Cognitive deficits and the Paced Auditory Serial Addition Test performance among patients with multiple sclerosis

Multiple sclerosis (MS) is a chronic, inflammatory disease of the central nervous system, characterized especially by myelin and axon damage. Cognitive impairment in MS is common but difficult to detect without a neuropsychological examination. Valid and reliable methods are needed in clinical practice and research to detect deficits, follow their natural evolution, and verify treatment effects. The Paced Auditory Serial Addition Test (PASAT) is a measure of sustained and divided attention, working memory, and information processing speed, and it is widely used in MS patients neuropsychological evaluation. Additionally, the PASAT is the sole cognitive measure in an assessment tool primarly designed for MS clinical trials, the Multiple Sclerosis Functional Composite (MSFC). The aims of the present study were to determine a) the frequency, characteristics, and evolution of cognitive impairment among relapsing-remitting MS patients, and b) the validity and reliability of the PASAT in measuring cognitive performance in MS patients. The subjects were 45 relapsing-remitting MS patients from Seinäjoki Central Hospital, Department of Neurology and 48 healthy controls. Both groups underwent comprehensive neuropsychological assessments, including the PASAT, twice in a one-year follow-up, and additionally a sample of 10 patients and controls were evaluated with the PASAT in serial assessments five times in one month. The frequency of cognitive dysfunction among relapsing-remitting MS patients in the present study was 42%. Impairments were characterized especially by slowed information processing speed and memory deficits. During the one-year follow-up, the cognitive performance was relatively stable among MS patients on a group level. However, the practice effects in cognitive tests were less pronounced among MS patients than healthy controls. At an individual level the spectrum of MS patients cognitive deficits was wide in regards to their characteristics, severity, and evolution. The PASAT was moderately accurate in detecting MS-associated cognitive impairment, and 69% of patients were correctly classified as cognitively impaired or unimpaired when comprehensive neuropsychological assessment was used as a "gold standard". Self-reported nervousness and poor arithmetical skills seemed to explain misclassifications. MS-related fatigue was objectively demonstrated as fading performance towards the end of the test. Despite the observed practice effect, the reliability of the PASAT was excellent, and it was sensitive to the cognitive decline taking place during the follow-up in a subgroup of patients. The PASAT can be recommended for use in the neuropsychological assessment of MS patients. The test is fairly sensitive, but less specific; consequently, the reasons for low scores have to be carefully identified before interpreting them as clinically significant.

Forest inventory-based large-scale forest biomass and carbon budget assessment: new enhanced methods and use of remote sensing for verification

In recent years, concern has arisen over the effects of increasing carbon dioxide (CO2) in the earth's atmosphere due to the burning of fossil fuels. One way to mitigate increase in atmospheric CO2 concentration and climate change is carbon sequestration to forest vegeta-tion through photosynthesis. Comparable regional scale estimates for the carbon balance of forests are therefore needed for scientific and political purposes. The aim of the present dissertation was to improve methods for quantifying and verifying inventory-based carbon pool estimates of the boreal forests in the mineral soils. Ongoing forest inventories provide a data based on statistically sounded sampling for estimating the level of carbon stocks and stock changes, but improved modelling tools and comparison of methods are still needed. In this dissertation, the entire inventory-based large-scale forest carbon stock assessment method was presented together with some separate methods for enhancing and comparing it. The enhancement methods presented here include ways to quantify the biomass of understorey vegetation as well as to estimate the litter production of needles and branches. In addition, the optical remote sensing method illustrated in this dis-sertation can be used to compare with independent data. The forest inventory-based large-scale carbon stock assessment method demonstrated here provided reliable carbon estimates when compared with independent data. Future ac-tivity to improve the accuracy of this method could consist of reducing the uncertainties regarding belowground biomass and litter production as well as the soil compartment. The methods developed will serve the needs for UNFCCC reporting and the reporting under the Kyoto Protocol. This method is principally intended for analysts or planners interested in quantifying carbon over extensive forest areas.

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.