De nos jours, avec l'abondance croissante de données de très grande taille, les problèmes de classification de grande dimension ont été mis en évidence comme un challenge dans la communauté d'apprentissage automatique et ont beaucoup attiré l'attention des chercheurs dans le domaine. Au cours des dernières années, les techniques d'apprentissage avec la parcimonie et l'optimisation stochastique se sont prouvées être efficaces pour ce type de problèmes. Dans cette thèse, nous nous concentrons sur le développement des méthodes d'optimisation pour résoudre certaines classes de problèmes concernant ces deux sujets. Nos méthodes sont basées sur la programmation DC (Difference of Convex functions) et DCA (DC Algorithm) étant reconnues comme des outils puissants d'optimisation non convexe. La thèse est composée de trois parties. La première partie aborde le problème de la sélection des variables. La deuxième partie étudie le problème de la sélection de groupes de variables. La dernière partie de la thèse liée à l'apprentissage stochastique. Dans la première partie, nous commençons par la sélection des variables dans le problème discriminant de Fisher (Chapitre 2) et le problème de scoring optimal (Chapitre 3), qui sont les deux approches différentes pour la classification supervisée dans l'espace de grande dimension, dans lequel le nombre de variables est beaucoup plus grand que le nombre d'observations. Poursuivant cette étude, nous étudions la structure du problème d'estimation de matrice de covariance parcimonieuse et fournissons les quatre algorithmes appropriés basés sur la programmation DC et DCA (Chapitre 4). Deux applications en finance et en classification sont étudiées pour illustrer l'efficacité de nos méthodes. La deuxième partie étudie la L_p,0régularisation pour la sélection de groupes de variables (Chapitre 5). En utilisant une approximation DC de la L_p,0norme, nous prouvons que le problème approché, avec des paramètres appropriés, est équivalent au problème original. Considérant deux reformulations équivalentes du problème approché, nous développons différents algorithmes basés sur la programmation DC et DCA pour les résoudre. Comme applications, nous mettons en pratique nos méthodes pour la sélection de groupes de variables dans les problèmes de scoring optimal et d'estimation de multiples matrices de covariance. Dans la troisième partie de la thèse, nous introduisons un DCA stochastique pour des problèmes d'estimation des paramètres à grande échelle (Chapitre 6) dans lesquelles la fonction objectif est la somme d'une grande famille des fonctions non convexes. Comme une étude de cas, nous proposons un schéma DCA stochastique spécial pour le modèle loglinéaire incorporant des variables latentes / These days with the increasing abundance of data with high dimensionality, high dimensional classification problems have been highlighted as a challenge in machine learning community and have attracted a great deal of attention from researchers in the field. In recent years, sparse and stochastic learning techniques have been proven to be useful for this kind of problem. In this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in these two topics. Our methods are based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) which are wellknown as one of the most powerful tools in optimization. The thesis is composed of three parts. The first part tackles the issue of variable selection. The second part studies the problem of group variable selection. The final part of the thesis concerns the stochastic learning. In the first part, we start with the variable selection in the Fisher's discriminant problem (Chapter 2) and the optimal scoring problem (Chapter 3), which are two different approaches for the supervised classification in the high dimensional setting, in which the number of features is much larger than the number of observations. Continuing this study, we study the structure of the sparse covariance matrix estimation problem and propose four appropriate DCA based algorithms (Chapter 4). Two applications in finance and classification are conducted to illustrate the efficiency of our methods. The second part studies the L_p,0regularization for the group variable selection (Chapter 5). Using a DC approximation of the L_p,0norm, we indicate that the approximate problem is equivalent to the original problem with suitable parameters. Considering two equivalent reformulations of the approximate problem we develop DCA based algorithms to solve them. Regarding applications, we implement the proposed algorithms for group feature selection in optimal scoring problem and estimation problem of multiple covariance matrices. In the third part of the thesis, we introduce a stochastic DCA for large scale parameter estimation problems (Chapter 6) in which the objective function is a large sum of nonconvex components. As an application, we propose a special stochastic DCA for the loglinear model incorporating latent variables

