Uncertainty assessment approach for composite structures based on global sensitivity indices

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.

Modelling an expert GIS system based on knowledge to evaluate oil spill environmental sensitivity

Oil spills cause great damage to coastal habitats, especially when rapid and suitable response measures are not taken. Establishing high priority areas is fundamental for the operation of response teams. Under this context and considering the need for keeping all geographical information up-to-date for emergencial use, the present study proposes employing a decision tree coupled with a knowledge-based approach using GIS to assign oil sensitivity indices to Brazilian coastal habitats. The modelled system works based on rules set by the official standards of Brazilian Federal Environment Organ. We tested it on one of the littoral regions of Brazil where transportation of petroleum is most intense: the coast of the municipalities of Sao Sebastiao and Caraguatatuba in the northern littoral of São Paulo state, Brazil. The system automatically ranked the littoral sensitivity index of the study area habitats according to geographical conditions during summer and winter; since index ranks of some habitats varied between these seasons because of sediment alterations. The obtained results illustrate the great potential of the proposed system in generating ESI maps and in aiding response teams during emergency operations. (C) 2009 Elsevier Ltd. All rights reserved.

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

The role of K121Q ENPP1 polymorphism in diabetes mellitus and its complications

The aim of the present study was to analyze the frequency of K121Q polymorphism in the ENPP1 gene of Brazilian subjects according to ethnic origin and to determine its possible association with diabetes mellitus (DM) and/or diabetic complications. A cross-sectional study was conducted on 1027 type 2 DM patients and 240 anonymous blood donors (BD). Ethnicity was classified based on self-report of European and African descent. The Q allele frequency was increased in African descendant type 2 DM patients (KK = 25.9%, KQ = 48.2%, and QQ = 25.9%) and BD (KK = 22.0%, KQ = 53.8%, and QQ = 24.2%) compared to European descendant type 2 DM patients (KK = 62.7%, KQ = 33.3%, and QQ = 4.1%) and BD (KK = 61.0%, KQ = 35.6%, and QQ = 3.4%). However, there was no difference in genotype distribution or Q allele frequency between diabetic and non-diabetic subjects (European descendants: DM = 0.21 vs BD = 0.21, P = 0.966, and African descendants: DM = 0.50 vs BD = 0.51, P = 0.899). In addition, there were no differences in clinical, laboratory or insulin resistance indices among the three genotypes. The prevalence of DM complications was also similar. In conclusion, K121Q polymorphism is more common among Afro-Brazilian descendants regardless of glycemic status or insulin sensitivity indices. Likewise, insulin sensitivity and DM chronic complications appear not to be related to the polymorphism in this sample.

Demographic and metabolic characteristics of individuals with progressive glucose tolerance

We evaluated changes in glucose tolerance of 17 progressors and 62 non-progressors for 9 years to improve our understanding of the pathogenesis of type 2 diabetes mellitus. Changes in anthropometric measurements and responses to an oral glucose tolerance test (OGTT) were analyzed. We identified 14 pairs of individuals, one from each group, who were initially normal glucose tolerant and were matched for gender, age, weight, and girth. We compared initial plasma glucose and insulin curves (from OGTT), insulin secretion (first and second phases) and insulin sensitivity indices (from hyperglycemic clamp assay) for both groups. In the normal glucose tolerant phase, progressors presented: 1) a higher OGTT blood glucose response with hyperglycemia in the second hour and a similar insulin response vs non-progressors; 2) a reduced first-phase insulin secretion (2.0 ± 0.3 vs 2.3 ± 0.3 pmol/L; P < 0.02) with a similar insulin sensitivity index and a lower disposition index (3.9 ± 0.2 vs 4.1 ± 0.2 µmol·kg-1·min-1 ; P < 0.05) vs non-progressors. After 9 years, both groups presented similar increases in weight and fasting blood glucose levels and progressors had an increased glycemic response at 120 min (P < 0.05) and reduced early insulin response to OGTT (progressors, 1st: 2.10 ± 0.34 vs 2nd: 1.87 ± 0.25 pmol/mmol; non-progressors, 1st: 2.15 ± 0.28 vs 2nd: 2.03 ± 0.39 pmol/mmol; P < 0.05). Theses data suggest that β-cell dysfunction might be a risk factor for type 2 diabetes mellitus.

