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

Glycaemic control in diabetic rats treated with islet transplantation using plasma combined with hydroxypropylmethyl cellulose hydrogel

Islet transplantation is one of the most efficient cell therapies used in clinics and could treat a large proportion of patients with diabetes. However, it is limited by the high requirement of pancreas necessary to provide the sufficient surviving islet mass in the hepatic tissue and restore normoglycaemia. Reduction in organ procurement requirements could be achieved by extrahepatic transplantation using a biomaterial that enhances islet survival and function. We report a plasma-supplemented hydroxypropyl methylcellulose (HPMC) hydrogel, engineered specifically using a newly developed technique for intra-omental islet infusion, known as hOMING (h-Omental Matrix Islet filliNG). The HPMC hydrogel delivered islets with better performance than that of the classical intrahepatic infusion. After the validation of the HPMC suitability for islets in vivo and in vitro, plasma supplementation modified the rheological properties of HPMC without affecting its applicability with hOMING. The biomaterial association was proven to be more efficient both in vitro and in vivo, with better islet viability and function than that of the current clinical intrahepatic delivery technique. Indeed, when the islet mass was decreased by 25% or 35%, glycaemia control was observed in the group of plasma-supplemented hydrogels, whereas no regulation was observed in the hepatic group. Plasma gelation, observed immediately post infusion, decreased anoïkis and promoted vascularisation. To conclude, the threshold mass for islet transplantation could be decreased using HPMC-Plasma combined with the hOMING technique. The simplicity of the hOMING technique and the already validated use of its components could facilitate its transfer to clinics

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

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Background: Neuroscience lacks a reliable method of screening the early stages of dementia.Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains.Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes.Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p &amp;gt; 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p &amp;lt; 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p &amp;gt; 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age.Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.</jats:p