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Climate models today depend critically on confident initial conditions, a reasonably plausible snapshot of the Earth from which all future predictions emerge. However, given the inherently chaotic nature of our system, this constraint is complicated by sensitivity dependence, where small uncertainties can lead to exponentially diverging outcomes over time. This challenge is particularly salient at global spatial scales and over centennial timescales, where data gaps are not just common but expected. The source of uncertainty is two-fold: (1) sparse, noisy observations from satellites and ground stations, and (2) variability stemming from simplifying approximations within the models themselves. In practice, data assimilation methods are used to reconcile this missing information by conditioning model states on available observations. Our work builds on this idea but operates at the extreme end of sparsity. We propose a conditional data imputation framework that reconstructs full temperature fields from as little as 1% observational coverage. The method leverages a diffusion model guided by a prekriged mask, effectively inferring the full-state fields from minimal data points. We validate our framework over the Southern Great Plains, focusing on afternoon through night (12:00 PM–12:00 AM) temperature fields during the summer months of 2018–2021. Across varying observational densities—from swath data to isolated in situ sensors—our model achieves strong reconstruction accuracy, highlighting its potential to fill in critical data gaps in both historical reanalysis and real-time forecasting pipelines. 

Abstract BackgroundOropharyngeal cancer (OPC) exhibits varying responses to chemoradiation therapy, making treatment outcome prediction challenging. Traditional imaging‐based methods often fail to capture the spatial heterogeneity within tumors, which influences treatment resistance and disease progression. Advances in modeling techniques allow for more nuanced analysis of this heterogeneity, identifying distinct tumor regions, or habitats, that drive patient outcomes. PurposeTo interrogate the association between treatment‐induced changes in spatial heterogeneity and chemoradiation resistance of oropharyngeal cancer (OPC) based on a novel tumor habitat analysis. MethodsA mathematical model was used to estimate tumor time dynamics of patients with OPC based on the applied analysis of partial differential equations. The position and momentum of each voxel was propagated according to Fokker‐Planck dynamics, that is, a common model in statistical mechanics. The boundary conditions of the Fokker‐Planck equation were solved based on pre‐ and intra‐treatment (i.e., after 2 weeks of therapy)¹⁸F‐FDG‐PET SUV images of patients (n = 56) undergoing definitive (chemo)radiation for OPC as part of a previously conducted prospective clinical trial. Tumor‐specific time dynamics, measured based on the solution of the Fokker‐Planck equation, were generated for each patient. Tumor habitats (i.e., non‐overlapping subregions of the primary tumor) were identified by measuring vector similarity in voxel‐level time dynamics through a fuzzy c‐means clustering algorithm. The robustness of our habitat construction method was quantified using a mean silhouette metric to measure intra‐habitat variability. Fifty‐four habitat‐specific radiomic texture features were extracted from pre‐treatment SUV images and normalized by habitat volume. Univariate Kaplan‐Meier analyses were implemented as a feature selection method, where statistically significant features (p < 0.05, log‐rank) were used to construct a multivariate Cox proportional‐hazards model. Parameters from the resulting Cox model were then used to construct a risk score for each patient, based on habitat‐specific radiomic expression. The patient cohort was stratified by median risk score value and association with recurrence‐free survival (RFS) was evaluated via log‐rank tests. ResultsDynamic tumor habitat analysis partitioned the gross disease of each patient into three spatial subregions. Voxels within each habitat suggested differential response rates in different compartments of the tumor. The minimum mean silhouette value was 0.57 and maximum mean silhouette value was 0.8, where values above 0.7 indicated strong intra‐habitat consistency and values between 0.5 and 0.7 indicated reasonable intra‐habitat consistency. Nine radiomic texture features (three GLRLM, two GLCOM, and three GLSZM) and SUVmax were found to be prognostically significant and were used to build the multivariate Cox model. The resulting risk score was associated with RFS (p = 0.032). By contrast, potential confounding factors (primary tumor volume and mean SUV) were not significantly associated with RFS (p = 0.286 andp = 0.231, respectively). ConclusionWe interrogated spatial heterogeneity of oropharyngeal tumors through the application of a novel algorithm to identify spatial habitats on SUV images. Our habitat construction technique was shown to be robust and habitat‐specific feature spaces revealed distinct underlying radiomic expression patterns. Radiomic features were extracted from dynamic habitats and used to build a risk score which demonstrated prognostic value. 

The value of scientists engaging with community members and other public audiences is widely recognized, and there is a growing literature devoted to the theory and practice of public engagement with science. However, as a group of professionals concerned with how public engagement is understood and practiced in the fields of ecology and environmental science, we see a need for accessible guidance for scientists who want to engage effectively, and for scientific leaders who want to support successful public engagement programs in their institutions. Here, we highlight six attributes of successful public engagement efforts led by scientists and scientific institutions: (1) strategic, (2) cumulative, (3) reciprocal, (4) reflexive, (5) equitable, and (6) evidence‐based. By designing and developing practices that incorporate these attributes, scientists and scientific organizations will be better poised to build two‐way linkages with communities that, over time, support science‐informed decision‐making in society and societally informed decision‐making in science. 

Electroadsorption analysis enables the determination of reaction intermediates to accelerate the design of next generation electrocatalysts. 

Crosslinking DLP 3D printable elastomers using scissile mechanophores increases tear resistance, and enables suturing of the material. 

An often-underacknowledged component of racial ethnic identity development concerns youth's multiple social identities, which affect how and when youth receive racial ethnic socialization (RES) from parents and caregivers. Here, we review how a child's or adolescent's gender, immigration status, skin tone, and socioeconomic status can influence the RES they receive. Additionally, we use the social psychology model of social complexity theory to demonstrate how these social identities may present themselves in distinct ways within a single individual (whether identities are intersected, compartmentalized, etc.) using a developmental lens. Understanding how a person's multiple social identities can hold differential salience allows us to more accurately measure RES by considering the factors that may influence its presentation and prevalence. Examples and implications for how multiple identities may converge and influence RES are discussed.