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.

Multi-Country Event Study Methods

Which event study methods are best in non-U.S. multi-country samples? Nonparametric tests, especially the rank and generalized sign, are better specified and more powerful than common parametric tests, especially in multi-day windows. The generalized sign test is the best statistic but must be applied to buy-and-hold abnormal returns for correct specification. Market-adjusted and market-model methods with local market indexes, without conversion to a common currency, work well. The results are robust to limiting the samples to situations expected to be problematic for test specification or power. Applying the tests that perform best in simulation to merger announcements produces reasonable results.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

A new insight in stress recovery tecniques and hessian reconstruction methods

Stress recovery techniques have been an active research topic in the last few years since, in 1987, Zienkiewicz and Zhu proposed a procedure called Superconvergent Patch Recovery (SPR). This procedure is a last-squares fit of stresses at super-convergent points over patches of elements and it leads to enhanced stress fields that can be used for evaluating finite element discretization errors. In subsequent years, numerous improved forms of this procedure have been proposed attempting to add equilibrium constraints to improve its performances. Later, another superconvergent technique, called Recovery by Equilibrium in Patches (REP), has been proposed. In this case the idea is to impose equilibrium in a weak form over patches and solve the resultant equations by a last-square scheme. In recent years another procedure, based on minimization of complementary energy, called Recovery by Compatibility in Patches (RCP) has been proposed in. This procedure, in many ways, can be seen as the dual form of REP as it substantially imposes compatibility in a weak form among a set of self-equilibrated stress fields. In this thesis a new insight in RCP is presented and the procedure is improved aiming at obtaining convergent second order derivatives of the stress resultants. In order to achieve this result, two different strategies and their combination have been tested. The first one is to consider larger patches in the spirit of what proposed in [4] and the second one is to perform a second recovery on the recovered stresses. Some numerical tests in plane stress conditions are presented, showing the effectiveness of these procedures. Afterwards, a new recovery technique called Last Square Displacements (LSD) is introduced. This new procedure is based on last square interpolation of nodal displacements resulting from the finite element solution. In fact, it has been observed that the major part of the error affecting stress resultants is introduced when shape functions are derived in order to obtain strains components from displacements. This procedure shows to be ultraconvergent and is extremely cost effective, as it needs in input only nodal displacements directly coming from finite element solution, avoiding any other post-processing in order to obtain stress resultants using the traditional method. Numerical tests in plane stress conditions are than presented showing that the procedure is ultraconvergent and leads to convergent first and second order derivatives of stress resultants. In the end, transverse stress profiles reconstruction using First-order Shear Deformation Theory for laminated plates and three dimensional equilibrium equations is presented. It can be seen that accuracy of this reconstruction depends on accuracy of first and second derivatives of stress resultants, which is not guaranteed by most of available low order plate finite elements. RCP and LSD procedures are than used to compute convergent first and second order derivatives of stress resultants ensuring convergence of reconstructed transverse shear and normal stress profiles respectively. Numerical tests are presented and discussed showing the effectiveness of both procedures.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Application of FTIR microscopy to cultural heritage materials

