Politiche Antitrust e crescita economica

Negli ultimi anni le istituzioni e la regolamentazione hanno svolto un ruolo sempre più importante nell’analisi della crescita economica. Tuttavia, non è facile interpretare le istituzioni e gli effetti dei regolamenti sulla crescita attraverso indicatori che tendono a “misurare” le istituzioni. Lo scopo di questa ricerca è analizzare la relazione di lungo periodo tra la crescita economica e la regolamentazione e il ruolo della regolamentazione antitrust sulla crescita economica. La stima econometrica dei modelli di crescita con la concorrenza e gli indicatori di potere di mercato si base su un dataset appositamente costruito che copre 211 Paesi, su un arco temporale massimo di 50 anni (da 1960 a 2009). In particolare, cerchiamo di identificare un quadro analitico volto a integrare l’analisi istituzionale ed economica al fine di valutare il ruolo della regolamentazione e, più in generale, il ruolo delle istituzioni nella crescita economica. Dopo una revisione della letteratura teorica ed empirica sulla crescita e le istituzioni, vi presentiamo l’analisi dell'impatto normativo (RIA) in materia di concorrenza, e analizziamo le principali misure di regolamentazione, la governance e le misure antitrust. Per rispondere alla nostra domanda di ricerca si stimano modelli di crescita prendendo in considerazione tre diverse misure di regolamentazione: la Regulation Impact (RI), la Governance (GOV), e la libertà economica (LIB). Nel modello a effetti fissi, RI, gli effetti della legislazione antitrust sulla crescita economica sono significativi e positivi, e gli effetti di durata antitrust sono significativi, ma negativi. Nel pannel dinamico, GOV, gli effetti dell’indicatore di governance sulla crescita sono notevoli, ma negativo. Nel pannel dinamico, LIB, gli effetti della LIB sono significativi e negativi.

Multidimensional analysis of complex networks

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Incompressible limit and well-posedness of PDE models of tissue growth

Both compressible and incompressible porous medium models are used in the literature to describe the mechanical aspects of living tissues. Using a stiff pressure law, it is possible to build a link between these two different representations. In the incompressible limit, compressible models generate free boundary problems where saturation holds in the moving domain. Our work aims at investigating the stiff pressure limit of reaction-advection-porous medium equations motivated by tumor development. Our first study concerns the analysis and numerical simulation of a model including the effect of nutrients. A coupled system of equations describes the cell density and the nutrient concentration and the derivation of the pressure equation in the stiff limit was an open problem for which the strong compactness of the pressure gradient is needed. To establish it, we use two new ideas: an L3-version of the celebrated Aronson-Bénilan estimate, and a sharp uniform L4-bound on the pressure gradient. We further investigate the sharpness of this bound through a finite difference upwind scheme, which we prove to be stable and asymptotic preserving. Our second study is centered around porous medium equations including convective effects. We are able to extend the techniques developed for the nutrient case, hence finding the complementarity relation on the limit pressure. Moreover, we provide an estimate of the convergence rate at the incompressible limit. Finally, we study a multi-species system. In particular, we account for phenotypic heterogeneity, including a structured variable into the problem. In this case, a cross-(degenerate)-diffusion system describes the evolution of the phenotypic distributions. Adapting methods recently developed in the context of two-species systems, we prove existence of weak solutions and we pass to the incompressible limit. Furthermore, we prove new regularity results on the total pressure, which is related to the total density by a power law of state.

