O IMPACTO DA ESPIRITUALIDADE NO TRABALHO E DE PERCEPÇÃO DE SAÚDE ORGANIZACIONAL SOBRE COMPORTAMENTOS DE CIDADANIA ORGANIZACIONAL

Cada vez mais o fator humano está em evidência no mercado competitivo e globalizado. Por isso, constantemente, as empresas se preocupam com a qualidade do trabalho, para que seus colaboradores sintam prazer em sua realização, não sendo tão agredidos pelas pressões do dia a dia. Nesse contexto, esta dissertação teve como objetivo geral analisar as relações entre espiritualidade no trabalho, percepção de saúde organizacional e comportamentos de cidadania organizacional entre professores universitários. Participaram deste estudo 82 trabalhadores de ambos os sexos, com idade entre 28 e 63 anos, os quais atuam no Estado de São Paulo em Instituições de Ensino Superior públicas e privadas. Como instrumento para coleta de dados foi utilizado um questionário de autopreenchimento composto de três escalas que mediram as variáveis da pesquisa. Assim, o estudo se propôs a apresentar, interpretar e discutir as relações entre as variáveis, como também, testar hipóteses referentes ao modelo conceitual proposto, por meio de uma pesquisa com abordagem quantitativa, cujos dados coletados foram analisados por aplicação de técnicas estatísticas paramétricas (cálculo de estatísticas descritivas: médias, desvios padrão, índice de precisão de medidas, teste t e correlações; cálculos de estatísticas multivariadas: regressão linear múltipla padrão. A análise dos dados foi realizada pelo software estatístico Statistical Package for the Social Science SPSS, versão 19.0 para Windows. Os resultados obtidos demonstraram que dentre as três dimensões de comportamentos de cidadania organizacional, apenas duas divulgação da imagem da organização e cooperação com os colegas receberam impacto positivo de percepção de saúde organizacional e espiritualidade no trabalho. Para estas duas classes de ações de cidadania organizacional ficou mais evidenciado o poder de impacto no trabalho. O estudo possibilitou concluir que ações de sugestões criativas realizadas pelos empregados, não sofre influências do quanto eles acreditam que a empresa tem uma saúde satisfatória e do quanto eles vivenciam, ou não, a espiritualidade no ambiente organizacional.

Methods of studying the planar distribution of objects in histological sections of brain tissue

This article reviews the statistical methods that have been used to study the planar distribution, and especially clustering, of objects in histological sections of brain tissue. The objective of these studies is usually quantitative description, comparison between patients or correlation between histological features. Objects of interest such as neurones, glial cells, blood vessels or pathological features such as protein deposits appear as sectional profiles in a two-dimensional section. These objects may not be randomly distributed within the section but exhibit a spatial pattern, a departure from randomness either towards regularity or clustering. The methods described include simple tests of whether the planar distribution of a histological feature departs significantly from randomness using randomized points, lines or sample fields and more complex methods that employ grids or transects of contiguous fields, and which can detect the intensity of aggregation and the sizes, distribution and spacing of clusters. The usefulness of these methods in understanding the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Creutzfeldt-Jakob disease is discussed. © 2006 The Royal Microscopical Society.

A semantic modelling approach to knowledge based statistical software

The topic of this thesis is the development of knowledge based statistical software. The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Some of the issues involved in the development of knowledge based software are presented and a review is given of some of the systems that have been developed so far. The majority of these have moved away from conventional architectures by adopting what can be termed an expert systems approach. The thesis then proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented, the system has been designed as an enhanced variant of a conventional style statistical package. This involved developing a semantic data model to represent some of the statistically relevant knowledge about data and identifying sets of requirements that should be met for the application of the statistical techniques to be valid. Those areas of statistics covered in the prototype are measures of association and tests of location.

