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Previous studies have documented that concerns about genetic discrimination (GD) may influence access to and participation in medically necessary care. We sought to characterize how GD issues influence current cancer genetics professional (CGP) practice, determine if their attitudes regarding GD have changed over time, and compare their knowledge and attitudes regarding laws prohibiting GD to a contemporary cohort of non-genetics clinicians. Members of the National Society of Genetic Counselors Familial Cancer Special Interest Group were invited to complete a 39 item online survey, adapted from previously published instruments. The resulting data were compared to a survey of CGPs published in 2000 and to a contemporary cohort of non-genetics clinicians (n = 1,181). There were 153 qualified respondents. Compared to the historical CGP cohort (n = 163), a significantly greater proportion said they would bill insurance for the cost of genetic testing for themselves (P < 0.0001). Most CGPs (94%) considered the risk of GD to be low to theoretical, concordant with 64% who expressed confidence in existing federal laws prohibiting GD. The mean knowledge score of CGPs regarding GD protective laws was significantly greater than that of non-genetics clinicians (P < 0.001). As barometers of change, CGPs show a migration in opinion over the past 8 years, with decreased fear of GD and greater knowledge of laws prohibiting GD compared to non-genetics clinicians. Better knowledge of GD and protective legislation, may facilitate non-genetics clinician utilization of genetics and personalized medicine.

The B-lymphocyte accessory molecule Ig-alpha (Ig-α) is encoded by the mouse B cell-specific gene (mb-1), and along with the Ig-beta (Ig-β) molecule and a membrane bound immunoglobulin (mIg) makes up the B-cell receptor (BCR). Ig-α and Ig-β form a heterodimer structure that upon antigen binding and receptor clustering primarily initiates and controls BCR intracellular signaling via a phosphorylation cascade, ultimately triggering an effector response. The signaling capacity of Ig-α is contained within its immunoreceptor tyrosine-based activation motif (ITAM), which is also a key component for intracellular signaling initiation in other immune cell-specific receptors. Although numerous studies have been devoted to the mb-1 gene product, Ig-α, and its signaling mechanism, an evolutionary analysis of the mb-1 gene has been lacking until now. In this study, mb-1 coding sequences from 19 species were compared using Bayesian inference. Analysis revealed a gene phylogeny consistent with an expected species divergence pattern, clustering species from the primate order separate from lower mammals and other species. In addition, an overall comparison of non-synonymous and synonymous nucleotide mutational changes suggests that the mb-1 gene has undergone purifying selection throughout its evolution.

Computer Supported Collaborative Science (CSCS) is a teaching pedagogy that uses collaborative web-based resources to engage all learners in the collection, analysis, and interpretation of whole-class data sets, and is useful for helping secondary and college students learn to think like scientists and engineers. This chapter presents the justification for utilizing whole-class data analysis as an important aspect of the CSCS pedagogy and demonstrates how it aligns with the Next Generation Science Standards (NGSS). The chapter achieves this end in several ways. First, it reviews rationale outlined in the NGSS science and engineering practices for adapting 21st century technologies to teach students 21st century science inquiry skills. Second, it provides a brief overview of the basis for our pedagogical perspective for engaging learners in pooled data analysis and presents five principles of CSCS instruction. Third, we offer several real-world and research-based excerpts as illustrative examples indicating the value and merit of utilizing CSCS whole-class data analysis. Fourth, we postulate recommendations for improving the ways science, as well as other subject matter content areas, will need to be taught as the U.S. grapples with the role-out of new Common Core State Standards (CCSS) and NGSS. Taken together, these components of CSCS whole-class data analysis help constitute a pedagogical model for teaching that functionally shifts the focus of science teaching from cookbook data collection to pooled data analysis, resulting in deeper understanding.

Continuous Formative Assessment (CFA) is a strategy that employs free and accessible collaborative cloud-based technologies to collect, stream, and archive evidence of student knowledge, reasoning, and understanding during STEM lessons, so that instructors and students can make evidence-based decisions for adjusting lessons to optimize learning. Writing samples, diagrams, equations, drawings, photos, and movies are collected from all students and archived in cloud-based databases so that instructors can assess student understanding during instruction, and monitor learning gains over time. This chapter introduces and explains CFA techniques and provides preliminary research pertaining to the effectiveness of CFA instructional strategies in promoting student accountability, metacognition, and engagement in STEM courses, and suggests avenues for future research.