Long-term respiratory consequences of COVID-19 related pneumonia: a cohort study.

Background: Our aims were to describe respiratory sequelae up to 12 months after discharge in COVID-19 patients with severe pneumonia requiring non-invasive respiratory support therapies.

Methods: This study was undertaken at University Hospital Doctor Josep Trueta (Girona, Spain) between March 2020 and June 2020. Three months after discharge, we evaluated their dyspnoea and performed Saint George's respiratory questionnaire, pulmonary function tests, blood test, 6-min walking test, and a high-resolution CT (HRCT).

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level.

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France.

Changes in Onset of Vegetation Growth on Svalbard, 2000-2020.

The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30'N and 80°50'N. A cloud-free time series of MODIS-NDVI data was processed.

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN).

Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions.

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain.