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The addition of terrestrial inputs to the ocean can have cascading impacts on coastal biogeochemistry by directly altering the water chemistry and indirectly changing ecosystem metabolism, which also influences water chemistry. Here, we use submarine groundwater discharge (SGD) as a model system to examine the direct geochemical and indirect biologically mediated effects of terrestrial nutrient subsidies on a fringing coral reef. We hypothesize that the addition of new solutes from SGD alters ecosystem metabolic processes including net ecosystem production and calcification, thereby changing the patterns of uptake and release of carbon by benthic organisms. SGD is a common land–sea connection that delivers terrestrially sourced nutrients, carbon dioxide, and organic matter to coastal ecosystems. Our research was conducted at two distinct coral reefs in Moʻorea, French Polynesia, characterized by contrasting flow regimes and SGD biogeochemistry. Using a Bayesian structural equation model, our research elucidates the direct geochemical and indirect biologically mediated effects of SGD on both dissolved organic and inorganic carbon pools. We reveal that SGD‐derived nutrients enhance both net ecosystem production and respiration. Furthermore, the study demonstrates that SGD‐induced alterations in net ecosystem production significantly influence pH dynamics, ultimately impacting net ecosystem calcification. Notably, the study underscores the context‐dependent nature of these cascading direct and indirect effects resulting from SGD, with flow conditions and the composition of the terrestrial inputs playing pivotal roles. Our research provides valuable insights into the interplay between terrestrial inputs and coral reef ecosystems, advancing our understanding of coastal carbon cycling and the broader implications of allochthonous inputs on ecosystem functioning. 

This study examines the impact of a femalized Architecture, Engineering, and Construction Kinesthetic Learning Model (fAEC-KLM) intervention on the AEC knowledge of African American middle school girls. Fourteen (14) middle school girls completed both pre- and post-surveys and tests that assessed their knowledge of AEC concepts, including roles and responsibilities, gender and racial diversity, and salary benefits. Paired-sample t-tests were utilized to evaluate statistically significant differences in knowledge before and after fAEC-KLM intervention. Results showed notable improvements in students' knowledge of AEC salary and benefits, as well as the underrepresentation of females, particularly African American females, and AEC roles and responsibilities, demonstrating that the intervention effectively increased awareness of AEC careers and the financial rewards. While the fAEC-KLM intervention positively influenced certain aspects of AEC knowledge, further refinement of assessment tools and expanded sample size are needed to assess the intervention's effectiveness fully. 

Abstract Studies of community assembly often explore the role of niche selection in limiting the diversity of functional traits (underdispersion) or increasing the diversity of functional traits (overdispersion) within local communities. While these patterns have primarily been explored with morphological functional traits related to environmental tolerances and resource acquisition, plant metabolomics may provide an additional functional dimension of community assembly to expand our understanding of how niche selection changes along environmental gradients. Here, we examine how the functional diversity of leaf secondary metabolites and traditional morphological plant traits changes along local environmental gradients in three temperate forest ecosystems across North America. Specifically, we asked whether co‐occurring tree species exhibit local‐scale over‐ or underdispersion of metabolomic and morphological traits, and whether differences in trait dispersion among local communities are associated with environmental gradients of soil resources and topography. Across tree species, we find that most metabolomic traits are not correlated with morphological traits, adding a unique dimension to functional trait space. Within forest plots, metabolomic traits tended to be overdispersed while morphological traits tended to be underdispersed. Additionally, local environmental gradients had site‐specific effects on metabolomic and morphological trait dispersion patterns. Taken together, these results show that different suites of traits can result in contrasting patterns of functional diversity along environmental gradients and suggest that multiple community assembly mechanisms operate simultaneously to structure functional diversity in temperate forest ecosystems. 

Methods to synthesize diverse collections of sub- stituted piperidines are valuable due to the prevalence of this heterocycle in pharmaceutical compounds. Here, we present a general strategy to access N-(hetero)arylpiperidines using a pyridine ring-opening and ring-closing approach via Zincke imine inter- mediates. This process generates pyridinium salts from a wide variety of substituted pyridines and (heteroaryl)anilines; hydro- genation reactions and nucleophilic additions then access the N- (hetero)arylpiperidine derivatives. We successfully applied high- throughput experimentation (HTE) using pharmaceutically relevant pyridines and (heteroaryl)anilines as inputs and developed a one-pot process using anilines as nucleophiles in the pyridinium salt-forming processes. This strategy is viable for generating piperidine libraries and applications such as the convergent coupling of complex fragments. 

Despite the positive gains towards student learning outcomes and engagement, active learning has been shown to potentially increase student anxiety due to a fear of negative evaluation. A pedagogical strategy proposed to mediate this issue is known as error framing; it asks instructors to encourage a perception of errors as being a natural part of the learning process. Previous work on this project investigated how graduate teaching assistants (GTAs) operationalized error framing during their training in a mixed-reality simulator but did not investigate their usage of it in their classrooms. This analysis characterizes the error framing statements made by GTAs during a set of classroom observations. We find that GTAs who employ error framing effectively avoid statements that might decrease student comfort and instead tend towards implicit, indirect strategies. 

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this article we propose a novel optimization strategy that enables a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including 𝓁1, Adaptive-𝓁1, SCAD, and 𝓁0. The computational framework only requires the proximal operator for each regularizer to be readily computable, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization. Supplementary materials for this article are available online.