919 resultados para Linear network analysis


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OBJECTIVE: Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no 'cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. APPROACH: Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. MAIN RESULTS: It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). SIGNIFICANCE: The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.

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The United Kingdom is committed to a raft of requirements to create a low-carbon economy. Buildings consume approximately 40% of UK energy demand. Any improvement on the energy performance of buildings therefore can significantly contribute to the delivery of a low-carbon economy. The challenge for the construction sector and its clients is how to meet the policy requirements to deliver low and zero carbon (LZC) buildings, which spans broader than the individual building level, to requirements at the local and regional levels, and wider sustainability pressures. Further, the construction sector is reporting skills shortages coupled with the need for ‘new skills’ for the delivery of LZC buildings. The aim of this paper is to identify, and better understand, the skills required by the construction sector and its clients for the delivery of LZC buildings within a region. The theoretical framing for this research is regional innovation system (RIS) using a socio-technical network analysis (STNA) methodology. A case study of a local authority region is presented. Data is drawn from a review of relevant local authority documentation, observations and semi-structured interviews from one (project 1) of five school retrofit projects within the region. The initial findings highlight the complexity surrounding the form and operation of the LZC network for project 1. The skills required by the construction sector and its clients are connected to different actor roles surrounding the delivery of the project. The key actors involved and their required skills are: project management and energy management skills required by local authority; project management skills (in particular project planning), communication and research skills required by school end-users; and a ‘technical skill’ relating to knowledge of a particular energy efficient measure (EEM) and use of equipment to implement the EEM is required by the EEM contractors.

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In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

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We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Nino-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with the Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 prediction skill, providing targets for model improvement.

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Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.

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Objective: To describe the results of a nutritional intervention programme among Japanese-Brazilians according to gender. Design: A non-controlled experimental study. Setting: The research included three points of clinical, nutritional and physical activity evaluation: at baseline (in 2005), after the first year and at the end of the second year (in 2007). The paired Student t test and multiple linear regression analysis were used to evaluate changes in the subjects` profile (clinical, nutritional and physical activity variables). Subjects: Japanese-Brazilians (n 575) of both genders, aged over 30 years. Results: We verified statistically significant reductions in body weight (0.9 kg), waist circumference (2.9 cm), blood pressure, fasting blood glucose (>3 mg/dl) and total cholesterol (>20 mg/dl) and its fractions, in both genders. We also found reductions in intake of energy (among men), protein (among women) and fat (both genders) and increases in intake of total fibre (among women) and carbohydrate (among men). Conclusions: The intervention programme indicated meaningful benefits for the intervention subjects, with changes in their habits that led to a `healthier` lifestyle positively impacting their nutritional and metabolic profile.

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The aim of this study was to evaluate the relationship between iron concentration in mature breast milk and characteristics of 136 donors of a Brazilian milk bank. Iron, vitamin A, zinc, and copper concentrations were assessed in human milk and maternal blood. Data were collected on maternal anthropometrics, obstetric, socioeconomic, demographic, and lifestyle factors. Iron, zinc, and copper in milk and zinc and copper in blood were detected by spectrophotometry. Vitamin A in milk and blood was determined by high-performance liquid chromatography. Hemoglobin was measured by electronic counting and serum iron and ferritin by colorimetry and chemoluminescence, respectively. Transferrin and ceruloplasmin were determined by nephelometry. According to multivariate linear regression analysis, iron in milk was positively associated with vitamin A in milk and with smoking but negatively associated with timing of breast milk donation (P < .001). These results indicate that iron concentration in milk of Brazilian donors may be influenced by nutritional factors and smoking. J Hum Lact. 26(2):175-179

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Epidemiological studies suggest that glucocorticoid excess in the fetus may contribute to the pathophysiology of cardiovascular diseases in adulthood. However, the impact of maternal glucocorticoid on the cardiovascular system of the offspring has not been much explored in studies involving humans, especially in childhood. The objective of this study was to assess the influence of maternal cortisol concentrations on child arterial elasticity. One hundred and thirty pregnant women followed from 1997 to 2000, and respective children 5-7 years of age followed from 2004 to 2006 were included in the study. Maternal cortisol was determined in saliva by an enzyme immunoassay utilizing the mean concentration of nine samples of saliva. Arterial elasticity was assessed by the large artery elasticity index (LAEI; the capacitive elasticity of large arteries) by recording radial artery pulse wave, utilizing the equipment HDI/PulseWave CR-2000 Cardiovascular Profiling System (R). The nutritional status of the children was determined by the body mass index (BMI). Insulin concentration was assessed by chemiluminescence, and insulin resistance by the homeostasis model assessment. Blood glucose, total cholesterol and fractions (LDL-c and HDL-c) and triglyceride concentrations were determined by automated enzymatic methods. The association between maternal cortisol and child arterial elasticity was assessed by multivariate linear regression analysis. There was a statistically significant association between maternal cortisol and LAEI (P=0.02), controlling for birth weight, age, BMI and HDL-c of the children. This study suggests that exposure to higher glucocorticoid concentrations in the prenatal period is associated to lower arterial elasticity in childhood, an earlier cardiovascular risk marker.

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Objective: To compare and evaluate longitudinally the dental arch relationships from 4.5 to 13.5 years of age with the Bauru-BCLP Yardstick in a large sample of patients with bilateral cleft lip and palate (BCLP). Design: Retrospective longitudinal intercenter outcome study. Patients: Dental casts of 204 consecutive patients with complete BCLP were evaluated at 6, 9, and 12 years of age. All models were identified only by random identification numbers. Setting: Three cleft palate centers with different treatment protocols. Main Outcome Measures: Dental arch relationships were categorized with the Bauru-BCLP yardstick. Increments for each interval (from 6 to 9 years, 6 to 12 years, and 9 to 12 years) were analyzed by logistic and linear regression models. Results: There were no significant differences in outcome measures between the centers at age 12 or at age 9. At age 6, center B showed significantly better results (p = .027), but this difference diminished as the yardstick score for this group increased over time (linear regression analysis), the difference with the reference category (center C, boys) for the intervals 6 to 12 and 9 to 12 years being 10.4% (p = .041) and 12.9% (p = .009), respectively. Conclusions: Despite different treatment protocols, dental arch relationships in the three centers were comparable in final scores at age 9 and 12 years. Delaying hard palate closure and employing infant orthopedics did not appear to be advantageous in the long run. Premaxillary osteotomy employed in center B appeared to be associated with less favorable development of the dental arch relationship between 9 and 12 years.

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Estimating the sizes of hard-to-count populations is a challenging and important problem that occurs frequently in social science, public health, and public policy. This problem is particularly pressing in HIV/AIDS research because estimates of the sizes of the most at-risk populations-illicit drug users, men who have sex with men, and sex workers-are needed for designing, evaluating, and funding programs to curb the spread of the disease. A promising new approach in this area is the network scale-up method, which uses information about the personal networks of respondents to make population size estimates. However, if the target population has low social visibility, as is likely to be the case in HIV/AIDS research, scale-up estimates will be too low. In this paper we develop a game-like activity that we call the game of contacts in order to estimate the social visibility of groups, and report results from a study of heavy drug users in Curitiba, Brazil (n = 294). The game produced estimates of social visibility that were consistent with qualitative expectations but of surprising magnitude. Further, a number of checks suggest that the data are high-quality. While motivated by the specific problem of population size estimation, our method could be used by researchers more broadly and adds to long-standing efforts to combine the richness of social network analysis with the power and scale of sample surveys. (C) 2010 Elsevier B.V. All rights reserved.

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In this paper we show the results of a comparison simulation study for three classification techniques: Multinomial Logistic Regression (MLR), No Metric Discriminant Analysis (NDA) and Linear Discriminant Analysis (LDA). The measure used to compare the performance of the three techniques was the Error Classification Rate (ECR). We found that MLR and LDA techniques have similar performance and that they are better than DNA when the population multivariate distribution is Normal or Logit-Normal. For the case of log-normal and Sinh(-1)-normal multivariate distributions we found that MLR had the better performance.

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Background: The relationship between CETP and postprandial hyperlipemia is still unclear. We verified the effects of varying activities of plasma CETP on postprandial lipemia and precocious atherosclerosis in asymptomatic adult women. Methods: Twenty-eight women, selected from a healthy population sample (n = 148) were classified according to three CETP levels, all statistically different: CETP deficiency (CETPd <= 4.5%, n = 8), high activity (CETPi >= 23.8, n = 6) and controls (CTL, CETP >= 4.6% and <= 23.7%, n = 14). After a 12 h fast they underwent an oral fat tolerance test (40 g of fat/m(2) of body surface area) for 8 hours. TG, TG-rich-lipoproteins (TRL), cholesterol and TRL-TG measurements (AUC, AUIC, AR, RR and late peaks) and comparisons were performed on all time points. Lipases and phospholipids transfer protein (PLTP) were determined. Correlation between carotid atherosclerosis (c-IMT) and postprandial parameters was determined. CETP TaqIB and I405V and ApoE-epsilon 3/epsilon 2/epsilon 4 polymorphisms were examined. To elucidate the regulation of increased lipemia in CETPd a multiple linear regression analysis was performed. Results: In the CETPi and CTL groups, CETP activity was respectively 9 and 5.3 higher compared to the CETPd group. Concentrations of all HDL fractions and ApoA-I were higher in the CETPd group and clearance was delayed, as demonstrated by modified lipemia parameters (AUC, AUIC, RR, AR and late peaks and meal response patterns). LPL or HL deficiencies were not observed. No genetic determinants of CETP deficiency or of postprandial lipemia were found. Correlations with c-IMT in the CETPd group indicated postprandial pro-atherogenic associations. In CETPd the regression multivariate analysis (model A) showed that CETP was largely and negatively predicted by VLDL-C lipemia (R(2) = 92%) and much less by TG, LDL-C, ApoAI, phospholipids and non-HDL-C. CETP (model B) influenced mainly the increment in ApoB-100 containing lipoproteins (R(2) = 85% negatively) and phospholipids (R(2) = 13%), at the 6(th)h point. Conclusion: The moderate CETP deficiency phenotype included a paradoxically high HDL-C and its sub fractions (as earlier described), positive associations with c-IMT, a postprandial VLDL-C increment predicting negatively CETP activity and CETP activity regulating inversely the increment in ApoB100-containing lipoproteins. We hypothesize that the enrichment of TG content in triglyceride-rich ApoB-containing lipoproteins and in TG rich remnants increases lipoproteins` competition to active lipolysis sites, reducing their catabolism and resulting on postprandial lipemia with atherogenic consequences.

