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Helicase regulation involves modulation of unwinding speed to maintain coordination of DNA replication fork activities and is vital for replisome progression. Currently, mechanisms for helicase regulation that involve interactions with both DNA strands through a steric exclusion and wrapping (SEW) model and conformational shifts between dilated and constricted states have been examined in vitro . To better understand the mechanism and cellular impact of helicase regulation, we used CRISPR-Cas9 genome editing to study four previously identified SEW-deficient mutants of the bacterial replicative helicase DnaB. We discovered that these four SEW mutations stabilize constricted states, with more fully constricted mutants having a generally greater impact on genomic stress, suggesting a dynamic model for helicase regulation that involves both excluded strand interactions and conformational states. These dnaB mutations result in increased chromosome complexities, less stable genomes, and ultimately less viable and fit strains. Specifically, dnaB : mut strains present with increased mutational frequencies without significantly inducing SOS, consistent with leaving single-strand gaps in the genome during replication that are subsequently filled with lower fidelity. This work explores the genomic impacts of helicase dysregulation in vivo , supporting a combined dynamic regulatory mechanism involving a spectrum of DnaB conformational changes and relates current mechanistic understanding to functional helicase behavior at the replication fork. 

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans. 

Cultural‐ecological theories posit that ethnic‐racial identity (ERI) development is shaped by transactions between contexts of ethnic‐racial socialization, yet research considering intersections among multiple contexts is limited. In this study, Black, Latino, White, and Asian American adolescents (N = 98;M_age = 16.26,SD = 1.09; 55.1% female identifying) participated in surveys and focus group discussions (2013–2014) to share insights into ERI development in context. Using consensual qualitative research, results indicated: (a) family ethnic‐racial socialization intersects with community‐based, peer, media, and school socialization; (b) ethnic‐racial socialization occurs outside family through intersections between peer, school, community‐based, and media settings; and (c) ethnic‐racial socialization is embedded within systems of racial oppression across contexts. Discussion includes implications for future research and interventions supporting youth ERI.