Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.

Characterization of the molecular mechanisms mediated by the insulin-like growth factor-2 mRNA-binding proteins and Ephrin receptor A2 (EPHA2) signaling in the malignancy of CIC-DUX4 sarcomas

Ewing sarcoma (EWS) and CIC-DUX4 sarcoma (CDS) are pediatric fusion gene-driven tumors of mesenchymal origin characterized by an extremely stable genome and limited clinical solutions. Post-transcriptional regulatory mechanisms are crucial for understanding the development of this class of tumors. RNA binding proteins (RBPs) play a crucial role in the aggressiveness of these tumors. Numerous RBP families are dysregulated in cancer, including IGF2BPs. Among these, IGF2BP3 is a negative prognostic factor in EWS because it promotes cell growth, chemoresistence, and induces the metastatic process. Based on preliminary RNA sequencing data from clinical samples of EWS vs CDS patients, three major axes that are more expressed in CDS have been identified, two of which are dissected in this PhD work. The first involves the transcription factor HMGA2, IGF2BP2-3, and IGF2; the other involves the ephrin receptor system, particularly EphA2. EphA2 is involved in numerous cellular functions during embryonic stages, and its increased expression in adult tissues is often associated with pathological conditions. In tumors, its role is controversial because it can be associated with both pro- and anti-tumoral mechanisms. In EWS, it has been shown to play a role in promoting cell migration and neoangiogenesis. Our study has confirmed that the HMGA2/IGF2BPs/IGF2 axis contributes to CDS malignancy, and Akt hyperactivation has a strong impact on migration. Using loss/gain of function models for EphA2, we confirmed that it is a substrate of Akt, and Akt hyperactivation in CDS triggers ligand-independent activation of EphA2 through phosphorylation of S897. Moreover, the combination of Trabectedin and NVP/BEZ235 partially inhibits Akt/mTOR activation, resulting in reduced tumor growth in vivo. Inhibition of EphA2 through ALWII 41_27 significantly reduces migration in vitro. The project aim is the identification of target molecules in CDS that can distinguish it from EWS and thus develop new targeted therapeutic strategies.