Starch distribution in pear tree organs in relation to training systems, rootstocks and fruit quality

Starch is the main form in which plants store carbohydrates reserves, both in terms of amounts and distribution among different plant species. Carbohydrates are direct products of photosynthetic activity, and it is well know that yield efficiency and production are directly correlated to the amount of carbohydrates synthesized and how these are distributed among vegetative and reproductive organs. Nowadays, in pear trees, due to the modernization of orchards, through the introduction of new rootstocks and the development of new training systems, the understanding and the development of new approaches regarding the distribution and storage of carbohydrates, are required. The objective of this research work was to study the behavior of carbohydrate reserves, mainly starch, in different pear tree organs and tissues: i.e., fruits, leaves, woody organs, roots and flower buds, at different physiological stages during the season. Starch in fruit is accumulated at early stages, and reached a maximum concentration during the middle phase of fruit development; after that, its degradation begins with a rise in soluble carbohydrates. Moreover, relationships between fruit starch degradation and different fruit traits, soluble sugars and organic acids were established. In woody organs and roots, an interconversion between starch and soluble carbohydrates was observed during the dormancy period that confirms its main function in supporting the growth and development of new tissues during the following spring. Factors as training systems, rootstocks, types of bearing wood, and their position on the canopy, influenced the concentrations of starch and soluble carbohydrates at different sampling dates. Also, environmental conditions and cultural practices must be considered to better explain these results. Thus, a deeper understanding of the dynamics of carbohydrates reserves within the plant could provide relevant information to improve several management practices to increase crop yield efficiency.

Silence after bombs. Practice, role and training of interpreters in conflict zones. A case study of the wars in Bosnia and Herzegovina and Croatia.

This dissertation investigates the role, training and practice of the interpreters that worked during the wars in Bosnia and Herzegovina and Croatia in the 1990s, both at a high political level and on the ground for peackeeping troops. Adopting a historical method that uses interviews, newspaper articles, videos, archival documents and pictures the author tries to retrace how those interpreters were hired, employed and what challenges they faced in their daily work. The aim is to give voice to a category that has long been forgotten, to investigate how mediated interaction is shaped by violent conflict and to offer hindsight to improve the recruitment and management of local interpreters by armed forces.

Formal planning rationality in public sector professional work: between discourse and practice

Although rational models of formal planning have been seriously criticized by strategy literature, they not only remain a widely used organizational practice in private firms, but they have increasingly been entering public, professional organizations too, as part of public sector managerial reforms. This research addresses this apparent paradox, exploring the meaning of formal planning in public sector professional work. Curiously, this is an issue that remains under-investigated in the literature: the long debate on formal planning in strategy research devoted scant attention to its diffusion in the public sector, and public sector studies have scrutinized the introduction of other management tools in professional work, but very limitedly formal planning itself. In fact, little is known on the actual meaning of formal planning in public, professional services. This research is based upon a case of adoption of formal planning tools in a public hospital. Embracing a discourse analytical lens, it examines which formal planning discourse entered professional work, to what extent, and how professionals interpret it and engage with it in their practice. The analysis uncovers dynamics of social construction of meaning where, eventually, a formal planning discourse both shapes and is shaped by professional practice. In particular, it is found that formal planning rationality largely penetrated professional work, but not to the detriment of professional values. Morevover, formal planning ‘fails’ as a tool for rational decision making, but it takes up a knowledge work and a social value in professional work, as a tool for explicitation of action courses and for dialogue between otherwise more disconnected parts of the organization.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.