Pacientes com câncer de mama localmente avançado são submetidas à quimioterapia neoadjuvante na tentativa de reduzir a dimensão do tumor e aumentar a possibilidade da realização de uma cirurgia conservadora. Nosso grupo identificou previamente através da tecnologia de cDNA microarray, trios de genes, incluindo BZRP, CLPTM1, MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2 e XLHSRF-1, cuja expressão era capaz de predizer a resposta à quimioterapia neoadjuvante com doxorrubicina e ciclofosfamida em pacientes com câncer de mama. No presente estudo, avaliamos se a expressão destes genes é reprodutível na identificação de pacientes responsivas e não-responsivas através de RT-PCR em tempo real, que representa uma técnica mais acessível. Avaliamos inicialmente amostras de 28 pacientes anteriormente estudadas (grupo de validação técnica = 23 responsivas e cinco não-responsivas) e a seguir um grupo de 14 novas pacientes (grupo de validação biológica = 11 responsivas e três não-responsivas). Dentre os trios de genes inicialmente identificados, a expressão de RPL37A + XLHSRF-1 + NOTCH1 e RPL37A + XLHSRF-1 + NUP210 classificou corretamente 86% (24/28) das amostras do grupo de validação técnica e 71% (10/14) das amostras do grupo de validação biológica, através de análise de classificação discriminante. Desse modo, esses trios não demonstraram a mesma precisão em comparação com resultados de cDNA microarray. Uma nova análise combinatória foi realizada na procura do melhor modelo preditivo utilizando valores de expressão obtidos por RT-PCR em tempo real. Identificamos então um novo trio, composto pelos genes RPL37A, SMYD2 e MTSS1, cuja expressão classificou corretamente 93% das amostras do grupo de validação técnica (22/23 responsivas e 4/5 não-responsivas) e 79% do grupo de validação biológica (8/11 responsivas e 3/3 não-responsivas). Portanto, o teste apresentou 88% de sensibilidade e especificidade em detectar pacientes responsivas para o total de amostras analisadas. Ao verificarmos o poder de classificação do mesmo grupo de genes, utilizando os valores de expressão pela análise de cDNA microarray, observamos um resultado semelhante (91% de sensibilidade e especificidade em reconhecer as amostras responsivas). Dessa forma, demonstramos que o perfil de expressão gênica obtido com cDNA microarray é reprodutível através do uso de RT-PCR em tempo real. Um estudo integrando um maior número de pacientes e uma plataforma de cDNA microarray mais abrangente pode auxiliar na identificação de um modelo preditivo baseado em grupos de genes mais acurado para antever a resposta ao tratamento com quimioterapia baseada em doxorrubicina. / Patients with locally advanced breast cancer are submitted to primary chemotherapy as an attempt to reduce tumor dimension and increase breast conserving surgery rates. Our group has previously identified through cDNA microarray technology gene trios, including BZRP, CLPTM1, MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2 and XLHSRF-1, whose expression was capable of predicting response to neoadjuvant chemotherapy with doxorubicin and cyclophosphamide in breast cancer patients. In the current study, it was evaluated whether expression of these genes is reproducible in the identification of responsive and non-responsive patients by real time RT-PCR, which represents a more accessible technique. We initially evaluated samples from 28 patients earlier studied (technical validation group = 23 responsive and 5 non-responsive) and subsequent to a new 14 patients set (biological validation group = 11 responsive and three non-responsive). Among the initially identified gene trios, RPL37A + XLHSRF-1 + NOTCH1 and RPL37A + XLHSRF-1 + NUP210 expression correctly classify 86% (24/28) samples from the technical validation group and 71% (10/14) samples from the biological validation group, through discriminant classification analysis. Therefore, these trios didnt demonstrate the same precision as compared with cDNA microarray results. A new combinatorial analysis was also performed in search of the best predictive model using real time RT-PCR expression values. A new trio was identified, represented by RPL37A, SMYD2 and MTSS1 genes, whose expression correctly classified 93% samples from technical validation group (22/23 responsive and 4/5 non-responsive) and 79% samples from biological validation group (8/11 responsive samples and 3/3 non-responsive samples). Therefore, the test presented 88% sensibility and specificity in identifying responsive patients for all samples analyzed. By means of verifying the classification strength of the same gene group, using cDNA microarray expression values, we observed a similar result (91% sensibility and specificity in recognizing responsive samples). Thus, we demonstrated that gene expression profile obtained by cDNA microarray is reproducible through real time RT-PCR. A study integrating a larger number of patients and a more comprehensive cDNA microarray platform may help the identification of a more accurate predictive model based on gene groups to foresee response to doxorubicin-based chemotherapy treatment.

Ce travail concerne les analyses visant à étudier les données où les individus sont structurés en différents groupes (données multi-groupes). La thèse aborde la question des données multi-groupes ayant une structure en un seul tableau, plusieurs tableaux, trois voies et deux blocs (régression). Cette thèse présente plusieurs méthodes d'analyse de données multi-groupes dans le cadre de l'analyse factorielle. Notre travail comporte trois parties. La première partie traite de l'analyse de données multi-groupes (un bloc de variables divisé en sous-groupes d'individus). Le but est soit descriptif (analyse intra-groupes) ou prédictif (analyse discriminante ou analyse inter-groupe). Nous commençons par une description exhaustive des méthodes multi-groupes. En outre, nous proposons deux méthodes : l'Analyse Procrustéenne duale et l'Analyse en Composantes Communes et Poids Spécifiques duale. Nous exposons également de nouvelles propriétés et algorithmes pour l'Analyse en Composantes Principales multi-groupes. La deuxième partie concerne l'analyse multi-blocs et multi-groupes et l'analyse trois voies et multi-groupes. Nous présentons les méthodes existantes. Par ailleurs, nous proposons deux méthodes, l'ACP multi-blocs et multi-groupes et l'ACP multi-blocs et multi-groupes pondérée, vues comme des extensions d'Analyse en Composantes Principales multi-groupes. L'analyse en deux blocs et multi-groupes est prise en compte dans la troisième partie. Tout d'abord, nous présentons des méthodes appropriées pour trouver la relation entre un ensemble de données explicatives et un ensemble de données à expliquer, les deux tableaux présentant une structure de groupe entre les individus. Par la suite, nous proposons quatre méthodes pouvant être vues comme des extensions de la régression PLS au cas multi-groupes, et parmi eux, nous en sélectionnons une et la développons dans une stratégie de régression. Les méthodes proposées sont illustrées sur la base de plusieurs jeux de données réels dans le domaine de la biologie. Toutes les stratégies d'analyse sont programmées sur le logiciel libre R. / This work deals with multi-group analysis, to study multi-group data where individuals are a priori structured into different groups. The thesis tackles the issue of multi-group data in a multivariate, multi-block, three-way and two-block (regression) setting. It presents several methods of multi-group data analysis in the framework of factorial analysis. It includes three sections. The first section concerns the case of multivariate multi-group data. The aim is either descriptive (within-group analysis) or predictive (discriminant analysis, between-group analysis). We start with a comprehensive review of multi-group methods. Furthermore, we propose two methods namely Dual Generalized Procrustes Analysis and Dual Common Component and Specific Weights Analysis. We also exhibit new properties and algorithms for multi-group Principal Component Analysis. The second section deals with multiblock multi-group and three-way multi-group data analysis. We give a general review of multiblock multi-group methods. In addition, we propose two methods, namely multiblock and multi-group PCA and Weighted-multiblock and multi-group PCA, as extensions of multi-group Principal Component Analysis. The two-block multi-group analysis is taken into account in the third section. Firstly, we give a presentation of appropriate methods to investigate the relationship between an explanatory dataset and a dependent dataset where there is a group structure among individuals. Thereafter, we propose four methods, namely multi-group PLS, in the PLS approach, and among them we select one and develop it into a regression strategy. The proposed methods are illustrated on the basis of several real datasets in the field of biology. All the strategies of analysis are implemented within the framework of R.