Analisi di espressione di microRNA in Tumore a Cellule Giganti: identificazione dei target con ruolo di potenziali biomarcatori

Il Tumore a Cellule Giganti dell’osso (TCG) è una rara neoplasia che rappresenta il 5% dei tumori di natura ossea; sebbene venga considerato un tumore a decorso benigno può manifestare caratteri di aggressività locale dando origine a recidive locali nel 10-25% dei casi, e nel 2-4% dei casi metastatizza a livello polmonare. In questo studio è stata valutata l’espressione dei miRNA mediante miRNA microarray in 10 pazienti affetti da TCG, 5 con metastasi e 5 liberi da malattia; sono stati riscontrati miRNA differenzialmente espressi tra i 2 gruppi di pazienti e la successiva validazione mediante Real Time PCR ha confermato una differenza significativa per il miR-136 (p=0.04). Mediante analisi bioinformatica con il software TargetScan abbiamo identificato RANK e NF1B come target del miR-136 e ne abbiamo studiato l’espressione mediante Real Time PCR su una più ampia casistica di pazienti affetti da TCG, metastatico e non, evidenziando una maggior espressione di NF1B nel gruppo di pazienti metastatici, mentre RANK non ha dimostrato una differenza significativa. L’analisi di Western Blot ha rilevato una maggiore espressione di entrambe le proteine nei pazienti metastatici rispetto ai non metastatici. Successivamente è stato condotto uno studio di immunoistochimica su TMA di 163 campioni di pazienti affetti da TCG a diverso decorso clinico che ha dimostrato una maggiore e significativa espressione di entrambe i target nei pazienti con metastasi rispetto ai non metastatici; le analisi di popolazione mediante Kaplan-Meier hanno confermato la correlazione tra over-espressione di RANK, NF1B e ricaduta con metastasi (p=0.001 e p<0.0005 rispettivamente). Lo studio di immunoistochimica è stato ampliato alle proteine maggiormente coinvolte nell’osteolisi che risultano avere un significato prognostico; tuttavia mediante analisi di ROC, la co-over-espressione di RANK, RANKL e NF1B rappresenta il migliore modello per predire la comparsa di metastasi (AUC=0.782, p<0.0005).

Local Trigonometric Methods for Time Series Smoothing.

The thesis is concerned with local trigonometric regression methods. The aim was to develop a method for extraction of cyclical components in time series. The main results of the thesis are the following. First, a generalization of the filter proposed by Christiano and Fitzgerald is furnished for the smoothing of ARIMA(p,d,q) process. Second, a local trigonometric filter is built, with its statistical properties. Third, they are discussed the convergence properties of trigonometric estimators, and the problem of choosing the order of the model. A large scale simulation experiment has been designed in order to assess the performance of the proposed models and methods. The results show that local trigonometric regression may be a useful tool for periodic time series analysis.

About stabilization of non-minimum phase systems by output feedback

This thesis work has been motivated by an internal benchmark dealing with the output regulation problem of a nonlinear non-minimum phase system in the case of full-state feedback. The system under consideration structurally suffers from finite escape time, and this condition makes the output regulation problem very hard even for very simple steady-state evolution or exosystem dynamics, such as a simple integrator. This situation leads to studying the approaches developed for controlling Non-minimum phase systems and how they affect feedback performances. Despite a lot of frequency domain results, only a few works have been proposed for describing the performance limitations in a state space system representation. In particular, in our opinion, the most relevant research thread exploits the so-called Inner-Outer Decomposition. Such decomposition allows splitting the Non-minimum phase system under consideration into a cascade of two subsystems: a minimum phase system (the outer) that contains all poles of the original system and an all-pass Non-minimum phase system (the inner) that contains all the unavoidable pathologies of the unstable zero dynamics. Such a cascade decomposition was inspiring to start working on functional observers for linear and nonlinear systems. In particular, the idea of a functional observer is to exploit only the measured signals from the system to asymptotically reconstruct a certain function of the system states, without necessarily reconstructing the whole state vector. The feature of asymptotically reconstructing a certain state functional plays an important role in the design of a feedback controller able to stabilize the Non-minimum phase system.

Models for reliability estimation of HVDC cable systems

The work carried out in this thesis aims at: - studying – in both simulative and experimental methods – the effect of electrical transients (i.e., Voltage Polarity Reversals VPRs, Temporary OverVoltages TOVs, and Superimposed Switching Impulses SSIs) on the aging phenomena in HVDC extruded cable insulations. Dielectric spectroscopy, conductivity measurements, Fourier Transform Infra-Red FTIR spectroscopy, and space charge measurements show variation in the insulating properties of the aged Cross-Linked Polyethylene XLPE specimens compared to non-aged ones. Scission in XLPE bonds and formation of aging chemical bonds is also noticed in aged insulations due to possible oxidation reactions. The aged materials show more ability to accumulate space charges compared to non-aged ones. An increase in both DC electrical conductivity and imaginary permittivity has been also noticed. - The development of life-based geometric design of HVDC cables in a detailed parametric analysis of all parameters that affect the design. Furthermore, the effect of both electrical and thermal transients on the design is also investigated. - The intrinsic thermal instability in HVDC cables and the effect of insulation characteristics on the thermal stability using a temperature and field iterative loop (using numerical methods – Finite Difference Method FDM). The dielectric loss coefficient is also calculated for DC cables and found to be less than that in AC cables. This emphasizes that the intrinsic thermal instability is critical in HVDC cables. - Fitting electrical conductivity models to the experimental measurements using both models found in the literature and modified models to find the best fit by considering the synergistic effect between field and temperature coefficients of electrical conductivity.

Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was supported with spectroradiometer ground measurements.