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Responding to the need to teach remotely due to COVID-19, we used readily available computational approaches (and developed associated tutorials (https://mdh-cures-community.squarespace.com/virtual-cures-and-ures)) to teach virtual Course-Based Undergraduate Research Experience (CURE) laboratories that fulfil generally accepted main components of CUREs or Undergraduate Research Experiences (UREs): Scientific Background, Hypothesis Development, Proposal, Experiments, Teamwork, Data Analysis, Conclusions, and Presentation1. We then developed and taught remotely, in three phases, protein-centric CURE activities that are adaptable to virtually any protein, emphasizing contributions of noncovalent interactions to structure, binding and catalysis (an ASBMB learning framework2 foundational concept). The courses had five learning goals (unchanged in the virtual format),focused on i) use of primary literature and bioinformatics, ii) the roles of non-covalent interactions, iii) keeping accurate laboratory notebooks, iv) hypothesis development and research proposal writing, and, v) presenting the project and drawing evidence based conclusions The first phase, Developing a Research Proposal, contains three modules, and develops hallmarks of a good student-developed hypothesis using available literature (PubMed3) and preliminary observations obtained using bioinformatics, Module 1: Using Primary Literature and Data Bases (Protein Data Base4, Blast5 and Clustal Omega6), Module 2: Molecular Visualization (PyMol7 and Chimera8), culminating in a research proposal (Module 3). Provided rubrics guide student expectations. In the second phase, Preparing the Proteins, students prepared necessary proteins and mutants using Module 4: Creating and Validating Models, which leads users through creating mutants with PyMol, homology modeling with Phyre29 or Missense10, energy minimization using RefineD11 or ModRefiner12, and structure validation using MolProbity13. In the third phase, Computational Experimental Approaches to Explore the Questions developed from the Hypothesis, students selected appropriate tools to perform their experiments, chosen from computational techniques suitable for a CURE laboratory class taught remotely. Questions, paired with computational approaches were selected from Modules 5: Exploring Titratable Groups in a Protein using H++14, 6: Exploring Small Molecule Ligand Binding (with SwissDock15), 7: Exploring Protein-Protein Interaction (with HawkDock16), 8: Detecting and Exploring Potential Binding Sites on a Protein (with POCASA17 and SwissDock), and 9: Structure-Activity Relationships of Ligand Binding & Drug Design (with SwissDock, Open Eye18 or the Molecular Operating Environment (MOE)19). All involve freely available computational approaches on publicly accessible web-based servers around the world (with the exception of MOE). Original literature/Journal club activities on approaches helped students suggest tie-ins to wet lab experiments they could conduct in the future to complement their computational approaches. This approach allowed us to continue using high impact CURE teaching, without changing our course learning goals. Quantitative data (including replicates) was collected and analyzed during regular class periods. Students developed evidence-based conclusions and related them to their research questions and hypotheses. Projects culminated in a presentation where faculty feedback was facilitated with the Virtual Presentation platform from QUBES20 These computational approaches are readily adaptable for topics accessible for first to senior year classes and individual research projects (UREs). We used them in both partial and full semester CUREs in various institutional settings. We believe this format can benefit faculty and students from a wide variety of teaching institutions under conditions where remote teaching is necessary. 

Heat conduction is strongly suppressed in turbulent, weakly collisional, and magnetized plasmas. 

Understanding magnetic-field generation and amplification in turbulent plasma is essential to account for observations of magnetic fields in the universe. A theoretical framework attributing the origin and sustainment of these fields to the so-called fluctuation dynamo was recently validated by experiments on laser facilities in low-magnetic-Prandtl-number plasmas ( P m < 1 ). However, the same framework proposes that the fluctuation dynamo should operate differently when P m ≳ 1 , the regime relevant to many astrophysical environments such as the intracluster medium of galaxy clusters. This paper reports an experiment that creates a laboratory P m ≳ 1 plasma dynamo. We provide a time-resolved characterization of the plasma’s evolution, measuring temperatures, densities, flow velocities, and magnetic fields, which allows us to explore various stages of the fluctuation dynamo’s operation on seed magnetic fields generated by the action of the Biermann-battery mechanism during the initial drive-laser target interaction. The magnetic energy in structures with characteristic scales close to the driving scale of the stochastic motions is found to increase by almost three orders of magnitude and saturate dynamically. It is shown that the initial growth of these fields occurs at a much greater rate than the turnover rate of the driving-scale stochastic motions. Our results point to the possibility that plasma turbulence produced by strong shear can generate fields more efficiently at the driving scale than anticipated by idealized magnetohydrodynamics (MHD) simulations of the nonhelical fluctuation dynamo; this finding could help explain the large-scale fields inferred from observations of astrophysical systems. 

Readily available, free, computational approaches, adaptable for topics accessible for first to senior year classes and individual research projects, emphasizing contributions of noncovalent interactions to structure, binding and catalysis were used to teach Course‐based Undergraduate Research Experiences that fulfil generally accepted main CURE components: Scientific Background, Hypothesis Development, Proposal, Experiments, Teamwork, Data Analysis of quantitative data, Conclusions, and Presentation.