Zinc Single Atom Confinement Effects on Catalysis in 1T-Phase Molybdenum Disulfide.

Active sites are atomic sites within catalysts that drive reactions and are essential for catalysis. Spatially confining guest metals within active site microenvironments has been predicted to improve catalytic activity by altering the electronic states of active sites. Using the hydrogen evolution reaction (HER) as a model reaction, we show that intercalating zinc single atoms between layers of 1T-MoS (Zn SAs/1T-MoS) enhances HER performance by decreasing the overpotential, charge transfer resistance, and kinetic barrier.

Variance Components Models for Analysis of Big Family Data of Health Outcomes in the Lifelines Cohort Study.

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals.

Confidence intervals for intraclass correlation coefficients in a nonlinear dose-response meta-analysis.

This work is motivated by a meta-analysis case study on antipsychotic medications. The Michaelis-Menten curve is employed to model the nonlinear relationship between the dose and D2 receptor occupancy across multiple studies. An intraclass correlation coefficient (ICC) is used to quantify the heterogeneity across studies.

Confidence intervals for intraclass correlation coefficients in variance components models.

Confidence intervals for intraclass correlation coefficients in agreement studies with continuous outcomes are model-specific and no generic approach exists. This paper provides two generic approaches for intraclass correlation coefficients of the form [Formula: see text] The first approach uses Satterthwaite's approximation and an F-distribution. The second approach uses the first and second moments of the intraclass correlation coefficient estimate in combination with a Beta distribution.

Probability genotype imputation method and integrated weighted lasso for QTL identification.

Background: Many QTL studies have two common features: (1) often there is missing marker information, (2) among many markers involved in the biological process only a few are causal. In statistics, the second issue falls under the headings "sparsity" and "causal inference". The goal of this work is to develop a two-step statistical methodology for QTL mapping for markers with binary genotypes.

Association of tissue promoter methylation levels of APC, TGFβ2, HOXD3 and RASSF1A with prostate cancer progression.

Aberrant promoter methylation is known to silence tumor-suppressor genes in prostate cancer (PCa). We correlated quantitative promoter methylation levels of APC, TGFβ2 and RASSF1A in 219 radical prostatectomies diagnosed between 1998 and 2001 with clinicopathological follow-up data available including Gleason Pattern (GP), Gleason Score (GS) and pathological stage and explored their potential in predicting biochemical recurrence using univariate and multivariate analyses. We observed that the average methylation levels of APC increased significantly from GS ≤ 6 to GS7, and pT2 to pT3a, and that of TGFβ2 increased from GS ≤ 6 to GS7, but not for RASSF1A.

DNA methylation of HOXD3 as a marker of prostate cancer progression.

DNA methylation in gene promoters causes gene silencing and is a common event in cancer development and progression. The ability of aberrant methylation events to serve as diagnostic and prognostic markers is being appreciated for many cancers, including prostate cancer. Using quantitative MethyLight technology, we evaluated the relationship between HOXD3 methylation and clinicopathological parameters including biochemical recurrence, pathological stage, Gleason score (GS), and Gleason pattern in a series of 232 radical prostatectomies performed between 1998 and 2001.