Contributo para o desenvolvimento de um Instrumento de Investigação Neuropsicológica

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Diagnosing Network-Wide Traffic Anomalies

Anomalies are unusual and significant changes in a network's traffic levels, which can often involve multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data. In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively using Principal Component Analysis. Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow. We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Use of 16S ribosomal RNA gene analyses to characterize the bacterial signature associated with poor oral health in West Virginia.

BACKGROUND: West Virginia has the worst oral health in the United States, but the reasons for this are unclear. This pilot study explored the etiology of this disparity using culture-independent analyses to identify bacterial species associated with oral disease. METHODS: Bacteria in subgingival plaque samples from twelve participants in two independent West Virginia dental-related studies were characterized using 16S rRNA gene sequencing and Human Oral Microbe Identification Microarray (HOMIM) analysis. Unifrac analysis was used to characterize phylogenetic differences between bacterial communities obtained from plaque of participants with low or high oral disease, which was further evaluated using clustering and Principal Coordinate Analysis. RESULTS: Statistically different bacterial signatures (P<0.001) were identified in subgingival plaque of individuals with low or high oral disease in West Virginia based on 16S rRNA gene sequencing. Low disease contained a high frequency of Veillonella and Streptococcus, with a moderate number of Capnocytophaga. High disease exhibited substantially increased bacterial diversity and included a large proportion of Clostridiales cluster bacteria (Selenomonas, Eubacterium, Dialister). Phylogenetic trees constructed using 16S rRNA gene sequencing revealed that Clostridiales were repeated colonizers in plaque associated with high oral disease, providing evidence that the oral environment is somehow influencing the bacterial signature linked to disease. CONCLUSIONS: Culture-independent analyses identified an atypical bacterial signature associated with high oral disease in West Virginians and provided evidence that the oral environment influenced this signature. Both findings provide insight into the etiology of the oral disparity in West Virginia.

Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events.

BACKGROUND: Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. METHODS AND RESULTS: We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("event-replication" group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01). CONCLUSIONS: Metabolite profiles are associated with CAD and subsequent cardiovascular events.

Spatial, seasonal and long-term fluctuations of plankton in relation to hydroclimatic features in the English Channel, Celtic Sea and Bay of Biscay

Plankton collected by the Continuous Plankton Recorder (CPR) survey were investigated for the English Channel, Celtic Sea and Bay of Biscay from 1979 to 1995. The main goal was to study the relationship between climate and plankton and to understand the factors influencing it. In order to take into account the spatial and temporal structure of biological data, a three-mode principal component analysis (PCA) was developed. It not only identified 5 zones characterised by their similar biological composition and by the seasonal and inter-annual evolution of the plankton, it also made species associations based on their location and year-to-year change. The studied species have stronger year-to-year fluctuations in abundance over the English Channel and Celtic Sea than the species offshore in the Bay of Biscay. The changes in abundance of plankton in the English Channel are negatively related to inter-annual changes of climatic conditions from December to March (North Atlantic Oscillation [NAO] index and air temperature). Thus, the negative relationship shown by Fromentin and Planque (1996; Mar Ecol Prog Ser 134:111-118) between year-to-year changes of Calanus finmarchicus abundance in the northern North Atlantic and North Sea and NAO was also found for the most abundant copepods in the Channel. However, the hypothesis proposed to explain the plankton/NAO relationship is different for this region and a new hypothesis is proposed. In the Celtic Sea, a relationship between the planktonic assemblage and the air temperature was detected, but it is weaker than for the English Channel. No relationship was found for the Bay of Biscay. Thus, the local physical environment and the biological composition of these zones appear to modify the relationship between winter climatic conditions and the year-to-year fluctuations of the studied planktonic species. This shows, therefore, that the relationship between climate and plankton is difficult to generalise.

Covariance among North Sea ecosystem state indicators during the past 50 years - contrasts between coastal and open waters

Regime shift and principal component analysis of a spatially disaggregated database capturing time-series of climatic, nutrient and plankton variables in the North Sea revealed considerable covariance between groups of ecosystem indicators. Plankton and climate time-series span the period 1958–2003, those of nutrients start in 1980. In both regions, the period from 1989 to 2001 identified in principal component 1 had warmer surface waters, higher Atlantic inflow and stronger winds, than the periods before or after. However, it was preceded by a regime shift in both open (PC2) and coastal (PC3) waters during 1977 towards more hours of sunlight and higher water temperature, which lasted until 1997. The relative influence of nutrient availability and climatic forcing differed between open and coastal North Sea regions. Inter-annual variability in phytoplankton dynamics of the open North Sea was primarily regulated by climatic forcing, specifically by sea surface temperature, Atlantic inflow and co-varying wind stress and NAO. Coastal phytoplankton variability, however, was regulated by insolation and sea surface temperature, as well as Si availability, but not by N or P. Regime shifts in principal components of hydrographic and climatic variables (explaining 55 and 61% of the variance in coastal and open water variables) were detected using Rodionov's sequential t-test. These shifts in hydroclimatic variables which occurred around 1977, 1989, 1997 and 2001, were synchronized in open and coastal waters, and were tracked by open water chlorophyll and copepods, but not by coastal plankton. North–central–south or open-coastal spatial breakdowns of the North Sea explained similar amounts of variability in most ecosystem indicators with the exception of diatom abundance and chlorophyll concentration, which were clearly better explained using the open-coastal configuration.

Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder

The Continuous Plankton Recorder (CPR) dataset on fish larvae has an extensive spatio-temporal coverage that allows the responses of fish populations to past changes in climate variability, including abrupt changes such as regime shifts, to be investigated. The newly available dataset offers a unique opportunity to investigate long-term changes over decadal scales in the abundance and distribution of fish larvae in relation to physical and biological factors. A principal component analysis (PCA) using 7 biotic and abiotic parameters is applied to investigate the impact of environmental changes in the North Sea on 5 selected taxa of fish larvae during the period 1960 to 2004. The analysis revealed 4 periods of time (1960–1976; 1977–1982; 1983–1996; 1997–2004) reflecting 3 different ecosystem states. The larvae of clupeids, sandeels, dab and gadoids seemed to be affected mainly by changes in the plankton ecosystem, while the larvae of migratory species such as Atlantic mackerel responded more to hydrographic changes. Climate variability seems more likely to influence fish populations through bottom-up control via a cascading effect from changes in the North Atlantic Oscillation (NAO) impacting on the hydro dynamic features of the North Sea, in turn impacting on the plankton available as prey for fish larvae. The responses and adaptability of fish larvae to changing environmental conditions, parti cularly to changes in prey availability, are complex and species-specific. This complexity is enhanced with fishing effects interacting with climate effects and this study supports furthering our under - standing of such interactions before attempting to predict how fish populations respond to climate variability

Biomonitoring of air pollutants by using lichens (Evernia prunastri) in areas between Kenitra and Mohammedia cities in Morocco.

In this study, Evernia prunastri, a lichen growing in its natural habitat in Morocco was analysed for the concentration of five heavy metals (Fe, Pb, Zn, Cu and Cr) from eleven sites between Kenitra and Mohammedia cities. The control site was Dar Essalam, an isolated area with low traffic density and dense vegetation. In the investigated areas, the concentration of heavy metals was correlated with vehicular traffic, industrial activity and urbanization. The total metal concentration was highest in Sidi Yahya, followed by Mohammedia and Bouznika. The coefficient of variation was higher for Pb and lower for Cu, Zn and Fe. The concentrations of most heavy metals in the thalli differed significantly between sites (p<0.01). Principal component analysis (PCA) revealed a significant correlation between heavy metal accumulation and atmospheric purity index. This study demonstrated also that the factors most strongly affecting the lichen flora were traffic density, the petroleum industry and paper factories in these areas. Overall, these results suggest that the index of atmospheric purity and assessment of heavy metals in lichen thalli are good indicators of the air quality at the studied sites.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

Effect of excitation wavelength on the Raman spectroscopy of the porcine photoreceptor layer from the area centralis.

Raman microscopy, based upon the inelastic scattering (Raman) of light by molecular species, has been applied as a specific structural probe in a wide range of biomedical samples. The purpose of the present investigation was to assess the potential of the technique for spectral characterization of the porcine outer retina derived from the area centralis, which contains the highest proportion of cone:rod cell ratio in the pig retina. METHODS: Retinal cross-sections, immersion-fixed in 4% (w/v) PFA and cryoprotected, were placed on salinized slides and air-dried prior to direct Raman microscopic analysis at three excitation wavelengths, 785 nm, 633 nm, and 514 nm. RESULTS: Raman spectra of each of the photoreceptor inner and outer segments (PIS, POS) and of the outer nuclear layer (ONL) of the retina acquired at 785 nm were dominated by vibrational features characteristic of proteins and lipids. There was a clear difference between the inner and outer domains in the spectroscopic regions, amide I and III, known to be sensitive to protein conformation. The spectra recorded with 633 nm excitation mirrored those observed at 785 nm excitation for the amide I region, but with an additional pattern of bands in the spectra of the PIS region, attributed to cytochrome c. The same features were even more enhanced in spectra recorded with 514 nm excitation. A significant nucleotide contribution was observed in the spectra recorded for the ONL at all three excitation wavelengths. A Raman map was constructed of the major spectral components found in the retinal outer segments, as predicted by principal component analysis of the data acquired using 633 nm excitation. Comparison of the Raman map with its histological counterpart revealed a strong correlation between the two images. CONCLUSIONS: It has been demonstrated that Raman spectroscopy offers a unique insight into the biochemical composition of the light-sensing cells of the retina following the application of standard histological protocols. The present study points to the considerable promise of Raman microscopy as a component-specific probe of retinal tissue.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

Differences In Metabolic Profile In the Testes of Streptozotocin Induced Diabetes Mellitus Mice As Detected By 1H NMR

Contrary to the traditional view, recent studies suggest that diabetes mellitus has an adverse influence on male reproductive function. Our aim was to determine the affect of diabetes on the testicular environment by identifying and then assessing perturbations in small molecule metabolites. Testes were obtained from control and streptozotocin induced diabetic C57BL/6 mice, two, four and eight weeks post treatment. Diabetic status was confirmed by HbA1c, non fasting blood glucose, physiological condition and body weight. Protein free, low molecular weight, water soluble extracts were assessed using 1H NMR spectroscopy. Principal Component Analysis of the derived profiles was used to classify any variations and specific metabolites were identified based on their spectral pattern. Characteristic metabolite profiles were identified for control and diabetic animals with the most distinctive being from mice with the greatest physical deterioration and loss of bodyweight. Eight streptozotocin treated animals did not develop diabetes and displayed profiles similar to controls. Diabetic mice had decreases in creatine, choline and carnitine and increases in lactate, alanine and myo-inositol. Betaine levels were found to be increased in the majority of diabetic mice but decreased in two animals with severe loss of body weight and physical condition. The association between perturbations in a number of small molecule metabolites known to be influential in sperm function, with diabetic status and physiological condition, adds further impetus to the proposal that diabetes influences important spermatogenic pathways and mechanisms in a subtle and previously unrecognised manner.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.