Uncertainty Quantification of Heavy Gas Release Over a Barrier

In this study a procedure for input uncertainty quantification (UQ) in computational fluid dynamics (CFD) simulations is proposed. The suggested procedure has been applied to a test case. The test case concerns the modeling of a heavy gas release into an atmospheric boundary layer over a barrier. The following uncertain parameters are investigated in their respective intervals: release velocity (18 m/s, 22 m/s), release temperature (270 K, 310 K) and the atmospheric boundary layer velocity (3 m/s, 7 m/s). The Stochastic Collocation (SC) method is used to perform the probabilistic propagation of the uncertain parameters. The uncertainty analysis was performed with two sets of sampling grids (full and sparse grids) for the uncertain parameters. The results show which of the selected uncertain parameters have the largest impact on the dispersed gas plume and the local concentrations in the gas cloud. Additionally, using sparse grids shows potential to reduce the computational effort of the uncertainty analysis

Stochastic analysis of the impact of freestream conditions on the aerodynamics of a rectangular 5:1 cylinder

Uncertainty plays a significant role in the Benchmark on the Aerodynamics of a Rectangular Cylinder (BARC) with a chord-to-depth ratio of 5. In particular, besides modeling and numerical errors, in numerical simulations it is difficult to exactly reproduce the experimental conditions due to uncertainties in the set-up parameters, which sometimes cannot be exactly controlled or characterized. In this study, the impact of the uncertainties in the inflow conditions of the BARC configuration is investigated by using probabilistic methods and two-dimensional URANS simulations. The following uncertain set-up parameters are investigated: the angle of incidence, the freestream longitudinal turbulence intensity and the freestream turbulence length scale. The stochastic collocation method is employed to perform the probabilistic propagation of the uncertainty in the three set-up parameters. This results in 25 URANS simulations based on the Smolyak sparse grid extension of the level-2 Clenshaw-Curtis quadrature points. The discretization error is estimated by repeating the same analysis on different grid sizes. Similarly, the effect of turbulence modeling is appraised by carrying out the uncertainty quantification for the Reynolds stress and the SST k-. ω models. Finally, the results obtained for different assumed probability density functions of the set-up parameters are compared. The propagation of the considered uncertainties does not explain alone the dispersion of the BARC experimental data. For certain quantities of interest, the effect of turbulence modeling is more important than the impact of the uncertainties in inflow conditions. The sensitivity to the considered uncertainties also varies with the turbulence model, with a larger variability of the results obtained with the Reynolds stress model. The inflow turbulence length scale is in all cases the least important parameter

Feature Selection of Post-Graduation Income of College Students in the United States

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure

Molecular characterization of MRSA collected during national surveillance between 2008 and 2019 in the Netherlands

BACKGROUND: Although the Netherlands is a country with a low endemic level, methicillin-resistant Staphylococcus aureus (MRSA) poses a significant health care problem. Therefore, high coverage national MRSA surveillance has been in place since 1989. To monitor possible changes in the type-distribution and emergence of resistance and virulence, MRSA isolates are molecularly characterized.METHODS: All 43,321 isolates from 36,520 persons, collected 2008-2019, were typed by multiple-locus variable number tandem repeats analysis (MLVA) with simultaneous PCR detection of the mecA, mecC and lukF-PV genes, indicative for PVL. Next-generation sequencing data of 4991 isolates from 4798 persons were used for whole genome multi-locus sequence typing (wgMLST) and identification of resistance and virulence genes.RESULTS: We show temporal change in the molecular characteristics of the MRSA population with the proportion of PVL-positive isolates increasing from 15% in 2008-2010 to 25% in 2017-2019. In livestock-associated MRSA obtained from humans, PVL-positivity increases to 6% in 2017-2019 with isolates predominantly from regions with few pig farms. wgMLST reveals the presence of 35 genogroups with distinct resistance, virulence gene profiles and specimen origin. Typing shows prolonged persistent MRSA carriage with a mean carriage period of 407 days. There is a clear spatial and a weak temporal relationship between isolates that clustered in wgMLST, indicative for regional spread of MRSA strains.CONCLUSIONS: Using molecular characterization, this exceptionally large study shows genomic changes in the MRSA population at the national level. It reveals waxing and waning of types and genogroups and an increasing proportion of PVL-positive MRSA.</p

Distinct Genomic Profiles Are Associated with Treatment Response and Survival in Ovarian Cancer

