Short-term effects of repeated-sprint training on vertical jump ability and aerobic fitness in collegiate volleyball players during pre-season

International Journal of Exercise Science 15(6): 1040-1051, 2022. The aim of this study was to assess the effect of repeated-sprint training (RST) on vertical jump ability and aerobic power in college volleyball players. Nineteen male volleyball players, aged between 18-24 years, were randomized into the RST group (RST; n = 10) and control group (CG; n = 9). The RST included 2-3 sets of 6x30m all-out sprints, twice per week, in addition to the regular training routine. The control group performed only the regular volleyball training sessions (i.e. mainly of technical-tactical drills). All players performed a maximal graded treadmill test, vertical countermovement jump (CMJ), and repeated-vertical jump ability (RVJA) test before and after 6-weeks of the training program. The following variables were determined from the RVJA: peak (RVJApeak), average (RVJAmean), and rate of decrement (RVJADec). A two-way ANOVA with repeated measures showed an interaction effect on CMJ (F(1,17) = 6.92; p = 0.018; η2 = 0.289), RVJApeak (F(1,17) = 4.92; p = 0.040; η2 = 0.225), maximal oxygen uptake (F(1,17) = 9.29; p = 0.007; η2 = 0.353) and maximal speed attained in the treadmill test (F(1,17) = 8.66; p = 0.009; η2 = 0.337), with significant improvements only on the RST group. In conclusion, RST, twice per week, improved RVJA and aerobic power in comparison to regular skill-based volleyball training

Artificial neural network model of soil heat flux over multiple land covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America

Perspectivas da Mauritia flexuosa como potencial agente anti-hipertensivo / Perspectives of Mauritia flexuosa as a potential anti-hypertensive agent

INTRODUÇÃO: As doenças cardiovasculares representam desafio de saúde pública, destacando-se a hipertensão arterial sistêmica (HAS), devido ao alto índice de incidência populacional. O buriti (Mauritia flexuosa (MF)) é uma palmeira, encontrada nas regiões norte, nordeste e centro oeste, abundante em carotenoides e polifenóis, sendo empregado, farmacologicamente, como agente cicatrizante e antibacteriano. OBJETIVO: Investigar as perspectivas da MF como potencial agente anti-hipertensivo. METODOLOGIA: Trata-se de uma revisão de literatura, realizada de dezembro/2021 a fevereiro/2022 através dos bancos de dados on-line: PubMed, Scielo e Lilacs, utilizando os descritores: Mauritia flexuosa, buriti, sistema cardiovascular e hipertensão, nas línguas inglesa e portuguesa. Os artigos selecionados atendem ao período de 2008 a 2016, abordando-se a relação da MF com o sistema cardiovascular (SCV). Os dados foram organizados e expressos em tabela, utilizando-se o programa Excel®. RESULTADOS: De 46 artigos encontrados, 6 foram selecionados. Nenhum estudo emprega o buriti como agente hipotensor e anti-hipertensivo. Entretanto, constatou-se que a MF é rica em substâncias químicas com potencial antioxidante, antitrombótica e anti-plaquetária, tais como o ß-caroteno e ácidos graxos. CONCLUSÃO: Há escassez de estudos acerca da ação da MF sobre o SCV. Contudo, os constituintes químicos da MF representam potente perspectiva anti-hipertensiva, necessitando estudos farmacológicos do seu papel na fisiopatologia da HAS

Artificial neural network model of soil heat flux over multiple land covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America

Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types