A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Modelling coupled physical-biogeochemical processes in ice-covered oceans

The last decades have seen a large effort of the scientific community to study and understand the physics of sea ice. We currently have a wide - even though still not exhaustive - knowledge of the sea ice dynamics and thermodynamics and of their temporal and spatial variability. Sea ice biogeochemistry is instead largely unknown. Sea ice algae production may account for up to 25% of overall primary production in ice-covered waters of the Southern Ocean. However, the influence of physical factors, such as the location of ice formation, the role of snow cover and light availability on sea ice primary production is poorly understood. There are only sparse localized observations and little knowledge of the functioning of sea ice biogeochemistry at larger scales. Modelling becomes then an auxiliary tool to help qualifying and quantifying the role of sea ice biogeochemistry in the ocean dynamics. In this thesis, a novel approach is used for the modelling and coupling of sea ice biogeochemistry - and in particular its primary production - to sea ice physics. Previous attempts were based on the coupling of rather complex sea ice physical models to empirical or relatively simple biological or biogeochemical models. The focus is moved here to a more biologically-oriented point of view. A simple, however comprehensive, physical model of the sea ice thermodynamics (ESIM) was developed and coupled to a novel sea ice implementation (BFM-SI) of the Biogeochemical Flux Model (BFM). The BFM is a comprehensive model, largely used and validated in the open ocean environment and in regional seas. The physical model has been developed having in mind the biogeochemical properties of sea ice and the physical inputs required to model sea ice biogeochemistry. The central concept of the coupling is the modelling of the Biologically-Active-Layer (BAL), which is the time-varying fraction of sea ice that is continuously connected to the ocean via brines pockets and channels and it acts as rich habitat for many microorganisms. The physical model provides the key physical properties of the BAL (e.g., brines volume, temperature and salinity), and the BFM-SI simulates the physiological and ecological response of the biological community to the physical enviroment. The new biogeochemical model is also coupled to the pelagic BFM through the exchange of organic and inorganic matter at the boundaries between the two systems . This is done by computing the entrapment of matter and gases when sea ice grows and release to the ocean when sea ice melts to ensure mass conservation. The model was tested in different ice-covered regions of the world ocean to test the generality of the parameterizations. The focus was particularly on the regions of landfast ice, where primary production is generally large. The implementation of the BFM in sea ice and the coupling structure in General Circulation Models will add a new component to the latters (and in general to Earth System Models), which will be able to provide adequate estimate of the role and importance of sea ice biogeochemistry in the global carbon cycle.

Investigating the role of single point mutations in the human proteome: a computational study

In the post genomic era with the massive production of biological data the understanding of factors affecting protein stability is one of the most important and challenging tasks for highlighting the role of mutations in relation to human maladies. The problem is at the basis of what is referred to as molecular medicine with the underlying idea that pathologies can be detailed at a molecular level. To this purpose scientific efforts focus on characterising mutations that hamper protein functions and by these affect biological processes at the basis of cell physiology. New techniques have been developed with the aim of detailing single nucleotide polymorphisms (SNPs) at large in all the human chromosomes and by this information in specific databases are exponentially increasing. Eventually mutations that can be found at the DNA level, when occurring in transcribed regions may then lead to mutated proteins and this can be a serious medical problem, largely affecting the phenotype. Bioinformatics tools are urgently needed to cope with the flood of genomic data stored in database and in order to analyse the role of SNPs at the protein level. In principle several experimental and theoretical observations are suggesting that protein stability in the solvent-protein space is responsible of the correct protein functioning. Then mutations that are found disease related during DNA analysis are often assumed to perturb protein stability as well. However so far no extensive analysis at the proteome level has investigated whether this is the case. Also computationally methods have been developed to infer whether a mutation is disease related and independently whether it affects protein stability. Therefore whether the perturbation of protein stability is related to what it is routinely referred to as a disease is still a big question mark. In this work we have tried for the first time to explore the relation among mutations at the protein level and their relevance to diseases with a large-scale computational study of the data from different databases. To this aim in the first part of the thesis for each mutation type we have derived two probabilistic indices (for 141 out of 150 possible SNPs): the perturbing index (Pp), which indicates the probability that a given mutation effects protein stability considering all the “in vitro” thermodynamic data available and the disease index (Pd), which indicates the probability of a mutation to be disease related, given all the mutations that have been clinically associated so far. We find with a robust statistics that the two indexes correlate with the exception of all the mutations that are somatic cancer related. By this each mutation of the 150 can be coded by two values that allow a direct comparison with data base information. Furthermore we also implement computational methods that starting from the protein structure is suited to predict the effect of a mutation on protein stability and find that overpasses a set of other predictors performing the same task. The predictor is based on support vector machines and takes as input protein tertiary structures. We show that the predicted data well correlate with the data from the databases. All our efforts therefore add to the SNP annotation process and more importantly found the relationship among protein stability perturbation and the human variome leading to the diseasome.

Catalytic processes for the transformation of ethanol into acetonitrile

This thesis deals with the transformation of ethanol into acetonitrile. Two approaches are investigated: (a) the ammoxidation of ethanol to acetonitrile and (b) the amination of ethanol to acetonitrile. The reaction of ethanol ammoxidation to acetonitrile has been studied using several catalytic systems, such as vanadyl pyrophosphate, supported vanadium oxide, multimetal molibdates and antimonates. The main conclusions are: (I) The surface acidity must be very low, because acidity catalyzes several undesired reactions, such as the formation of ethylene, and of heavy compounds as well. (II) Supported vanadium oxide is the catalyst showing the best catalytic behaviour, but the role of the support is of crucial importance. (III) Both metal molybdates and antimonates show interesting catalytic behaviour, but are poorly active, and probably require harder conditions than those used with the V oxide-based catalysts. (IV) One key point in the reaction network is the rate of reaction between acetaldehyde (the first intermediate) and ammonia, compared to the parallel rates of acetaldehyde transformation into by-products (CO, CO2, HCN, heavy compounds). Concerning the non-oxidative process, two possible strategies are investigated: (a) the ethanol ammonolysis to ethylamine coupled with ethylamine dehydrogenation, and (b) the direct non-reductive amination of ethanol to acetonitrile. Despite the good results obtained in each single step, the former reaction does not lead to good results in terms of yield to acetonitrile. The direct amination can be catalyzed with good acetonitrile yield over catalyst based on supported metal oxides. Strategies aimed at limiting catalyst deactivation have also been investigated.

Innovative processes for syngas production

The research of new advanced processes for syngas production is a part of a European project for the production of a new Gas to Liquid Process (NextGTL). The crucial points in the production of GTL process are the energy required for the air separation used in autothermal reforming or the heat required for steam reforming and the efficiency in carbon utilization. Therefore a new multistep oxy-reforming process scheme was developed at lower temperature with intermediate H2 membrane separation to improve the crucial parameter. The process is characterized by a S/C of 0.7 and O2/C of 0.21 having a smoothed temperature profile in which kinetic regime is easily obtained. Active catalysts for low temperature oxy-reforming process have been studied working at low pressure to discriminate among the catalyst and at high pressure to prove it on industrial condition. It allows the selection of the Rh as active phase among single and bimetallic VIII group metal. The study of the matrix composition and thermal treatment has been carried out on Rh-Mg/Al hydrotalcite selected as reference catalyst. The research to optimize the catalyst lead to enhanced performances through the identification of a limitation of the Rh reduction from the oxides matrix as key point to increase the Rh performances. The Rh loading have been studied to allow the catalyst scale up for pilot process in Chieti in a shape of Rh-HT on honeycomb ceramic material. The developed catalyst has enhanced methane conversion in a inch diameter monolith reactor if compared with the semi-industrial catalyst chosen in the project as the best reference.

Assessment and optimization of chemical industrial processes from a life cycle perspective

During the PhD program in chemistry, curriculum in environmental chemistry, at the University of Bologna the sustainability of industry was investigated through the application of the LCA methodology. The efforts were focused on the chemical sector in order to investigate reactions dealing with the Green Chemistry and Green Engineering principles, evaluating their sustainability in comparison with traditional pathways by a life cycle perspective. The environmental benefits associated with a reduction in the synthesis steps and the use of renewable feedstock were assessed through a holistic approach selecting two case studies with high relevance from an industrial point of view: the synthesis of acrylonitrile and the production of acrolein. The current approach wants to represent a standardized application of LCA methodology to the chemical sector, which could be extended to several case studies, and also an improvement of the current databases, since the lack of data to fill the inventories of the chemical productions represent a huge limitation, difficult to overcome and that can affects negatively the results of the studies. Results emerged from the analyses confirms that the sustainability in the chemical sector should be evaluated from a cradle-to-gate approach, considering all the stages and flows involved in each pathways in order to avoid shifting the environmental burdens from a steps to another. Moreover, if possible, LCA should be supported by other tools able to investigate the other two dimensions of sustainability represented by the social and economic issues.

Microbial ecology of biotechnological processes

The investigation of phylogenetic diversity and functionality of complex microbial communities in relation to changes in the environmental conditions represents a major challenge of microbial ecology research. Nowadays, particular attention is paid to microbial communities occurring at environmental sites contaminated by recalcitrant and toxic organic compounds. Extended research has evidenced that such communities evolve some metabolic abilities leading to the partial degradation or complete mineralization of the contaminants. Determination of such biodegradation potential can be the starting point for the development of cost effective biotechnological processes for the bioremediation of contaminated matrices. This work showed how metagenomics-based microbial ecology investigations supported the choice or the development of three different bioremediation strategies. First, PCR-DGGE and PCR-cloning approaches served the molecular characterization of microbial communities enriched through sequential development stages of an aerobic cometabolic process for the treatment of groundwater contaminated by chlorinated aliphatic hydrocarbons inside an immobilized-biomass packed bed bioreactor (PBR). In this case the analyses revealed homogeneous growth and structure of immobilized communities throughout the PBR and the occurrence of dominant microbial phylotypes of the genera Rhodococcus, Comamonas and Acidovorax, which probably drive the biodegradation process. The same molecular approaches were employed to characterize sludge microbial communities selected and enriched during the treatment of municipal wastewater coupled with the production of polyhydroxyalkanoates (PHA). Known PHA-accumulating microorganisms identified were affiliated with the genera Zooglea, Acidovorax and Hydrogenophaga. Finally, the molecular investigation concerned communities of polycyclic aromatic hydrocarbon (PAH) contaminated soil subjected to rhizoremediation with willow roots or fertilization-based treatments. The metabolic ability to biodegrade naphthalene, as a representative model for PAH, was assessed by means of stable isotope probing in combination with high-throughput sequencing analysis. The phylogenetic diversity of microbial populations able to derive carbon from naphthalene was evaluated as a function of the type of treatment.

Physiological studies to optimize algal biomass production in phytoremediation processes

Nowadays microalgae are studied, and a number of species already mass-cultivated, for their application in many fields: food and feed, chemicals, pharmaceutical, phytoremediation and renewable energy. Phytoremediation, in particular, can become a valid integrated process in many algae biomass production systems. This thesis is focused on the physiological and biochemical effects of different environmental factors, mainly macronutrients, lights and temperature on microalgae. Microalgal species have been selected on the basis of their potential in biotechnologies, and nitrogen occurs in all chapters due to its importance in physiological and applicative fields. There are 5 chapters, ready or in preparation to be submitted, with different specific matters: (i) to measure the kinetic parameters and the nutrient removal efficiencies for a selected and local strain of microalgae; (ii) to study the biochemical pathways of the microalga D. communis in presence of nitrate and ammonium; (iii) to improve the growth and the removal efficiency of a specific green microalga in mixotrophic conditions; (iv) to optimize the productivity of some microalgae with low growth-rate conditions through phytohormones and other biostimulants; and (v) to apply the phyto-removal of ammonium in an effluent from anaerobic digestion. From the results it is possible to understand how a physiological point of view is necessary to provide and optimize already existing biotechnologies and applications with microalgae.