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Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T2), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

Quorum sensing (QS) in vitro controls production of plant cell wall degrading enzymes (PCWDEs) and other virulence factors in the soft rotting enterobacterial plant pathogen Pectobacterium atrosepticum (Pba). Here, we demonstrate the genome-wide regulatory role of QS in vivo during the Pba-potato interaction, using a Pba-specific microarray. We show that 26% of the Pba genome exhibited differential transcription in a QS (expI-) mutant, compared to the wild-type, suggesting that QS may make a greater contribution to pathogenesis than previously thought. We identify novel components of the QS regulon, including the Type I and II secretion systems, which are involved in the secretion of PCWDEs; a novel Type VI secretion system (T6SS) and its predicted substrates Hcp and VgrG; more than 70 known or putative regulators, some of which have been demonstrated to control pathogenesis and, remarkably, the Type III secretion system and associated effector proteins, and coronafacoyl-amide conjugates, both of which play roles in the manipulation of plant defences. We show that the T6SS and a novel potential regulator, VirS, are required for full virulence in Pba, and propose a model placing QS at the apex of a regulatory hierarchy controlling the later stages of disease progression in Pba. Our findings indicate that QS is a master regulator of phytopathogenesis, controlling multiple other regulators that, in turn, co-ordinately regulate genes associated with manipulation of host defences in concert with the destructive arsenal of PCWDEs that manifest the soft rot disease phenotype.

The performance of Radio-Isotope Identification (RIID) algorithms using NaI-based γ spectroscopy is increasingly important. For example, sensors at locations that screen for illicit nuclear material rely on isotope identification using NaI detectors to distinguish innocent nuisance alarms, arising from naturally occurring radioactive material, from alarms arising from threat isotopes. Recent data collections for RIID testing consist of repeat measurements for each of several measurement scenarios to test RIID algorithms. It is anticipated that vendors can modify their algorithms on the basis of performance on chosen measurement scenarios and then test modified algorithms on data for other measurement scenarios. It is therefore timely to review the current status of RIID algorithms on NaI detectors. This review describes γ spectra from NaI detectors, measurement issues and challenges, current RIID algorithms, data preprocessing steps, the role and current quality of synthetic spectra, and opportunities for improvements.

Quorum sensing describes the ability of bacteria to sense their population density and respond by modulating gene expression. In the plant soft-rotting bacteria, such as Erwinia, an arsenal of plant cell wall-degrading enzymes is produced in a cell density-dependent manner, which causes maceration of plant tissue. However, quorum sensing is central not only to controlling the production of such destructive enzymes, but also to the control of a number of other virulence determinants and secondary metabolites. Erwinia synthesizes both N-acylhomoserine lactone (AHL) and autoinducer-2 types of quorum sensing signal, which both play a role in regulating gene expression in the phytopathogen. We review the models for AHL-based regulation of carbapenem antibiotic production in Erwinia. We also discuss the importance of quorum sensing in the production and secretion of virulence determinants by Erwinia, and its interplay with other regulatory systems.

Background Epigenome-wide association studies (EWAS) have identified DNA methylation loci involved in adiposity. However, EWAS on adiposity in sub-Saharan Africans are lacking despite the high burden of adiposity among African populations. We undertook an EWAS for anthropometric indices of adiposity among Ghanaians aiming to identify DNA methylation loci that are significantly associated. Methods The Illumina 450k DNA methylation array was used to profile DNA methylation in whole blood samples of 547 Ghanaians from the Research on Obesity and Diabetes among African Migrants (RODAM) study. Differentially methylated positions (DMPs) and differentially methylation regions (DMRs) were identified for BMI and obesity (BMI ≥ 30 kg/m 2 ), as well as for waist circumference (WC) and abdominal obesity (WC ≥ 102 cm in men, ≥88 cm in women). All analyses were adjusted for age, sex, blood cell distribution estimates, technical covariates, recruitment site and population stratification. We also did a replication study of previously reported EWAS loci for anthropometric indices in other populations. Results We identified 18 DMPs for BMI and 23 for WC. For obesity and abdominal obesity, we identified three and one DMP, respectively. Fourteen DMPs overlapped between BMI and WC. DMP cg00574958 annotated to gene CPT1A was the only DMP associated with all outcomes analysed, attributing to 6.1 and 5.6% of variance in obesity and abdominal obesity, respectively. DMP cg07839457 ( NLRC5 ) and cg20399616 ( BCAT1 ) were significantly associated with BMI, obesity and with WC and had not been reported by previous EWAS on adiposity. Conclusions This first EWAS for adiposity in Africans identified three epigenome-wide significant loci ( CPT1A , NLRC5 and BCAT1 ) for both general adiposity and abdominal adiposity. The findings are a first step in understanding the role of DNA methylation in adiposity among sub-Saharan Africans. Studies on other sub-Saharan African populations as well as translational studies are needed to determine the role of these DNA methylation variants in the high burden of adiposity among sub-Saharan Africans.

