A spectroscopic and photometric study of stellar populations in a sample of clusters in the Magellanic Clouds

This Ph.D. Thesis has been carried out in the framework of a long-term and large project devoted to describe the main photometric, chemical, evolutionary and integrated properties of a representative sample of Large and Small Magellanic Cloud (LMC and SMC respectively) clusters. The globular clusters system of these two Irregular galaxies provides a rich resource for investigating stellar and chemical evolution and to obtain a detailed view of the star formation history and chemical enrichment of the Clouds. The results discussed here are based on the analysis of high-resolution photometric and spectroscopic datasets obtained by using the last generation of imagers and spectrographs. The principal aims of this project are summarized as follows: • The study of the AGB and RGB sequences in a sample of MC clusters, through the analysis of a wide near-infrared photometric database, including 33 Magellanic globulars obtained in three observing runs with the near-infrared camera SOFI@NTT (ESO, La Silla). • The study of the chemical properties of a sample of MCs clusters, by using optical and near-infrared high-resolution spectra. 3 observing runs have been secured to our group to observe 9 LMC clusters (with ages between 100 Myr and 13 Gyr) with the optical high-resolution spectrograph FLAMES@VLT (ESO, Paranal) and 4 very young (<30 Myr) clusters (3 in the LMC and 1 in the SMC) with the near-infrared high-resolution spectrograph CRIRES@VLT. • The study of the photometric properties of the main evolutive sequences in optical Color- Magnitude Diagrams (CMD) obtained by using HST archive data, with the final aim of dating several clusters via the comparison between the observed CMDs and theoretical isochrones. The determination of the age of a stellar population requires an accurate measure of the Main Sequence (MS) Turn-Off (TO) luminosity and the knowledge of the distance modulus, reddening and overall metallicity. For this purpose, we limited the study of the age just to the clusters already observed with high-resolution spectroscopy, in order to date only clusters with accurate estimates of the overall metallicity.

Detailed study of the seismotectonic setting in the Lucanian Apennines and surrounding areas (Southern Italy)

In this research work I analyzed the instrumental seismicity of Southern Italy in the area including the Lucanian Apennines and Bradano foredeep, making use of the most recent seismological database available so far. I examined the seismicity occurred during the period between 2001 and 2006, considering 514 events with magnitudes M ≥ 2.0. In the first part of the work, P- and S-wave arrival times, recorded by the Italian National Seismic Network (RSNC) operated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), were re-picked along with those of the SAPTEX temporary array (2001–2004). For some events located in the Upper Val d'Agri, I also used data from the Eni-Agip oil company seismic network. I computed the VP/VS ratio obtaining a value of 1.83 and I carried out an analysis for the one-dimensional (1D) velocity model that approximates the seismic structure of the study area. After this preliminary analysis, making use of the records obtained in the SeSCAL experiment, I incremented the database by handpicking new arrival times. My final dataset consists of 15,666 P- and 9228 S-arrival times associated to 1047 earthquakes with magnitude ML ≥ 1.5. I computed 162 fault-plane solutions and composite focal mechanisms for closely located events. I investigated stress field orientation inverting focal mechanism belonging to the Lucanian Apennine and the Pollino Range, both areas characterized by more concentrated background seismicity. Moreover, I applied the double difference technique (DD) to improve the earthquake locations. Considering these results and different datasets available in the literature, I carried out a detailed analysis of single sub-areas and of a swarm (November 2008) recorded by SeSCAL array. The relocated seismicity appears more concentrated within the upper crust and it is mostly clustered along the Lucanian Apennine chain. In particular, two well-defined clusters were located in the Potentino and in the Abriola-Pietrapertosa sector (central Lucanian region). Their hypocentral depths are slightly deeper than those observed beneath the chain. I suggest that these two seismic features are representative of the transition from the inner portion of the chain with NE-SW extension to the external margin characterized by dextral strike-slip kinematics. In the easternmost part of the study area, below the Bradano foredeep and the Apulia foreland, the seismicity is generally deeper and more scattered and is associated to the Murge uplift and to the small structures present in the area. I also observed a small structure NE-SW oriented in the Abriola-Pietrapertosa area (activated with a swarm in November 2008) that could be considered to act as a barrier to the propagation of a potential rupture of an active NW-SE striking faults system. Focal mechanisms computed in this study are in large part normal and strike-slip solutions and their tensional axes (T-axes) have a generalized NE-SW orientation. Thanks to denser coverage of seismic stations and the detailed analysis, this study is a further contribution to the comprehension of the seismogenesis and state of stress of the Southern Apennines region, giving important contributions to seismotectonic zoning and seismic hazard assessment.

Automated Segmentation and Non-Rigid Registration for the Quantitative Evaluation of Myocardial Perfusion from Magnetic Resonance Images

Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation and non-rigid registration as a basis for perfusion quantification is presented. The proposed technique is based on three steps: reference frame selection, myocardial segmentation and non-rigid registration. In the first step, the reference frame in which both endo- and epicardial segmentation will be performed is chosen. Endocardial segmentation is achieved by means of a statistical region-based level-set technique followed by a curvature-based regularization motion. Epicardial segmentation is achieved by means of an edge-based level-set technique followed again by a regularization motion. To take into account the changes in position, size and shape of myocardium throughout the sequence due to out of plane respiratory motion, a non-rigid registration algorithm is required. The proposed non-rigid registration scheme consists in a novel multiscale extension of the normalized cross-correlation algorithm in combination with level-set methods. The myocardium is then divided into standard segments. Contrast enhancement curves are computed measuring the mean pixel intensity of each segment over time, and perfusion indices are extracted from each curve. The overall approach has been tested on synthetic and real datasets. For validation purposes, the sequences have been manually traced by an experienced interpreter, and contrast enhancement curves as well as perfusion indices have been computed. Comparisons between automatically extracted and manually obtained contours and enhancement curves showed high inter-technique agreement. Comparisons of perfusion indices computed using both approaches against quantitative coronary angiography and visual interpretation demonstrated that the two technique have similar diagnostic accuracy. In conclusion, the proposed technique allows fast, automated and accurate measurement of intra-myocardial contrast dynamics, and may thus address the strong clinical need for quantitative evaluation of myocardial perfusion.

