Search

In this work, we prepared sulfur-zinc nanoparticles (ZnS-TGA) functionalized with thioglycolic acid by a hydrothermal method and tested their photodegradation ability by solar irradiation. ZnS-TGA were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), high-resolution transmission electron microscope (HR-TEM), UV–Vis spectrophotometer and photoluminescence spectroscopy. In the characterization of these nanoparticles, thioglycolic acid proved to be a strong capping ligand, with a specific surface area of 36.82 m2/g and an average size of 7.15 nm. To test the photocatalytic degradability of the product, methylene blue (MB) was used as a model pollutant. Various operational variables were investigated, including pH, amount of nanoparticles, dye concentration, contact time and temperature. The equilibrium adsorption tests, and the statistical physical calculations allowed the analysis of the energetic and steric variables of the adsorption of MB dye molecules on the surface of these nanoparticles. The equilibrium data were well fitted with Langmuir-Freundlich (L-F) and the adsorption kinetics with pseudo-first order. The maximum adsorption capacity of the MB dye removal process was 30.92 mg g−1 at pH 7 and 298 K, and this process was spontaneous and exothermic. The dye molecules and the surface of the nanoparticles exhibited physical interactions with adsorption energies of 23.31–25.92 kJ/mol. The photocatalytic activity of these nanoparticles resulted in a dye degradation efficiency of 91.1 % in 180 min. The photocatalytic efficiency remained almost unchanged after five consecutive degradation cycles, resulting in a methylene blue degradation of 85 %. According to these results, these environmentally friendly nanoparticles have the potential to purify industrial and urban liquids contaminated with harmful organic compounds such as dye molecules.

ABSTRACT: The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

The objective of this paper is to assess the potential of machine learning algorithms in predicting the indoor air temperature in a greenhouse using the outdoor data. A dataset gathering the main weather data and the indoor air temperature of a greenhouse located in Agadir, Morocco was used for this purpose. Machine learning models including support vector machine based-regression, ensemble trees and Gaussian process regression are compared against multiple linear regression models. This comparison was carried out on the basis of a 5-fold cross validation framework and across unseen data. The results show that all predictive models are capable of describing the indoor air temperature of the greenhouse and perform well (R2 > 0.9), with only 10% fraction of the dataset as training data. The Gaussian process regression outperforms all models, with R2 = 0.94 in the 5-fold cross validation test. However, the computational time related to the training of Gaussian process regression model is slightly higher than other machine learning models.

The data presented in this paper stand as supplementary information of the associated article “Karyological characterization of the common chameleon (Chamaeleo chamaeleon) provides insights on the evolution and diversification of sex chromosomes in Chamaeleonidae” [1]. This work provides (i) raw experimental data on the karyology of the common chameleon Chamaeleo chamaeleon and (ii) the results of bioinformatic analysis on sex-specific and repeated DNA sequences found in the same species. The karyological information here presented includes traditional staining method (Giemsa staining) and sequential C-banding + fluorochromes performed on Tunisian samples of the species. The sequence data include the alignments of the isolated DNA sequences with homologous sequences found in squamate Short Read Archives (SRAs) and the results of searches in public nucleic acid databases.

Since early times, the people of Morocco use medicinal and aromatic plants as traditional medicine to heal different human ailments. However, little studies have been made in the past to properly document and promote the traditional knowledge. This study was carried out in the Rif (North of Morocco), it aimed to identify medicinal and aromatic plant used by the local people to treat metabolic diseases, together with the associated ethnomedicinal knowledge. The ethnomedical information collected was from 582 traditional healers using semi-structured interviews, free listing and focus group. Family use value (FUV), use value (UV), plant part value (PPV), fidelity level (FL) and informant agreement ratio (IAR) were employed in data analysis. Medicinal and aromatic plant were collected, identified and kept at the natural resources and biodiversity laboratory, Ibn Tofail University, Kenitra. During the present study 30 medicinal plant species belonging to 14 families has been documented. The most frequent ailments reported were diabetes (IAR = 0.98). The majority of the remedies were prepared from infusion (53.9%). Leaves were the most frequently used plant part (PPV 0.633) and Rosmarinus officinalis L. (UV = 0.325) was the specie most commonly prescribed by local herbalists. The results of this study showed that people living in the Rif of Morocco are still dependent on medicinal and aromatic plants. The documented plants can serve as a basis for further studies on the regions medicinal plants knowledge and for future phytochemical and pharmacological studies.