The VIMOS-VLT Deep Survey: the evolution of type-1 AGN

Quasars and AGN play an important role in many aspects of the modern cosmology. Of particular interest is the issue of the interplay between AGN activity and formation and evolution of galaxies and structures. Studies on nearby galaxies revealed that most (and possibly all) galaxy nuclei contain a super-massive black hole (SMBH) and that between a third and half of them are showing some evidence of activity (Kormendy and Richstone, 1995). The discovery of a tight relation between black holes mass and velocity dispersion of their host galaxy suggests that the evolution of the growth of SMBH and their host galaxy are linked together. In this context, studying the evolution of AGN, through the luminosity function (LF), is fundamental to constrain the theories of galaxy and SMBH formation and evolution. Recently, many theories have been developed to describe physical processes possibly responsible of a common formation scenario for galaxies and their central black hole (Volonteri et al., 2003; Springel et al., 2005a; Vittorini et al., 2005; Hopkins et al., 2006a) and an increasing number of observations in different bands are focused on collecting larger and larger quasar samples. Many issues remain however not yet fully understood. In the context of the VVDS (VIMOS-VLT Deep Survey), we collected and studied an unbiased sample of spectroscopically selected faint type-1 AGN with a unique and straightforward selection function. Indeed, the VVDS is a large, purely magnitude limited spectroscopic survey of faint objects, free of any morphological and/or color preselection. We studied the statistical properties of this sample and its evolution up to redshift z 4. Because of the contamination of the AGN light by their host galaxies at the faint magnitudes explored by our sample, we observed that a significant fraction of AGN in our sample would be missed by the UV excess and morphological criteria usually adopted for the pre-selection of optical QSO candidates. If not properly taken into account, this failure in selecting particular sub-classes of AGN could, in principle, affect some of the conclusions drawn from samples of AGN based on these selection criteria. The absence of any pre-selection in the VVDS leads us to have a very complete sample of AGN, including also objects with unusual colors and continuum shape. The VVDS AGN sample shows in fact redder colors than those expected by comparing it, for example, with the color track derived from the SDSS composite spectrum. In particular, the faintest objects have on average redder colors than the brightest ones. This can be attributed to both a large fraction of dust-reddened objects and a significant contamination from the host galaxy. We have tested these possibilities by examining the global spectral energy distribution of each object using, in addition to the U, B, V, R and I-band magnitudes, also the UV-Galex and the IR-Spitzer bands, and fitting it with a combination of AGN and galaxy emission, allowing also for the possibility of extinction of the AGN flux. We found that for 44% of our objects the contamination from the host galaxy is not negligible and this fraction decreases to 21% if we restrict the analysis to a bright subsample (M1450 <-22.15). Our estimated integral surface density at IAB < 24.0 is 500 AGN per square degree, which represents the highest surface density of a spectroscopically confirmed sample of optically selected AGN. We derived the luminosity function in B-band for 1.0 < z < 3.6 using the 1/Vmax estimator. Our data, more than one magnitude fainter than previous optical surveys, allow us to constrain the faint part of the luminosity function up to high redshift. A comparison of our data with the 2dF sample at low redshift (1 < z < 2.1) shows that the VDDS data can not be well fitted with the pure luminosity evolution (PLE) models derived by previous optically selected samples. Qualitatively, this appears to be due to the fact that our data suggest the presence of an excess of faint objects at low redshift (1.0 < z < 1.5) with respect to these models. By combining our faint VVDS sample with the large sample of bright AGN extracted from the SDSS DR3 (Richards et al., 2006b) and testing a number of different evolutionary models, we find that the model which better represents the combined luminosity functions, over a wide range of redshift and luminosity, is a luminosity dependent density evolution (LDDE) model, similar to those derived from the major Xsurveys. Such a parameterization allows the redshift of the AGN density peak to change as a function of luminosity, thus fitting the excess of faint AGN that we find at 1.0 < z < 1.5. On the basis of this model we find, for the first time from the analysis of optically selected samples, that the peak of the AGN space density shifts significantly towards lower redshift going to lower luminosity objects. The position of this peak moves from z 2.0 for MB <-26.0 to z 0.65 for -22< MB <-20. This result, already found in a number of X-ray selected samples of AGN, is consistent with a scenario of “AGN cosmic downsizing”, in which the density of more luminous AGN, possibly associated to more massive black holes, peaks earlier in the history of the Universe (i.e. at higher redshift), than that of low luminosity ones, which reaches its maximum later (i.e. at lower redshift). This behavior has since long been claimed to be present in elliptical galaxies and it is not easy to reproduce it in the hierarchical cosmogonic scenario, where more massive Dark Matter Halos (DMH) form on average later by merging of less massive halos.

