An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Development of a new student evaluation instrument of instructor effectiveness in online courses

The purpose of this study was to (a) develop an evaluation instrument capable of rating students' perceptions of the instructional quality of an online course and the instructor’s performance, and (b) validate the proposed instrument with a study conducted at a major public university. The instrument was based upon the Seven Principles of Good Practice for Undergraduate Education (Chickering & Gamson, 1987). The study examined four specific questions. 1. Is the underlying factor structure of the new instrument consistent with Chickering and Gamson's Seven Principles? 2. Is the factor structure of the new instrument invariant for male and female students? 3. Are the scores on the new instrument related students’ expected grades? 4. Are the scores on the new instrument related to the students' perceived course workload? ^ The instrument was designed to measure students’ levels of satisfaction with their instruction, and also gathered information concerning the students’ sex, the expected grade in the course, and the students’ perceptions of the amount of work required by the course. A cluster sample consisting of an array of online courses across the disciplines yielded a total 297 students who responded to the online survey. The students for each course selected were asked to rate their instructors with the newly developed instrument. ^ Question 1 was answered using exploratory factor analysis, and yielded a factor structure similar to the Seven Principles.^ Question 2 was answered by separately factor-analyzing the responses of male and female students and comparing the factor structures. The resulting factor structures for men and women were different. However, 14 items could be realigned under five factors that paralleled some of the Seven Principles. When the scores of only those 14 items were entered in two principal components factor analyses using only men and only women, respectively and restricting the factor structure to five factors, the factor structures were the same for men and women.^ A weak positive relationship between students’ expected grades and their scores on the instrument was found (Question 3). There was no relationship between students’ perceived workloads for the course and their scores on the instrument (Question 4).^

Stream dissolved organic matter bioavailability and composition in watersheds underlain with discontinuous permafrost

We examined the impact of permafrost on dissolved organic matter (DOM) composition in Caribou-Poker Creeks Research Watershed (CPCRW), a watershed underlain with discontinuous permafrost, in interior Alaska. We analyzed long term data from watersheds underlain with varying degrees of permafrost, sampled springs and thermokarsts, used fluorescence spectroscopy, and measured the bioavailabity of dissolved organic carbon (DOC). Permafrost driven patterns in hydrology and vegetation influenced DOM patterns in streams, with the stream draining the high permafrost watershed having higher DOC and dissolved organic nitrogen (DON) concentrations, higher DOC:- DON and greater specific ultraviolet absorbance (SUVA) than the streams draining the low and medium permafrost watersheds. Streams, springs and thermokarsts exhibited a wide range of DOC and DON concentrations (1.5–37.5 mgC/L and 0.14–1.26 mgN/L, respectively), DOC:DON (7.1–42.8) and SUVA (1.5–4.7 L mgC-1 m-1). All sites had a high proportion of humic components, a low proportion of protein components, and a low fluorescence index value (1.3–1.4), generally consistent with terrestrially derivedDOM. Principal component analysis revealed distinct groups in our fluorescence data determined by diagenetic processing and DOM source. The proportion of bioavailable DOC ranged from 2 to 35%, with the proportion of tyrosine- and tryptophan-like fluorophores in the DOM being a major predictor of DOC loss (p\0.05, R2 = 0.99). Our results indicate that the degradation of permafrost in CPCRW will result in a decrease in DOC and DON concentrations, a decline in DOC:DON, and a reduction in SUVA, possibly accompanied by

Comparative study of dissolved organic matter from groundwater and surface water in the Florida coastal Everglades using multi-dimensional spectrofluorometry combined with multivariate statistics

Dissolved organic matter (DOM) in groundwater and surface water samples from the Florida coastal Everglades were studied using excitation–emission matrix fluorescence modeled through parallel factor analysis (EEM-PARAFAC). DOM in both surface and groundwater from the eastern Everglades S332 basin reflected a terrestrial-derived fingerprint through dominantly higher abundances of humic-like PARAFAC components. In contrast, surface water DOM from northeastern Florida Bay featured a microbial-derived DOM signature based on the higher abundance of microbial humic-like and protein-like components consistent with its marine source. Surprisingly, groundwater DOM from northeastern Florida Bay reflected a terrestrial-derived source except for samples from central Florida Bay well, which mirrored a combination of terrestrial and marine end-member origin. Furthermore, surface water and groundwater displayed effects of different degradation pathways such as photodegradation and biodegradation as exemplified by two PARAFAC components seemingly indicative of such degradation processes. Finally, Principal Component Analysis of the EEM-PARAFAC data was able to distinguish and classify most of the samples according to DOM origins and degradation processes experienced, except for a small overlap of S332 surface water and groundwater, implying rather active surface-to-ground water interaction in some sites particularly during the rainy season. This study highlights that EEM-PARAFAC could be used successfully to trace and differentiate DOM from diverse sources across both horizontal and vertical flow profiles, and as such could be a convenient and useful tool for the better understanding of hydrological interactions and carbon biogeochemical cycling.

