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Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise. 

The problem of few-shot graph classification targets at assigning class labels for graph samples, where only limited labeled graphs are provided for each class. To solve the problem brought by label scarcity, recent studies have proposed to adopt the prevalent few-shot learning framework to achieve fast adaptations to graph classes with limited labeled graphs. In particular, these studies typically propose to accumulate meta-knowledge across a large number of meta-training tasks, and then generalize such meta-knowledge to meta-test tasks sampled from a disjoint class set. Nevertheless, existing studies generally ignore the crucial task correlations among meta-training tasks and treat them independently. In fact, such task correlations can help promote the model generalization to meta-test tasks and result in better classification performance. On the other hand, it remains challenging to capture and utilize task correlations due to the complex components and interactions in meta-training tasks. To deal with this, we propose a novel few-shot graph classification framework FAITH to capture task correlations via learning a hierarchical task structure at different granularities. We further propose a task-specific classifier to incorporate the learned task correlations into the few-shot graph classification process. Moreover, we derive FAITH+, a variant of FAITH that can improve the sampling process for the hierarchical task structure. The extensive experiments on four prevalent graph datasets further demonstrate the superiority of FAITH and FAITH+ over other state-of-the-art baselines. 

Proximity labeling expansion microscopy (PL-ExM) visualizes superresolution structures of interactome on widely accessible light microscopes, enabling the assessment of the precision and efficiency of proximity labeling techniques. 

Polymers that release small molecules in response to mechanical force are promising candidates as next-generation on-demand delivery systems. Despite advancements in the development of mechanophores for releasing diverse payloads through careful molecular design, the availability of scaffolds capable of discharging biomedically significant cargos in substantial quantities remains scarce. In this report, we detail a nonscissile mechanophore built from an 8-thiabicyclo[3.2.1]octane 8,8-dioxide (TBO) motif that releases one equivalent of sulfur dioxide (SO2) from each repeat unit. The TBO mechanophore exhibits high thermal stability but is activated mechanochemically using solution ultrasonication in either organic solvent or aqueous media with up to 63% efficiency, equating to 206 molecules of SO2 released per 143.3 kDa chain. We quantified the mechanochemical reactivity of TBO by single-molecule force spectroscopy and resolved its single-event activation. The force-coupled rate constant for TBO opening reaches ∼9.0 s–1 at ∼1520 pN, and each reaction of a single TBO domain releases a stored length of ∼0.68 nm. We investigated the mechanism of TBO activation using ab initio steered molecular dynamic simulations and rationalized the observed stereoselectivity. These comprehensive studies of the TBO mechanophore provide a mechanically coupled mechanism of multi-SO2 release from one polymer chain, facilitating the translation of polymer mechanochemistry to potential biomedical applications.