A number of small, self-cleaving ribozyme classes have been identified including the hammerhead, hairpin, hepatitis delta virus (HDV), Varkud satellite (VS), glmS, twister, hatchet, pistol, and twister sister ribozymes. Within the active sites of these ribozymes, myriad functional groups contribute to catalysis. There has been extensive structure-function analysis of individual ribozymes, but the extent to which catalytic devices are shared across different ribozyme classes is unclear. As such, emergent catalytic principles for ribozymes may await discovery. Identification of conserved catalytic devices can deepen our understanding of RNA catalysis specifically and of enzymic catalysis generally. To probe similarities and differences amongst ribozyme classes, active sites from more than 80 high-resolution crystal structures of self-cleaving ribozymes were compared computationally. We identify commonalities amongst ribozyme classes pertaining to four classic catalytic devices: deprotonation of the 2′OH nucleophile (γ), neutralization of the non-bridging oxygens of the scissile phosphate (β), neutralization of the O5′ leaving group (δ), and in-line nucleophilic attack (α). In addition, we uncover conservation of two catalytic devices, each of which centers on the activation of the 2′OH nucleophile by a guanine: one to acidify the 2′OH by hydrogen bond donation to it (γ′) and one to acidify the 2′OH by releasing it from nonproductive interactions by competitive hydrogen bonding (γ′′). Our findings reveal that the amidine functionalities of G, A, and C are especially important for these strategies, and help explain absence of U at ribozyme active sites. The identified γ′ and γ′′ catalytic strategies help unify the catalytic strategies shared amongst catalytic RNAs and may be important for large ribozymes, as well as protein enzymes that act on nucleic acids.

The instruction of high enrollment general and organic chemistry laboratories at a large public 10 university always have curricular, administrative, and logistical challenges. Herein, we describe how we met these challenges in the transition to remote teaching during the COVID-19 pandemic. We discuss the reasoning behind our approach, the utilization of our existing web-based course content, the additions and alterations to our curriculum, replacement of experimental work with videos, the results of both student and TA surveys, and lessons learned for iterations of these courses in the near 15 future. File list (3) download file view on ChemRxiv CHEMRXIV_REVISED-Online in No Time.pdf (1.34 MiB) download file view on ChemRxiv Online in No Time Supporting Information.pdf (181.10 KiB) download file view on ChemRxiv bigbrother_python_code.py (3.15 KiB)

The instruction of high enrollment general and organic chemistry laboratories at a large public 10 university always have curricular, administrative, and logistical challenges. Herein, we describe how we met these challenges in the transition to remote teaching during the COVID-19 pandemic. We discuss the reasoning behind our approach, the utilization of our existing web-based course content, the additions and alterations to our curriculum, replacement of experimental work with videos, the results of both student and TA surveys, and lessons learned for iterations of these courses in the near 15 future.

Mechanical properties of cells are important features that are tightly regulated and are dictated by various pathologies. Deformability cytometry allows for the characterization of the mechanical properties at a rate of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that approaches the maximum potential classification accuracy for single cells. In order to find this maximum accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations and provides a comprehensive set of mechanotyping parameters. We track the shape dynamics of individual cells with high time resolution and apply sequence-based deep learning models for feature extraction. In order to create a dataset based solely on differing mechanical properties, a model system was created with treated and untreated HL60 cells. Treated cells were exposed to chemical agents that perturb either the actin or microtubule networks. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes and were found to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified two of the sub-populations of HL60 cells with an accuracy over 90%, significantly higher than the 75% we achieved with traditional methods. This increase in accuracy corresponds to a fivefold increase in potential enrichment of a sample for a target population. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.

Remote delivery approaches to laboratory courses in the response to the COVID-19 pandemic have included a spectrum spanning passive options such as providing students with prerecorded videos of experiments to replacing in-person laboratory experiences with immersive virtual reality environments. While interactive activities that require students to make choices about experimental design or procedure, mimicking levels of inquiry present in in-person laboratory experiments, are preferred, creating custom activities of this type often require expensive equipment, a media production team, and knowledge of multiple programming languages. The open source, nonlinear storytelling platform, Twine, provides a free alternative that allows instructors to create custom interactive choose-your-own-adventure activities based on their existing laboratory curriculum and inclusive of text, images, and video clips. We used Twine to create a choose-your-own-adventure laboratory activity as the final experiment in a remote delivery format organic chemistry laboratory course that allowed students to obtain customized data based on their experimental choices. We expanded this work to create an entire course of Twine choose-your-own-adventure laboratory activities for a third term organic chemistry laboratory course.

The instruction of high enrollment general and organic chemistry laboratories at a large public 10 university always have curricular, administrative, and logistical challenges. Herein, we describe how we met these challenges in the transition to remote teaching during the COVID-19 pandemic. We discuss the reasoning behind our approach, the utilization of our existing web-based course content, the additions and alterations to our curriculum, replacement of experimental work with videos, the results of both student and TA surveys, and lessons learned for iterations of these courses in the near 15 future.

Mechanobiology has shown great success in revealing complex cellular dynamics in various pathologies and physiologies. Most methods for assessing a cell's mechanical properties, however, generally extract only a few physical constants such as Young's modulus. This can limit the potential for accurate classification given the wide variety of rheological properties of cells, there are many ways for cells to differ. While it was recently shown that deep learning can classify cells more accurately than traditional approaches, it is not clear how this may be extended to unsupervised classification. In this work, we showcase the potential for a deep learning model to classify cells in an unsupervised fashion using a blend of physical properties. We introduce the combination of a variational autoencoder and a previously described clustering loss for classifying cells in an unsupervised fashion.

Remote delivery approaches to laboratory courses in the response to the COVID-19 pandemic have included a spectrum spanning passive options such as providing students with prerecorded videos of experiments to replacing in-person laboratory experiences with immersive virtual reality environments. While interactive activities that require students to make choices about experimental design or procedure, mimicking levels of inquiry present in in-person laboratory experiments are preferred, creating custom activities of this type often require expensive equipment, a media production team, and knowledge of multiple programming languages. The open source, non-linear storytelling platform, Twine, provides a free alternative that allows instructors to create custom interactive choose-your-own-adventure activities based on their existing laboratory curriculum and inclusive of text, images, and video clips. We used Twine to create a choose-your-own-adventure laboratory activity as the final experiment in a remote delivery format organic chemistry laboratory course that allowed students to obtain customized data based on their experimental choices. We expanded this work to create an entire course of Twine choose-your-own-adventure laboratory activities for a third term organic chemistry laboratory course.