The Opportunities and Challenges of Research Data and Software for Libraries and Institutional Repositories

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Software Citation Checklist for Developers

This document provides a minimal, generic checklist that developers of software (either open or closed source) used in research can use to ensure they are following good practice around software citation. This will help developers get credit for the software they create, and improve transparency, reproducibility, and reuse

Effective health care for older people living and dying in care homes: A realist review

Background: Care home residents in England have variable access to health care services. There is currently no coherent policy or consensus about the best arrangements to meet these needs. The purpose of this review was to explore the evidence for how different service delivery models for care home residents support and/or improve wellbeing and health-related outcomes in older people living and dying in care homes. Methods: We conceptualised models of health care provision to care homes as complex interventions. We used a realist review approach to develop a preliminary understanding of what supported good health care provision to care homes. We completed a scoping of the literature and interviewed National Health Service and Local Authority commissioners, providers of services to care homes, representatives from the Regulator, care home managers, residents and their families. We used these data to develop theoretical propositions to be tested in the literature to explain why an intervention may be effective in some situations and not others. We searched electronic databases and related grey literature. Finally the findings were reviewed with an external advisory group. Results: Strategies that support and sustain relational working between care home staff and visiting health care professionals explained the observed differences in how health care interventions were accepted and embedded into care home practice. Actions that encouraged visiting health care professionals and care home staff jointly to identify, plan and implement care home appropriate protocols for care, when supported by ongoing facilitation from visiting clinicians, were important. Contextual factors such as financial incentives or sanctions, agreed protocols, clinical expertise and structured approaches to assessment and care planning could support relational working to occur, but of themselves appeared insufficient to achieve change. Conclusion: How relational working is structured between health and care home staff is key to whether health service interventions achieve health related outcomes for residents and their respective organisations. The belief that either paying clinicians to do more in care homes and/or investing in training of care home staff is sufficient for better outcomes was not supported.This research was funded by National Institute of Health Research Health Service Delivery and Research programme (HSDR 11/021/02)

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype

Photometry of the Didymos System across the DART Impact Apparition

On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the satellite of binary near-Earth asteroid (65803) Didymos. This demonstrated the efficacy of a kinetic impactor for planetary defense by changing the orbital period of Dimorphos by 33 minutes. Measuring the period change relied heavily on a coordinated campaign of lightcurve photometry designed to detect mutual events (occultations and eclipses) as a direct probe of the satellite’s orbital period. A total of 28 telescopes contributed 224 individual lightcurves during the impact apparition from 2022 July to 2023 February. We focus here on decomposable lightcurves, i.e., those from which mutual events could be extracted. We describe our process of lightcurve decomposition and use that to release the full data set for future analysis. We leverage these data to place constraints on the postimpact evolution of ejecta. The measured depths of mutual events relative to models showed that the ejecta became optically thin within the first ∼1 day after impact and then faded with a decay time of about 25 days. The bulk magnitude of the system showed that ejecta no longer contributed measurable brightness enhancement after about 20 days postimpact. This bulk photometric behavior was not well represented by an HG photometric model. An HG 1 G 2 model did fit the data well across a wide range of phase angles. Lastly, we note the presence of an ejecta tail through at least 2023 March. Its persistence implied ongoing escape of ejecta from the system many months after DART impact

Publisher Correction: Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease