The effect of siesta in parameters of cardiac structure and in interpretation of ambulatory arterial blood pressure monitoring

OBJECTIVE: To evaluate the influence of the siesta in ambulatory blood pressure (BP) monitoring and in cardiac structure parameters. METHODS: 1940 ambulatory arterial blood pressure monitoring tests were analyzed (Spacelabs 90207, 15/15 minutes from 7:00 to 22:00 hours and 20/20 minutes from 22:01 to 6.59hours) and 21% of the records indicated that the person had taken a siesta (263 woman, 52±14 years). The average duration of the siesta was 118±58 minutes. RESULTS: (average ± standard deviation) The average of systolic/diastolic pressures during wakefulness, including the napping period, was less than the average for the period not including the siesta (138±16/85±11 vs 139±16/86±11 mmHg, p<0.05); 2) pressure loads during wakefulness including the siesta, were less than those observed without the siesta); 3) the averages of nocturnal sleep blood pressures were similar to those of the siesta, 4) nocturnal sleep pressure drops were similar to those in the siesta including wakefulness with and without the siesta; 5) the averages of BP in men were higher (p<0.05) during wakefulness with and without the siesta, during the siesta and nocturnal sleep in relation to the average obtained in women; 6) patients with a reduction of 0- 5% during the siesta had thickening of the interventricular septum and a larger posterior wall than those with a reduction during the siesta >5%. CONCLUSION: The siesta influenced the heart structure parameters and from a statistical point of view the average of systolic and diastolic pressures and the respective pressure loads of the wakeful period.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Implications of the variability in soil penetration resistance for statistical analysis

Soil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %. The values of 15 and 26 % were the thresholds separating the low/intermediate and the high/very high coefficient variation classes of PR in this Ultisol.

Beaufils ranges to assess the cotton nutrient status in the southern region of Mato Grosso

The relationships between nutrient contents and indices of the Diagnosis and Recommendation Integrated System (DRIS) are a useful basis to determine appropriate ranges for the interpretation of leaf nutrient contents. The purpose of this study was to establish Beaufils ranges from statistical models of the relationship between foliar concentrations and DRIS indices, generated by two systems of DRIS norms - the F value and natural logarithm transformation - and assess the nutritional status of cotton plants, based on these Beaufils ranges. Yield data from plots (average acreage 100 ha) and foliar concentrations of macro and micronutrients of cotton (Gossypium hirsutum r. latifolium) plants, in the growing season 2004/2005, were stored in a database. The criterion to define the reference population consisted of plots with above-average yields + 0.5 standard deviation (over 4,575 kg ha-1 seed cotton yield). The best-fitting statistical model of the relationship between foliar nutrient concentrations and DRIS indices was linear, with R² &gt; 0.8090, p < 0.01, except for N, with R² = 0.5987, p < 0.01. The two criteria were effective to diagnose the plant nutritional status. The diagnoses were not random, but based on the effectiveness of the chi-square-tested method. The agreement between the methods to assess the nutritional status was 92.59-100 %, except for S, with 74.07 % agreement.

Estimation of soil moisture in the root-zone from remote sensing data

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (&#923;) were measured by the Bowen ratio method. Soil moisture (&#952;) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected &#923; and &#952; values and was applied using the &#923; values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated &#952; values (p<0.05, R² = 0.84) and there were no significant differences according to the Student t-test (p<0.01). The statistical analyses showed that the empirical relationship can be applied to estimate the root-zone soil moisture of irrigated soils, i.e. when the evaporative fraction is greater than 0.45.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Three-dimensional interpolation of soil data: fertility and pedomorphological features in southern Brazil

The graphical representation of spatial soil properties in a digital environment is complex because it requires a conversion of data collected in a discrete form onto a continuous surface. The objective of this study was to apply three-dimension techniques of interpolation and visualization on soil texture and fertility properties and establish relationships with pedogenetic factors and processes in a slope area. The GRASS Geographic Information System was used to generate three-dimensional models and ParaView software to visualize soil volumes. Samples of the A, AB, BA, and B horizons were collected in a regular 122-point grid in an area of 13 ha, in Pinhais, PR, in southern Brazil. Geoprocessing and graphic computing techniques were effective in identifying and delimiting soil volumes of distinct ranges of fertility properties confined within the soil matrix. Both three-dimensional interpolation and the visualization tool facilitated interpretation in a continuous space (volumes) of the cause-effect relationships between soil texture and fertility properties and pedological factors and processes, such as higher clay contents following the drainage lines of the area. The flattest part with more weathered soils (Oxisols) had the highest pH values and lower Al3+ concentrations. These techniques of data interpolation and visualization have great potential for use in diverse areas of soil science, such as identification of soil volumes occurring side-by-side but that exhibit different physical, chemical, and mineralogical conditions for plant root growth, and monitoring of plumes of organic and inorganic pollutants in soils and sediments, among other applications. The methodological details for interpolation and a three-dimensional view of soil data are presented here.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Annonaceae substitution rates: a codon model perspective

The Annonaceae includes cultivated species of economic interest and represents an important source of information for better understanding the evolution of tropical rainforests. In phylogenetic analyses of DNA sequence data that are used to address evolutionary questions, it is imperative to use appropriate statistical models. Annonaceae are cases in point: Two sister clades, the subfamilies Annonoideae and Malmeoideae, contain the majority of Annonaceae species diversity. The Annonoideae generally show a greater degree of sequence divergence compared to the Malmeoideae, resulting in stark differences in branch lengths in phylogenetic trees. Uncertainty in how to interpret and analyse these differences has led to inconsistent results when estimating the ages of clades in Annonaceae using molecular dating techniques. We ask whether these differences may be attributed to inappropriate modelling assumptions in the phylogenetic analyses. Specifically, we test for (clade-specific) differences in rates of non-synonymous and synonymous substitutions. A high ratio of nonsynonymous to synonymous substitutions may lead to similarity of DNA sequences due to convergence instead of common ancestry, and as a result confound phylogenetic analyses. We use a dataset of three chloroplast genes (rbcL, matK, ndhF) for 129 species representative of the family. We find that differences in branch lengths between major clades are not attributable to different rates of non-synonymous and synonymous substitutions. The differences in evolutionary rate between the major clades of Annonaceae pose a challenge for current molecular dating techniques that should be seen as a warning for the interpretation of such results in other organisms.

Analysis of laboratory intercomparison data: a matter of independence

When laboratory intercomparison exercises are conducted, there is no a priori dependence of the concentration of a certain compound determined in one laboratory to that determined by another(s). The same applies when comparing different methodologies. A existing data set of total mercury readings in fish muscle samples involved in a Brazilian intercomparison exercise was used to show that correlation analysis is the most effective statistical tool in this kind of experiments. Problems associated with alternative analytical tools such as mean or paired 't'-test comparison and regression analysis are discussed.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Retrospective canine skin peripheral nerve sheath tumors data with emphasis on histologic, immunohistochemical and prognostic factors

Abstract: In this retrospective study was determined the frequency of canine skin peripheral nerve sheath tumors (PNST) in cases diagnosed by the Setor de Patologia Veterinária of the Universidade Federal do Rio Grande do Sul (SPV-UFRGS), Brazil, between the years 2000 and 2012. The canine profiles, as well as histological, immunohistochemical and prognostic aspects of the tumors were based on 70 samples, comprising 40 females, 29 males and one unspecified sample. Between 2000 and 2012, 2,984 skin tumors of dogs were diagnosed in the SPV-UFRGS, totaling 2.34% of skin neoplasms in dogs. Animals that comprised the largest amount of samples (43%) were those with no breed (SRD), followed by German Shepherds (10%). Females were more affected than males (40/70 - 57% and 29/70 - 41% respectively). Skin PNST of this research showed predominant localization on the limbs (40% in the forelimbs and 29% in the hindlimbs); affecting adult dogs, mostly aged between 8 and 11 years (54%). The samples were routinely processed for hematoxylin and eosin, and were also evaluated by toluidine blue and Masson's trichrome staining, and immunohistochemistry (IHC) anti-vimentin, -S-100, -GFAP, -actin, von Willebrand factor and neurofilament. Anisocytosis and anisokaryosis, mitotic index, intratumoral necrosis, invasion of adjacent tissues, tumor location, local recurrence and metastasis were related to the diagnosis of benign (49/70) or malignant tumor (21/70). The Antoni A histological pattern was observed more frequently in benign tumors. The immunohistochemistry helped to diagnose PNST, and anti-vimentin and anti-protein S-100 showed the highest rates of immunostaining. Throughout statistical analysis of animals with tumor recurrence, it was found that the chance of an animal with a malignant peripheral nerve sheath tumor to develop recurrence is 4.61 times higher than in an animal that had a benign tumor.

