Sex differences in 3- to 5-year-old children's motor competence : a pooled cross-sectional analysis of 6241 children

There is some, albeit inconsistent, evidence supporting sex differences in preschoolers' motor competence (MC), with these observations not uniform when analyzed by age, and cultural groups. Thus, this study examined sex differences across ages in 3- to 5-year-old children's MC. A cross-country pooled sample of 6241 children aged 3–5 years (49.6% girls) was assessed for MC using the Test of Gross Motor Development—2nd/3rd edition, and children were categorized into groups of age in months. Multiple linear regression models and predictive margins were calculated to explore how sex and age in months affect scores of MC (i.e., locomotor and ball skills), with adjustments for country and BMI. The Chow's Test was used to test for the presence of a structural break in the data. Significant differences in favor of girls were seen at 57–59 and 66–68 months of age for locomotor skills; boys performed better in ball skills in all age periods, except for 42–44 and 45–47 months of age. The higher marginal effects were observed for the period between 45–47 and 48–50 months for locomotor skills (F = 30.21; and F = 25.90 for girls and boys, respectively), and ball skills (F = 19.01; and F = 42.11 for girls and boys, respectively). A significantly positive break point was seen at 45–47 months, highlighting the age interval where children's MC drastically improved. The identification of this breakpoint provides an evidence-based metric for when we might expect MC to rapidly increase, and an indicator of early delay when change does not occur at that age

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Frequência da artéria calosomarginal e proposição de hipótese quanto ao seu significado filogenético

A frequência da artéria calosomarginal foi estudada em 52 cérebros humanos, adultos e de ambos os sexos. Neste estudo foram considerados a origem e os padrões de ramificação da artéria calosomarginal. Com bases no trabalho de Watts23 em primatas subhumanos, foi proposta uma hipótese do significado evolutivo da artéria calosomarginal

Uncovering the bioactivity of Aurantiochytrium sp.: a comparison of extraction methodologies

Aurantiochytrium sp. is an emerging alternative source of polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA), and squalene, playing an important role in the phasing out of traditional fish sources for these compounds. Novel lipid extraction techniques with a focus on sustainability and low environmental footprint are being developed for this organism, but the exploration of other added-value compounds within it is still very limited. In this work, a combination of novel green extraction techniques (high hydrostatic pressure extraction (HPE) and supercritical fluid extraction (SFE)) and traditional techniques (organic solvent Soxhlet extraction and hydrodistillation (HD)) was used to obtain lipophilic extracts of Aurantiochytrium sp., which were then screened for antioxidant (DPPH radical reduction capacity and ferric-reducing antioxidant potential (FRAP) assays), lipid oxidation protection, antimicrobial, anti-aging enzyme inhibition (collagenase, elastase and hyaluronidase), and anti-inflammatory (inhibition of NO production) activities. The screening revealed promising extracts in nearly all categories of biological activity tested, with only the enzymatic inhibition being low in all extracts. Powerful lipid oxidation protection and anti-inflammatory activity were observed in most SFE samples. Ethanolic HPEs inhibited both lipid oxidation reactions and microbial growth. The HD extract demonstrated high antioxidant, antimicrobial, and antiinflammatory activities making, it a major contender for further studies aiming at the valorization of Aurantiochytrium sp. Taken together, this study presents compelling evidence of the bioactive potential of Aurantiochytrium sp. and encourages further exploration of its composition and application.info:eu-repo/semantics/publishedVersio