Nondestructive assay (NDA) of special nuclear material (SNM) is used in nonproliferation applications, including identification of SNM at border crossings, and quantifying SNM at safeguarded facilities. No assay method is complete without “error bars,” which provide one widely used way to express confidence in assay results. NDA specialists typically partition total uncertainty into “random” and “systematic” components so that, for example, an error bar can be developed for the SNM mass estimate in one item or for the total SNM mass estimate in multiple items. Uncertainty quantification (UQ) for NDA has always been important, but greater rigor is needed and achievable using modern statistical methods. To this end, we describe the extent to which the guideline for expressing uncertainty in measurements (GUM) can be used for NDA. Also, we describe possible extensions to the GUM by illustrating UQ challenges in NDA that it does not address, including calibration with errors in predictors, model error, and item-specific biases. A case study is presented using gamma spectra and applying the enrichment meter principle to estimate the 235U mass in an item. The case study illustrates how to update the ASTM international standard test method for application of the enrichment meter principle using gamma spectra.

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement − prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.

Quantitative conclusions by the International Atomic Energy Agency (IAEA) regarding States' nuclear material inventories and flows are provided in the form of material balance evaluations (MBEs). MBEs use facility estimates of the material unaccounted for together with verification data to monitor for possible nuclear material diversion. Verification data consist of paired measurements (usually operators' declarations and inspectors' verification results) that are analysed one-item-at-a-time to detect significant differences. Also, to check for patterns, an overall difference of the operator-inspector values using a “D (difference) statistic” is used. The estimated DP and false alarm probability (FAP) depend on the assumed measurement error model and its random and systematic error variances, which are estimated using data from previous inspections (which are used for metrology studies to characterize measurement error variance components). Therefore, the sample sizes in both the previous and current inspections will impact the estimated DP and FAP, as is illustrated by simulated numerical examples. The examples include application of a new expression for the variance of the D statistic assuming the measurement error model is multiplicative and new application of both random and systematic error variances in one-item-at-a-time testing.

Two-dimensional polyacrylamide gel electrophoresis of the secreted proteins of Erwinia carotovora subsp. atroseptica revealed alow-abundance protein that was identified by mass spectrometry as a homologue of a Xanthomonas campestris avirulence protein with unknown function. The predicted Svx protein has an N-terminal signal sequence and zinc binding-region signature, and the mature protein is post-translationally modified. A 2D difference gel electrophoresis (DIGE) showed that the protein is secreted by the type II (out) secretion apparatus, which is also responsible for the secretion of the major known virulence factors, PelC and CelV. Transcription of the svx gene is under Nacyl- homoserine lactone-mediated quorum-sensing control. The svx gene was inactivated by transposon insertion. The mutant showed a decrease in virulence in potato plant assays, demonstrating a role for Svx in the pathogenicity of E. carotovora subsp. atroseptica. These results show that Svx is a previously unidentified virulence determinant which is secreted by the out machinery and is regulated by quorum sensing, two systems employed by several other virulence factors. Thus, the type II secretory machine is a conduit for virulence factors other than the main pectinnases and cellulase in E. carotovora subsp. atroseptica.