The foodomics approach to investigate the impact of the matrix structure on food quality: applications of magnetic resonance spectroscopy, relaxometry and imaging

The aim of this thesis is to explore the possible influence of the food matrix on food quality attributes. Using nuclear magnetic resonance techniques, the matrix-dependent properties of different foods were studied and some useful indices were defined to classify food products based on the matrix behaviour when responding to processing phenomena. Correlations were found between fish freshness indices, assessed by certain geometric parameters linked to the morphology of the animal, i.e. a macroscopic structure, and the degradation of the product structure. The same foodomics approach was also applied to explore the protective effect of modified atmospheres on the stability of fish fillets, which are typically susceptible to oxidation of the polyunsaturated fatty acids incorporated in the meat matrix. Here, freshness is assessed by evaluating the time-dependent change in the fish metabolome, providing an established freshness index, and its relationship to lipid oxidation. In vitro digestion studies, focusing on food products with different matrixes, alone and in combination with other meal components (e.g. seasoning), were conducted to investigate possible interactions between enzymes and food, modulated by matrix structure, which influence digestibility. The interaction between water and the gelatinous matrix of the food, consisting of a network of protein gels incorporating fat droplets, was also studied by means of nuclear magnetic relaxometry, in order to create a prediction tool for the correct classification of authentic and counterfeit food products protected by a quality label. This is one of the first applications of an NMR method focusing on the supramolecular structure of the matrix, rather than the chemical composition, to assess food authenticity. The effect of innovative processing technologies, such as PEF applied to fruit products, has been assessed by magnetic resonance imaging, exploiting information associated with the rehydration kinetics exerted by a modified food structure.

Radiocarbon and stable isotopes in the shell organic matrix: a new approach for the use of mollusk shells in the study of human evolution

Mollusk shells are often found in archeological sites, given their great preservation potential and high value as a multipurpose resource. They are often the only available material to use for radiocarbon dating, due to a lack of well-preserved bones in many archeological sites, especially for the key period of the Middle to Upper Paleolithic transition. However, radiocarbon dating on mollusk shells is often regarded as less reliable compared to bones, wood, or charcoals due to the various factors influencing their radiocarbon content (e.g., Isotope fractionation, marine reservoir effect etc.). For the development of more accurate chronologies using shells, it is fundamental to continue improving the precision of the techniques applied, as has been done for other materials (wood and bones). Thus, improving the chemical pretreatment on mollusk shells might allow researchers to obtain more reliable radiocarbon determinations allowing for the construction of new radiocarbon chronologies in archeological sites where so far it has not been possible. Furthermore, mollusk shells can provide information on the climatic and environmental variables present during their growth. Using shells for paleoclimatic reconstruction adds more evidence helpful for the interpretation of scenarios of human migration, adaptation, and behavior. Standard methods for both radiocarbon and stable isotope studies use the carbonate fraction of the shell. However, being biogenic structures, mollusk shells also consist of a minor organic fraction. The shell organic matrix has an important role in the formation of the calcium carbonate structure and is still not fully understood. This thesis explores the potential of using the shell organic matrix for radiocarbon dating and paleoenvironmental studies. The results of the work performed for this thesis represent a starting point for future research to build on, and further develop the approach and methodology proposed here.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

A dynamical system approach to data assimilation in chaotic models

The Assimilation in the Unstable Subspace (AUS) was introduced by Trevisan and Uboldi in 2004, and developed by Trevisan, Uboldi and Carrassi, to minimize the analysis and forecast errors by exploiting the flow-dependent instabilities of the forecast-analysis cycle system, which may be thought of as a system forced by observations. In the AUS scheme the assimilation is obtained by confining the analysis increment in the unstable subspace of the forecast-analysis cycle system so that it will have the same structure of the dominant instabilities of the system. The unstable subspace is estimated by Breeding on the Data Assimilation System (BDAS). AUS- BDAS has already been tested in realistic models and observational configurations, including a Quasi-Geostrophicmodel and a high dimensional, primitive equation ocean model; the experiments include both fixed and“adaptive”observations. In these contexts, the AUS-BDAS approach greatly reduces the analysis error, with reasonable computational costs for data assimilation with respect, for example, to a prohibitive full Extended Kalman Filter. This is a follow-up study in which we revisit the AUS-BDAS approach in the more basic, highly nonlinear Lorenz 1963 convective model. We run observation system simulation experiments in a perfect model setting, and with two types of model error as well: random and systematic. In the different configurations examined, and in a perfect model setting, AUS once again shows better efficiency than other advanced data assimilation schemes. In the present study, we develop an iterative scheme that leads to a significant improvement of the overall assimilation performance with respect also to standard AUS. In particular, it boosts the efficiency of regime’s changes tracking, with a low computational cost. Other data assimilation schemes need estimates of ad hoc parameters, which have to be tuned for the specific model at hand. In Numerical Weather Prediction models, tuning of parameters — and in particular an estimate of the model error covariance matrix — may turn out to be quite difficult. Our proposed approach, instead, may be easier to implement in operational models.

Obsessed With Work: A multi-causal and multi-rater approach to workaholism

Workaholism is defined as the combination of two underlying dimensions: working excessively and working compulsively. The present thesis aims at achieving the following purposes: 1) to test whether the interaction between environmental and personal antecedents may enhance workaholism; 2) to develop a questionnaire aimed to assess overwork climate in the workplace; 3) to contrast focal employees’ and coworkers’ perceptions of employees’ workaholism and engagement. Concerning the first purpose, the interaction between overwork climate and person characteristics (achievement motivation, perfectionism, conscientiousness, self-efficacy) was explored on a sample of 333 Dutch employees. The results of moderated regression analyses showed that the interaction between overwork climate and person characteristics is related to workaholism. The second purpose was pursued with two interrelated studies. In Study 1 the Overwork Climate Scale (OWCS) was developed and tested using a principal component analysis (N = 395) and a confirmatory factor analysis (N = 396). Two overwork climate dimensions were distinguished, overwork endorsement and lacking overwork rewards. In Study 2 the total sample (N = 791) was used to explore the association of overwork climate with two types of working hard: work engagement and workaholism. Lacking overwork rewards was negatively associated with engagement, whereas overwork endorsement showed a positive association with workaholism. Concerning the third purpose, using a sample of 73 dyads composed by focal employees and their coworkers, a multitrait-multimethod matrix and a correlated trait-correlated method model, i.e. the CT-C(M–1) model, were examined. Our results showed a considerable agreement between raters on focal employees' engagement and workaholism. In contrast, we observed a significant difference concerning the cognitive dimension of workaholism, working compulsively. Moreover, we provided further evidence for the discriminant validity between engagement and workaholism. Overall, workaholism appears as a negative work-related state that could be better explained by assuming a multi-causal and multi-rater approach.

A multidisciplinary approach to the study of protein dynamics and signal transmission

Allostery is a phenomenon of fundamental importance in biology, allowing regulation of function and dynamic adaptability of enzymes and proteins. Despite the allosteric effect was first observed more than a century ago allostery remains a biophysical enigma, defined as the “second secret of life”. The challenge is mainly associated to the rather complex nature of the allosteric mechanisms, which manifests itself as the alteration of the biological function of a protein/enzyme (e.g. ligand/substrate binding at the active site) by binding of “other object” (“allos stereos” in Greek) at a site distant (> 1 nanometer) from the active site, namely the effector site. Thus, at the heart of allostery there is signal propagation from the effector to the active site through a dense protein matrix, with a fundamental challenge being represented by the elucidation of the physico-chemical interactions between amino acid residues allowing communicatio n between the two binding sites, i.e. the “allosteric pathways”. Here, we propose a multidisciplinary approach based on a combination of computational chemistry, involving molecular dynamics simulations of protein motions, (bio)physical analysis of allosteric systems, including multiple sequence alignments of known allosteric systems, and mathematical tools based on graph theory and machine learning that can greatly help understanding the complexity of dynamical interactions involved in the different allosteric systems. The project aims at developing robust and fast tools to identify unknown allosteric pathways. The characterization and predictions of such allosteric spots could elucidate and fully exploit the power of allosteric modulation in enzymes and DNA-protein complexes, with great potential applications in enzyme engineering and drug discovery.

Mechanical behaviour and durability assessment of Fibre Reinforced Cementitious Matrix (FRCM) strengthening systems.

In recent years, the seismic vulnerability of existing masonry buildings has been underscored by the destructive impacts of earthquakes. Therefore, Fibre Reinforced Cementitious Matrix (FRCM) retrofitting systems have gained prominence due to their high strength-to-weight ratio, compatibility with substrates, and potential reversibility. However, concerns linger regarding the durability of these systems when subjected to long-term environmental conditions. This doctoral dissertation addressed these concerns by studying the effects of mild temperature variations on three FRCM systems, featuring basalt, glass, and aramid fibre textiles with lime-based mortar matrices. The study subjected various specimens, including mortar triplets, bare textile specimens, FRCM coupons, and single-lap direct shear wallets, to thermal exposure. A novel approach utilizing embedded thermocouple sensors facilitated efficient monitoring and active control of the conditioning process. A shift in the failure modes was obtained in the single lap-direct shear tests, alongside a significant impact on tensile capacity for both textiles and FRCM coupons. Subsequently, bond tests results were used to indirectly calibrate an analytical approach based on mode-II fracture mechanics. A comparison between Cohesive Material Law (CML) functions at various temperatures was conducted for each of the three systems, demonstrating a good agreement between the analytical model and experimental curves. Furthermore, the durability in alkaline environment of two additional FRCM systems, characterized by basalt and glass fibre textiles with lime-based mortars, was studied through an extensive experimental campaign. Tests conducted on single yarn and textile specimens after exposure at different durations and temperatures revealed a significant impact on tensile capacity. Additionally, FRCM coupons manufactured with conditioned textile were tested to understand the influence of aged textile and curing environment on the final tensile behavior. These results contributed significantly to the existing knowledge on FRCM systems and could be used to develop a standardized alkaline testing protocol, still lacking in the scientific literature.