Analysis of cumulative effects of multiple stressors on saltmarshes and consideration of management options

Natural systems face pressures exerted by natural physical-chemical forcings and a myriad of co-occurring human stressors that may interact to cause larger than expected effects, thereby presenting a challenge to ecosystem management. This thesis aimed to develop new information that can contribute to reduce the existing knowledge gaps hampering the holistic management of multiple stressors. I undertook a review of the state-of-the-art methods to detect, quantify and predict stressor interactions, identifying techniques that could be applied in this thesis research. Then, I conducted a systematic review of saltmarsh multiple stressor studies in conjunction with a multiple stressor mapping exercise for the study system in order to infer potential important synergistic stressor interactions. This analysis identified key stressors that are affecting the study system, but also pointed to data gaps in terms of driver and pressure data and raised issues for potentially overlooked stressors. Using field mesocosms, I explored how a local stressor (nutrient availability) affects the responses of saltmarsh vegetation to a global stressor (increased inundation) in different soil types. Results indicate that saltmarsh vegetation would be more drastically affected by increased inundation in low than in medium organic matter soils, and especially in estuaries already under high nutrient availability. In another field experiment, I examined the challenges of managing co-occurring and potentially interacting local stressors on saltmarsh vegetation: recreational trampling and smothering by deposition of excess macroalgal wrack due to high nutrient loads. Trampling and wrack prevention had interacting effects, causing non-linear responses of the vegetation to simulated management of these stressors, such that vegetation recovered only in those treatments simulating the combined prevention of both stressors. During this research I detected, using molecular genetic methods, a widespread presence of S. anglica (and to a lesser extent S. townsendii), two previously unrecorded non-native Spartinas in the study areas.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Reconstruction and analysis of the NF-kB pathway interactome: a systems biology approach

Background. One of the phenomena observed in human aging is the progressive increase of a systemic inflammatory state, a condition referred to as “inflammaging”, negatively correlated with longevity. A prominent mediator of inflammation is the transcription factor NF-kB, that acts as key transcriptional regulator of many genes coding for pro-inflammatory cytokines. Many different signaling pathways activated by very diverse stimuli converge on NF-kB, resulting in a regulatory network characterized by high complexity. NF-kB signaling has been proposed to be responsible of inflammaging. Scope of this analysis is to provide a wider, systemic picture of such intricate signaling and interaction network: the NF-kB pathway interactome. Methods. The study has been carried out following a workflow for gathering information from literature as well as from several pathway and protein interactions databases, and for integrating and analyzing existing data and the relative reconstructed representations by using the available computational tools. Strong manual intervention has been necessarily used to integrate data from multiple sources into mathematically analyzable networks. The reconstruction of the NF-kB interactome pursued with this approach provides a starting point for a general view of the architecture and for a deeper analysis and understanding of this complex regulatory system. Results. A “core” and a “wider” NF-kB pathway interactome, consisting of 140 and 3146 proteins respectively, were reconstructed and analyzed through a mathematical, graph-theoretical approach. Among other interesting features, the topological characterization of the interactomes shows that a relevant number of interacting proteins are in turn products of genes that are controlled and regulated in their expression exactly by NF-kB transcription factors. These “feedback loops”, not always well-known, deserve deeper investigation since they may have a role in tuning the response and the output consequent to NF-kB pathway initiation, in regulating the intensity of the response, or its homeostasis and balance in order to make the functioning of such critical system more robust and reliable. This integrated view allows to shed light on the functional structure and on some of the crucial nodes of thet NF-kB transcription factors interactome. Conclusion. Framing structure and dynamics of the NF-kB interactome into a wider, systemic picture would be a significant step toward a better understanding of how NF-kB globally regulates diverse gene programs and phenotypes. This study represents a step towards a more complete and integrated view of the NF-kB signaling system.

Molecular analysis of special type breast carcinomas

The project was developed into three parts: the analysis of p63 isoform in breast tumours; the study of intra-tumour eterogeneicity in metaplastic breast carcinoma; the analysis of oncocytic breast carcinoma. p63 is a sequence-specific DNA-binding factor, homologue of the tumour suppressor and transcription factor p53. The human p63 gene is composed of 15 exons and transcription can occur from two distinct promoters: the transactivating isoforms (TAp63) are generated by a promoter upstream of exon 1, while the alternative promoter located in intron 3 leads to the expression of N-terminal truncated isoforms (ΔNp63). It has been demonstrated that anti-p63 antibodies decorate the majority of squamous cell carcinomas of different organs; moreover tumours with myoepithelial differentiation of the breast show nuclear p63 expression. Two new isoforms have been described with the same sequence as TAp63 and ΔNp63 but lacking exon 4: d4TAp63 and ΔNp73L, respectively. Purpose of the study was to investigate the molecular expression of N-terminal p63 isoforms in benign and malignant breast tissues. In the present study 40 specimens from normal breast, benign lesions, DIN/DCIS, and invasive carcinomas were analyzed by immunohistochemistry and RT-PCR (Reverse Transcriptase-PCR) in order to disclose the patterns of p63 expression. We have observed that the full-length isoforms can be detected in non neoplastic and neoplastic lesions, while the short isoforms are only present in the neoplastic cells of invasive carcinomas. Metaplastic carcinomas of the breast are a heterogeneous group of neoplasms which exhibit varied patterns of metaplasia and differentiation. The existence of such non-modal populations harbouring distinct genetic aberrations may explain the phenotypic diversity observed within a given tumour. Intra-tumour morphological heterogeneity is not uncommon in breast cancer and it can often be appreciated in metaplastic breast carcinomas. Aim of this study was to determine the existence of intra-tumour genetic heterogeneity in metaplastic breast cancers and whether areas with distinct morphological features in a given tumour might be underpinned by distinct patterns of genetic aberrations. 47 cases of metaplastic breast carcinomas were retrieved. Out of the 47 cases, 9 had areas that were of sufficient dimensions to be independently microdissected. Our results indicate that at least some breast cancers are composed of multiple non-modal populations of clonally related cells and provide direct evidence that at least some types of metaplastic breast cancers are composed of multiple non-modal clones harbouring distinct genetic aberrations. Oncocytic tumours represent a distinctive set of lesions with typical granular cytoplasmatic eosinophilia of the neoplastic cells. Only rare example of breast oncocytic carcinomas have been reported in literature and the incidence is probably underestimated. In this study we have analysed 33 cases of oncocytic invasive breast carcinoma of the breast, selected according to morphological and immunohistochemical criteria. These tumours were morphologically classified and studied by immunohistochemistry and aCGH. We have concluded that oncocytic breast carcinoma is a morphologic entity with distinctive ultrastructural and histological features; immunohistochemically is characterized by a luminal profile, it has a frequency of 19.8%, has not distinctive clinical features and, at molecular level, shows a specific constellation of genetic aberration.

Analysis of the nuclear properties of Brightest Cluster Galaxies and comparison with "normal" radio galaxies.

The aim of this PhD thesis is the study of the nuclear properties of radio loud AGN. Multiple and/or recent mergers in the host galaxy and/or the presence of cool core in galaxy clusters can play a role in the formation and evolution of the radio source. Being a unique class of objects (Lin & Mohr 2004), we focus on Brightest Cluster Galaxies (BCGs). We investigate their parsec scale radio emission with VLBI (Very Long Baseline Interferometer) observations. From literature or new data , we collect and analyse VLBA (Very Long Baseline) observations at 5 GHz of a complete sample of BCGs and ``normal'' radio galaxies (Bologna Complete Sample , BCS). Results on nuclear properties of BCGs are coming from the comparison with the results for the Bologna COmplete Sample (BCS). Our analysis finds a possible dichotomy between BCGs in cool-core clusters and those in non-cool-core clusters. Only one-sided BCGs have similar kinematic properties with FRIs. Furthermore, the dominance of two-sided jet structures only in cooling clusters suggests sub-relativistic jet velocities. The different jet properties can be related to a different jet origin or to the interaction with a different ISM. We larger discuss on possible explanation of this.

