Awareness of visceral leishmaniasis and its relationship to canine infection in riverside endemic areas in Northeastern Brazil

IntroductionAn awareness of visceral leishmaniasis (VL) is necessary to encourage the population to participate in prevention and control in collaboration with more efficient, centrally organized health programs. The aim of this study was to evaluate the awareness of the riverside population regarding VL and the association between awareness and the prevalence of canine visceral leishmaniasis (CVL).MethodsIn total, 71 people living in riverside areas in the City of Mossoró in State of Rio Grande do Norte participated of the study, and 71 dogs were tested for CVL by polymerase chain reaction (PCR). Association analysis of several variables related to knowledge of the riverside population regarding CVL positivity was performed, yielding odds ratios (OR) and 95% confidence intervals (CI), and significance was determined using chi-square (χ2) and Fisher's exact tests.ResultsAmong individuals whose dogs tested positive for CVL, 60% did not know the cure for CVL, and these subjects were three times more likely to have a dog test positive for CVL than those who were aware the cure for CVL. Knowledge of CVL cure was the only variable that remained in the logistic model after the successive removal of variables, with an adjusted OR of 3.11 (95%CI: 1.1-8,799; p=0.032).ConclusionsInsufficient awareness regarding VL in riverside areas with CVL-positive dogs was associated with increased rates of canine infection, which suggests that changes in habits and the adoption of attitudes and preventive practices may contribute to the control and prevention of this disease. This study reinforces the need to invest in better health education programs regarding VL.

The determinants of net interest margins & net interest spreads in the Russian & Japanese Commercial Banking sectors

This study presents an empirical investigation of the determinants of net interest margins and spreads in the Russian and Japanese banking sectors with a particular focus on commercial banks. Net interest mar-gins and spreads serve as indicators of financial intermediation efficiency. This paper employed a bank-level unbalanced panel dataset prolonging from 2005 to 2014. My main empirical results show that bank characteristics explain the most of the variation in not only net interest margins but also in spreads. Capi-talization, liquidity risk, inflation, economic growth, private and government debt are important determi-nants of margin in Russia. In Japan to the contrary loan and deposit market concentration along with bank size do predominate. Common significant variables in both countries are the substitution effect, cost effi-ciency and profitability. Turning to net interest spreads, micro- and macro-specific variables are the main significant drivers in Russia. I reach the conclusion that there are no significant determinants of net interest spreads in Japan within the original selection of variables, but operating efficiency and deposits to total funding seem to prevail. In both countries, there are solid differences in the net interest margins as well as spreads once the pre- and the post-crisis periods are considered.

Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.

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

Lecture Notes in Computer Science, 9273

Study of the spin and parity of the Higgs boson in diboson decays with the ATLAS detector

Studies of the spin, parity and tensor couplings of the Higgs boson in the H→ZZ∗→4ℓ , H→WW∗→eνμν and H→γγ decay processes at the LHC are presented. The investigations are based on 25 fb−1 of pp collision data collected by the ATLAS experiment at s√=7 TeV and s√=8 TeV. The Standard Model (SM) Higgs boson hypothesis, corresponding to the quantum numbers JP=0+, is tested against several alternative spin scenarios, including non-SM spin-0 and spin-2 models with universal and non-universal couplings to fermions and vector bosons. All tested alternative models are excluded in favour of the SM Higgs boson hypothesis at more than 99.9% confidence level. Using the H→ZZ∗→4ℓ and H→WW∗→eνμν decays, the tensor structure of the HVV interaction in the spin-0 hypothesis is also investigated. The observed distributions of variables sensitive to the non-SM tensor couplings are compatible with the SM predictions and constraints on the non-SM couplings are derived.

Some predictors of cardiovascular mortality among the elderly population of Botucatu (SP)

OBJECTIVE: To detect factors associated with cardiovascular mortality in the elderly of Botucatu. METHODS: We evaluated 29 variables of interest in a cohort of patients aged ³60 using data from a survey conducted between 1983/84. The elderly cohort was analyzed in 1992 to detect the occurrence of cardiovascular deaths. Survival analysis was performed using the Kaplan-Meier method, the log-rank test, and Cox regression analysis. Three models were adapted for each group of variables, and a final model was chosen from those variables selected from each group. RESULTS: We identified predictor for cardiovascular death according to age for elderly males not supporting the family, not possessing a vehicle, and previous cardiovascular disease. In elderly females, the predictor variables were previous cardiovascular disease and diabetes mellitus. CONCLUSION: Socioeconomic indicators (family heading and vehicle ownerrship) may be added to well stabilished medical factors (diabete mellitus and hypertension to select target groups for programs intended to reduce deaths due to cardiovascular diseases in elderly people.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Calculation of the variance in surveys of the economic climate

Public opinion surveys have become progressively incorporated into systems of official statistics. Surveys of the economic climate are usually qualitative because they collect opinions of businesspeople and/or experts about the long-term indicators described by a number of variables. In such cases the responses are expressed in ordinal numbers, that is, the respondents verbally report, for example, whether during a given trimester the sales or the new orders have increased, decreased or remained the same as in the previous trimester. These data allow to calculate the percent of respondents in the total population (results are extrapolated), who select every one of the three options. Data are often presented in the form of an index calculated as the difference between the percent of those who claim that a given variable has improved in value and of those who claim that it has deteriorated. As in any survey conducted on a sample the question of the measurement of the sample error of the results has to be addressed, since the error influences both the reliability of the results and the calculation of the sample size adequate for a desired confidence interval. The results presented here are based on data from the Survey of the Business Climate (Encuesta de Clima Empresarial) developed through the collaboration of the Statistical Institute of Catalonia (Institut d’Estadística de Catalunya) with the Chambers of Commerce (Cámaras de Comercio) of Sabadell and Terrassa.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.

The use of the likelihood ratio for evaluative and investigative purposes in comparative forensic handwriting examination

This paper extends previous research and discussion on the use of multivariate continuous data, which are about to become more prevalent in forensic science. As an illustrative example, attention is drawn here on the area of comparative handwriting examinations. Multivariate continuous data can be obtained in this field by analysing the contour shape of loop characters through Fourier analysis. This methodology, based on existing research in this area, allows one describe in detail the morphology of character contours throughout a set of variables. This paper uses data collected from female and male writers to conduct a comparative analysis of likelihood ratio based evidence assessment procedures in both, evaluative and investigative proceedings. While the use of likelihood ratios in the former situation is now rather well established (typically, in order to discriminate between propositions of authorship of a given individual versus another, unknown individual), focus on the investigative setting still remains rather beyond considerations in practice. This paper seeks to highlight that investigative settings, too, can represent an area of application for which the likelihood ratio can offer a logical support. As an example, the inference of gender of the writer of an incriminated handwritten text is forwarded, analysed and discussed in this paper. The more general viewpoint according to which likelihood ratio analyses can be helpful for investigative proceedings is supported here through various simulations. These offer a characterisation of the robustness of the proposed likelihood ratio methodology.

Using VARs and TVP-VARs with Many Macroeconomic Variables

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

Prognostic value of postoperative carcinoembryonic antigen concentration and extent of invasion of resection margins after hepatic resection for colorectal metastases.

OBJECTIVE: To evaluate the prognostic value of postoperative concentration of carcinoembryonic antigen (CEA) and extent of surgical margins after resection of liver metastases from colorectal cancer. DESIGN: Retrospective study. SETTING: Teaching hospital, Switzerland. SUBJECTS: 49 patients with hepatic metastases after primary colorectal cancer. INTERVENTIONS: Resection of hepatic metastases MAIN OUTCOME MEASURES: Assessment of prognostic value of variables by univariate and multivariate analysis. RESULTS: Median survival was 24 months (range 5-86 months). Resection margins were clear (> 1-cm) in 10, close (< 1-cm) in 25 and invaded in 9 patients. On univariate analysis, a postoperative concentration of CEA of <4ng/ml was correlated with prolonged survival (p < 0.001), but the width of the resection margin was not of prognostic importance. There was no correlation between width of resection margins and postoperative concentration of CEA (p = 0.5). On multivariate analysis, postoperative concentrations of CEA of 4 ng/ml or more were associated with increased risk of death (relative risk 7.3; 95% confidence interval (CI) 2.8-18.7, p < 0.001). CONCLUSION: Postoperative CEA offers better prognostic discrimination than the width of resection margins after resection of liver metastases from colorectal tumours. Some patients with invaded resection margins did survive for 3 years, but no patient did whose CEA concentration was 4 ng/ml or more. The definition of a potentially curative hepatic resection should include a postoperative CEA concentration of <4 ng/ml (within the reference range).

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

On the stability of the optimal value and the optimal set in optimization problems

The paper develops a stability theory for the optimal value and the optimal set mapping of optimization problems posed in a Banach space. The problems considered in this paper have an arbitrary number of inequality constraints involving lower semicontinuous (not necessarily convex) functions and one closed abstract constraint set. The considered perturbations lead to problems of the same type as the nominal one (with the same space of variables and the same number of constraints), where the abstract constraint set can also be perturbed. The spaces of functions involved in the problems (objective and constraints) are equipped with the metric of the uniform convergence on the bounded sets, meanwhile in the space of closed sets we consider, coherently, the Attouch-Wets topology. The paper examines, in a unified way, the lower and upper semicontinuity of the optimal value function, and the closedness, lower and upper semicontinuity (in the sense of Berge) of the optimal set mapping. This paper can be seen as a second part of the stability theory presented in [17], where we studied the stability of the feasible set mapping (completed here with the analysis of the Lipschitz-like property).

The impact of social relationships on the maintenance of independence in advanced old age: findings of a Swiss Longitudinal Study .

The impact of social relationships on the maintenance of independence over periods of 12-18 months in a group of 306 octogenarians is assessed in this study. The study is based on the results of the Swilsoo (Swiss Interdisciplinary Longitudinal Study on the Oldest Old). Participants (80-84 years old at baseline) were interviewed five times between 1994 and 1999. Independence was defined as the capacity to perform without assistance eight activities of daily living. We distinguished in our analyses kinship and friendship networks and evaluated social relationships with the help of a series of variables serving as indicators of network composition and contact frequency. Logistic regression models were used to identify the short-term effects of social relationships on independence, after controlling for sociodemographic and health-related variables; independence at a given wave of interviews was interpreted in the light of social factors measured at the previous wave. Our analyses indicate that the existence of a close friend has a significant impact on the maintenance of independence (OR=1.58, p<0.05), which is not the case with the other variables concerning network composition. Kinship contacts were also observed to have a positive impact on independence (OR=1.12, p<0.01).