Patient doses in ct, dental cone beam ct and projection radiography in Finland, with emphasis on paediatric patients

Diagnostic radiology represents the largest man-made contribution to population radiation doses in Europe. To be able to keep the diagnostic benefit versus radiation risk ratio as high as possible, it is important to understand the quantitative relationship between the patient radiation dose and the various factors which affect the dose, such as the scan parameters, scan mode, and patient size. Paediatric patients have a higher probability for late radiation effects, since longer life expectancy is combined with the higher radiation sensitivity of the developing organs. The experience with particular paediatric examinations may be very limited and paediatric acquisition protocols may not be optimised. The purpose of this thesis was to enhance and compare different dosimetric protocols, to promote the establishment of the paediatric diagnostic reference levels (DRLs), and to provide new data on patient doses for optimisation purposes in computed tomography (with new applications for dental imaging) and in paediatric radiography. Large variations in radiation exposure in paediatric skull, sinus, chest, pelvic and abdominal radiography examinations were discovered in patient dose surveys. There were variations between different hospitals and examination rooms, between different sized patients, and between imaging techniques; emphasising the need for harmonisation of the examination protocols. For computed tomography, a correction coefficient, which takes individual patient size into account in patient dosimetry, was created. The presented patient size correction method can be used for both adult and paediatric purposes. Dental cone beam CT scanners provided adequate image quality for dentomaxillofacial examinations while delivering considerably smaller effective doses to patient compared to the multi slice CT. However, large dose differences between cone beam CT scanners were not explained by differences in image quality, which indicated the lack of optimisation. For paediatric radiography, a graphical method was created for setting the diagnostic reference levels in chest examinations, and the DRLs were given as a function of patient projection thickness. Paediatric DRLs were also given for sinus radiography. The detailed information about the patient data, exposure parameters and procedures provided tools for reducing the patient doses in paediatric radiography. The mean tissue doses presented for paediatric radiography enabled future risk assessments to be done. The calculated effective doses can be used for comparing different diagnostic procedures, as well as for comparing the use of similar technologies and procedures in different hospitals and countries.

Hydrogen soil deposition and atmospheric variations in the boreal zone

This research has been prompted by an interest in the atmospheric processes of hydrogen. The sources and sinks of hydrogen are important to know, particularly if hydrogen becomes more common as a replacement for fossil fuel in combustion. Hydrogen deposition velocities (vd) were estimated by applying chamber measurements, a radon tracer method and a two-dimensional model. These three approaches were compared with each other to discover the factors affecting the soil uptake rate. A static-closed chamber technique was introduced to determine the hydrogen deposition velocity values in an urban park in Helsinki, and at a rural site at Loppi. A three-day chamber campaign to carry out soil uptake estimation was held at a remote site at Pallas in 2007 and 2008. The atmospheric mixing ratio of molecular hydrogen has also been measured by a continuous method in Helsinki in 2007 - 2008 and at Pallas from 2006 onwards. The mean vd values measured in the chamber experiments in Helsinki and Loppi were between 0.0 and 0.7 mm s-1. The ranges of the results with the radon tracer method and the two-dimensional model were 0.13 - 0.93 mm s-1 and 0.12 - 0.61 mm s-1, respectively, in Helsinki. The vd values in the three-day campaign at Pallas were 0.06 - 0.52 mm s-1 (chamber) and 0.18 - 0.52 mm s-1 (radon tracer method and two-dimensional model). At Kumpula, the radon tracer method and the chamber measurements produced higher vd values than the two-dimensional model. The results of all three methods were close to each other between November and April, except for the chamber results from January to March, while the soil was frozen. The hydrogen deposition velocity values of all three methods were compared with one-week cumulative rain sums. Precipitation increases the soil moisture, which decreases the soil uptake rate. The measurements made in snow seasons showed that a thick snow layer also hindered gas diffusion, lowering the vd values. The H2 vd values were compared to the snow depth. A decaying exponential fit was obtained as a result. During a prolonged drought in summer 2006, soil moisture values were lower than in other summer months between 2005 and 2008. Such conditions were prevailing in summer 2006 when high chamber vd values were measured. The mixing ratio of molecular hydrogen has a seasonal variation. The lowest atmospheric mixing ratios were found in the late autumn when high deposition velocity values were still being measured. The carbon monoxide (CO) mixing ratio was also measured. Hydrogen and carbon monoxide are highly correlated in an urban environment, due to the emissions originating from traffic. After correction for the soil deposition of H2, the slope was 0.49±0.07 ppb (H2) / ppb (CO). Using the corrected hydrogen-to-carbon-monoxide ratio, the total hydrogen load emitted by Helsinki traffic in 2007 was 261 t (H2) a-1. Hydrogen, methane and carbon monoxide are connected with each other through the atmospheric methane oxidation process, in which formaldehyde is produced as an important intermediate. The photochemical degradation of formaldehyde produces hydrogen and carbon monoxide as end products. Examination of back-trajectories revealed long-range transportation of carbon monoxide and methane. The trajectories can be grouped by applying cluster and source analysis methods. Thus natural and anthropogenic emission sources can be separated by analyzing trajectory clusters.

Applications of dimensional reduction to electroweak and QCD matter

When heated to high temperatures, the behavior of matter changes dramatically. The standard model fields go through phase transitions, where the strongly interacting quarks and gluons are liberated from their confinement to hadrons, and the Higgs field condensate melts, restoring the electroweak symmetry. The theoretical framework for describing matter at these extreme conditions is thermal field theory, combining relativistic field theory and quantum statistical mechanics. For static observables the physics is simplified at very high temperatures, and an effective three-dimensional theory can be used instead of the full four-dimensional one via a method called dimensional reduction. In this thesis dimensional reduction is applied to two distinct problems, the pressure of electroweak theory and the screening masses of mesonic operators in quantum chromodynamics (QCD). The introductory part contains a brief review of finite-temperature field theory, dimensional reduction and the central results, while the details of the computations are contained in the original research papers. The electroweak pressure is shown to converge well to a value slightly below the ideal gas result, whereas the pressure of the full standard model is dominated by the QCD pressure with worse convergence properties. For the mesonic screening masses a small positive perturbative correction is found, and the interpretation of dimensional reduction on the fermionic sector is discussed.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

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.

Modelling and Forecasting Property Rents and Returns (summary section only)

Recently, focus of real estate investment has expanded from the building-specific level to the aggregate portfolio level. The portfolio perspective requires investment analysis for real estate which is comparable with that of other asset classes, such as stocks and bonds. Thus, despite its distinctive features, such as heterogeneity, high unit value, illiquidity and the use of valuations to measure performance, real estate should not be considered in isolation. This means that techniques which are widely used for other assets classes can also be applied to real estate. An important part of investment strategies which support decisions on multi-asset portfolios is identifying the fundamentals of movements in property rents and returns, and predicting them on the basis of these fundamentals. The main objective of this thesis is to find the key drivers and the best methods for modelling and forecasting property rents and returns in markets which have experienced structural changes. The Finnish property market, which is a small European market with structural changes and limited property data, is used as a case study. The findings in the thesis show that is it possible to use modern econometric tools for modelling and forecasting property markets. The thesis consists of an introduction part and four essays. Essays 1 and 3 model Helsinki office rents and returns, and assess the suitability of alternative techniques for forecasting these series. Simple time series techniques are able to account for structural changes in the way markets operate, and thus provide the best forecasting tool. Theory-based econometric models, in particular error correction models, which are constrained by long-run information, are better for explaining past movements in rents and returns than for predicting their future movements. Essay 2 proceeds by examining the key drivers of rent movements for several property types in a number of Finnish property markets. The essay shows that commercial rents in local markets can be modelled using national macroeconomic variables and a panel approach. Finally, Essay 4 investigates whether forecasting models can be improved by accounting for asymmetric responses of office returns to the business cycle. The essay finds that the forecast performance of time series models can be improved by introducing asymmetries, and the improvement is sufficient to justify the extra computational time and effort associated with the application of these techniques.

Inference on Cointegration in Vector Autoregressive Models (summary section only)

In the thesis we consider inference for cointegration in vector autoregressive (VAR) models. The thesis consists of an introduction and four papers. The first paper proposes a new test for cointegration in VAR models that is directly based on the eigenvalues of the least squares (LS) estimate of the autoregressive matrix. In the second paper we compare a small sample correction for the likelihood ratio (LR) test of cointegrating rank and the bootstrap. The simulation experiments show that the bootstrap works very well in practice and dominates the correction factor. The tests are applied to international stock prices data, and the .nite sample performance of the tests are investigated by simulating the data. The third paper studies the demand for money in Sweden 1970—2000 using the I(2) model. In the fourth paper we re-examine the evidence of cointegration between international stock prices. The paper shows that some of the previous empirical results can be explained by the small-sample bias and size distortion of Johansen’s LR tests for cointegration. In all papers we work with two data sets. The first data set is a Swedish money demand data set with observations on the money stock, the consumer price index, gross domestic product (GDP), the short-term interest rate and the long-term interest rate. The data are quarterly and the sample period is 1970(1)—2000(1). The second data set consists of month-end stock market index observations for Finland, France, Germany, Sweden, the United Kingdom and the United States from 1980(1) to 1997(2). Both data sets are typical of the sample sizes encountered in economic data, and the applications illustrate the usefulness of the models and tests discussed in the thesis.

Excimer laser refractive surgery : corneal wound healing and clinical results

Although the first procedure in a seeing human eye using excimer laser was reported in 1988 (McDonald et al. 1989, O'Connor et al. 2006) just three studies (Kymionis et al. 2007, O'Connor et al. 2006, Rajan et al. 2004) with a follow-up over ten years had been published when this thesis was started. The present thesis aims to investigate 1) the long-term outcomes of excimer laser refractive surgery performed for myopia and/or astigmatism by photorefractive keratectomy (PRK) and laser-in situ- keratomileusis (LASIK), 2) the possible differences in postoperative outcomes and complications when moderate-to-high astigmatism is treated with PRK or LASIK, 3) the presence of irregular astigmatism that depend exclusively on the corneal epithelium, and 4) the role of corneal nerve recovery in corneal wound healing in PRK enhancement. Our results revealed that in long-term the number of eyes that achieved uncorrected visual acuity (UCVA)≤0.0 and ≤0.5 (logMAR) was higher after PRK than after LASIK. Postoperative stability was slightly better after PRK than after LASIK. In LASIK treated eyes the incidence of myopic regression was more pronounced when the intended correction was over >6.0 D and in patients aged <30 years.Yet the intended corrections in our study were higher for LASIK than for PRK eyes. No differences were found in percentages of eyes with best corrected visual acuity (BCVA) or loss of two or more lines of visual acuity between PRK and LASIK in the long-term. The postoperative long-term outcomes of PRK with two different delivery systems broad beam and scanning laser were compared and revealed no differences. Postoperative outcomes of moderate-to-high astigmatism yielded better results in terms of UCVA and less compromise or loss of two more lines of BCVA after LASIK that after PRK.Similar stability for both procedures was revealed. Vector analysis showed that LASIK outcomes tended to be more accurate than PRK outcomes, yet no statistically differences were found. Irregular astigmatism secondary to recurrent corneal erosion due to map-dot-fingerprint was successfully treated with phototherapeutic keratectomy (PTK). Preoperative videokeratographies (VK) showed irregular astigmatism. However, postoperatively, all eyes showed a regular pattern. No correlation was found between pre- and postoperative VK patterns. Postoperative outcomes of late PRK in eyes originally subjected to LASIK showed that all (7/7) eyes achieved UCVA ≤0.5 at last follow-up (range 3 — 11 months), and no eye lost lines of BCVA. Postoperatively all eyes developed and initial mild haze (0.5 — 1) into the first month. Yet, at last follow-up 5/7 eyes showed a haze of 0.5 and this was no longer evident in 2/7 eyes. Based on these results, we demonstrated that the long-term outcomes after PRK and LASIK were safe and efficient, with similar stability for both procedures. The PRK outcomes were similar when treated by broad-beam or scanning slit laser. LASIK was better than PRK to correct moderate-to-high astigmatism, yet both procedures showed a tendency of undercorrection. Irregular astigmatism was proven to be able to depend exclusively from the corneal epithelium. If this kind of astigmatism is present in the cornea and a customized PRK/LASIK correction is done based on wavefront measurements an irregular astigmatism may be produced rather than treated. Corneal sensory nerve recovery should have an important role in the modulation of the corneal wound healing and post-operative anterior stromal scarring. PRK enhancement may be an option in eyes with previous LASIK after a sufficient time interval that in at least 2 years.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Carbon Dioxide emissions from an Acacia plantation on peatland in Sumatra, Indonesia

