Current targets to protect environment not enough

Artificial intelligence can provide important insights into how complex chemical mixes in rivers affect aquatic life, paving the path for more effective environmental protection.

The University of South Australia has published new research showing that children born to obese mothers both before and during pregnancy are more likely to experience neuropsychiatric and behavioural disorders, such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD).

Tuning this environment by controlling how the CoPc catalyst was distributed on the carbon nanotube surface allowed carbon dioxide to be as much as eight times more likely to produce methanol, a discovery that could increase the efficiency of other catalytic processes and have a widespread impact on other fields, said Robert Baker, co-author of the study and a professor in chemistry and biochemistry at The Ohio State University.

Researchers have identified specific genetic changes that can result in newborns having little to no immune protection against illness.

Researchers have developed a simple strategy that improves the accuracy of approaches used to understand how environmental elements like temperature affect gene activity in plants.

Researchers have identified a microscopic worm species that infects and kills insects.

Researchers identified a microscopic worm species that infects and kills insects.

Studies conducted by scientists at the University of Queensland revealed the critical function of saturated fatty acids in the brain's memory consolidation process.

The purpose of exodermal suberin has long been unknown, but a new study published in Nature Plants by researchers at the University of California, Davis demonstrates that it fulfils the same function as endodermal suberin and that tomato plants are less able to cope with water stress without it. This knowledge could aid scientists in developing drought-resistant crops.

To address this gap in soil MP research, a group of scientists led by Prof. Yong Sik Ok employed machine learning (ML) algorithms to assess and forecast the influence of MPs on soil parameters.