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

Solar radiation–enhanced dissolution of particulate organic matter from coastal marine sediments

We investigated the influence of solar radiation on the transfer of organic matter from the particulate to dissolved phase during resuspension of coastal sediments collected from seven sites across Florida Bay (organic carbon values ranged from 2% to 9% by weight). Sediments were resuspended in oligotrophic seawater for 48 h in 1-liter quartz flasks in the dark and under simulated solar radiation (SunTest XLS+) at wet weight concentrations of 100 mg L21 and 1 g L21 (dry weights ranged from 27 to 630 mg L21). There were little to no dissolved organic carbon (DOC) increases in dark resuspensions, but substantial DOC increases occurred in irradiated resuspensions. DOC levels increased 4 mg C L21 in an irradiated 1 g L21 suspension (dry weight 400 mg L21) of an organic-rich (7% organic carbon) sediment. At a particle load commonly found in coastal waters (dry weight 40 mg L21), an irradiated suspension of the same organic-rich sediment produced 1 mg C L21. DOC increases in irradiated resuspensions were well-correlated with particulate organic carbon (POC) added. Photodissolution of POC ranged from 6% to 15% at high sediment levels and 10% to 33% at low sediment levels. Parallel factor analysis modeling of excitation-emission matrix fluorescence data (EEM PARAFAC) suggested the dissolved organic matter (DOM) produced during photodissolution included primarily humic-like components and a less important input of protein-like components. Principal component analysis (PCA) of EEM data revealed a marked similarity in the humic character of photodissolved DOM from organic-rich sediments and the humic character of Florida Bay waters.

Occupational therapists' attitudes toward family -centered care

The purpose of this study was to examine pediatric occupational therapists attitudes towards family-centered care. Specific attributes identified by the literature (professional characteristics, educational experiences and organizational culture) were investigated to determine their influence on these attitudes. Study participants were 250 pediatric occupational therapists who were randomly selected from the American Occupational Therapy Association special interest sections. ^ Participants received a mail packet with three instruments to complete and mail back within 2 weeks. The instruments were (a) the Professional Attitude Scale, (b) the Professional Characteristics Questionnaire, and (c) the Family-Centered Program Rating Scale. There was a 50% return rate. Data analysis was conducted in SPSS using descriptive statistics, correlations and regression analysis. ^ The analysis showed that pediatric occupational therapists working in various practice settings demonstrate favorable attitudes toward family-centered care as measured by the Professional Attitude Scale. There was no correlation between professional characteristics and educational experiences to therapists' attitudes. A moderate correlation (r = .368, p < .05) was found between the occupational therapists attitudes and the organizational culture of their workplaces. A factor analysis was conducted on the organizational culture instrument (FamPRS) as this sample was exclusively pediatric occupational therapists and the original sample was interdisciplinary professionals. Two factors were extracted using a principal components extraction and varimax rotation, in addition to examination of the scree plot. These two factors accounted for 50% of the total variance of the scores on the instrument. Factor 1, called empowerment accounted for 45.6% of the variance, and Factor 2, responsiveness accounted for 4.3% of the variance of the entire instrument. Stepwise regression analysis demonstrated that these two factors accounted for 16% of the variance toward attitudes clinicians hold toward family-centered care. These factors support the tenets of family-centered care; empowering parents to be leaders in their child's health care and helping organizations become more responsive to family needs. ^ These study findings suggest that organizational culture has some influence on occupational therapists attitudes toward family-centered care (R 2 = .16). These findings suggest educators should consider families as valuable resources when considering program planning in family-centered care at preservice and workplace settings. ^

Evaluating the Distribution of Terrestrial Dissolved Organic Matter in a Complex Coastal Ecosystem Using Fluorescence Spectroscopy

The coastal zone of the Florida Keys features the only living coral reef in the continental United States and as such represents a unique regional environmental resource. Anthropogenic pressures combined with climate disturbances such as hurricanes can affect the biogeochemistry of the region and threaten the health of this unique ecosystem. As such, water quality monitoring has historically been implemented in the Florida Keys, and six spatially distinct zones have been identified. In these studies however, dissolved organic matter (DOM) has only been studied as a quantitative parameter, and DOM composition can be a valuable biogeochemical parameter in assessing environmental change in coastal regions. Here we report the first data of its kind on the application of optical properties of DOM, in particular excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC), throughout these six Florida Keys regions in an attempt to assess spatial differences in DOM sources. Our data suggests that while DOM in the Florida Keys can be influenced by distant terrestrial environments such as the Everglades, spatial differences in DOM distribution were also controlled in part by local surface runoff/fringe mangroves, contributions from seasgrass communities, as well as the reefs and waters from the Florida Current. Application of principal component analysis (PCA) of the relative abundance of EEM-PARAFAC components allowed for a clear distinction between the sources of DOM (allochthonous vs. autochthonous), between different autochthonous sources and/or the diagenetic status of DOM, and further clarified contribution of terrestrial DOM in zones where levels of DOM were low in abundance. The combination between EEM-PARAFAC and PCA proved to be ideally suited to discern DOM composition and source differences in coastal zones with complex hydrology and multiple DOM sources.

Annotated record of the detailed examination of Mn deposits from the Snellius II - Cruise 4 stations in the vicinity of the island of Misool, Eastern Indonesia

The chemical analyses of ferromanganese encrustations found on the seabed west of Misool, eastern Indonesia, indicate that these deposits formed in a way different from that of world-wide occurring manganese nodules. Ferromanganese coated pebbles and fragments that were found in the deeper parts of the study area probably originate from nearby ridges. The ferromanganese crust on the upper part of a dolomite fragment of ?30 kg is likely to be formed by hydrogenous processes, whereas that from the lower part seems to be formed by diagenetic processes mainly. These assumptions are supported by pore-water data from two box cores taken in the same area. The manganese and iron profiles versus depth in these cores indicate a high flux of these metals to the uppermost sediment layer, and possibly into the overlying bottom water. Factor analysis for the principal components of the microprobe analytical results of the mainly hydrogenous ferromanganese crust demonstrates a strong correlation of manganese with the trace metals, of iron with phosphorus and an antipathetic relationship between iron and manganese. Similar results have also been reported for abyssal manganese nodules in the world oceans. Factor analysis for the principal components of the analytical data obtained for the diagenetic ferromanganese crust results in a clear dolomite (Ca/Mg) dilution factor only.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Multivariate Techniques in Biomechanical Analysis

This work outlines the theoretical advantages of multivariate methods in biomechanical data, validates the proposed methods and outlines new clinical findings relating to knee osteoarthritis that were made possible by this approach. New techniques were based on existing multivariate approaches, Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF) and validated using existing data sets. The new techniques developed, PCA-PLS-LDA (Principal Component Analysis – Partial Least Squares – Linear Discriminant Analysis), PCA-PLS-MLR (Principal Component Analysis – Partial Least Squares –Multiple Linear Regression) and Waveform Similarity (based on NMF) were developed to address the challenging characteristics of biomechanical data, variability and correlation. As a result, these new structure-seeking technique revealed new clinical findings. The first new clinical finding relates to the relationship between pain, radiographic severity and mechanics. Simultaneous analysis of pain and radiographic severity outcomes, a first in biomechanics, revealed that the knee adduction moment’s relationship to radiographic features is mediated by pain in subjects with moderate osteoarthritis. The second clinical finding was quantifying the importance of neuromuscular patterns in brace effectiveness for patients with knee osteoarthritis. I found that brace effectiveness was more related to the patient’s unbraced neuromuscular patterns than it was to mechanics, and that these neuromuscular patterns were more complicated than simply increased overall muscle activity, as previously thought.

Expressing Sentiments in Game Reviews

Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.

