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Claire Linturn Group

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Valentine Ershov
Valentine Ershov

Graphical Rapid Analysis Of Structures Program.epub



It is challenging to conduct and quickly disseminate findings from in-depth qualitative analyses, which can impede timely implementation of interventions because of its time-consuming methods. To better understand tradeoffs between the need for actionable results and scientific rigor, we present our method for conducting a framework-guided rapid analysis (RA) and a comparison of these findings to an in-depth analysis of interview transcripts.




Graphical Rapid Analysis Of Structures Program.epub



Timeline for conducting rapid and in-depth analysis. Some transcript coding took place as part of CFIR codebook development (i.e., the first 93 days). CFIR Consolidated Framework for Implementation Research


With respect to consistency of our RA and in-depth analysis findings [14], themes from the RA were well-aligned with the CFIR domains and constructs from the in-depth analysis. Considering the CFIR was embedded throughout the evaluation, including the design of interview guides and indirectly in development of the summary tables, these findings are not entirely unexpected. Upon further reflection, we could have elected to more explicitly incorporate the CFIR constructs into the RA summary tables rather than indirectly through the interview guides, and this may have made RA even faster. This would still be considered a rapid analytic approach, but would have carried the CFIR more transparently throughout the RA portion of the project. Depending on the anticipated uses of similar evaluation data, this may further streamline the method.


Given the complexity of the CFIR (i.e., multiple constructs per domain), rapid analytic methods like ours may be helpful when working with large numbers of interviews where line-by-line coding and analysis may not be possible, and/or when evaluating highly complex interventions where one needs to quickly identify key aspects of implementation. However, careful consideration should be taken prior to adopting this approach to limit the potential for bias and to limit the potential for providing an overly narrow interpretation of the data. It is important to keep in mind that the combination of the strength and frequency of qualitative comments is what helps us understand their relative importance and contributions to our research [20], regardless of whether you are using a rapid or in-depth analytic approach.


Achieving balance between the need for actionable results and scientific rigor is challenging. The use of rapid analytic methods for the analysis of data from a process evaluation of a successful AD program proved to be adequate for providing our operations partner with actionable suggestions in a relatively short timeframe.


Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at and A record of this paper's transparent peer review process is included in the Supplemental Information.


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