Qualidade de vida: o papel do estado nutricional e da percepção do relacionamento familiar de crianças do município de Indaiatuba/SP

Introdução: A importância da investigação da qualidade de vida de crianças está diretamente relacionada ao fato de que muitos problemas na vida adulta têm sua origem na infância. Objetivo: analisar a contribuição da percepção do relacionamento familiar e do estado nutricional sobre a qualidade de vida de crianças do município de Indaiatuba. Metodologia: Na etapa 1 foi realizado o estudo de validade do instrumento APGAR Familiar adaptado à crianças de 7 a 11 anos, utilizando como medida de confiabilidade a técnica do Teste-Reteste e para a avaliação da validade convergente foi utilizada a Escala de Avaliação de Qualidade de Vida Infantil. Na etapa 2 foram avaliados os determinantes da Qualidade de Vida de crianças sob aspectos familiares, estado nutricional e socioeconômicos e demográficos. Os dados foram analisados por meio de Regressão Linear Múltipla com método dos Mínimos Quadrados Ordinários. Resultados: Na etapa 1 a análise de confiabilidade obteve a correlação de 0,764 do coeficiente de Spearman-Brown. Na análise de Validade Convergente o coeficiente de correlação de Rô de Spearman entre os escores dos dois instrumentos foi de 0,570 (p<0,01). Na etapa 2 foi estimado um modelo dos determinantes da qualidade de vida a partir de uma amostra de 1028 crianças de 7 a 11 anos de ambos os sexos. As variáveis independentes foram capazes de explicar a Qualidade de Vida de crianças a uma significância de 1% (Z = 8,417), sendo o R² ajustado de 0,104. A idade da criança e a percepção do relacionamento familiar foram as variáveis estatisticamente significativas, ao passo que o estado nutricional, o tamanho da família, o sexo da criança, a classe social e a escolaridade do responsável, não foram estatisticamente significantes. Conclusão: O instrumento APGAR Familiar apresentou índices preliminares de validade e precisão; a idade e o relacionamento familiar foram as variáveis que explicaram a qualidade de vida percebida sob a perspectiva subjetiva de bem-estar.

Aplicação de métodos geoestatísticos no estudo das distribuições espaciais de condutividade hidráulica em áreas do Sistema Aquífero Guarani (SAG) e do Sistema Aquífero Bauru (SAB) no Estado de São Paulo

A condutividade hidráulica (K) é um dos parâmetros controladores da magnitude da velocidade da água subterrânea, e consequentemente, é um dos mais importantes parâmetros que afetam o fluxo subterrâneo e o transporte de solutos, sendo de suma importância o conhecimento da distribuição de K. Esse trabalho visa estimar valores de condutividade hidráulica em duas áreas distintas, uma no Sistema Aquífero Guarani (SAG) e outra no Sistema Aquífero Bauru (SAB) por meio de três técnicas geoestatísticas: krigagem ordinária, cokrigagem e simulação condicional por bandas rotativas. Para aumentar a base de dados de valores de K, há um tratamento estatístico dos dados conhecidos. O método de interpolação matemática (krigagem ordinária) e o estocástico (simulação condicional por bandas rotativas) são aplicados para estimar os valores de K diretamente, enquanto que os métodos de krigagem ordinária combinada com regressão linear e cokrigagem permitem incorporar valores de capacidade específica (Q/s) como variável secundária. Adicionalmente, a cada método geoestatístico foi aplicada a técnica de desagrupamento por célula para comparar a sua capacidade de melhorar a performance dos métodos, o que pode ser avaliado por meio da validação cruzada. Os resultados dessas abordagens geoestatísticas indicam que os métodos de simulação condicional por bandas rotativas com a técnica de desagrupamento e de krigagem ordinária combinada com regressão linear sem a técnica de desagrupamento são os mais adequados para as áreas do SAG (rho=0.55) e do SAB (rho=0.44), respectivamente. O tratamento estatístico e a técnica de desagrupamento usados nesse trabalho revelaram-se úteis ferramentas auxiliares para os métodos geoestatísticos.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

ANALISANDO A RELAÇÃO DOS VOLUMES DE EXPORTAÇÃO E DE IMPORTAÇÃO, PIB, TAXAS DE CÂMBIO E INFLAÇÃO NO PERÍODO DE 2004 A 2014.