Normative data for the Brazilian population in the Boston Diagnostic Aphasia Examination: influence of schooling

In Neurolinguistics, the use of diagnostic tests developed in other countries can create difficulties in the interpretation of results due to cultural, demographic and linguistic differences. In a country such as Brazil, with great social contrasts, schooling exerts a powerful influence on the abilities of normal individuals. The objective of the present study was to identify the influence of schooling on the performance of normal Brazilian individuals in the Boston Diagnostic Aphasia Examination (BDAE), in order to obtain reference values for the Brazilian population. We studied 107 normal subjects ranging in age from 15 to 84 years (mean ± SD = 47.2 ± 17.6 years), with educational level ranging from 1 to 24 years (9.9 ± 4.8 years). Subjects were compared for scores obtained in the 28 subtests of the BDAE after being divided into groups according to age (15 to 30, N = 24, 31 to 50, N = 33 and 51 years or more, N = 50) and education (1 to 4, N = 26, 5 to 8, N = 17 and 9 years or more, N = 61). Subjects with 4 years or less of education performed poorer in Word Discrimination, Visual Confrontation Naming, Reading of Sentences and Paragraphs, and Primer-Level Dictation (P < 0.05). When breakdown by schooling was 8 years or less, subjects performed poorer in all subtests (P < 0.05), except Responsive Naming, Word Recognition and Word-Picture Matching. The elderly performed poorer (P < 0.05) in Complex Ideational Material, Visual Confrontation Naming, Comprehension of Oral Spelling, Written Confrontation Naming, and Sentences to Dictation. We present the reference values for the cut-off scores according to educational level.

The effect of performing corrections on reported uterine cancer mortality data in the city of São Paulo

Reports of uterine cancer deaths that do not specify the subsite of the tumor threaten the quality of the epidemiologic appraisal of corpus and cervix uteri cancer mortality. The present study assessed the impact of correcting the estimated corpus and cervix uteri cancer mortality in the city of São Paulo, Brazil. The epidemiologic assessment of death rates comprised the estimation of magnitudes, trends (1980-2003), and area-level distribution based on three strategies: i) using uncorrected death certificate information; ii) correcting estimates of corpus and cervix uteri mortality by fully reallocating unspecified deaths to either one of these categories, and iii) partially correcting specified estimates by maintaining as unspecified a fraction of deaths certified as due to cancer of "uterus not otherwise specified". The proportion of uterine cancer deaths without subsite specification decreased from 42.9% in 1984 to 20.8% in 2003. Partial and full corrections resulted in considerable increases of cervix (31.3 and 48.8%, respectively) and corpus uteri (34.4 and 55.2%) cancer mortality. Partial correction did not change trends for subsite-specific uterine cancer mortality, whereas full correction did, thus representing an early indication of decrease for cervical neoplasms and stability for tumors of the corpus uteri in this population. Ecologic correlations between mortality and socioeconomic indices were unchanged for both strategies of correcting estimates. Reallocating unspecified uterine cancer mortality in contexts with a high proportion of these deaths has a considerable impact on the epidemiologic profile of mortality and provides more reliable estimates of cervix and corpus uteri cancer death rates and trends.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.