Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Wood-derived biomaterials for biomedical applications

Driven by the global trend in the sustainable economy development and environmental concerns, the exploring of plant-derived biomaterials or biocomposites for potential biomedical and/or pharmaceutical applications has received tremendous attention. Therefore, the work of this thesis is dedicated to high-value and high-efficiency utilization of plant-derived materials, with the focus on cellulose and hemicelluloses in the field of biomedical applications in a novel biorefinery concept. The residual cellulose of wood processing waste, sawdust, was converted into cellulose nanofibrils (CNFs) with tunable surface charge density and geometric size through 2,2,6,6-tetramethylpiperidinyloxy (TEMPO)-mediated oxidation and mechanical defibrillation. The sawdust-based CNFs and its resultant free-standing films showed comparable or even better mechanical properties than those from a commercial bleached kraft pulp at the same condition, demonstrating the feasibility of producing CNFs and films thereof with outstanding mechanical properties from birch sawdust by a process incorporated into a novel biorefinery platform recovering also polymeric hemicelluloses for other applications. Thus, it is providing an efficient route to upgrade sawdust waste to valuable products. The surface charge density and geometric size of the CNFs were found to play key roles in the stability of the CNF suspension, as well as the gelling properties, swelling behavior, mechanical stiffness, morphology and microscopic structural properties, and biocompatibility of CNF-based materials (i.e. films, hydrogels, and aerogels). The CNFs with tunable surface chemistry and geometric size was found promising applications as transparent and tough barrier materials or as reinforcing additive for production of biocomposites. The CNFs was also applied as structural matrices for the preparation of biocomposites possessing electrical conductivity and antimicrobial activity by in situ polymerization and coating of polypyrrole, and incorporation of silver nanoparticles, which make the material possible for potential wound healing application. The CNF-based matrices (films, hydrogels, and aerogels) with tunable structural and mechanical properties and biocompatibility were further prepared towards an application as 3D scaffolds in tissue engineering. The structural and mechanical strength of the CNF matrices could be tuned by controlling the charge density of the nanocellulose, as well as the pH and temperature values of the hydrogel formation conditions. Biological tests revealed that the CNF scaffolds could promote the survival and proliferation of tumor cells, and enhance the transfection of exogenous DNA into the cells, suggesting the usefulness of the CNF-based 3D matrices in supporting crucial cellular processes during cell growth and proliferation. The CNFs was applied as host materials to incorporate biomolecules for further biomedical application. For example, to investigate how the biocompatibility of a scaffold is influenced by its mechanical and structural properties, these properties of CNF-based composite matrices were controlled by incorporation of different hemicelluloses (O-acetyl galactoglucomanan (GGM), xyloglucan (XG), and xylan) into CNF hydrogel networks in different ratios and using two different approaches. The charge density of the CNFs, the incorporated hemicellulose type and amount, and the swelling time of the hydrogels were found to affect the pore structure, the mechanical strength, and thus the cells growth in the composite hydrogel scaffolds. The mechanical properties of the composite hydrogels were found to have an influence on the cell viability during the wound healing relevant 3T3 fibroblast cell culture. The thusprepared CNF composite hydrogels may work as promising scaffolds in wound healing application to provide supporting networks and to promote cells adhesion, growth, and proliferation.