Analysis of the prevalence of dyslipidemia in individuals with HIV and its association with antiretroviral therapy

IntroductionAntiretroviral therapy (ART) has been used to treat large numbers of patients living with human immunodeficiency virus (HIV) infection. Lipid disorders are often observed in these patients, and include elevations in total cholesterol (TC) and triglycerides (TG).MethodsA cross-sectional study was performed using 333 patient records from the Regional Hospital of São José Doutor Homero de Miranda Gomes (HRSJHMG). The study population consisted of patients with HIV who were under medical follow up, either on or off drug treatment. The data were entered into Excel and exported to SPSS 16.0 for analysis using chi-square testing. We used prevalence ratios as the measure of association.ResultsLipid abnormalities were observed in 78.9% of individuals who received ART. Of the 308 subjects on ART, 59.1%, 41.9%, and 33.1% had TG, TC and low-density lipoprotein (LDL) abnormalities, respectively. The prevalence of LDL changes was 2.57-fold higher in individuals who had been using ART for more than 12 months, compared to those using ART for 6 to 12 months.ConclusionsHIV patients showed a significant increase in the association between TC and TG levels and the use of ART. In particular, changes in TC, LDL and TG were greater in individuals who had received ART for over more than 12 months.

The domestic architecture of Bracara Augusta and its evolution

The excavations carried out under the rescue “Project of Bracara Augusta” have generated significant amounts of data that enabled the reconstruction of Bracara Augusta urban evolution and the characterization of its buildings and blocks. This paper aims to enhance the existing data related with the domestic architecture of the roman town, which was mainly represented by the houses of domus type.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.

Safety climate and its relationship with furniture companies’ safety performance and workers’ risk acceptance

This study aims to analyse the relationship between safety climate and the level of risk acceptance, as well as its relationship with workplace safety performance. The sample includes 14 companies and 403 workers. The safety climate assessment was performed by the application of a Safety Climate in Wood Industries questionnaire and safety performance was assessed with a checklist. Judgements about risk acceptance were measured through questionnaires together with four other variables: trust, risk perception, benefit perception and emotion. Safety climate was found to be correlated with workgroup safety performance, and it also plays an important role in workers’ risk acceptance levels. Risk acceptance tends to be lower when safety climate scores of workgroups are high, and subsequently, their safety performance is better. These findings seem to be relevant, as they provide Occupational, Safety and Health practitioners with a better understanding of workers’ risk acceptance levels and of the differences among workgroups.

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.

Jet energy measurement and its systematic uncertainty in proton-proton collisions at s√=7 TeV with the ATLAS detector

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector using proton–proton collision data with a centre-of-mass energy of s√=7 TeV corresponding to an integrated luminosity of 4.7 fb −1 . Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells using the anti- kt algorithm with distance parameters R=0.4 or R=0.6 , and are calibrated using MC simulations. A residual JES correction is applied to account for differences between data and MC simulations. This correction and its systematic uncertainty are estimated using a combination of in situ techniques exploiting the transverse momentum balance between a jet and a reference object such as a photon or a Z boson, for 20≤pjetT<1000 GeV and pseudorapidities |η|<4.5 . The effect of multiple proton–proton interactions is corrected for, and an uncertainty is evaluated using in situ techniques. The smallest JES uncertainty of less than 1 % is found in the central calorimeter region ( |η|<1.2 ) for jets with 55≤pjetT<500 GeV . For central jets at lower pT , the uncertainty is about 3 %. A consistent JES estimate is found using measurements of the calorimeter response of single hadrons in proton–proton collisions and test-beam data, which also provide the estimate for pjetT>1 TeV. The calibration of forward jets is derived from dijet pT balance measurements. The resulting uncertainty reaches its largest value of 6 % for low- pT jets at |η|=4.5 . Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also discussed. The magnitude of these uncertainties depends on the event sample used in a given physics analysis, but typically amounts to 0.5–3 %.