Stem Cells as a therapy for myocardial infarction in animal models

Advances in stem cell biology have challenged the notion that infarcted myocardium is irreparable. The pluripotent ability of stem cells to differentiate into specialized cell lines began to garner intense interest within cardiology when it was shown in animal models that intramyocardial injection of bone marrow stem cells (MSCs), or the mobilization of bone marrow stem cells with spontaneous homing to myocardium, could improve cardiac function and survival after induced myocardial infarction (MI) [1, 2]. Furthermore, the existence of stem cells in myocardium has been identified in animal heart [3, 4], and intense research is under way in an attempt to clarify their potential clinical application for patients with myocardial infarction. To date, in order to identify the best one, different kinds of stem cells have been studied; these have been derived from embryo or adult tissues (i.e. bone marrow, heart, peripheral blood etc.). Currently, three different biologic therapies for cardiovascular diseases are under investigation: cell therapy, gene therapy and the more recent “tissue-engineering” therapy . During my Ph.D. course, first I focalised my study on the isolation and characterization of Cardiac Stem Cells (CSCs) in wild-type and transgenic mice and for this purpose I attended, for more than one year, the Cardiovascular Research Institute of the New York Medical College, in Valhalla (NY, USA) under the direction of Doctor Piero Anversa. During this period I learnt different Immunohistochemical and Biomolecular techniques, useful for investigating the regenerative potential of stem cells. Then, during the next two years, I studied the new approach of cardiac regenerative medicine based on “tissue-engineering” in order to investigate a new strategy to regenerate the infracted myocardium. Tissue-engineering is a promising approach that makes possible the creation of new functional tissue to replace lost or failing tissue. This new discipline combines isolated functioning cells and biodegradable 3-dimensional (3D) polymeric scaffolds. The scaffold temporarily provides the biomechanical support for the cells until they produce their own extracellular matrix. Because tissue-engineering constructs contain living cells, they may have the potential for growth and cellular self-repair and remodeling. In the present study, I examined whether the tissue-engineering strategy within hyaluron-based scaffolds would result in the formation of alternative cardiac tissue that could replace the scar and improve cardiac function after MI in syngeneic heterotopic rat hearts. Rat hearts were explanted, subjected to left coronary descending artery occlusion, and then grafted into the abdomen (aorta-aorta anastomosis) of receiving syngeneic rat. After 2 weeks, a pouch of 3 mm2 was made in the thickness of the ventricular wall at the level of the post-infarction scar. The hyaluronic scaffold, previously engineered for 3 weeks with rat MSCs, was introduced into the pouch and the myocardial edges sutured with few stitches. Two weeks later we evaluated the cardiac function by M-Mode echocardiography and the myocardial morphology by microscope analysis. We chose bone marrow-derived mensenchymal stem cells (MSCs) because they have shown great signaling and regenerative properties when delivered to heart tissue following a myocardial infarction (MI). However, while the object of cell transplantation is to improve ventricular function, cardiac cell transplantation has had limited success because of poor graft viability and low cell retention, that’s why we decided to combine MSCs with a biopolimeric scaffold. At the end of the experiments we observed that the hyaluronan fibres had not been substantially degraded 2 weeks after heart-transplantation. Most MSCs had migrated to the surrounding infarcted area where they were especially found close to small-sized vessels. Scar tissue was moderated in the engrafted region and the thickness of the corresponding ventricular wall was comparable to that of the non-infarcted remote area. Also, the left ventricular shortening fraction, evaluated by M-Mode echocardiography, was found a little bit increased when compared to that measured just before construct transplantation. Therefore, this study suggests that post-infarction myocardial remodelling can be favourably affected by the grafting of MSCs delivered through a hyaluron-based scaffold

Retrieval of trace gases vertical profile in the lower atmosphere combining. Differential Optical Absorption Spectroscopy with radiative transfer models

The motivation for the work presented in this thesis is to retrieve profile information for the atmospheric trace constituents nitrogen dioxide (NO2) and ozone (O3) in the lower troposphere from remote sensing measurements. The remote sensing technique used, referred to as Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), is a recent technique that represents a significant advance on the well-established DOAS, especially for what it concerns the study of tropospheric trace consituents. NO2 is an important trace gas in the lower troposphere due to the fact that it is involved in the production of tropospheric ozone; ozone and nitrogen dioxide are key factors in determining the quality of air with consequences, for example, on human health and the growth of vegetation. To understand the NO2 and ozone chemistry in more detail not only the concentrations at ground but also the acquisition of the vertical distribution is necessary. In fact, the budget of nitrogen oxides and ozone in the atmosphere is determined both by local emissions and non-local chemical and dynamical processes (i.e. diffusion and transport at various scales) that greatly impact on their vertical and temporal distribution: thus a tool to resolve the vertical profile information is really important. Useful measurement techniques for atmospheric trace species should fulfill at least two main requirements. First, they must be sufficiently sensitive to detect the species under consideration at their ambient concentration levels. Second, they must be specific, which means that the results of the measurement of a particular species must be neither positively nor negatively influenced by any other trace species simultaneously present in the probed volume of air. Air monitoring by spectroscopic techniques has proven to be a very useful tool to fulfill these desirable requirements as well as a number of other important properties. During the last decades, many such instruments have been developed which are based on the absorption properties of the constituents in various regions of the electromagnetic spectrum, ranging from the far infrared to the ultraviolet. Among them, Differential Optical Absorption Spectroscopy (DOAS) has played an important role. DOAS is an established remote sensing technique for atmospheric trace gases probing, which identifies and quantifies the trace gases in the atmosphere taking advantage of their molecular absorption structures in the near UV and visible wavelengths of the electromagnetic spectrum (from 0.25 μm to 0.75 μm). Passive DOAS, in particular, can detect the presence of a trace gas in terms of its integrated concentration over the atmospheric path from the sun to the receiver (the so called slant column density). The receiver can be located at ground, as well as on board an aircraft or a satellite platform. Passive DOAS has, therefore, a flexible measurement configuration that allows multiple applications. The ability to properly interpret passive DOAS measurements of atmospheric constituents depends crucially on how well the optical path of light collected by the system is understood. This is because the final product of DOAS is the concentration of a particular species integrated along the path that radiation covers in the atmosphere. This path is not known a priori and can only be evaluated by Radiative Transfer Models (RTMs). These models are used to calculate the so called vertical column density of a given trace gas, which is obtained by dividing the measured slant column density to the so called air mass factor, which is used to quantify the enhancement of the light path length within the absorber layers. In the case of the standard DOAS set-up, in which radiation is collected along the vertical direction (zenith-sky DOAS), calculations of the air mass factor have been made using “simple” single scattering radiative transfer models. This configuration has its highest sensitivity in the stratosphere, in particular during twilight. This is the result of the large enhancement in stratospheric light path at dawn and dusk combined with a relatively short tropospheric path. In order to increase the sensitivity of the instrument towards tropospheric signals, measurements with the telescope pointing the horizon (offaxis DOAS) have to be performed. In this circumstances, the light path in the lower layers can become very long and necessitate the use of radiative transfer models including multiple scattering, the full treatment of atmospheric sphericity and refraction. In this thesis, a recent development in the well-established DOAS technique is described, referred to as Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS). The MAX-DOAS consists in the simultaneous use of several off-axis directions near the horizon: using this configuration, not only the sensitivity to tropospheric trace gases is greatly improved, but vertical profile information can also be retrieved by combining the simultaneous off-axis measurements with sophisticated RTM calculations and inversion techniques. In particular there is a need for a RTM which is capable of dealing with all the processes intervening along the light path, supporting all DOAS geometries used, and treating multiple scattering events with varying phase functions involved. To achieve these multiple goals a statistical approach based on the Monte Carlo technique should be used. A Monte Carlo RTM generates an ensemble of random photon paths between the light source and the detector, and uses these paths to reconstruct a remote sensing measurement. Within the present study, the Monte Carlo radiative transfer model PROMSAR (PROcessing of Multi-Scattered Atmospheric Radiation) has been developed and used to correctly interpret the slant column densities obtained from MAX-DOAS measurements. In order to derive the vertical concentration profile of a trace gas from its slant column measurement, the AMF is only one part in the quantitative retrieval process. One indispensable requirement is a robust approach to invert the measurements and obtain the unknown concentrations, the air mass factors being known. For this purpose, in the present thesis, we have used the Chahine relaxation method. Ground-based Multiple AXis DOAS, combined with appropriate radiative transfer models and inversion techniques, is a promising tool for atmospheric studies in the lower troposphere and boundary layer, including the retrieval of profile information with a good degree of vertical resolution. This thesis has presented an application of this powerful comprehensive tool for the study of a preserved natural Mediterranean area (the Castel Porziano Estate, located 20 km South-West of Rome) where pollution is transported from remote sources. Application of this tool in densely populated or industrial areas is beginning to look particularly fruitful and represents an important subject for future studies.

Physical model of PD behavior and relevant damage growth from micro-cavities in polyethylene-based material under AC voltage

In the framework of developing defect-based life models, in which breakdown is explicitly associated with partial discharge (PD)-induced damage growth from a defect, ageing tests and PD measurements were carried out in the lab on polyethylene (PE) layered specimens containing artificial cavities. PD activity was monitored continuously during aging. A quasi-deterministic series of stages can be observed in the behavior of the main PD parameters (i.e. discharge repetition rate and amplitude). Phase-resolved PD patterns at various ageing stages were reproduced by numerical simulation which is based on a physical discharge model devoid of adaptive parameters. The evolution of the simulation parameters provides insight into the physical-chemical changes taking place at the dielectric/cavity interface during the aging process. PD activity shows similar time behavior under constant cavity gas volume and constant cavity gas pressure conditions, suggesting that the variation of PD parameters may not be attributed to the variation of the gas pressure. Brownish PD byproducts, consisting of oxygen containing moieties, and degradation pits were found at the dielectric/cavity interface. It is speculated that the change of PD activity is related to the composition of the cavity gas, as well as to the properties of dielectric/cavity interface.

Performance evaluation of low environmental impact asphalt concretes using the mechanistic empirical design method based on laboratory fatigue and permanent deformation models

The "sustainability" concept relates to the prolonging of human economic systems with as little detrimental impact on ecological systems as possible. Construction that exhibits good environmental stewardship and practices that conserve resources in a manner that allow growth and development to be sustained for the long-term without degrading the environment are indispensable in a developed society. Past, current and future advancements in asphalt as an environmentally sustainable paving material are especially important because the quantities of asphalt used annually in Europe as well as in the U.S. are large. The asphalt industry is still developing technological improvements that will reduce the environmental impact without affecting the final mechanical performance. Warm mix asphalt (WMA) is a type of asphalt mix requiring lower production temperatures compared to hot mix asphalt (HMA), while aiming to maintain the desired post construction properties of traditional HMA. Lowering the production temperature reduce the fuel usage and the production of emissions therefore and that improve conditions for workers and supports the sustainable development. Even the crumb-rubber modifier (CRM), with shredded automobile tires and used in the United States since the mid 1980s, has proven to be an environmentally friendly alternative to conventional asphalt pavement. Furthermore, the use of waste tires is not only relevant in an environmental aspect but also for the engineering properties of asphalt [Pennisi E., 1992]. This research project is aimed to demonstrate the dual value of these Asphalt Mixes in regards to the environmental and mechanical performance and to suggest a low environmental impact design procedure. In fact, the use of eco-friendly materials is the first phase towards an eco-compatible design but it cannot be the only step. The eco-compatible approach should be extended also to the design method and material characterization because only with these phases is it possible to exploit the maximum potential properties of the used materials. Appropriate asphalt concrete characterization is essential and vital for realistic performance prediction of asphalt concrete pavements. Volumetric (Mix design) and mechanical (Permanent deformation and Fatigue performance) properties are important factors to consider. Moreover, an advanced and efficient design method is necessary in order to correctly use the material. A design method such as a Mechanistic-Empirical approach, consisting of a structural model capable of predicting the state of stresses and strains within the pavement structure under the different traffic and environmental conditions, was the application of choice. In particular this study focus on the CalME and its Incremental-Recursive (I-R) procedure, based on damage models for fatigue and permanent shear strain related to the surface cracking and to the rutting respectively. It works in increments of time and, using the output from one increment, recursively, as input to the next increment, predicts the pavement conditions in terms of layer moduli, fatigue cracking, rutting and roughness. This software procedure was adopted in order to verify the mechanical properties of the study mixes and the reciprocal relationship between surface layer and pavement structure in terms of fatigue and permanent deformation with defined traffic and environmental conditions. The asphalt mixes studied were used in a pavement structure as surface layer of 60 mm thickness. The performance of the pavement was compared to the performance of the same pavement structure where different kinds of asphalt concrete were used as surface layer. In comparison to a conventional asphalt concrete, three eco-friendly materials, two warm mix asphalt and a rubberized asphalt concrete, were analyzed. The First Two Chapters summarize the necessary steps aimed to satisfy the sustainable pavement design procedure. In Chapter I the problem of asphalt pavement eco-compatible design was introduced. The low environmental impact materials such as the Warm Mix Asphalt and the Rubberized Asphalt Concrete were described in detail. In addition the value of a rational asphalt pavement design method was discussed. Chapter II underlines the importance of a deep laboratory characterization based on appropriate materials selection and performance evaluation. In Chapter III, CalME is introduced trough a specific explanation of the different equipped design approaches and specifically explaining the I-R procedure. In Chapter IV, the experimental program is presented with a explanation of test laboratory devices adopted. The Fatigue and Rutting performances of the study mixes are shown respectively in Chapter V and VI. Through these laboratory test data the CalME I-R models parameters for Master Curve, fatigue damage and permanent shear strain were evaluated. Lastly, in Chapter VII, the results of the asphalt pavement structures simulations with different surface layers were reported. For each pavement structure, the total surface cracking, the total rutting, the fatigue damage and the rutting depth in each bound layer were analyzed.

Stochastic models and dynamic measures for the characterization of bistable circuits

During the last few years, a great deal of interest has risen concerning the applications of stochastic methods to several biochemical and biological phenomena. Phenomena like gene expression, cellular memory, bet-hedging strategy in bacterial growth and many others, cannot be described by continuous stochastic models due to their intrinsic discreteness and randomness. In this thesis I have used the Chemical Master Equation (CME) technique to modelize some feedback cycles and analyzing their properties, including experimental data. In the first part of this work, the effect of stochastic stability is discussed on a toy model of the genetic switch that triggers the cellular division, which malfunctioning is known to be one of the hallmarks of cancer. The second system I have worked on is the so-called futile cycle, a closed cycle of two enzymatic reactions that adds and removes a chemical compound, called phosphate group, to a specific substrate. I have thus investigated how adding noise to the enzyme (that is usually in the order of few hundred molecules) modifies the probability of observing a specific number of phosphorylated substrate molecules, and confirmed theoretical predictions with numerical simulations. In the third part the results of the study of a chain of multiple phosphorylation-dephosphorylation cycles will be presented. We will discuss an approximation method for the exact solution in the bidimensional case and the relationship that this method has with the thermodynamic properties of the system, which is an open system far from equilibrium.In the last section the agreement between the theoretical prediction of the total protein quantity in a mouse cells population and the observed quantity will be shown, measured via fluorescence microscopy.

Development of 3D Cancer Cell Models to Test Metabolic Interventions as an Anticancer Strategy

In recent years, it has become evident that the role of mitochondria in the metabolic rewiring is essential for cancer development and progression. The metabolic profile during tumorigenesis has been performed mainly in traditional 2D cell models, including cell lines of various lineages and phenotypes. Although useful in many ways, their relevance can be often debatable, as they lack the interactions between different cells of the tumour microenvironment and/or interaction with the extracellular matrix 1,2. Improved models are now being developed using 3D cell culture technology, contributing with increased physiological relevance 3,4. In this work, we improved a method for the generation of 3D models from healthy and tumour colon tissue, based on organoid technology, and performed their molecular and biochemical characterization and validation. Further, in-plate cryopreservation was applied to these models, and optimal results were obtained in terms of cell viability and functionality of the cryopreserved models. We also cryopreserved colon fibroblasts with the aim to introduce them in a co-culture cryopreserved model with organoids. This technology allows the conversion of cell models into “plug and play” formats. Therefore, cryopreservation in-plate facilitates the accessibility of specialized cell models to cell-based research and application, in cases where otherwise such specialized models would be out of reach. Finally, we briefly explored the field of bioprinting, by testing a new matrix to support the growth of colon tumour organoids, which revealed promising preliminary results. To facilitate the reader, we organized this thesis into chapters, divided by the main points of work which include development, characterization and validation of the model, commercial output, and associated applications. Each chapter has a brief introduction, followed by results and discussion and a final conclusion. The thesis has also a general discussion and conclusion section in the end, which covers the main results obtained during this work.

Comprehensive characterization of SDH-deficient GIST using NGS data and iPSC models

