A research agenda for the future of urban water management: exploring the potential of non-grid, small-grid, and hybrid solutions. Environmental Science and Technology.

Summary: The structural relationship model is recommended for the comparison of analytical methods in clinical chemistry. This model is based on the partition of the observed measurements of the different methods in 2 hypothetical random variables: the "expected values", which represent the correct value of the analyte with no error of measurement, and the "error term" representing the measurement errors. It is assumed, that both these variables are normally distributed.There exists a linear structural relation between different analytical methods for the same analyte, provided the correlation coefficient between each pair of the expected values of these methods is 1. This linear structural relationship is expressed by the mean values ^ and standard deviations aj of the expected values, whereas the standard deviations of the error terms determine the precision of the methods. As a measure of the precision the coefficient of determination R? is recommended.The model of structural relationship is an extension of the well known regression models and gives a more realistic approach to the comparison of 2 or more analytical methods. With 2 methods the standardized principle component should therefore replace the regression analysis. The slope of this principle component is identical with the ratio s y /s x . Statistical methods for the estimation and for tests of hypotheses of the parameters are derived and demonstrated with an example.

The human leukocyte antigen (HLA) distribution in donor registry data is typically nonrandom as, mostly for economical reasons, typing additional loci or resolving ambiguities is selectively performed based on the previously known HLA type. Analyzing a sample of over 1 million German stem cell donors, we practically show the extent of the bias caused by the restriction of the input data for HLA haplotype frequency (HF) estimation to subsets selected according to their higher HLA typing resolution and, conversely, the correctness of estimates based on unselected data with a methodology suitable for heterogeneous resolution. We discuss algorithmic aspects of this approach and, also because of the sample size, provide some new insights into the distribution of HLA-DRB1 alleles in the German population and the application of HFs in unrelated donor search.

Urban water management represents a core economic sector exposed to global water-related challenges. Recently, small modular system configurations have been identified to enable a potential sustainability transition in this lasting and rather conservative sector. The identification of current market potentials of decentralised wastewater treatment is a first step to assess whether decentralised treatment technologies could potentially be deployed on a larger scale in Europe, which would allow current decentralised wastewater treatment technologies to develop and mature. The paper elaborates a method to assess the market potential for decentralised wastewater treatment systems by starting from a raster-based geospatial modelling framework, to determine the optimal degrees of centralisation for the case of Switzerland. The resulting market potential is shown to be twenty times higher than the current market share of decentralised systems. In order to extrapolate these findings to other countries, the calculated optimal degrees of centralisation were correlated with different spatial density measures to determine a reliable and widely available proxy: population density. Based on this indicator, the European market potentials for decentralised treatment systems are estimated to be about 100'000 units per annum serving around 35 million population equivalents. The paper concludes by discussing implications for future sustainability transitions in urban water management by large-scale installation of small modular wastewater treatment systems.

Estimation of human leukocyte antigen (HLA) haplotype frequencies from unrelated stem cell donor registries presents a challenge because of large sample sizes and heterogeneity of HLA typing data. For the 14th International HLA and Immunogenetics Workshop, five bioinformatics groups initiated the 'Registry Diversity Component' aiming to cross-validate and improve current haplotype estimation tools. Five datasets were derived from different donor registries and then used as input for five different computer programs for haplotype frequency estimation. Because of issues related to heterogeneity and complexity of HLA typing data identified in the initial phase, the same five implementations, and two new ones, were used on simulated datasets in a controlled experiment where the correct results were known a priori. These datasets contained various fractions of missing HLA-DR modeled after European haplotype frequencies. We measured the contribution of sampling fluctuation and estimation error to the deviation of the frequencies from their true values, finding equivalent contributions of each for the chosen samples. Because of patient-directed activities, selective prospective typing strategies and the variety and evolution of typing technology, some donors have more complete and better HLA data. In this setting, we show that restricting estimation to fully typed individuals introduces biases that could be overcome by including all donors in frequency estimation. Our study underlines the importance of critical review and validation of tools in registry-related activity and provides a sustainable framework for validating the computational tools used. Accurate frequencies are essential for match prediction to improve registry operations and to help more patients identify suitably matched donors.