Methods for determining the biomass content of waste.

As CO2 emission trading in Europe has been established it is of essential importance to distinguish between biogenic and fossil emissions. Emissions resulting from bio-fuels and biogenous fractions are categorized as climate-neutral. Determination of plants using only fossil or bio-fuels is simple but categorization becomes more difficult for plants using a mix of fossil and bio-fuel such as solid recovered fuels. In the meantime, different methods for solving this problem have been developed. Using different approaches and technologies, all of these methods have the same goal: determining the biomass content (biogenic fraction), for example, in solid recovered fuels or in the off-gas of a mono- or co-incineration plant in order to calculate the biogenic carbon dioxide emissions. In the following article, the most common methods for determining the biogenic fraction of fuels, namely the Selective Dissolution Method, the Balance Method and the 14C-Method will be explained in detail.     

Evaluation and classification of severity for 176 genes on an expanded carrier screening panel

Background

Disease severity is important when considering genes for inclusion on reproductive expanded carrier screening (ECS) panels. We applied a validated and previously published algorithm that classifies diseases into four severity categories (mild, moderate, severe, and profound) to 176 genes screened by ECS. Disease traits defining severity categories in the algorithm were then mapped to four severity-related ECS panel design criteria cited by the American College of Obstetricians and Gynecologists (ACOG).

Methods

Eight genetic counselors (GCs) and four medical geneticists (MDs) applied the severity algorithm to subsets of 176 genes. MDs and GCs then determined by group consensus how each of these disease traits mapped to ACOG severity criteria, enabling determination of the number of ACOG severity criteria met by each gene.

Results

Upon consensus GC and MD application of the severity algorithm, 68 (39%) genes were classified as profound, 71 (40%) as severe, 36 (20%) as moderate, and one (1%) as mild. After mapping of disease traits to ACOG severity criteria, 170 out of 176 genes (96.6%) were found to meet at least one of the four criteria, 129 genes (73.3%) met at least two, 73 genes (41.5%) met at least three, and 17 genes (9.7%) met all four.

Conclusion

This study classified the severity of a large set of Mendelian genes by collaborative clinical expert application of a trait-based algorithm. Further, it operationalized difficult to interpret ACOG severity criteria via mapping of disease traits, thereby promoting consistency of ACOG criteria interpretation.

     

Optimal municipal investment in boomtowns An empirical analysis

The primary concern of this paper is the use of wage-infrastructure trade-offs as a measure for the social benefits of municipal infrastructure in boomtowns. A regression based on pooled cross sectional and time series data for 26 towns in the Rocky Moutain region suggests that individuals will “trade off” a 1% increase in per capita stocks of municipal infrastructure for a 0.035% decline in wages. These results are then used to calculate “optimal” levels of infrastructure investment for an example boomtown.