Advancing Photodynamic Antimicrobial Strategy: Sustainable Fabrication of Novel Lauryl Gallate-Chitosan Hydrophobic Films with Rapid Bacterial Capture and Biofilms Elimination Capabilities for Promoting Wound Healing.

Bioinspired photoactive composites, in terms of photodynamic inactivation, cost-effectiveness, and biosafety, are promising alternatives to antibiotics for combating bacterial infections while avoiding antibacterial resistance. However, the weak bacterial membrane affinity of the photoactive substrate and the lack of synergistic antibacterial effect remain crucial shortcomings for their antibacterial applications. Herein, we developed a hydrophobic film from food antioxidant lauryl gallate covalently functionalized chitosan (LG--CS conjugates) through a green radical-induced grafting reaction that utilizes synergistic bacteria capture, contact-killing, and photodynamic inactivation activities to achieve enhanced bactericidal and biofilm elimination capabilities.

Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments.

The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution.

Bayesian Model Averaging for Propensity Score Analysis.

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach via Markov chain Monte Carlo sampling (MCMC) to account for uncertainty in both parameters and models.

Dialect awareness and lexical comprehension of mainstream american english in african american english-speaking children.

Purpose: This study was designed to examine the relationships among minority dialect use, language ability, and young African American English (AAE)-speaking children's understanding and awareness of Mainstream American English (MAE).

Method: Eighty-three 4- to 8-year-old AAE-speaking children participated in 2 experimental tasks. One task evaluated their awareness of differences between MAE and AAE, whereas the other task evaluated their lexical comprehension of MAE in contexts that were ambiguous in AAE but unambiguous in MAE.

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study.

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching.