Evaluating Implementation Contexts, Mechanisms, and Outcomes: A Normalization Process Theory Coding Manual for Qualitative Research and Instrument Development

Background. Qualitative studies, especially those conducted by teams of researchers, may benefit from clearly structured, parsimonious, coding manuals. The process of creating rigorous and robust coding manuals for individual studies is rarely described, and generalizable coding manuals are rare. Normalization Process Theory (NPT) provides conceptual tools to facilitate understanding of the dynamics of adoption, implementation, and sustainment of socio-technical and organizational innovations. As a widely used theory, a generalizable coding manual would be of utility to implementation researchersObjectives. To make the application of NPT simple for the user, to describe the development of a coding manual for qualitative content analytic studies using NPT, and present this for wider use.Method. Concept Selection and Structuring. Qualitative Content Analysis of selected published papers and interview transcripts. Results. All identifiable theoretical concepts (n=149) embedded in papers and chapters that developed NPT between 2006 and 2020 were identified and extracted from their texts. Overlapping, ambiguous, and duplicate versions of concepts were eliminated, as were concepts derived from other theories. This left 38 core concept definitions. These were piloted in coding of qualitative transcripts collected in two implementation studies, and by collaboratively coding papers collected for a systematic review of implementation studies. At the end of this process, a further process of elimination of overlapping or ambiguous concepts was undertaken leaving 12 primary NPT concepts. Conclusion. The process of coding manual resulted in the presentation of NPT concepts according to the Context-Mechanism-Outcomes configuration of realist evaluation research. A coding manual for NPT that is in accordance with realistic evaluation research was successfully produced and is now freely available to researchers who wish to use NPT in primary and secondary research that employs qualitative methods.

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

In the era of big data, new storage media are urgently needed because the storage capacity for global data cannot meet the exponential growth of information. Deoxyribonucleic acid (DNA) storage, where primer and address sequences play a crucial role, is one of the most promising storage media because of its high density, large capacity and durability. In this study, we describe an enhanced gradient-based optimizer that includes the Cauchy and Levy mutation strategy (CLGBO) to construct DNA coding sets, which are used as primer and address libraries. Our experimental results show that the lower bounds of DNA storage coding sets obtained using the CLGBO algorithm are increased by 4.3–13.5% compared with previous work. The non-adjacent subsequence constraint was introduced to reduce the error rate in the storage process. This helps to resolve the problem that arises when consecutive repetitive subsequences in the sequence cause errors in DNA storage. We made use of the CLGBO algorithm and the non-adjacent subsequence constraint to construct larger and more highly robust coding sets.

An Accurate Bioinformatics Tool For Anti-Cancer Peptide Generation Through Deep Learning Omics

ABSTRACTThe Anti-cancer targets play crucial role in signalling processes of cells. We have developed an Anti-Cancer Scanner (ACS) tool for identification of Anti-cancer targets in form of peptides. ACS tool also allows fast fingerprinting of the Anti-cancer targets of significance in the current bioinformatics research. There are tools currently available which predicts the above-mentioned features in single platform. In the present work, we have compared the features predicted by ACS with other on-line available methods and evaluated the performance of the ACS tool. ACS scanned the Anti-cancer target protein sequences provided by the user against the Anti-cancer target data-sets. It has been developed in PERL language and it is scalable having an extensible application in bioinformatics with robust coding architecture. It achieves a prediction accuracy of 95%, which is much higher than the existing tools.