DEVELOPMENT OF AN ALGORITHM TO ANALYZE CARTOGRAPHIC FEATURES EXTRACTION METHODS IN DIGITAL IMAGES

The algorithm creates a buffer area around the cartographic features of interest in one of the images and compare it with the other one. During the comparison, the algorithm calculates the number of equals and different points and uses it to calculate the statistical values of the analysis. One calculated statistical value is the correctness, which shows the user the percentage of points that were correctly extracted. Another one is the completeness that shows the percentage of points that really belong to the interest feature. And the third value shows the idea of quality obtained by the extraction method, since that in order to calculate the quality the algorithm uses the correctness and completeness previously calculated. All the performed tests using this algorithm were possible to use the statistical values calculated to represent quantitatively the quality obtained by the extraction method executed. So, it is possible to say that the developed algorithm can be used to analyze extraction methods of cartographic features of interest, since that the results obtained were promising.

Radiographic evaluation of root canal cleaning, main and laterals, using different methods of final irrigation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Effect of cleansing methods on saliva-contaminated zirconia: an evaluation of resin bond durability

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effectiveness of two methods for preparation of autologous platelet-rich plasma: an experimental study in rabbits

The purpose of this study was to compare the quantity and quality of platelets in platelet-rich plasma (PRP) samples prepared using either the single- or the double-centrifugation protocol. Ten adult white New Zealand rabbits were used. Ten ml of blood were drawn from each animal via cardiac puncture. Each blood sample was divided into two equal parts for PRP preparation: 5 ml of blood were centrifuged according to a single-centrifugation protocol (Group I), and 5 ml were centrifuged according to a double-centrifugation protocol (Group II). Manual platelet counts were performed on the whole blood and PRP samples of each group. Smears were also done on all samples in order to see the morphology of the platelets. The data obtained in the manual platelet count were submitted to statistical analysis (repeated measures ANOVA, Tukey, P<.05). The average whole blood platelet count was 446,389/μl. The PRP samples in Group II presented an average platelet amount significantly higher than that of Group I (1,986,875 ± 685,020/μl and 781,875 ± 217,693/μl, respectively). The PRP smears from Group II were the only one to present platelets with altered morphology (75% of the smears). A few lymphocytes with increased cytoplasm were observed in the PRP smears of both Groups I (25% of the smears) and II (62.5% of the smears). Within the limits of this study, it can be concluded that the double-centrifugation protocol resulted in higher platelet concentrations than did the single-centrifugation protocol. However, the double-centrifugation protocol caused alterations in platelet morphology and was more sensitive to small processing errors.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Comparative clinical study of the effectiveness of different dental bleaching methods - two year follow-up

This study evaluated color change, stability, and tooth sensitivity in patients submitted to different bleaching techniques. Material and methods: In this study, 48 patients were divided into five groups. A half-mouth design was conducted to compare two in-office bleaching bleaching techniques (with and without light activation): G1: 35% hydrogen peroxide (HP) (Lase Peroxide - DMC Equipments, Sao Carlos, SP, Brazil) + hybrid light (HL) (LED/Diode Laser, Whitening Lase II DMC Equipments, Sao Carlos, SP, Brazil); G2: 35% HP; G3: 38% HP (X-traBoost - Ultradent, South Jordan UT, USA) + HL; G4: 38% HP; and G5: 15% carbamide peroxide (CP) (Opalescence PF - Ultradent, South Jordan UT, USA). For G1 and G3, HP was applied on the enamel surface for 3 consecutive applications activated by HL. Each application included 3x3' HL activations with 1' between each interval; for G2 and G4, HP was applied 3x15' with 15' between intervals; and for G5, 15% CP was applied for 120'/10 days at home. A spectrophotometer was used to measure color change before the treatment and after 24 h, 1 week, 1, 6, 12, 18 and 24 months. A VAS questionnaire was used to evaluate tooth sensitivity before the treatment, immediately following treatment, 24 h after and finally 1 week after. Results: Statistical analysis did not reveal any significant differences between in-office bleaching with or without HL activation related to effectiveness; nevertheless the time required was less with HL. Statistical differences were observed between the result after 24 h, 1 week and 1, 6, 12, 18 and 24 months (integroup). Immediately, in-office bleaching increased tooth sensitivity. The groups activated with HL required less application time with gel. Conclusion: All techniques and bleaching agents used were effective and demonstrated similar behaviors.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

In vivo accuracy of conventional and digital radiographic methods in confirming root canal working length determination by Root ZX

Objectives: To compare, in vivo, the accuracy of conventional and digital radiographic methods in determining root canal working length. Material and Methods: Twenty-five maxillary incisor or canine teeth from 22 patients were used in this study. Considering the preoperative radiographs as the baseline, a 25 K file was inserted into the root canal to the point where the Root ZX electronic apex locator indicated the APEX measurement in the screen. From this measurement, 1 mm was subtracted for positioning the file. The radiographic measurements were made using a digital sensor (Digora 1.51) or conventional type-E films, size 2, following the paralleling technique, to determine the distance of the file tip and the radiographic apex. Results: The Student "t" test indicated mean distances of 1.11 mm to conventional and 1.20 mm for the digital method and indicated a significant statistical difference (p<0.05). Conclusions: The conventional radiographic method was found to be superior to the digital one in determining the working length of the root canal.