Research in art conservation has been developed from the early 1950s, giving a significant contribution to the conservation-restoration of cultural heritage artefacts. In fact, only through a profound knowledge about the nature and conditions of constituent materials, suitable decisions on the conservation and restoration measures can thus be adopted and preservation practices enhanced. The study of ancient artworks is particularly challenging as they can be considered as heterogeneous and multilayered systems where numerous interactions between the different components as well as degradation and ageing phenomena take place. However, difficulties to physically separate the different layers due to their thickness (1-200 µm) can result in the inaccurate attribution of the identified compounds to a specific layer. Therefore, details can only be analysed when the sample preparation method leaves the layer structure intact, as for example the preparation of embedding cross sections in synthetic resins. Hence, spatially resolved analytical techniques are required not only to exactly characterize the nature of the compounds but also to obtain precise chemical and physical information about ongoing changes. This thesis focuses on the application of FTIR microspectroscopic techniques for cultural heritage materials. The first section is aimed at introducing the use of FTIR microscopy in conservation science with a particular attention to the sampling criteria and sample preparation methods. The second section is aimed at evaluating and validating the use of different FTIR microscopic analytical methods applied to the study of different art conservation issues which may be encountered dealing with cultural heritage artefacts: the characterisation of the artistic execution technique (chapter II-1), the studies on degradation phenomena (chapter II-2) and finally the evaluation of protective treatments (chapter II-3). The third and last section is divided into three chapters which underline recent developments in FTIR spectroscopy for the characterisation of paint cross sections and in particular thin organic layers: a newly developed preparation method with embedding systems in infrared transparent salts (chapter III-1), the new opportunities offered by macro-ATR imaging spectroscopy (chapter III-2) and the possibilities achieved with the different FTIR microspectroscopic techniques nowadays available (chapter III-3). In chapter II-1, FTIR microspectroscopy as molecular analysis, is presented in an integrated approach with other analytical techniques. The proposed sequence is optimized in function of the limited quantity of sample available and this methodology permits to identify the painting materials and characterise the adopted execution technique and state of conservation. Chapter II-2 describes the characterisation of the degradation products with FTIR microscopy since the investigation on the ageing processes encountered in old artefacts represents one of the most important issues in conservation research. Metal carboxylates resulting from the interaction between pigments and binding media are characterized using synthesised metal palmitates and their production is detected on copper-, zinc-, manganese- and lead- (associated with lead carbonate) based pigments dispersed either in oil or egg tempera. Moreover, significant effects seem to be obtained with iron and cobalt (acceleration of the triglycerides hydrolysis). For the first time on sienna and umber paints, manganese carboxylates are also observed. Finally in chapter II-3, FTIR microscopy is combined with further elemental analyses to characterise and estimate the performances and stability of newly developed treatments, which should better fit conservation-restoration problems. In the second part, in chapter III-1, an innovative embedding system in potassium bromide is reported focusing on the characterisation and localisation of organic substances in cross sections. Not only the identification but also the distribution of proteinaceous, lipidic or resinaceous materials, are evidenced directly on different paint cross sections, especially in thin layers of the order of 10 µm. Chapter III-2 describes the use of a conventional diamond ATR accessory coupled with a focal plane array to obtain chemical images of multi-layered paint cross sections. A rapid and simple identification of the different compounds is achieved without the use of any infrared microscope objectives. Finally, the latest FTIR techniques available are highlighted in chapter III-3 in a comparative study for the characterisation of paint cross sections. Results in terms of spatial resolution, data quality and chemical information obtained are presented and in particular, a new FTIR microscope equipped with a linear array detector, which permits reducing the spatial resolution limit to approximately 5 µm, provides very promising results and may represent a good alternative to either mapping or imaging systems.

Innovative analytical methods for Central Nervous System Drug analysis in biological fluids

During recent years a consistent number of central nervous system (CNS) drugs have been approved and introduced on the market for the treatment of many psychiatric and neurological disorders, including psychosis, depression, Parkinson disease and epilepsy. Despite the great advancements obtained in the treatment of CNS diseases/disorders, partial response to therapy or treatment failure are frequent, at least in part due to poor compliance, but also genetic variability in the metabolism of psychotropic agents or polypharmacy, which may lead to sub-therapeutic or toxic plasma levels of the drugs, and finally inefficacy of the treatment or adverse/toxic effects. With the aim of improving the treatment, reducing toxic/side effects and patient hospitalisation, Therapeutic Drug Monitoring (TDM) is certainly useful, allowing for a personalisation of the therapy. Reliable analytical methods are required to determine the plasma levels of psychotropic drugs, which are often present at low concentrations (tens or hundreds of nanograms per millilitre). The present PhD Thesis has focused on the development of analytical methods for the determination of CNS drugs in biological fluids, including antidepressants (sertraline and duloxetine), antipsychotics (aripiprazole), antiepileptics (vigabatrin and topiramate) and antiparkinsons (pramipexole). Innovative methods based on liquid chromatography or capillary electrophoresis coupled to diode-array or laser-induced fluorescence detectors have been developed, together with the suitable sample pre-treatment for interference removal and fluorescent labelling in case of LIF detection. All methods have been validated according to official guidelines and applied to the analysis of real samples obtained from patients, resulting suitable for the TDM of psychotropic drugs.

Evaluation of fluid transport processes in dental enamel. Methods to assess the relevance of enamel permeability in caries prevention and etching treatments.