Oil Market Modelling: A Comparative Analysis of Fundamental and Latent Factor Approaches

We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short- medium- and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

Obésité abdominale et autres déterminants du risque cardiométabolique dans deux populations urbaines noires apparentées : Port-au-Prince (Haïti) et Cotonou (Bénin)

L’obésité est un problème de santé publique reconnu. Dans la dernière décennie l’obésité abdominale (OA) a été considérée comme une maladie métabolique qui contribue davantage au risque de diabète et de maladies cardiovasculaires que l’obésité générale définie par l’indice de masse corporelle. Toutefois, dans les populations d’origine africaine, la relation entre l’OA et les autres biomarqueurs de risque cardiométabolique (RCM) demeure obscure à cause du manque d’études chez ces populations et de l’absence de valeurs-seuils spécifiques pour juger d’une OA. Cette étude visait à comparer la prévalence des biomarqueurs de RCM (OA, hypertension artérielle, hyperglycémie, dyslipidémie, résistance à l'insuline et inflammation pré-clinique) chez les Béninois de Cotonou et les Haïtiens de Port-au-Prince (PAP), à étudier l’association de l’OA avec les autres biomarqueurs de RCM, à documenter le rôle du niveau socio-économique (NSE) et du mode de vie dans cette association et à ’identifier les indicateurs anthropométriques de l’OA -tour de taille (TT) et le ratio TT/hauteur (TT/H)- et les seuils qui prédisent le mieux le RCM à Cotonou et à PAP. Il s’est agi d’une analyse de données transversales chez 452 adultes (52 % hommes) apparemment en bonne santé, âgés de 25 à 60 ans, avec 200 sujets vivant à Cotonou (Bénin) et 252 sujets à PAP (Haïti). Les biomarqueurs de RCM considérés étaient : le syndrome métabolique (SMet) d’après les critères harmonisés de 2009 et ses composantes individuelles - une OA à partir d’un TT ≥ 94cm chez les hommes et ≥ 80cm chez les femmes, une hypertension, une dyslipidémie et une hyperglycémie; la résistance à l’insuline définie chez l’ensemble des sujets de l’étude à partir du 75e centile de l’Homeostasis Model Assessment (HOMA-IR); un ratio d’athérogénicité élevé (Cholestérol sérique total/HDL-Cholestérol); et l’inflammation pré-clinique mesurée à partir d’un niveau de protéine C-réactive ultrasensible (PCRus) entre 3 et 10 mg/l. Le ratio TT/H était aussi considéré pour définir l’OA à partir d’un seuil de 0,5. Les données sur les habitudes alimentaires, la consommation d’alcool, le tabagisme, les caractéristiques sociodémographiques et les conditions socio-économiques incluant le niveau d’éducation et un proxy du revenu (basé sur l’analyse par composante principale des biens et des possessions) ont été recueillies au moyen d’un questionnaire. Sur la base de données de fréquence de consommation d’aliments occidentaux, urbains et traditionnels, des schémas alimentaires des sujets de chaque ville ont été identifiés par analyse typologique. La validité et les valeurs-seuils de TT et du ratio TT/H prédictives du RCM ont été définies à partir des courbes ROC (Receiver Operating Characteristics). Le SMet était présent chez 21,5 % et 16,1 % des participants, respectivement à Cotonou et à PAP. La prévalence d’OA était élevée à Cotonou (52,5 %) qu’à PAP (36%), avec une prévalence plus élevée chez les femmes que chez les hommes. Le profil lipidique sérique était plus athérogène à PAP avec 89,3 % d’HDL-c bas à PAP contre 79,7 % à Cotonou et un ratio CT/HDL-c élevé de 73,4 % à PAP contre 42 % à Cotonou. Les valeurs-seuils spécifiques de TT et du TT/H étaient respectivement 94 cm et 0,59 chez les femmes et 80 cm et 0,50 chez les hommes. Les analyses multivariées de l’OA avec les biomarqueurs de RCM les plus fortement prévalents dans ces deux populations montraient que l’OA était associée à un risque accru de résistance à l’insuline, d’athérogénicité et de tension artérielle élevée et ceci, indépendamment des facteurs socio-économiques et du mode de vie. Deux schémas alimentaires ont émergé, transitionnel et traditionnel, dans chaque ville, mais ceux-ci ne se révélaient pas associés aux biomarqueurs de RCM bien qu’ils soient en lien avec les variables socio-économiques. La présente étude confirme la présence de plusieurs biomarqueurs de RCM chez des sujets apparemment sains. En outre, l’OA est un élément clé du RCM dans ces deux populations. Les seuils actuels de TT devraient être reconsidérés éventuellement à la lumière d’études de plus grande envergure, afin de mieux définir l’OA chez les Noirs africains ou d’origine africaine, ce qui permettra une surveillance épidémiologique plus adéquate des biomarqueurs de RCM.