Coastline Kriging: A Bayesian Approach

Statistical interpolation of chemical concentrations at new locations is an important step in assessing a worker's exposure level. When measurements are available from coastlines, as is the case in coastal clean-up operations in oil spills, one may need a mechanism to carry out spatial interpolation at new locations along the coast. In this paper we present a simple model for analyzing spatial data that is observed over a coastline. We demonstrate four different models using two different representations of the coast using curves. The four models were demonstrated on simulated data and one of them was also demonstrated on a dataset from the GuLF STUDY. Our contribution here is to offer practicing hygienists and exposure assessors with a simple and easy method to implement Bayesian hierarchical models for analyzing and interpolating coastal chemical concentrations

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method’s performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications

SARS-CoV-2 variant Alpha has a spike-dependent replication advantage over the ancestral B.1 strain in human cells with low ACE2 expression

Epidemiological data demonstrate that Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) Alpha and Delta are more transmissible, infectious, and pathogenic than previous variants. Phenotypic properties of VOC remain understudied. Here, we provide an extensive functional study of VOC Alpha replication and cell entry phenotypes assisted by reverse genetics, mutational mapping of spike in lentiviral pseudotypes, viral and cellular gene expression studies, and infectivity stability assays in an enhanced range of cell and epithelial culture models. In almost all models, VOC Alpha spread less or equally efficiently as ancestral (B.1) SARS-CoV-2. B.1. and VOC Alpha shared similar susceptibility to serum neutralization. Despite increased relative abundance of specific sgRNAs in the context of VOC Alpha infection, immune gene expression in infected cells did not differ between VOC Alpha and B.1. However, inferior spreading and entry efficiencies of VOC Alpha corresponded to lower abundance of proteolytically cleaved spike products presumably linked to the T716I mutation. In addition, we identified a bronchial cell line, NCI-H1299, which supported 24-fold increased growth of VOC Alpha and is to our knowledge the only cell line to recapitulate the fitness advantage of VOC Alpha compared to B.1. Interestingly, also VOC Delta showed a strong (595-fold) fitness advantage over B.1 in these cells. Comparative analysis of chimeric viruses expressing VOC Alpha spike in the backbone of B.1, and vice versa, showed that the specific replication phenotype of VOC Alpha in NCI-H1299 cells is largely determined by its spike protein. Despite undetectable ACE2 protein expression in NCI-H1299 cells, CRISPR/Cas9 knock-out and antibody-mediated blocking experiments revealed that multicycle spread of B.1 and VOC Alpha required ACE2 expression. Interestingly, entry of VOC Alpha, as opposed to B.1 virions, was largely unaffected by treatment with exogenous trypsin or saliva prior to infection, suggesting enhanced resistance of VOC Alpha spike to premature proteolytic cleavage in the extracellular environment of the human respiratory tract. This property may result in delayed degradation of VOC Alpha particle infectivity in conditions typical of mucosal fluids of the upper respiratory tract that may be recapitulated in NCI-H1299 cells closer than in highly ACE2-expressing cell lines and models. Our study highlights the importance of cell model evaluation and comparison for in-depth characterization of virus variant-specific phenotypes and uncovers a fine-tuned interrelationship between VOC Alpha- and host cell-specific determinants that may underlie the increased and prolonged virus shedding detected in patients infected with VOC Alpha

Pair-Wise Regulation of Convergence and Extension Cell Movements by Four Phosphatases via RhoA

Various signaling pathways regulate shaping of the main body axis during early vertebrate development. Here, we focused on the role of protein-tyrosine phosphatase signaling in convergence and extension cell movements. We identified Ptpn20 as a structural paralogue of PTP-BL and both phosphatases were required for normal gastrulation cell movements. Interestingly, knockdowns of PTP-BL and Ptpn20 evoked similar developmental defects as knockdown of RPTPα and PTPε. Co-knockdown of RPTPα and PTP-BL, but not Ptpn20, had synergistic effects and conversely, PTPε and Ptpn20, but not PTP-BL, cooperated, demonstrating the specificity of our approach. RPTPα and PTPε knockdowns were rescued by constitutively active RhoA, whereas PTP-BL and Ptpn20 knockdowns were rescued by dominant negative RhoA. Consistently, RPTPα and PTP-BL had opposite effects on RhoA activation, both in a PTP-dependent manner. Downstream of the PTPs, we identified NGEF and Arhgap29, regulating RhoA activation and inactivation, respectively, in convergence and extension cell movements. We propose a model in which two phosphatases activate RhoA and two phosphatases inhibit RhoA, resulting in proper cell polarization and normal convergence and extension cell movements

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Parental Factors Associated With the Decision to Participate in a Neonatal Clinical Trial

Importance: It remains poorly understood how parents decide whether to enroll a child in a neonatal clinical trial. This is particularly true for parents from racial or ethnic minority populations. Understanding factors associated with enrollment decisions may improve recruitment processes for families, increase enrollment rates, and decrease disparities in research participation. Objective: To assess differences in parental factors between parents who enrolled their infant and those who declined enrollment for a neonatal randomized clinical trial. Design, setting, and participants: This survey study conducted from July 2017 to October 2019 in 12 US level 3 and 4 neonatal intensive care units included parents of infants who enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial or who were eligible but declined enrollment. Data were analyzed October 2019 through July 2020. Exposure: Parental choice of enrollment in neonatal clinical trial. Main outcomes and measures: Percentages and odds ratios (ORs) of parent participation as categorized by demographic characteristics, self-assessment of child's medical condition, study comprehension, and trust in medical researchers. Survey questions were based on the hypothesis that parents who enrolled their infant in HEAL differ from those who declined enrollment across 4 categories: (1) infant characteristics and parental demographic characteristics, (2) perception of infant's illness, (3) study comprehension, and (4) trust in clinicians and researchers. Results: Of a total 387 eligible parents, 269 (69.5%) completed the survey and were included in analysis. This included 183 of 242 (75.6%) of HEAL-enrolled and 86 of 145 (59.3%) of HEAL-declined parents. Parents who enrolled their infant had lower rates of Medicaid participation (74 [41.1%] vs 47 [55.3%]; P = .04) and higher rates of annual income greater than $55 000 (94 [52.8%] vs 30 [37.5%]; P = .03) compared with those who declined. Black parents had lower enrollment rates compared with White parents (OR, 0.35; 95% CI, 0.17-0.73). Parents who reported their infant's medical condition as more serious had higher enrollment rates (OR, 5.7; 95% CI, 2.0-16.3). Parents who enrolled their infant reported higher trust in medical researchers compared with parents who declined (mean [SD] difference, 5.3 [0.3-10.3]). There was no association between study comprehension and enrollment. Conclusions and relevance: In this study, the following factors were associated with neonatal clinical trial enrollment: demographic characteristics (ie, race/ethnicity, Medicaid status, and reported income), perception of illness, and trust in medical researchers. Future work to confirm these findings and explore the reasons behind them may lead to strategies for better engaging underrepresented groups in neonatal clinical research to reduce enrollment disparities