Spatial integration of optic flow information in direction of heading judgments

While we know that humans are extremely sensitive to optic flow information about direction of heading, we do not know how they integrate information across the visual field. We adapted the standard cue perturbation paradigm to investigate how young adult observers integrate optic flow information from different regions of the visual field to judge direction of heading. First, subjects judged direction of heading when viewing a three-dimensional field of random dots simulating linear translation through the world. We independently perturbed the flow in one visual field quadrant to indicate a different direction of heading relative to the other three quadrants. We then used subjects' judgments of direction of heading to estimate the relative influence of flow information in each quadrant on perception. Human subjects behaved similarly to the ideal observer in terms of integrating motion information across the visual field with one exception: Subjects overweighted information in the upper half of the visual field. The upper-field bias was robust under several different stimulus conditions, suggesting that it may represent a physiological adaptation to the uneven distribution of task-relevant motion information in our visual world

Evaluating the articulation of programme theory in practice as observed in Quality Improvement initiatives

Background: The Action-Effect Method(AEM) was co-developed by NIHR CLAHRC Northwest London (CLAHRC NWL) researchers and QI practitioners, building on Driver Diagrams(DD). This study aimed to determine AEM effectiveness in terms of technical aspects (how diagrams produced in practice compared with theoretical ideals) and social aspects (how engagement with the method related to social benefits). Methods Diagrams were scored on criteria developed on theoretical ideals of programme theory. 65 programme theory diagrams were reviewed (21 published Driver Diagrams (External DDs), 22 CLAHRC NWL Driver Diagrams (Internal DDs), and 21 CLAHRC NWL Action-Effect Diagrams(AEDs)). Social functions were studied through ethnographic observation of frontline QI teams in AEM sessions facilitated by QI experts. Qualitative analysis used inductive and deductive coding. Results ANOVA indicated the AEM significantly improved the quality of programme theory diagrams over Internal and External DDs on an average of 5 criteria from an 8-point assessment. Articulated aims were more likely to be patient-focused and high-level in AEDs than DDs. The cause/effect relationships from intervention to overall aim also tended to be clearer and were more likely than DDs to contain appropriate measure concepts. Using the AEM also served several social functions such as facilitating dialogue among multidisciplinary teams, and encouraging teams to act scientifically and pragmatically about planning and measuring QI interventions. Implications: The Action-Effect Method developed by CLAHRC NWL resulted in improvements over Driver Diagrams in articulating programme theory, which has wide-ranging benefits to quality improvement, including encouraging broad multi-disciplinary buy-in to clear aims and pre-planning a rigorous evaluation strategy

Criteria for evaluating programme theory diagrams in quality improvement initiatives: a structured method for appraisal

Background: Despite criticisms that many Quality Improvement (QI) initiatives fail due to incomplete programme theory, there is no defined way to evaluate how programme theory has been articulated. The objective of this research was to develop, and assess the usability and reliability of, scoring criteria to evaluate programme theory diagrams. Methods: Criteria development was informed by published literature and QI experts. Inter-rater reliability was tested between two evaluators. 63 programme theory diagrams (42 driver diagrams and 21 action effect diagrams) were reviewed to establish whether the criteria could support comparative analysis of different approaches to constructing diagrams. Results: Components of the scoring criteria include: assessment of overall aim, logical overview, clarity of components, cause/effect relationships, evidence, and measurement. Independent reviewers had 78% inter-rater reliability. Scoring enabled direct comparison of different approaches to developing programme theory; Action-Effect diagrams were found to have had a statistically significant but moderate improvement in programme theory quality over Driver Diagrams; no significant differences were observed based on the setting in which Driver Diagrams were developed. Conclusions: The scoring criteria summarise the necessary components of programme theory that are thought to contribute to successful QI projects. The viability of the scoring criteria for practical application was demonstrated. Future uses include assessment of individual programme theory diagrams, and comparison of different approaches (e.g. methodological, teaching or other QI support) to produce programme theory. The criteria can be used as a tool to guide the production of better programme theory diagrams, and also highlights where additional support for QI teams could be needed

A pilot survey of junior doctors’ attitudes and awareness around medication review: time to change our educational approach?

