Variables Associated With The Performance Of Centers For Dental Specialties In Brazil.

The aim of this study was to evaluate the performance of the Centers for Dental Specialties (CDS) in the country and associations with sociodemographic indicators of the municipalities, structural variables of services and primary health care organization in the years 2004-2009. The study used secondary data from procedures performed in the CDS to the specialties of periodontics, endodontics, surgery and primary care. Bivariate analysis by χ2 test was used to test the association between the dependent variable (performance of the CDS) with the independents. Then, Poisson regression analysis was performed. With regard to the overall achievement of targets, it was observed that the majority of CDS (69.25%) performance was considered poor/regular. The independent factors associated with poor/regular performance of CDS were: municipalities belonging to the Northeast, South and Southeast regions, with lower Human Development Index (HDI), lower population density, and reduced time to deployment. HDI and population density are important for the performance of the CDS in Brazil. Similarly, the peculiarities related to less populated areas as well as regional location and time of service implementation CDS should be taken into account in the planning of these services.

Sexual dysfunction in epilepsy: identifying the psychological variables

In order to evaluate the psychological variables that affect sexual dysfunction (SD) in epilepsy, where compared 60 epileptics (Group 1) with 60 healthy individuals (Group 2), through the State-Trait Anxiety Inventory (Spielberger et al., 1970), Beck Depression Inventory (Beck, 1974) and Sexual Behavior Interview (Souza, 1995). Sexual dysfunction (SD), anxiety and depression were found more frequently in Group 1 than in Group 2 and were not related to sex. Variables such as the onset duration and frequency of seizures as well as the use to medication were not associated with SD. Temporal lobe epilepsy was related to SD (p = 0.035) but not to anxiety or depression. Anxiety and depression were related to SD in both groups. Perception in controlling the seizures was closely related to anxiety (p = 0) and depression (p = 0.009). We conclude that psychological factors play an important role in the alteration of sexual behavior in epileptics and that suitable attention must be given to the control of these variables.

Identification of family variables in parents' groups of children with epilepsy

OBJECTIVE: To verify the effectiveness of the support group in the identification of family variables linked to epilepsy. METHOD: Pre-test were applied to parents of 21 children with benign epilepsy of childhood recently diagnosed, from 5 to 15 years, who participated in the groups at HC/Unicamp. There was a presentation of an educational video, discussion and application of the post-test 1. After six months, the post-test 2 was applied. RESULTS: The beliefs were: fear of swallowing the tongue during the seizures (76.19%) and of a future mental disease (66.67%). Facing the epilepsy, fear and sadness appeared. 76.19% of the parents presented overprotection and 90.48%, expected a new seizure. In the post-test 1, the parents affirmed that the information offered had modified the beliefs. In the post-test 2, 80.95% didn't report great doubts about epilepsy and 90.48% considered their relationship with their children better. CONCLUSIONS: The demystification of beliefs supplied from the groups influenced the family positively, prevented behavior alterations and guaranteed effective care in the attendance to the child with epilepsy.

Analysis of the effects of conical indentation variables on the indentation response of elastic-plastic materials through factorial design of experiment

In this work, the effects of conical indentation variables on the load-depth indentation curves were analyzed using finite element modeling and dimensional analysis. A factorial design 2(6) was used with the aim of quantifying the effects of the mechanical properties of the indented material and of the indenter geometry. Analysis was based on the input variables Y/E, R/h(max), n, theta, E, and h(max). The dimensional variables E and h(max) were used such that each value of dimensionless Y/E was obtained with two different values of E and each value of dimensionless R/h(max) was obtained with two different h(max) values. A set of dimensionless functions was defined to analyze the effect of the input variables: Pi(1) = P(1)/Eh(2), Pi(2) = h(c)/h, Pi(3) = H/Y, Pi(4) = S/Eh(max), Pi(6) = h(max)/h(f) and Pi(7) = W(P)/W(T). These six functions were found to depend only on the dimensionless variables studied (Y/E, R/h(max), n, theta). Another dimension less function, Pi(5) = beta, was not well defined for most of the dimensionless variables and the only variable that provided a significant effect on beta was theta. However, beta showed a strong dependence on the fraction of the data selected to fit the unloading curve, which means that beta is especially Susceptible to the error in the Calculation of the initial unloading slope.

