Studies on orographic effects in a numerical weather prediction model

Numerical models, used for atmospheric research, weather prediction and climate simulation, describe the state of the atmosphere over the heterogeneous surface of the Earth. Several fundamental properties of atmospheric models depend on orography, i.e. on the average elevation of land over a model area. The higher is the models' resolution, the more the details of orography directly influence the simulated atmospheric processes. This sets new requirements for the accuracy of the model formulations with respect to the spatially varying orography. Orography is always averaged, representing the surface elevation within the horizontal resolution of the model. In order to remove the smallest scales and steepest slopes, the continuous spectrum of orography is normally filtered (truncated) even more, typically beyond a few gridlengths of the model. This means, that in the numerical weather prediction (NWP) models, there will always be subgridscale orography effects, which cannot be explicitly resolved by numerical integration of the basic equations, but require parametrization. In the subgrid-scale, different physical processes contribute in different scales. The parametrized processes interact with the resolved-scale processes and with each other. This study contributes to building of a consistent, scale-dependent system of orography-related parametrizations for the High Resolution Limited Area Model (HIRLAM). The system comprises schemes for handling the effects of mesoscale (MSO) and small-scale (SSO) orographic effects on the simulated flow and a scheme of orographic effects on the surface-level radiation fluxes. Representation of orography, scale-dependencies of the simulated processes and interactions between the parametrized and resolved processes are discussed. From the high-resolution digital elevation data, orographic parameters are derived for both momentum and radiation flux parametrizations. Tools for diagnostics and validation are developed and presented. The parametrization schemes applied, developed and validated in this study, are currently being implemented into the reference version of HIRLAM.

Molecular basis of colorectal cancer predisposition

Colorectal cancer (CRC) is one of the most frequent malignancies in Western countries. Inherited factors have been suggested to be involved in 35% of CRCs. The hereditary CRC syndromes explain only ~6% of all CRCs, indicating that a large proportion of the inherited susceptibility is still unexplained. Much of the remaining genetic predisposition for CRC is probably due to undiscovered low-penetrance variations. This study was conducted to identify germline and somatic changes that contribute to CRC predisposition and tumorigenesis. MLH1 and MSH2, that underlie Hereditary non-polyposis colorectal cancer (HNPCC) are considered to be tumor suppressor genes; the first hit is inherited in the germline and somatic inactivation of the wild type allele is required for tumor initiation. In a recent study, frequent loss of the mutant allele in HNPCC tumors was detected and a new model, arguing against the two-hit hypothesis, was proposed for somatic HNPCC tumorigenesis. We tested this hypothesis by conducting LOH analysis on 25 colorectal HNPCC tumors with a known germline mutation in the MLH1 or MSH2 genes. LOH was detected in 56% of the tumors. All the losses targeted the wild type allele supporting the classical two-hit model for HNPCC tumorigenesis. The variants 3020insC, R702W and G908R in NOD2 predispose to Crohn s disease. Contribution of NOD2 to CRC predisposition has been examined in several case-control series, with conflicting results. We have previously shown that 3020insC does not predispose to CRC in Finnish CRC patients. To expand our previous study the variants R702W and G908R were genotyped in a population-based series of 1042 Finnish CRC patients and 508 healthy controls. Association analyses did not show significant evidence for association of the variants with CRC. Single nucleotide polymorphism (SNP) rs6983267 at chromosome 8q24 was the first CRC susceptibility variant identified through genome-wide association studies. To characterize the role of rs6983267 in CRC predisposition in the Finnish population, we genotyped the SNP in the case-control material of 1042 cases and 1012 controls and showed that G allele of rs6983267 is associated with the increased risk of CRC (OR 1.22; P=0.0018). Examination of allelic imbalance in the tumors heterozygous for rs6983267 revealed that copy number increase affected 22% of the tumors and interestingly, it favored the G allele. By utilizing a computer algorithm, Enhancer Element Locator (EEL), an evolutionary conserved regulatory motif containing rs6983267 was identified. The SNP affected the binding site of TCF4, a transcription factor that mediates Wnt signaling in cells, and has proven to be crucial in colorectal neoplasia. The preferential binding of TCF4 to the risk allele G was showed in vitro and in vivo. The element drove lacZ marker gene expression in mouse embryos in a pattern that is consistent with genes regulated by the Wnt signaling pathway. These results suggest that rs6983267 at 8q24 exerts its effect in CRC predisposition by regulating gene expression. The most obvious target gene for the enhancer element is MYC, residing ~335 kb downstream, however further studies are required to establish the transcriptional target(s) of the predicted enhancer element.

