive part will present multivariate statistical analysis methods, such as multiple regression, principal component analysis and cluster analysis, including examples and suggestions as to when one might want to use these techniques. Finally, the course will conclude with considerations for communicating statistical analysis results.