Innovative treatments for shelf-life extension of whole and fresh-cut fruit

Maintaining the postharvest quality of whole and fresh-cut fruit during storage and distribution is the major challenge facing fruit industry. For this purpose, industry adopt a wide range of technologies to enable extended shelf-life. Many factors can lead to loss of quality in fresh product, hence the common description of these products as ‘perishable’. As a consequence normal factors such as transpiration and respiration lead ultimately to water loss and senescence of the product. Fruits and vegetables are living commodities and their rate of respiration is of key importance to maintenance of quality. It has been commonly observed that the greater the respiration rate of a product, the shorter the shelf-life. The principal problem for fresh-cut fruit industries is the relative shorter shelf-life of minimally processed fruit (MPF) compared to intact product. This fact is strictly connected with the higher ethylene production of fruit tissue stimulated during fresh-cut processing (peeling, cutting, dipping). 1-Methylcyclopropene (1-MCP) is an inhibitor of ethylene action and several researches have shown its effectiveness on the inhibition of ripening and senescence incidence for intact fruit and consequently on their shelf-life extension. More recently 1-MCP treatment has been tested also for shelf-life extension of MPF but discordant results have been obtained. Considering that in some countries 1-MCP is already a commercial product registered for the use on a number of horticultural products, the main aim of this actual study was to enhance our understanding on the effects of 1-MCP treatment on the quality maintenance of whole and fresh-cut climacteric and non-climacteric fruit (apple, kiwifruit and pineapple). Concerning the effects of 1-MCP on whole fruit, was investigated the effects of a semi-commercial postharvest treatment with 1-MCP on the quality of Pink Lady apples as functions of fruit ripening stage, 1-MCP dose, storage time and also in combination with controlled atmospheres storage in order to better understand what is the relationship among these parameters and if is possible to maximize the 1-MCP treatment to meet the market/consumer needs and then in order to put in the market excellent fruit. To achieve this purpose an incomplete three-level three-factor design was adopted. During the storage were monitored several quality parameters: firmness, ripening index, ethylene and carbon dioxide production and were also performed a sensory evaluations after 6 month of storage. In this study the higher retention of firmness (at the end of storage) was achieved by applying the greatest 1-MCP concentration to fruits with the lowest maturity stage. This finding means that in these semi-commercial conditions we may considerate completely blocked the fruit softening. 1-MCP was able to delay also the ethylene and CO2 production and the maturity parameters (soluble solids content and total acidity). Only in some cases 1-MCP generate a synergistic effect with the CA storage. The results of sensory analyses indicated that, the 1-MCP treatment did not affect the sweetness and whole fruit flavour while had a little effect on the decreasing cut fruit flavour. On the contrary the treated apple was more sour, crisp, firm and juicy. The effects of some treatment (dipping and MAP) on the nutrient stability were also investigated showing that in this case study the adopted treatments did not have drastic effects on the antioxidant compounds on the contrary the dipping may enhance the total antioxidant activity by the accumulation of ascorbic acid on the apple cut surface. Results concerning the effects of 1-MCP in combination with MAP on the quality parameters behaviour of the kiwifruit were not always consistent and clear: in terms of colour maintenance, it seemed to have a synergistic effect with N2O MAP; as far as ripening index is concerned, 1-MCP had a preservative effect, but just for sample packed in air.

Use of essential oils and biocontrol cultures for the improvement of shelf-life of fresh cut products

The demand of minimally processed fruits and vegetables has increased in the last years. However, their intrinsic characteristics may favor the growth of pathogens and spoilage microbiota. The negative effects on human health reported for some traditional chemical sanitizers have justified the search for substitutes to guarantee food safety and quality. In this work we have evaluate the potential of some essential oils and their components to improve the safety and the shelf life of Lamb’s lettuce (Valerianella locusta) and apples (Golden delicious). Moreover, the effects of selected lactic acid bacteria alone or in combination with essential oils or their components, on the shelf-life and safety as well as organoleptic properties of minimally processed products, were evaluated. Since the lack of knowledge of microbial cell targets of essential oils represent one of the most important limit to the use of these molecules at industrial level, another aim of this thesis was the study of the action mechanisms of essential oils and their components. The results obtained showed the beneficial effects of the natural antimicrobials as well as the selected lactic acid bacteria on minimally processed fruit and vegetable safety and shelf-life, without detrimental effects on the quality parameters. The beneficial effects obtained by the use of the selected biocontrol agents were further increased combining them with selected natural antimicrobials. The natural antimicrobial employed induced noticeable modifications of membrane fatty acid profiles and volatile compounds produced by microbial cells during the growth. The modification of the expression in genes involved in fatty acid biosynthesis suggesting that the cytoplasmic membrane of microbial cells is one of the major cellular target of essential oils and their components. The comprehension of microbial stress response mechanisms can contribute to the scaling up of natural antimicrobials and bio-control agents at industrial level.

Graph algorithms for bioinformatics

Biological data are inherently interconnected: protein sequences are connected to their annotations, the annotations are structured into ontologies, and so on. While protein-protein interactions are already represented by graphs, in this work I am presenting how a graph structure can be used to enrich the annotation of protein sequences thanks to algorithms that analyze the graph topology. We also describe a novel solution to restrict the data generation needed for building such a graph, thanks to constraints on the data and dynamic programming. The proposed algorithm ideally improves the generation time by a factor of 5. The graph representation is then exploited to build a comprehensive database, thanks to the rising technology of graph databases. While graph databases are widely used for other kind of data, from Twitter tweets to recommendation systems, their application to bioinformatics is new. A graph database is proposed, with a structure that can be easily expanded and queried.

Network assimilation methodology and sectorization of water distribution systems by graph mining and optimization algorithm

Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

Identification of outcome-oriented cut-offs for copy number alterations in multiple myeloma: predictive biomarkers with improved prognostic accuracy

Aim of the present study was to develop a statistical approach to define the best cut-off Copy number alterations (CNAs) calling from genomic data provided by high throughput experiments, able to predict a specific clinical end-point (early relapse, 18 months) in the context of Multiple Myeloma (MM). 743 newly diagnosed MM patients with SNPs array-derived genomic and clinical data were included in the study. CNAs were called both by a conventional (classic, CL) and an outcome-oriented (OO) method, and Progression Free Survival (PFS) hazard ratios of CNAs called by the two approaches were compared. The OO approach successfully identified patients at higher risk of relapse and the univariate survival analysis showed stronger prognostic effects for OO-defined high-risk alterations, as compared to that defined by CL approach, statistically significant for 12 CNAs. Overall, 155/743 patients relapsed within 18 months from the therapy start. A small number of OO-defined CNAs were significantly recurrent in early-relapsed patients (ER-CNAs) - amp1q, amp2p, del2p, del12p, del17p, del19p -. Two groups of patients were identified either carrying or not ≥1 ER-CNAs (249 vs. 494, respectively), the first one with significantly shorter PFS and overall survivals (OS) (PFS HR 2.15, p<0001; OS HR 2.37, p<0.0001). The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51; p=0.01) and by low quality (< stable disease) clinical response (HR=2.59 p=0.004). Notably, the type of induction therapy was not descriptive, suggesting that ER is strongly related to patients’ baseline genomic architecture. In conclusion, the OO- approach employed allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls’ accuracy to identify MM patients with the highest probability to ER. As being outcome-dependent, the OO-approach is dynamic and might be adjusted according to the selected outcome variable of interest.