Towards computer-aided design of cellular structure.

Cells are complex machines with tremendous potential for applications in medicine and biotechnology. Although much effort has been devoted to engineering the metabolic, genetic, and signaling pathways of cells, methods for systematically engineering the physical structure of cells are less developed. Here we consider how coarse-grained models for cellular geometry at the organelle level can be used to build computer-aided design (CAD) tools for cellular structure.

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.

Organelle size scaling of the budding yeast vacuole is tuned by membrane trafficking rates.

Organelles serve as biochemical reactors in the cell, and often display characteristic scaling trends with cell size, suggesting mechanisms that coordinate their sizes. In this study, we measure the vacuole-cell size scaling trends in budding yeast using optical microscopy and a novel, to our knowledge, image analysis algorithm. Vacuole volume and surface area both show characteristic scaling trends with respect to cell size that are consistent among different strains. Rapamycin treatment was found to increase vacuole-cell size scaling trends for both volume and surface area. Unexpectedly, these increases did not depend on macroautophagy, as similar increases in vacuole size were observed in the autophagy deficient mutants atg1Δ and atg5Δ. Rather, rapamycin appears to act on vacuole size by inhibiting retrograde membrane trafficking, as the atg18Δ mutant, which is defective in retrograde trafficking, shows similar vacuole size scaling to rapamycin-treated cells and is itself insensitive to rapamycin treatment. Disruption of anterograde membrane trafficking in the apl5Δ mutant leads to complementary changes in vacuole size scaling. These quantitative results lead to a simple model for vacuole size scaling based on proportionality between cell growth rates and vacuole growth rates.

High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio.

Four-dimensional fluorescence microscopy--which records 3D image information as a function of time--provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.