Aging leads to sex-dependent effects on pair bonding and increased number of oxytocin-producing neurons in monogamous prairie voles.

Pair bonds powerfully modulate health, which becomes particularly important when facing the detrimental effects of aging. To examine the impact of aging on relationship formation and response to loss, we examined behavior in naive 6-, 12-, and 18-month male and female prairie voles, a monogamous species that forms mating-based pair bonds. We found that older males (18-months) bonded quicker than younger voles, while similarly aged female voles increased partner directed affiliative behaviors.

Aging leads to sex-dependent effects on pair bonding and increased number of oxytocin-producing neurons in monogamous prairie voles.

Unlabelled: Pair bonds powerfully modulate health, which becomes particularly important when facing the detrimental effects of aging. To examine the impact of aging on relationship formation and response to loss, we examined behavior in 6-, 12-, and 18-month male and female prairie voles, a monogamous species that forms mating-based pair bonds. We found that older males (18-months) bonded quicker than younger voles, while similarly aged female voles increased partner directed affiliative behaviors.

Standardization parameters and synergism of source plant materials for the antidiabetic efficacy of the Rauvolfia-Citrus tea.

The introduction of glucagon-like peptide 1 (GLP-1)-based therapies has greatly improved the management of type 2 diabetes (T2D), as they ensure good blood glucose control and promote weight loss. Ingestion of standardized herbal remedies that promote the same endogenous metabolic processes affected by the GLP-1-based treatments could provide cheaper alternatives in low- and middle-income countries, where there is currently an increase in the incidence of T2D. The focus in this study was to determine quality control parameters and the prime factors for the Rauvolfia-Citrus tea (RC-tea), as used in Nigerian traditional medicine to treat T2D.

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations.