Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Computational modelling of human stem cell-derived cardiomyocytes - Texture descriptors for biological image processing

This thesis investigates two distinct research topics. The main topic (Part I) is the computational modelling of cardiomyocytes derived from human stem cells, both embryonic (hESC-CM) and induced-pluripotent (hiPSC-CM). The aim of this research line lies in developing models of the electrophysiology of hESC-CM and hiPSC-CM in order to integrate the available experimental data and getting in-silico models to be used for studying/making new hypotheses/planning experiments on aspects not fully understood yet, such as the maturation process, the functionality of the Ca2+ hangling or why the hESC-CM/hiPSC-CM action potentials (APs) show some differences with respect to APs from adult cardiomyocytes. Chapter I.1 introduces the main concepts about hESC-CMs/hiPSC-CMs, the cardiac AP, and computational modelling. Chapter I.2 presents the hESC-CM AP model, able to simulate the maturation process through two developmental stages, Early and Late, based on experimental and literature data. Chapter I.3 describes the hiPSC-CM AP model, able to simulate the ventricular-like and atrial-like phenotypes. This model was used to assess which currents are responsible for the differences between the ventricular-like AP and the adult ventricular AP. The secondary topic (Part II) consists in the study of texture descriptors for biological image processing. Chapter II.1 provides an overview on important texture descriptors such as Local Binary Pattern or Local Phase Quantization. Moreover the non-binary coding and the multi-threshold approach are here introduced. Chapter II.2 shows that the non-binary coding and the multi-threshold approach improve the classification performance of cellular/sub-cellular part images, taken from six datasets. Chapter II.3 describes the case study of the classification of indirect immunofluorescence images of HEp2 cells, used for the antinuclear antibody clinical test. Finally the general conclusions are reported.

Analisi d'immagine, patologia quantitativa e dimensione frattale in patologia veterinaria: Densita' e forme

Fino dagli albori della metodica scientifica, l’osservazione e la vista hanno giocato un ruolo fondamentale. La patologia è una scienza visiva, dove le forme, i colori, le interfacce e le architetture di organi, tessuti, cellule e componenti cellulari guidano l’occhio del patologo e ne indirizzano la scelta diagnostico-classificativa. L’osservazione del preparato istologico in microscopia ottica si attua mediante l’esame e la caratterizzazione di anomalie ad ingrandimenti progressivamente crescenti, a diverse scale spaziali, che partono dalla valutazione dell’assetto architettonico sovracellulare, per poi spostarsi ad investigare e descrivere le cellule e le peculiarità citomorfologiche delle stesse. A differenza di altri esami di laboratorio che sono pienamente quantificabili, l’analisi istologica è intrinsecamente soggettiva, e quindi incline ad un alto grado di variabilità nei risultati prodotti da differenti patologi. L’analisi d’immagine, l’estrazione da un’immagine digitale di contenuti utili, rappresenta una metodica oggettiva, valida e robusta ormai largamente impiegata a completamento del lavoro del patologo. Si sottolinea come l’analisi d’immagine possa essere vista come fase descrittiva quantitativa di preparati macroscopici e microscopici che poi viene seguita da una interpretazione. Nuovamente si sottolinea come questi descrittori siano oggettivi, ripetibili e riproducibili, e non soggetti a bassa concordanza inter operatore. La presente tesi si snoda attraverso un percorso concettuale orientato ad applicazioni di analisi d’immagine e patologia quantitativa che parte dalle applicazioni più elementari (densità, misure lineari), per arrivare a nozioni più avanzate, quali lo studio di complessità delle forme mediante l’analisi frattale e la quantificazione del pattern spaziale di strutture sovracellulari.