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-14T18:15:08Z No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) / Made available in DSpace on 2017-08-14T18:15:08Z (GMT). No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) Previous issue date: 2008-02-25 / Female breast cancer is the major cause of death in western countries. Efforts in Computer Vision have been made in order to add improve the diagnostic accuracy by radiologists. In this work, we present a methodology that uses independent component analysis (ICA) along with support vector machine (SVM) and linear discriminant analysis (LDA) to distinguish between mass or non-mass and benign or malign tissues from mammograms. As a result, it was found that: LDA reaches 90,11% of accuracy to discriminante between mass or non-mass and 95,38% to discriminate between benign or malignant tissues in DDSM database and in mini-MIAS database we obtained 85% to discriminate between mass or non-mass and 92% of accuracy to discriminate between benign or malignant tissues; SVM reaches 99,55% of accuracy to discriminate between mass or non-mass and the same percentage to discriminate between benign or malignat tissues in DDSM database whereas, and in MIAS database it was obtained 98% to discriminate between mass or non-mass and 100% to discriminate between benign or malignant tissues. / Câncer de mama feminino é o câncer que mais causa morte nos países ocidentais. Esforços em processamento de imagens foram feitos para melhorar a precisão dos diagnósticos por radiologistas. Neste trabalho, nós apresentamos uma metodologia que usa análise de componentes independentes (ICA) junto com análise discriminante linear (LDA) e máquina de vetor de suporte (SVM) para distinguir as imagens entre nódulos ou não-nódulos e os tecidos em benignos ou malignos. Como resultado, obteve-se com LDA 90,11% de acurácia na discriminação entre nódulo ou não-nódulo e 95,38% na discriminação de tecidos benignos ou malignos na base de dados DDSM. Na base de dados mini- MIAS, obteve-se 85% e 92% na discriminação entre nódulos ou não-nódulos e tecidos benignos ou malignos respectivamente. Com SVM, alcançou-se uma taxa de até 99,55% na discriminação de nódulos ou não-nódulos e a mesma porcentagem na discriminação entre tecidos benignos ou malignos na base de dados DDSM enquanto que na base de dados mini-MIAS, obteve-se 98% e até 100% na discriminação de nódulos ou não-nódulos e tecidos benignos ou malignos, respectivamente.

Die chemische Zusammensetzung pharmazeutisch verwendeter Gläser (Ampullen, Infusionsglas, Fertigspritzenbehältnisse) wurde mit Laser-Ablation-ICP-Massenspektrometrie (LA-ICP-MS) untersucht. Dazu kam ein Nd:YAG-Laser mit einer Frequenz von 10 Hz im q-switch mode mit der Grundwellenlänge sowie der 4. Harmonischen (1064 und 266 nm) zum Einsatz. Ziel war die Identifizierung verschiedener Arten pharmazeutisch eingesetzter Gläser. Folgende Isotope wurden zur Charakterisierung bestimmt: 7Li, 11B, 23Na, 24Mg, 27Al, 28Si, 29Si, 30Si, 39K, 42Ca, 47Ti, 57Fe, 90Zr, 121Sb, 137Ba. Die quantitativen Ergebnisse zeigten relative Standardabweichungen von 1.8 % bis 8.0 %. Die Normierung der Daten erfolgte unter Bezug auf 29Si. Mit Referenzmaterial verschiedener Zusammensetzung (Natron-Kalk-Glas, Borosilikatglas und Bleiglas) wurden externe Mehrpunktkalibrationen erstellt. Für alle gemessenen Isotope konnten lineare Kalibrierfunktionen festgestellt werden. Die Richtigkeit des Verfahrens wurde durch Aufschluß des Standardmaterials wie auch der untersuchten Proben mit Flußsäure/Salpetersäure und Messung der Aufschlußlösungen nach externer Kalibration