Laser-Induced Plasma Analysis for Surrogate Nuclear Debris

This work identifies analytical lines in laser-induced plasma for chemical analyses of major elements found in surrogate nuclear debris. These lines are evaluated for interferences and signal strength to insure they would be useful to measure relative concentrations. Compact, portable instruments are employed and can be included as part of a mobile nuclear forensics laboratory for field screening of nuclear debris and contamination. The average plasma temperature is measured using the well-established Boltzmann plot technique, and plasma\u27s average electron density is determined using empirical formulae based on Stark broadening of the H-alpha line. These measurements suggest existence of partial local thermal equilibrium

The human fungal pathogen Aspergillus fumigatus can produce the highest known number of meiotic crossovers

Sexual reproduction involving meiosis is essential in most eukaryotes. This produces offspring with novel genotypes, both by segregation of parental chromosomes as well as crossovers between homologous chromosomes. A sexual cycle for the opportunistic human pathogenic fungus Aspergillus fumigatus is known, but the genetic consequences of meiosis have remained unknown. Among other Aspergilli, it is known that A. flavus has a moderately high recombination rate with an average of 4.2 crossovers per chromosome pair, whereas A. nidulans has in contrast a higher rate with 9.3 crossovers per chromosome pair. Here, we show in a cross between A. fumigatus strains that they produce an average of 29.9 crossovers per chromosome pair and large variation in total map length across additional strain crosses. This rate of crossovers per chromosome is more than twice that seen for any known organism, which we discuss in relation to other genetic model systems. We validate this high rate of crossovers through mapping of resistance to the laboratory antifungal acriflavine by using standing variation in an undescribed ABC efflux transporter. We then demonstrate that this rate of crossovers is sufficient to produce one of the common multidrug resistant haplotypes found in the cyp51A gene (TR34/L98H) in crosses among parents harboring either of 2 nearby genetic variants, possibly explaining the early spread of such haplotypes. Our results suggest that genomic studies in this species should reassess common assumptions about linkage between genetic regions. The finding of an unparalleled crossover rate in A. fumigatus provides opportunities to understand why these rates are not generally higher in other eukaryotes

Development and psychometric evaluation of the CO-PARTNER tool for collaboration and parent participation in neonatal care

Background Active parent participation in neonatal care and collaboration between parents and professionals during infant hospitalization in the neonatal intensive care unit (NICU) is beneficial for infants and their parents. A tool is needed to support parents and to study the effects and implementation of parent-partnered models of neonatal care.Methods We developed and psychometrically evaluated a tool measuring active parent participation and collaboration in neonatal care within six domains: Daily Care, Medical Care, Acquiring Information, Parent Advocacy, Time Spent with Infant and Closeness and Comforting the Infant. Items were generated in focus group discussions and in-depth interviews with professionals and parents. The tool was completed at NICU-discharge by 306 parents (174 mothers and 132 fathers) of preterm infants. Subsequently, we studied structural validity with confirmatory factor analysis (CFA), construct validity, using the Average Variance Extracted and Heterotrait-Monotrait ratio of correlations, and hypothesis testing with correlations and univariate linear regression. For internal consistency we calculated composite reliability (CR). We performed multiple imputations by chained equations for missing data.Results A 31 item tool for parent participation and collaboration in neonatal care was developed. CFA revealed high factor loadings of items within each domain. Internal consistency was 0.558 to 0.938. Convergent validity and discriminant validity were strong. Higher scores correlated with less parent depressive symptoms (r = -0.141, 95%CI -0.240; -0.029, p = 0.0141), less impaired parent-infant bonding (r = -0.196, 95%CI -0.302; -0.056, pConclusion The CO-PARTNER tool explicitly measures parents' participation and collaboration with professionals in neonatal care incorporating their unique roles in care provision, leadership, and connection to their infant. The tool consists of 31 items within six domains with good face, content, construct and structural validity.</p

The COVID University Challenge: A hazard analysis of critical control points assessment of the return of students to higher education establishments

The COVID-19 pandemic has disrupted economies and societies throughout the world since early 2020. Education is especially affected, with schools and universities widely closed for long periods. People under 25 years have the lowest risk of severe disease but their activities can be key to persistent ongoing community transmission. A challenge arose for how to provide education, including university level, without the activities of students increasing wider community SARS-CoV-2 infections. We used a Hazard Analysis of Critical Control Points (HACCP) framework to assess the risks associated with university student activity and recommend how to mitigate these risks. This tool appealed because it relies on multiagency collaboration and interdisciplinary expertise and yet is low cost, allowing rapid generation of evidence-based recommendations. We identified key critical control points associated with university student’ activities, lifestyle, and interaction patterns both on-and-off campus. Unacceptable contact thresholds and the most up-to-date guidance were used to identify levels of risk for potential SARS-CoV-2 transmission, as well as recommendations based on existing research and emerging evidence for strategies that can reduce the risks of transmission. Employing the preventative measures we suggest can reduce the risks of SARS-CoV-2 transmission among and from university students. Reduction of infectious disease transmission in this demographic will reduce overall community transmission, lower demands on health services and reduce risk of harm to clinically vulnerable individuals while allowing vital education activity to continue. HACCP assessment proved a flexible tool for risk analysis in a specific setting in response to an emerging infectious disease threat. Systematic approaches to assessing hazards and risk critical control points (#HACCP) enable robust strategies for protecting students and staff in HE settings during #COVID19 pandemic

Applications of Portable LIBS for Actinide Analysis

A portable LIBS device was used for rapid elemental impurity analysis of plutonium alloys. This device demonstrates the potential for fast, accurate in-situ chemical analysis and could significantly reduce the fabrication time of plutonium alloys

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA),partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. A boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting

Analytical comparisons of handheld LIBS and XRF devices for rapid quantification of gallium in a plutonium surrogate matrix

This work compares a portable laser-induced breakdown spectroscopy (LIBS) analyzer to a portable X-ray fluorescence (XRF) device for quantification of gallium (Ga) in a plutonium surrogate matrix of cerium (Ce) for the first time. Calibration methods are developed with spectra of Ce–Ga samples from both devices. Metrics such as limit of detection (LoD) and mean average percent error (MAPE) are examined to evaluate calibration performance. While the portable LIBS device can yield a nearly instantaneous analytical measurement, its accuracy is hampered by self-absorption. By employing a self-absorption correction and increasing gating delay, LIBS calibrations with errors in the low single percents and LoDs of 0.1% Ga were constructed. The XRF device produces calibrations with superlative sensitivity, yielding LoDs for gallium in the low tens of parts-per-million (ppm), two orders of magnitude lower than the corrected LIBS models. However, a clear trade-off of measurement fidelity is established between the instantaneous analysis of the LIBS device and the minutes-long XRF measurement yielding superior detection limits

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys