Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.

Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease.

Robust but weak winter atmospheric circulation response to future Arctic sea ice loss.

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.

Interaction between decadal-to-multidecadal oceanic variability and sudden stratospheric warmings.

Major sudden stratospheric warmings (SSWs) are the most important phenomena of the wintertime boreal stratospheric variability. During SSWs, the polar temperature increases abruptly, and easterlies prevail in the stratosphere. Their effects extend farther from the polar stratosphere, affecting near-surface circulation.

Estimating Subseasonal Variability and Trends in Global Atmosphere Using Reanalysis Data.

A new measure of subseasonal variability is introduced that provides a scale-dependent estimation of vertically and meridionally integrated atmospheric variability in terms of the normal modes of linearized primitive equations. Applied to the ERA-Interim data, the new measure shows that subseasonal variability decreases for larger zonal wave numbers. Most of variability is due to balanced (Rossby mode) dynamics but the portion associated with the inertio-gravity (IG) modes increases as the scale reduces.