Esta dissertação visa deslumbrar uma análise macroeconômica do Brasil, especialmente no que se refere à relação dos índices mensais dos volumes das exportações e das importações com os volumes mensais do PIB, da Taxa SELIC e as Taxas de Câmbio, conforme dados coletados no período de janeiro de 2004 a dezembro de 2014, através de pesquisa literária referente aos históricos sobre cada conceito envolvido no âmbito da macroeconomia das varáveis estudadas. Foi realizado um estudo de caso embasado em dados de sites governamentais, no período delimitado, empregando-se o método de regressão linear, com base na Teoria da correlação de Pearson, demonstrando os resultados obtidos no período do estudo para as varáveis estudadas. Desta maneira, conseguiu-se estudar e analisar como as variáveis dependentes (resposta): volume das exportações e volume das importações estão relacionadas com as varáveis independentes (explicativas): PIB, Taxa Selic e taxa de Câmbio. Os resultados apurados no presente estudo permitem identificar que existe correlação moderada e negativa, quando analisadas a Taxa Selic e a Taxa de Câmbio com os volumes das exportações e das importações, enquanto o PIB apresenta correlação forte e positiva na análise com os volumes das exportações e das importações. Palavras-

Clustering patterns of neurofibrillary tangles in Alzheimer’s disease

Clustering of cellular neurofibrillary tangles (NFT) was studied in the cerebral cortex and hippocampus in cases of Alzheimer’s disease (AD) using a regression method. The objective of the study was to test the hypothesis that clustering of NFTs reflects the degeneration of the cortico-cortical pathways. In 25/38 (66%) of analyses of individual brain areas, a significant peak to trough and peak to peak distance was obtained suggesting that the clusters of NFTs were regularly distributed in bands parallel to the tissue boundary. In analyses of cortical tissues with regularly distributed clusters, peak to peak distance was between 1000 and 1600 microns in 13/24 (54%) of analyses, >1600 microns in 10/24 (42%) and <1000 microns in 1/24 (4%) of analyses. A regular distribution of NFT clusters was less evident in the CA sectors of the hippocampus than in the cortex. Hence, in a significant proportion of brain areas, the spacing of NFT clusters along the cerebral cortex was consistent with the predicted distribution of the cells of origin of specific cortico-cortical projections. However, in many brain regions, the sizes of the NFT clusters were larger than predicted which may be attributable to the spread of NFTs to adjacent groups of cells as the disease progresses.

Hepatite C crônica em serviço de referência no nordeste do Brasil: um estudo retrospectivo de dezoito anos de acompanhamento