Demographic and metabolic characteristics of individuals with progressive glucose tolerance

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Distribution network planning using a constructive heuristic algorithm

An optimization technique to solve distribution network planning (DNP) problem is presented. This is a very complex mixed binary nonlinear programming problem. A constructive heuristic algorithm (CHA) aimed at obtaining an excellent quality solution for this problem is presented. In each step of the CHA, a sensitivity index is used to add a circuit or a substation to the distribution network. This sensitivity index is obtained solving the DNP problem considering the numbers of circuits and substations to be added as continuous variables (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an efficient nonlinear optimization solver. A local improvement phase and a branching technique were implemented in the CHA. Results of two tests using a distribution network are presented in the paper in order to show the ability of the proposed algorithm. ©2009 IEEE.

O hiperandrogenismo influencia no desenvolvimento de síndrome metabólica em pacientes com síndrome dos ovários policísticos?

Pós-graduação em Ginecologia, Obstetrícia e Mastologia - FMB

Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

Background: Decreased heart rate variability (HRV) is related to higher morbidity and mortality. In this study we evaluated the linear and nonlinear indices of the HRV in stable angina patients submitted to coronary angiography. Methods. We studied 77 unselected patients for elective coronary angiography, which were divided into two groups: coronary artery disease (CAD) and non-CAD groups. For analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 40 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal], NN50 [total number of adjacent RR intervals with a difference of duration greater than 50ms] and RMSSD [root-mean square of differences]) and frequency domains ultra-low frequency (ULF) ≤ 0,003 Hz, very low frequency (VLF) 0,003 - 0,04 Hz, low frequency (LF) (0.04-0.15 Hz), and high frequency (HF) (0.15-0.40 Hz) as well as the ratio between LF and HF components (LF/HF). In relation to the nonlinear indices we evaluated SD1, SD2, SD1/SD2, approximate entropy (-ApEn), α1, α2, Lyapunov Exponent, Hurst Exponent, autocorrelation and dimension correlation. The definition of the cutoff point of the variables for predictive tests was obtained by the Receiver Operating Characteristic curve (ROC). The area under the ROC curve was calculated by the extended trapezoidal rule, assuming as relevant areas under the curve ≥ 0.650. Results: Coronary arterial disease patients presented reduced values of SDNN, RMSSD, NN50, HF, SD1, SD2 and -ApEn. HF ≤ 66 ms§ssup§2§esup§, RMSSD ≤ 23.9 ms, ApEn ≤-0.296 and NN50 ≤ 16 presented the best discriminatory power for the presence of significant coronary obstruction. Conclusion: We suggest the use of Heart Rate Variability Analysis in linear and nonlinear domains, for prognostic purposes in patients with stable angina pectoris, in view of their overall impairment. © 2012 Pivatelli et al.; licensee BioMed Central Ltd.

Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

Abstract Background Decreased heart rate variability (HRV) is related to higher morbidity and mortality. In this study we evaluated the linear and nonlinear indices of the HRV in stable angina patients submitted to coronary angiography. Methods We studied 77 unselected patients for elective coronary angiography, which were divided into two groups: coronary artery disease (CAD) and non-CAD groups. For analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 40 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal], NN50 [total number of adjacent RR intervals with a difference of duration greater than 50ms] and RMSSD [root-mean square of differences]) and frequency domains ultra-low frequency (ULF) ≤ 0,003 Hz, very low frequency (VLF) 0,003 – 0,04 Hz, low frequency (LF) (0.04–0.15 Hz), and high frequency (HF) (0.15–0.40 Hz) as well as the ratio between LF and HF components (LF/HF). In relation to the nonlinear indices we evaluated SD1, SD2, SD1/SD2, approximate entropy (−ApEn), α1, α2, Lyapunov Exponent, Hurst Exponent, autocorrelation and dimension correlation. The definition of the cutoff point of the variables for predictive tests was obtained by the Receiver Operating Characteristic curve (ROC). The area under the ROC curve was calculated by the extended trapezoidal rule, assuming as relevant areas under the curve ≥ 0.650. Results Coronary arterial disease patients presented reduced values of SDNN, RMSSD, NN50, HF, SD1, SD2 and -ApEn. HF ≤ 66 ms2, RMSSD ≤ 23.9 ms, ApEn ≤−0.296 and NN50 ≤ 16 presented the best discriminatory power for the presence of significant coronary obstruction. Conclusion We suggest the use of Heart Rate Variability Analysis in linear and nonlinear domains, for prognostic purposes in patients with stable angina pectoris, in view of their overall impairment.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Concussion in contact sports: Reliable change indices of impairment and recovery

This paper reports a follow-up study to an article on the sensitivity of three tests of speed of information processing to impairment after concussion (Hinton-Bayre, Geffen, BL McFarland, 1997). Group analyses showed that practice effects can obscure the effects of concussion on information processing, thereby making the assessment of functional impairment and recovery after injury unreliable. A Reliable Change Index (RCI) was used to assess individual variations following concussion. It was found that 16 of the 20 concussed professional rugby league players were impaired 1-3 days following injury. It was also demonstrated that 7 players still displayed cognitive deficits at 1-2 weeks, before returning to preseason levels at 3-5 weeks. The RCI permits comparisons between different tests, players, and repeated assessments, thereby providing a quantitative basis for decisions regarding return to play.

