Tissue engineers have developed a model to mimic the effects of aging to improve prevention and treatment of age-related macular degeneration and other age-related diseases, such as Alzheimer’s. 

Tender tea shoots are where many of tea’s most valued sensory compounds are made, transported, and transformed. 

Genomic tools are beginning to change how perennial fruit trees are bred, offering a way to identify promising seedlings before years of field evaluation. 

Research sponsored by the International Space Station (ISS) National Laboratory shows that microgravity disrupts how engineered microbes absorb nutrients, limiting their ability to produce useful materials in space. Published in npj Microgravity, the study examined Escherichia coli engineered to produce melanin—a protective pigment that could help shield astronauts and spacecraft from radiation and environmental stress. While the microbes successfully activated the genetic pathway for melanin production, nutrient uptake was impaired in microgravity, leaving key resources unused. Complementary ground-based experiments confirmed that fluid mixing and nutrient transport are major challenges. In contrast, fungal strains tested in the study remained resilient and continued producing melanin, highlighting their potential for space-based biomanufacturing. The findings will inform the design of future bioreactors and systems to enable reliable production of protective materials, medicines, and other valuable compounds during long-duration missions.

A University of Florida-led study of 1,051 Floridians finds strategic environmental communication significantly boosts public support for roadside pollinator habitat, with agricultural benefit frames driving the strongest attitudinal shifts.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with poor survival. This review explores CAR-T therapy, a revolutionary immunotherapy, as a potential treatment. It analyzes 74 studies, discussing targets like MSLN and HER2, challenges such as immune evasion, and combination strategies to enhance efficacy.

In spectroscopic analysis, the development of cost-effective handheld infrared spectrometers has revolutionized monitoring practices, particularly in industries like agriculture and food production. Analysis time and efficiency of processing the raw spectral data are further boosted by the implementation of machine learning models for identification and quantification purposes. However, the inherent spectral variations arising from the random spatial arrangement of inhomogeneous granular media pose accuracy challenges. To enable real-time monitoring with enhanced precision, a scanning approach is presented herein. It involves the utilization of microelectromechanical systems-based infrared spectral sensors with spot sizes of 3 to 20 mm, facilitating spectrospatial averaging by scanning over the sample surface. A model is developed for the absorbance repeatability of the nonhomogeneous samples. It takes into account the effects of spatial and temporal noise, the sample scanned area, and the optical throughput of the device, also revealing an optimum point for the light spot size. Through experimental validation performed on wheat, chosen among the major cereals, we demonstrate that the spatial scanning method considerably improves absorbance repeatability of the 10-mm sensor by up to 5 times compared to stationary measurements, showing a good agreement with the theoretical model. The impact of sample scanning on artificial intelligence-based chemometrics model accuracy is rigorously assessed using heterogeneous feed samples, revealing substantial enhancements in the accuracy of prediction models. The 10-mm sensor exhibits a substantial reduction in root mean square error for protein (−60%) and for moisture (−43.5%), 2 major nutritional quality indicators of cereal grains.