Kinematic models of interseismic deformation from inversion of GPS and InSAR measurements to estimate fault parameters and coupling degree

We have used kinematic models in two Italian regions to reproduce surface interseismic velocities obtained from InSAR and GPS measurements. We have considered a Block modeling, BM, approach to evaluate which fault system is actively accommodating the occurring deformation in both considered areas. We have performed a study for the Umbria-Marche Apennines, obtaining that the tectonic extension observed by GPS measurements is explained by the active contribution of at least two fault systems, one of which is the Alto Tiberina fault, ATF. We have estimated also the interseismic coupling distribution for the ATF using a 3D surface and the result shows an interesting correlation between the microseismicity and the uncoupled fault portions. The second area analyzed concerns the Gargano promontory for which we have used jointly the available InSAR and GPS velocities. Firstly we have attached the two datasets to the same terrestrial reference frame and then using a simple dislocation approach, we have estimated the best fault parameters reproducing the available data, providing a solution corresponding to the Mattinata fault. Subsequently we have considered within a BM analysis both GPS and InSAR datasets in order to evaluate if the Mattinata fault may accommodate the deformation occurring in the central Adriatic due to the relative motion between the North-Adriatic and South-Adriatic plates. We obtain that the deformation occurring in that region should be accommodated by more that one fault system, that is however difficult to detect since the poor coverage of geodetic measurement offshore of the Gargano promontory. Finally we have performed also the estimate of the interseismic coupling distribution for the Mattinata fault, obtaining a shallow coupling pattern. Both of coupling distributions found using the BM approach have been tested by means of resolution checkerboard tests and they demonstrate that the coupling patterns depend on the geodetic data positions.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Tumori primitivi multipli del polmone. Profilo clinico e biologico in pazienti affetti da neoplasie polmonari multiple. Analisi di elementi clinici e marcatori biologici come possibili fattori di differenziazione dei tumori polmonari multipli

Background: MPLC represents a diagnostic challenge. Topic of the discussion is how to distinguish these patients as a metastatic or a multifocal disease. While in case of the different histology there are less doubt on the opposite in case of same histology is mandatory to investigate on other clinical features to rule out this question. Matherials and Methods: A retrospective review identified all patients treated surgically for a presumed diagnosis of SPLC. Pre-operative staging was obtained with Total CT scan and fluoro-deoxy positron emission tomography and mediastinoscopy. Patients with nodes interest or extra-thoracic location were excluded from this study. Epidermal growth factor receptor (EGFR) expression with complete immunohistochemical analisis was evaluated. Survival was estimated using Kaplan-Meyer method, and clinical features were estimated using a long-rank test or Cox proportional hazards model for categorical and continuous variable, respectively. Results: According to American College Chest Physician, 18 patients underwent to surgical resection for a diagnosis of MPLC. Of these, 8 patients had 3 or more nodules while 10 patients had less than 3 nodules. Pathologic examination demonstrated that 13/18(70%) of patients with multiple histological types was Adenocarcinoma, 2/18(10%) Squamous carcinoma, 2/18(10%) large cell carcinoma and 1/18(5%) Adenosquamosu carcinoma. Expression of EGFR has been evaluated in all nodules: in 7 patients of 18 (38%) the percentage of expression of each nodule resulted different. Conclusions: MPLC represent a multifocal disease where interactions of clinical informations with biological studies reinforce the diagnosis. EGFR could contribute to differentiate the nodules. However, further researches are necessary to validate this hypothesis.

Automotive Radars:towards smarter vehicles

Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

Methodologies for the analysis and interpretation of environmental datasets from areas interested by offshore activities

The exploitation of hydrocarbon reservoirs by the oil and gas industries represents one of the most relevant and concerning anthropic stressor in various marine areas worldwide and the presence of extractive structures can have severe consequences on the marine environment. Environmental monitoring surveys are carried out to monitor the effects and impacts of offshore energy facilities. Macrobenthic communities, inhabiting the soft-bottom, represent a key component of these surveys given their great responsiveness to natural and anthropic changes. A comprehensive collection of monitoring data from four Italian seas was used to investigate distributional pattern of macrozoobenthos assemblages confirming a high spatial variability in relation to the environmental variables analyzed. Since these datasets could represent a powerful tool for the industrial and scientific research, the steps and standardized procedures needed to obtain robust and comparable high-quality data were investigated and outlined. Over recent years, decommissioning of old platforms is a growing topic in this sector, involving many actors in the various decision-making processes. A Multi-Criteria Decision Analysis, specific for the Adriatic Sea, was developed to investigate the impacts of decommissioning of a gas platform on environmental and socio-economic aspects, to select the best decommissioning scenario. From the scenarios studied, the most impacting one has resulted to be total removal, affecting all the faunal component considered in the study. Currently, the European nations are increasing the production of energy from offshore wind farms with an exponential expansion. A comparative study of methodologies used five countries of the North Sea countries was carried out to investigate the best approaches to monitor the effects of wind farms on the benthic communities. In the foreseeable future, collaboration between industry, scientific communities, national and international policies are needed to gain knowledge concerning the effects of these industrial activities on the ecological status of the ecosystems.