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The Implicit Association Test (IAT), like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, many IATs appear to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for IATs based on the geometric similarity representation (GSR) model. This model leverages both response times and accuracy on IATs to make inferences about representational similarity between the stimuli and categories. The model disentangles processes related to response caution, stimulus encoding, similarities between concepts and categories, and response processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in IATs is almost entirely attributable to the methods used to analyze data from the task: GSR model parameters show test-retest reliability around .80-.90, on par with reliable self-report measures. Furthermore, we demonstrate how model parameters result in greater validity compared to the IAT D-score, Quad model, and simple diffusion model contrasts, predicting outcomes related to intergroup contact and motivation. Finally, we present a simple point-and-click software tool for fitting the model, which uses a pre-trained neural network to estimate best-fit parameters of the GSR model. This approach allows easy and instantaneous fitting of IAT data with minimal demands on coding or technical expertise on the part of the user, making the new model accessible and effective. 

The heterodimerization of wild-type (WT) Cu, Zn superoxide dismutase-1 (SOD1) and mutant SOD1 might be a critical step in the pathogenesis of SOD1-linked amyotrophic lateral sclerosis (ALS). Post-translational modifications that accelerate SOD1 heterodimerization remain unidentified. Here, we used capillary electrophoresis to quantify the effect of cysteine-111 oxidation on the rate and free energy of ALS mutant/WT SOD1 heterodimerization. The oxidation of Cys111-β-SH to sulfinic and sulfonic acid (by hydrogen peroxide) increased rates of heterodimerization (with unoxidized protein) by ∼3-fold. Cysteine oxidation drove the equilibrium free energy of SOD1 heterodimerization by up to ΔΔG = −5.11 ± 0.36 kJ mol–1. Molecular dynamics simulations suggested that this enhanced heterodimerization, between oxidized homodimers and unoxidized homodimers, was promoted by electrostatic repulsion between the two “dueling” Cys111-SO2–/SO3–, which point toward one another in the homodimeric state. Together, these results suggest that oxidation of Cys-111 promotes subunit exchange between oxidized homodimers and unoxidized homodimers, regardless of whether they are mutant or WT dimers. 

Abstract Computationally modeling how mutations affect protein–protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high‐throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long‐distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high‐throughput Rosetta‐based side‐chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long‐distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long‐distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein–protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions. 

Abstract For many ecologists, publishing data in a data repository is a new, unfamiliar task. To reduce the learning curve, the Environmental Data Initiative has developed user‐friendly software to make capturing and submitting data and metadata a simple process. In this article, we introduce ezEML and discuss use cases for researchers who publish data infrequently or information managers who regularly update multiple datasets. 

Abstract Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.