A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

<p>Abstract</p> <p>Background</p> <p>In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.</p> <p>Results</p> <p>Instead of the two traditional methods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate single, master, and modified calibration curves. The mean and the percentiles of the posterior distribution were used as point and interval estimates of unknown parameters such as intercepts, slopes and DNA concentrations. The software WinBUGS was used to perform all simulations and to generate the posterior distributions of all the unknown parameters of interest.</p> <p>Conclusion</p> <p>The Bayesian approach defined in this study allowed for the estimation of DNA concentrations from environmental samples using absolute standard curves generated by real-time qPCR. The approach accounted for uncertainty from multiple sources such as experiment-to-experiment variation, variability between replicate measurements, as well as uncertainty introduced when employing calibration curves generated from absolute plasmid DNA standards.</p

A Measure of Student Engagement for Serious Games and IoT

Student Engagement has been a strong topic of research for the avoidance of student drop out and the increase in grading. Serious games have highlighted benefits in engaging students, primarily through edutainment, educating via games. This article suggests a Computer Algorithm, purposed at measuring and encouraging student engagement. In addition, the algorithm accounts for sensor networks accessed both directly and through the Internet, extending its application to the Internet of Things (IoT)

Improving Scientific Research and Writing Skills through Peer Review and Empirical Group Learning

Here we describe a semester-long, multipart activity called “Read and wRite to reveal the Research process” (R3) that was designed to teach students the elements of a scientific research paper. We implemented R3 in an advanced immunology course. In R3, we paralleled the activities of reading, discussion, and presentation of relevant immunology work from primary research papers with student writing, discussion, and presentation of their own lab findings. We used reading, discussing, and writing activities to introduce students to the rationale for basic components of a scientific research paper, the method of composing a scientific paper, and the applications of course content to scientific research. As a final part of R3, students worked collaboratively to construct a Group Research Paper that reported on a hypothesis-driven research project, followed by a peer review activity that mimicked the last stage of the scientific publishing process. Assessment of student learning revealed a statistically significant gain in student performance on writing in the style of a research paper from the start of the semester to the end of the semester