ALBERT

Description: Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

Description: Although early childhood education is crucial for development, it is considered less important than other educational stages. For this reason, we sought to understand teachers’ perceptions about the effects of educational habits on 3–6 year old children’s learning, in addition to their engagement and level of commitment to make families assume greater responsibility over their children’s acquisition of habits. Further, differences of opinion were examined according to age, teaching experience, and years of experience at the center. The instrument consisted of twenty variables and four dimensions: working habits and effort, environmental factors and personal hygiene, healthy diets, and collaboration and cooperation. The instrument was sent out via email. Non-probability convenience sampling was performed (n = 320). The methodology used a descriptive and cross-sectional study, incorporating correlational (Pearson correlation) and inferential analyses. Statistics included one-way ANOVA, statistical power, effect size, and Scheffé’s test for multiple comparisons. Educational habits were deemed to have a positive impact, which favored studying. Effects were accentuated in relation to hard work and effort towards learning, with these outcomes being associated with other measured variables. Teachers aged between 34 and 40 years old showed greater commitment and attributed more importance to these habits. Teachers who had been at the school for longer evaluated environmental and hygiene habits more positively.

Description: Gamification is an innovative pedagogical approach to addressing problems related to social behaviour, student motivation and academic performance at different educational stages. Therefore, this research aimed to analyse its impact on the motivations and academic performances of university students. The research was carried out in the training of future teachers specialising in physical education during two academic courses. In total, 127 students participated in the study, divided into a gamified experimental group (n = 62) and a control group (n = 65). The participants completed a questionnaire to assess motivation in physical education before and after the intervention and performed a final exam to assess academic performance. The results indicated an increase in external regulation in the experimental group only. Furthermore, this group achieved significantly better academic performance. The findings of this study suggest that gamified implementation is beneficial for academic performance at the university stage, even though intrinsic motivation does not change. Furthermore, the nature of rewards or punishments, as characteristic of this pedagogical approach, could play an important role in the expected results, since external regulation increased significantly after the intervention.

Description: The phenolic compounds of extra-virgin olive oil can act at various levels to protect individuals against cardiovascular and neurodegenerative diseases, cancer, and osteoporosis, among others. Polyphenols in extra-virgin olive oil can stimulate the proliferation of osteoblasts, modify their antigen profile, and promote alkaline phosphatase synthesis. The objective of this work was to determine the effect of different extra-virgin olive oil phenolic compounds on the gene expression of osteoblast-related markers. The cells of the MG63 osteoblast line were cultured for 24 h with 10-6 M of the phenolic compounds ferulic acid, caffeic acid, coumaric acid, apigenin, or luteolin. The expression of studied markers was quantified using quantitative real-time polymerase chain reaction (q-RT-PCR). The expression by MG63 osteoblasts of growth and differentiation/maturation markers was modified after 24 h of treatment with 10-6 M of the phenolic compounds under study, most of which increased the gene expression of the transforming growth factor β1 (TGF-β1), TGF-β receptor 1,2 and 3 (TGF-βR1, TGF-βR2, TGF-βR3), bone morphogenetic protein 2 and 7 (BMP2, BMP7), run-related transcription factor 2 (RUNX-2), Alkaline phosphatase (ALP), Osteocalcin (OSC), Osterix (OSX), Collagen type I (Col-I) and osteoprotegerin (OPN). The extra-virgin olive oil phenolic compounds may have a beneficial effect on bone by modulating osteoblast physiology, which would support their protective effect against bone pathologies.

Description: The aromatic metabolites derived from yeast metabolism determine the characteristics of aroma and taste in wines, so they are considered of great industrial interest. Volatile esters represent the most important group and therefore, their presence is extremely important for the flavor profile of the wine. In this work, we use and compare two Saccharomyces cerevisiae yeast strains: P29, typical of sparkling wines resulting of second fermentation in a closed bottle; G1, a flor yeast responsible for the biological aging of Sherry wines. We aimed to analyze and compare the effect of endogenous CO2 overpressure on esters metabolism with the proteins related in these yeast strains, to understand the yeast fermentation process in sparkling wines. For this purpose, protein identification was carried out using the OFFGEL fractionator and the LTQ Orbitrap, following the detection and quantification of esters with gas chromatograph coupled to flame ionization detector (GC-FID) and stir-bar sorptive extraction, followed by thermal desorption and gas chromatography-mass spectrometry (SBSE-TD-GC-MS). Six acetate esters, fourteen ethyl esters, and five proteins involved in esters metabolism were identified. Moreover, significant correlations were established between esters and proteins. Both strains showed similar behavior. According to these results, the use of this flor yeast may be proposed for the sparkling wine production and enhance the diversity and the typicity of sparkling wine yeasts.

Description: Sparkling wines elaboration has been studied by several research groups, but this is the first report on analysis of biological processes according to the Gene Ontology terms (GO terms) and related to proteins expressed by yeast cells during the second fermentation of sparkling wines. This work provides a comprehensive study of the most relevant biological processes in Saccharomyces cerevisiae P29, a sparkling wine strain, during the second fermentation under two conditions (without and with endogenous CO2 overpressure) in the middle and the end of second fermentation. Consequently, a proteomic analysis with the OFFGEL fractionator and protein identification with LTQ Orbitrap XL coupled to HPLC were performed. The classification of biological processes was carried out using the tools provided by the Saccharomyces Genome Database. Results indicate that a greater number of biological processes were identified under condition without CO2 overpressure and in the middle of the fermentation versus the end of the second fermentation. The biological processes highlighted under condition without CO2 overpressure in the middle of the fermentation were involved in the carbohydrate and lipid metabolic processes and catabolic and biosynthetic processes. However, under CO2 overpressure, specific protein expression in response to stress, transport, translation, and chromosome organization and specific processes were not found. At the end of fermentation, there were higher specific processes under condition without CO2 overpressure; most were related to cell division, growth, biosynthetic process, and gene transcription resulting in increased cell viability in this condition. Under CO2 overpressure condition, the most representative processes were related to translation as tRNA metabolic process, chromosome organization, mRNA processing, ribosome biogenesis, and ribonucleoprotein complex assembly, probably in response to the stress caused by the hard fermentation conditions. Therefore, a broader knowledge of the adaptation of the yeast, and its behavior under typical conditions to produce sparkling wine, might improve and favor the wine industry and the selection of yeast for obtaining a high-quality wine.

Description: Sparkling wine is a distinctive wine. Saccharomyces cerevisiae flor yeasts is innovative and ideal for the sparkling wine industry due to the yeasts’ resistance to high ethanol concentrations, surface adhesion properties that ease wine clarification, and the ability to provide a characteristic volatilome and odorant profile. The objective of this work is to study the proteins in a flor yeast and a conventional yeast that are responsible for the production of the volatile compounds released during sparkling wine elaboration. The proteins were identified using the OFFGEL fractionator and LTQ Orbitrap. We identified 50 and 43 proteins in the flor yeast and the conventional yeast, respectively. Proteomic profiles did not show remarkable differences between strains except for Adh1p, Fba1p, Tdh1p, Tdh2p, Tdh3p, and Pgk1p, which showed higher concentrations in the flor yeast versus the conventional yeast. The higher concentration of these proteins could explain the fuller body in less alcoholic wines obtained when using flor yeasts. The data presented here can be thought of as a proteomic map for either flor or conventional yeasts which can be useful to understand how these strains metabolize the sugars and release pleasant volatiles under sparkling wine elaboration conditions.

Description: A correlation between autophagy and autolysis has been proposed in order to accelerate the acquisition of wine organoleptic properties during sparkling wine elaboration. In this context, a proteomic analysis was carried out in two industrial Saccharomyces cerevisiae strains (P29, conventional sparkling wine strain and G1, implicated in sherry wine elaboration) with the aim of studying the autophagy-related proteome and comparing the effect of CO2 overpressure during sparkling wine elaboration. In general, a detrimental effect of pressure and second fermentation development on autophagy-related proteome was observed in both strains, although it was more pronounced in flor yeast strain G1. Proteins mainly involved in autophagy regulation and autophagosome formation in flor yeast G1, and those required for vesicle nucleation and expansion in P29 strain, highlighted in sealed bottle. Proteins Sec2 and Sec18 were detected 3-fold under pressure conditions in P29 and G1 strains, respectively. Moreover, ‘fingerprinting’ obtained from multivariate data analysis established differences in autophagy-related proteome between strains and conditions. Further research is needed to achieve more solid conclusions and design strategies to promote autophagy for an accelerated autolysis, thus reducing cost and time production, as well as acquisition of good organoleptic properties.

Description: In this study, a first proteomic approach was carried out to characterize the adaptive response of cell wall-related proteins to endogenous CO2 overpressure, which is typical of second fermentation conditions, in two wine Saccharomyces cerevisiae strains (P29, a conventional second fermentation strain, and G1, a flor yeast strain implicated in sherry wine making). The results showed a high number of cell wall proteins in flor yeast G1 under pressure, highlighting content at the first month of aging. The cell wall proteomic response to pressure in flor yeast G1 was characterized by an increase in both the number and content of cell wall proteins involved in glucan remodeling and mannoproteins. On the other hand, cell wall proteins responsible for glucan assembly, cell adhesion, and lipid metabolism stood out in P29. Over-represented proteins under pressure were involved in cell wall integrity (Ecm33p and Pst1p), protein folding (Ssa1p and Ssa2p), and glucan remodeling (Exg2p and Scw4p). Flocculation-related proteins were not identified under pressure conditions. The use of flor yeasts for sparkling wine elaboration and improvement is proposed. Further research based on the genetic engineering of wine yeast using those genes from protein biomarkers under pressure alongside the second fermentation in bottle is required to achieve improvements.

Description: Apoptosis and later autolysis are biological processes which take place in Saccharomyces cerevisiae during industrial fermentation processes, which involve costly and time-consuming aging periods. Therefore, the identification of potential cell death biomarkers can contribute to the creation of a long-term strategy in order to improve and accelerate the winemaking process. Here, we performed a proteomic analysis based on the detection of possible apoptosis and autolysis protein biomarkers in two industrial yeast strains commonly used in post-fermentative processes (sparkling wine secondary fermentation and biological aging) under typical sparkling wine elaboration conditions. Pressure had a negatively effect on viability for flor yeast, whereas the sparkling wine strain seems to be more adapted to these conditions. Flor yeast strain experienced an increase in content of apoptosis-related proteins, glucanases and vacuolar proteases at the first month of aging. Significant correlations between viability and apoptosis proteins were established in both yeast strains. Multivariate analysis based on the proteome of each process allowed to distinguish among samples and strains. The proteomic profile obtained in this study could provide useful information on the selection of wine strains and yeast behavior during sparkling wine elaboration. Additionally, the use of flor yeasts for sparkling wine improvement and elaboration is proposed.