Percutaneous Cement Discoplasty: Biomechanical and clinical assessment of a minimally invasive treatment of lumbar intervertebral disc disease

With population ageing, spine diseases have an increasing prevalence and induce high economic and social costs. The development of minimally invasive surgeries allows reducing the surgery-associated risks in elderly and polymorbid patients, and save costs by treating more patients in shorter time and reducing the complications. Percutaneous Cement Discoplasty (PCD) is a minimally invasive technique developed to treat highly degenerated intervertebral discs exhibiting a vacuum phenomenon. Filling the disc with bone cement creates a stand-alone spacer which partially restores the disc height and re-opens the foraminal space. PCD has recently been introduced to clinical use. However, the spine biomechanics following this treatment remained unravelled. The aim of this PhD thesis is to bridge the clinical experience with in vitro methodologies, to provide a multilateral evaluation of PCD outcome and a better understanding of its impact on the spine biomechanics, and of its possible contraindications. Firstly, a suitable in vitro porcine model to test the biomechanics of discoplasty by comparing specimens in the preoperative and postoperative conditions was developed. The methodology was then applied to investigate the biomechanics of discoplasty in cadaveric human segments. The in vitro specimens were mechanically investigated in flexion and extension, while a DIC system quantified the range of motion, disc height, and strains on the disc surface. Then, a versatile tool to measure the impact of discoplasty on the foramen space was developed and applied both to clinical and experimental work. The vertebrae reconstructed from CT scans were registered to match the loading configuration, using ex vivo DIC measurements under loading. The foramen volumetric changes caused by PCD was measured using a 3D geometrical method clinically developed by the research group. In conclusion, this project significantly extended the understanding of PCD biomechanics, highlighting its benefits in the treatment of advanced cases of intervertebral disc degeneration.

Development of a new galaxy classification technique and its application to the zCOSMOS survey

The purpose of this Thesis is to develop a robust and powerful method to classify galaxies from large surveys, in order to establish and confirm the connections between the principal observational parameters of the galaxies (spectral features, colours, morphological indices), and help unveil the evolution of these parameters from $z \sim 1$ to the local Universe. Within the framework of zCOSMOS-bright survey, and making use of its large database of objects ($\sim 10\,000$ galaxies in the redshift range $0 < z \lesssim 1.2$) and its great reliability in redshift and spectral properties determinations, first we adopt and extend the \emph{classification cube method}, as developed by Mignoli et al. (2009), to exploit the bimodal properties of galaxies (spectral, photometric and morphologic) separately, and then combining together these three subclassifications. We use this classification method as a test for a newly devised statistical classification, based on Principal Component Analysis and Unsupervised Fuzzy Partition clustering method (PCA+UFP), which is able to define the galaxy population exploiting their natural global bimodality, considering simultaneously up to 8 different properties. The PCA+UFP analysis is a very powerful and robust tool to probe the nature and the evolution of galaxies in a survey. It allows to define with less uncertainties the classification of galaxies, adding the flexibility to be adapted to different parameters: being a fuzzy classification it avoids the problems due to a hard classification, such as the classification cube presented in the first part of the article. The PCA+UFP method can be easily applied to different datasets: it does not rely on the nature of the data and for this reason it can be successfully employed with others observables (magnitudes, colours) or derived properties (masses, luminosities, SFRs, etc.). The agreement between the two classification cluster definitions is very high. ``Early'' and ``late'' type galaxies are well defined by the spectral, photometric and morphological properties, both considering them in a separate way and then combining the classifications (classification cube) and treating them as a whole (PCA+UFP cluster analysis). Differences arise in the definition of outliers: the classification cube is much more sensitive to single measurement errors or misclassifications in one property than the PCA+UFP cluster analysis, in which errors are ``averaged out'' during the process. This method allowed us to behold the \emph{downsizing} effect taking place in the PC spaces: the migration between the blue cloud towards the red clump happens at higher redshifts for galaxies of larger mass. The determination of $M_{\mathrm{cross}}$ the transition mass is in significant agreement with others values in literature.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.