Results 231 to 240 of about 283,645 (308)

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

open access: yesAdvanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu   +5 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

Multimodal Imaging Reveals Rapid Catecholamine Uptake and Release by Neutrophils

open access: yesAdvanced Science, EarlyView.
We show that immune cells (neutrophils) synthesize, uptake, and store catecholamine neurotransmitters such as dopamine or adrenaline. They also release them in response to specific stimuli (serotonin), which we directly visualize using fluorescent nanosensors. We further demonstrate that catecholamines affect neutrophil functions (NETosis) and platelet
Jennifer Mohr   +19 more
wiley   +1 more source

Schooling Trajectories and the Development of Brain Dynamics: A Comparative Study of Montessori and Traditional Education

open access: yesAdvanced Science, EarlyView.
We investigate whether Montessori and traditional schooling systems shape the developmental trajectory of large‐scale brain dynamics in different ways. We quantify the arrow of time (“non‐reversibility”) in neural activity during resting state and movie‐watching, revealing distinct maturational patterns.
Elvira del Agua   +6 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

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