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The purpose of this mixed-method research study was to better understand community college students' engagement in global learning. The study, supported by the Florida Consortium for International Education, was conducted during the 2015-2016 academic year in Florida across nine community colleges drawing from a 55-question survey with an 11% response rate (n = 175). Both qualitative and quantitative methods were used, with data analysis conducted using NVivo and SPSS software. An electronic survey was piloted with one institution, and a refined version was sent to select professors across nine community colleges in the state for distribution to students in the fall semester of 2015. A total of 175 students completed the survey. The majority of students responding were interested in global learning; however, they reported that this interest stemmed primarily from family encouragement and personal experiences rather than from their academic experiences. Students did report that professors were promoting global learning in their classes, although the results were not statistically significant. Students who did not express an interest in global learning felt as though it was not important because it was unrelated to their daily lives. It is the hope that this study will inform community college administrators and faculty to better understand how and why students become interested in global learning to more effectively promote their engagement both on and off campus.

Building and evaluating artifacts specifically for K-12 education, technologists committed to design sciences are needed along with an approach to evaluation increasing the systemic transfer from research and development into school settings. The authors describe THE VIRTUAL LAB scanning electronic microscope simulation, including (a) its development from a design and project management perspective at NASA and (b) the formative evaluation, drawing on a usability study conducted in high school science classrooms by University of Central Florida. The authors offer a four-level assessment that begins with the overarching vision that guided developers. Over time, a team of evaluators (a) verify (the artifact), (b) check variation (of content), (c) verify (in context), and (d) document value (in the field of education). THE VIRTUAL LAB usability study is offered as a specific example in the evaluation process using both quantitative and qualitative data while responding and contributing to an overarching vision for education. (Contains 3 figures and 2 tables.)

Background Pioneering views at the dawn of the GWAS era held that nodes of genetic convergence downstream of any individual molecule per se regulate genetic networks subserving the core neurophysiological elements affected in schizophrenia (SCZ [1]). These nodes of genetic convergence might be, for example, transcription factors or miRNAs representing entry points into biologically valid pathways that affect cellular, systems-level and behavioral phenotypes. Since many of the SCZ risk variants are non-coding and may control gene expression, it is plausible that the regulatory elements of gene expression represent a mechanism of risk. Using Weighted Gene Co-expression Network Analysis (WGCNA), we have previously shown that genetic networks including SCZ risk genes indexed via co-expression quantitative traits loci (co-eQTLs) are associated with core neurophysiological systems-level processes affected in SCZ, like working memory brain activity patterns [2]. However, genetic regulatory elements linking risk genes with altered regulation of gene expression at multiple risk loci are still missing. Methods In two RNA sequencing studies, we first identified a gene co-expression module enriched for miR-137 targets and for SCZ risk, validated it in a mouse cell model, and found that its co-eQTLs are associated with brain activity. In our first study, we computed polygenic scores obtained either combining co-eQTLs associated with target genes of miR-137 or SCZ risk variants within target genes of miR-137; then, we compared their effects on two neurobiological phenotypes of SCZ, i.e. brain activation during working memory and emotion recognition. In our second study, we adopted an approach agnostic of regulators and identified gene co-expression modules enriched for SCZ risk genes. Finally, we associated SCZ risk modules with treatment response to olanzapine in two patients' cohorts. Results We found different neurobiological readouts of the two scores, i.e. polygenic risk for SCZ was associated with working memory, while co-expression scores were associated with emotion recognition. These results suggest that co-eQTLs index a different signal compared to SCZ risk variants, although both are associated with SCZ phenotypes. In the second study, SCZ risk genes were co-expressed to a significant extent and were co-regulated by miR-101, miR-374, and miR-28. Furthermore, in two independent samples of patients with SCZ, the co-eQTLs of a SCZ risk module were associated with the inter-individual variation between patients in terms of positive symptoms decrease following 1-month treatment. Discussion These findings suggest that genetic risk for SCZ translates into illness via gene co-regulation pathways mediated by molecular factors potentially susceptible to genetic influences. Furthermore, these results suggest that data mining of the transcriptome can effectively stratify patients in terms of their neurobiological and clinical phenotypes. This is relevant to prospective approaches entailing personalized pharmacological treatment. References: 1. Harrison, P. J. & Weinberger, D. R. Molecular psychiatry 10, 40–68; (2005). 2. Pergola, G. et al. Translational psychiatry 7, e1006; (2017). [ABSTRACT FROM AUTHOR]

Background Schizophrenia (SCZ) is associated with genetic factors, and specific risk loci have been identified. Still, the biology and clinical translation of genetic risk remain largely unknown. Gene co-expression networks are relevant to functional and clinical translation of SCZ risk. We hypothesized that SCZ risk genes may converge into co-expression pathways which, in turn, may be associated with clinical phenotypes in SCZ patients. Methods We used Weighted Gene Co-expression Network Analysis to identify co-expression modules in two prefrontal cortex post-mortem RNA sequencing datasets, including 379 healthy controls (HC) and 309 SCZ. We used four replication datasets (HC=339). To identify modules enriched for SCZ risk genes, we computed hypergeometric tests and corrected for multiple comparisons. To translate post mortem information into clinical phenotypes, we identified polymorphisms predicting co-expression and combined them to obtain an index approximating module co-expression (Polygenic Co-expression Index: PCI). We used two independent replication datasets (HC=131). Finally, we tested the association between PCI and treatment response in two independent SCZ cohorts (SCZ=167). Results We identified and replicated (all p-values<.001) a single module enriched for SCZ risk genes (13 genes in 10 loci, corrected-p-value<.001). PCI-co-expression association was replicated in two datasets (p-values<.05). PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (p-values<.05). Discussion These findings show that co-expression pathways are relevant to the neurobiology of SCZ and part of the risk genes is co-expressed in the prefrontal cortex. Results provide evidence for the clinical relevance of this co-expression pathway and its associated polymorphisms. [ABSTRACT FROM AUTHOR]