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Abstract Zirconium carbide (ZrC), a high‐performance refractory ceramic, exhibits complex defect dynamics that critically influence its behavior in extreme environments. In this work, we employ density functional theory (DFT) simulations to determine the formation energies and migration barriers of four defect types—isolated carbon vacancies, divacancies, Frenkel pairs, and Schottky pairs—across various charge states. The calculated formation energies reveal that isolated carbon vacancies are the most energetically favorable (1.13 eV), followed by Frenkel pairs (3.29 eV), while divacancies (6.86 eV) and Schottky pairs (8.29 eV) require higher formation energies, indicating their lower intrinsic concentrations. Isolated carbon vacancies exhibit the highest migration barrier (4.11 eV) in ZrC, with a modest increase to 4.13 eV upon adding one electron to 64‐atom supercell and a decrease to 4.06 eV with two electrons/64‐atom supercell—reflecting charge redistribution that stabilizes the local environment and weakens nearby Zr–C bonds. In contrast, Frenkel and Schottky pairs show barrier increases with electron doping and decreases with holes (ranging from 3.26 to 3.44 eV and 3.37 to 3.73 eV, respectively), while divacancies display increases (carbon vacancies: 2.69 to 2.93 eV; zirconium vacancies: 3.60 to 3.69 eV) upon electron addition. These results reveal the defect‐specific impact of charge carriers on mobility in ZrC, offering key insights for optimizing its performance in extreme environments. 

Abstract DNA methylation plays an important role in many biological processes. The mechanisms underlying the establishment and maintenance of DNA methylation are well understood thanks to decades of research using DNA methylation mutants, primarily in Arabidopsis (Arabidopsis thaliana) accession Col-0. Recent genome-wide association studies (GWASs) using the methylomes of natural accessions have uncovered a complex and distinct genetic basis of variation in DNA methylation at the population level. Sequencing following bisulfite treatment has served as an excellent method for quantifying DNA methylation. Unlike studies focusing on specific accessions with reference genomes, population-scale methylome research often requires an additional round of sequencing beyond obtaining genome assemblies or genetic variations from whole-genome sequencing data, which can be cost prohibitive. Here, we provide an overview of recently developed bisulfite-free methods for quantifying methylation and cost-effective approaches for the simultaneous detection of genetic and epigenetic information. We also discuss the plasticity of DNA methylation in a specific Arabidopsis accession, the contribution of DNA methylation to plant adaptation, and the genetic determinants of variation in DNA methylation in natural populations. The recently developed technology and knowledge will greatly benefit future studies in population epigenomes. 

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device. 

Abstract Epigenetic variations contribute greatly to the phenotypic plasticity and diversity. Current functional studies on epialleles have predominantly focused on protein-coding genes, leaving the epialleles of non-coding RNA (ncRNA) genes largely understudied. Here, we uncover abundant DNA methylation variations of ncRNA genes and their significant correlations with plant adaptation among 1001 naturalArabidopsisaccessions. Through genome-wide association study (GWAS), we identify large numbers of methylation QTL (methylQTL) that are independent of known DNA methyltransferases and enriched in specific chromatin states. Proximal methylQTL closely located to ncRNA genes have a larger effect on DNA methylation than distal methylQTL. We ectopically tether a DNA methyltransferase MQ1v to miR157a by CRISPR-dCas9 and show de novo establishment of DNA methylation accompanied with decreased miR157a abundance and early flowering. These findings provide important insights into the genetic basis of epigenetic variations and highlight the contribution of epigenetic variations of ncRNA genes to plant phenotypes and diversity. 

Plasmonic catalysis is uniquely positioned between photo/electrochemistry and thermal chemistry such that multiple factors may compete to dominate the reaction enhancement mechanism. The adoption of norms originating in both photochemistry and thermal chemistry has resulted in the use of language and methods of data analysis, which, in the context of plasmonic catalysis, may be implicitly contradictory. This article tracks several years of research towards understanding thermal and nonthermal effects in plasmonic catalysis and culminates with a discussion on how the choice of language and presentation of data can be tuned to avoid subtle yet significant contradictory implications. 

Plasmonic photocatalysis presents a promising method for light-to-matter conversion. However, most current studies focused on understanding the relative importance of thermal and nonthermal effects while their synergistic effects remained less studied. Here, we propose an index, termed Overall Light Effectiveness (OLE), to capture the combined impact of these light effects on reactions. By systematically varying the thickness of catalyst layers, we isolated thermal and nonthermal contributions and optimized them to achieve maximum light enhancement. We demonstrate the approach using carbon dioxide hydrogenation reaction on titania-supported rhodium nanoparticles as a model reaction system. It shows a generalizable potential in designing catalyst systems with optimum combinations of heating and light illumination, especially with broadband light illumination such as sunlight, for achieving the most economical light-to-matter conversion in plasmonic catalysis.