Recent findings and future directions for the intersection of genetic and environmental contributions to schizophrenia.

It is well established that both genetic and environmental factors contribute to risk for schizophrenia (SCZ), and much progress has been made in identifying the specific factors conferring risk. However, the nature and extent of interactions between them has long been a topic of debate. Both the data and methods available to address this have evolved rapidly, enabling new prospects for identifying gene-environment interactions in SCZ.

Higher temperatures shorten inter-nesting periods in olive ridley turtles.

Climate change is impacting sea turtles worldwide with the effects varying between species and populations. For example, rising temperatures have variable effects on the duration of the inter-nesting period (IP)-the time between two consecutive nests during a single nesting season. Specifically, a negative correlation between water temperature and IP has been reported in green (Chelonia mydas), hawksbill (Eretmochelys imbricata), and loggerhead (Caretta caretta) turtles.

Solar-Driven Atmospheric Water Production Through Hierarchically Ordered Porous Carbon for Self-Sustaining Green Hydrogen Production.

Green hydrogen production by proton exchange membrane water electrolysis (PEMWE) powered by clean energy is a promising and environmentally friendly technology. However, it relies on a high-purity water source, which is limited in regions facing water scarcity. Here, a coupled self-sustaining solar-enabled system is reported that couples atmospheric water harvesting with PEM water electrolysis (AWH-PEMWE), offering a novel pathway for clean water generation and green hydrogen production.

Light use efficiency (LUE) based bimonthly gross primary productivity (GPP) for global grasslands at 30 m spatial resolution (2000-2022).

The article describes production of a high spatial resolution (30 m) bimonthly light use efficiency (LUE) based gross primary productivity (GPP) data set representing grasslands for the period 2000 to 2022. The data set is based on using reconstructed global complete consistent bimonthly Landsat archive (400TB of data), combined with 1 km MOD11A1 temperature data and 1° CERES Photosynthetically Active Radiation (PAR). First, the LUE model was implemented by taking the biome-specific productivity factor (maximum LUE parameter) as a global constant, producing a global bimonthly (uncalibrated) productivity data for the complete land mask.