Soil legacy data rescue via GlobalSoilMap and other international and national initiatives The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.

Most common machine learning (ML) algorithms usually work well on balanced training sets, that is, datasets in which all classes are approximately represented equally. Otherwise, the accuracy estimates may be unreliable and classes with only a few values are often misclassified or neglected. This is known as a class imbalance problem in machine learning and datasets that do not meet this criterion are referred to as imbalanced data. Most datasets of soil classes are, therefore, imbalanced data. One of our main objectives is to compare eight resampling strategies that have been developed to counteract the imbalanced data problem. We compared the performance of five of the most common ML algorithms with the resampling approaches.The highest increase in prediction accuracy was achieved with SMOTE (the synthetic minority oversampling technique). In comparison to the baseline prediction on the original dataset, we achieved an increase of about 10, 20 and 10% in the overall accuracy, kappa index and F-score, respectively. Regarding the ML approaches, random forest (RF) showed the best performance with an overall accuracy, kappa index and F-score of 66, 60 and 57%, respectively. Moreover, the combination of RF and SMOTE improved the accuracy of the individual soil classes, compared to RF trained on the original dataset and allowed better prediction of soil classes with a low number of samples in the corresponding soil profile database, in our case for Chernozems. Our results show that balancing existing soil legacy data using synthetic sampling strategies can significantly improve the prediction accuracy in digital soil mapping (DSM). Highlights• Spatial distribution of soil classes in Iran can be predicted using machine learning (ML) algorithms. • The synthetic minority oversampling technique overcomes the drawback of imbalanced and highly biased soil legacy data. • When combining a random forest model with synthetic sampling strategies the prediction accuracy of the soil model improves significantly.

Psoriasis is a chronic inflammatory skin disease characterized by excessive cellular replication. Apolipoproteins are genetically determined molecule whose role has been implied in cardiovascular pathology. Vascular adhesion protein-1 (VAP-1) is an adhesion molecule with an enzymatic activity that partakes in the migration process of lymphocytes into sites of inflammation. Our purpose was to evaluate the plasma lipid profiles, apolipoproteins (A1, B) and Lp (a) and VAP-1 in order to compare the lipid profile in psoriatic patients with non-affected persons and correlation between VAP-1 and Lp (a). We determined serum concentrations of lipids, lipoproteins , apolipoproteins and VAP-1 in 90 patients with psoriasis and 90 age matched controls. Serum Lp (a), apo A1 and apo B were measured by immunoprecipitation assays, and the lipids and lipoproteins were measured by enzymatic methods.The VAP-1 were measured by ELISA method. The mean levels of total cholesterol, LDL, apo B and VAP-1 in patients with psoriasis were found to be significantly higher than those of healthy subjects (P<0.05. In psoriatic patients, elevation of VAP-1 correlated with elevation of Lp (a) (p = 0.025). This study shows that high serum lipid level and VAP-1, is significantly more common in psoriasis. This fact may be responsible for higher prevalence of cardiovascular accident in psoriatic patients.

No abstract