Changes in Cholesterol, Triglycerides and Body Composition in Pregnant Mares

Background: Mares are very different from other species during pregnancy, and studies on the physiological changes of this period are important. During late pregnancy, the distribution of weight and body fat are often used as indicators of adequate nutrition. This is a physiological period that results in an increase in metabolic demand. There is a tendency for the Criollo breed to have a higher body condition score that becomes more evident during pregnancy, a period when mares tend to gain more weight. The current study monitored serum cholesterol and triglyceride levels in pregnant mares during late gestation to determine a possible correlation with the distribution of fat or body weight.Materials, Methods & Results: Four body parameters of thirty-four Criollo-type mares in late gestation were evaluated: body weight measured with a weight scale, body weight using a commercial weight tape, total body fat and fat thickness and the serum levels of total cholesterol and triglycerides. The fat thickness was measured in an ultrasound device and the prediction of total body fat was calculated using an equation. According to the days prior foaling, biometric monitoring and blood collection were carried out in five periods: F-90 (± 90 days prior to foaling) n = 33; F-60 (± 60 days prior to foaling) n = 33; F-30 (± 30 days prior to foaling) n = 31; F-15 (± 15 days prior to foaling) n = 29 and Foaling (at day of foaling) n = 14. Mares were monitored daily and accompanied foaling was also performed, ensuring collection at the right time. Comparisons of means were performed between variables in addition to the Pearson correlation test. Statistical significance was established at P 0.05). A strong positive correlation was observed between the average weights (P 0.068). There was a strong positive correlation between weights (P 0.191). There was also no correlation with the body composition (P > 0.068).Discussion: The absence of difference between the periods in relation to the weight measures and the correlations existing in these measures is related to the period in which they were collected, since the maximum relative weight of the foal is reached in ten months, causing the mare's weight stability. Interestingly, an unexplained increase in total cholesterol levels was found on the day of foaling. As the same change in triglycerides was not observed and there was no change in the diet or feeding behavior of the mares, the effects of the diet can be excluded in this case, which requires further studies to explain this result. Our hypothesis is that this increase is linked to hormones that tend to change in this pre-delivery period and that have their metabolism strongly linked to cholesterol levels. Levels of body fat and mare weight can therefore be correlated in the late gestation, allowing for their use as indicators of adequate nutritional and energy reserves

The germline mutational landscape of BRCA1 and BRCA2 in Brazil

The detection of germline mutations in BRCA1 and BRCA2 is essential to the formulation of clinical management strategies, and in Brazil, there is limited access to these services, mainly due to the costs/availability of genetic testing. Aiming at the identification of recurrent mutations that could be included in a low-cost mutation panel, used as a first screening approach, we compiled the testing reports of 649 probands with pathogenic/likely pathogenic variants referred to 28 public and private health care centers distributed across 11 Brazilian States. Overall, 126 and 103 distinct mutations were identified in BRCA1 and BRCA2, respectively. Twenty-six novel variants were reported from both genes, and BRCA2 showed higher mutational heterogeneity. Some recurrent mutations were reported exclusively in certain geographic regions, suggesting a founder effect. Our findings confirm that there is significant molecular heterogeneity in these genes among Brazilian carriers, while also suggesting that this heterogeneity precludes the use of screening protocols that include recurrent mutation testing only. This is the first study to show that profiles of recurrent mutations may be unique to different Brazilian regions. These data should be explored in larger regional cohorts to determine if screening with a panel of recurrent mutations would be effective.This work was supported in part by grants from Barretos Cancer Hospital (FINEP - CT-INFRA, 02/2010), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, 2013/24633-2 and 2103/23277-8), Fundação de Apoio à Pesquisa do Rio Grande do Norte (FAPERN), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), Ministério da Saúde, the Breast Cancer Research Foundation (Avon grant #02-2013-044) and National Institute of Health/National Cancer Institute (grant #RC4 CA153828-01) for the Clinical Cancer Genomics Community Research Network. Support in part was provided by grants from Fundo de Incentivo a Pesquisa e Eventos (FIPE) from Hospital de Clínicas de Porto Alegre, by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, BioComputacional 3381/2013, Rede de Pesquisa em Genômica Populacional Humana), Secretaria da Saúde do Estado da Bahia (SESAB), Laboratório de Imunologia e Biologia Molecular (UFBA), INCT pra Controle do Câncer and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). RMR and PAP are recipients of CNPq Productivity Grants, and Bárbara Alemar received a grant from the same agencyinfo: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 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

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Author correction: One sixth of Amazonian tree diversity is dependent on river floodplains

In the version of the article initially published, the affiliation of Edgardo Manuel Latrubesse was incorrect and has now been amended to Environmental Sciences Graduate Program-CIAMB, Federal University of Goiás, Goiânia, Brazil in the HTML and PDF versions of the article