Gastrointestinal stromal tumors (GIST) are the most common di tumors of the gastrointestinal tract, arising from the interstitial cells of Cajal (ICCs) or their precursors. The vast majority of GISTs (75–85% of GIST) harbor KIT or PDGFRA mutations. A small percentage of GIST (about 10‐15%) do not harbor any of these driver mutations and have historically been called wild-type (WT). Among them, from 20% to 40% show loss of function of the succinate dehydrogenase complex (SDH), also defined as SDH‐deficient GIST. SDH-deficient GISTs display distinctive clinical and pathological features, and can be sporadic or associated with Carney triad or Carney-Stratakis syndrome. These tumors arise most frequently in the stomach with predilection to distal stomach and antrum, have a multi-nodular growth, display a histological epithelioid phenotype, and present frequent lympho-vascular invasion. Occurrence of lymph node metastases and indolent course are representative features of SDH-deficient GISTs. This subset of GIST is known for the immunohistochemical loss of succinate dehydrogenase subunit B (SDHB), which signals the loss of function of the entire SDH-complex. The overall aim of my PhD project consists of the comprehensive characterization of SDH deficient GIST. Throughout the project, clinical, molecular and cellular characterizations were performed using next-generation sequencing technologies (NGS), that has the potential to allow the identification of molecular patterns useful for the diagnosis and development of novel treatments. Moreover, while there are many different cell lines and preclinical models of KIT/PDGFRA mutant GIST, no reliable cell model of SDH-deficient GIST has currently been developed, which could be used for studies on tumor evolution and in vitro assessments of drug response. Therefore, another aim of this project was to develop a pre-clinical model of SDH deficient GIST using the novel technology of induced pluripotent stem cells (iPSC).

3D bioprinted organ models for drug screening

In recent years, 3D bioprinting has emerged as an innovative and versatile technology able to produce in vitro models that resemble the native spatial organization of organ tissues, by employing or more bioinks composed of various types of cells suspended in hydrogels. Natural and semi-synthetic hydrogels are extensively used for 3D bioprinting models since they can mimic the natural composition of the tissues, they are biocompatible and bioactive with customizable mechanical properties, allowing to support cell growth. The possibility to tailor hydrogels mechanical properties by modifying the chemical structures to obtain photo-crosslinkable materials, while maintaining their biocompatibility and biomimicry, make their use versatile and suitable to simulate a broad spectrum of physiological features. In this PhD Thesis, 3D bioprinted in vitro models with tailored mechanical properties and physiologically-like features were fabricated. AlgMa-based bioinks were employed to produce a living platform with gradient stiffness, with the aim to create an easy to handle and accessible biological tool to evaluate mechanobiology. In addition, GelMa, collagen and IPN of GelMa and collagen were used as bioinks to fabricate a proof-of-concept of 3D intestinal barrier, which include multiple cell components and multi-layered structure. A useful rheological guide to drive users to the selection of the suitable bioinks for 3D bioprinting and to correlate the model’s mechanical stability after crosslinking is proposed. In conclusion, a platform capable to reproduce models with physiological gradient stiffness was developed and the fabrication of 3D bioprinted intestinal models displaying a good hierarchical structure and cells composition was fully reported and successfully achieved. The good biological results obtained demonstrated that 3D bioprinting can be used for the fabrications of 3D models and that the mechanical properties of the external environment plays a key role on the cell pathways, viability and morphology.

3D Printing and bioprinting of osteomimetic materials for 3D in vitro models and tissue repair applications

Bone disorders have severe impact on body functions and quality life, and no satisfying therapies exist yet. The current models for bone disease study are scarcely predictive and the options existing for therapy fail for complex systems. To mimic and/or restore bone, 3D printing/bioprinting allows the creation of 3D structures with different materials compositions, properties, and designs. In this study, 3D printing/bioprinting has been explored for (i) 3D in vitro tumor models and (ii) regenerative medicine. Tumor models have been developed by investigating different bioinks (i.e., alginate, modified gelatin) enriched by hydroxyapatite nanoparticles to increase printing fidelity and increase biomimicry level, thus mimicking the organic and inorganic phase of bone. High Saos-2 cell viability was obtained, and the promotion of spheroids clusters as occurring in vivo was observed. To develop new syntethic bone grafts, two approaches have been explored. In the first, novel magnesium-phosphate scaffolds have been investigated by extrusion-based 3D printing for spinal fusion. 3D printing process and parameters have been optimized to obtain custom-shaped structures, with competent mechanical properties. The 3D printed structures have been combined to alginate porous structures created by a novel ice-templating technique, to be loaded by antibiotic drug to address infection prevention. Promising results in terms of planktonic growth inhibition was obtained. In the second strategy, marine waste precursors have been considered for the conversion in biogenic HA by using a mild-wet conversion method with different parameters. The HA/carbonate ratio conversion efficacy was analysed for each precursor (by FTIR and SEM), and the best conditions were combined to alginate to develop a composite structure. The composite paste was successfully employed in custom-modified 3D printer for the obtainment of 3D printed stable scaffolds. In conclusion, the osteomimetic materials developed in this study for bone models and synthetic grafts are promising in bone field.

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.