Although the anaerobic biodegradation of methyl tert-butyl ether (MTBE) and tert-butyl alcohol (TBA) has been documented in the laboratory and the field, knowledge of the microorganisms and mechanisms involved is still lacking. In this study, DNA-stable isotope probing (SIP) was used to identify microorganisms involved in anaerobic fuel oxygenate biodegradation in a sulfate-reducing MTBE and TBA plume. Microorganisms were collected in the field using Bio-Sep® beads amended with ¹³C₅-MTBE, ¹³C₁-MTBE (only methoxy carbon labeled), or¹³C₄-TBA. ¹³C-DNA and ¹²C-DNA extracted from the Bio-Sep beads were cloned and 16S rRNA gene sequences were used to identify the indigenous microorganisms involved in degrading the methoxy group of MTBE and the tert-butyl group of MTBE and TBA. Results indicated that microorganisms were actively degrading ¹³C-labeled MTBE and TBA in situ and the ¹³C was incorporated into their DNA. Several sequences related to known MTBE- and TBA-degraders in the Burkholderiales and the Sphingomonadales orders were detected in all three¹³C clone libraries and were likely to be primary degraders at the site. Sequences related to sulfate-reducing bacteria and iron-reducers, such as Geobacter and Geothrix, were only detected in the clone libraries where MTBE and TBA were fully labeled with ¹³C, suggesting that they were involved in processing carbon from the tert-butyl group. Sequences similar to the Pseudomonas genus predominated in the clone library where only the methoxy carbon of MTBE was labeled with ¹³C. It is likely that members of this genus were secondary degraders cross-feeding on ¹³C-labeled metabolites such as acetate.

Instructor Talk-noncontent language used by instructors in classrooms-is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk framework to noncontent language used by instructors in novel course contexts. We analyzed Instructor Talk in eight additional biology courses in their entirety and in 61 biology courses using an emergent sampling strategy. We observed widespread use of Instructor Talk with variation in the amount and category type used. The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework. Additionally, a new form of Instructor Talk-Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process-was detected in these novel course contexts. Finally, the emergent sampling strategy described here may allow investigation of Instructor Talk in even larger numbers of courses across institutions and disciplines. Given its widespread use, potential influence on students in learning environments, and ability to be sampled, Instructor Talk may be a key variable to consider in future research on teaching and learning in higher education.

Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.