Abstract. Peat surface CO2 emission, groundwater table depth and peat temperature were monitored for two years along transects in an Acacia plantation on thick tropical peat (>4 m) in Sumatra, Indonesia. A total of 2300 emission measurements were taken at 144 locations. The autotrophic root respiration component of the CO2 emission was separated from heterotrophic emissions caused by peat oxidation in three ways: (i) by comparing CO2 emissions within and beyond the tree rooting zone, (ii) by comparing CO2 emissions with and without peat trenching (i.e. cutting any roots remaining in the peat beyond the tree rooting zone), and (iii) by comparing CO2 emissions before and after Acacia tree harvesting. On average, the contribution of root respiration to daytime CO2 emission is 21 % along transects in mature tree stands. At locations 0.5 m from trees this is up to 80 % of the total emissions, but it is negligible at locations more than 1.3 m away. This means that CO2 emission measurements well away from trees are free of any root respiration contribution and thus represent only peat oxidation emission. We find daytime mean annual CO2 emission from peat oxidation alone of 94 t ha−1 yr−1 at a mean water table depth of 0.8 m, and a minimum emission value of 80 t ha−1 yr−1 after correction for the effect of diurnal temperature fluctuations, which resulted in a 14.5 % reduction of the daytime emission. There is a positive correlation between mean long-term water table depths and peat oxidation CO2 emission. However, no such relation is found for instantaneous emission/water table depth within transects and it is clear that factors other than water table depth also affect peat oxidation and total CO2 emissions. The increase in the temperature of the surface peat due to plantation development may explain over 50 % of peat oxidation emissions.

Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.

Microbial identification by detection of ligation probes on DNA microarray

Microbes in natural and artificial environments as well as in the human body are a key part of the functional properties of these complex systems. The presence or absence of certain microbial taxa is a correlate of functional status like risk of disease or course of metabolic processes of a microbial community. As microbes are highly diverse and mostly notcultivable, molecular markers like gene sequences are a potential basis for detection and identification of key types. The goal of this thesis was to study molecular methods for identification of microbial DNA in order to develop a tool for analysis of environmental and clinical DNA samples. Particular emphasis was placed on specificity of detection which is a major challenge when analyzing complex microbial communities. The approach taken in this study was the application and optimization of enzymatic ligation of DNA probes coupled with microarray read-out for high-throughput microbial profiling. The results show that fungal phylotypes and human papillomavirus genotypes could be accurately identified from pools of PCR amplicons generated from purified sample DNA. Approximately 1 ng/μl of sample DNA was needed for representative PCR amplification as measured by comparisons between clone sequencing and microarray. A minimum of 0,25 amol/μl of PCR amplicons was detectable from amongst 5 ng/μl of background DNA, suggesting that the detection limit of the test comprising of ligation reaction followed by microarray read-out was approximately 0,04%. Detection from sample DNA directly was shown to be feasible with probes forming a circular molecule upon ligation followed by PCR amplification of the probe. In this approach, the minimum detectable relative amount of target genome was found to be 1% of all genomes in the sample as estimated from 454 deep sequencing results. Signal-to-noise of contact printed microarrays could be improved by using an internal microarray hybridization control oligonucleotide probe together with a computational algorithm. The algorithm was based on identification of a bias in the microarray data and correction of the bias as shown by simulated and real data. The results further suggest semiquantitative detection to be possible by ligation detection, allowing estimation of target abundance in a sample. However, in practise, comprehensive sequence information of full length rRNA genes is needed to support probe design with complex samples. This study shows that DNA microarray has the potential for an accurate microbial diagnostic platform to take advantage of increasing sequence data and to replace traditional, less efficient methods that still dominate routine testing in laboratories. The data suggests that ligation reaction based microarray assay can be optimized to a degree that allows good signal-tonoise and semiquantitative detection.