Automated Analysis Methods for the Assessment of Bicycle Infrastructure and Demand

A first phase of the research activity has been related to the study of the state of art of the infrastructures for cycling, bicycle use and methods for evaluation. In this part, the candidate has studied the "bicycle system" in countries with high bicycle use and in particular in the Netherlands. Has been carried out an evaluation of the questionnaires of the survey conducted within the European project BICY on mobility in general in 13 cities of the participating countries. The questionnaire was designed, tested and implemented, and was later validated by a test in Bologna. The results were corrected with information on demographic situation and compared with official data. The cycling infrastructure analysis was conducted on the basis of information from the OpenStreetMap database. The activity consisted in programming algorithms in Python that allow to extract data from the database infrastructure for a region, to sort and filter cycling infrastructure calculating some attributes, such as the length of the arcs paths. The results obtained were compared with official data where available. The structure of the thesis is as follows: 1. Introduction: description of the state of cycling in several advanced countries, description of methods of analysis and their importance to implement appropriate policies for cycling. Supply and demand of bicycle infrastructures. 2. Survey on mobility: it gives details of the investigation developed and the method of evaluation. The results obtained are presented and compared with official data. 3. Analysis cycling infrastructure based on information from the database of OpenStreetMap: describes the methods and algorithms developed during the PhD. The results obtained by the algorithms are compared with official data. 4. Discussion: The above results are discussed and compared. In particular the cycle demand is compared with the length of cycle networks within a city. 5. Conclusions

Genetic analysis of tumour initiation and progression in a Drosophila model of epithelial cancer

Neoplastic overgrowth depends on the cooperation of several mutations ultimately leading to major rearrangements in cellular behaviour. The molecular crosstalk occurring between precancerous and normal cells strongly influences the early steps of the tumourigenic process as well as later stages of the disease. Precancerous cells are often removed by cell death from normal tissues but the mechanisms responsible for such fundamental safeguard processes remain in part elusive. To gain insight into these phenomena I took advantage of the clonal analysis methods available in Drosophila for studying the phenotypes due to loss of function of the neoplastic tumour suppressor lethal giant larvae (lgl). I found that lgl mutant cells growing in wild-type imaginal wing discs are subject to the phenomenon of cell competition and are eliminated by JNK-dependent cell death because they express very low levels of dMyc oncoprotein compared to those in the surrounding tissue. Indeed, in non-competitive backgrounds lgl mutant clones are able to overgrow and upregulate dMyc, overwhelming the neighbouring tissue and forming tumourous masses that display several cancer hallmarks. These phenotypes are completely abolished by reducing dMyc abundance within mutant cells while increasing it in lgl clones growing in a competitive context re-establishes their tumourigenic potential. Similarly, the neoplastic growth observed upon the oncogenic cooperation between lgl mutation and activated Ras/Raf/MAPK signalling was found to be characterised by and dependent on the ability of cancerous cells to upregulate dMyc with respect to the adjacent normal tissue, through both transcriptional and post-transcriptional mechanisms, thereby confirming its key role in lgl-induced tumourigenesis. These results provide first evidence that the dMyc oncoprotein is required in lgl mutant tissue to promote invasive overgrowth in developing and adult epithelial tissues and that dMyc abundance inside versus outside lgl mutant clones plays a key role in driving neoplastic overgrowth.

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

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.

Percezione della direzione del proprio movimento: dalla registrazione dell'attività corticale al modello computazionale.

The main aim of this thesis is strongly interdisciplinary: it involves and presumes a knowledge on Neurophysiology, to understand the mechanisms that undergo the studied phenomena, a knowledge and experience on Electronics, necessary during the hardware experimental set-up to acquire neuronal data, on Informatics and programming to write the code necessary to control the behaviours of the subjects during experiments and the visual presentation of stimuli. At last, neuronal and statistical models should be well known to help in interpreting data. The project started with an accurate bibliographic research: until now the mechanism of perception of heading (or direction of motion) are still poorly known. The main interest is to understand how the integration of visual information relative to our motion with eye position information happens. To investigate the cortical response to visual stimuli in motion and the integration with eye position, we decided to study an animal model, using Optic Flow expansion and contraction as visual stimuli. In the first chapter of the thesis, the basic aims of the research project are presented, together with the reasons why it’s interesting and important to study perception of motion. Moreover, this chapter describes the methods my research group thought to be more adequate to contribute to scientific community and underlines my personal contribute to the project. The second chapter presents an overview on useful knowledge to follow the main part of the thesis: it starts with a brief introduction on central nervous system, on cortical functions, then it presents more deeply associations areas, which are the main target of our study. Furthermore, it tries to explain why studies on animal models are necessary to understand mechanism at a cellular level, that could not be addressed on any other way. In the second part of the chapter, basics on electrophysiology and cellular communication are presented, together with traditional neuronal data analysis methods. The third chapter is intended to be a helpful resource for future works in the laboratory: it presents the hardware used for experimental sessions, how to control animal behaviour during the experiments by means of C routines and a software, and how to present visual stimuli on a screen. The forth chapter is the main core of the research project and the thesis. In the methods, experimental paradigms, visual stimuli and data analysis are presented. In the results, cellular response of area PEc to visual stimuli in motion combined with different eye positions are shown. In brief, this study led to the identification of different cellular behaviour in relation to focus of expansion (the direction of motion given by the optic flow pattern) and eye position. The originality and importance of the results are pointed out in the conclusions: this is the first study aimed to investigate perception of motion in this particular cortical area. In the last paragraph, a neuronal network model is presented: the aim is simulating cellular pre-saccadic and post-saccadic response of neuron in area PEc, during eye movement tasks. The same data presented in chapter four, are further analysed in chapter fifth. The analysis started from the observation of the neuronal responses during 1s time period in which the visual stimulation was the same. It was clear that cells activities showed oscillations in time, that had been neglected by the previous analysis based on mean firing frequency. Results distinguished two cellular behaviour by their response characteristics: some neurons showed oscillations that changed depending on eye and optic flow position, while others kept the same oscillations characteristics independent of the stimulus. The last chapter discusses the results of the research project, comments the originality and interdisciplinary of the study and proposes some future developments.

Networking: the "making of" social networks. A closer look at the process and antecedents of some resourcing-oriented behaviors in organizations.

Social networks are one of the “hot” themes in people’s life and contemporary social research. Considering our “embeddedness” in a thick web of social relations is a study perspective that could unveil a number of explanations of how people may manage their personal and social resources. Looking at people’s behaviors of building and managing their social networks, seems to be an effective way to find some possible rationalization about how to help people getting the best from their resources . The main aim of this dissertation is to give a closer look at the role of networking behaviors. Antecedents, motivations, different steps and measures about networking behaviors and outcomes are analyzed and discussed. Results seem to confirm, in a different setting and time perspective, that networking behaviors include different types and goals that change over time. Effects of networking behaviors seem to find empirical confirmation through social network analysis methods. Both personality and situational self-efficacy seem to predict networking behaviors. Different types of motivational drivers seem to be related to diverse networking behaviors.

Numerical methods for the dispersion analysis of Guided Waves

The use of guided ultrasonic waves (GUW) has increased considerably in the fields of non-destructive (NDE) testing and structural health monitoring (SHM) due to their ability to perform long range inspections, to probe hidden areas as well as to provide a complete monitoring of the entire waveguide. Guided waves can be fully exploited only once their dispersive properties are known for the given waveguide. In this context, well stated analytical and numerical methods are represented by the Matrix family methods and the Semi Analytical Finite Element (SAFE) methods. However, while the former are limited to simple geometries of finite or infinite extent, the latter can model arbitrary cross-section waveguides of finite domain only. This thesis is aimed at developing three different numerical methods for modelling wave propagation in complex translational invariant systems. First, a classical SAFE formulation for viscoelastic waveguides is extended to account for a three dimensional translational invariant static prestress state. The effect of prestress, residual stress and applied loads on the dispersion properties of the guided waves is shown. Next, a two-and-a-half Boundary Element Method (2.5D BEM) for the dispersion analysis of damped guided waves in waveguides and cavities of arbitrary cross-section is proposed. The attenuation dispersive spectrum due to material damping and geometrical spreading of cavities with arbitrary shape is shown for the first time. Finally, a coupled SAFE-2.5D BEM framework is developed to study the dispersion characteristics of waves in viscoelastic waveguides of arbitrary geometry embedded in infinite solid or liquid media. Dispersion of leaky and non-leaky guided waves in terms of speed and attenuation, as well as the radiated wavefields, can be computed. The results obtained in this thesis can be helpful for the design of both actuation and sensing systems in practical application, as well as to tune experimental setup.