durch Multielement-Standards gezeigt. Aufschluß- und Laserablation-Ergebnisse zeigten gute Übereinstimmung und wichen um bis zu 8 % voneinander ab. Zur Prüfung repräsentativer Ablation der Proben wurden die Isotopenverhältnisse bestimmt. Auch hier zeigte sich eine ausreichende Übereinstimmung mit den theoretischen Werten. Weiterhin wurden Präzision, Empfindlichkeit, Selektivität, Nachweisgrenze, Bestimmungsgrenze und Robustheit des Verfahrens bestimmt. Im Unterschied zu den Ergebnissen unter der Grundwellenlänge führte die Verwendung der Laserwellenlänge 266 nm zu keinen signifikanten Verbesserungen der Resultate. Die Aufnahme transienter Signale sollte zur Erkennung von Elementverteilungen in Glasoberflächen dienen. Am Beispiel eines Natron-Kalk-Glases wurden in einzelnen Schichten verschiedene Elementkonzentrationen festgestellt. Weiterhin konnten mit dieser Technik Glasampullen erkannt werden, deren Oberflächen durch ein Silikonisierungsverfahren vergütet waren. Zum Abschluß der Untersuchungen wurde versucht, auf Grundlage der unnormierten Intensitätsdaten der LA-Messungen Gläser zu klassifizieren. Hierzu wurde die lineare Diskriminanzanalyse eingesetzt, mit deren Hilfe nach Variablenreduktion (8 von 13 Isotopen) alle eingesetzten Glasarten korrekt identifiziert werden konnten. Durch das entwickelte und validierte LA-ICP-MS-Verfahren steht eine leistungsfähige Technik zur quantitativen Untersuchung der chemischen Zusammensetzung von Gläsern sowie zur Identifizierung der jeweiligen Glassorte zur Verfügung. / The chemical composition of pharmaceutical glasses (ampoules, infusion bottles, plunger) has been determined by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). A Nd:YAG laser with 10 Hz repetition rate in the q-switch mode at its fundamental wavelength and its 4th harmonic (1064 and 266 nm) was used to identify common types of pharmaceutical glasses. The following isotopes were used for measurements: 7Li, 11B, 23Na, 24Mg, 27Al, 28Si, 29Si, 30Si, 39K, 42Ca, 47Ti, 57Fe, 90Zr, 121Sb, 137Ba. Relative standard deviations between 1.8 % and 8.0 % of the quantitative results were obtained. 29Si was used as internal standard. Standard reference materials (soda-lime-, lead- and borosilicate glasses) were used for external calibration of laser sampling. Linear calibration functions for each isotope were found. The accuracy was determined by digestion of all samples and standard materials in a two-step-procedure by nitric/fluoric acid, measurement and external calibration by ICP-MS with multi-elemental standard solutions. Digestion and laser ablation results agreed within 8 % with the certified values. The proof of representative ablation was given by sufficient agreement of intensity ratios of most isotopes with the corresponding theoretical values. Further precision, sensititivity, selectivity, limit of detection, limit of quantitation and robustness were determined. In contrast to the fundamental wavelength the laserablation technique with 266 nm showed no significant improvement in the quality of the results. By measuring transient signals element concentrations of surface layers should be detected. Differing concentrations could be determined in soda-lime-glass samples. Also different surface-treated borosilicate-glass ampoules were examined by this method. Silicon-treated glass surfaces could be identified. Finally raw data of LA measurements were used for applying linear discriminant analysis. After reduction of the used variables (8 of 13 isotopes) all types of glasses could be distinguished only by their intensity data of LA measurements. The developed LA-ICP-MS method is a powerful technique to distinguish different types of pharmaceutical glasses and to examine their chemical composition.

<p>The present thesis proposes new methods for obtaining objective and accurate diagnoses in modern healthcare. Non-invasive techniques have been used to examine or diagnose three different medical conditions, namely neuropathy among diabetics, radiotherapy induced erythema (skin redness) among breast cancer patients and diagnoses of cutaneous malignant melanoma. The techniques used were Near-InfraRed spectroscopy (NIR), Multi Frequency Bio Impedance Analysis of whole body (MFBIA-body), Laser Doppler Imaging (LDI) and Digital Colour Photography (DCP).</p><p>The neuropathy for diabetics was studied in papers I and II. The first study was performed on diabetics and control subjects of both genders. A separation was seen between males and females and therefore the data had to be divided in order to obtain good models. NIR spectroscopy was shown to be a viable technique for measuring neuropathy once the division according to gender was made. The second study on diabetics, where MFBIA-body was added to the analysis, was performed on males exclusively. Principal component analysis showed that healthy reference subjects tend to separate from diabetics. Also, diabetics with severe neuropathy separate from persons less affected.</p><p>The preliminary study presented in paper III was performed on breast cancer patients in order to investigate if NIR, LDI and DCP were able to detect radiotherapy induced erythema. The promising results in the preliminary study motivated a new and larger study. This study, presented in papers IV and V, intended to investigate the measurement techniques further but also to examine the effect that two different skin lotions, Essex and Aloe vera have on the development of erythema. The Wilcoxon signed rank sum test showed that DCP and NIR could detect erythema, which is developed during one week of radiation treatment. LDI was able to detect erythema developed during two weeks of treatment. None of the techniques could detect any differences between the two lotions regarding the development of erythema.</p><p>The use of NIR to diagnose cutaneous malignant melanoma is presented as unpublished results in this thesis. This study gave promising but inconclusive results. NIR could be of interest for future development of instrumentation for diagnosis of skin cancer.</p>

In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.

In the last decade, there has been a growing interest in the use of differential sensing for molecular recognition. Inspired by the mammalian olfactory system, differential sensing employs an array of non-selective receptors, which through cross-reactive interactions, create a distinct pattern for each analyte tested. The unique fingerprints obtained for each analyte with differential sensing are studied with statistical analysis techniques, such as principal component analysis and linear discriminant analysis. It was postulated that serum albumin proteins would be applicable to differential sensing schemes due to significant differences in sequence identity between different serum albumin species, and due to the wide range of hydrophobic molecules which are known to bind to these proteins. Consequently, cross-reactive serum albumin arrays were developed, utilizing hydrophobic fluorescent indicators to detect hydrophobic molecules. As such, serum albumin cross-reactive arrays were employed to discriminate subtly different hydrophobic analytes, and mixtures of these analytes, in the form of terpenes and perfumes, plasticizers and plastic explosive mixtures, and glycerides and adipocyte extracts. In this doctoral work, a detailed review of the field of differential sensing, and a thorough study of principal component analysis and linear discriminant analysis in various differential sensing scenarios, are given. These introductory chapters aid in better understanding the methods and techniques applied in later experimental chapters. In chapter 3, serum albumins, a PRODAN indicator, and an additive are shown to discriminate five terpene analytes and terpene doped perfumes. Chapter 4 describes an array with serum albumins, two dansyl fluorophores, and an additive which successfully differentiate the plasticizers found within the plastic explosives C4 and Semtex and simulated C4 and Semtex mixtures. Discrimination of these simulated mixtures was also achieved with this array in the presence of soil contaminants, demonstrating the potential real-world applicability of this sensing ensemble. Finally, chapter 5 details an array consisting of serum albumins, several fluorescent indicators, and a Grubb's olefin metathesis reaction, to differentiate saturated and unsaturated triglycerides, diglycerides, and monoglycerides. Mixtures of glycerides in adipocyte extracts taken from rats with different health states were then successfully discriminated, showing promise for clinical applications in differentiating adipoctyes from pre-diabetic, type 2 diabetic, and non-diabetic individuals. / text

