727 research outputs found

    Soil pyrogenic carbon in southern Amazonia: Interaction between soil, climate, and above-ground biomass

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    This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement: The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s.The Amazon forest represents one of the world’s largest terrestrial carbon reservoirs. Here, we evaluated the role of soil texture, climate, vegetation, and distance to savanna on the distribution and stocks of soil pyrogenic carbon (PyC) in intact forests with no history of recent fire spanning the southern Amazonia forest-Cerrado Zone of Transition (ZOT). In 19 one hectare forest plots, including three Amazonian Dark Earth (ADE, terra preta) sites with high soil PyC, we measured all trees and lianas with diameter ≥ 10 cm and analyzed soil physicochemical properties, including texture and PyC stocks. We quantified PyC stocks as a proportion of total organic carbon using hydrogen pyrolysis. We used multiple linear regression and variance partitioning to determine which variables best explain soil PyC variation. For all forests combined, soil PyC stocks ranged between 0.9 and 6.8 Mg/ha to 30 cm depth (mean 2.3 ± 1.5 Mg/ha) and PyC, on average, represented 4.3% of the total soil organic carbon (SOC). The most parsimonious model (based on AICc) included soil clay content and above-ground biomass (AGB) as the main predictors, explaining 71% of soil PyC variation. After removal of the ADE plots, PyC stocks ranged between 0.9 and 3.8 Mg/ha (mean 1.9 ± 0.8 Mg/ha–1) and PyC continued to represent ∼4% of the total SOC. The most parsimonious models without ADE included AGB and sand as the best predictors, with sand and PyC having an inverse relationship, and sand explaining 65% of the soil PyC variation. Partial regression analysis did not identify any of the components (climatic, environmental, and edaphic), pure or shared, as important in explaining soil PyC variation with or without ADE plots. We observed a substantial amount of soil PyC, even excluding ADE forests; however, contrary to expectations, soil PyC stocks were not higher nearer to the fire-dependent Cerrado than more humid regions of Amazonia. Our findings that soil texture and AGB explain the distribution and amount of soil PyC in ZOT forests will help to improve model estimates of SOC change with further climatic warming.Coordination for the Improvement of Higher Education Personnel (CAPES)Natural Environment Research Council (NERC

    Transtorno autístico e doença celíaca : sem evidências de associação

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    Objective: To evaluate the possible association between celiac disease (CD) and/or gluten sensitivity (GS) and autism spectrum disorder (ASD). Methods: Occurrences of CD were determined in a group of children and adolescents affected by ASD and, conversely, occurrences of ASD were assessed in a group of biopsy-proven celiac patients. To detect the possible existence of GS, the levels of antigliadin antibodies in ASD patients were assessed and compared with the levels in a group of non-celiac children. Results: The prevalence of CD or GS in ASD patients was not greater than in groups originating from the same geographical area. Similarly the prevalence of ASD was not greater than in a group of biopsy-proven CD patients. Conclusion: No statistically demonstrable association was found between CD or GS and ASD. Consequently, routine screening for CD or GS in all patients with ASD is, at this moment, neither justifed nor cost-effective. ___________________________________________________________________________________ RESUMOObjetivo: Avaliar a possível associação entre doença celíaca (DC) e/ou sensibilidade ao glúten (SG) e transtorno do espectro autista (TEA). Métodos: Ocorrências de DC foram determinadas em um grupo de crianças e adolescentes afetados pelo TEA e a ocorrência d TEA foi avaliada em um grupo de pacientes com DC comprovada por biópsia. Para detectar a possível existência de SG, foram determinados níveis de anticorpos antigliadina em pacientes com TEA e comparados ao grupo de crianças sem a doença celíaca. Resultados: A prevalência de DC ou SG não foi maior no grupo de pacientes com TEA quando comparada a grupos de indivíduos originários da mesma região geográfca. De modo similar, a prevalência do TEA não foi maior ao ser comparada ao grupo de pacientes com DC. Conclusão: Não houve associação estatisticamente demonstrável entre DC ou SG e TEA. Consequentemente, não são justifcáveis, no momento, exames de rotina para detecção de DC ou SG em pacientes com TEA

    Soil water-holding capacity and monodominance in Southern Amazon tropical forests

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    Background and aims: We explored the hypothesis that low soil water-holding capacity is the main factor driving the monodominance of Brosimum rubescens in a monodominant forest in Southern Amazonia. Tropical monodominant forests are rare ecosystems with low diversity and high dominance of a single tree species. The causes of this atypical condition are still poorly understood. Some studies have shown a relationship between monodominance and waterlogging or soil attributes, while others have concluded that edaphic factors have little or no explanatory value, but none has accounted for soil-moisture variation other than waterlogging. This study is the first to explicitly explore how low soil water-holding capacity influences the monodominance of tropical forests. Methods: We conducted in situ measurements of vertical soil moisture using electrical resistance collected over 1 year at 0–5; 35–40 and 75–80 cm depths in a B. rubescens monodominant forest and in an adjacent mixed-species forest in the Amazon-Cerrado transition zone, Brazil. Minimum leaf water potential (Ψmin) of the seven most common species, including B. rubescens, and soil water-holding capacity for both forests were determined. Results: The vertical soil moisture decay pattern was similar in both forests for all depths. However, the slightly higher water availability in the monodominant forest and Ψmin similarity between B. rubescens and nearby mixed forest species indicate that low water-availability does not cause the monodominance. Conclusions: We reject the hypothesis that monodominance of B. rubescens is primarily determined by low soil water-holding capacity, reinforcing the idea that monodominance in tropical forests is not determined by a single factor

    Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

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    <p>Abstract</p> <p>Background</p> <p>Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome.</p> <p>Methods</p> <p>Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed.</p> <p>Results</p> <p>Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846), Barnetson (0.850), MMRpro (0.821) and Wijnen (0.807) models did not present significant statistical difference. The Myriad model presented lower AUC (0.704) than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad) and 0.87 (Wijnen) and specificity ranged from 0 (Myriad) to 0.38 (Barnetson).</p> <p>Conclusions</p> <p>The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.</p
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