SIMPLE SUMMARY: In most patients with ovarian cancer, their disease eventually becomes resistant to chemotherapy. The timing and type of treatment given are therefore highly important. Currently, treatment choice is mainly based on the subtype of cancer (from a histological point of view), prior response to chemotherapy, and the time it takes for the disease to recur. In this study, we combined complete genome data of the tumor with clinical data to better understand treatment responses. In total, 132 tumor samples were included, all from patients with disease that had spread beyond the primary location. By clustering the samples based on genetic characteristics, we have identified subgroups with distinct response rates and survival outcomes. We suggest that in the future, this data can be used to make more informed treatment choices for individuals with ovarian cancer. ABSTRACT: The majority of patients with ovarian cancer ultimately develop recurrent chemotherapy-resistant disease. Treatment stratification is mainly based on histological subtype and stage, prior response to platinum-based chemotherapy, and time to recurrent disease. Here, we integrated clinical treatment, treatment response, and survival data with whole-genome sequencing profiles of 132 solid tumor biopsies of metastatic epithelial ovarian cancer to explore genome-informed stratification opportunities. Samples from primary and recurrent disease harbored comparable numbers of single nucleotide variants and structural variants. Mutational signatures represented platinum exposure, homologous recombination deficiency, and aging. Unsupervised hierarchical clustering based on genomic input data identified specific ovarian cancer subgroups, characterized by homologous recombination deficiency, genome stability, and duplications. The clusters exhibited distinct response rates and survival probabilities which could thus potentially be used for genome-informed therapy stratification for more personalized ovarian cancer treatment

A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility

<p>Abstract</p> <p>Background</p> <p>Microarray diagnostics of tumour samples is based on measurement of prognostic and/or predictive gene expression profiles. Typically, diagnostic profiles have been developed using bulk tumour samples with a sufficient amount of tumour cells (usually >50%). Consequentially, a diagnostic results depends on the minimal percentage of tumour cells within a sample. Currently, tumour cell percentage is assessed by conventional histopathological review. However, even for experienced pathologists, such scoring remains subjective and time consuming and can lead to ambiguous results.</p> <p>Methods</p> <p>In this study we investigated whether we could use transcriptional activity of a specific set of genes instead of histopathological review to identify samples with sufficient tumour cell content. Genome-wide gene expression measurements were used to develop a transcriptional gene profile that could accurately assess a sample's tumour cell percentage.</p> <p>Results</p> <p>Supervised analysis across 165 breast tumour samples resulted in the identification of a set of 13 genes which expression correlated with presence of tumour cells. The developed gene profile showed a high performance (AUC 0.92) for identification of samples that are suitable for microarray diagnostics. Validation on 238 additional breast tumour samples indicated a robust performance for correct classification with an overall accuracy of 91 percent and a kappa score of 0.63 (95%CI 0.47–0.73).</p> <p>Conclusion</p> <p>The developed 13-gene profile provides an objective tool for assessment whether a breast cancer sample contains sufficient tumour cells for microarray diagnostics. It will improve the efficiency and throughput for diagnostic gene expression profiling as it no longer requires histopathological analysis for initial tumour percentage scoring. Such profile will also be very use useful for assessment of tumour cell percentage in biopsies where conventional histopathology is difficult, such as fine needle aspirates.</p

Remote mental health care interventions during the COVID-19 pandemic: an umbrella review

Mitigating the COVID-19 related disruptions in mental health care services is crucial in a time of increased mental health disorders. Numerous reviews have been conducted on the process of implementing technology- based mental health care during the pandemic. The research question of this umbrella review was to examine what the impact of COVID-19 was on access and delivery of mental health services and how mental health services have changed during the pandemic. A systematic search for systematic reviews and meta-analyses was conducted up to August 12, 2022, and 38 systematic reviews were identified. Main disruptions during COVID-19 were reduced access to outpatient mental health care and reduced admissions and earlier discharge from inpatient care. In response, synchronous telemental health tools such as videoconferencing were used to provide remote care similar to pre-COVID care, and to a lesser extent asynchronous virtual mental health tools such as apps. Implementation of synchronous tools were facilitated by time-efficiency and flexibility during the pandemic but there was a lack of accessibility for specific vulnerable populations. Main barriers among prac- titioners and patients to use digital mental health tools were poor technological literacy, particularly when preexisting inequalities existed, and beliefs about reduced therapeutic alliance particularly in case of severe mental disorders. Absence of organizational support for technological implementation of digital mental health interventions due to inadequate IT infrastructure, lack of funding, as well as lack of privacy and safety, chal- lenged implementation during COVID-19. Reviews were of low to moderate quality, covered heterogeneously designed primary studies and lacked findings of implementation in low- and middle-income countries. These gaps in the evidence were particularly prevalent in studies conducted early in the pandemic. This umbrella review shows that during the COVID-19 pandemic, practitioners and mental health care institutions mainly used synchronous telemental health tools, and to a lesser degree asynchronous tools to enable continued access to mental health care for patients. Numerous barriers to these tools were identified, and call for further improve- ments. In addition, more high quality research into comparative effectiveness and working mechanisms may improve scalability of mental health care in general and in future infectious disease outbreaks