Investigating Instructor Talk in Novel Contexts: Widespread Use, Unexpected Categories, and an Emergent Sampling Strategy.

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.

Classroom sound can be used to classify teaching practices in college science courses.

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.

Functional overlap between eIF4G isoforms in Saccharomyces cerevisiae.

Initiation factor eIF4G is a key regulator of eukaryotic protein synthesis, recognizing proteins bound at both ends of an mRNA to help recruit messages to the small (40S) ribosomal subunit. Notably, the genomes of a wide variety of eukaryotes encode multiple distinct variants of eIF4G. We found that deletion of eIF4G1, but not eIF4G2, impairs growth and global translation initiation rates in budding yeast under standard laboratory conditions. Not all mRNAs are equally sensitive to loss of eIF4G1; genes that encode messages with longer poly(A) tails are preferentially affected. However, eIF4G1-deletion strains contain significantly lower levels of total eIF4G, relative to eIF4G2-delete or wild type strains. Homogenic strains, which encode two copies of either eIF4G1 or eIF4G2 under native promoter control, express a single isoform at levels similar to the total amount of eIF4G in a wild type cell and have a similar capacity to support normal translation initiation rates. Polysome microarray analysis of these strains and the wild type parent showed that translationally active mRNAs are similar. These results suggest that total eIF4G levels, but not isoform-specific functions, determine mRNA-specific translational efficiency.

Myosin light chain kinase A is activated by cGMP-dependent and cGMP-independent pathways.

Stimulation of Dictyostelium cells with the chemoattractant cAMP results in transient phosphorylation of the myosin regulatory light chain (RLC). We show that myosin light chain kinase A (MLCK-A) is responsible for RLC phosphorylation during chemotaxis, and that MLCK-A itself is transiently phosphorylated on threonine-166, dramatically increasing its catalytic activity. MLCK-A activation during chemotaxis is highly responsive to cellular cGMP levels and the cGMP-binding protein GbpC. MLCK-A- cells have a partial cytokinesis defect, and do not phosphorylate RLC in response to concanavalin A (conA), but cells lacking cGMP or GbpC divide normally and phosphorylate in response to conA. Thus MLCK-A is activated by a cGMP/GbpC-independent mechanism activated during cytokinesis or by conA, and a cGMP/GbpC-dependent pathway during chemotaxis.

Induced fit and "lock and key" recognition of 5S RNA by zinc fingers of transcription factor IIIA.

Transcription factor IIIA (TFIIIA) is a Cys2His2 zinc finger protein that regulates expression of the 5 S ribosomal RNA gene by binding specifically to the internal control element. TFIIIA also functions in transport and storage of 5 S RNA by binding directly to the RNA transcript. To obtain insights into the mechanism by which TFIIIA recognizes 5 S RNA, we determined the solution structure of the middle three zinc fingers bound to the central core of 5 S RNA. Finger 4 utilizes "lock and key" recognition to bind in the widened major groove of the pre-structured RNA loop E motif. This interaction is mediated by direct hydrogen bonding interactions with bases. In contrast, recognition of loop A, a flexible junction of three helices, occurs by an induced fit mechanism that involves reorganization of the conserved CAUA motif and structuring of the finger 5-finger 6 interface to form a complementary RNA binding surface.