Chronic Hepatitis C is the leading cause of chronic liver disease in advanced final stage of hepatocellular carcinoma (HCC) and of death related to liver disease. Evolves progressively in time 20-30 years. Evolutionary rates vary depending on factors virus, host and behavior. This study evaluated the impact of hepatitis C on the lives of patients treated at a referral service in Hepatology of the University Hospital Onofre Lopes - Liver Study Group - from May 1995 to December 2013. A retrospective evaluation was performed on 10,304 records, in order to build a cohort of patients with hepatitis C, in which all individuals had their diagnosis confirmed by gold standard molecular biological test. Data were obtained directly from patient charts and recorded in an Excel spreadsheet, previously built, following an elaborate encoding with the study variables, which constitute individual data and prognostic factors defined in the literature in the progression of chronic hepatitis C. The Research Ethics Committee approved the project. The results were statistically analyzed with the Chi-square test and Fisher's exact used to verify the association between variable for the multivariate analysis, we used the Binomial Logistic regression method. For both tests, it was assumed significance p < 0.05 and 95%. The results showed that the prevalence of chronic hepatitis C in NEF was 4.96 %. The prevalence of cirrhosis due to hepatitis C was 13.7%. The prevalence of diabetes in patients with Hepatitis C was 8.78 % and diabetes in cirrhotic patients with hepatitis C 38.0 %. The prevalence of HCC was 5.45%. The clinical follow-up discontinuation rates were 67.5 %. The mortality in confirmed cases without cirrhosis was 4.10% and 32.1% in cirrhotic patients. The factors associated with the development of cirrhosis were genotype 1 (p = 0.0015) and bilirubin > 1.3 mg % (p = 0.0017). Factors associated with mortality were age over 35 years, abandon treatment, diabetes, insulin use, AST> 60 IU, ALT> 60 IU, high total bilirubin, extended TAP, INR high, low albumin, treatment withdrawal, cirrhosis and hepatocarcinoma. The occurrence of diabetes mellitus increased mortality of patients with hepatitis C in 6 times. Variables associated with the diagnosis of cirrhosis by us were blood donor (odds ratio 0.24, p = 0.044) and professional athlete (odds ratio 0.18, p = 0.35). It is reasonable to consider a revaluation in screening models for CHC currently proposed. The condition of cirrhosis and diabetes modifies the clinical course of patients with chronical hepatitis C, making it a disease more mortality. However, being a blood donor or professional athlete is a protective factor that reduces the risk of cirrhosis, independent of alcohol consumption. Public policies to better efficient access, hosting and resolution are needed for this population.

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.

A Corrupção burocrática inibe o empreendedorismo? uma análise empírica dos estados brasileiros

This work aims to investigate the relationship between the entrepreneurship and the incidence of bureaucratic corruption in the states of Brazil and Federal District. The main hypothesis of this study is that the opening of a business in Brazilian states is negatively affected by the incidence of corruption. The theoretical reference is divided into Entrepreneurship and bureaucratic corruption, with an emphasis on materialistic perspective (objectivist) of entrepreneurship and the effects of bureaucratic corruption on entrepreneurial activity. By the regression method with panel data, we estimated the models with pooled data and fixed and random effects. To measure corruption, I used the General Index of Corruption for the Brazilian states (BOLL, 2010), and to represent entrepreneurship, firm entry per capita by state. Tests (Chow, Hausman and Breusch-Pagan) indicate that the random effects model is more appropriate, and the preliminary results indicate a positive impact of bureaucratic corruption on entrepreneurial activity, contradicting the hypothesis expected and found in previous articles to Brazil, and corroborating the proposition of Dreher and Gassebner (2011) that, in countries with high regulation, bureaucratic corruption can be grease in the wheels of entrepreneurship

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

Characterization of the methanol recovery process at Prio Biocombustíveis S. A.

In a industrial environment, to know the process one is working with is crucial to ensure its good functioning. In the present work, developed at Prio Biocombustíveis S.A. facilities, using process data, collected during the present work, and historical process data, the methanol recovery process was characterized, having started with the characterization of key process streams. Based on the information retrieved from the stream characterization, Aspen Plus® process simulation software was used to replicate the process and perform a sensitivity analysis with the objective of accessing the relative importance of certain key process variables (reflux/feed ratio, reflux temperature, reboiler outlet temperature, methanol, glycerol and water feed compositions). The work proceeded with the application of a set of statistical tools, starting with the Principal Components Analysis (PCA) from which the interactions between process variables and their contribution to the process variability was studied. Next, the Design of Experiments (DoE) was used to acquire experimental data and, with it, create a model for the water amount in the distillate. However, the necessary conditions to perform this method were not met and so it was abandoned. The Multiple Linear Regression method (MLR) was then used with the available data, creating several empiric models for the water at distillate, the one with the highest fit having a R2 equal to 92.93% and AARD equal to 19.44%. Despite the AARD still being relatively high, the model is still adequate to make fast estimates of the distillate’s quality. As for fouling, its presence has been noticed many times during this work. Not being possible to directly measure the fouling, the reboiler inlet steam pressure was used as an indicator of the fouling growth and its growth variation with the amount of Used Cooking Oil incorporated in the whole process. Comparing the steam cost associated to the reboiler’s operation when fouling is low (1.5 bar of steam pressure) and when fouling is high (reboiler’s steam pressure of 3 bar), an increase of about 58% occurs when the fouling increases.

Prevalence and associated factors of TB/HIV co-infection among HIV Infected patients in Amhara region, Ethiopia.

Background: Tuberculosis is one of the world’s most common causes of death in the era of Human immunodeficiency virus. The purpose of this study was to determine the prevalence and associated factors of TB/HIV co-infection. Methods: Hospital based retrospective studies were conducted among adult HIV-positive patients. Logistic regression method and Chi square test were applied. Results: A total of 571 HIV positive study participants were enrolled. Of these, 158 (27.7%) were found to have pulmonary tuberculosis. Lower baseline CD4 count<200cell/μl, patients who drunk alcohol, patients who were ambulatory at the initiation of ART, patients whose marital status was single were significant predictors for increased risk of tuberculosis in PLWHIV (P <0.05). Non smoker patients, patients in WHO clinical stage I, patients in WHO clinical stage II and ownership of the house had significant protective benefit against risk of TB (P <0.05). Conclusion: The prevalence of TB/HIV co-infection in adults on ART in our study was moderately high. Having advanced clinical status and presence of risk factors were found to be the predicting factors for co-infection. The health office should open TB/HIV co-infection units in the hospitals and health workers should be cautious when a patient has an advanced disease.

A Corrupção burocrática inibe o empreendedorismo? uma análise empírica dos estados brasileiros

This work aims to investigate the relationship between the entrepreneurship and the incidence of bureaucratic corruption in the states of Brazil and Federal District. The main hypothesis of this study is that the opening of a business in Brazilian states is negatively affected by the incidence of corruption. The theoretical reference is divided into Entrepreneurship and bureaucratic corruption, with an emphasis on materialistic perspective (objectivist) of entrepreneurship and the effects of bureaucratic corruption on entrepreneurial activity. By the regression method with panel data, we estimated the models with pooled data and fixed and random effects. To measure corruption, I used the General Index of Corruption for the Brazilian states (BOLL, 2010), and to represent entrepreneurship, firm entry per capita by state. Tests (Chow, Hausman and Breusch-Pagan) indicate that the random effects model is more appropriate, and the preliminary results indicate a positive impact of bureaucratic corruption on entrepreneurial activity, contradicting the hypothesis expected and found in previous articles to Brazil, and corroborating the proposition of Dreher and Gassebner (2011) that, in countries with high regulation, bureaucratic corruption can be grease in the wheels of entrepreneurship

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 differentcompositional datasets and modelled the first canonical variable using a segmented regression modelsolely based on an observation about the scatter plots. In this paper, multiple linear regressions areapplied to different datasets to confirm the validity of our proposed model. In addition to dating theunknown tephras by calibration as discussed previously, another method of mapping the unknown tephrasinto samples of the reference set or missing samples in between consecutive reference samples isproposed. The application of these methodologies is demonstrated with both simulated and real datasets.This new proposed methodology provides an alternative, more acceptable approach for geologists as theirfocus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age ofunknown tephra.Kew words: Tephrochronology; Segmented regression

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 different compositional datasets and modelled the first canonical variable using a segmented regression model solely based on an observation about the scatter plots. In this paper, multiple linear regressions are applied to different datasets to confirm the validity of our proposed model. In addition to dating the unknown tephras by calibration as discussed previously, another method of mapping the unknown tephras into samples of the reference set or missing samples in between consecutive reference samples is proposed. The application of these methodologies is demonstrated with both simulated and real datasets. This new proposed methodology provides an alternative, more acceptable approach for geologists as their focus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age of unknown tephra. Kew words: Tephrochronology; Segmented regression