The influence of six polymorphisms of uncoupling protein 3 (UCP3) gene and childhood obesity: a case-control study.

BACKGROUND: Obesity is defined as a multifactorial disease, marked by excessive accumulation of body fat, responsible for compromising the individual's health over the years. The energy balance is essential for the proper functioning of the body, as the individual needs to earn and spend energy in a compensatory way. Mitochondrial Uncoupling Proteins (UCP) help in energy expenditure through heat release and genetic polymorphisms could be responsible for reducing energy consumption to release heat and consequently generate an excessive accumulation of fat in the body. Thus, this study aimed to investigate the potential association between six UCP3 polymorphisms, that have not yet been represented in ClinVar®, and pediatric obesity susceptibility. METHODS: A case-control study was conducted with 225 children from Central Brazil. The groups were subdivided into obese (123) and eutrophic (102) individuals. The polymorphisms rs15763, rs1685354, rs1800849, rs11235972, rs647126, and rs3781907 were determined by real-time Polymerase Chain Reaction (qPCR). RESULTS: Biochemical and anthropometric evaluation of obese group showed higher levels of triglycerides, insulin resistance, and LDL-C and low level of HDL-C. Insulin resistance, age, sex, HDL-C, fasting glucose, triglyceride levels, and parents' BMI explained up to 50% of body mass deposition in the studied population. Additionally, obese mothers contribute 2 × more to the Z-BMI of their children than the fathers. The SNP rs647126 contributed to 20% to the risk of obesity in children and the SNP rs3781907 contribute to 10%. Mutant alleles of UCP3 increase the risk for triglycerides, total cholesterol, and HDL-C levels. The polymorphism rs3781907 is the only one that could not be a biomarker for obesity as the risk allele seem to be protective gains the increase in Z-BMI in our pediatric population. Haplotype analysis demonstrated two SNP blocks (rs15763, rs647126, and rs1685534) and (rs11235972 and rs1800849) that showed linkage disequilibrium, with LOD 76.3% and D' = 0.96 and LOD 57.4% and D' = 0.97, respectively. CONCLUSIONS: The causality between UCP3 polymorphism and obesity were not detected. On the other hand, the studied polymorphism contributes to Z-BMI, HOMA-IR, triglycerides, total cholesterol, and HDL-C levels. Haplotypes are concordant with the obese phenotype and contribute minimally to the risk of obesity.

Probabilistic fire spread forecast as a management tool in an operational setting.

BACKGROUND: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. RESULTS: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial-temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. CONCLUSION: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires.

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations.

Predicting wildfire spread is a challenging task fraught with uncertainties. 'Perfect' predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any fire model user to do so.