Espacialização da temperatura do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais

The aim of this study was to establish a digital elevation model and its horizontal resolution to interpolate the annual air temperature for the Alagoas State by means of multiple linear regression models. A multiple linear regression model was adjusted to series (11 to 34 years) of annual air temperatures obtained from 28 weather stations in the states of Alagoas, Bahia, Pernambuco and Sergipe, in the Northeast of Brazil, in function of latitude, longitude and altitude. The elevation models SRTM and GTOPO30 were used in the analysis, with original resolutions of 90 and 900 m, respectively. The SRTM was resampled for horizontal resolutions of 125, 250, 500, 750 and 900 m. For spatializing the annual mean air temperature for the state of Alagoas, a multiple linear regression model was used for each elevation and spatial resolution on a grid of the latitude and longitude. In Alagoas, estimates based on SRTM data resulted in a standard error of estimate (0.57 degrees C) and dispersion (r(2) = 0.62) lower than those obtained from GTOPO30 (0.93 degrees C and 0.20). In terms of SRTM resolutions, no significant differences were observed between the standard error (0.55 degrees C; 750 m - 0.58 degrees C; 250m) and dispersion (0.60; 500 m - 0.65; 750 m) estimates. The spatialization of annual air temperature in Alagoas, via multiple regression models applied to SRTM data showed higher concordance than that obtained with the GTOPO30, independent of the spatial resolution.

Depressive symptoms in old age: relations among sociodemographic and self-reported health variables

Background: Depression in old age is a complex multifactorial phenomenon that is influenced by several biopsychosocial variables. Depressive symptoms are associated with the presence of chronic diseases, with being female, with low education and low income levels, and with poor perceived health assessment. In impoverished areas, older adults may have more physical disability, as they may have less access to health services. Therefore, they may be more likely to report depressive symptoms. Methods: Population-based cross-sectional research was undertaken using data from the FIBRA study conducted in Ermelino Matarazzo, a poor subdistrict of the city of Sao Paulo, Brazil. The participants comprised 303 elderly people, aged 65 years and over, who attended a single-session data collection effort carried out at community centers. The protocol comprised sociodemographic and self-reported health variables, and the Geriatric Depression Scale. Results: The majority of the subjects reported five or fewer symptoms of depression (79.21%), reported one or two self-reported chronic diseases (56.86%), declared themselves to have one or two self-reported health problems (46.15%), and had good perceived health assessment (40.27%). The presence of depressive symptoms was associated with a higher number of self-reported health problems, poor perceived health assessment, and lower schooling levels, in the total sample and in analyses including men only. For women, depressive symptoms were associated with the number of self-reported health problems and family income. Conclusion: The presence of health problems, such as falls and memory problems, lower perceived health, and low education (and low family income for women) were associated with a higher presence of depressive symptoms among elderly people in this poor area of Sao Paulo.

An exploratory analysis of relationships between socioeconomic, land use, activity participation variables and travel patterns

This paper aims to find relations between the socioeconomic characteristics, activity participation, land use patterns and travel behavior of the residents in the Sao Paulo Metropolitan Area (SPMA) by using Exploratory Multivariate Data Analysis (EMDA) techniques. The variables influencing travel pattern choices are investigated using: (a) Cluster Analysis (CA), grouping and characterizing the Traffic Zones (17), proposing the independent variable called Origin Cluster and, (b) Decision Tree (DT) to find a priori unknown relations among socioeconomic characteristics, land use attributes of the origin TZ and destination choices. The analysis was based on the origin-destination home-interview survey carried out in SPMA in 1997. The DT application revealed the variables of greatest influence on the travel pattern choice. The most important independent variable considered by DT is car ownership, followed by the Use of Transportation ""credits"" for Transit tariff, and, finally, activity participation variables and Origin Cluster. With these results, it was possible to analyze the influence of a family income, car ownership, position of the individual in the family, use of transportation ""credits"" for transit tariff (mainly for travel mode sequence choice), activities participation (activity sequence choice) and Origin Cluster (destination/travel distance choice). (c) 2010 Elsevier Ltd. All rights reserved.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Approximate algorithms for credal networks with binary variables

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. (C) 2007 Elsevier Inc. All rights reserved.

On the use of correlated beta random variables with animal population modelling

We give reasons why demographic parameters such as survival and reproduction rates are often modelled well in stochastic population simulation using beta distributions. In practice, it is frequently expected that these parameters will be correlated, for example with survival rates for all age classes tending to be high or low in the same year. We therefore discuss a method for producing correlated beta random variables by transforming correlated normal random variables, and show how it can be applied in practice by means of a simple example. We also note how the same approach can be used to produce correlated uniform triangular, and exponential random variables. (C) 2008 Elsevier B.V. All rights reserved.

