Considering Knowledge Uptake within a Cycle of Transforming Data,Information, and Knowledge

Knowledge uptake, having decision makers assimilate the ideas of experts, is recognized as an important stimulus to bringing about policy change. This is particularly true in the realm of environmental policymaking, which is characterized by knowledge intensity, complexity, and multifaceted concerns. Using examples from an innovative watershed management organization, this article presents a heuristic for understanding how knowledge uptake occurs within a cycle of organizational reasoning. This cycle is driven by activities that transform data, information, and knowledge and that link specialists with decision makers. The heuristic can be used as a diagnostic tool to identify breaks in the transformation process that impede mandate fulfillment and impair capacity building. Lack of appreciation of the dynamic relationship between data, information and knowledge leads to mistimed and ineffective policy interventions that do not result in the hoped for progress in science intended to underpin policy advances.     

Empirical analysis of the STR profiles resulting from conceptual mixtures

Samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors can be challenging and associated uncertainties can have dramatic effects on the interpretation of testing results. Using an FBI genotypes dataset, containing complete genotype information from the 13 Combined DNA Index System (CODIS) loci for 959 individuals, all possible mixtures of three individuals were exhaustively and empirically computed. Allele sharing between pairs of individuals in the original dataset, a randomized dataset and datasets of generated cousins and siblings was evaluated as were the number of loci that were necessary to reliably deduce the number of contributors present in simulated mixtures of four or less contributors. The relatively small number of alleles detectable at most CODIS loci and the fact that some alleles are likely to be shared between individuals within a population can make the maximum number of different alleles observed at any tested loci an unreliable indicator of the maximum number of contributors to a mixed DNA sample. This analysis does not use other data available from the electropherograms (such as peak height or peak area) to estimate the number of contributors to each mixture. As a result, the study represents a worst case analysis of mixture characterization. Within this dataset, approximately 3% of three-person mixtures would be mischaracterized as two-person mixtures and more than 70% of four-person mixtures would be mischaracterized as two- or three-person mixtures using only the maximum number of alleles observed at any tested locus.     

Risk factors for sexual victimization among male and female homeless and runaway youth

Risk factors associated with the likelihood of being sexually victimized by a stranger or friend/acquaintance since being on the street was examined among 372 homeless and runaway youth. Young people were interviewed on the streets and in shelters by outreach workers using a systematic sampling strategy. Youth who engaged in more high-risk behaviors were expected to be at greater risk for sexual victimization by both known and unknown assailants. Results indicated that for females, running from home for the first time at an earlier age was associated with sexual victimization by both a stranger and friend/acquaintance. However, engaging in deviant subsistence strategies, survival sex, and grooming predicted being sexually victimized by a friend/acquaintance. For males, survival sex and grooming predicted stranger sexual victimization, whereas sexual orientation was associated with sexual victimization by a friend/acquaintance. Overall, 35% of the sample had been sexually victimized.     

Systematic differences in electropherogram peak heights reported by different versions of the GeneScan software

DNA profiling using STRs on the 310 and 3100 Genetic Analyzers routinely generates electropherograms that are analyzed with the GeneScan software available from the instrument's manufacturer, Applied Biosystems. Users have been able to choose from three different smoothing options that have been known to result in significant differences in the peak heights that are reported. Improvements in the underlying algorithm of the most recent version of the software also result in significant and somewhat predictable differences in peak height values. Laboratories that have performed validation studies using older versions of GeneScan should either reanalyze the data generated in those validation studies with the newest version of the software or otherwise take into consideration the systematically higher peak height values obtained as they begin following the recommendation of the manufacturer and use the new algorithm.