Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.

Rolling tachyons, random matrices, and Coulomb gas thermodynamics

Time-dependent backgrounds in string theory provide a natural testing ground for physics concerning dynamical phenomena which cannot be reliably addressed in usual quantum field theories and cosmology. A good, tractable example to study is the rolling tachyon background, which describes the decay of an unstable brane in bosonic and supersymmetric Type II string theories. In this thesis I use boundary conformal field theory along with random matrix theory and Coulomb gas thermodynamics techniques to study open and closed string scattering amplitudes off the decaying brane. The calculation of the simplest example, the tree-level amplitude of n open strings, would give us the emission rate of the open strings. However, even this has been unknown. I will organize the open string scattering computations in a more coherent manner and will argue how to make further progress.

Disjunct eddy covariance measurements of volatile organic compound fluxes using proton transfer reaction mass spectrometry

Volatile organic compounds (VOCs) are emitted into the atmosphere from natural and anthropogenic sources, vegetation being the dominant source on a global scale. Some of these reactive compounds are deemed major contributors or inhibitors to aerosol particle formation and growth, thus making VOC measurements essential for current climate change research. This thesis discusses ecosystem scale VOC fluxes measured above a boreal Scots pine dominated forest in southern Finland. The flux measurements were performed using the micrometeorological disjunct eddy covariance (DEC) method combined with proton transfer reaction mass spectrometry (PTR-MS), which is an online technique for measuring VOC concentrations. The measurement, calibration, and calculation procedures developed in this work proved to be well suited to long-term VOC concentration and flux measurements with PTR-MS. A new averaging approach based on running averaged covariance functions improved the determination of the lag time between wind and concentration measurements, which is a common challenge in DEC when measuring fluxes near the detection limit. The ecosystem scale emissions of methanol, acetaldehyde, and acetone were substantial. These three oxygenated VOCs made up about half of the total emissions, with the rest comprised of monoterpenes. Contrary to the traditional assumption that monoterpene emissions from Scots pine originate mainly as evaporation from specialized storage pools, the DEC measurements indicated a significant contribution from de novo biosynthesis to the ecosystem scale monoterpene emissions. This thesis offers practical guidelines for long-term DEC measurements with PTR-MS. In particular, the new averaging approach to the lag time determination seems useful in the automation of DEC flux calculations. Seasonal variation in the monoterpene biosynthesis and the detailed structure of a revised hybrid algorithm, describing both de novo and pool emissions, should be determined in further studies to improve biological realism in the modelling of monoterpene emissions from Scots pine forests. The increasing number of DEC measurements of oxygenated VOCs will probably enable better estimates of the role of these compounds in plant physiology and tropospheric chemistry. Keywords: disjunct eddy covariance, lag time determination, long-term flux measurements, proton transfer reaction mass spectrometry, Scots pine forests, volatile organic compounds

Practical Proposals for Specifying k-Nearest Neighbours Weights Matrices

In this article we introduce and evaluate testing procedures for specifying the number k of nearest neighbours in the weights matrix of spatial econometric models. The spatial J-test is used for specification search. Two testing procedures are suggested: an increasing neighbours testing procedure and a decreasing neighbours testing procedure. Simulations show that the increasing neighbours testing procedures can be used in large samples to determine k. The decreasing neighbours testing procedure is found to have low power, and is not recommended for use in practice. An empirical example involving house price data is provided to show how to use the testing procedures with real data.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Field intercomparison of four methane gas analysers suitable for eddy covariance flux measurements

Eddy covariance (EC)-flux measurement technique is based on measurement of turbulent motions of air with accurate and fast measurement devices. For instance, in order to measure methane flux a fast methane gas analyser is needed which measures methane concentration at least ten times in a second in addition to a sonic anemometer, which measures the three wind components with the same sampling interval. Previously measurement of methane flux was almost impossible to carry out with EC-technique due to lack of fast enough gas analysers. However during the last decade new instruments have been developed and thus methane EC-flux measurements have become more common. Performance of four methane gas analysers suitable for eddy covariance measurements are assessed in this thesis. The assessment and comparison was performed by analysing EC-data obtained during summer 2010 (1.4.-26.10.) at Siikaneva fen. The four participating methane gas analysers are TGA-100A (Campbell Scientific Inc., USA), RMT-200 (Los Gatos Research, USA), G1301-f (Picarro Inc., USA) and Prototype-7700 (LI-COR Biosciences, USA). RMT-200 functioned most reliably throughout the measurement campaign and the corresponding methane flux data had the smallest random error. In addition, methane fluxes calculated from data obtained from G1301-f and RMT-200 agree remarkably well throughout the measurement campaign. The calculated cospectra and power spectra agree well with corresponding temperature spectra. Prototype-7700 functioned only slightly over one month in the beginning of the measurement campaign and thus its accuracy and long-term performance is difficult to assess.

Liquid Chromatography-Mass Spectrometry in Studies of Drug Metabolism and Permeability

Poor pharmacokinetics is one of the reasons for the withdrawal of drug candidates from clinical trials. There is an urgent need for investigating in vitro ADME (absorption, distribution, metabolism and excretion) properties and recognising unsuitable drug candidates as early as possible in the drug development process. Current throughput of in vitro ADME profiling is insufficient because effective new synthesis techniques, such as drug design in silico and combinatorial synthesis, have vastly increased the number of drug candidates. Assay technologies for larger sets of compounds than are currently feasible are critically needed. The first part of this work focused on the evaluation of cocktail strategy in studies of drug permeability and metabolic stability. N-in-one liquid chromatography-tandem mass spectrometry (LC/MS/MS) methods were developed and validated for the multiple component analysis of samples in cocktail experiments. Together, cocktail dosing and LC/MS/MS were found to form an effective tool for increasing throughput. First, cocktail dosing, i.e. the use of a mixture of many test compounds, was applied in permeability experiments with Caco-2 cell culture, which is a widely used in vitro model for small intestinal absorption. A cocktail of 7-10 reference compounds was successfully evaluated for standardization and routine testing of the performance of Caco-2 cell cultures. Secondly, cocktail strategy was used in metabolic stability studies of drugs with UGT isoenzymes, which are one of the most important phase II drug metabolizing enzymes. The study confirmed that the determination of intrinsic clearance (Clint) as a cocktail of seven substrates is possible. The LC/MS/MS methods that were developed were fast and reliable for the quantitative analysis of a heterogenous set of drugs from Caco-2 permeability experiments and the set of glucuronides from in vitro stability experiments. The performance of a new ionization technique, atmospheric pressure photoionization (APPI), was evaluated through comparison with electrospray ionization (ESI), where both techniques were used for the analysis of Caco-2 samples. Like ESI, also APPI proved to be a reliable technique for the analysis of Caco-2 samples and even more flexible than ESI because of the wider dynamic linear range. The second part of the experimental study focused on metabolite profiling. Different mass spectrometric instruments and commercially available software tools were investigated for profiling metabolites in urine and hepatocyte samples. All the instruments tested (triple quadrupole, quadrupole time-of-flight, ion trap) exhibited some good and some bad features in searching for and identifying of expected and non-expected metabolites. Although, current profiling software is helpful, it is still insufficient. Thus a time-consuming largely manual approach is still required for metabolite profiling from complex biological matrices.

Methodological and Empirical Advances in the Quantitative Analysis of Spontaneous Responses in Psychophysiological Time Series

The aim of the present study was to advance the methodology and use of time series analysis to quantify dynamic structures in psychophysiological processes and thereby to produce information on spontaneously coupled physiological responses and their behavioral and experiential correlates. Series of analyses using both simulated and empirical cardiac (IBI), electrodermal (EDA), and facial electromyographic (EMG) data indicated that, despite potential autocorrelated structures, smoothing increased the reliability of detecting response coupling from an interindividual distribution of intraindividual measures and that especially the measures of covariance produced accurate information on the extent of coupled responses. This methodology was applied to analyze spontaneously coupled IBI, EDA, and facial EMG responses and vagal activity in their relation to emotional experience and personality characteristics in a group of middle-aged men (n = 37) during the administration of the Rorschach testing protocol. The results revealed new characteristics in the relationship between phasic end-organ synchronization and vagal activity, on the one hand, and individual differences in emotional adjustment to novel situations on the other. Specifically, it appeared that the vagal system is intimately related to emotional and social responsivity. It was also found that the lack of spontaneously synchronized responses is related to decreased energetic arousal (e.g., depression, mood). These findings indicate that the present process analysis approach has many advantages for use in both experimental and applied research, and that it is a useful new paradigm in psychophysiological research. Keywords: Autonomic Nervous System; Emotion; Facial Electromyography; Individual Differences; Spontaneous Responses; Time Series Analysis; Vagal System

Ryhmäidentiteetin mittaaminen verkkoroolipelissä World of Warcraft

The objective of the study was to explore the dimensions of group identity in the guilds of World of Warcraft. Previous research shows that social interaction has an important role in playing games for many players. Social identities are an important aspect of self-concept and since group related cues are more salient than personal clues in computer-mediated communication, the social gaming experience was approached through group identity. In the study a new scale will be developed to measure the group identity in games. Secondary goal is to study how different guild attributes affect the group identity and third goal is to explore the connection between group identity and gaming experience and amount of play. Subjects were 1203 guild members and 106 players not in a guild. The data was gathered by an Internet survey which measured group identity with nine scales, gaming experience with three scales and guild attributes with four scales. Also various background data was gathered. The construct of group identity was analyzed with explorative factor analysis. The typical experiences of group identity was analyzed with cluster analysis and effects of guild attributes with multivariate analysis of covariance. As a result of the study a new scale was developed which measured group identity on six dimensions: self-stereotyping, public and private evaluation, importance, interconnection of self and others and awareness of content. Group identity was experienced strongest in elder middle-sized guilds that had formal rules and that emphasized social interaction. The players with strong group identity had more positive gaming experience and played World of Warcraft more per week than the players who were not in a guild or identified to guild weakly. This result encourages game developers to produce environments that enhance group identity as it seems to increase the enjoyment in games. As a whole this study proposes that group identity in guilds is constructed from the same elements as in traditional groups. If this is truly the case, guild membership may have similar positive effects on individual’s mental well-being as traditional positively evaluated group memberships have.

Toxicity of dioxin to developing teeth and salivary glands : An experimental study

Dioxins are ubiquitous environmental poisons having unequivocal adverse health effects on various species. The majority of their effects are thought to be mediated by the aryl hydrocarbon receptor (AhR). Developing human teeth may be sensitive to dioxins and the most toxic dioxin congener, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), is developmentally toxic to rodent teeth. Mechanisms of TCDD toxicity can be studied only experimentally. The aim of the present thesis work was to delineate morphological end points of developmental toxicity of TCDD in rat and mouse teeth and salivary glands in vivo and in vitro and to characterize their cellular and molecular background. Mouse embryonic teeth and submandibular gland explants were grown in organ culture without/with TCDD at various concentrations, examined stereomicroscopically and processed for histological examination. The effects of TCDD on cellular mechanisms essential for organogenesis were investigated. The expression of various genes eliciting the response to TCDD exposure or involved in tooth and salivary gland development was studied at the mRNA and/or protein levels by in situ hybridization and immunohistochemistry. Association of the dental effects of TCDD with the resistance of a rat strain to TCDD acute lethality was analyzed in two lactationally exposed rat strains. The effect of TCDD on rat molar tooth mineralization was studied in tissue sections. TCDD dose- and developmental stage-dependently interfered with tooth formation. TCDD prevented early mouse molar tooth morphogenesis and altered cuspal morphology by enhancing programmend cell death, or apoptosis, in dental epithelial cells programmed to undergo apotosis. Cell proliferation was not affected. TCDD impaired mineralization of rat molar dental matrices, possibly by specifically reducing the expression of the mineralization-related dentin sialophosphoprotein gene shown in cultured mouse teeth. The impaired mineralization of rat teeth was accompanied by decreased expression of AhR and the TCDD-inducible xenobiotic-metabolozing enzyme P4501 A1 (CYP1A1), suggesting mediation of the TCDD effect by the AhR pathway. The severe interference by TCDD with rat incisor formation was independent of the genotypic variation of AhR determining the resistance of a rat strain to TCDD acute lethality. The impairment by TCDD of mouse submandibular gland branching morphogenesis was associated with CYP1A1 induction and involved blockage of EGF receptor signalling. In conclusion, TCDD exposure is likely to have activated the AhR pathway in target organs with the consequent activation of other signalling pathways involving developmentally regulated genes. The resultant phenotype is organ specific and modified by epithelial-mesenchymal interactions and dependent on dose as well as the stage of organogenesis at the time of TCDD exposure. Teeth appear to be responsive to TCDD exposure throughout their development.

Fen ecosystem carbon gas dynamics in changing hydrological conditions

Northern peatlands are thought to store one third of all soil carbon (C). Besides the C sink function, peatlands are one of the largest natural sources of methane (CH4) to the atmosphere. Climate change may affect the C gas dynamics as well as the labile C pool. Because the peatland C sequestration and CH4 emissions are governed by high water levels, changes in hydrology are seen as the driving factor in peatland ecosystem change. This study aimed to quantify the carbon dioxide (CO2) and CH4 dynamics of a fen ecosystem at different spatial scales: plant community components scale, plant community scale and ecosystem scale, under hydrologically normal and water level drawdown conditions. C gas exchange was measured in two fens in southern Finland applying static chamber and eddy covariance techniques. During hydrologically normal conditions, the ecosystem was a CO2 sink and CH4 source to the atmosphere. Sphagnum mosses and sedges were the most important contributors to the community photosynthesis. The presence of sedges had a major positive impact on CH4 emissions while dwarf shrubs had a slightly attenuating impact. C fluxes varied considerably between the plant communities. Therefore, their proportions determined the ecosystem scale fluxes. An experimental water level drawdown markedly reduced the photosynthesis and respiration of sedges and Sphagnum mosses and benefited shrubs. Consequently, changes were smaller at the ecosystem scale than at the plant group scale. The decrease in photosynthesis and the increase in respiration, mostly peat respiration, made the fen a smaller CO2 sink. CH4 fluxes were significantly lowered, close to zero. The impact of natural droughts was similar to, although more modest than, the impact of the experimental water level drawdown. The results are applicable to the short term impacts of the water level drawdown and to climatic conditions in which droughts become more frequent.

Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit