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-g-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. Besides, the grafting reaction mechanism between LG and CS in the ascorbic acid (AA)/H2O2 redox system was further proposed. The LG-g-CS films feature hydrophobic side chains and photoactive phenolic hydroxyl groups, facilitating dual bactericidal activities through bacteria capture and contact-killing via strong hydrophobic and electrostatic interactions with bacterial membranes as well as blue light (BL)-driven photodynamic bacterial eradication through the enhanced generation of reactive oxygen species. As a result, the LG-g-CS films efficiently capture and immobilize bacteria and exhibit excellent photodynamic antibacterial activity against model bacteria (Escherichia coli and Staphylococcus aureus) and their biofilms under BL irradiation. Moreover, LG-g-CS films could significantly promote the healing process of S. aureus-infected wounds. This research demonstrates a new strategy for designing and fabricating sustainable bactericidal and biofilm-removing materials with a high bacterial membrane affinity and photodynamic activity.

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. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis, and power priors. Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.

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. Receptive and expressive vocabulary, receptive syntax, and dialect density were also assessed. RESULTS: The results of a series of mixed-effect models showed that children with larger expressive vocabularies performed better on both experimental tasks, relative to children with smaller expressive vocabularies. Dialect density was a significant predictor only of MAE lexical comprehension; children with higher levels of dialect density were less accurate on this task. CONCLUSIONS: Both vocabulary size and dialect density independently influenced MAE lexical comprehension. The results suggest that children with high levels of nonmainstream dialect use have more difficulty understanding words in MAE, at least in challenging contexts, and suggest directions for future research.

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. A detailed study of our approach examines the differences in the causal estimate when incorporating noninformative versus informative priors in the model averaging stage. We examine these approaches under common methods of propensity score implementation. In addition, we evaluate the impact of changing the size of Occam's window used to narrow down the range of possible models. We also assess the predictive performance of both Bayesian model averaging propensity score approaches and compare it with the case without Bayesian model averaging. Overall, results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates, as expected. Both Bayesian model averaging approaches offer slightly better prediction of the propensity score compared with the Bayesian approach with a single propensity score equation. Covariate balance checks for the case study show that both Bayesian model averaging approaches offer good balance. The fully Bayesian model averaging approach also provides posterior probability intervals of the balance indices.

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. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative.