Establishing a risk-assessment process for release of genetically modified wine yeast into the environment

2009 ◽  
Vol 55 (8) ◽  
pp. 990-1002 ◽  
Author(s):  
Heidi Schoeman ◽  
Gideon M. Wolfaardt ◽  
Alfred Botha ◽  
Pierre van Rensburg ◽  
Isak S. Pretorius
Keyword(s):  
Risk Assessment ◽  
Genetically Modified ◽  
Wine Yeast ◽  
The United States ◽  
Assessment Process ◽  
Saturated Sand ◽  
Wine Yeasts ◽  
Flow Cells ◽  
Risk Assessment Process ◽  
Biofilm Community

The use and release of genetically modified organisms (GMOs) is an issue of intense public concern and, in the case of food and beverages, products containing GMOs or products thereof carry the risk of consumer rejection. The recent commercialization of 2 GM wine yeasts in the United States and Canada has made research and development of risk assessments for GM microorganisms a priority. The purpose of this study was to take a first step in establishing a risk-assessment process for future use and potential release of GM wine yeasts into the environment. The behaviour and spread of a GM wine yeast was monitored in saturated sand columns, saturated sand flow cells, and conventional flow cells. A widely used commercial Saccharomyces cerevisiae wine yeast, VIN13, a VIN13 transgenic strain (LKA1, which carries the LKA1 α-amylase gene of Lipomyces kononenkoae ), a soil bacterium ( Dyadobacter fermentens ), and a nonwine soil-borne yeast ( Cryptococcus laurentii ) were compared in laboratory-scale microcosm systems designed to monitor microbial mobility behaviour, survival, and attachment to surfaces. It was found that LKA1 cells survived in saturated sand columns, but showed little mobility in the porous matrix, suggesting that the cells attached with high efficiency to sand. There was no significant difference between the mobility patterns of LKA1 and VIN13. All 3 yeasts (VIN13, LKA1, and C. laurentii) were shown to form stable biofilms; the 2 S. cerevisiae strains either had no difference in biofilm density or the LKA1 biofilm was less dense than that of VIN13. When co-inoculated with C. laurentii, LKA1 had no negative influence on the breakthrough of the Cryptococcus yeast in a sand column or on its ability to form biofilms. In addition, LKA1 did not successfully integrate into a stable mixed-biofilm community, nor did it disrupt the biofilm community. Overall, it was concluded that the LKA1 transgenic yeast had the same reproductive success as VIN13 in these 3 microcosms and had no selective advantage over the untransformed parental strain.

Cumulative Risk Assessment Conceptual Model

2010 ◽  
Author(s):  
Lorna Harron ◽  
Doug McCutcheon
Keyword(s):  
Risk Assessment ◽  
Natural Gas ◽  
Cumulative Risk ◽  
The United States ◽  
Assessment Process ◽  
Annual Reports ◽  
Baseline Risk ◽  
Risk Level ◽  
Risk Assessment Process ◽  
Operating Risk

The energy transportation network of the United States consists of over 2.5 million miles of pipelines operated by approximately 3,000 companies. Based on data generated from annual reports to PHMSA from pipeline operators, the network includes approximately: • 173,000 miles of hazardous liquid pipeline; • 324,000 miles of gas transmission and gathering pipelines; • 2,037,000 miles of natural gas distribution mains and service pipelines; • 113 LNG plants connected to natural gas systems. There are 580,000 kilometers of pipeline in Canada, transferring oil and natural gas to various locations within the country, North America, and to ports, where products can then be shipped globally. As organizations change and grow, there is a need to determine not only the risk of a specific project or new asset, but the effect of that project or new asset on the risk profile of the facility or pipeline. Different types of risk evaluations may be performed at a location, so obtaining a risk score that can integrate various risk assessment techniques can be a challenge. This paper proposes a new technique developed to meet this need, called the cumulative risk assessment process. The cumulative risk assessment provides a quantified value for the operating risk at a facility based on the following formulae: BaselineRiskValue=L×C(1)OperatingRiskValue=BaselineRiskValue×(1×10−MitCredits)(2)CumulativeRiskValue=Σ(OperatingRiskValue)scen(3) Baseline risk is defined as the risk value in the absence of mitigation or risk control. Operating risk is the current risk level with existing mitigation and risk controls in place, evaluated in the calculation as mitigation credits. For the baseline risk calculation (L) refers to Likelihood and (C) refers to Consequence. Both baseline and operating risk are evaluated per scenario, with all scenarios summed to obtain the cumulative risk value for a location, pipeline or pipeline segment. This paper describes the cumulative risk assessment process and provides examples of how this risk assessment technique can be applied to an existing facility with new assets constructed and to a segment of operating mainline pipe.

Risk Assessment and Chemical Regulatory Policy in the United States and Abroad

2017 ◽  
Author(s):  
Heather B. Patisaul ◽  
Scott M. Belcher
Keyword(s):  
United States ◽  
Risk Assessment ◽  
Screening Program ◽  
Regulatory Policy ◽  
The United States ◽  
Assessment Process ◽  
Critical Study ◽  
The European Union ◽  
Chemical Safety ◽  
Risk Assessment Process

This chapter presents an overview of the risk assessment process with an in-depth description of the related terminology. Critical study features that should be included to maximize utility of data for risk assessment for any experimental study are presented as an aid for academic scientists interested in designing studies with utility in the risk assessment process. The second half of this chapter summarizes the current state of regulatory policy regarding EDCs in the United States and abroad. Topics addressed include the Toxic Substances Control Act (TSCA) and a detailed accounting of the changes enacted by the recent 2016 revisions to TSCA. These policies are compared to the Registration Evaluation Authorization and Restriction of Chemicals (REACH) laws that govern chemical safety assessment in the European Union. The Endocrine Disruptor Screening Program (EDSP) and current efforts toward developing high-throughput methods for screening chemicals for endocrine-disrupting activity are also summarized.

scholarly journals Risk assessment of cartilaginous fish populations

2014 ◽  
Vol 72 (3) ◽  
pp. 1057-1068 ◽  
Author(s):  
Enric Cortés ◽  
Elizabeth N. Brooks ◽  
Kyle W. Shertzer
Keyword(s):  
United States ◽  
Risk Assessment ◽  
The United States ◽  
Cartilaginous Fish ◽  
Assessment Methods ◽  
Reference Points ◽  
Assessment Process ◽  
Model Complexity ◽  
Stock Assessments ◽  
Demographic Methods

Abstract We review three broad categories of risk assessment methodology used for cartilaginous fish: productivity-susceptibility analysis (PSA), demographic methods, and quantitative stock assessments. PSA is generally a semi-quantitative approach useful as an exploratory or triage tool that can be used to prioritize research, group species with similar vulnerability or risk, and provide qualitative management advice. Demographic methods are typically used in the conservation arena and provide quantitative population metrics that are used to quantify extinction risk and identify vulnerable life stages. Stock assessments provide quantitative estimates of population status and the associated risk of exceeding biological reference points, such as maximum sustainable yield. We then describe six types of uncertainty (process, observation, model, estimation, implementation, and institutional) that affect the risk assessment process, identify which of the three risk assessment methods can accommodate each type of uncertainty, and provide examples mostly for sharks drawn from our experience in the United States. We also review the spectrum of stock assessment methods used mainly for sharks in the United States, and present a case study where multiple methods were applied to the same species (dusky shark, Carcharinus obscurus) to illustrate differing degrees of model complexity and type of uncertainty considered. Finally, we address the common and problematic case of data-poor bycatch species. Our main recommendation for future work is to use Management Strategy Evaluation or similar simulation approaches to explore the effect of different sources of uncertainty, identify the most critical data to satisfy predetermined management objectives, and develop harvest control rules for cartilaginous fish. We also propose to assess the performance of data-poor and -rich methods through stepwise model construction.

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