Quantitative methods in neuropathology

The last decade has seen a considerable increase in the application of quantitative methods in the study of histological sections of brain tissue and especially in the study of neurodegenerative disease. These disorders are characterised by the deposition and aggregation of abnormal or misfolded proteins in the form of extracellular protein deposits such as senile plaques (SP) and intracellular inclusions such as neurofibrillary tangles (NFT). Quantification of brain lesions and studying the relationships between lesions and normal anatomical features of the brain, including neurons, glial cells, and blood vessels, has become an important method of elucidating disease pathogenesis. This review describes methods for quantifying the abundance of a histological feature such as density, frequency, and 'load' and the sampling methods by which quantitative measures can be obtained including plot/quadrat sampling, transect sampling, and the point-quarter method. In addition, methods for determining the spatial pattern of a histological feature, i.e., whether the feature is distributed at random, regularly, or is aggregated into clusters, are described. These methods include the use of the Poisson and binomial distributions, pattern analysis by regression, Fourier analysis, and methods based on mapped point patterns. Finally, the statistical methods available for studying the degree of spatial correlation between pathological lesions and neurons, glial cells, and blood vessels are described.

Priority evaluation in transportation policies and programmes

This work is concerned with the development of techniques for the evaluation of large-scale highway schemes with particular reference to the assessment of their costs and benefits in the context of the current transport planning (T.P.P.) process. It has been carried out in close cooperation with West Midlands County Council, although its application and results are applicable elsewhere. The background to highway evaluation and its development in recent years has been described and the emergence of a number of deficiencies in current planning practise noted. One deficiency in particular stood out, that stemming from inadequate methods of scheme generation and the research has concentrated upon improving this stage of appraisal, to ensure that subsequent stages of design, assessment and implementation are based upon a consistent and responsive foundation. Deficiencies of scheme evaluation were found to stem from inadequate development of appraisal methodologies suffering from difficulties of valuation, measurement and aggregation of the disparate variables that characterise highway evaluation. A failure to respond to local policy priorities was also noted. A 'problem' rather than 'goals' based approach to scheme generation was taken, as it represented the current and foreseeable resource allocation context more realistically. A review of techniques with potential for highway problem based scheme generation, which would work within a series of practical and theoretical constraints were assessed and that of multivariate analysis, and classical factor analysis in particular, was selected, because it offerred considerable application to the difficulties of valuation, measurement and aggregation that existed. Computer programs were written to adapt classical factor analysis to the requirements of T.P.P. highway evaluation, using it to derive a limited number of factors which described the extensive quantity of highway problem data. From this, a series of composite problem scores for 1979 were derived for a case study area of south Birmingham, based upon the factorial solutions, and used to assess highway sites in terms of local policy issues. The methodology was assessed in the light of its ability to describe highway problems in both aggregate and disaggregate terms, to guide scheme design, coordinate with current scheme evaluation methods, and in general to improve upon current appraisal. Analysis of the results was both in subjective, 'common-sense' terms and using statistical methods to assess the changes in problem definition, distribution and priorities that emerged. Overall, the technique was found to improve upon current scheme generation methods in all respects and in particular in overcoming the problems of valuation, measurement and aggregation without recourse to unsubstantiated and questionable assumptions. A number of deficiencies which remained have been outlined and a series of research priorities described which need to be reviewed in the light of current and future evaluation needs.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.

Application of Statistical Experimental Design in Food Sciences

The development of new, health supporting food of high quality and the optimization of food technological processes today require the application of statistical methods of experimental design. The principles and steps of statistical planning and evaluation of experiments will be explained. By example of the development of a gluten-free rusk (zwieback), which is enriched by roughage compounds the application of a simplex-centroid mixture design will be shown. The results will be illustrated by different graphics.

Infecções por enterobacteriaceae resistente aos carbapenêmicos em hospital de ensino: epidemiologia e caracterização molecular

Introduction: The production of KPC (Klebsiella pneumoniae carbapenemase) has become an important mechanism of carbapenem-resistance among Enterobacteriaceae strains. In Brazil, KPC is already widespread and its incidence has increased significantly, reducing treatment options. The “perfect storm” combination of the absence of new drug developmentand the emergence of multidrug-resistant strains resulted in the need for the use of older drugs, with greater toxicity, such as polymyxins. Aims: To determine the occurrence of carbapenemase-producing strains in carbapenem-resistant Enterobacteriaceae isolated from patients with nosocomial infection/colonization during September/2014 to August/2015, to determine the risk factors associated with 30-day- mortality and the impact of inappropriate therapy. Materials and Methods: We performed a case control study to assess the risk factors (comorbidities, invasive procedures and inappropriate antimicrobial therapy) associated with 30-day-mortality, considering the first episode of infection in 111 patients. The resistance genes blaKPC, blaIMP, blaVIM and blaNDM-1 were detected by polymerase chain reaction technique. Molecular typing of the strains involved in the outbreak was performed by pulsed field gel electrophoresis technique. The polymyxin resistance was confirmed by the microdilution broth method. Results: 188 episodes of carbapenem-resistant Enterobacteriaceae infections/colonizations were detected; of these, 122 strains were recovered from the hospital laboratory. The presence of blaKPC gene were confirmed in the majority (74.59%) of these isolates. It was not found the presence of blaIMP , blaVIM and blaNDM-1 genes. K. pneumoniae was the most frequent microorganism (77,13%), primarily responsible for urinary tract infections (21,38%) and infections from patients of the Intensive Care Unit (ICU) (61,38%). Multivariate statistical analysis showed as predictors independently associated with mortality: dialysis and bloodstream infection. The Kaplan-Meier curve showed a lower probability of survival in the group of patients receiving antibiotic therapy inappropriately. Antimicrobial use in adult ICU varied during the study period, but positive correlation between increased incidence of strains and the consumption was not observed. In May and July 2015, the occurrence rates of carbapenem-resistant Enterobacteriaceae KPC-producing per 1000 patient-days were higher than the control limit established, confirming two outbreaks, the first caused by colistin-susceptible KPC-producing K. pneumoniae isolates, with a polyclonal profile and the second by a dominant clone of colistin-resistant (≥ 32 μg/mL) KPC-producing K. pneumoniae. The cross transmission between patients became clear by the temporal and spatial relationships observed in the second outbreak, since some patients occupied the same bed, showing problems in hand hygiene adherence among healthcare workers and inadequate terminal disinfection of environment. The outbreak was contained when the ICU was closed to new admissions. Conclusions: The study showed an endemicity of K. pneumoniae KPC-producing in adult ICU, progressing to an epidemic monoclonal expansion, resulted by a very high antibiotic consumption of carbapenems and polymyxins and facilitated by failures in control measures the unit.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Methods for Hypothesis Testing in Animal Carcinogenicity Experiments

Thesis (Ph.D.)--University of Washington, 2016-08

Flexible modeling and estimation for high-dimensional graphs

Thesis (Ph.D.)--University of Washington, 2016-08

Ecomorfologia de dez espécies de peixes marinhos mais abundantes de Galinhos/RN

Ecomorphology is a science based on the idea that morphological differences among species could be associated with distinct biological and environmental pressures suffered by them. These differences can be studied employing morphological and biometric indexes denominated Ecomorphological attributes , representing standards that express characteristics of the individual in relation to its environment, and can be interpreted as indicators of life habits or adaptations suffered due its occupation of different habitats. This work aims to contribute for the knowledge of the ecomorphology of the Brazilian marine ichthyofauna, specifically from Galinhos, located at Rio Grande do Norte state. 10 different species of fish were studied, belonging the families Gerreidae (Eucinostomus argenteus), Haemulidae (Orthopristis ruber,Pomadasyscorvinaeformis,Haemulonaurolineatum,Haemulonplumieri,Haemulonsteindachneri), Lutjanidae (Lutjanus synagris), Paralichthyidae (Syaciummicrurum), Bothidae (Bothus ocellatus) and Tetraodontidae (Sphoeroidestestudineus), which were obtained during five collections, in the period time of September/2004 to April/2005, utilizing three special nets. The ecomorphological study was performed at the laboratory. Eight to ten samples of each fish specie were measured. Fifteen morphological aspects were considered to calculate twelve ecomorphological attributes. Multivariate statistical analysis methods such as Principal Component Analysis (PCA) and Cluster Analysis were done to identify ecmorphological patterns to describe the data set obtained. As results, H.aurolineatumwas the most abundant specie found (23,03%) and S.testudineusthe less one with 0,23%. The 1st Principal component showed variation of 60,03% with influence of the ecomorphological attribute related to body morphology, while the 2nd PC with 23,25% variation had influence of the ecomorphological attribute related to oral morphology. The Cluster Analiysis promoted the identification of three distinct groups Perciformes, Pleuronectiformes and Tetraodontiformes. Based on the obtained data, considering morphological characters differences among the species studied, we suggest that all of them live at the medium (E.argenteus,O.rubber, P.corvinaeformis,H.aurolineatum,H.plumieri,H.steindachneri,L.synagris) and bottom (S.micrurum,B.ocellatus,S.testudineus) region of column water.