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Tropical cyclones (e.g., hurricanes and tropical storms), are considered one of the world's most destructive climatological forces, causing substantial damage especially in urban areas. However, for some arid ecosystems, tropical cyclones represent natural disturbance events, providing important sources of fresh water that support ecosystem functioning. For subsistence populations living in these regions, it is unclear whether they experience these events negatively due to the associated damages or positively within a predictable disturbance regime. Here, we assess these phenomena with traditional ranchers from Baja California Sur, Mexico, following Hurricane Kay (September 2022). We find that despite significant damage caused by the hurricane, nearly the entire sample perceived this tropical cyclone event as a net positive on their lives. This traditional ranching population has a culture that is adapted to the seasonal tropical cyclone disturbance regime, and expects extreme rain events annually to support ecosystem functioning, and therefore their economic livelihoods. To these ranchers, the climate shock is not when the hurricanes come, but rather, when hurricanes do not come. We situate our results within a disturbance ecology framework, highlighting the role of increasing aridity and probability of drought in the North American Arid West. 

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. How- ever, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability pro- files. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials. 

Abstract Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences,in vivogenomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained onin vitroTF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific,in vivobinding profiles. Conversely, deep learning models, trained onin vivoTF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models ofin vitroandin vivoTF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinitiesde-novofrom deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diversein vitroassays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant ofin vivobinding, suggest that deep learning models ofin vivobinding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughputin silicoexperiments to explore the influence of sequence context and variation on both intrinsic affinity andin vivooccupancy.