This thesis evaluated in vivo and in vitro enamel permeability in different physiological and clinical conditions by means of SEM inspection of replicas of enamel surface obtained from polyvinyl siloxane impressions subsequently later cast in polyether impression ma-terial. This technique, not invasive and risk-free, allows the evaluation of fluid outflow from enamel surface and is able to detect the presence of small quantities of fluid, visu-alized as droplets. Fluid outflow on enamel surface represents enamel permeability. This property has a paramount importance in enamel physiolgy and pathology although its ef-fective role in adhesion, caries pathogenesis and prevention today is still not fully under-stood. The aim of the studies proposed was to evaluate enamel permeability changes in differ-ent conditions and to correlate the findings with the actual knowledge about enamel physiology, caries pathogenesis, fluoride and etchinhg treatments. To obtain confirmed data the replica technique has been supported by others specific techniques such as Ra-man and IR spectroscopy and EDX analysis. The first study carried out visualized fluid movement through dental enamel in vivo con-firmed that enamel is a permeable substrate and demonstrated that age and enamel per-meability are closely related. Examined samples from subjects of different ages showed a decreasing number and size of droplets with increasing age: freshly erupted permanent teeth showed many droplets covering the entire enamel surface. Droplets in permanent teeth were prominent along enamel perikymata. These results obtained through SEM inspection of replicas allowed innovative remarks in enamel physiology. An analogous testing has been developed for evaluation of enamel permeability in primary enamel. The results of this second study showed that primary enamel revealed a substantive permeability with droplets covering the entire enamel sur-face without any specific localization accordingly with histological features, without changes during aging signs of post-eruptive maturation. These results confirmed clinical data that showed a higher caries susceptibility for primary enamel and suggested a strong relationship between this one and enamel permeability. Topical fluoride application represents the gold standard for caries prevention although the mechanism of cariostatic effect of fluoride still needs to be clarified. The effects of topical fluoride application on enamel permeability were evaluated. Particularly two dif-ferent treatments (NaF and APF), with different pH, were examined. The major product of topical fluoride application was the deposition of CaF2-like globules. Replicas inspec-tion before and after both treatments at different times intervals and after specific addi-tional clinical interventions showed that such globule formed in vivo could be removed by professional toothbrushing, sonically and chemically by KOH. The results obtained in relation to enamel permeability showed that fluoride treatments temporarily reduced enamel water permeability when CaF2-like globules were removed. The in vivo perma-nence of decreased enamel permeability after CaF2 globules removal has been demon-strated for 1 h for NaF treated teeth and for at least 7 days for APF treated teeth. Important clinical consideration moved from these results. In fact the caries-preventing action of fluoride application may be due, in part, to its ability to decrease enamel water permeability and CaF2 like-globules seem to be indirectly involved in enamel protection over time maintaining low permeability. Others results obtained by metallographic microscope and SEM/EDX analyses of or-thodontic resins fluoride releasing and not demonstrated the relevance of topical fluo-ride application in decreasing the demineralization marks and modifying the chemical composition of the enamel in the treated area. These data obtained in both the experiments confirmed the efficacy of fluoride in caries prevention and contribute to clarify its mechanism of action. Adhesive dentistry is the gold standard for caries treatment and tooth rehabilitation and is founded on important chemical and physical principles involving both enamel and dentine substrates. Particularly acid etching of dental enamel enamel has usually employed in bonding pro-cedures increasing microscopic roughness. Different acids have been tested in the litera-ture suggesting several etching procedures. The acid-induced structural transformations in enamel after different etching treatments by means of Raman and IR spectroscopy analysis were evaluated and these findings were correlated with enamel permeability. Conventional etching with 37% phosphoric acid gel (H3PO4) for 30 s and etching with 15 % HCl for 120 s were investigated. Raman and IR spectroscopy showed that the treatment with both hydrochloric and phosphoric acids induced a decrease in the carbonate content of the enamel apatite. At the same time, both acids induced the formation of HPO42- ions. After H3PO4 treatment the bands due to the organic component of enamel decreased in intensity, while in-creased after HCl treatment. Replicas of H3PO4 treated enamel showed a strongly reduced permeability while replicas of HCl 15% treated samples showed a maintained permeability. A decrease of the enamel organic component, as resulted after H3PO4 treatment, involves a decrease in enamel permeability, while the increase of the organic matter (achieved by HCl treat-ment) still maintains enamel permeability. These results suggested a correlation between the amount of the organic matter, enamel permeability and caries. The results of the different studies carried out in this thesis contributed to clarify and improve the knowledge about enamel properties with important rebounds in theoretical and clinical aspects of Dentistry.

Application-oriented Mixed Integer Non-Linear Programming

In the most recent years there is a renovate interest for Mixed Integer Non-Linear Programming (MINLP) problems. This can be explained for different reasons: (i) the performance of solvers handling non-linear constraints was largely improved; (ii) the awareness that most of the applications from the real-world can be modeled as an MINLP problem; (iii) the challenging nature of this very general class of problems. It is well-known that MINLP problems are NP-hard because they are the generalization of MILP problems, which are NP-hard themselves. However, MINLPs are, in general, also hard to solve in practice. We address to non-convex MINLPs, i.e. having non-convex continuous relaxations: the presence of non-convexities in the model makes these problems usually even harder to solve. The aim of this Ph.D. thesis is to give a flavor of different possible approaches that one can study to attack MINLP problems with non-convexities, with a special attention to real-world problems. In Part 1 of the thesis we introduce the problem and present three special cases of general MINLPs and the most common methods used to solve them. These techniques play a fundamental role in the resolution of general MINLP problems. Then we describe algorithms addressing general MINLPs. Parts 2 and 3 contain the main contributions of the Ph.D. thesis. In particular, in Part 2 four different methods aimed at solving different classes of MINLP problems are presented. Part 3 of the thesis is devoted to real-world applications: two different problems and approaches to MINLPs are presented, namely Scheduling and Unit Commitment for Hydro-Plants and Water Network Design problems. The results show that each of these different methods has advantages and disadvantages. Thus, typically the method to be adopted to solve a real-world problem should be tailored on the characteristics, structure and size of the problem. Part 4 of the thesis consists of a brief review on tools commonly used for general MINLP problems, constituted an integral part of the development of this Ph.D. thesis (especially the use and development of open-source software). We present the main characteristics of solvers for each special case of MINLP.

Bioimaging of animal embryogenesis: mathematical methods and computational algorithms

Some fundamental biological processes such as embryonic development have been preserved during evolution and are common to species belonging to different phylogenetic positions, but are nowadays largely unknown. The understanding of cell morphodynamics leading to the formation of organized spatial distribution of cells such as tissues and organs can be achieved through the reconstruction of cells shape and position during the development of a live animal embryo. We design in this work a chain of image processing methods to automatically segment and track cells nuclei and membranes during the development of a zebrafish embryo, which has been largely validates as model organism to understand vertebrate development, gene function and healingrepair mechanisms in vertebrates. The embryo is previously labeled through the ubiquitous expression of fluorescent proteins addressed to cells nuclei and membranes, and temporal sequences of volumetric images are acquired with laser scanning microscopy. Cells position is detected by processing nuclei images either through the generalized form of the Hough transform or identifying nuclei position with local maxima after a smoothing preprocessing step. Membranes and nuclei shapes are reconstructed by using PDEs based variational techniques such as the Subjective Surfaces and the Chan Vese method. Cells tracking is performed by combining informations previously detected on cells shape and position with biological regularization constraints. Our results are manually validated and reconstruct the formation of zebrafish brain at 7-8 somite stage with all the cells tracked starting from late sphere stage with less than 2% error for at least 6 hours. Our reconstruction opens the way to a systematic investigation of cellular behaviors, of clonal origin and clonal complexity of brain organs, as well as the contribution of cell proliferation modes and cell movements to the formation of local patterns and morphogenetic fields.

Biofilms on exposed monumental stones: mechanism of formation and development of new control methods

Within the stone monumental artefacts artistic fountains are extremely favorable to formation of biofilms, giving rise to biodegradation processes related with physical-chemical and visual aspect alterations, because of their particular exposure conditions. Microbial diversity of five fountains (two from Spain and three from Italy) was investigated. It was observed an ample similarity between the biodiversity of monumental stones reported in literature and that one found in studied fountains. Mechanical procedures and toxic chemical products are usually employed to remove such phototrophic patinas. Alternative methods based on natural antifouling substances are recently experimented in the marine sector, due to their very low environmental impact and for the bio settlement prevention on partially immersed structures of ships. In the present work groups of antibiofouling agents (ABAs) were selected from literature for their ability to interfere, at molecular level, with the microbial communication system “quorum sensing”, inhibiting the initial phase of biofilm formation. The efficacy of some natural antibiofoulants agents (ABAs) with terrestrial (Capsaicine - CS, Cinnamaldehyde - CI) and marine origin (Zosteric Acid - ZA, poly-Alkyl Pyridinium Salts – pAPS and Ceramium botryocarpum extract - CBE), incorporated into two commercial coatings (Silres BS OH 100 - S and Wacker Silres BS 290 - W) commonly used in stone conservation procedures were evaluated. The formation of phototrophic biofilms in laboratory conditions (on Carrara marble specimens and Sierra Elvira stone) and on two monumental fountains (Tacca’s Fountain 2 - Florence, Italy and Fountain from Patio de la Lindaraja - Alhambra Palace, Granada, Spain) has been investigated in the presence or absence of these natural antifouling agents. The natural antibiofouling agents, at tested concentrations, demonstrated a certain inhibitory effect. The silane-siloxane based silicone coating (W) mixing with ABAs was more suitable with respect to ethyl silicate coating (S) and proved efficacy against biofilm formation only when incompletely cured. The laboratory results indicated a positive action in inhibiting the patina formation, especially for poly-alkyl pyridinium salts, zosteric acid and cinnamaldehyde, while on site tests revealed a good effect for zosteric acid.

Coastal flood vulnerability assessment with geomatic methods: Test sites of western Thailand, Sydney (Australia) and aeolian islands (south tyrrhenian sea, Italy)

The work undertaken in this PhD thesis is aimed at the development and testing of an innovative methodology for the assessment of the vulnerability of coastal areas to marine catastrophic inundation (tsunami). Different approaches are used at different spatial scales and are applied to three different study areas: 1. The entire western coast of Thailand 2. Two selected coastal suburbs of Sydney – Australia 3. The Aeolian Islands, in the South Tyrrhenian Sea – Italy I have discussed each of these cases study in at least one scientific paper: one paper about the Thailand case study (Dall’Osso et al., in review-b), three papers about the Sydney applications (Dall’Osso et al., 2009a; Dall’Osso et al., 2009b; Dall’Osso and Dominey-Howes, in review) and one last paper about the work at the Aeolian Islands (Dall’Osso et al., in review-a). These publications represent the core of the present PhD thesis. The main topics dealt with are outlined and discussed in a general introduction while the overall conclusions are outlined in the last section.

Heteronuclear recoupling methods in solid-state NMR

This thesis is focused on the development of heteronuclear correlation methods in solid-state NMR spectroscopy, where the spatial dependence of the dipolar coupling is exploited to obtain structural and dynamical information in solids. Quantitative results on dipolar coupling constants are extracted by means of spinning sideband analysis in the indirect dimension of the two-dimensional experiments. The principles of sideband analysis were established and are currently widely used in the group of Prof. Spiess for the special case of homonuclear 1H double-quantum spectroscopy. The generalization of these principles to the heteronuclear case is presented, with special emphasis on naturally abundant 13C-1H systems. For proton spectroscopy in the solid state, line-narrowing is of particular importance, and is here achieved by very-fast sample rotation at the magic angle (MAS), with frequencies up to 35 kHz. Therefore, the heteronuclear dipolar couplings are suppressed and have to be recoupled in order to achieve an efficient excitation of the observed multiple-quantum modes. Heteronuclear recoupling is most straightforwardly accomplished by performing the known REDOR experiment, where pi-pulses are applied every half rotor period. This experiment was modified by the insertion of an additional spectroscopic dimension, such that heteronuclear multiple-quantum experiments can be carried out, which, as shown experimentally and theoretically, closely resemble homonuclear double-quantum experiments. Variants are presented which are well-suited for the recording of high-resolution 13C-1H shift correlation and spinning-sideband spectra, by means of which spatial proximities and quantitative dipolar coupling constants, respectively, of heteronuclear spin pairs can be determined. Spectral editing of 13C spectra is shown to be feasible with these techniques. Moreover, order phenomena and dynamics in columnar mesophases with 13C in natural abundance were investigated. Two further modifications of the REDOR concept allow the correlation of 13C with quadrupolar nuclei, such as 2H. The spectroscopic handling of these nuclei is challenging in that they cover large frequency ranges, and with the new experiments it is shown how the excitation problem can be tackled or circumvented altogether, respectively. As an example, one of the techniques is used for the identification of a yet unknown motional process of the H-bonded protons in the crystalline parts of poly(vinyl alcohol).