Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

Distraction by task-irrelevant stimuli: the effects of endogenous spatial attention and visual working memory load

Salient stimuli, like sudden changes in the environment or emotional stimuli, generate a priority signal that captures attention even if they are task-irrelevant. However, to achieve goal-driven behavior, we need to ignore them and to avoid being distracted. It is generally agreed that top-down factors can help us to filter out distractors. A fundamental question is how and at which stage of processing the rejection of distractors is achieved. Two circumstances under which the allocation of attention to distractors is supposed to be prevented are represented by the case in which distractors occur at an unattended location (as determined by the deployment of endogenous spatial attention) and when the amount of visual working memory resources is reduced by an ongoing task. The present thesis is focused on the impact of these factors on three sources of distraction, namely auditory and visual onsets (Experiments 1 and 2, respectively) and pleasant scenes (Experiment 3). In the first two studies we recorded neural correlates of distractor processing (i.e., Event-Related Potentials), whereas in the last study we used interference effects on behavior (i.e., a slowing down of response times on a simultaneous task) to index distraction. Endogenous spatial attention reduced distraction by auditory stimuli and eliminated distraction by visual onsets. Differently, visual working memory load only affected the processing of visual onsets. Emotional interference persisted even when scenes occurred always at unattended locations and when visual working memory was loaded. Altogether, these findings indicate that the ability to detect the location of salient task-irrelevant sounds and identify the affective significance of natural scenes is preserved even when the amount of visual working memory resources is reduced by an ongoing task and when endogenous attention is elsewhere directed. However, these results also indicate that the processing of auditory and visual distractors is not entirely automatic.

Advanced studies on two animal models, murine and fish. RadKI21A626T mouse model: new insights into molecular mechanisms of enteric neuro-epithelial pathology. European sea bass: study of the effects of essential oils in different diets on the gastric system.

My PhD research period was focused on the anatomical, physiological and functional study of the gastrointestinal system on two different animal models. In two different contexts, the purpose of these two lines of research was contribute to understand how a specific genetic mutation or the adoption of a particular dietary supplement can affect gastrointestinal function. Functional gastrointestinal disorders are chronic conditions characterized by symptoms for which no organic cause can be found. Although symptoms are generally mild, a small subset of cases shows severe manifestations. This subset of patients may also have recurrent intestinal sub-occlusive episodes, but in absence of mechanical causes. This condition is referred to as chronic intestinal pseudo-obstruction, a rare, intractable chronic disease. Some mutations have been associated with CIPO. A novel causative RAD21 missense mutation was identified in a large consanguineous family, segregating a recessive form of CIPO. The present thesis was aimed to elucidate the mechanisms leading to neuropathy underlying CIPO via a recently developed conditional KI mouse carrying the RAD21 mutation. The experimental studies are based on the characterization and functional analysis of the conditional KI Rad21A626T mouse model. On the other hand aquaculture is increasing the global supply of foods. The species selected and feeds used affects the nutrients available from aquaculture, with a need to improve feed efficiency, both for economic and environmental reasons, but this will require novel innovative approaches. Nutritional strategies focused on the use of botanicals have attracted interest in animal production. Previous research indicates the positive results of using essential oils (EOs) as natural feed additives for several farmed animals. Therefore, the present study was designed to compare the effects of feed EO supplementation in two different forms (natural and composed of active ingredients obtained by synthesis) on the gastric mucosa in European sea bass.

Oltre il ghetto. Dinamiche residenziali e traiettorie migranti a Bologna

La tesi intende contribuire a livello teorico ed empirico al dibattito in tema di segregazione residenziale su base etnica. Negli ultimi anni, infatti, si stanno sviluppando studi e ricerche che mirano a (ri)definire la categoria di segregazione residenziale e le forme che essa può assumere a livello urbano (micro-segregazione, interstizio, segregazione verticale) proprio a partire dall’analisi delle caratteristiche del contesto Mediterraneo o dell’Europa del Sud. In questo quadro, il disegno di ricerca si articola a partire dal caso studio di Bologna e analizza la distribuzione residenziale della popolazione residente straniera in prospettiva diacronica, utilizzando strumenti di analisi georeferenziata e considerando diverse variabili (nazionalità, genere, status-socioeconomico). L’analisi quantitativa viene integrata da una seconda parte, che si compone di 20 interviste biografiche di tipo recall, che ricostruisce le traiettorie abitative di migranti residenti nella Città Metropolitana di Bologna, al fine di comprendere le modalità in cui le dinamiche strutturali che investono l’housing system si concretizzino nelle storie di vita di persone migranti. Dall’analisi emerge che la popolazione residente straniera è investita da un processo di periferizzazione che si manifesta a livello spaziale-territoriale e nei percorsi di vita. Adottandone una definizione estensiva, la categoria di periferizzazione individua una specifica forma di segregazione residenziale che non denota solo un processo di mobilità territoriale ma anche un più ampio e articolato “modo di abitare” di chi sta ai margini.

Role of Shiga toxins in the pathogenesis of hemolytic uremic syndrome

During the pathogenesis of hemolytic uremic syndrome (HUS), a severe sequela of Shiga toxin (Stx)-producing Escherichia coli (STEC) gastrointestinal infections, before the toxin acts on the target endothelial cells of the kidney and brain, several Stx forms are transported in the bloodstream: free Stx; Stx bound to circulating cells through Gb3Cer and TLR4 receptors; and Stx associated to blood cell-derived microvesicles. The latter form is mainly responsible for the development of life-threatening HUS in 15% of STEC-infected patients. Stx consist of five B subunits non-covalently bound to a single A subunit (uncleaved Stx) which can be cleaved in two fragments (A1 and A2) held by a disulfide bond (cleaved Stx). After reduction, the enzymatically active A1 fragment responsible for toxicity is released. Cleaved and uncleaved Stx are biologically active but functionally different, thus their presence in patients’ blood could affect the onset of HUS. Currently, there are no effective therapies for the treatment of STEC-infected patients and the gold standard strategies available for the diagnosis are very expensive and time-consuming. In this thesis, by exploiting the resolving power of SERS technology (Amplified Raman Spectroscopy on Surfaces), a plasmonic biosensor was developed as effective diagnostic tool for early detection of Stx in patients’ sera. An acellular protein synthesis system for detecting cleaved Stx2a in human serum based on its greater translation inhibition after treatment with reducing agents was developed and used to identify cleaved Stx in STEC-infected patients’ sera. Pathogenic microvesicles from Stx2a-challenged blood from healthy donors were isolated and characterized. The antibiotic NAB815, acting as inhibitor of toxin binding to TLR4 expressed by circulating cells, was found to be effective in impairing the formation of blood cell-derived microvesicles containing Stx2a, also having a protective effect in cellular models. This approach could be proposed as an innovative treatment for HUS prevention.

Le contexte linguistique et la tradition de langue hebraïque de la Secunda (deuxième colonne des Hexaples d'Origène)

Le terme Secunda désigne la deuxième colonne de la synopse hexaplaire d’Origène. Cette synopse comportait six colonnes, d’où le nom Hexapla utilisé pour la désigner : la première contenait le texte hébreu original de l’Ancien Testament, la deuxième (Secunda) sa transcription phonétique en caractères grecs, les quatre autres les différentes traductions grecques de la Bible. La présence de graphèmes de vocaliques grecs dans la Secunda permet de mener une étude grammaticale complète de cette source, d’un point de vue phonétique et morphologique. Il manque encore actuellement une recherche qui développe le rapport entre la tradition hébraïque de la Secunda, telle qu’elle ressort de la transcription, et les autres traditions hébraïques attestées : celles sans graphèmes vocaliques (c’est-à-dire la tradition samaritaine et le corpus qumranien) et les traditions plus tardives et vocalisées pendant la période médiévale (les traditions massorétique tibérienne, babylonienne et palestinienne). Ce dernier point est précisément l’objet de cette thèse, qui vise à mieux comprendre le statut de l’hébreu de la Secunda, ses relations avec les autres traditions hébraïques et sa place dans l’histoire de la langue. Cette question est abordée à travers différentes étapes : en partant d’une étude phonétique et morphologique de la langue hébraïque de la colonne, on arrive à une hypothèse de datation qui permet une comparaison directe entre l’hébreu hexaplaire et les autres traditions mentionnées ci-dessus. La comparaison entre la Secunda et les autres traditions est cruciale pour situer correctement la Secunda dans l’histoire de la langue hébraïque : au niveau synchronique, elle permet de mettre en évidence ses éléments dialectaux, documentés dans les transcriptions de la Secunda et dans les traditions de la même époque ; au niveau diachronique, elle fournit des terminus ante ou post quem pour des phénomènes bien attestés dans les traditions tardives.

Dynamic food systems and climate-induced risks transmission for food and nutritional security

Agriculture market instability impedes achieving the global goal of sustainable and resilient food systems. Currently, the support to producers reaches the mammoth USD 540 billion a year and is projected to reach USD 1.8 trillion by 2030. This gigantic increase requires a repurposing agricultural support strategy (RASS), considering the market country-specific circumstances. These circumstances may vary with geographic locations, marketing structures, and product value chains. The fruit production system is crucial for health-conscious consumers and profit-oriented producers for food and nutritional security. Export is one of the main driving forces behind the expansion of the fruit sector, and during the year 2010-2018, trade significantly outpaced production increases. The previous literature states that irregular and unpredictable behaviour — Chaos — can arise from entirely rational economic decision-making within markets. Different markets' direct/indirect linkages through trade create trade hubs, and uncertainty may function as an avenue to transmit adverse shocks and increase vulnerability rather than contribute to resilience. Therefore, distinguishing Chaos into an endogenous and exogenous pattern of behaviour is cradled to formulate an effective RASS for resilient food systems and to understand global food crises. The present research is aimed at studying the market dynamics of three regional trade hubs, i.e., Brazil (South America), Italy (Europe), and Pakistan (Asia), each representing advanced to traditional value chains to control uncertainty (risks). The present research encompasses 1) a systematic review to highlight the research dynamism and identify grey-areas of research. Based on the findings, we have investigated the 2) nonlinear impacts of climate-induced price responsiveness in monopsony markets. Once we highlighted the importance of marketing structures/arrangements, 3) we developed a risk transmission framework to address the co-evolving impacts in complex dynamic interactions.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.

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.

Dragonlifter - Un lifter da p-code a C

L'analisi di codice compilato è un'attività sempre più richiesta e necessaria, critica per la sicurezza e stabilità delle infrastrutture informatiche utilizzate in tutto il mondo. Le tipologie di file binari da analizzare sono numerose e in costante evoluzione, si può passare da applicativi desktop o mobile a firmware di router o baseband. Scopo della tesi è progettare e realizzare Dragonlifter, un convertitore da codice compilato a C che sia estendibile e in grado di supportare un numero elevato di architetture, sistemi operativi e formati file. Questo rende possibile eseguire programmi compilati per altre architetture, tracciare la loro esecuzione e modificarli per mitigare vulnerabilità o cambiarne il comportamento.

Ottimizzazione impiantistica di servizi di climatizzazione e acqua calda con produzione di energia da fotovoltaico: il caso studio del progetto "e-SAFE"

In questa tesi si è approfondito dal punto di vista impiantistico ed energetico il primo progetto pilota “e-SAFE”, relativo ad un condominio di edilizia popolare sito a Catania. Il progetto europeo “e-SAFE” mira alla riqualificazione “globale” del patrimonio edilizio UE, con obiettivi quali la decarbonizzazione, tramite raggiungimento di edifici nZEB, e la sicurezza sismica, includendo finalità sociali. È stata svolta la progettazione degli impianti tecnici relativi ai servizi di climatizzazione (riscaldamento e raffrescamento) e produzione acqua calda sanitaria (ACS), tramite adozione di sistema a pompa di calore abbinato ad impianto fotovoltaico provvisto di batterie di accumulo, al fine di massimizzare l’utilizzo di fonti energetiche rinnovabili. Una soluzione innovativa è stata prevista per il sistema ACS tramite bollitori decentralizzati che permettono l’eliminazione della rete di ricircolo e dotano il servizio centralizzato di una certa “individualità”, con adozione di logiche di controllo che consentono di sfruttare al massimo l’energia da fotovoltaico. Sono state quindi svolte analisi energetiche, relative a tre casi impiantistici (senza PV, con PV e con batterie d’accumulo), con modellazione del sistema edificio-impianto adottando una procedura di calcolo orario, per poi confrontare i consumi elettrici, le emissioni e i principali indicatori energetici al fine di dimostrare la bontà delle scelte progettuali effettuate. Si è dimostrato come l’impiego dell’impianto fotovoltaico, abbinato ad un corretto dimensionamento delle batterie di accumulo, consente di ottimizzare la contemporaneità di produzione e consumo di energia. Nonché permette di minimizzare il prelievo di energia elettrica dalla rete nazionale, consumando e stoccando in loco l’elettricità autoprodotta da fonti energetiche totalmente rinnovabili.

SRAE: Sustainable Regressive Auto Encoder

Al giorno d’oggi viviamo in una realtà dove lo sviluppo economico, l’innovazione tecnologica, la qualità della vita e l’impatto ambientale sono i protagonisti assoluti. Tutti, persino gli Stati del mondo, si trovano a fare i conti con varie problematiche riguardanti i quattro aspetti sopracitati e qui possiamo dire che la sostenibilità ne è il punto chiave e che al momento non sembra esistere ancora una metrica riconosciuta e approvata per consigliare, a chi di interesse, come modificare certi aspetti per crescere in modo sostenibile. Le Nazioni Unite hanno deciso, di comune accordo, di stilare una lista di obiettivi da raggiungere entro il 2030 dove è possibile trovare argomenti in linea con quanto descritto finora. Questa raccolta è principalmente divisa in aspetti economici, sociali e ambientali che sono le stesse categorie di dati impiegate per il calcolo del Sustainable Development Index. In questo elaborato ci si propone di progettare e sviluppare una rete neurale predittiva da affiancare a un sistema di feedback per realizzare un prodotto che sia abile di: descrivere il contesto di partenza tramite l’SDI e/o consigliare comportamenti per migliorare la situazione in modo sostenibile.

Latent representations for traditional music analysis and generation

We create and study a generative model for Irish traditional music based on Variational Autoencoders and analyze the learned latent space trying to find musically significant correlations in the latent codes' distributions in order to perform musical analysis on data. We train two kinds of models: one trained on a dataset of Irish folk melodies, one trained on bars extrapolated from the melodies dataset, each one in five variations of increasing size. We conduct the following experiments: we inspect the latent space of tunes and bars in relation to key, time signature, and estimated harmonic function of bars; we search for links between tunes in a particular style (i.e. "reels'") and their positioning in latent space relative to other tunes; we compute distances between embedded bars in a tune to gain insight into the model's understanding of the similarity between bars. Finally, we show and evaluate generative examples. We find that the learned latent space does not explicitly encode musical information and is thus unusable for musical analysis of data, while generative results are generally good and not strictly dependent on the musical coherence of the model's internal representation.

Generazione automatica di una knowledge base con applicazione ai Sustainable Development Goals

Questa tesi di laurea compie uno studio sull’ utilizzo di tecniche di web crawling, web scraping e Natural Language Processing per costruire automaticamente un dataset di documenti e una knowledge base di coppie verbo-oggetto utilizzabile per la classificazione di testi. Dopo una breve introduzione sulle tecniche utilizzate verrà presentato il metodo di generazione, prima in forma teorica e generalizzabile a qualunque classificazione basata su un insieme di argomenti, e poi in modo specifico attraverso un caso di studio: il software SDG Detector. In particolare quest ultimo riguarda l’applicazione pratica del metodo esposto per costruire una raccolta di informazioni utili alla classificazione di documenti in base alla presenza di uno o più Sustainable Development Goals. La parte relativa alla classificazione è curata dal co-autore di questa applicazione, la presente invece si concentra su un’analisi di correttezza e performance basata sull’espansione del dataset e della derivante base di conoscenza.