© 2015, BMJ Publishing Group. All rights reserved.Objectives Our aim was to explore junior doctors attitudes and awareness around concepts related to medication review, in order to find ways to change the culture for reviewing, altering and stopping inappropriate or unnecessary medicines. Having already demonstrated the value of team working with senior doctors and pharmacists and the use of a medication review tool, we are now looking to engage first year clinicians and undergraduates in the process. Method An online survey about medication review was distributed among all 42 foundation year one (FY1) doctors at the Chelsea and Westminster Hospital NHS Foundation Trust in November 2014. Descriptive statistics were used for analysis. Results Twenty doctors completed the survey (48%). Of those, 17 believed that it was the pharmacists duty to review medicines; and 15 of 20 stated the general practitioner (GP). Sixteen of 20 stated that they would consult a senior doctor first before stopping medication. Eighteen of 20 considered the GP and consultant to be responsible for alterations, rather than themselves. Sixteen of 20 respondents were not aware of the availability of a medication review tool. Seventeen of 20 felt that more support from senior staff would help them become involved with medication review. Conclusions Junior doctors report feeling uncomfortable altering mediations without consulting a senior first. They appear to be building confidence with prescribing in their first year but not about the medication review process or questioning the drugs already prescribed. Consideration should be given to what we have termed a bottom-up educational approach to provide early experience of and change the culture around medication review, to include the education of undergraduate and foundation doctors and pharmacists

The Alzheimer’s Disease Sequencing Project – Follow Up Study (ADSP‐FUS): APOE genotype status and demographic characteristics across datasets

Abstract Background The ADSP‐FUS is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for Alzheimer Disease (AD) by expanding the ADSP beyond non‐Hispanic Whites of European Ancestry (NHW‐EA) populations. Given the lack of diversity in the ADSP, the ADSP‐FUS was designed to whole genome sequence (WGS) existing ethnically diverse and unique cohorts. The upcoming phase ADSP‐ FUS 2.0: The Diverse Population Initiative, focuses on inclusion of Hispanic/Latino (HL), non‐Hispanic Black with African Ancestry (NHB‐AA), and Asian populations. Methods ADSP‐FUS cohorts consist of studies of AD, dementia, and age‐related conditions. Clinical classifications are assigned based on standard criteria from clinical measures and history, as well as additional neuropathologic data. In addition to production of WGS, genome‐wide array and APOE genotyping is acquired or performed for all ADSP‐FUS samples. Results The ADSP‐FUS currently consists of 38 cohorts comprised of ∼40,000 individuals, with plan to sequence >100,000 individuals from diverse ancestries. Genotyping, sequencing, and clinical adjudication has been performed on 23,428 participants (cases N = 6,961, median age = 73; controls N = 13,007, median age = 72; ADRD N = 3,460, median age = 77. More participants are female (62.3%) than male and are evenly distributed across cases (61.0%), controls (63.1%), and ADRD (61.8%). As expected, the most prevalent APOE genotype is APOE 3/3 (% by cases/controls for 2/2 = 0.2,0.4; 2/3 = 4.3, 8.2; 2/4 = 2.2, 1.8; 3/3 = 43.8, 64.4; 3/4 = 39.5, 23.0; 4/4 = 10.1, 2.2). These proportions vary greatly between ethnicities, with the highest for APOE 4/4 observed in Asian participants (8.8%) and the lowest in Hispanic participants (2.5%), for example. Mean Braak stage for AD cases is higher (5.1+1.2) than controls (2.6+1.3) and ADRD participants (3.5+1.6). Conclusion The results provide an overview of features of ADSP‐FUS cohorts. As the ADSP‐FUS expands in size and diversity, this genomic resource, available via NIAGADS, will be integrated with ADSP programs focused on phenotype harmonization, association analyses, functional genomics, and machine learning. In concert with these programs, the ADSP‐FUS will accelerate the identification and understanding of potential genetic risk and protective variants for AD across all populations with the target of developing new treatments that are globally effective

ADSP Whole Genome Sequencing (WGS) Release 4 Data Update from Genome Center for Alzheimer’s Disease

Abstract Background The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data with the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated through collaboration between investigators from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. With the goal of minimizing data heterogeneity, introduced by different sequencing protocols and assays, GCAD processes all samples using standardized pipelines and performs quality control (QC)/quality assurance (QA) checks. Methods Raw sequencing data (FASTQs or BAMs) were aligned to GRCh38/hg38 by BWA, and variant calling and joint genotyping on single nucleotide variants (SNVs), insertions and deletions (indels), were done by GATK. Structural variants (SVs) were called per sample using the Smoove, Manta, and Strelka packages. Preliminary QA checks including sex check, contamination, and genotype concordance were performed followed by QC per ADSP protocol to evaluate the quality of samples and variants. To facilitate access and usage of massive joint‐genotype called VCF files, a compact version for storing variant info and sample genotypes only was released first. Results We dropped 275 (0.7%) samples of poor coverage (362M bi‐allelic variants, >58M multi‐allelic variants, with 95% of variants remaining after QC. SV calling is ongoing and data will be ready prior to the conference. Conclusion The ADSP and GCAD generate high quality SNVs, indels and SV calls. Currently GCAD is preparing the next release of ∼60,000 more ancestrally‐diverse WGS samples sequenced primarily through the ADSP Follow‐Up Study, which we anticipate will be released in 2023 to greatly benefit the AD genetics community