Les Indigènes canadiens vivent une rapide transition nutritionnelle marquée par une consommation accrue des produits commercialisés au dépit des aliments traditionnels. Ce mémoire cherche à identifier les patrons alimentaires associés à une meilleure alimentation des femmes autochtones vivant dans les réserves en Colombie Britannique. L’échantillon (n=493) a été sélectionné de l’étude ‘First Nations Food, Nutrition, and Environment Study’. L’étude a utilisé des rappels alimentaires de 24 heures. Pour identifier les patrons alimentaires, un indice de qualité alimentaire (QA) basé sur 10 éléments nutritionnels (fibre alimentaire, gras totaux/saturés, folate, magnésium, calcium, fer, vitamines A, C, D) a permis de classifier les sujets en trois groupes (tertiles). Ces groupes ont été comparés sur leur consommation de 25 groupes alimentaires (GAs) en employant des tests statistiques non-paramétriques (Kruskal-Wallis et ANCOVA). Une analyse discriminante (AD) a confirmé les GAs associés à la QA. La QA des sujets était globalement faible car aucun rappel n’a rencontré les consommations recommandées pour tous les 10 éléments nutritionnels. L'AD a confirmé que les GAs associés de façon significative à la QA étaient ‘légumes et produits végétaux’, ‘fruits’, ‘aliments traditionnels’, ‘produits laitiers faibles en gras’, ‘soupes et bouillons’, et ‘autres viandes commercialisées’ (coefficients standardisés= 0,324; 0,295; 0,292; 0,282; 0,157; -0.189 respectivement). Le pourcentage de classifications correctes était 83.8%. Nos résultats appuient la promotion des choix alimentaires recommandés par le « Guide Alimentaire Canadien- Premières Nations, Inuits, et Métis ». Une consommation accrue de légumes, fruits, produits laitiers faibles en gras, et aliments traditionnels caractérise les meilleurs patrons alimentaires. / Indigenous Canadians are going through a rapid nutrition transition marked by an increased consumption of market foods and a decreased intake of traditional products. The aim of this research is to identify dietary patterns associated with a better diet quality among Indigenous female adults living on reserve in British Columbia. The sample (n=493) was selected from the First Nations Food, Nutrition, and Environment Study. The study used 24-hour food recalls. To identify dietary patterns, individuals were classified in three groups (tertiles) according to points obtained on a dietary score (based on Dietary Reference Intakes for dietary fiber, total fat, saturated fat, folate, magnesium, calcium, iron, vitamins A, C, D). The tertiles were compared for their consumption of 25 food groups (FGs) using statistical non-parametric tests (i.e. Kruskal-Wallis and ANCOVA tests). A discriminant analysis was used to confirm the FGs significantly associated with diet quality. Generally, subjects had poor diet quality since no food recall met the recommended intakes for all selected nutritional elements. The discriminant analysis confirmed that the FGs significantly associated with diet quality were “vegetables and vegetable products”, “fruits”, “traditional foods”, “low-fat dairy products”, “soups and broth”, and “other market meat” (standardized discriminant function coefficient= 0.324, 0.295, 0.292, 0.282, 0.157, -0.189 respectively). The percentage of correct classifications was 83.8%. In conclusion, our findings support the promotion of dietary choices according to the “Eating well with the Canadian Food Guide – First Nations, Inuit, and Métis”. It is greater use of vegetables, fruits, low-fat dairy products, and traditional foods that characterizes better dietary patterns.

When we develop and use species distribution models to predict species' current or potential distributions, we are faced with the trade-offs between model generality, precision, and realism. It is important to know how to improve and validate model generality while maintaining good model precision and realism. However, it is difficult for ecologists to evaluate species distribution models using field-sampled data alone because the true species response function to environmental or ecological factors is unknown. Species distribution models should be able to approximate the true characteristics and distributions of species if ecologists want to use them as reliable tools. Simulated data provide the advantage of being able to know the true species-environment relationships and control the causal factors of interest to obtain insights into the effects of these factors on model performance. I used a case study on Bythotrephes longimanus distributions from several hundred Ontario lakes and a simulation study to explore the effects on model performance caused by several factors: the choice of predictor variables, the model evaluation methods, the quantity and quality of the data used for developing models, and the strengths and weaknesses of different species distribution models. Linear discriminant analysis, multiple logistic regression, random forests, and artificial neural networks were compared in both studies. Results based on field data sampled from lakes indicated that the predictive performance of the four models was more variable when developed on abiotic (physical and chemical) conditions alone, whereas the generality of these models improved when including biotic (relevant species) information. When using simulated data, although the overall performance of random forests and artificial neural networks was better than linear discriminant analysis and multiple logistic regression, linear discriminant analysis and multiple logistic regression had relatively good and stable model sensitivity at different sample size and detection limit levels, which may be useful for predicting species presences when data are limited. Random forests performed consistently well at different sample size levels, but was more sensitive to high detection limit. The performance of artificial neural networks was affected by both sample size and detection limit, and it was more sensitive to small sample size.