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Proceedings of the National Academy of Sciences


Machine-learning detection of neurodevelopmental disorders

A machine learning algorithm identified altered pupil diameter fluctuations in mouse models of autism spectrum disorders, allowing early detection of developmental disorders. The algorithm distinguished Rett syndrome patients from controls based on heart rate fluctuations, suggesting a potential biomarker for early detection.

SourceProceedings of the National Academy of Sciences·JournalProceedings of the National Academy of Sciences·DateJul 22, 2019

DNA analysis of Gibraltar Neanderthals

Researchers analyzed DNA from Gibraltar Neanderthal remains found in 1848 and 1926, finding that some sequences were deaminated due to damage, while others showed significant human DNA contamination. The study suggests it is possible to analyze ancient DNA in highly contaminated specimens using a specific preparation method.

SourceProceedings of the National Academy of Sciences·JournalProceedings of the National Academy of Sciences·DateJul 15, 2019

B and T cells in celiac disease

Researchers discovered that B cells specifically binding N-terminal epitopes of transglutaminase 2 (TG2) more efficiently take up and present TG2-gluten complexes to gluten-specific T cells. This suggests that B cells with this specificity are the main antigen-presenting cells for pathogenic T cells in celiac disease.

SourceProceedings of the National Academy of Sciences·JournalProceedings of the National Academy of Sciences·DateJul 8, 2019

Arctic lead pollution and economic history

A study on Arctic lead pollution reveals a significant increase in emissions from European industries during the Middle Ages, coinciding with technological advancements and economic growth. The pollution level declined substantially after pollution abatement policies were enacted, but still remains much higher than historical levels.

SourceProceedings of the National Academy of Sciences·JournalProceedings of the National Academy of Sciences·DateJul 8, 2019

Improving wind farm efficiency

Researchers developed a wake steering technique that increases total power production while reducing variability in wind speeds, leading to improved efficiency for near-average wind conditions. The study used field experiments on an array of 6 turbines over 10 days at a wind farm in Alberta, Canada.

SourceProceedings of the National Academy of Sciences·JournalProceedings of the National Academy of Sciences·DateJul 1, 2019