Much of the work done by faculty at both public and private universities has significant public dimensions: it is often paid for by public funds; it is often aimed at serving the public good; and it is often subject to public evaluation. To understand how the public dimensions of faculty work are valued, we analyzed review, promotion, and tenure documents from a representative sample of 129 universities in the US and Canada. Terms and concepts related to public and community are mentioned in a large portion of documents, but mostly in ways that relate to service, which is an undervalued aspect of academic careers. Moreover, the documents make significant mention of traditional research outputs and citation-based metrics: however, such outputs and metrics reward faculty work targeted to academics, and often disregard the public dimensions. Institutions that seek to embody their public mission could therefore work towards changing how faculty work is assessed and incentivized.
The data that support the findings of this study are available in the Harvard Dataverse with the identifier https://doi.org/10.7910/DVN/VY4TJE (Alperin et al., 2019). These data include the list of institutions and academic units for which we have acquired documents along with an indicator of whether each term and concept studied was found in the documents for the institution or academic unit. The data also include the aggregated values and chi-square calculations reported. The code used for computing these aggregations can be found on Github https://github.com/ScholCommLab/rpt-project (Alperin, 2019). The documents collected are available on request from the corresponding author (JPA). These documents are not publicly available due to copyright restrictions.
Terms and Concepts found in Tenure and Promotion Guidelines from the US and CanadaHarvard Dataverse, 2018-05-22.
- Juan Pablo Alperin
- Meredith T Niles
- Erin C McKiernan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Emma Pewsey, eLife, United Kingdom
© 2019, Alperin et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least 6 of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 milliseconds of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin’s binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 mM vs. 0.36 mM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.
Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data is typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n=12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n=2) during an instrumental task from calcium fluorescence in orbitofrontal cortex (OFC). DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array (FPGA) hardware for real time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.