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Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess 'do-ability'. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers' knowledge of terms including algorithm and code; a concept of 'emergent algorithms' is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study's results are generalisable more widely.

Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts we rename the LOA levels: problem, design, code and running the code. In our qualitative exploratory study, we interviewed five K-5 teachers on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses we found our teachers used design as an instructional strategy and for assessment. Our teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess 'do-ability'. Teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their understanding of student understanding. One issue was of our teachers' knowledge of terms including algorithm and code; we propose a concept of "emergent algorithms". Our findings suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether our results are generalisable more widely.

Recent experimental and bioinformatic advances enable the recovery of genomes belonging to yet-uncultured microbial lineages directly from environmental samples. Here, we report on the recovery and characterization of single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) representing candidate phylum LCP-89, previously defined based on 16S rRNA gene sequences. Analysis of LCP-89 genomes recovered from Zodletone Spring, an anoxic spring in Oklahoma, predicts slow-growing, rod-shaped organisms. LCP-89 genomes contain genes for cell wall lipopolysaccharide (LPS) production but lack the entire machinery for peptidoglycan biosynthesis, suggesting an atypical cell wall structure. The genomes, however, encode S-layer homology domain-containing proteins, as well as machinery for the biosynthesis of CMP-legionaminate, inferring the possession of an S-layer glycoprotein. A nearly complete chemotaxis machinery coupled to the absence of flagellar synthesis and assembly genes argues for the utilization of alternative types of motility. A strict anaerobic lifestyle is predicted, with dual respiratory (nitrite ammonification) and fermentative capacities. Predicted substrates include a wide range of sugars and sugar alcohols and a few amino acids. The capability of rhamnose metabolism is confirmed by the identification of bacterial microcompartment genes to sequester the toxic intermediates generated. Comparative genomic analysis identified differences in oxygen sensitivities, respiratory capabilities, substrate utilization preferences, and fermentation end products between LCP-89 genomes and those belonging to its four sister phyla (Calditrichota, SM32-31, AABM5-125-24, and KSB1) within the broader FCB (Fibrobacteres-Chlorobi-Bacteroidetes) superphylum. Our results provide a detailed characterization of members of the candidate division LCP-89 and highlight the importance of reconciling 16S rRNA-based and genome-based phylogenies.IMPORTANCE Our understanding of the metabolic capacities, physiological preferences, and ecological roles of yet-uncultured microbial phyla is expanding rapidly. Two distinct approaches are currently being utilized for characterizing microbial communities in nature: amplicon-based 16S rRNA gene surveys for community characterization and metagenomics/single-cell genomics for detailed metabolic reconstruction. The occurrence of multiple yet-uncultured bacterial phyla has been documented using 16S rRNA surveys, and obtaining genome representatives of these yet-uncultured lineages is critical to our understanding of the role of yet-uncultured organisms in nature. This study provides a genomics-based analysis highlighting the structural features and metabolic capacities of a yet-uncultured bacterial phylum (LCP-89) previously identified in 16S rRNA surveys for which no prior genomes have been described. Our analysis identifies several interesting structural features for members of this phylum, e.g., lack of peptidoglycan biosynthetic machinery and the ability to form bacterial microcompartments. Predicted metabolic capabilities include degradation of a wide range of sugars, anaerobic respiratory capacity, and fermentative capacities. In addition to the detailed structural and metabolic analysis provided for candidate division LCP-89, this effort represents an additional step toward a unified scheme for microbial taxonomy by reconciling 16S rRNA gene-based and genomics-based taxonomic outlines.

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data. [ABSTRACT FROM AUTHOR]

BackgroundMusculoskeletal conditions account for 16% of global disability, resulting in a negative effect on millions of patients and an increasing burden on health care utilization. Digital technologies that improve health care outcomes and efficiency are considered a priority; however, innovations are often inadequately developed and poorly adopted. Further, they are rarely tested with sufficient rigor in clinical trials—the gold standard for clinical proof of efficacy. We have developed a new musculoskeletal Digital Assessment Routing Tool (DART) that allows users to self-assess and be directed to the right care. DART requires usability testing in preparation for clinical trials. ObjectiveThis study will use the iterative convergent mixed methods design to assess and mitigate all serious usability issues to optimize user experience and adoption. Using this methodology, we will provide justifiable confidence to progress to full-scale randomized controlled trials when DART is integrated into clinical management pathways. This study protocol will provide a blueprint for future usability studies of mobile health solutions. MethodsWe will collect qualitative and quantitative data from 20-30 participants aged 18 years and older for 4 months. The exact number of participants recruited will be dependent on the number of iterative cycles required to reach the study end points. Building on previous internal testing and stakeholder involvement, quantitative data collection is defined by the constructs within the ISO 9241-210-2019 standard and the system usability scale, providing a usability score for DART. Guided by the participant responses to quantitative questioning, the researcher will focus the qualitative data collection on specific usability problems. These will then be graded to provide the rationale for further DART system improvements throughout the iterative cycles. ResultsThis study received approval from the Queen Mary University of London Ethics of Research Committee (QMREC2018/48/048) on June 4, 2020. At manuscript submission, study recruitment was on-going, with data collection to be completed and results published in 2021. ConclusionsThis study will provide evidence concerning mobile health DART system usability and acceptance determining system improvements required to support user adoption and minimize suboptimal system usability as a potential confounder within subsequent noninferiority clinical trials. Success should produce a safe effective system with excellent usability, facilitating quicker and easier patient access to appropriate care while reducing the burden on primary and secondary care musculoskeletal services. This deliberately rigorous approach to mobile health innovation could be used as a guide for other developers of similar apps. International Registered Report Identifier (IRRID)DERR1-10.2196/27205

BackgroundLow back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts. ObjectiveThis paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. MethodsWe propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network. ResultsEthical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity. ConclusionsWe have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions. International Registered Report Identifier (IRRID)DERR1-10.2196/21804

Objectives Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway. Methods People with RA were recruited from Barts Health NHS Trust rheumatology clinics to complete paper questionnaires and clinicians were recruited from a variety of centres in the UK to complete an online questionnaire. Data collected included demographics, current technology use, challenges managing RA, RA medications and monitoring, clinic appointments, opportunities for technology and barriers to technology. Results A total of 109 patient and 41 clinician questionnaires were completed. A total of 83.5% of patients and 93.5% of clinicians use smartphones daily. However, only 25% had ever used an arthritis app and only 5% had persisted with one. Both groups identified managing pain, flares and RA medications as areas of existing need. Access to care, medication support and disease education were mutually agreeable opportunities; however, discrepancies existed between groups with clinicians prioritizing education over access, likely due to concerns of data overwhelm (80.6% considered this a barrier). Conclusions In spite of high technology use and willingness from both sides, our cohort did not utilize technology to support care, suggesting inadequacies in the existing software. The lack of an objective biomarker for RA disease activity, existing challenges in the healthcare system and the need for integration with existing technical systems were identified as the greatest barriers. Trial registration Registered on the Clinical Research Network registry (IRAS ID: 264690). [ABSTRACT FROM AUTHOR]