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The oxygen reduction reaction (ORR) is a critical process in energy conversion systems, influencing the efficiency and performance of various devices such as fuel cells, batteries, and electrolyzers. Perovskite-supported metal materials (metal/perovskite) offer several advantages as ORR electrocatalysts, including strong metal-support interactions, oxygen vacancy formation in the perovskite lattice, and synergistic triple-phase boundary (TPB) activity at the interface. Despite their significance, the mechanistic understanding of ORR on metal/perovskite catalysts remains incomplete, particularly at metal/perovskite interfaces. This study investigates ORR on BaZrO3 (BZO) perovskite-supported metal clusters (Pt or Ag) using density functional theory (DFT) to unravel critical insights into charge redistribution at the metal/BZO interface. Energy profiles for elemental steps along two different ORR pathways—oxygen adsorption on the metal cluster surface and direct oxygen adsorption at the TPB—were calculated to explore the effects of different active sites. The results provide a deeper understanding of ORR on metal/perovskite catalysts, emphasizing the role of interfacial interactions and pathway-dependent reaction mechanisms. This work paves the way for guiding the design of high-performance electrocatalysts for ORR in terms of composition, interface design, and local environment modification for a broad range of energy applications. 

The development of statistical methods to infer species phylogenies with reticulations (species networks) has led to many discoveries of gene flow between distinct species. These methods typically assume only incomplete lineage sorting and introgression. Given that phylogenetic networks can be arbitrarily complex, these methods might compensate for model misspecification by increasing the number of dimensions beyond the true value. Herein, we explore the effect of potential model misspecification, including the negligence of gene tree estimation error (GTEE) and assumption of a single substitution rate for all genomic loci, on the accuracy of phylogenetic network inference using both simulated and biological data. In particular, we assess the accuracy of estimated phylogenetic networks as well as test statistics for determining whether a network is the correct evolutionary history, as opposed to the simpler model that is a tree.We found that while GTEE negatively impacts the performance of test statistics to determine the “treeness” of the evolutionary history of a data set, running those tests on triplets of taxa and correcting for multiple-testing significantly ameliorates the problem. We also found that accounting for substitution rate heterogeneity improves the reliability of full Bayesian inference methods of phylogenetic networks, whereas summary statistic methods are robust to GTEE and rate heterogeneity, though currently require manual inspection to determine the network complexity. 

Abstract BackgroundWe hypothesized that alemtuzumab use is safe in pediatric kidney transplant recipients (KTRs) with equivalent long‐term outcomes compared to other induction agents. MethodsUsing pediatric kidney transplant recipient data in the UNOS database between January 1, 2000, and June 30, 2022, multivariate logistic regression, multivariable Cox regression, and survival analyses were utilized to estimate the likelihoods of 1st‐year and all‐time hospitalizations, acute rejection, CMV infection, delayed graft function (DGF), graft loss, and patient mortality among recipients of three common induction regimens (ATG, alemtuzumab, and basiliximab). ResultsThere were no differences in acute rejection or graft failure among induction or maintenance regimens. Basiliximab was associated with lower odds of DGF in deceased donor recipients (OR 0.77 [0.60–0.99],p = .04). Mortality was increased in patients treated with steroid‐containing maintenance (HR 1.3 [1.005–1.7]p = .045). Alemtuzumab induction correlated with less risk of CMV infection than ATG (OR 0.76 [0.59–0.99],p = .039). Steroid‐containing maintenance conferred lower rate of PTLD compared to steroid‐free maintenance (HR 0.59 [0.4–0.8]p = .001). Alemtuzumab was associated with less risk of hospitalization within 1 year (OR 0.79 [0.67–0.95]p = .012) and 5 years (HR 0.54 [0.46–0.65]p < .001) of transplantation. Steroid maintenance also decreased 5 years hospitalization risk (HR 0.78 [0.69–0.89]p < .001). ConclusionsPediatric KTRs may be safely treated with alemtuzumab induction without increased risk of acute rejection, DGF, graft loss, or patient mortality. The decreased risk of CMV infections and lower hospitalization rates compared to other agents make alemtuzumab an attractive choice for induction in pediatric KTRs, especially in those who cannot tolerate ATG. 

Recent studies have shown that climate change and global warming considerably increase the risks of hurricane winds, floods, and storm surges in coastal communities. Turbulent processes in Hurricane Boundary Layers (HBLs) play a major role in hurricane dynamics and intensification. Most of the existing turbulence parameterizations in the current numerical weather prediction (NWP) models rely on the Planetary Boundary Layer (PBL) schemes. Previous studies (Zhang 2010; Momen et al. 2021) showed that there is a significant distinction between turbulence characteristics in HBLs and regular atmospheric boundary layers (ABLs) due to the strong rotational effects of hurricane flows. Nevertheless, such differences are not considered in the current schemes of NWPs, and they are primarily designed and tested for regular ABLs. In this talk, we aim to bridge this knowledge gap by conducting new hurricane simulations using the Weather Research and Forecasting (WRF) model as well as large-eddy simulations. We investigate the role of the PBL parameterizations and momentum roughness length in multiple hurricanes by probing the parameter space of the problem. Our simulations have shown that the most widely used WRF PBL schemes do not capture the hurricane intensification properly and underestimate their intensity. We will present that decreasing the roughness length close to the values of observational estimates and theoretical hurricane intensity models in high wind regimes (≳ 45 m s-1) led to significant improvements in the intensity forecasts of strong hurricanes. Furthermore, by decreasing the existing vertical diffusion values, on average more than 20% improvements in hurricane intensity forecasts were obtained compared to the default runs. Our results provide new insights into the role of turbulence parameterizations in hurricane dynamics and can be employed to improve the accuracy of real hurricane forecasts. The implications of these results and improvements for coastal resiliency and fluid-structure interactions will also be discussed.