REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Reporting on methods of subgroup analysis in clinical trials: a survey of four scientific journals

Results of subgroup analysis (SA) reported in randomized clinical trials (RCT) cannot be adequately interpreted without information about the methods used in the study design and the data analysis. Our aim was to show how often inaccurate or incomplete reports occur. First, we selected eight methodological aspects of SA on the basis of their importance to a reader in determining the confidence that should be placed in the author's conclusions regarding such analysis. Then, we reviewed the current practice of reporting these methodological aspects of SA in clinical trials in four leading journals, i.e., the New England Journal of Medicine, the Journal of the American Medical Association, the Lancet, and the American Journal of Public Health. Eight consecutive reports from each journal published after July 1, 1998 were included. Of the 32 trials surveyed, 17 (53%) had at least one SA. Overall, the proportion of RCT reporting a particular methodological aspect ranged from 23 to 94%. Information on whether the SA preceded/followed the analysis was reported in only 7 (41%) of the studies. Of the total possible number of items to be reported, NEJM, JAMA, Lancet and AJPH clearly mentioned 59, 67, 58 and 72%, respectively. We conclude that current reporting of SA in RCT is incomplete and inaccurate. The results of such SA may have harmful effects on treatment recommendations if accepted without judicious scrutiny. We recommend that editors improve the reporting of SA in RCT by giving authors a list of the important items to be reported.

Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains

The aim of the present study was to compare heart rate variability (HRV) at rest and during exercise using a temporal series obtained with the Polar S810i monitor and a signal from a LYNX® signal conditioner (BIO EMG 1000 model) with a channel configured for the acquisition of ECG signals. Fifteen healthy subjects aged 20.9 ± 1.4 years were analyzed. The subjects remained at rest for 20 min and performed exercise for another 20 min with the workload selected to achieve 60% of submaximal heart rate. RR series were obtained for each individual with a Polar S810i instrument and with an ECG analyzed with a biological signal conditioner. The HRV indices (rMSSD, pNN50, LFnu, HFnu, and LF/HF) were calculated after signal processing and analysis. The unpaired Student t-test and intraclass correlation coefficient were used for data analysis. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV at rest and during exercise. The intraclass correlation coefficient demonstrated satisfactory correlation between the values obtained by the devices at rest (pNN50 = 0.994; rMSSD = 0.995; LFnu = 0.978; HFnu = 0.978; LF/HF = 0.982) and during exercise (pNN50 = 0.869; rMSSD = 0.929; LFnu = 0.973; HFnu = 0.973; LF/HF = 0.942). The calculation of HRV values by means of temporal series obtained from the Polar S810i instrument appears to be as reliable as those obtained by processing the ECG signal captured with a signal conditioner.

Association of adult weight gain and nonalcoholic fatty liver in a cross-sectional study in Wan Song Community, China

Our objective was to examine associations of adult weight gain and nonalcoholic fatty liver disease (NAFLD). Cross-sectional interview data from 844 residents in Wan Song Community from October 2009 to April 2010 were analyzed in multivariate logistic regression models to examine odds ratios (OR) and 95% confidence intervals (CI) between NAFLD and weight change from age 20. Questionnaires, physical examinations, laboratory examinations, and ultrasonographic examination of the liver were carried out. Maximum rate of weight gain, body mass index, waist circumference, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, fasting blood glucose, cholesterol, triglycerides, uric acid, and alanine transaminase were higher in the NAFLD group than in the control group. HDL-C in the NAFLD group was lower than in the control group. As weight gain increased (measured as the difference between current weight and weight at age 20 years), the OR of NAFLD increased in multivariate models. NAFLD OR rose with increasing weight gain as follows: OR (95%CI) for NAFLD associated with weight gain of 20+ kg compared to stable weight (change <5 kg) was 4.23 (2.49-7.09). Significantly increased NAFLD OR were observed even for weight gains of 5-9.9 kg. For the “age 20 to highest lifetime weight” metric, the OR of NAFLD also increased as weight gain increased. For the “age 20 to highest lifetime weight” metric and the “age 20 to current weight” metric, insulin resistance index (HOMA-IR) increased as weight gain increased (P<0.001). In a stepwise multivariate regression analysis, significant association was observed between adult weight gain and NAFLD (OR=1.027, 95%CI=1.002-1.055, P=0.025). We conclude that adult weight gain is strongly associated with NAFLD.