To Evaluate the Synergistic Effect of Different Crosslinking Strategies on Collagen Derivatives to Develop Funcional Biomimetic Scaffolds

The preliminary objective of this work was to study how the effect of different crosslinking methodologies can functionally modify various characteristics of biological macromolecules relevant for scaffold development in bone tissue engineering. The research study was classified and studied in three different phases: (i) different crosslinking strategies in gelatin functionalization, (ii) ribose mediated crosslinking in collagen-hydroxyapatite scaffold (iii) different crosslinking mechanisms in functional modification of bone-like scaffold. The obtained results were highly positive in all the three investigated studies. Though the core aim of this research was to explore the available crosslinking strategies in different biological macromolecules, the present study generated significant findings, largely contributing to provide optimum solutions in understanding how the crosslinking density can fine-tune the overall performance of a scaffold, relevant for its functioning in vivo. In particular, this study demonstrated that different crosslinkers at different conditions (pH and temperature) can modify the functional properties of the scaffolds differently, therefore this optimization strategies on these crosslinkers as obtained from this study results will help material scientists in the design and development of bioactive hybrid biomaterials for hard tissue regeneration.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Metabolomics to investigate the effects of treatments on food and of food consumption on health

Metabolomics has established itself as a discipline that can offer a unique point of view on how a technological treatment could impact on the charactersitics of a food. Even more, the same analytical platforms necessary for the purpose can also effectively unravel intricate interactions between such food and human health upon consumption. This PhD thesis investigates the application of metabolomics in understanding the impact of technological treatments on food and their subsequent effects on human health, utilizing 1H-NMR as the analytical platform. The study involves the development of standard operating procedures (SOPs) to ensure a fast and stable preparation of seafood samples, incorporating novel algorithms to enhance the accuracy of metabolome profiles. To gain insight on how metabolomics can allow exploring the effects of a technological treatment on a food, we performed three sets of experiments to investigate the application of metabolomics in studying the impact of high hydrostatic pressure (HHP) treatment on seafood metabolome during storage. The first experiment employs untargeted metabolomic analysis on chill-stored rose shrimp, revealing significant post-HHP treatment metabolic alterations and mechanisms. The investigation is extended to grey mullet in the second experiment, utilizing both untargeted and targeted metabolomic analyses to account for matrix-related effects. The third experiment assesses the targeted metabolome of striped prawns, showing that HHP significantly influences metabolic pathways, positively impacting freshness and taste through alterations in related metabolites. Shifting focus to the effects of food on humans, the study explores the impact of multistrain probiotics on cirrhosis patients using 1H-NMR. The platform reveals notable alterations in glutamine/glutamate metabolism, enhancing the patients' ammonia detoxification capacity. This research underscores the potential of metabolomics in uncovering intricate interactions between technological treatments, food, and human health, providing valuable insights for both the food industry and healthcare interventions.