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The anisotropic properties of Janus NPs are crucial for their ability to disrupt the negative-surface bacterial membrane modelviathe combination of hydrophobic and electrostatic interactions. 

Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time‐honored methods, we can predict theoretical leaves that are possible, enabling us to understand the genetics, development, and environmental responses of plants in new ways. SummaryGrapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genusVitispredicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution.We measure the shapes of 139 grapevine leaves using 1672 pseudo‐landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo‐landmarks that results in a high‐resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10Vitisspp.We visualize a high‐resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers,Vitis vinifera,Vitisspp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models.By predicting a high‐resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genusVitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high‐resolution space, or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. 

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.