Effects of alcohol consumption on indices of iron stores and of iron stores on alcohol intake markers

Background: Alcohol increases body iron stores. Alcohol and iron may increase oxidative stress and the risk of alcohol-related liver disease. The relationship between low or safe levels of alcohol use and indices of body iron stores, and the factors that affect the alcohol-iron relationship, have not been fully characterized. Other aspects of the biological response to alcohol use have been reported to depend on iron status. Methods: We have measured serum iron, transferrin, and ferritin as indices of iron stores in 3375 adult twin subjects recruited through the Australian Twin Registry. Information on alcohol use and dependence and smoking was obtained from questionnaires and interviews. Results: Serum iron and ferritin increased progressively across classes of alcohol intake. The effects of beer consumption were greater than those of wine or spirits. Ferritin concentration was significantly higher in subjects who had ever been alcohol dependent. There was no evidence of interactions between HFE genotype or body mass index and alcohol. Alcohol intake-adjusted carbohydrate-deficient transferrin was increased in women in the lowest quartile of ferritin results, whereas adjusted gamma -glutamyltransferase, aspartate aminotransferase, and alanine aminotransferase values were increased in subjects with high ferritin. Conclusions: Alcohol intake at low level increases ferritin and, by inference, body iron stores. This may be either beneficial or harmful, depending on circumstances. The response of biological markers of alcohol intake can be affected by body iron stores; this has implications for test sensitivity and specificity and for variation in biological responses to alcohol use.

Evaluation of the predictive indices for candidemia in an adult intensive care unit

INTRODUCTION: To evaluate predictive indices for candidemia in an adult intensive care unit (ICU) and to propose a new index. METHODS: A prospective cohort study was conducted between January 2011 and December 2012. This study was performed in an ICU in a tertiary care hospital at a public university and included 114 patients staying in the adult ICU for at least 48 hours. The association of patient variables with candidemia was analyzed. RESULTS: There were 18 (15.8%) proven cases of candidemia and 96 (84.2%) cases without candidemia. Univariate analysis revealed the following risk factors: parenteral nutrition, severe sepsis, surgical procedure, dialysis, pancreatitis, acute renal failure, and an APACHE II score higher than 20. For the Candida score index, the odds ratio was 8.50 (95% CI, 2.57 to 28.09); the sensitivity, specificity, positive predictive value, and negative predictive value were 0.78, 0.71, 0.33, and 0.94, respectively. With respect to the clinical predictor index, the odds ratio was 9.45 (95%CI, 2.06 to 43.39); the sensitivity, specificity, positive predictive value, and negative predictive value were 0.89, 0.54, 0.27, and 0.96, respectively. The proposed candidemia index cutoff was 8.5; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.77, 0.70, 0.33, and 0.94, respectively. CONCLUSIONS: The Candida score and clinical predictor index excluded candidemia satisfactorily. The effectiveness of the candidemia index was comparable to that of the Candida score.

Absorbance enhancement in microplate wells for improved-sensitivity biosensors

A generic optical biosensing strategy was developed that relies on the absorbance enhancement phenomenon occurring in a multiple scattering matrix. Experimentally, inserts made of glass fiber membrane were placed into microplate wells in order to significantly lengthen the trajectory of the incident light through the sample and therefore increase the corresponding absorbance. Enhancement factor was calculated by comparing the absorbance values measured for a given amount of dye with and without the absorbance-enhancing inserts in the wells. Moreover, the dilution of dye in solutions with different refractive indices (RI) clearly revealed that the enhancement factor increased with the ΔRI between the membrane and the surrounding medium, reaching a maximum value (EF>25) when the membranes were dried. On this basis, two H2O2-biosensing systems were developed based on the biofunctionalization of the glass fiber inserts either with cytochrome c or horseradish peroxidase (HRP) and the analytical performances were systematically compared with the corresponding bioassay in solution. The efficiency of the absorbance-enhancement approach was particularly clear in the case of the cytochrome c-based biosensor with a sensitivity gain of 40 folds and wider dynamic range. Therefore, the developed strategy represents a promising way to convert standard colorimetric bioassays into optical biosensors with improved sensitivity.