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Summary Powdery mildew is an economically important disease caused byc. 1000 different fungal species.Erysiphe vacciniiis an emerging powdery mildew species that is impacting the blueberry industry. Once confined to North America,E. vacciniiis now spreading rapidly across major blueberry‐growing regions, including China, Morocco, Mexico, and the USA, threatening millions in losses.This study documents its recent global spread by analyzing both herbarium specimens, some over 150‐yr‐old, and fresh samples collected world‐wide.Our findings were integrated into a ‘living phylogeny’ via T‐BAS to simplify pathogen identification and enable rapid responses to new outbreaks. We identified 50 haplotypes, two primary introductions world‐wide, and revealed a shift from a generalist to a specialist pathogen.This research provides insights into the complexities of host specialization and highlights the need to address this emerging global threat to blueberry production. 

How do practitioners who develop consumer AI products scope, motivate, and conduct privacy work? Respecting pri- vacy is a key principle for developing ethical, human-centered AI systems, but we cannot hope to better support practitioners without answers to that question. We interviewed 35 industry AI practitioners to bridge that gap. We found that practitioners viewed privacy as actions taken against pre-defined intrusions that can be exacerbated by the capabilities and requirements of AI, but few were aware of AI-specific privacy intrusions documented in prior literature. We found that their privacy work was rigidly defined and situated, guided by compliance with privacy regulations and policies, and generally demoti- vated beyond meeting minimum requirements. Finally, we found that the methods, tools, and resources they used in their privacy work generally did not help address the unique pri- vacy risks introduced or exacerbated by their use of AI in their products. Collectively, these findings reveal the need and opportunity to create tools, resources, and support structures to improve practitioners’ awareness of AI-specific privacy risks, motivations to do AI privacy work, and ability to ad- dress privacy harms introduced or exacerbated by their use of AI in consumer products. 

Predicting the behavior of nanomaterials under various conditions presents a significant challenge due to their complex microstructures. While high-fidelity modeling techniques, such as molecular dynamics (MD) simulations, are effective, they are also computationally demanding. Machine learning (ML) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN) to predict the evolution of grain boundary (GB) networks in nanocrystalline materials under mechanical loads, incorporating both morphological and atomic details. We conducted MD simulations on nanocrystalline tungsten and used the resulting ground-truth data to train our cGAN model. We assessed the performance of our cGAN model by comparing it to a Convolutional Autoencoder (ConvAE) model and examining the impact of changes in geometric morphology and loading conditions on the model's performance. Our cGAN model demonstrated high accuracy in predicting GB network evolution under a variety of conditions. This developed framework shows potential for predicting various materials' behaviors across a wide range of nanomaterials. 

Abstract Nickel stable isotopes (δ⁶⁰Ni) provide insight to Ni biogeochemistry in the modern and past oceans. Here, we present the first Pacific Ocean high‐resolution dissolved Ni concentration and δ⁶⁰Ni data, from the US GEOTRACES GP15 cruise. As in other ocean basins, increases in δ⁶⁰Ni toward the surface ocean are observed across the entire transect, reflecting preferential biological uptake of light Ni isotopes, however the observed magnitude of fractionation is larger in the tropical Pacific than the North Pacific Subtropical Gyre. Such surface ocean fractionation by phytoplankton should accumulate isotopically lighter Ni in the deep Pacific, yet we find that North Pacific deep ocean δ⁶⁰Ni is similar to previously reported values from the deep Atlantic. Finally, we find that seawater dissolved δ⁶⁰Ni in regions with hydrothermal input can be either higher or lower than background deep ocean δ⁶⁰Ni, depending on vent geochemistry and proximity.