A landmark study published in PNAS has unveiled that brain aging follows a distinct yet nonlinear trajectory with critical transition points. The research, conducted by an international team of scientists led by Lilianne R. Mujica-Parodi, PhD, of Stony Brook University, offers new insights into when interventions to prevent cognitive decline might be most effective.

The brain plays a vital role in controlling reproductive functions. It helps to maintain a delicate balance of hormones, all of which can be affected by the metabolism. Investigating the impact of the metabolism on reproductive development and function is critical to a better understanding of health and diseases. Professor Carol Fuzeti Elias and Dr Cristina Sáenz de Miera Patín from the University of Michigan in the USA, carry out groundbreaking research in neuroscience, exploring the molecular and neural mechanisms at play.

Movie watching may provide scientists a window into infant brain activity and organization.

Cross-sectional age-skill profiles suggest that cognitive skills start declining by age 30 if not earlier. If accurate, such age-driven skill losses pose a major threat to the human capital of societies with rapidly aging populations. We estimate actual age-skill profiles from individual changes in literacy and numeracy skills at different ages. We use the unique German longitudinal component of the Programme of the International Assessment of Adult Competencies (PIAAC-L) that retested a large representative sample of adults after 3.5 years. Our empirical approach separates age from cohort effects and corrects for measurement error from reversion to the mean. Two main results emerge. First, average skills increase strongly into the forties before decreasing slightly in literacy and more strongly in numeracy. Second, skills decline at older ages only for those with below-average skill usage. White-collar and higher-educated workers with above-average usage show increasing skills even beyond their forties. Women have larger skill losses at older age, particularly in numeracy.

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach, for swiftly designing antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 micrograms per milliliter against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.

Researchers from the University of Würzburg are opening up new horizons in chemistry: They present the world's first triple bond between the atoms boron and carbon.

• Researchers found that infectious bacteria in diabetic mice rapidly evolved resistance to antibiotics.
• Controlling blood sugar in the mice via insulin significantly reduced the emergence of drug-resistant bacteria.

• Scientists found that, when deprived of amino acids, cancer cells cooperated to extract and share them from their environment.
• Blocking a protein called CNDP2 shut down this cooperative survival strategy, suggesting a new potential target for cancer treatment.

Experiments in tiny freshwater animals suggest that certain tumors manipulate their host’s body to increase the likelihood of being transmitted to the next generation.