Validation of Statistical Sampling Algorithms in Visual Sample Plan (VSP): Summary Report

The U.S. Department of Homeland Security, Office of Technology Development (OTD) contracted with a set of U.S. Department of Energy national laboratories, including the Pacific Northwest National Laboratory (PNNL), to write a Remediation Guidance for Major Airports After a Chemical Attack. The report identifies key activities and issues that should be considered by a typical major airport following an incident involving release of a toxic chemical agent. Four experimental tasks were identified that would require further research in order to supplement the Remediation Guidance. One of the tasks, Task 4, OTD Chemical Remediation Statistical Sampling Design Validation, dealt with statistical sampling algorithm validation. This report documents the results of the sampling design validation conducted for Task 4. In 2005, the Government Accountability Office (GAO) performed a review of the past U.S. responses to Anthrax terrorist cases. Part of the motivation for this PNNL report was a major GAO finding that there was a lack of validated sampling strategies in the U.S. response to Anthrax cases. The report (GAO 2005) recommended that probability-based methods be used for sampling design in order to address confidence in the results, particularly when all sample results showed no remaining contamination. The GAO also expressed a desire that the methods be validated, which is the main purpose of this PNNL report. The objective of this study was to validate probability-based statistical sampling designs and the algorithms pertinent to within-building sampling that allow the user to prescribe or evaluate confidence levels of conclusions based on data collected as guided by the statistical sampling designs. Specifically, the designs found in the Visual Sample Plan (VSP) software were evaluated. VSP was used to calculate the number of samples and the sample location for a variety of sampling plans applied to an actual release site. Most of the sampling designs validated are probability based, meaning samples are located randomly (or on a randomly placed grid) so no bias enters into the placement of samples, and the number of samples is calculated such that IF the amount and spatial extent of contamination exceeds levels of concern, at least one of the samples would be taken from a contaminated area, at least X% of the time. Hence, "validation" of the statistical sampling algorithms is defined herein to mean ensuring that the "X%" (confidence) is actually met

ENMTools 1.0: an R package for comparative ecological biogeography

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages

An environmental sampling model for combining judgment and randomly placed samples

In the event of the release of a lethal agent (such as anthrax) inside a building, law enforcement and public health responders take samples to identify and characterize the contamination. Sample locations may be rapidly chosen based on available incident details and professional judgment. To achieve greater confidence of whether or not a room or zone was contaminated, or to certify that detectable contamination is not present after decontamination, we consider a Bayesian model for combining the information gained from both judgment and randomly placed samples. We investigate the sensitivity of the model to the parameter inputs and make recommendations for its practical use

The Genetic Signatures of Noncoding RNAs

The majority of the genome in animals and plants is transcribed in a developmentally regulated manner to produce large numbers of non–protein-coding RNAs (ncRNAs), whose incidence increases with developmental complexity. There is growing evidence that these transcripts are functional, particularly in the regulation of epigenetic processes, leading to the suggestion that they compose a hitherto hidden layer of genomic programming in humans and other complex organisms. However, to date, very few have been identified in genetic screens. Here I show that this is explicable by an historic emphasis, both phenotypically and technically, on mutations in protein-coding sequences, and by presumptions about the nature of regulatory mutations. Most variations in regulatory sequences produce relatively subtle phenotypic changes, in contrast to mutations in protein-coding sequences that frequently cause catastrophic component failure. Until recently, most mapping projects have focused on protein-coding sequences, and the limited number of identified regulatory mutations have been interpreted as affecting conventional cis-acting promoter and enhancer elements, although these regions are often themselves transcribed. Moreover, ncRNA-directed regulatory circuits underpin most, if not all, complex genetic phenomena in eukaryotes, including RNA interference-related processes such as transcriptional and post-transcriptional gene silencing, position effect variegation, hybrid dysgenesis, chromosome dosage compensation, parental imprinting and allelic exclusion, paramutation, and possibly transvection and transinduction. The next frontier is the identification and functional characterization of the myriad sequence variations that influence quantitative traits, disease susceptibility, and other complex characteristics, which are being shown by genome-wide association studies to lie mostly in noncoding, presumably regulatory, regions. There is every possibility that many of these variations will alter the interactions between regulatory RNAs and their targets, a prospect that should be borne in mind in future functional analyses

Validation of Statistical Sampling Algorithms in Visual Sample Plan (VSP): Summary Report

The U.S. Department of Homeland Security, Office of Technology Development (OTD) contracted with a set of U.S. Department of Energy national laboratories, including the Pacific Northwest National Laboratory (PNNL), to write a Remediation Guidance for Major Airports After a Chemical Attack. The report identifies key activities and issues that should be considered by a typical major airport following an incident involving release of a toxic chemical agent. Four experimental tasks were identified that would require further research in order to supplement the Remediation Guidance. One of the tasks, Task 4, OTD Chemical Remediation Statistical Sampling Design Validation, dealt with statistical sampling algorithm validation. This report documents the results of the sampling design validation conducted for Task 4. In 2005, the Government Accountability Office (GAO) performed a review of the past U.S. responses to Anthrax terrorist cases. Part of the motivation for this PNNL report was a major GAO finding that there was a lack of validated sampling strategies in the U.S. response to Anthrax cases. The report (GAO 2005) recommended that probability-based methods be used for sampling design in order to address confidence in the results, particularly when all sample results showed no remaining contamination. The GAO also expressed a desire that the methods be validated, which is the main purpose of this PNNL report. The objective of this study was to validate probability-based statistical sampling designs and the algorithms pertinent to within-building sampling that allow the user to prescribe or evaluate confidence levels of conclusions based on data collected as guided by the statistical sampling designs. Specifically, the designs found in the Visual Sample Plan (VSP) software were evaluated. VSP was used to calculate the number of samples and the sample location for a variety of sampling plans applied to an actual release site. Most of the sampling designs validated are probability based, meaning samples are located randomly (or on a randomly placed grid) so no bias enters into the placement of samples, and the number of samples is calculated such that IF the amount and spatial extent of contamination exceeds levels of concern, at least one of the samples would be taken from a contaminated area, at least X% of the time. Hence, "validation" of the statistical sampling algorithms is defined herein to mean ensuring that the "X%" (confidence) is actually met

HIGH-FREQUENCY, HEAT TREATMENT-INDUCED INACTIVATION OF THE PHOSPHINOTHRICIN RESISTANCE GENE IN TRANSGENIC SINGLE CELL-SUSPENSION CULTURES OF MEDICAGO-SATIVA

WALTER C, BROER I, HILLEMANN D, Pühler A. HIGH-FREQUENCY, HEAT TREATMENT-INDUCED INACTIVATION OF THE PHOSPHINOTHRICIN RESISTANCE GENE IN TRANSGENIC SINGLE CELL-SUSPENSION CULTURES OF MEDICAGO-SATIVA. MOLECULAR & GENERAL GENETICS. 1992;235(2-3):189-196.One descendant of the Medicago sativa Ra-3 transformant T304 was analysed with respect to the somatic stability of the synthetic phosphinothricin-N-acetyltransferase (pat) gene which was used as a selective marker and was under the control of the 5'/3' expression signals of the cauliflower mosaic virus (CaMV) gene VI. In order to quantify gene instability, we developed a system for culturing and regenerating individual cells. Single cell suspension cultures derived from T304 and the ancestral non-transgenic M. sativa cultivar Ra-3, were established. The cells were regenerated into monoclonal calli. In transgenic calli, the phosphinothricin (Pt)-resistance phenotype was retained after more than 2 months of non-selective growth. In contrast, up to 12% of the suspension culture cells grown under nonselective conditions and at constant temperature (25-degrees-C) lost the herbicide-resistance phenotype within 150 days. Surprisingly, a heat treatment (37-degrees-C), lasting for 10 days, during the culture period resulted in an almost complete (95%) loss of the Pt resistance of the suspension culture cells. However, the frequency of cell division was identical in cultures grown under normal and heat treatment conditions. A biochemical test revealed that no phosphinothricin-N-acetyltransferase activity was present in heat treated, Pt-sensitive cells. The resistance level of the Pt-sensitive transgenic cells was equivalent to that of the wild-type cells. A PCR analysis confirmed the presence of the pat gene in heat treated, Pt-sensitive cells. From these results it is concluded that the Pt resistance gene was heat-inactivated at a high frequency in the M. sativa suspension cultures

ENMTools 1.0: an R package for comparative ecological biogeography

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.DLW was funded by ARC DECRA award # DE140101675 and supported by subsidy funding to OIST. NJM was funded by Marsden grants 16‐UOA‐277 and 18‐UOA‐034, and U. Auckland FRDF #3722433. JCP is funded by a doctoral fellowship supported by the Agencia Canaria de la Investigación, Innovación y Sociedad de la Información and the European Social Fund (Operational Programme of the Canary Islands 2014‐2020). MC acknowledges support from Australian Research Council Discovery Project DP110103168.Peer reviewe

Banana contains a diverse array of endogenous badnaviruses

Banana streak disease is caused by several distinct badnavirus species, one of which is Banana streak Obino I'Ewai virus. Banana streak Obino I'Ewai virus has severely hindered international banana (Musa spp.) breeding programmes, as new hybrids are frequently infected with this virus, curtailing any further exploitation. This infection is thought to arise from, viral DNA integrated in the nuclear genome of Musa balbisiana (B genome), one of the wild species contributing to many of the banana cultivars currently grown. In order to determine whelther the DNA of other badnavirus species is integrated in the Musa genome, PCR-amplified DNA fragments from Musa acuminata, M. balbisiana and Musa schizocalpa, as well as cultivars 'Obino I'Ewai' and 'Klue Tiparot', were cloned. In total, 103 clones were sequenced and all had similarity to open reading frame III in the badnavirus genome, although there was remarkable variation, with 36 distinct sequences being recognized with less than 85% nucleotide identify to each other. There was no commonality in the sequences amplified from M. acuminaia and M. balbisiana, suggesting that integration occurred following the separation of these species Analysis of rates of non-synonymous and synonymous substitution suggested that the integrated sequences evolved under a high degree of selective constraint as might be expected for a living badnavirus, and that each distinct sequence resulted from an independent integration event

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification