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Meiotic recombination generates new genetic combinations and ensures proper chromosome segregation, yet its regulation remains unclear in many species. Here, we show that Mediator of paramutation1 (Mop1), a regulator of DNA methylation, affects the recombination landscape in maize. Highresolution crossover mapping reveals that loss of MOP1 reduces crossover numbers during male meiosis, while the total number in female meiosis remains largely unchanged despite local shifts in crossover distribution. We find that new crossovers occur on miniature inverted-repeat transposable elements (MITEs), particularly in regions with greater genetic diversity. Analysis of DNA methylation and histone modifications, together with gene expression, shows that loss of MOP1 reducesmethylation at MITEs near genes enriched with histone marks linked to open and active chromatin, and downregulates a gene required for chromosome organization. Together, these findings suggest that both chromosome structure and transposonmethylation contribute to recombination control, providing insights into epigenetic control of genetic variation. 

Clustering methods are often used in physics education research (PER) to identify subgroups of individuals within a population who share similar response patterns or characteristics. Among these, $k$-means (or $k$-modes, for categorical data) is one of the most commonly used clustering methods in PER. This algorithm, however, is distance-based rather than model-based: it relies on algorithmic partitioning and assigns each individual to one subgroup through hard assignment. Researchers must also conduct analyses to relate subgroup membership to other variables. Mixture models are a model-based alternative that offers several statistical tools for choosing the optimal number of subgroups, accounts for classification errors by assigning individuals probabilities of belonging to each subgroup rather than hard assignment, and allows researchers to directly integrate subgroup membership into a broader latent variable framework. In this paper, we outline the theoretical similarities and differences between $k$-modes clustering and latent class analysis (one type of mixture model for categorical data). We also present parallel analyses using each method to address the same research questions in order to demonstrate these similarities and differences. We provide the data and code to replicate the worked example presented in the paper for researchers interested in using mixture models. 

We designed a few polymyxin derivatives which exhibit broad-spectrum antimicrobial activity. Lead compound P1 could disrupt bacterial membranes rapidly without developing resistance, inhibit biofilms formed by E. coli , and exhibit excellent in vivo activity in an MRSA-infected thigh burden mouse model.