A VEP study in sleeping and awake one-month-old infants and its relation with social behavior

With the present study we aimed to analyze the relationship between infants' behavior and their visual evoked-potential (VEPs) response. Specifically, we want to verify differences regarding the VEP response in sleeping and awake infants and if an association between VEP components, in both groups, with neurobehavioral outcome could be identified. To do so, thirty-two full-term and healthy infants, approximately 1-month of age, were assessed through a VEP unpatterned flashlight stimuli paradigm, offered in two different intensities, and were assessed using a neurobehavioral scale. However, only 18 infants have both assessments, and therefore, these is the total included in both analysis. Infants displayed a mature neurobehavioral outcome, expected for their age. We observed that P2 and N3 components were present in both sleeping and awake infants. Differences between intensities were found regarding the P2 amplitude, but only in awake infants. Regression analysis showed that N3 amplitude predicted an adequate social interactive and internal regulatory behavior in infants who were awake during the stimuli presentation. Taking into account that social orientation and regulatory behaviors are fundamental keys for social-like behavior in 1-month-old infants, this study provides an important approach for assessing physiological biomarkers (VEPs) and its relation with social behavior, very early in postnatal development. Moreover, we evidence the importance of the infant's state when studying differences regarding visual threshold processing and its association with behavioral outcome.

Mineralogical and geochemical influences on sediment color of Amazon wetlands analyzed by visible spectrophotometry

Based on sedimentological and geochemical data, this work relates spectrophotometric measurements with sediment composition and its application in palaeoecological studies of Amazon wetlands. The CIELAB values are directly related to mineralogical and chemical composition, mostly involving quartz, iron oxyhydroxides and sulfides (e.g. pyrite), and total organic carbon. Total organic carbon contents between 0.4-1%, 1-2%, 3-5% and 15-40% were related to L* (lightness) data of 27, 26-15, 7-10 and 7 or less, respectively. The CIELAB values of a deposit in Marabá, Pará, were proportional to variations in quartz and total organic carbon contents, but changes in zones of similar color, mainly in the +a* (red) and +b* (yellow) values of deposits in Calçoene, Amapá and Soure, Pará, indicate a close relationship between total organic carbon content and iron oxyhydroxides and sulfides. Furthermore, the Q7/4 diagram (ratio between the % re?ectance value at 700 nm to that at 400 nm, coupled with L*) indicated iron-rich sediments in the bioturbated mud facies of the Amapá deposit, bioturbated mud and bioturbated sand facies of Soure deposit, and cross-laminated sand and massive sand facies of the Marabá core. Also, organic-rich sediments were found in the bioturbated mud facies of the Amapá deposit, lenticular heterolithic and bioturbated mud facies of the Soure deposit, and laminated mud and peat facies of the Marabá deposit. At the Marabá site, the data suggest an autochthonous influence with peat formation. The coastal wetland sites at Marajó and Amapá represent the development of a typical tidal flat setting with sulfide and iron oxyhydroxides formation during alternated flooding and drying.

Poker learner: modelação de jogadores através de data mining

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Lipid profile of nutrition students and its association with cardiovascular disease risk factors

OBJECTIVE: To describe the lipid profile and to verify its relationship with cardiovascular disease risk factors in students at a public university in São Paulo. METHODS: After obtaining clinical, anthropomorphic, and lipid profile data from 118 students, variables of the lipid profile were related to other risk factors. RESULTS: The mean age of the students was 20.3 years (SD=1.5). The risk of cardiovascular disease was characterized by a positive family history of ischemic heart disease in 38.9%; sedentariness in 35.6%; limiting and increased total and LDL-C cholesterol levels in 17.7% and 10.2%, respectively; decreased HDL-C levels in 11.1%; increased triglyceride levels in 11.1%; body mass index >25 in 8.5%, and smoking in 6.7% of the subjects. Students' diet was found to be inadequate regarding protein, total fat, saturated fat, sodium, and fiber contents. A statistically significant association between cholesterol and contraceptive use, between HDL-C and contraceptive use, age and percent body fat, and triglycerides and percent lean weight was observed. CONCLUSION: A high prevalence of some risk factors of cardiovascular disease as well as the association between these factors with altered lipid profiles was observed in the young population studied.