Homogeneous model theory of metric structures

This thesis studies homogeneous classes of complete metric spaces. Over the past few decades model theory has been extended to cover a variety of nonelementary frameworks. Shelah introduced the abstact elementary classes (AEC) in the 1980s as a common framework for the study of nonelementary classes. Another direction of extension has been the development of model theory for metric structures. This thesis takes a step in the direction of combining these two by introducing an AEC-like setting for studying metric structures. To find balance between generality and the possibility to develop stability theoretic tools, we work in a homogeneous context, thus extending the usual compact approach. The homogeneous context enables the application of stability theoretic tools developed in discrete homogeneous model theory. Using these we prove categoricity transfer theorems for homogeneous metric structures with respect to isometric isomorphisms. We also show how generalized isomorphisms can be added to the class, giving a model theoretic approach to, e.g., Banach space isomorphisms or operator approximations. The novelty is the built-in treatment of these generalized isomorphisms making, e.g., stability up to perturbation the natural stability notion. With respect to these generalized isomorphisms we develop a notion of independence. It behaves well already for structures which are omega-stable up to perturbation and coincides with the one from classical homogeneous model theory over saturated enough models. We also introduce a notion of isolation and prove dominance for it.

Society by Numbers : Studies on Model-Based Explanations in the Social Sciences

The aim of this dissertation is to provide conceptual tools for the social scientist for clarifying, evaluating and comparing explanations of social phenomena based on formal mathematical models. The focus is on relatively simple theoretical models and simulations, not statistical models. These studies apply a theory of explanation according to which explanation is about tracing objective relations of dependence, knowledge of which enables answers to contrastive why and how-questions. This theory is developed further by delineating criteria for evaluating competing explanations and by applying the theory to social scientific modelling practices and to the key concepts of equilibrium and mechanism. The dissertation is comprised of an introductory essay and six published original research articles. The main theses about model-based explanations in the social sciences argued for in the articles are the following. 1) The concept of explanatory power, often used to argue for the superiority of one explanation over another, compasses five dimensions which are partially independent and involve some systematic trade-offs. 2) All equilibrium explanations do not causally explain the obtaining of the end equilibrium state with the multiple possible initial states. Instead, they often constitutively explain the macro property of the system with the micro properties of the parts (together with their organization). 3) There is an important ambivalence in the concept mechanism used in many model-based explanations and this difference corresponds to a difference between two alternative research heuristics. 4) Whether unrealistic assumptions in a model (such as a rational choice model) are detrimental to an explanation provided by the model depends on whether the representation of the explanatory dependency in the model is itself dependent on the particular unrealistic assumptions. Thus evaluating whether a literally false assumption in a model is problematic requires specifying exactly what is supposed to be explained and by what. 5) The question of whether an explanatory relationship depends on particular false assumptions can be explored with the process of derivational robustness analysis and the importance of robustness analysis accounts for some of the puzzling features of the tradition of model-building in economics. 6) The fact that economists have been relatively reluctant to use true agent-based simulations to formulate explanations can partially be explained by the specific ideal of scientific understanding implicit in the practise of orthodox economics.

Management of multi-scale forest resource data over time

During the last decades there has been a global shift in forest management from a focus solely on timber management to ecosystem management that endorses all aspects of forest functions: ecological, economic and social. This has resulted in a shift in paradigm from sustained yield to sustained diversity of values, goods and benefits obtained at the same time, introducing new temporal and spatial scales into forest resource management. The purpose of the present dissertation was to develop methods that would enable spatial and temporal scales to be introduced into the storage, processing, access and utilization of forest resource data. The methods developed are based on a conceptual view of a forest as a hierarchically nested collection of objects that can have a dynamically changing set of attributes. The temporal aspect of the methods consists of lifetime management for the objects and their attributes and of a temporal succession linking the objects together. Development of the forest resource data processing method concentrated on the extensibility and configurability of the data content and model calculations, allowing for a diverse set of processing operations to be executed using the same framework. The contribution of this dissertation to the utilisation of multi-scale forest resource data lies in the development of a reference data generation method to support forest inventory methods in approaching single-tree resolution.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

A quantum chemical investigation of the metal centres in cytochrome c oxidase

Quantum effects are often of key importance for the function of biological systems at molecular level. Cellular respiration, where energy is extracted from the reduction of molecular oxygen to water, is no exception. In this work, the end station of the electron transport chain in mitochondria, cytochrome c oxidase, is investigated using quantum chemical methodology. Cytochrome c oxidase contains two haems, haem a and haem a3. Haem a3, with its copper companion, CuB, is involved in the final reduction of oxygen into water. This binuclear centre receives the necessary electrons from haem a. Haem a, in turn, receives its electrons from a copper ion pair in the vicinity, called CuA. Density functional theory (DFT) has been used to clarify the charge and spin distributions of haem a, as well as changes in these during redox activity. Upon reduction, the added electron is shown to be evenly distributed over the entire haem structure, important for the accommodation of the prosthetic group within the protein. At the same time, the spin distribution of the open-shell oxidised state is more localised to the central iron. The exact spin density distribution has been disputed in the literature, however, different experiments indicating different distributions of the unpaired electron. The apparent contradiction is shown to be due to the false assumption of a unit amount of unpaired electron density; in fact, the oxidised state has about 1.3 unpaired electrons. The validity of the DFT results have been corroborated by wave function based coupled cluster calculations. Point charges, for use in classical force field based simulations, have been parameterised for the four metal centres, using a newly developed methodology. In the procedure, the subsystem for which point charges are to be obtained, is surrounded by an outer region, with the purpose of stabilising the inner region, both electronically and structurally. Finally, the possibility of vibrational promotion of the electron transfer step between haem a and a3 has been investigated. Calculating the full vibrational spectra, at DFT level, of a combined model of the two haems, revealed several normal modes that do shift electron density between the haems. The magnitude of the shift was found to be moderate, at most. The proposed mechanism could have an assisting role in the electron transfer, which still seems to be dominated by electron tunnelling.

Theoretical Studies on Coupled Electron and Proton Transfer in Cytochrome c Oxidase

The complexity of life is based on an effective energy transduction machinery, which has evolved during the last 3.5 billion years. In aerobic life, the utilization of the high oxidizing potential of molecular oxygen powers this machinery. Oxygen is safely reduced by a membrane bound enzyme, cytochrome c oxidase (CcO), to produce an electrochemical proton gradient over the mitochondrial or bacterial membrane. This gradient is used for energy-requiring reactions such as synthesis of ATP by F0F1-ATPase and active transport. In this thesis, the molecular mechanism by which CcO couples the oxygen reduction chemistry to proton-pumping has been studied by theoretical computer simulations. By building both classical and quantum mechanical model systems based on the X-ray structure of CcO from Bos taurus, the dynamics and energetics of the system were studied in different intermediate states of the enzyme. As a result of this work, a mechanism was suggested by which CcO can prevent protons from leaking backwards in proton-pumping. The use and activation of two proton conducting channels were also enlightened together with a mechanism by which CcO sorts the chemical protons from pumped protons. The latter problem is referred to as the gating mechanism of CcO, and has remained a challenge in the bioenergetics field for more than three decades. Furthermore, a new method for deriving charge parameters for classical simulations of complex metalloenzymes was developed.