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To identify chemical descriptors to distinguish Cuban from non-Cuban rums, analyses of 44 samples of rum from 15 different countries are described. To provide the chemical descriptors, analyses of the the mineral fraction, phenolic compounds, caramel, alcohols, acetic acid, ethyl acetate, ketones, and aldehydes were carried out. The analytical data were treated through the following chemometric methods: principal component analysis (PCA), partial least square-discriminate analysis (PLS-DA), and linear discriminate analysis (LDA). These analyses indicated 23 analytes as relevant chemical descriptors for the separation of rums into two distinct groups. The possibility of clustering the rum samples investigated through PCA analysis led to an accumulative percentage of 70.4% in the first three principal components, and isoamyl alcohol, n-propyl alcohol, copper, iron, 2-furfuraldehyde (furfuraldehyde), phenylmethanal (benzaldehyde), epicatechin, and vanillin were used as chemical descriptors. By applying the PLS-DA technique to the whole set of analytical data, the following analytes have been selected as descriptors: acetone, sec-butyl alcohol, isobutyl alcohol, ethyl acetate, methanol, isoamyl alcohol, magnesium, sodium, lead, iron, manganese, copper, zinc, 4-hydroxy3,5-dimethoxybenzaldehyde (syringaldehyde), methaldehyde (formaldehyde), 5-hydroxymethyl-2furfuraldehyde (5-HMF), acetalclehyde, 2-furfuraldehyde, 2-butenal (crotonaldehyde), n-pentanal (valeraldehyde), iso-pentanal (isovaleraldehyde), benzaldehyde, 2,3-butanodione monoxime, acetylacetone, epicatechin, and vanillin. By applying the LIDA technique, a model was developed, and the following analytes were selected as descriptors: ethyl acetate, sec-butyl alcohol, n-propyl alcohol, n-butyl alcohol, isoamyl alcohol, isobutyl alcohol, caramel, catechin, vanillin, epicatechin, manganese, acetalclehyde, 4-hydroxy-3-methoxybenzoic acid, 2-butenal, 4-hydroxy-3,5-dimethoxybenzoic acid, cyclopentanone, acetone, lead, zinc, calcium, barium, strontium, and sodium. This model allowed the discrimination of Cuban rums from the others with 88.2% accuracy.

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Tendo como motivação o desenvolvimento de uma representação gráfica de redes com grande número de vértices, útil para aplicações de filtro colaborativo, este trabalho propõe a utilização de superfícies de coesão sobre uma base temática multidimensionalmente escalonada. Para isso, utiliza uma combinação de escalonamento multidimensional clássico e análise de procrustes, em algoritmo iterativo que encaminha soluções parciais, depois combinadas numa solução global. Aplicado a um exemplo de transações de empréstimo de livros pela Biblioteca Karl A. Boedecker, o algoritmo proposto produz saídas interpretáveis e coerentes tematicamente, e apresenta um stress menor que a solução por escalonamento clássico.

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A estratégia empresarial é uma disciplina jovem. Comparado com os campos de estudo de economia e sociologia o campo de estratégia empresarial pode ser visto como um fenômeno de formação mais recente, embora extremamente dinâmico em sua capacidade de criar abordagens teóricas diferenciadas. Este trabalho discute a recente proliferação de teorias em estratégia empresarial, propondo um modelo de classificação destas teorias baseado na análise empírica do modelo de escolas de pensamento em estratégia empresarial desenvolvido por Mintzberg, Ahlstrand e Lampel em seu livro Safári de Estratégia (1998). As possíveis conseqüências relativas à interação entre teoria e prática são também discutidas apresentando o que definimos como a síndrome do ornitorrinco.