Evaluation of the detection and quantification limits in electroanalysis using two popular methods: application in the case study of paraquat determination

In this work, the reduction reaction of paraquat herbicide was used to obtain analytical signals using electrochemical techniques of differential pulse voltammetry, square wave voltammetry and multiple square wave voltammetry. Analytes were prepared with laboratory purified water and natural water samples (from Mogi-Guacu River, SP). The electrochemical techniques were applied to 1.0 mol L-1 Na2SO4 solutions, at pH 5.5, and containing different concentrations of paraquat, in the range of 1 to 10 mu mol L-1, using a gold ultramicroelectrode. 5 replicate experiments were conducted and in each the mean value for peak currents obtained -0.70 V vs. Ag/AgCl yielded excellent linear relationships with pesticide concentrations. The slope values for the calibration plots (method sensitivity) were 4.06 x 10(-3), 1.07 x 10(-2) and 2.95 x 10(-2) A mol(-1) L for purified water by differential pulse voltammetry, square wave voltammetry and multiple square wave voltammetry, respectively. For river water samples, the slope values were 2.60 x 10(-3), 1.06 x 10(-2) and 3.35 x 10(-2) A mol(-1) L, respectively, showing a small interference from the natural matrix components in paraquat determinations. The detection limits for paraquat determinations were calculated by two distinct methodologies, i.e., as proposed by IUPAC and a statistical method. The values obtained with multiple square waves voltammetry were 0.002 and 0.12 mu mol L-1, respectively, for pure water electrolytes. The detection limit from IUPAC recommendations, when inserted in the calibration curve equation, an analytical signal (oxidation current) is smaller than the one experimentally observed for the blank solution under the same experimental conditions. This is inconsistent with the definition of detection limit, thus the IUPAC methodology requires further discussion. The same conclusion can be drawn by the analyses of detection limits obtained with the other techniques studied.

Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright (c) 2011 John Wiley & Sons, Ltd.

Comparison of different dosimetric methods for red marrow absorbed dose calculation in thyroid cancer therapy

Several dosimetric methods have been proposed for estimating red marrow absorbed dose (RMAD) when radionuclide therapy is planned for differentiated thyroid cancer, although to date, there is no consensus as to whether dose calculation should be based on blood-activity concentration or not. Our purpose was to compare RMADs derived from methods that require collecting patients' blood samples versus those involving OLINDA/EXM software, thereby precluding this invasive procedure. This is a retrospective study that included 34 patients under treatment for metastatic thyroid disease. A deviation of 10 between RMADs was found, when comparing the doses from the most usual invasive dosimetric methods and those from OLINDA/EXM. No statistical difference between the methods was discovered, whereby the need for invasive procedures when calculating the dose is questioned. The use of OLINDA/EXM in clinical routine could possibly diminish data collection, thus giving rise to a simultaneous reduction in time and clinical costs, besides avoiding any kind of discomfort on the part of the patients involved.

Evaluating different methods of microarray data normalization

Abstract Background With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. Results Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. Conclusion In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Comparison of two laboratory-developed PCR methods for the diagnosis of Pulmonary Tuberculosis in Brazilian patients with and without HIV infection

Abstract Background Direct smear examination with Ziehl-Neelsen (ZN) staining for the diagnosis of pulmonary tuberculosis (PTB) is cheap and easy to use, but its low sensitivity is a major drawback, particularly in HIV seropositive patients. As such, new tools for laboratory diagnosis are urgently needed to improve the case detection rate, especially in regions with a high prevalence of TB and HIV. Objective To evaluate the performance of two in house PCR (Polymerase Chain Reaction): PCR dot-blot methodology (PCR dot-blot) and PCR agarose gel electrophoresis (PCR-AG) for the diagnosis of Pulmonary Tuberculosis (PTB) in HIV seropositive and HIV seronegative patients. Methods A prospective study was conducted (from May 2003 to May 2004) in a TB/HIV reference hospital. Sputum specimens from 277 PTB suspects were tested by Acid Fast Bacilli (AFB) smear, Culture and in house PCR assays (PCR dot-blot and PCR-AG) and their performances evaluated. Positive cultures combined with the definition of clinical pulmonary TB were employed as the gold standard. Results The overall prevalence of PTB was 46% (128/277); in HIV+, prevalence was 54.0% (40/74). The sensitivity and specificity of PCR dot-blot were 74% (CI 95%; 66.1%-81.2%) and 85% (CI 95%; 78.8%-90.3%); and of PCR-AG were 43% (CI 95%; 34.5%-51.6%) and 76% (CI 95%; 69.2%-82.8%), respectively. For HIV seropositive and HIV seronegative samples, sensitivities of PCR dot-blot (72% vs 75%; p = 0.46) and PCR-AG (42% vs 43%; p = 0.54) were similar. Among HIV seronegative patients and PTB suspects, ROC analysis presented the following values for the AFB smear (0.837), Culture (0.926), PCR dot-blot (0.801) and PCR-AG (0.599). In HIV seropositive patients, these area values were (0.713), (0.900), (0.789) and (0.595), respectively. Conclusion Results of this study demonstrate that the in house PCR dot blot may be an improvement for ruling out PTB diagnosis in PTB suspects assisted at hospitals with a high prevalence of TB/HIV.