Production of F4 Fimbrial Adhesin in Plants: a Model for Oral Porcine Vaccine against Enterotoxigenic Escherichia coli

F4 fimbriae of enterotoxigenic Escherichia coli (ETEC) are highly stable multimeric structures with a capacity to evoke mucosal immune responses. With these characters F4 offer a unique model system to study oral vaccination against ETEC-induced porcine postweaning diarrhea. Postweaning diarrhea is a major problem in piggeries worldwide and results in significant economic losses. No vaccine is currently available to protect weaned piglets against ETEC infections. Transgenic plants provide an economically feasible platform for large-scale production of vaccine antigens for animal health. In this study, the capacity of transgenic plants to produce FaeG protein, the major structural subunit and adhesin of F4 fimbria, was evaluated. Using the model plant tobacco, the optimal subcellular location for FaeG accumulation was examined. Targeting of FaeG into chloroplasts offered a superior accumulation level of 1% of total soluble proteins (TSP) over the other investigated subcellular locations, namely, the endoplasmic reticulum and the apoplast. Moreover, we determined whether the FaeG protein, when isolated from its fimbrial background and produced in a plant cell, would retain the key properties of an oral vaccine, i.e. stability in gastrointestinal conditions, binding to porcine intestinal F4 receptors (F4R), and inhibition of the F4-possessing (F4+) ETEC attachment to F4R. The chloroplast-derived FaeG protein did show resistance against low pH and proteolysis in the simulated gastrointestinal conditions and was able to bind to the F4R, subsequently inhibiting the F4+ ETEC binding in a dose-dependent manner. To investigate the oral immunogenicity of FaeG protein, the edible crop plant alfalfa was transformed with the chloroplast-targeting construct and equally to tobacco plants, a high-yield FaeG accumulation of 1% of TSP was obtained. A similar yield was also obtained in the seeds of barley, a valuable crop plant, when the FaeG-encoding gene was expressed under an endosperm-specific promoter and subcellularly targeted into the endoplasmic reticulum. Furthermore, desiccated alfalfa plants and barley grains were shown to have a capacity to store FaeG protein in a stable form for years. When the transgenic alfalfa plants were administred orally to weaned piglets, slight F4-specific systemic and mucosal immune responses were induced. Co-administration of the transgenic alfalfa and the mucosal adjuvant cholera toxin enhanced the F4-specific immune response; the duration and number of F4+ E. coli excretion following F4+ ETEC challenge were significantly reduced as compared with pigs that had received nontransgenic plant material. In conclusion, the results suggest that transgenic plants producing the FaeG subunit protein could be used for production and delivery of oral vaccines against porcine F4+ ETEC infections. The findings here thus present new approaches to develop the vaccination strategy against porcine postweaning diarrhea.

Monte Carlo Simulations of Molecular Clusters in Nucleation

A better understanding of the limiting step in a first order phase transition, the nucleation process, is of major importance to a variety of scientific fields ranging from atmospheric sciences to nanotechnology and even to cosmology. This is due to the fact that in most phase transitions the new phase is separated from the mother phase by a free energy barrier. This barrier is crossed in a process called nucleation. Nowadays it is considered that a significant fraction of all atmospheric particles is produced by vapor-to liquid nucleation. In atmospheric sciences, as well as in other scientific fields, the theoretical treatment of nucleation is mostly based on a theory known as the Classical Nucleation Theory. However, the Classical Nucleation Theory is known to have only a limited success in predicting the rate at which vapor-to-liquid nucleation takes place at given conditions. This thesis studies the unary homogeneous vapor-to-liquid nucleation from a statistical mechanics viewpoint. We apply Monte Carlo simulations of molecular clusters to calculate the free energy barrier separating the vapor and liquid phases and compare our results against the laboratory measurements and Classical Nucleation Theory predictions. According to our results, the work of adding a monomer to a cluster in equilibrium vapour is accurately described by the liquid drop model applied by the Classical Nucleation Theory, once the clusters are larger than some threshold size. The threshold cluster sizes contain only a few or some tens of molecules depending on the interaction potential and temperature. However, the error made in modeling the smallest of clusters as liquid drops results in an erroneous absolute value for the cluster work of formation throughout the size range, as predicted by the McGraw-Laaksonen scaling law. By calculating correction factors to Classical Nucleation Theory predictions for the nucleation barriers of argon and water, we show that the corrected predictions produce nucleation rates that are in good comparison with experiments. For the smallest clusters, the deviation between the simulation results and the liquid drop values are accurately modelled by the low order virial coefficients at modest temperatures and vapour densities, or in other words, in the validity range of the non-interacting cluster theory by Frenkel, Band and Bilj. Our results do not indicate a need for a size dependent replacement free energy correction. The results also indicate that Classical Nucleation Theory predicts the size of the critical cluster correctly. We also presents a new method for the calculation of the equilibrium vapour density, surface tension size dependence and planar surface tension directly from cluster simulations. We also show how the size dependence of the cluster surface tension in equimolar surface is a function of virial coefficients, a result confirmed by our cluster simulations.

Molecular dynamics studies of nanoparticle impacts

This thesis concerns the dynamics of nanoparticle impacts on solid surfaces. These impacts occur, for instance, in space, where micro- and nanometeoroids hit surfaces of planets, moons, and spacecraft. On Earth, materials are bombarded with nanoparticles in cluster ion beam devices, in order to clean or smooth their surfaces, or to analyse their elemental composition. In both cases, the result depends on the combined effects of countless single impacts. However, the dynamics of single impacts must be understood before the overall effects of nanoparticle radiation can be modelled. In addition to applications, nanoparticle impacts are also important to basic research in the nanoscience field, because the impacts provide an excellent case to test the applicability of atomic-level interaction models to very dynamic conditions. In this thesis, the stopping of nanoparticles in matter is explored using classical molecular dynamics computer simulations. The materials investigated are gold, silicon, and silica. Impacts on silicon through a native oxide layer and formation of complex craters are also simulated. Nanoparticles up to a diameter of 20 nm (315000 atoms) were used as projectiles. The molecular dynamics method and interatomic potentials for silicon and gold are examined in this thesis. It is shown that the displacement cascade expansionmechanism and crater crown formation are very sensitive to the choice of atomic interaction model. However, the best of the current interatomic models can be utilized in nanoparticle impact simulation, if caution is exercised. The stopping of monatomic ions in matter is understood very well nowadays. However, interactions become very complex when several atoms impact on a surface simultaneously and within a short distance, as happens in a nanoparticle impact. A high energy density is deposited in a relatively small volume, which induces ejection of material and formation of a crater. Very high yields of excavated material are observed experimentally. In addition, the yields scale nonlinearly with the cluster size and impact energy at small cluster sizes, whereas in macroscopic hypervelocity impacts, the scaling 2 is linear. The aim of this thesis is to explore the atomistic mechanisms behind the nonlinear scaling at small cluster sizes. It is shown here that the nonlinear scaling of ejected material yield disappears at large impactor sizes because the stopping mechanism of nanoparticles gradually changes to the same mechanism as in macroscopic hypervelocity impacts. The high yields at small impactor size are due to the early escape of energetic atoms from the hot region. In addition, the sputtering yield is shown to depend very much on the spatial initial energy and momentum distributions that the nanoparticle induces in the material in the first phase of the impact. At the later phases, the ejection of material occurs by several mechanisms. The most important mechanism at high energies or at large cluster sizes is atomic cluster ejection from the transient liquid crown that surrounds the crater. The cluster impact dynamics detected in the simulations are in agreement with several recent experimental results. In addition, it is shown that relatively weak impacts can induce modifications on the surface of an amorphous target over a larger area than was previously expected. This is a probable explanation for the formation of the complex crater shapes observed on these surfaces with atomic force microscopy. Clusters that consist of hundreds of thousands of atoms induce long-range modifications in crystalline gold.

Factors controlling the surface energy budget over snow and ice

Polar Regions are an energy sink of the Earth system, as the Sun rays do not reach the Poles for half of the year, and hit them only at very low angles for the other half of the year. In summer, solar radiation is the dominant energy source for the Polar areas, therefore even small changes in the surface albedo strongly affect the surface energy balance and, thus, the speed and amount of snow and ice melting. In winter, the main heat sources for the atmosphere are the cyclones approaching from lower latitudes, and the atmosphere-surface heat transfer takes place through turbulent mixing and longwave radiation, the latter dominated by clouds. The aim of this thesis is to improve the knowledge about the surface and atmospheric processes that control the surface energy budget over snow and ice, with particular focus on albedo during the spring and summer seasons, on horizontal advection of heat, cloud longwave forcing, and turbulent mixing during the winter season. The critical importance of a correct albedo representation in models is illustrated through the analysis of the causes for the errors in the surface and near-surface air temperature produced in a short-range numerical weather forecast by the HIRLAM model. Then, the daily and seasonal variability of snow and ice albedo have been examined by analysing field measurements of albedo, carried out in different environments. On the basis of the data analysis, simple albedo parameterizations have been derived, which can be implemented into thermodynamic sea ice models, as well as numerical weather prediction and climate models. Field measurements of radiation and turbulent fluxes over the Bay of Bothnia (Baltic Sea) also allowed examining the impact of a large albedo change during the melting season on surface energy and ice mass budgets. When high contrasts in surface albedo are present, as in the case of snow covered areas next to open water, the effect of the surface albedo heterogeneity on the downwelling solar irradiance under overcast condition is very significant, although it is usually not accounted for in single column radiative transfer calculations. To account for this effect, an effective albedo parameterization based on three-dimensional Monte Carlo radiative transfer calculations has been developed. To test a potentially relevant application of the effective albedo parameterization, its performance in the ground-based retrieval of cloud optical depth was illustrated. Finally, the factors causing the large variations of the surface and near-surface temperatures over the Central Arctic during winter were examined. The relative importance of cloud radiative forcing, turbulent mixing, and lateral heat advection on the Arctic surface temperature were quantified through the analysis of direct observations from Russian drifting ice stations, with the lateral heat advection calculated from reanalysis products.

CDNF and MANF in an experimental model of Parkinson's disease in rats

Parkinson s disease (PD) is a neurodegenerative disorder associated with a progressive loss of dopaminergic neurons of the substantia nigra (SN). Current therapies of PD do not stop the progression of the disease and the efficacy of these treatments wanes over time. Neurotrophic factors are naturally occurring proteins promoting the survival and differentiation of neurons and the maintenance of neuronal contacts. Neurotrophic factors are attractive candidates for neuroprotective or even neurorestorative treatment of PD. Thus, searching for and characterizing trophic factors are highly important approaches to degenerative diseases. CDNF (cerebral dopamine neurotrophic factor) and MANF (mesencephalic astrocyte-derived neurotrophic factor) are secreted proteins that constitute a novel, evolutionarily conserved neurotrophic factor family expressed in vertebrates and invertebrates. The present study investigated the neuroprotective and restorative effects of human CDNF and MANF in rats with unilateral partial lesion of dopamine neurons by 6-hydroxydopamine (6-OHDA) using both behavioral (amphetamine-induced rotation) and immunohistochemical analyses. We also investigated the distribution and transportation profiles of intrastriatally injected CDNF and MANF in rats. Intrastriatal CDNF and MANF protected nigrostriatal dopaminergic neurons when administered six hours before or four weeks after the neurotoxin 6-OHDA. More importantly, the function of the lesioned nigrostriatal dopaminergic system was partially restored even when the neurotrophic factors were administered four weeks after 6-OHDA. A 14-day continuous infusion of CDNF but not of MANF restored the function of the midbrain neural circuits controlling movement when initiated two weeks after unilateral injection of 6-OHDA. Continuous infusion of CDNF also protected dopaminergic TH-positive cell bodies from toxin-induced degeneration in the substantia nigra pars compacta (SNpc) and fibers in the striatum. When injected into the striatum, CDNF and GDNF had similar transportation profiles from the striatum to the SNpc; thus CDNF may act via the same nerve tracts as GDNF. Intrastriatal MANF was transported to cortical areas which may reflect a mechanism of neurorestorative action that is different from that of CDNF and GDNF. CDNF and MANF were also shown to distribute more readily than GDNF. In conclusion, CDNF and MANF are potential therapeutic proteins for the treatment of PD.

