Reexploring the conception of heat-health risk: From the perspectives of dimensionality and spatiality.

Extreme heat events are more frequent and intense as a result of global climate change, thus posing tremendous threats to public health. However, extant literature exploring the multidimensional features of heat-health risks from a spatial perspective is limited. This study revisits extreme heat-health risk and decomposes this concept by integrating multi-sourced datasets, identifying compositional features, examining spatial patterns, and comparing classified characteristics based on local conditions.

Comparing RMSEA-Based Indices for Assessing Measurement Invariance in Confirmatory Factor Models.

Fit indices are descriptive measures that can help evaluate how well a confirmatory factor analysis (CFA) model fits a researcher's data. In multigroup models, before between-group comparisons are made, fit indices may be used to evaluate measurement invariance by assessing the degree to which multiple groups' data are consistent with increasingly constrained nested models. One such fit index is an adaptation of the root mean square error of approximation (RMSEA) called RMSEA.

Targeting Unique Ligand Binding Domain Structural Features Downregulates DKK1 in Y537S Mutant Breast Cancer Cells.

Resistance to endocrine therapies remains a major clinical hurdle in breast cancer. Mutations to estrogen receptor alpha (ERα) arise after continued therapeutic pressure. Next generation selective estrogen receptor modulators and degraders/downregulators (SERMs and SERDs) show clinical efficacy, but responses are often non-durable.

Targeting Unique Ligand Binding Domain Structural Features Downregulates DKK1 in Y537S Mutant Breast Cancer Cells.

Resistance to endocrine therapies remains a major clinical hurdle in breast cancer. Mutations to estrogen receptor alpha (ERα) arise after continued therapeutic pressure. Next generation selective estrogen receptor modulators and degraders/downregulators (SERMs and SERDs) show clinical efficacy, but responses are often non-durable.

Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors.