Assessing Social Learning Outcomes Through Participatory Mind Mapping

This article presents a method for using mind mapping to assess social learning outcomes in collaborative environmental restoration and participatory natural resource management initiatives. Using mind mapping for preassessment and postassessment can reveal changes in individual and collective thinking about critical social and ecological issues. On the basis of results from four youth-based environmental restoration programs in Boulder, Colorado, and New York and Cattaraugus Territory, New York, we suggest that mind mapping can serve as an effective data collection strategy and as a method for analyzing cognitive change in environmental restoration programs and civic ecology more broadly

Causal Impact of the Hospital Readmissions Reduction Program on Hospital Readmissions and Mortality

Estimating causal effects of the Hospital Readmissions Reduction Program (HRRP), part of the Affordable Care Act, has been very controversial. Associational studies have demonstrated decreases in hospital readmissions, consistent with the intent of the program, although analyses with different data sources and methods have differed in estimating effects on patient mortality. To address these issues, we define the estimands of interest in the context of potential outcomes, we formalize a Bayesian structural time-series model for causal inference, and discuss the necessary assumptions for estimation of effects using observed data. The method is used to estimate the effect of the passage of HRRP on both the 30-day readmissions and 30-day mortality. We show that for acute myocardial infarction and congestive heart failure, HRRP caused reduction in readmissions while it had no statistically significant effect on mortality. However, for pneumonia, HRRP had no statistically significant effect on readmissions but caused an increase in mortality.Comment: 10 pages, 1 figure, 2 table

Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana.

BackgroundGenome-wide association (GWA) is gaining popularity as a means to study the architecture of complex quantitative traits, partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of adaptive quantitative traits. GSL are key anti-herbivory defenses that impart adaptive advantages within field trials. While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA, GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA.Methodology/principal findingsTo understand how development and environment can influence GWA, we conducted a study using 96 Arabidopsis thaliana accessions, >40 GSL phenotypes across three conditions (one developmental comparison and one environmental comparison) and ∼230,000 SNPs. Developmental stage had dramatic effects on the outcome of GWA, with each stage identifying different loci associated with GSL traits. Further, while the molecular bases of numerous quantitative trait loci (QTL) controlling GSL traits have been identified, there is currently no estimate of how many additional genes may control natural variation in these traits. We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A. thaliana using single gene isogenic lines.Conclusions/significanceTogether, these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism. Additionally, while GWA is highly conditional upon genetics, the use of additional genomic information can rapidly identify causal loci en masse

Genetic variation in the nuclear and organellar genomes modulates stochastic variation in the metabolome, growth, and defense

Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype