Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

Physico-chemical and microstructural properties of food dispersions

The macroscopic properties of oily food dispersions, such as rheology, mechanical strength, sensory attributes (e.g. mouth feel, texture and even flavour release) and as well as engineering properties are strongly determined by their microstructure, that is considered a key parameter in the understanding of the foods behaviour . In particular the rheological properties of these matrices are largely influenced by their processing techniques, particle size distribution and composition of ingredients. During chocolate manufacturing, mixtures of sugar, cocoa and fat are heated, cooled, pressurized and refined. These steps not only affect particle size reduction, but also break agglomerates and distribute lipid and lecithin-coated particles through the continuous phase, this considerably modify the microstructure of final chocolate. The interactions between the suspended particles and the continuous phase provide information about the existing network and consequently can be associated to the properties and characteristics of the final dispersions. Moreover since the macroscopic properties of food materials, are strongly determined by their microstructure, the evaluation and study of the microstructural characteristics, can be very important for a through understanding of the food matrices characteristics and to get detailed information on their complexity. The aim of this study was investigate the influence of formulation and each process step on the microstructural properties of: chocolate type model systems, dark milk and white chocolate types, and cocoa creams. At the same time the relationships between microstructural changes and the resulting physico-chemical properties of: chocolate type dispersions model systems dark milk and white chocolate were investigated.

Country-of-Origin Labeling for Processed Foods

The country-of-origin is the “nationality” of a food when it goes through customs in a foreign country, and is a “brand” when the food is for sale in a foreign market. My research on country-of-origin labeling (COOL) started from a case study on the extra virgin olive oil exported from Italy to China; the result shows that asymmetric and imperfect origin information may lead to market inefficiency, even market failure in emerging countries. Then, I used the Delphi method to conduct qualitative and systematic research on COOL; the panel of experts in food labeling and food policy was composed of 19 members in 13 countries; the most important consensus is that multiple countries of origin marking can provide accurate information about the origin of a food produced by two or more countries, avoiding misinformation for consumers. Moreover, I enhanced the research on COOL by analyzing the rules of origin and drafting a guideline for the standardization of origin marking. Finally, from the perspective of information economics I estimated the potential effect of the multiple countries of origin labeling on the business models of international trade, and analyzed the regulatory options for mandatory or voluntary COOL of main ingredients. This research provides valuable insights for the formulation of COOL policy.

Non conventional species as monitor of environmental pollution

This thesis collects several ecotoxicological studies focused on the quali- quantitative analysis of several classes of chemical compounds. Our studies have been conducted on different aquatic species occupying different food chain trophic levels and characterized by differences in biology, ethology, and nutrition, but all considered excellent bioindicators. This choice allowed us to have a broad overview of the contamination of aquatic environments. Detrimental effects of several chemical compounds on the species investigated have been discussed, considering the economic and public health implications linked to the pollution of the environment and the exposure to old and emerging xenobiotics. Our studies underline the importance of a multidisciplinary and integrated approach that includes the application of the one health concept to ensure the protection of public health and respect for natural environments. Studies collected in this thesis also aim to overcome some critical limitations of the branch of ecotoxicology, such as the lack of standardization in laboratory methods. Our data also underline the importance of expanding research to a greater number of various biological matrices than those indicated by the literature as target tissues for specific pollutants. This condition enables more detailed information on the kinetics of xenobiotics in animal organisms. Our studies also allow us to expand the knowledge related to the mechanisms of synergy and antagonism of mixtures of pollutants that can simultaneously accumulate in wildlife.

Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.