Uranium (U) interacts with organic ligands, subsequently controlling its aqueous chemistry. It is therefore imperative to assess the binding ability of natural organic molecules. We evidence that density functional theory (DFT) can be used as a practical protocol for predicting the stability of U organic ligand complexes, allowing for the development of a relative stability series for organic complexes with limited experimental data. Solvation methods and DFT settings were benchmarked to suggest a suitable off-the-shelf solution. The results indicate that the IEFPCM solvation method should be employed. A mixed solvation approach improves the accuracy of the calculated stability constant (log β); however, the calculated log β are approximately five times more favorable than experimental data. Different basis sets, functionals, and effective core potentials were tested to check that there were no major changes in molecular geometries and Δ G. The recommended method employed is the B3LYP functional, aug-cc-pVDZ basis set for ligands, MDF60 ECP and basis set for U, and the IEFPCM solvation model. Using the fitting approach employed in the literature with these updated DFT settings allows fitting of 1:1 U complexes with root-mean-square deviation of 1.38 log β units. Fitting multiple bound carboxylate ligands indicates a second, separate fitting for 1:2 and 1:3 complexes.
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http://dx.doi.org/10.1021/acs.jpca.8b06863 | DOI Listing |
Environ Toxicol Chem
January 2025
Department of Environmental Science, Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX, United States.
The glucocorticoid receptor (GR) is present in almost every vertebrate cell and is utilized in many biological processes. Despite an abundance of mammalian data, the structural conservation of the receptor and cross-species susceptibility, particularly for aquatic species, has not been well defined. Efforts to reduce, refine, and/or replace animal testing have increased, driving the impetus to advance development of new approach methodologies (NAMs).
View Article and Find Full Text PDFAm J Speech Lang Pathol
January 2025
Good Samaritan Medical Center Foundation, Lafayette, CO.
Purpose: The aim of this study was to gauge the impacts of cognitive empathy training experiential learning on traumatic brain injury (TBI) knowledge, awareness, confidence, and empathy in a pilot study of speech-language pathology graduate students.
Method: A descriptive quasi-experimental convergent parallel mixed methods design intervention pilot study (QUAL + QUANT) was conducted with a diverse convenience sample of 19 first- and second-year speech-language pathology graduate students who engaged in a half-day TBI point-of-view simulation. The simulation was co-constructed through a participatory design with those living with TBI based on Kolb's experiential learning model and followed the recommendations for point-of-view simulation ethics.
PLOS Digit Health
January 2025
Social Physics and Complexity (SPAC) Lab, LIP-Laboratory for Instrumentation and Experimental Particle Physics, Lisboa, Portugal.
Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious diseases, we review the state of Digital Epidemiology at the beginning of 2020 and how it changed after the COVID-19 pandemic, in both nature and breadth. We argue that Epidemiology's progressive use of data generated outside of clinical and public health systems creates several technical challenges, particularly in carrying specific biases that are almost impossible to correct for a priori.
View Article and Find Full Text PDFPLoS Biol
January 2025
Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America.
Pivotal to self-preservation is the ability to identify when we are safe and when we are in danger. Previous studies have focused on safety estimations based on the features of external threats and do not consider how the brain integrates other key factors, including estimates about our ability to protect ourselves. Here, we examine the neural systems underlying the online dynamic encoding of safety.
View Article and Find Full Text PDFBiological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex.
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