Alas! Those Chimes So Sweetly Stealing

Alas! Those Chimes So Sweetly Stealing: Wallace, W. V.: A Fiot: ca 1861-1863: Voice

Bohemian Waltz

80.7568.148 – “Bohemian Waltz” J. C. Viereck: A. Fiot: 1845: Piano

[The] Cradle Song

80.7568.243 – “[The] Cradle Song” Arranged by C. M. Von Weber: Fiot, Meigner & Co.: Philadelphia: Minstrel, Ballad, Love Song: n.d.: Solo Voice with Piano or guitar

Méthodes mathématiques d’analyse d’image pour les études de population transversales et longitudinales

In medicine, large scale population analysis aim to obtain statistical information in order to understand better diseases, identify their risk factors, develop preventive and curative treatments and improve the quality of life of the patients.In this thesis, we first introduce the medical context of Alzheimer’s disease, recall some concepts of statistical learning and the challenges that typically occurwhen applied in medical imaging. The second part focus on cross-sectional studies,i.e. at a single time point. We present an efficient method to classify white matter lesions based on support vector machines. Then we discuss the use of manifoldlearning techniques for image and shape analysis. Finally, we present extensions ofLaplacian eigenmaps to improve the low-dimension representations of patients usingthe combination of imaging and clinical data. The third part focus on longitudinalstudies, i.e. between several time points. We quantify the hippocampus deformations of patients via the large deformation diffeomorphic metric mapping frameworkto build disease progression classifiers. We introduce novel strategies and spatialregularizations for the classification and identification of biomarkers.En médecine, les analyses de population à grande échelle ont pour but d’obtenir des informations statistiques pour mieux comprendre des maladies, identifier leurs facteurs de risque, développer des traitements préventifs et curatifs et améliorer la qualité de vie des patients.Dans cette thèse, nous présentons d’abord le contexte médical de la maladie d’Alzheimer, rappelons certains concepts d’apprentissage statistique et difficultés rencontrées lors de l’application en imagerie médicale. Dans la deuxième partie,nous nous intéressons aux analyses transversales, c-a-d ayant un seul point temporel.Nous présentons une méthode efficace basée sur les séparateurs à vaste marge (SVM)permettant de classifier des lésions dans la matière blanche. Ensuite, nous étudions les techniques d’apprentissage de variétés pour l’analyse de formes et d’images, et présentons deux extensions des Laplacian eigenmaps améliorant la représentation de patients en faible dimension grâce à la combinaison de données d’imagerie et cliniques. Dans la troisième partie, nous nous intéressons aux analyses longitudinales, c-a-d entre plusieurs points temporels. Nous quantifions les déformations des hippocampus de patients via le modèle des larges déformations par difféomorphismes pour classifier les évolutions de la maladie. Nous introduisons de nouvelles stratégies et des régularisations spatiales pour la classification et l’identification de marqueurs biologiques

In Happy Moments

80.7568.553 – “In Happy Moments”: Wallace, W. V.: A. Fiot: n.d.: Voice. (2 copies

Structured Dimensionality Reduction for Additive Model Regression

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models are however not adapted to problems involving a large number (e.g., hundreds) of input variables, as they are prone to overfitting in addition to losing interpretability. In this paper, we introduce a novel framework for applying additive models to a large number of input variables. The key idea is to reduce the task dimensionality by deriving a small number of new covariates obtained by linear combinations of the inputs, where the linear weights are estimated with regard to the regression problem at hand. The weights are moreover constrained to prevent overfitting and facilitate the interpretation of the derived covariates. We establish identifiability of the proposed model under mild assumptions and present an efficient approximate learning algorithm. Experiments on synthetic and real-world data demonstrate that our approach compares favorably to baseline methods in terms of accuracy, while resulting in models of lower complexity and yielding practical insights into high-dimensional real-world regression tasks. Our framework broadens the applicability of additive models to high-dimensional problems while maintaining their interpretability and potential to provide practical insights

Analysis of additivity and synergism in the anti-plasmodial effect of purified compounds from plant extracts

In the search for antimalarials from ethnobotanical origin, plant extracts are chemically fractionated and biological tests guide the isolation of pure active compounds. To establish the responsibility of isolated active compound(s) to the whole antiplasmodial activity of a crude extract, the literature in this field was scanned and results were analysed quantitatively to find the contribution of the pure compound to the activity of the whole extract. It was found that, generally, the activity of isolated molecules could not account on their own for the activity of the crude extract. It is suggested that future research should take into account the “drugs beside the drug”, looking for those products (otherwise discarded along the fractionation process) able to boost the activity of isolated active compounds