931 resultados para Descriptive quantitative analysis


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The present work aims at analyzing how the adoption of a proactive environmental management via green operational practices (GOPs) correlates to the Green Performance (GrP) of a given set of ISO 9001-certified firms in Brazil. To this end, we elaborated a conceptual framework about environmental management, GOPs, and GrP Such theoretical foundation supported the development of empirical research through quantitative analysis. For the analysis, 75 questionnaires were collected from ISO 9001 certified companies. Data was analyzed by with statistical tools such as descriptive analysis, correlation analysis, and Structural Equation Modeling (SEM). The results demonstrate that the adoption of GOPs, in fact, exerts a positive impact on the GrP of the firms. This work contributes to a better understanding of green manufacturing in Brazil's industrial sector

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

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Objective The Brazilian National Hansens Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters. Method The study area included 373 municipalities in the four Brazilian states Maranha o, Para ', Tocantins and Piaui '. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases / 100 000 population, (ii) rate of cases < 15 years / 100 000 population, (iii) new cases with grade-2 disability / 100 000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic. Results A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates > 40 cases / 100 000 inhabitants). There was a concentration of municipalities with higher detection rates in Para ' and in the center of Maranha o. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate < 15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2 / population overlap with those of new cases detection and detection of children < 15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters. Conclusions Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

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OBJETIVO: identificar, entre os cursos de Mestrado Acadêmico dos programas de pós-graduação em enfermagem brasileiros, analisados por ocasião da avaliação trienal (2007-2009) da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, a carga horária informada pelos programas para a formação de mestre. MÉTODO: trata-se de estudo descritivo, quantitativo, documental, com dados coletados em julho de 2010, mediante consulta aos relatórios da citada Coordenação, no quesito "Propostas dos Programas", dos Cadernos de Indicadores, disponíveis na web. RESULTADOS: foram encontradas discrepâncias entre as cargas horárias dos 32 cursos de Mestrado Acadêmico das diversas regiões do Brasil, sendo que a menor carga horária foi de 315 horas e a maior de 1.530 horas. CONCLUSÕES: há necessidade de maior discussão acerca da carga horária necessária à formação das competências e habilidades para o perfil desejado do egresso. O estudo propicia avanços no conhecimento da enfermagem, pois não foram identificadas investigações pregressas que tratem de tal tema. Espera-se que os cursos de Mestrado Acadêmico de enfermagem percebam-se nesse processo e revejam suas decisões pedagógicas, à luz dos novos modelos de formação/capacitação dos profissionais de enfermagem, centrados na ciência, tecnologia e inovação na área.

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The objective of this study was to identify and analyze the nursing diagnoses that constitute risk factors for death in trauma victims in the first 6 hours post-event. This is a cross-sectional, descriptive and exploratory study using quantitative analysis. A total of 406 patients were evaluated over six months of data collection in a tertiary hospital in the municipality of Sao Paulo, according to an instrument created for this purpose. Of the total, 44 (10.7%) suffered death. Multivariate analysis indicated the nursing diagnoses ineffective respiratory pattern, impaired spontaneous ventilation, risk of bleeding and risk of ineffective gastrointestinal tissue perfusion as risk factors for death and ineffective airway clearance, impaired comfort, and acute pain as protective factors, data that can direct health teams for different interventionist actions faced with the complexity of the trauma.

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Statement of problem. The retention of an Aramany Class IV removable partial dental prosthesis can be compromised by a lack of support. The biomechanics of this obturator prosthesis result in an unusual stress distribution on the residual maxillary bone. Purpose. This study evaluated the biomechanics of an Aramany Class IV obturator prosthesis with finite element analysis and a digital 3-dimensional (3-D) model developed from a computed tomography scan; bone stress was evaluated according to the load placed on the prosthesis. Material and methods. A 3-D model of an Aramany Class IV maxillary resection and prosthesis was constructed. This model was used to develop a finite element mesh. A 120 N load was applied to the occlusal and incisal platforms corresponding to the prosthetic teeth. Qualitative analysis was based on the scale of maximum principal stress; values obtained through quantitative analysis were expressed in MPa. Results. Under posterior load, tensile and compressive stresses were observed; the tensile stress was greater than the compressive stress, regardless of the bone region, and the greatest compressive stress was observed on the anterior palate near the midline. Under an anterior load, tensile stress was observed in all of the evaluated bone regions; the tensile stress was greater than the compressive stress, regardless of the bone region. Conclusions. The Aramany Class IV obturator prosthesis tended to rotate toward the surgical resection when subjected to posterior or anterior loads. The amount of tensile and compressive stress caused by the Aramany Class IV obturator prosthesis did not exceed the physiological limits of the maxillary bone tissue. (J Prosthet Dent 2012;107:336-342)

