Visual Sample Plan Version 7.0 User's Guide

Matzke, Brett D.; Newburn, Lisa Ln; Hathaway, John E.; Bramer, Lisa; Wilson, John E.; Dowson, Scott; Sego, Landon H.; Pulsipher, Brent A.

doi:10.2172/1124046

Cited by 11 publications

(10 citation statements)

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“…We employed a ‘multiple increment sampling strategy’ based on randomly selected samples (Matzke et al ., ). Multiple increment sampling incorporates the intra‐ and inter‐assay variance (0.77 and 0.58 respectively) to calculate the number of samples that can be pooled together for final analysis and estimates how many pooled samples are needed to estimate the bacterial population size with 95% confidence.…”

Section: Methodsmentioning

confidence: 97%

Soil‐borne reservoirs of antibiotic‐resistant bacteria are established following therapeutic treatment of dairy calves

Liu

Zhao

Orfe

et al. 2015

Environmental Microbiology

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SummaryWe determined if antibiotics residues that are excreted from treated animals can contribute to persistence of resistant bacteria in agricultural environments. Administration of ceftiofur, a third-generation cephalosporin, resulted in a ∼ 3 log increase in ceftiofur-resistant Escherichia coli found in the faeces and pen soils by day 10 (P = 0.005). This resistant population quickly subsided in faeces, but was sustained in the pen soil (∼ 4.5 log bacteria g −1 ) throughout the trial (1 month). Florfenicol treatment resulted in a similar pattern although the loss of florfenicol-resistant E. coli was slower for faeces and remained stable at ∼ 6 log bacteria g −1 in the soil. Calves were treated in pens where eGFP-labelled E. coli were present in the bedding (∼ 2 log g −1 ) resulting in amplification of the eGFP E. coli population ∼ 2.1 log more than eGFP E. coli populations in pens with untreated calves (day 4; P < 0.005). Excreted residues accounted for > 10-fold greater contribution to the bedding reservoir compared with shedding of resistant bacteria in faeces. Treatment with therapeutic doses of ceftiofur or florfenicol resulted in 2-3 log g −1 more bacteria than the estimated ID50 (2.83 CFU g −1 ), consistent with a soil-borne reservoir emerging after antibiotic treatment that can contribute to the long-term persistence of antibiotic resistance in animal agriculture.

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Section: Methodsmentioning

confidence: 97%

Soil‐borne reservoirs of antibiotic‐resistant bacteria are established following therapeutic treatment of dairy calves

Liu

Zhao

Orfe

et al. 2015

Environmental Microbiology

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“…Google Earth was used to download a map of the Tract A-5-2 area, which was then incorporated into Visual Sampling Plan (VSP) software (Matzke et al 2010). The approximate boundary of the tract was then delineated as a sampling area (Figure 3).…”

Section: Statistically-based Evaluation For Number Of Samples Requirementioning

confidence: 99%

Sampling and Analysis Plan (SAP) for Assessment of LANL-Derived Residual Radionuclides in soils within Tract A-5-2 for Land Transfer Decisions

Whicker

Eisele

2014

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The A-5-2 Tract is located just west of the eastern boundary of DP Mesa, Technical Area-21 (TA-21) and south of Highway 502 (Figure 1). This A-5-2 Tract is a revision of the original tract and now stops approximately at the northern boundary of the DP Canyon floodplain an extends from the A-10 tract downstream. The tract consists of the DP Canyon portion of the "Airport Tract" (DOE 1999). This tract contains undeveloped hillslope and canyon bottom accessed from DP Road. Vegetation includes ponderosa and piñon-juniper woodlands with open shrub, grasslands, and wildflower areas; A-5-2 is considered potentially sensitive wildlife habitat. DP Canyon has an ephemeral stream and receives runoff from surrounding mesas and areas.

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“…Several of these approaches are implemented in the Visual Sample Plan (VSP) software (VSP Development Team 2012; Matzke et al 2010). However, there are too many possible sampling approaches to address in this work.…”

Section: Overview Of Statistical Sampling Approaches For Characterizamentioning

confidence: 99%

“…However, those implementations do not account for the uncertainties in the SAP, with one exception discussed subsequently. The methods and formulas for the sampling approaches implemented in VSP are included in the software documentation (Matzke et al 2010 and help files in VSP), related technical reports, and the refereed literature (references cited in subsequent sections). In Appendix A we provide instruction how to locate the various sampling approaches in VSP that are discussed in this report.…”

