22 resultados para Compositional data analysis-roots in geosciences


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Worldwide, the impact of meningococcal disease is substantial, and the potential for the introduction and spread of more virulent strains of N. meningitidis or strains with increased resistance to current antibiotics causes concern, making prevention essential. OBJECTIVES: Review the indications for meningococcal disease vaccines, considering the epidemiological status in Brazil. METHODS: A critical literature review on this issue using the Medline and Lilacs databases. RESULTS: In Brazil, MenB and MenC were the most important serogroups identified in the 1990s. Polysaccharide vaccines available against those serogroups can offer only limited protection for infants, the group at highest risk for meningococcal disease. Additionally, polysaccharide vaccines may induce a hypo-responsive state to MenC. New meningococcal C conjugate vaccines could partially solve these problems, but it is unlikely that in the next few years a vaccine against MenB that can promote good protection against multiple strains of MenB responsible for endemic and epidemic diseases will become available. CONCLUSIONS: In order to make the best decision about recommendations on immunization practices, better quality surveillance data are required. In Brazil, MenC was responsible for about 2,000 cases per year during the last 10 years. New conjugate vaccines against MenC are very effective and immunogenic, and they should be recommended, especially for children less than 5 years old. Polysaccharide vaccines should be indicated only in epidemic situations and for high-risk groups. Until new vaccines against MenC and MenB are available for routine immunization programs, the most important measure for controlling meningococcal disease is early diagnosis of these infections in order to treat patients and to offer chemoprophylaxis to contacts.

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Abstract Background: Due to the importance of coronary artery disease (CAD), continuous investigation of the risk factors (RFs) is needed. Objective: To evaluate the prevalence of RFs for CAD in cities in Rio Grande do Sul State, and compare it with that reported in a similar study conducted in the same cities in 2002. Methods: Cross-sectional study on 1,056 healthy adults, investigating the prevalence and absolute and relative frequencies of the following RFs for CAD: obesity, systemic arterial hypertension (SAH), dyslipidemias, smoking, sedentary lifestyle, diabetes mellitus, and family history, as well as age and sex. Data was collected in 19 cities, host of the Offices of the Regional Coordinators of Health, as in the 2002 study. Results: Twenty-six percent of the sample consisted of older adults and 57% were women. The prevalence of sedentary lifestyle was 44%, history family 50%, smoking 23%, overweight/obesity 68%, dyslipidemia (high cholesterol levels) 43%, SAH 40%, and diabetes 11%. When compared to the 2002 study, the prevalence of active smoking and sedentary behavior decreased, whereas the prevalence of hypertension, dyslipidemia and obesity increased. Obesity is the most prevalent RF in women, and SAH the most prevalent in men. Conclusions: The prevalence of RFs for CAD in Rio Grande do Sul State remains high. Hypertension, obesity and dyslipidemia are still prevalent and require major prevention programs. Smoking and physical inactivity have decreased in the state, suggesting the efficacy of related campaigns.

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The geographic information system approach has permitted integration between demographic, socio-economic and environmental data, providing correlation between information from several data banks. In the current work, occurrence of human and canine visceral leishmaniases and insect vectors (Lutzomyia longipalpis) as well as biogeographic information related to 9 areas that comprise the city of Belo Horizonte, Brazil, between April 2001 and March 2002 were correlated and georeferenced. By using this technique it was possible to define concentration loci of canine leishmaniasis in the following regions: East; Northeast; Northwest; West; and Venda Nova. However, as for human leishmaniasis, it was not possible to perform the same analysis. Data analysis has also shown that 84.2% of the human leishmaniasis cases were related with canine leishmaniasis cases. Concerning biogeographic (altitude, area of vegetation influence, hydrographic, and areas of poverty) analysis, only altitude showed to influence emergence of leishmaniasis cases. A number of 4673 canine leishmaniasis cases and 64 human leishmaniasis cases were georeferenced, of which 67.5 and 71.9%, respectively, were living between 780 and 880 m above the sea level. At these same altitudes, a large number of phlebotomine sand flies were collected. Therefore, we suggest control measures for leishmaniasis in the city of Belo Horizonte, giving priority to canine leishmaniasis foci and regions at altitudes between 780 and 880 m.

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Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

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In general, laboratory activities are costly in terms of time, space, and money. As such, the ability to provide realistically simulated laboratory data that enables students to practice data analysis techniques as a complementary activity would be expected to reduce these costs while opening up very interesting possibilities. In the present work, a novel methodology is presented for design of analytical chemistry instrumental analysis exercises that can be automatically personalized for each student and the results evaluated immediately. The proposed system provides each student with a different set of experimental data generated randomly while satisfying a set of constraints, rather than using data obtained from actual laboratory work. This allows the instructor to provide students with a set of practical problems to complement their regular laboratory work along with the corresponding feedback provided by the system's automatic evaluation process. To this end, the Goodle Grading Management System (GMS), an innovative web-based educational tool for automating the collection and assessment of practical exercises for engineering and scientific courses, was developed. The proposed methodology takes full advantage of the Goodle GMS fusion code architecture. The design of a particular exercise is provided ad hoc by the instructor and requires basic Matlab knowledge. The system has been employed with satisfactory results in several university courses. To demonstrate the automatic evaluation process, three exercises are presented in detail. The first exercise involves a linear regression analysis of data and the calculation of the quality parameters of an instrumental analysis method. The second and third exercises address two different comparison tests, a comparison test of the mean and a t-paired test.

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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.

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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.