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ABSTRACT Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a 3-dimensional array with elements that can be grouped according to 2 directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a full Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates. 

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. 

Polyploidy and subsequent post-polyploid diploidization (PPD) are key drivers of plant genome evolution, yet their contributions to evolutionary success remain debated. Here, we analyze the Malvaceae family as an exemplary system for elucidating the evolutionary role of polyploidy and PPD in angiosperms, leveraging 11 high-quality chromosome-scale genomes from all nine subfamilies, including newly sequenced, near telomere-to-telomere assemblies from four of these subfamilies. Our findings reveal a complex reticulate paleoallopolyploidy history early in the diversification of the Malvadendrina clade, characterized by multiple rounds of species radiation punctuated by ancient allotetraploidization (Mal-β) and allodecaploidization (Mal-α) events around the Cretaceous–Paleogene (K–Pg) boundary. We further reconstruct the evolutionary dynamics of PPD and find a strong correlation between dysploidy rate and taxonomic richness of the paleopolyploid subfamilies (R^2 ≥ 0.90, P < 1e-4), supporting the “polyploidy for survival and PPD for success” hypothesis. Overall, our study provides a comprehensive reconstruction of the evolutionary history of the Malvaceae and underscores the crucial role of polyploidy–dysploidy waves in shaping plant biodiversity. 

The Transparent Research Object Vocabulary (TROV) is a key element of the Transparency Certified (TRACE) approach to ensuring research trustworthiness. In contrast with methods that entail repeating computations in part or in full to verify that the descriptions of methods included in a publication are sufficient to reproduce reported results, the TRACE approach depends on a controlled computing environment termed a Transparent Research System (TRS) to guarantee that accurate, sufficiently complete, and otherwise trustworthy records are captured when results are obtained in the first place. Records identifying (1) the digital artifacts and computations that yielded a research result, (2) the TRS that witnessed the artifacts and supervised the computations, and (3) the specific conditions enforced by the TRS that warrant trust in these records, together constitute a Transparent Research Object (TRO). Digital signatures provided by the TRS and by a trusted third-party timestamp authority (TSA) guarantee the integrity and authenticity of the TRO. The controlled vocabulary TROV provides means to declare and query the properties of a TRO, to enumerate the dimensions of trustworthiness the TRS asserts for a TRO, and to verify that each such assertion is warranted by the documented capabilities of the TRS. Our approach for describing, publishing, and working with TROs imposes no restrictions on how computational artifacts are packaged or otherwise shared, and aims to be interoperable with, rather than to replace, current and future Research Object standards, archival formats, and repository layouts. 

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.