Musical tension in harmonic intervals: behavioral and neural correlates

Musical tension is what drives our emotional experience in music listening. However, the specific role of the musical elements involved in tension-resolution perception remains largely unclear. This dissertation aims to advance the understanding of tension perception dynamics related to sensory consonance-dissonance. The first experiment aimed to design and validate a new crossmodal proprioceptive device for tension rating that overcomes some of the limitations of known tools. As a result, a psychophysical equation for the matching of physical force and psychological force was presented. The same tool was subsequently used in the second and third experiments to collect ratings of perceived tension and movement in harmonic musical intervals and standard noises. Besides, a visual analog scale (VAS) was used to allow a comparison of these two methods. The results confirmed the close relationship between sensory dissonance and perceived tension. Moreover, stimuli in the higher pitch register were perceived as more tense, confirming the primary role of pitch as a mediator of tension. The comparison between ratings obtained with the proprioceptive device and the VAS highlighted the tendency to give higher tension ratings using the VAS compared to the proprioceptive device. In the last experiment, brain electrical activity was recorded during the presentation of short tension-resolution patterns created using the most tense (perfect unison, fourth, and fifth) and the least tense harmonic intervals (augmented fourth, minor second, and inverted major seventh) to understand how consonance-dissonance can convey meaningful information on perceived tension-resolution. Results showed overall larger effects during the ‘resolution’ condition compare to the ‘tension induction’ condition, indicating that the resolution of harmonic instability towards a state of stability may be more salient than its opposite. A late positive component (LPC) was elicited, possibly reflecting deeper processing of tension-related meaning within a minimal harmonic context.

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.

Thiophene- Based Materials for Photovoltaic Applications

Thiophene oligomers (OTs) and polymers (PTs) are currently attracting remarkable attention as organic materials showing semiconducting, fluorescent, nonlinear optical and liquid crystalline properties. All these properties can be fine-tuned through minor structural modifications. As a consequence, thiophene oligomers and polymers are among the most investigated compounds for applications in organic electronics, optoelectronics and thin film devices such as field effect transistors (FETs), light emitting diodes (LEDs) and photovoltaic devices (PVDs). Our research aims to explore the self-assembly features and the optical, electrical and photovoltaic properties of a class of thiophene based materials so far scarcely investigated, namely that of oligo- and polythiophenes head-to-head substituted with alkyl or S-alkyl chains. In particular, we synthesized these compounds in short reaction times, high yields, high purity and environmentally friendly procedures taking advantage of ultrasound (US) and microwave (MW) enabling technologies in Suzuki-Miyaura cross-couplings.