Flow cytometric method to measure urea-resistant alkaline phosphatase from blood neutrophils for use in the prenatal screening of Down's syndrome

The histochemical measurement of urea-resistant alkaline phosphatase from maternal blood neutrophils is known to have a high detection rate for the prenatal detection of Down's syndrome pregnancies. However, because the histochemical method is laborious and subjective to use, it has not gained widespread acceptance in prenatal screening programmes. We present a simple and objective method for the measurement of urea-resistant alkaline phosphatase by flow cytometry. The method should allow the design of larger studies aimed at evaluating the role of neutrophil urea-resistant alkaline phosphatase in the prenatal screening for Down's syndrome.     

Transport of particulate matter in natural waters — modelling and in situ-measurements

The significant processes controlling the fate of particulates are convection an dispersion on one hand, and sedimentation on the other hand. Due to inteparticulated reactions, larger aggregates can be formed from smaller units thus changing the sedimentation characteristics. These phenomena are summarized in a mathematical model whereby hydrodynamic effects as well as the control mechanisms of the dissolved phase are included. A relationship was derived on the basis of energy considerations leading to the formulation of a critical sedimentation velocity of the suspensa, which determines the transport capacity of the flowing system. The sedimentation term is calculated from the above discussed transport capacity, hydro-dynamic parameters and suspending media properties. Aggregation effects are taken into account as an increase of sedimentation velocities of the particles. The equations are solved in a particular computational routine such that the horizontal distribution of suspended solids in a natural system can be describe as function of the above discussed phenomena. The model was tested with in situ-measurements. It was found that the observed processes are described satisfactorily by this model.     

Using digital image projections to visualize forest landscape changes due to management activities and forest growth

A pedagogic problem in forestry and landscape management is to visualize future landscape effects of forest growth and current management activities in the forest. This paper presents a method for forecasting digital image projections of forest landscape dynamics. Static nonlinear regression functions estimate the digital numbers in a Landsat Thematic Mapper image. Regressors used are forest stand variables. By estimating the future forest stand data, based on intermediate treatment and growth, future satellite digital images are created. In a case study example, the future landscape of a forest block in the province of Västernorrland, Sweden, is projected to demonstrate the application of this visualization technique.     

Effect of 450 MHz Microwave Modulated with 217 Hz on Human EEG in Rest

Summary This study focuses on discrimination of changes, produced by low-level microwave exposure in intensity and time variability of the human EEG at rest. The power spectral density (PSD) method and nonlinear scaling analysis of the length distribution of low variability periods (LDLVP) were selected for analysis of the EEG signal. During the study, 19 healthy volunteers were exposed to a microwave (450 MHz) of 217 Hz frequency on-off modulation. The field power density at the scalp was 0.16 mW/cm². The experimental protocol consisted of ten cycles of repetitive microwave exposure. Signals from frontal, temporal, parietal and occipital EEG channels on EEG theta, alpha and beta rhythms were analysed. Exposure to microwave causes average increase of EEG activity. LDLVP analysis discriminated significant effect in time variability for 2 subjects (11%). PSD method detected significant changes in intensity for 4 subjects (21%). The effect of low-level microwave exposure is stronger on EEG beta rhythm in temporal and parietal regions of the human brain.     

Are national biodiversity strategies and action plans appropriate for building responsibilities for mainstreaming biodiversity across policy sectors? The case of Finland

The Convention on Biological Diversity's national biodiversity strategies and action plans (NBSAPs) are major mechanisms for mainstreaming biodiversity into national policies. This article examines whether and how the NBSAPs contribute to mainstreaming biodiversity across policy sectors in Finland in order to halt biodiversity loss. We have developed an innovative analytical framework where the concept of responsibility addresses how motivations for mainstreaming can be built, and the concept of social learning outcomes addresses the extent of institutional changes for biodiversity. The Finnish NBSAP processes have been able to build diverse forms of responsibility (liability, accountability, responsiveness and care) in different policy sectors by providing new knowledge, careful process design and developing institutional linkages. Despite pro-biodiversity outcomes in the targeted policy sectors, the responsibilities do not diffuse from the environmental administration to other policy sectors to a sufficient extent. Closing this ‘responsibility gap’ is a key challenge for building effective environmental policies.     

Using process-based models to filter out natural variability in observed concentrations of nitrogen and phosphorus in river water

Advances in process-based modelling of loads of nitrogen and phosphorus carried by rivers have created new possibilities to interpret time series of water quality data. We examined how model runs with constant anthropogenic forcing can be used to estimate and filter out weather-driven variation in observational data and, thereby, draw attention to other features of such data. An assessment of measured and modelled nutrient concentrations at the outlets of 45 Swedish rivers provided promising results for total nitrogen. In particular, joint analyses of observational data and outputs from the catchment model S-HYPE strengthened the evidence that downward trends in nitrogen were due to mitigation measures in agriculture. Evaluation of modelled and observed total phosphorus concentrations revealed considerable bias in the collection or chemical analysis of water samples and also identified weaknesses in the model outputs. Together, our results highlight the need for more efficient two-way communication between environmental modelling and monitoring.     

Land use influences macroinvertebrate community composition in boreal headwaters through altered stream conditions

Land use is known to alter the nature of land–water interactions, but the potential effects of widespread forest management on headwaters in boreal regions remain poorly understood. We evaluated the importance of catchment land use, land cover, and local stream variables for macroinvertebrate community and functional trait diversity in 18 boreal headwater streams. Variation in macroinvertebrate metrics was often best explained by in-stream variables, primarily water chemistry (e.g. pH). However, variation in stream variables was, in turn, significantly associated with catchment-scale forestry land use. More specifically, streams running through catchments that were dominated by young (11–50 years) forests had higher pH, greater organic matter standing stock, higher abundance of aquatic moss, and the highest macroinvertebrate diversity, compared to streams running through recently clear-cut and old forests. This indicates that catchment-scale forest management can modify in-stream habitat conditions with effects on stream macroinvertebrate communities and that characteristics of younger forests may promote conditions that benefit headwater biodiversity.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.