A Multilevel Model with Time Series Components for the Analysis of Tribal Art Prices

In the present work we perform an econometric analysis of the Tribal art market. To this aim, we use a unique and original database that includes information on Tribal art market auctions worldwide from 1998 to 2011. In Literature, art prices are modelled through the hedonic regression model, a classic fixed-effect model. The main drawback of the hedonic approach is the large number of parameters, since, in general, art data include many categorical variables. In this work, we propose a multilevel model for the analysis of Tribal art prices that takes into account the influence of time on artwork prices. In fact, it is natural to assume that time exerts an influence over the price dynamics in various ways. Nevertheless, since the set of objects change at every auction date, we do not have repeated measurements of the same items over time. Hence, the dataset does not constitute a proper panel; rather, it has a two-level structure in that items, level-1 units, are grouped in time points, level-2 units. The main theoretical contribution is the extension of classical multilevel models to cope with the case described above. In particular, we introduce a model with time dependent random effects at the second level. We propose a novel specification of the model, derive the maximum likelihood estimators and implement them through the E-M algorithm. We test the finite sample properties of the estimators and the validity of the own-written R-code by means of a simulation study. Finally, we show that the new model improves considerably the fit of the Tribal art data with respect to both the hedonic regression model and the classic multilevel model.

New computational biology tools for the systematic analysis of the structure and expression of human genes

From the late 1980s, the automation of sequencing techniques and the computer spread gave rise to a flourishing number of new molecular structures and sequences and to proliferation of new databases in which to store them. Here are presented three computational approaches able to analyse the massive amount of publicly avalilable data in order to answer to important biological questions. The first strategy studies the incorrect assignment of the first AUG codon in a messenger RNA (mRNA), due to the incomplete determination of its 5' end sequence. An extension of the mRNA 5' coding region was identified in 477 in human loci, out of all human known mRNAs analysed, using an automated expressed sequence tag (EST)-based approach. Proof-of-concept confirmation was obtained by in vitro cloning and sequencing for GNB2L1, QARS and TDP2 and the consequences for the functional studies are discussed. The second approach analyses the codon bias, the phenomenon in which distinct synonymous codons are used with different frequencies, and, following integration with a gene expression profile, estimates the total number of codons present across all the expressed mRNAs (named here "codonome value") in a given biological condition. Systematic analyses across different pathological and normal human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. The third approach is useful to studies the expression of human autism spectrum disorder (ASD) implicated genes. ASD implicated genes sharing microRNA response elements (MREs) for the same microRNA are co-expressed in brain samples from healthy and ASD affected individuals. The different expression of a recently identified long non coding RNA which have four MREs for the same microRNA could disrupt the equilibrium in this network, but further analyses and experiments are needed.

Analysis of Copy Number Variants identifies new candidate genes for Autism Spectrum Disorder and Intellectual Disability

Autism spectrum disorder (ASD) and Intellectual Disability (ID) are complex neuropsychiatric disorders characterized by extensive clinical and genetic heterogeneity and with overlapping risk factors. The aim of my project was to further investigate the role of Copy Numbers Variants (CNVs), identified through genome-wide studies performed by the Autism Geome Project (AGP) and the CHERISH consortium in large cohorts of ASD and ID cases, respectively. Specifically, I focused on four rare genic CNVs, selected on the basis of their impact on interesting ASD/ID candidate genes: a) a compound heterozygous deletion involving CTNNA3, predicted to cause the lack of functional protein; b) a 15q13.3 duplication containing CHRNA7; c) a 2q31.1 microdeletion encompassing KLHL23, SSB and METTL5; d) Lastly, I investigated the putative imprinting regulation of the CADPS2 gene, disrupted by a maternal deletion in two siblings with ASD and ID. This study provides further evidence for the role of CTNNA3, CHRNA7, KLHL23 and CADPS2 as ASD and/or ID susceptibility genes, and highlights that rare genetic variation contributes to disease risk in different ways: some rare mutations, such as those impacting CTNNA3, act in a recessive mode of inheritance, while other CNVs, such as those occurring in the 15q13.3 region, are implicated in multiple developmental and/or neurological disorders possibly interacting with other susceptibility variants elsewhere in the genome. On the other hand, the discovery of a tissue-specific monoallelic expression for the CADPS2 gene, implicates the involvement of epigenetic regulatory mechanisms as risk factors conferring susceptibility to ASD/ID.

Integrated molecular analysis of endometrial carcinoma: translation of biomarker profiles into the clinical practice

The Cancer Genome Atlas (TCGA) collaborative project identified four distinct prognostic groups of endometrial carcinoma (EC) based on molecular alterations: (i) the ultramutated subtype that encompassed POLE mutated (POLE) cases; (ii) the hypermutated subtype, characterized by MisMatch Repair deficiency (MMRd); (iii) the copy-number high subtype, with p53 abnormal/mutated features (p53abn); (iv) the copy-number low subtype, known as No Specific Molecular Profile (NSMP). Although the prognostic value of TCGA molecular classification, NSMP tumors present a wide variability in molecular alterations and biological aggressiveness. This study aims to investigate the impact of ARID1A and CTNNB1/β-catenin alterations by targeted Next-generation sequencing (NGS) and immunohistochemistry (IHC) in a consecutive series of 125 molecularly classified ECs. NGS and IHC were used to assign surrogate TCGA groups and to identify molecular alterations of multiple target genes including POLE, PTEN, ARID1A, CTNNB1, TP53. Associations with clinicopathologic parameters, molecular subtypes, and outcomes identified NSMP category as the most heterogeneous group in terms of clinicopathologic features and outcome. Integration of surrogate TCGA molecular classification with ARID1A and β-catenin analysis showed NSMP cases with ARID1A mutation characterized by the worst outcome with early recurrence, while NSMP tumors with ARID1A wild-type and β-catenin alteration had indolent clinicopathologic features and no recurrence. This study indicates how the identification of ARID1A and β-catenin alterations in EC represents a simple and effective way to characterize NSMP tumor aggressiveness and metastatic potential.

Biomimetic surfaces for greener coastal infrastructures: an analysis of factors that contribute to make artificial structures more “natural”

The growing ecological awareness of Ocean Sprawl impacts is promoting the adoption of eco-engineering strategies to enhance the ecological performance of coastal infrastructures. Biomimicry, as an eco-engineering tool, aims to design infrastructure more suitable for wildlife by manipulating structural factors to mimic natural habitats. However, little is known about the extent to which natural and artificial substrates differ in their structure and to what extent such differences affect the biota. To fill these knowledge gaps and consequently design biomimetic surfaces, I initially explored how much physical structure diverges between various types of natural and artificial substrates and tested to what extent differences in physical structure and material composition affect the epibenthic communities. By mean of an in-field mensurative experiment and a systematic review coupled with a meta-analysis, I found that, although communities tended to differ between natural and artificial coastal habitats, both physical structure and material composition reported an overall mild effect on epibenthic communities. However, an informed choice of building material and an appropriate combination of multiple structural manipulations can promote ecological benefits at multiple levels, from increasing the ecological performance in situ to reducing the impacts during the production process. Thus, I combined my findings in a final experiment, still in progress, where I am testing the combined role of shape, brightness and inclination of biomimetic surfaces I have designed in producing benefits at multiple levels. Overall, I suggest that biomimicry has the potential to increase the ecological value of artificial habitats especially when a wide range of aspects is simultaneously considered. Indeed, none of the structural factors, individually, can fully mimic the “natural conditions” to effectively improve the ecological performance of the artificial substrates. This emphasizes the need to include in future works a multi-level perspective to fully achieve the great potential of biomimicry.

Analysis of technological accidents triggered by natural events (Natech) in the perspective of climate change

The severe accidents deriving from the impact of natural events on industrial installations have become a matter of growing concern in the last decades. In the literature, these events are typically referred to as Natech accidents. Several peculiarities distinguish them from conventional industrial accidents caused by internal factors, such as the possible occurrence of multiple simultaneous failures, and the enhanced probability of cascading events. The research project provides a comprehensive overview of Natech accidents that occurred in the Chemical and Process Industry, allowing for the identification of relevant aspects of Natech events. Quantified event trees and probability of ignition are derived from the collected dataset, providing a step forward in the quantitative risk assessment of Natech accidents. The investigation of past Natech accidents also demonstrated that wildfires may cause technological accidents. Climate change and global warming are promoting the conditions for wildfire development and rapid spread. Hence, ensuring the safety of industrial facilities exposed to wildfires is paramount. This was achieved defining safety distances between wildland vegetation and industrial equipment items. In addition, an innovative methodology for the vulnerability assessment of Natech and Domino scenarios triggered by wildfires was developed. The approach accounted for the dynamic behaviour of wildfire events and related technological scenarios. Besides, the performance of the emergency response and the related intervention time in the case of cascading events caused by natural events were evaluated. Overall, the tools presented in this thesis represent a step forward in the Quantitative Risk Assessment of Natech accidents. The methodologies developed also provide a solid basis for the definition of effective strategies for risk mitigation and reduction. These aspects are crucial to improve the resilience of industrial plants to natural hazards, especially considering the effects that climate change may have on the severity of such events.

Multi-scale analysis of active landslides using two-pass differential interferometry

Landslides are common features of the landscape of the north-central Apennine mountain range and cause frequent damage to human facilities and infrastructure. Most of these landslides move periodically with moderate velocities and, only after particular rainfall events, some accelerate abruptly. Synthetic aperture radar interferometry (InSAR) provides a particularly convenient method for studying deforming slopes. We use standard two-pass interferometry, taking advantage of the short revisit time of the Sentinel-1 satellites. In this paper we present the results of the InSAR analysis developed on several study areas in central and Northern Italian Apennines. The aims of the work described within the articles contained in this paper, concern: i) the potential of the standard two-pass interferometric technique for the recognition of active landslides; ii) the exploration of the potential related to the displacement time series resulting from a two-pass multiple time-scale InSAR analysis; iii) the evaluation of the possibility of making comparisons with climate forcing for cognitive and risk assessment purposes. Our analysis successfully identified more than 400 InSAR deformation signals (IDS) in the different study areas corresponding to active slope movements. The comparison between IDSs and thematic maps allowed us to identify the main characteristics of the slopes most prone to landslides. The analysis of displacement time series derived from monthly interferometric stacks or single 6-day interferograms allowed the establishment of landslide activity thresholds. This information, combined with the displacement time series, allowed the relationship between ground deformation and climate forcing to be successfully investigated. The InSAR data also gave access to the possibility of validating geographical warning systems and comparing the activity state of landslides with triggering probability thresholds.

Analysis of urban infrastructure for sustainable mobility through instrumented bicycles for students

In Europe almost 80% of the continent's population lives in cities. It is estimated that by 2030 most regions in Europe which contain major cities will have even more inhabitants on 35–60% more than now. This process generates a consequent elevate human pressure on the natural environment, especially around large urban agglomerations. Cities could be seen as an ecosystem, represented by the dominance of humans that re-distribute organisms and fluxes and represent the result of co-evolving human and natural systems, emerging from the interactions between humans, natural and infrastructures. Roads have a relevant role in building links between urban components, creating the basis on which it is founded the urban ecosystem itself. This thesis is focused on the research for a comprehensive model, framed in European urban health & wellbeing programme, aimed to evaluate the determinants of health in urban populations. Through bicycles, GPS and sensor kits, specially developed and produced by University of Bologna for this purpose, it has been possible to conduct on Bologna different direct observations that oriented the novelty of the research: the categorization of university students cyclists, connection among environmental data awareness and level of cycling, and an early identification of urban attributes able to impact on road air quality and level of cycling. The categorization of university students’ cyclist has been defined through GPS analysis and focused survey, that both permit to identify behavioural and technical variables and attitudes towards urban cycling. The statistic relationship between level of cycling, seen as number of bicycles passages per lane and pollutants level, has been investigated through an inverse regression model, defined and tested through SPSS software on the basis of the data harvest. The research project that represents a sort of dynamic mobility laboratory on two wheels, that permits to harvest and study detected parameters.