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Abstract Genomic structural variants (SVs) are pervasive and can impose major phenotypic impacts. However, it is difficult to appreciate the individual significance of SVs when they are heterogeneously positioned across a genomic neighborhood. Further, ubiquitous variance in SV calling accuracy complicates SV counting and downstream analysis. Tools exist to simplify SV datasets, but they are not suited for all applications. Here, we present a new SV merger, SVCROWS: Structural Variation Consensus with Reciprocal Overlap and Weighted Sizes. This option-rich merger summarizes SV regions using a size-weighted reciprocal overlap framework, accounting for skewed impacts of variable-length SVs. User input directs stringency, enabling various levels of resolution in complex genome regions that harbor a spectrum of SV sizes. Further, by optimizing SVCROWS parameters, the user can tailor results to their study system. When compared to other SV merging programs, SVCROWS maintained accuracy and conserved rare genotypes from both simulated and real-world datasets. Visualization of merger output was critical for identifying how some algorithms derived erroneous conclusions while SVCROWS remained reliable, especially in complex regions. Overall, the novel SVCROWS algorithm presents an improved framework for SV interpretation; its intuitive nature and generalizability facilitate its application to virtually any workflow. 

Abstract Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs in only a few genomes are often overlooked but important; if beneficial, a de novo CNV that arises in a single genome can expand during selection to create a population of cells with novel characteristics. While single cell methods for studying de novo CNVs are increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich Plasmodium falciparum genome readily accumulates CNVs that facilitate rapid adaptation. We employed low-input genomics and specialized computational tools to evaluate the impact of sub-lethal stress on the de novo CNV rate. We observed a significant increase in genome-wide de novo CNVs following treatment with an antimalarial compound that inhibits replication. De novo CNVs encompassed genes from various cellular pathways participating in human infection. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen. 

You rate movies, you rate restaurants, and you can also rate mathematics solutions! 

Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, which is a framework for measuring and aligning the uncertainty of LLM-based planners such that they know when they don't know and ask for help when needed. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion while minimizing human help in complex multi-step planning settings. Experiments across a variety of simulated and real robot setups that involve tasks with different modes of ambiguity (e.g., from spatial to numeric uncertainties, from human preferences to Winograd schemas) show that KnowNo performs favorably over modern baselines (which may involve ensembles or extensive prompt tuning) in terms of improving efficiency and autonomy, while providing formal assurances. KnowNo can be used with LLMs out of the box without model-finetuning, and suggests a promising lightweight approach to modeling uncertainty that can complement and scale with the growing capabilities of foundation models. 

Mathematics assessments should allow all students opportunities to demonstrate their knowledge and skills as problem solvers. Looking at textbook word problems, we share a process for revising them using Universal Design for Learning.