Monte Carlo simulations of molecular clusters in nucleation

A better understanding of the limiting step in a first order phase transition, the nucleation process, is of major importance to a variety of scientific fields ranging from atmospheric sciences to nanotechnology and even to cosmology. This is due to the fact that in most phase transitions the new phase is separated from the mother phase by a free energy barrier. This barrier is crossed in a process called nucleation. Nowadays it is considered that a significant fraction of all atmospheric particles is produced by vapor-to liquid nucleation. In atmospheric sciences, as well as in other scientific fields, the theoretical treatment of nucleation is mostly based on a theory known as the Classical Nucleation Theory. However, the Classical Nucleation Theory is known to have only a limited success in predicting the rate at which vapor-to-liquid nucleation takes place at given conditions. This thesis studies the unary homogeneous vapor-to-liquid nucleation from a statistical mechanics viewpoint. We apply Monte Carlo simulations of molecular clusters to calculate the free energy barrier separating the vapor and liquid phases and compare our results against the laboratory measurements and Classical Nucleation Theory predictions. According to our results, the work of adding a monomer to a cluster in equilibrium vapour is accurately described by the liquid drop model applied by the Classical Nucleation Theory, once the clusters are larger than some threshold size. The threshold cluster sizes contain only a few or some tens of molecules depending on the interaction potential and temperature. However, the error made in modeling the smallest of clusters as liquid drops results in an erroneous absolute value for the cluster work of formation throughout the size range, as predicted by the McGraw-Laaksonen scaling law. By calculating correction factors to Classical Nucleation Theory predictions for the nucleation barriers of argon and water, we show that the corrected predictions produce nucleation rates that are in good comparison with experiments. For the smallest clusters, the deviation between the simulation results and the liquid drop values are accurately modelled by the low order virial coefficients at modest temperatures and vapour densities, or in other words, in the validity range of the non-interacting cluster theory by Frenkel, Band and Bilj. Our results do not indicate a need for a size dependent replacement free energy correction. The results also indicate that Classical Nucleation Theory predicts the size of the critical cluster correctly. We also presents a new method for the calculation of the equilibrium vapour density, surface tension size dependence and planar surface tension directly from cluster simulations. We also show how the size dependence of the cluster surface tension in equimolar surface is a function of virial coefficients, a result confirmed by our cluster simulations.

Struggles over legitimacy in global organizational restructuring: A rhetorical perspective on legitimation strategies and dynamics in a shutdown case

Critical organization scholars have focused increasing attention on industrial and organizational restructurings such as shutdown decisions. However, we know little about the rhetorical strategies used to legitimate or resist plant closures in organizational negotiations. In this paper, we draw from New Rhetoric to analyze rhetorical struggles, strategies and dynamics in unfolding organizational negotiations. We focus on the shutdown of the bus body unit of the Swedish company Volvo in Finland. We distinguish five types of rhetorical legitimation strategies and dynamics. These include the three classical dynamics of logos (rational arguments), pathos (emotional moral arguments), and ethos (authority-based arguments), but also autopoiesis (autopoietic narratives), and cosmos (cosmological constructions). Our analysis adds to the previous studies explaining how organizational restructuring as a phenomenon is legitimated, how this legitimation has changed over time, and how contemporary industrial closures are legitimated in the media. This study also increases our theoretical understanding of the role of rhetoric in legitimation more generally.