Elaborating Hungarian segment of the Global Map of Salt-Affected Soils (GSSmap)

<p>Recently, FAO and Global Soil Partnership (GSP) launched the Global Map of Salt-affected Soils (GSSmap) international initiative, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The objective of our study is to present how Hungary contributed to this international initiative by preparing its own SAS maps according to the GSSmap specifications. For this purpose, we used not just a combination of advanced machine learning and multivariate geostatistical techniques for predicting the spatial distribution of the selected SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and for the subsoil (30–100 cm), but also a number of image indices exploiting a huge amount of relevant information contained in Sentinel-2 satellite images. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling of SAS indicators was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.</p><p> </p><p><strong>Acknowledgment:</strong> Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).</p>

Optimization of 1,4-Naphthoquinone Hit Compound: A Computational, Phenotypic, and In Vivo Screening against Trypanosoma cruzi

Chagas disease (CD) still represents a serious public health problem in Latin America, even after more than 100 years of its discovery. Clinical treatments (nifurtimox and benznidazole) are considered inadequate, especially because of undesirable side effects and low efficacy in the chronic stages of the disease, highlighting the urgency for discovering new effective and safe drugs. A small library of compounds (1a–i and 2a–j) was designed based on the structural optimization of a Hit compound derived from 1,4-naphthoquinones (C2) previously identified. The biological activity, structure-activity relationship (SAR), and the in silico physicochemical profiles of the naphthoquinone derivatives were analyzed. Most modifications resulted in increased trypanocidal activity but some substitutions also increased toxicity. The data reinforce the importance of the chlorine atom in the thiophenol benzene ring for trypanocidal activity, highlighting 1g, which exhibit a drug-likeness profile, as a promising compound against Trypanosoma cruzi. SAR analysis also revealed 1g as cliff generator in the structure-activity similarity map (SAS maps). However, compounds C2 and 1g were unable to reduce parasite load, and did not prevent mouse mortality in T. cruzi acute infection. Phenotypic screening and computational analysis have provided relevant information to advance the optimization and design of new 1,4-naphthoquinone derivatives with a better pharmacological profile.

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.