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Copyright of Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte is the property of Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tomato is one of the most commercialised and consumed fruits worldwide. However, tomatoes are highly susceptible to Alternaria rot. Among the safe strategies proposed to control Alternaria rot is the induction of defence mechanisms through biological elicitors, such as chitin. Chitin and its oligosaccharides are an activate plant defence mechanisms, but studies of fruits exposed to fungal chitin fragments are scarce. Therefore, the present work aimed to obtain and partially characterise chitin oligomers of Alternaria alternata, and evaluate their effect on the defence mechanism of tomato fruits and their tolerance to Alternaria rot. The chitin oligomers obtained had a molecular weight of ≤ 1 kDa, 12% N-acetyl-glucosamine, 0.2% residual protein, and were 94% acetylated. These oligomers markedly increased the enzymatic activity of chitinase and β-1,3-glucanase in tomato fruits, and the development of Alternaria rot was inhibited by 78%. Chitin oligomers of A. alternata represent a promising alternative to attenuate Alternaria rot in tomato fruits through an enzymatic defence mechanism. [ABSTRACT FROM AUTHOR]

Copyright of Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte is the property of Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Molecular Dynamics (MD) simulations on grain collisions allow to incorporate complex properties of dust interactions. We performed simulations of collisions of porous grains, each with many particles, using the MD software LAMMPS. The simulations consisted of a projectile grain striking a larger immobile target grain, with different impact velocities. The disadvantage of this method is the large computational cost due to a large number of particles being modeled. Machine Learning (ML) has the power to manipulate large data and build predictive models that could reduce MD simulation times. Using ML algorithms (Support Vector Machine and Random Forest), we are able to predict the outcome of MD simulations regarding fragment formation after a number of steps smaller than in usual MD simulations. We achieved a time reduction of at least 46%, for 90% accuracy. These results show that SVM and RF can be powerful yet simple tools to reduce computational cost in collision fragmentation simulations. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Medicine & Science of Physical Activity & Sport / Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte is the property of Revista Internacional de Medicina y Ciencias de la Actividad Fisica y del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Develop a literature review of the use of metaverses and virtual reality focused on teaching. This article was developed under an exploratory, descriptive methodology; using the following categories: metaverses, virtual reality, and teaching, a search was made in the Science, Scopus and web Science databases, obtaining one hundred reference articles. Education is going through stages of improvement, where the use of new tools helps to promote education. The use of virtual platforms focused on virtual reality, and metaverses are, in essence, the tools for the bestcataloged modern teaching, where this proved to be useful for education. [ABSTRACT FROM AUTHOR]

Many studies have reported that the exposure of microalgae cultures to red light increases the production of carbohydrates, while blue light promotes the production of protein. There are several studies about Navicula, however there are few, if any, studies of the combined effects of wavelength and light intensity on the biochemical composition of this genus In this study we evaluated the combined effect of three wavelengths: white (400-750nm), blue (430-480nm), and red (650-750nm), at two light intensities (50 and100 μmol/m2/sec) on the growth and biochemical composition of Navicula sp. cultured on a laboratory scale. The experiment was carried out under controlled conditions utilizing a factorial design 2×3 (light intensity and wavelength) with white light as the control. The cell concentration was measured daily. Dry biomass of filtered cells was incinerated at 450°C in a muffle oven. The biochemical content was measured using micro methods. The cell concentration was higher with white light at both intensities (291,875 and 90,938 cells/mL at 50 and100 μmol photon/m2/sec, respectively). Microalgae grown under blue wavelengths at 100 μmol photon/m2/sec had the highest dry biomass (1607 pg/cell). The highest percentage of protein was obtained under the blue light (22.83%), carbohydrates under the white light (4.13) and lipids under the red light (35.25%) all these results were observed under the low light intensity (50 μmol photon/m2/sec). The highest cell concentration and growth rate was observed under the low light intensity the largest proportions of which were proteins produced under the blue light. Lipid composition was not affected by light intensity or wavelength. [ABSTRACT FROM AUTHOR]

Guillaume Moulis,1–3,* Berta Ibañez,4–6,* Aurore Palmaro,2,3 Felipe Aizpuru,6–8 Eduardo Millan,6,8 Maryse Lapeyre-Mestre,2,3,9 Laurent Sailler,1–3 Koldo Cambra5,6,10 1Department of Internal Medicine, Toulouse University Hospital, Toulouse, France; 2UMR1027 INSERM, University of Toulouse, Toulouse, France; 3Clinical Investigation Center 1436, Toulouse University Hospital, Toulouse, France; 4Navarrabiomed, Health Department, Public University of Navarra, Pamplona, Spain; 5IdiSNA, Pamplona, Spain; 6Health Service Research on Chronic Patients Network (REDISSEC), Pamplona, Spain; 7Research Unit Araba (BioAraba), Osakidetza-Basque Health Department, Vitoria-Gasteiz, Spain; 8Healthcare Services Sub-directorate, Osakidetza-Basque Health Service, Araba, Spain; 9Department of Medical and Clinical Pharmacology, Toulouse University Hospital, Toulouse, France; 10Institute of Public Health and Labour Health of Navarra, Pamplona, Spain *These authors contributed equally to this work Aim: The EPICHRONIC (EPIdemiology of CHRONIC diseases) project investigated the possibility of developing common procedures for French and Spanish electronic health care databases to enable large-scale pharmacoepidemiological studies on chronic diseases. A feasibility study assessed the prevalence of type 2 diabetes mellitus (T2DM) in Navarre and the Basque Country (Spain) and the Midi-Pyrénées region (France). Patients and methods: We described and compared database structures and the availability of hospital, outpatient, and drug-dispensing data from 5.9 million inhabitants. Due to differences in database structures and recorded data, we could not develop a common procedure to estimate T2DM prevalence, but identified an algorithm specific to each database. Patients were identified using primary care diagnosis codes previously validated in Spanish databases and a combination of primary care diagnosis codes, hospital diagnosis codes, and data on exposure to oral antidiabetic drugs from the French database. Results: Spanish and French databases (the latter termed Système National d’Information Inter-Régimes de l’Assurance Maladie [SNIIRAM]) included demographic, primary care diagnoses, hospital diagnoses, and outpatient drug-dispensing data. Diagnoses were encoded using the International Classification of Primary Care (version 2) and the International Classification of Diseases, version 9 and version 10 (ICD-9 and ICD-10) in the Spanish databases, whereas the SNIIRAM contained ICD-10 codes. All data were anonymized before transferring to researchers. T2DM prevalence in the population over 20 years was estimated to be 6.6–7.0% in the Spanish regions and 6.3% in the Midi-Pyrénées region with ~2% higher estimates for males in the three regions. Conclusion: Tailored procedures can be designed to estimate the prevalence of T2DM in population-based studies from Spanish and French electronic health care records. Keywords: epidemiology, pharmacoepidemiology, electronic health care database, cross-national study, population-based study, type 2 diabetes mellitus