This letter focuses on the importance of incorporating implicit bias into research integrity education, and the significant impact it can have on creating an equitable research environment.
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| http://dx.doi.org/10.1080/08989621.2023.2247974 | DOI Listing |
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Do reproductive endocrinology and infertility fellowship websites reflect a commitment to diversity, equity, and inclusion?
F S Rep
December 2024
Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University Hospitals Cleveland Medical Center, Beachwood, Ohio.
Objective: To characterize the frequency of diversity elements (DEs) in reproductive endocrinology and infertility (REI) fellowship websites and analyze these elements according to program characteristics.
Design: Forty-nine REI fellowship websites were assessed for 20 DEs that represent programmatic commitments to diversity, equity, and inclusion (DEI). Program websites were categorized by the number of discrete DEs featured: low (0-6); moderate (7-13); or high (14-20).
The curious case of dopaminergic prediction errors and learning associative information beyond value.
Nat Rev Neurosci
January 2025
Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA.
Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.
View Article and Find Full Text PDFHessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.
Sci Data
January 2025
IBM Research, Hursley, SO21 2JN, UK.
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.
View Article and Find Full Text PDFA novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine.
Sci Rep
December 2024
Center for Research on Microgrids (UPC CROM), Department of Electronic Engineering, Technical University of Catalonia, 08019, Barcelona, Spain.
With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system.
View Article and Find Full Text PDFComputational Design of (B)Chl Models: Structural and Chemical Modifications toward Enriched Properties.
J Phys Chem B
January 2025
Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, Via A. Moro 2, Siena 53100, Italy.
The functional units of natural photosynthetic systems control the process of converting sunlight into chemical energy. In this article, we explore a series of chemically and structurally modified bacteriochlorophyll and chlorophyll pigments through computational chemistry to evaluate their electronic spectroscopy properties. More specifically, we use multiconfigurational and time-dependent density functional theory methods, along with molecular dynamics simulations, to compute the models' energetics both in an implicit and explicit solvent environment.
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