The influence of environmental variables on capybara (Hydrochoerus hydrochaeris: Rodentia, Hydrochoeridae) detectability in anthropogenic environments of southeastern Brazil

Capybaras were monitored weekly from 1998 to 2006 by counting individuals in three anthropogenic environments (mixed agricultural fields, forest and open areas) of southeastern Brazil in order to examine the possible influence of environmental variables (temperature, humidity, wind speed, precipitation and global radiation) on the detectability of this species. There was consistent seasonality in the number of capybaras in the study area, with a specific seasonal pattern in each area. Log-linear models were fitted to the sample counts of adult capybaras separately for each sampled area, with an allowance for monthly effects, time trends and the effects of environmental variables. Log-linear models containing effects for the months of the year and a quartic time trend were highly significant. The effects of environmental variables on sample counts were different in each type of environment. As environmental variables affect capybara detectability, they should be considered in future species survey/monitoring programs.

Estimating hourly net radiation for leaf wetness duration using the Penman-Monteith equation

Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R-2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5h, which is sufficient for use in plant disease management schemes.

CORRELATION BETWEEN FLOWERING, FRUCTIFICATION AND ENVIRONMENTAL VARIABLES IN Solanum lycocarpum A. St.-Hil, SOLANACEAE

Solanum lycocarpum (lobeira) is a typical and abundant species of brazilian Cerrado, which occupies mainly surrounding disturbed areas. It has interesting characteristics from the point of view of reproductive biology, that probably are favoring the large occupation of habitats by the species. Based on the fact that the species produces flowers and fruit during all the year, the present study had the purpose to verify the association between flower and fruit production with environmental variables (temperature, relative humidity and precipitation), aiming to support future studies referring to reproductive biology and ecology of plant species from Cerrado biome. A population of S. lycocarpum composed of 34 plants in reproductive phase, situated in Morrinhos, south of the State of Goias, Brazil,, was evaluated. All the plants were geographically referenced with a GPS receptor. Observations were made monthly during 13 months (June, 2005 to July, 2006) quantifying open flowers and fruits produced in the intervals between the observations. It was possible suggest high conversion of flowers in fruits. The Spearman rank correlation showed positive correlation of flower number with precipitation and relative humidity. Fruit number was not correlated with the environmental variables.

Development of Ramulosis Disease of Cotton Under Controlled Environment and Field Conditions

Colletotrichum gossypii var. cephalosporioides, the fungus that causes ramulosis disease of cotton, is widespread in Brazil and can cause severe yield loss. Because weather conditions greatly affect disease development, the objective of this work was to develop weather-based models to assess disease favorability. Latent period, incidence, and severity of ramulosis symptoms were evaluated in controlled environment experiments using factorial combinations of temperature (15, 20, 25, 30, and 35 degrees C) and leaf wetness duration (0, 4, 8, 16, 32, and 64 h after inoculation). Severity was modeled as an exponential function of leaf wetness duration and temperature. At the optimum temperature of disease development, 27 degrees C, average latent period was 10 days. Maximum ramulosis severity occurred from 20 to 30 degrees C, with sharp decreases at lower and higher temperatures. Ramulosis severity increased as wetness periods were increased from 4 to 32 h. In field experiments at Piracicaba, Sao Paulo State, Brazil, cotton plots were inoculated (10(5) conidia ml(-1)) and ramulosis severity was evaluated weekly. The model obtained from the controlled environment study was used to generate a disease favorability index for comparison with disease progress rate in the field. Hourly measurements of solar radiation, temperature, relative humidity, leaf wetness duration, rainfall, and wind speed were also evaluated as possible explanatory variables. Both the disease favorability model and a model based on rainfall explained ramulosis growth rate well, with R(2) of 0.89 and 0.91, respectively. They are proposed as models of ramulosis development rate on cotton in Brazil, and weather-disease relationships revealed by this work can form the basis of a warning system for ramulosis development.

Screening of Variables Influencing the Clavulanic Acid Production by Streptomyces DAUFPE 3060 Strain

Clavulanic acid (CA) is a beta-lactam antibiotic, which has a potent beta-lactamase inhibiting activity. The influence of five variables, namely pH (6.0, 6.4, and 6.8), temperature (28A degrees C, 30A degrees C, and 32A degrees C), agitation intensity (150, 200, and 250 rpm), glycerol concentration (5.0, 7.5, and 10 g/L) and soybean flour concentration (5.0, 12.5, and 20 g/L), on CA production by a new isolate of Streptomyces (DAUFPE 3060) was investigated in 250-mL Erlenmeyer flasks using a fractional factorial design. Temperature and soybean flour concentration were shown to be the two variables that exerted the most important effects on the production of CA at 95% confidence level. The highest CA concentration (494 mg/L) was obtained after 48 h at 150 rpm, 32A degrees C, pH 6.0, 5.0 g/L glycerol, and 20 g/L soybean flour concentrations. Under these conditions, the yields of biomass and product on consumed substrate were 0.26 g(X)/g(S) and 64.3 mg(P)/g(S), respectively. Fermentations performed in 3.0-L bench-scale fermenter allowed increasing the CA production by about 60%.