Section: Overview Of Statistical Sampling Approaches For Characterizamentioning

confidence: 99%

“…These interactions are gratefully acknowledged. The authors would also like to thank Randy Long (DHS) for his leadership of the VSPWG and general guidance on this work.The authors acknowledge and thank PNNL staff members 1) Brent Pulsipher for discussions about the work in this report, about the capabilities in the Visual Sample Plan software (VSP Development Team 2012, Matzke et al 2010, and for reviewing and providing comments on a draft of this report, and 2) Maura Zimmerschied for editing, formatting, and preparing the report for publication. true, unknown mean of the random variable R A,S which is the recovery efficiency for a specific sample analysis method (including culturing and counting) and a specific set of conditions (all denoted by S) ) R ( Mean S , A estimate of the true, unknown mean of the random variable R A,S which is the recovery efficiency for a specific sample analysis method (including culturing and counting) and a specific set of conditions (all denoted by S) ) R ( Mean S , HST true, unknown mean of the random variable R HST,S , which is the recovery efficiency for a specific sample handling, storage, and transport method and a specific set S of conditions (all denoted by S) ) R ( Mean S , HST estimate of the true, unknown mean of the random variable R HST,S , which is the recovery efficiency for a specific sample handling, storage, and transport method and a specific set S of conditions (all denoted by S) ) R ( Mean S , P true, unknown mean of the random variable R P,S , which is the recovery efficiency for a specific sample processing/extraction method and a specific set S of conditions (all denoted by S) ) R ( Mean S , P estimate of the true, unknown mean of the random variable R P,S , which is the recovery efficiency for a specific sample processing/extraction method and a specific set S of conditions (all denoted by S) ) R ( Mean S , SC true, unknown mean of the random variable R SC,S , which is the recovery efficiency for a specific sample collection method and specific S set of conditions (all denoted by S) ) R ( Mean S , SC estimate of the true, unknown mean of the random variable R SC,S , which is the recovery efficiency for a specific sample collection method and specific S set of conditions (all denoted by S) ) R ( Mean S , SAP true, unknown mean of the random variable R SAP,S , which is the recovery efficiency for a specific set S of SAP methods and sampling conditions (e.g., surface material, surface concentration, humidity)…”

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confidence: 99%

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Calculating Confidence, Uncertainty, and Numbers of Samples When Using Statistical Sampling Approaches to Characterize and Clear Contaminated Areas

Piepel¹,

Matzke²,

Sego³

et al. 2013

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Executive SummaryThis report discusses selected methods, formulas, and inputs needed to make characterization and clearance decisions for Bacillus anthracis-contaminated and uncontaminated (or decontaminated) areas using a statistical sampling approach. Specifically, the report includes the methods and formulas for calculating the• number of samples required to achieve a specified confidence in characterization and clearance decisions• confidence in making characterization and clearance decisions for a specified number of samples for two common, statistically based, environmental sampling approaches. In particular, the report addresses an issue raised by the Government Accountability Office by providing methods and formulas to calculate the confidence that a decision area is uncontaminated (or successfully decontaminated) if all samples collected according to a statistical sampling approach have negative results. Key to addressing this topic is the probability that an individual sample result is a false negative, which is commonly referred to as the false negative rate (FNR).The two statistical sampling approaches currently discussed in this report are 1) hotspot sampling to detect small isolated contaminated locations during the characterization phase, and 2) combined judgment and random (CJR) sampling during the characterization or clearance phase. Typically, if contamination is widely distributed in a decision area, it will be detectable via judgment sampling during the characterization phrase. Hotspot sampling is appropriate for characterization situations where contamination is not widely distributed and may not be detected by judgment sampling. CJR sampling is appropriate during the characterization or clearance phase when it is desired to augment judgment samples with statistical (random) samples. Only these two statistical sampling approaches are addressed in this report because they are the only two approaches mentioned in a sampling reference document being developed by the Validated Sampling Plan Working Group (VSPWG). It was by agreement with the sponsor of this work (the Department of Homeland Security) that this report be limited to those two statistical sampling approaches for that reason. Other approaches which could reduce the number of samples required are being studied and may be included in future evaluations.The hotspot and CJR statistical sampling approaches are discussed in the report for four situations:1. qualitative data (detect and non-detect) when the FNR = 0 or when using statistical sampling methods that account for FNR > 0 2. qualitative data when the FNR > 0 but statistical sampling methods are used that assume the FNR = 0 3. quantitative data (e.g., contaminant concentrations expressed as CFU/cm 2 ) when the FNR = 0 or when using statistical sampling methods that account for FNR > 0 4. quantitative data when the FNR > 0 but statistical sampling methods are used that assume the FNR = 0.For Situation 2, the hotspot sampling approach provides for stating with Z% confidence that a ho...

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Visual Sample Plan Version 7.0 User's Guide

Cited by 11 publications

References 0 publications

Soil‐borne reservoirs of antibiotic‐resistant bacteria are established following therapeutic treatment of dairy calves

Soil‐borne reservoirs of antibiotic‐resistant bacteria are established following therapeutic treatment of dairy calves

Sampling and Analysis Plan (SAP) for Assessment of LANL-Derived Residual Radionuclides in soils within Tract A-5-2 for Land Transfer Decisions

Calculating Confidence, Uncertainty, and Numbers of Samples When Using Statistical Sampling Approaches to Characterize and Clear Contaminated Areas

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