Journeys to identity : why care records matter

Care experienced people often find themselves applying for their care records in search of answers — to address gaps and inconsistencies in the knowledge they hold about their childhoods and personal development, which may in turn affect their broader senses of self. This article, written from our own lived experiences, provides a commentary on a system of writing, accessing and reading records which is not aligned to the circumstances and purposes of care experienced people and which indeed frequently disempowers and (re-)traumatises. We share our experiences of applying for and reading our records, as an adoptee and as a care experienced person. We also draw on the discussions and experiences of a Who Cares? Scotland care records campaign group. This commentary reveals the power imbalance at the heart of record keeping where the rights to memory, identity, and childhood are effectively questioned. It also makes suggestions for future practice. It asks for a complete rethinking of how care records are regarded by professionals and the sector, advocating for a shift in power as regards the production and control of information and a significant improvement in the care offered to those of us who choose to access it

Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GP

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source

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X-ray crystallography was used to characterise the title compound for the first time, and the 1H NMR, 13C NMR and IR spectroscopic data from earlier reports were also updated.Publisher PDFPeer reviewe

DCMQI: An open source library for standardized communication of quantitative image analysis results using DICOM

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi

Review of Rural Development Instruments: DG Agri project 2006-G4-10. Final Report

The aim of the study was to review the policy instruments under the framework of the European Agricultural Fund for Rural Development (EAFRD), 2007-13, by: • reviewing intervention rationales and instruments and their use against the objectives, priorities and key actions in the EU Strategic Guidelines; • assessing whether and how RD rationales and instruments should be adapted to deliver these more effectively. The study involved 8 tasks, grouped into 3 themes of analysis: 1) the targeting of EU-27 rural development expenditure, 2000-13, including the development of databases of EU-27 rural area characteristics and ‘indicators of need’ for RD; 2) consideration of the adequacy of the current EAFRD framework, based upon an evaluation of instruments’ cost-effectiveness; the a priori development of a typology of RD interventions and catalogue of instruments; an analysis of delivery mechanisms; and assessment of instruments in ‘fiches’; 3) conclusions and recommendations. In the event, progress in finalising national and/or regional RDPs 2007-13 was delayed,over the study period. Thus, the approach was modified to incorporate more qualitative analysis and the expenditure analysis was made using incomplete figures (July 2007), so 4% of total EAFRD planned expenditure was missing

Forest Plant and Bird Communities in the Lau Group, Fiji

We examined species composition of forest and bird communities in relation to environmental and human disturbance gradients on Lakeba (55.9 km²), Nayau (18.4 km²), and Aiwa Levu (1.2 km²), islands in the Lau Group of Fiji, West Polynesia. The unique avifauna of West Polynesia (Fiji, Tonga, Samoa) has been subjected to prehistoric human-caused extinctions but little was previously known about this topic in the Lau Group. We expected that the degree of human disturbance would be a strong determinant of tree species composition and habitat quality for surviving landbirds, while island area would be unrelated to bird diversity.All trees > 5 cm diameter were measured and identified in 23 forest plots of 500 m² each. We recognized four forest species assemblages differentiated by composition and structure: coastal forest, dominated by widely distributed species, and three forest types with differences related more to disturbance history (stages of secondary succession following clearing or selective logging) than to environmental gradients (elevation, slope, rockiness). Our point counts (73 locations in 1 or 2 seasons) recorded 18 of the 24 species of landbirds that exist on the three islands. The relative abundance and species richness of birds were greatest in the forested habitats least disturbed by people. These differences were due mostly to increased numbers of columbid frugivores and passerine insectivores in forests on Lakeba and Aiwa Levu. Considering only forested habitats, the relative abundance and species richness of birds were greater on the small but completely forested (and uninhabited) island of Aiwa Levu than on the much larger island of Lakeba.Forest disturbance history is more important than island area in structuring both tree and landbird communities on remote Pacific islands. Even very small islands may be suitable for conservation reserves if they are protected from human disturbance