Il fascicolo sanitario elettronico tra Semantic Web e diritto alla privacy

Principale obiettivo della ricerca è quello di ricostruire lo stato dell’arte in materia di sanità elettronica e Fascicolo Sanitario Elettronico, con una precipua attenzione ai temi della protezione dei dati personali e dell’interoperabilità. A tal fine sono stati esaminati i documenti, vincolanti e non, dell’Unione europea nonché selezionati progetti europei e nazionali (come “Smart Open Services for European Patients” (EU); “Elektronische Gesundheitsakte” (Austria); “MedCom” (Danimarca); “Infrastruttura tecnologica del Fascicolo Sanitario Elettronico”, “OpenInFSE: Realizzazione di un’infrastruttura operativa a supporto dell’interoperabilità delle soluzioni territoriali di fascicolo sanitario elettronico nel contesto del sistema pubblico di connettività”, “Evoluzione e interoperabilità tecnologica del Fascicolo Sanitario Elettronico”, “IPSE - Sperimentazione di un sistema per l’interoperabilità europea e nazionale delle soluzioni di Fascicolo Sanitario Elettronico: componenti Patient Summary e ePrescription” (Italia)). Le analisi giuridiche e tecniche mostrano il bisogno urgente di definire modelli che incoraggino l’utilizzo di dati sanitari ed implementino strategie effettive per l’utilizzo con finalità secondarie di dati sanitari digitali , come Open Data e Linked Open Data. L’armonizzazione giuridica e tecnologica è vista come aspetto strategico per ridurre i conflitti in materia di protezione di dati personali esistenti nei Paesi membri nonché la mancanza di interoperabilità tra i sistemi informativi europei sui Fascicoli Sanitari Elettronici. A questo scopo sono state individuate tre linee guida: (1) armonizzazione normativa, (2) armonizzazione delle regole, (3) armonizzazione del design dei sistemi informativi. I principi della Privacy by Design (“prottivi” e “win-win”), così come gli standard del Semantic Web, sono considerate chiavi risolutive per il suddetto cambiamento.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

La città di Bologna e la sua storia: diffusione e condivisione delle conoscenze attraverso Webgis open source e Web Mapping

To understand a city and its urban structure it is necessary to study its history. This is feasible through GIS (Geographical Information Systems) and its by-products on the web. Starting from a cartographic view they allow an initial understanding of, and a comparison between, present and past data together with an easy and intuitive access to database information. The research done led to the creation of a GIS for the city of Bologna. It is based on varied data such as historical map, vector and alphanumeric historical data, etc.. After providing information about GIS we thought of spreading and sharing the collected data on the Web after studying two solutions available on the market: Web Mapping and WebGIS. In this study we discuss the stages, beginning with the development of Historical GIS of Bologna, which led to the making of a WebGIS Open Source (MapServer and Chameleon) and the Web Mapping services (Google Earth, Google Maps and OpenLayers).

The rupture process of recent tsunamigenic earthquakes by geophysical data inversion

Subduction zones are the favorite places to generate tsunamigenic earthquakes, where friction between oceanic and continental plates causes the occurrence of a strong seismicity. The topics and the methodologies discussed in this thesis are focussed to the understanding of the rupture process of the seismic sources of great earthquakes that generate tsunamis. The tsunamigenesis is controlled by several kinematical characteristic of the parent earthquake, as the focal mechanism, the depth of the rupture, the slip distribution along the fault area and by the mechanical properties of the source zone. Each of these factors plays a fundamental role in the tsunami generation. Therefore, inferring the source parameters of tsunamigenic earthquakes is crucial to understand the generation of the consequent tsunami and so to mitigate the risk along the coasts. The typical way to proceed when we want to gather information regarding the source process is to have recourse to the inversion of geophysical data that are available. Tsunami data, moreover, are useful to constrain the portion of the fault area that extends offshore, generally close to the trench that, on the contrary, other kinds of data are not able to constrain. In this thesis I have discussed the rupture process of some recent tsunamigenic events, as inferred by means of an inverse method. I have presented the 2003 Tokachi-Oki (Japan) earthquake (Mw 8.1). In this study the slip distribution on the fault has been inferred by inverting tsunami waveform, GPS, and bottom-pressure data. The joint inversion of tsunami and geodetic data has revealed a much better constrain for the slip distribution on the fault rather than the separate inversions of single datasets. Then we have studied the earthquake occurred on 2007 in southern Sumatra (Mw 8.4). By inverting several tsunami waveforms, both in the near and in the far field, we have determined the slip distribution and the mean rupture velocity along the causative fault. Since the largest patch of slip was concentrated on the deepest part of the fault, this is the likely reason for the small tsunami waves that followed the earthquake, pointing out how much the depth of the rupture plays a crucial role in controlling the tsunamigenesis. Finally, we have presented a new rupture model for the great 2004 Sumatra earthquake (Mw 9.2). We have performed the joint inversion of tsunami waveform, GPS and satellite altimetry data, to infer the slip distribution, the slip direction, and the rupture velocity on the fault. Furthermore, in this work we have presented a novel method to estimate, in a self-consistent way, the average rigidity of the source zone. The estimation of the source zone rigidity is important since it may play a significant role in the tsunami generation and, particularly for slow earthquakes, a low rigidity value is sometimes necessary to explain how a relatively low seismic moment earthquake may generate significant tsunamis; this latter point may be relevant for explaining the mechanics of the tsunami earthquakes, one of the open issues in present day seismology. The investigation of these tsunamigenic earthquakes has underlined the importance to use a joint inversion of different geophysical data to determine the rupture characteristics. The results shown here have important implications for the implementation of new tsunami warning systems – particularly in the near-field – the improvement of the current ones, and furthermore for the planning of the inundation maps for tsunami-hazard assessment along the coastal area.

Il linguaggio degli ambienti virtuali culturali: comunicare la storia di Bologna attraverso il progetto Nu.M.E.

The contemporary media landscape is characterized by the emergence of hybrid forms of digital communication that contribute to the ongoing redefinition of our societies cultural context. An incontrovertible consequence of this phenomenon is the new public dimension that characterizes the transmission of historical knowledge in the twenty-first century. Awareness of this new epistemic scenario has led us to reflect on the following methodological questions: what strategies should be created to establish a communication system, based on new technology, that is scientifically rigorous, but at the same time engaging for the visitors of museums and Internet users? How does a comparative analysis of ancient documentary sources form a solid base of information for the virtual reconstruction of thirteenth century Bologna in the Metaverse? What benefits can the phenomenon of cross-mediality give to the virtual heritage? The implementation of a new version of the Nu.M.E. project allowed for answering many of these instances. The investigation carried out between 2008 and 2010 has shown that, indeed, real-time 3D graphics and collaborative virtual environments can be feasible tools for representing philologically the urban medieval landscape and for communicating properly validated historical data to the general public. This research is focused on the study and implementation of a pipeline that permits mass communication of historical information about an area of vital importance in late medieval Bologna: Piazza di Porta Ravegnana. The originality of the developed project is not limited solely to the methodological dimension of historical research. Adopted technological perspective is an excellent example of innovation that digital technologies can bring to the cultural heritage. The main result of this research is the creation of Nu.ME 2010, a cross-media system of 3D real-time visualization based on some of the most advanced free software and open source technologies available today free of charge.

Innovation and economic performance of the firms in the service sector: relevant issues and open problems

This thesis is a collection of essays related to the topic of innovation in the service sector. The choice of this structure is functional to the purpose of single out some of the relevant issues and try to tackle them, revising first the state of the literature and then proposing a way forward. Three relevant issues has been therefore selected: (i) the definition of innovation in the service sector and the connected question of measurement of innovation; (ii) the issue of productivity in services; (iii) the classification of innovative firms in the service sector. Facing the first issue, chapter II shows how the initial width of the original Schumpeterian definition of innovation has been narrowed and then passed to the service sector form the manufacturing one in a reduce technological form. Chapter III tackle the issue of productivity in services, discussing the difficulties for measuring productivity in a context where the output is often immaterial. We reconstruct the dispute on the Baumol’s cost disease argument and propose two different ways to go forward in the research on productivity in services: redefining the output along the line of a characteristic approach; and redefining the inputs, particularly analysing which kind of input it’s worth saving. Chapter IV derives an integrated taxonomy of innovative service and manufacturing firms, using data coming from the 2008 CIS survey for Italy. This taxonomy is based on the enlarged definition of “innovative firm” deriving from the Schumpeterian definition of innovation and classify firms using a cluster analysis techniques. The result is the emergence of a four cluster solution, where firms are differentiated by the breadth of the innovation activities in which they are involved. Chapter 5 reports some of the main conclusions of each singular previous chapter and the points worth of further research in the future.

Data and behavioral contracts for web services

The recent trend in Web services is fostering a computing scenario where loosely coupled parties interact in a distributed and dynamic environment. Such interactions are sequences of xml messages and in order to assemble parties – either statically or dynamically – it is important to verify that the “contracts” of the parties are “compatible”. The Web Service Description Language (wsdl) is a standard used for describing one-way (asynchronous) and request/response (synchronous) interactions. Web Service Conversation Language extends wscl contracts by allowing the description of arbitrary, possibly cyclic sequences of exchanged messages between communicating parties. Unfortunately, neither wsdl nor wscl can effectively define a notion of compatibility, for the very simple reason that they do not provide any formal characterization of their contract languages. We define two contract languages for Web services. The first one is a data contract language and allow us to describe a Web service in terms of messages (xml documents) that can be sent or received. The second one is a behavioral contract language and allow us to give an abstract definition of the Web service conversation protocol. Both these languages are equipped with a sort of “sub-typing” relation and, therefore, they are suitable to be used for querying Web services repositories. In particular a query for a service compatible with a given contract may safely return services with “greater” contract.

Assimilation of Meteosat Second Generation (MSG) satellite data in a regional numerical weather prediction model using a one-dimensional variational approach

The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Computational analyses on the structure-function relation in ion channels

Ion channels are protein molecules, embedded in the lipid bilayer of the cell membranes. They act as powerful sensing elements switching chemicalphysical stimuli into ion-fluxes. At a glance, ion channels are water-filled pores, which can open and close in response to different stimuli (gating), and one once open select the permeating ion species (selectivity). They play a crucial role in several physiological functions, like nerve transmission, muscular contraction, and secretion. Besides, ion channels can be used in technological applications for different purpose (sensing of organic molecules, DNA sequencing). As a result, there is remarkable interest in understanding the molecular determinants of the channel functioning. Nowadays, both the functional and the structural characteristics of ion channels can be experimentally solved. The purpose of this thesis was to investigate the structure-function relation in ion channels, by computational techniques. Most of the analyses focused on the mechanisms of ion conduction, and the numerical methodologies to compute the channel conductance. The standard techniques for atomistic simulation of complex molecular systems (Molecular Dynamics) cannot be routinely used to calculate ion fluxes in membrane channels, because of the high computational resources needed. The main step forward of the PhD research activity was the development of a computational algorithm for the calculation of ion fluxes in protein channels. The algorithm - based on the electrodiffusion theory - is computational inexpensive, and was used for an extensive analysis on the molecular determinants of the channel conductance. The first record of ion-fluxes through a single protein channel dates back to 1976, and since then measuring the single channel conductance has become a standard experimental procedure. Chapter 1 introduces ion channels, and the experimental techniques used to measure the channel currents. The abundance of functional data (channel currents) does not match with an equal abundance of structural data. The bacterial potassium channel KcsA was the first selective ion channels to be experimentally solved (1998), and after KcsA the structures of four different potassium channels were revealed. These experimental data inspired a new era in ion channel modeling. Once the atomic structures of channels are known, it is possible to define mathematical models based on physical descriptions of the molecular systems. These physically based models can provide an atomic description of ion channel functioning, and predict the effect of structural changes. Chapter 2 introduces the computation methods used throughout the thesis to model ion channels functioning at the atomic level. In Chapter 3 and Chapter 4 the ion conduction through potassium channels is analyzed, by an approach based on the Poisson-Nernst-Planck electrodiffusion theory. In the electrodiffusion theory ion conduction is modeled by the drift-diffusion equations, thus describing the ion distributions by continuum functions. The numerical solver of the Poisson- Nernst-Planck equations was tested in the KcsA potassium channel (Chapter 3), and then used to analyze how the atomic structure of the intracellular vestibule of potassium channels affects the conductance (Chapter 4). As a major result, a correlation between the channel conductance and the potassium concentration in the intracellular vestibule emerged. The atomic structure of the channel modulates the potassium concentration in the vestibule, thus its conductance. This mechanism explains the phenotype of the BK potassium channels, a sub-family of potassium channels with high single channel conductance. The functional role of the intracellular vestibule is also the subject of Chapter 5, where the affinity of the potassium channels hEag1 (involved in tumour-cell proliferation) and hErg (important in the cardiac cycle) for several pharmaceutical drugs was compared. Both experimental measurements and molecular modeling were used in order to identify differences in the blocking mechanism of the two channels, which could be exploited in the synthesis of selective blockers. The experimental data pointed out the different role of residue mutations in the blockage of hEag1 and hErg, and the molecular modeling provided a possible explanation based on different binding sites in the intracellular vestibule. Modeling ion channels at the molecular levels relates the functioning of a channel to its atomic structure (Chapters 3-5), and can also be useful to predict the structure of ion channels (Chapter 6-7). In Chapter 6 the structure of the KcsA potassium channel depleted from potassium ions is analyzed by molecular dynamics simulations. Recently, a surprisingly high osmotic permeability of the KcsA channel was experimentally measured. All the available crystallographic structure of KcsA refers to a channel occupied by potassium ions. To conduct water molecules potassium ions must be expelled from KcsA. The structure of the potassium-depleted KcsA channel and the mechanism of water permeation are still unknown, and have been investigated by numerical simulations. Molecular dynamics of KcsA identified a possible atomic structure of the potassium-depleted KcsA channel, and a mechanism for water permeation. The depletion from potassium ions is an extreme situation for potassium channels, unlikely in physiological conditions. However, the simulation of such an extreme condition could help to identify the structural conformations, so the functional states, accessible to potassium ion channels. The last chapter of the thesis deals with the atomic structure of the !- Hemolysin channel. !-Hemolysin is the major determinant of the Staphylococcus Aureus toxicity, and is also the prototype channel for a possible usage in technological applications. The atomic structure of !- Hemolysin was revealed by X-Ray crystallography, but several experimental evidences suggest the presence of an alternative atomic structure. This alternative structure was predicted, combining experimental measurements of single channel currents and numerical simulations. This thesis is organized in two parts, in the first part an overview on ion channels and on the numerical methods adopted throughout the thesis is provided, while the second part describes the research projects tackled in the course of the PhD programme. The aim of the research activity was to relate the functional characteristics of ion channels to their atomic structure. In presenting the different research projects, the role of numerical simulations to analyze the structure-function relation in ion channels is highlighted.