Advancing understanding of land-atmosphere interactions by breaking discipline and scale barriers.

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses).

Reviewing the state of the art of probiotics as clinical modalities for brain-gut-microbiota axis associated disorders.

The rise in prevalence of mental and neurological disorders is causing a high burden on society, however adequate interventions are not always available. The brain-gut-microbiota axis (BGMA) may provide a new angle for the development of clinical modalities. Due to the intricate bi-directional signaling between the brain and the gut, it may be helpful to look into interventions that target the gut, such as probiotics.

Predicting atmospheric optical properties for radiative transfer computations using neural networks.

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP).

Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers.

Major Depressive Disorder (MDD) is a heterogeneous disorder with a considerable symptomatic overlap with other psychiatric and somatic disorders. This study aims at providing evidence for association of a set of serum and urine biomarkers with MDD. We analyzed urine and serum samples of 40 MDD patients and 47 age- and sex-matched controls using 40 potential MDD biomarkers (21 serum biomarkers and 19 urine biomarkers).