The WASCAL hydrometeorological observatory in the Sudan Savanna of Burkina Faso and Ghana

Watersheds with rich hydrometeorological equipment are still very limited in West Africa but are essential for an improved analysis of environmental changes and their impacts in this region. This study gives an overview of a novel hydrometeorological observatory that was established for two mesoscale watersheds in the Sudan Savanna of Southern Burkina Faso and Northern Ghana as part of the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL) program. The study area is characterized by severe land cover changes due to a strongly increasing demand of agricultural land. The observatory is designed for long-term measurements of >30 hydrometeorological variables in subhourly resolution and further variables such as CO2. This information is complemented by long-term daily measurements from national meteorological and hydrological networks, among several other datasets recently established for this region. A unique component of the observatory is a micrometeorological field experiment using eddy covariance stations implemented at three contrasting sites (near-natural, cropland, and degraded grassland) to assess the impact of land cover changes on water, energy, and CO2 fluxes. The datasets of the observatory are needed by many modeling and field studies conducted in this region and are made available via the WASCAL database. Moreover, the observatory forms an excellent platform for future investigations and can be used as observational foundation for environmental observatories for an improved assessment of environmental changes and their socioeconomic impacts for the savanna regions of West Africa

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

BackgroundHypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.ObjectiveWe aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.Materials and MethodsWe enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, HCO3-, K+, Na+, anion gap, C-reactive protein) served as ground truth.ResultsRadiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.ConclusionThe constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity

A Novel Activator of CBP/p300 Acetyltransferases Promotes Neurogenesis and Extends Memory Duration in Adult Mice

International audienc

Late-Life Environmental Enrichment Induces Acetylation Events and Nuclear Factor B-Dependent Regulations in the Hippocampus of Aged Rats Showing Improved Plasticity and Learning

International audienc