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Background: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal Findings: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/Significance: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

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Abstract Background Five species of the genus Schistosoma, a parasitic trematode flatworm, are causative agents of Schistosomiasis, a disease that is endemic in a large number of developing countries, affecting millions of patients around the world. By using SAGE (Serial Analysis of Gene Expression) we describe here the first large-scale quantitative analysis of the Schistosoma mansoni transcriptome, one of the most epidemiologically relevant species of this genus. Results After extracting mRNA from pooled male and female adult-worms, a SAGE library was constructed and sequenced, generating 68,238 tags that covered more than 6,000 genes expressed in this developmental stage. An analysis of the ordered tag-list shows the genes of F10 eggshell protein, pol-polyprotein, HSP86, 14-3-3 and a transcript yet to be identified to be the five top most abundant genes in pooled adult worms. Whereas only 8% of the 100 most abundant tags found in adult worms of S. mansoni could not be assigned to transcripts of this parasite, 46.9% of the total ditags could not be mapped, demonstrating that the 3 sequence of most of the rarest transcripts are still to be identified. Mapping of our SAGE tags to S. mansoni genes suggested the occurrence of alternative-polyadenylation in at least 13 gene transcripts. Most of these events seem to shorten the 3 UTR of the mRNAs, which may have consequences over their stability and regulation. Conclusion SAGE revealed the frequency of expression of the majority of the S. mansoni genes. Transcriptome data suggests that alternative polyadenylation is likely to be used in the control of mRNA stability in this organism. When transcriptome was compared with the proteomic data available, we observed a correlation of about 50%, suggesting that both transcriptional and post-transcriptional regulation are important for determining protein abundance in S. mansoni. The generation of SAGE tags from other life-cycle stages should contribute to reveal the dynamics of gene expression in this important parasite.

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Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

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The evaluation of the farmers’ communities’ approach to the Slow Food vision, their perception of the Slow Food role in supporting their activity and their appreciation and expectations from participating in the event of Mother Earth were studied. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted in an agro-food sector context. A survey was conducted, 120 questionnaires from farmers attending the Mother Earth in Turin in 2010 were collected. The descriptive statistical analysis showed that both Slow Food membership and participation to Mother Earth Meeting were much appreciated for the support provided to their business and the contribution to a more sustainable and fair development. A positive social, environmental and psychological impact on farmers also resulted. Results showed also an interesting perspective on the possible universality of the Slow Food and Mother Earth values. Farmers declared that Slow Food is supporting them by preserving the biodiversity and orienting them to the use of local resources and reducing the chemical inputs. Many farmers mentioned the language/culture and administration/bureaucratic issues as an obstacle to be a member in the movement and to participate to the event. Participation to Mother Earth gives an opportunity to exchange information with other farmers’ communities and to participate to seminars and debates, helpful for their business development. The absolute majority of positive answers associated to the farmers’ willingness to relate to Slow Food and participate to the next Mother Earth editions negatively influenced the UTAUT model results. A factor analysis showed that the variables associated to the UTAUT model constructs Performance Expectancy and Effort Expectancy were consistent, able to explain the construct variability, and their measurement reliable. Their inclusion in a simplest Technology Acceptance Model could be considered in future researches.

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Folates (vitamin B9) are essential water soluble vitamins, whose deficiency in humans may contribute to the onset of several diseases, such as anaemia, cancer, cardiovascular diseases, neurological problems as well as defects in embryonic development. Human and other mammals are unable to synthesize ex novo folate obtaining it from exogenous sources, via intestinal absorption. Recently the gut microbiota has been identified as an important source of folates and the selection and use of folate producing microorganisms represents an innovative strategy to increase human folate levels. The aim of this thesis was to gain a fundamental understanding of folate metabolism in Bifidobacterium adolescentis. The work was subdivided in three main phases, also aimed to solve different problems encountered working with Bifidobacterium strains. First, a new identification method (based on PCR-RFLP of hsp60 gene) was specifically developed to identify Bifidobacterium strains. Secondly, Bifidobacterium adolescentis biodiversity was explored in order to recognize representing strains of this species to be screened for their folate production ability. Results showed that this species is characterized by a wide variability and support the idea that a possible new taxonomic re-organization would be required. Finally B. adolescentis folate metabolism was studied using a double approach. A quantitative analysis of folate content was complemented by the examination of expression levels of genes involved in folate related pathways. For the normalization process, required to increase the robustness of the qRT-PCR analysis, an appropriate set of reference genes was tested using two different algorithms. Results demonstrate that B.adolescentis strains may represent an endogenous source of natural folate and they could be used to fortify fermented dairy products. This bio-fortification strategy presents many advantages for the consumer, providing native folate forms more bio-available, and not implicated in the discussed controversy concerning the safety of high intake of synthetic folic acid.