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The benefits of crystallization in a microgravity environment have been documented. Herein, we update the community on the results of a retrospective meta-analysis and data curation of 507 unique crystallization experiments that have been disseminated in the literature over a broad diversity of substrates. The analysis variables in the dataset that were evaluated include individual success metrics such as size, structural improvement, improved uniformity, increased resolution limit, and improved mosaicity. The overall databases were evaluated over time and by molecular complexity. Compared to ground experiments, crystals grown in a microgravity environment continue to show improvement across all metrics evaluated. The retrospective analysis demonstrates that success rates of crystallization experiments in microgravity improved over time. There also seems to be no correlation between complexity of molecule, using molecular weight as a surrogate for complexity, and successful crystallization under microgravity conditions. The microgravity environment provides improvement to crystal fabrication across metrics utilized for evaluation and molecular types, and the datasets utilized for this investigation are excellent tools for this evaluation.

Microgravity has been shown to be an excellent tool for protein crystal formation. A retrospective analysis of all publicly available crystallization data, including many that have not yet been published, clearly demonstrates the value of the microgravity environment for producing superior protein crystals. The parameters in the database (the Butler Microgravity Protein Crystal Database, BμCDB) that were evaluated pertain to both crystal morphology and diffraction quality. Success metrics were determined as improvements in size, definition, uniformity, mosaicity, diffraction quality, resolution limits, and B factor. The proteins in the databases were evaluated by molecular weight, protein type, the number of subunits, space group, and Mattew’s Coefficient. Compared to ground experiments, crystals grown in a microgravity environment continue to show improvement across all metrics evaluated. General trends as well as numerical differences are included in the assessment of the BμCDB. The microgravity environment improves crystal formation across a spectrum of metrics and the datasets utilized for this investigation are excellent tools for this evaluation.

A change order may increase or decrease the contract amount, or it may modify the contract time. Because of the perceived negative context, it is important that all parties carefully review and understand the circumstances that lead to change orders.

Background Abbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense breasts. Upscaling abMRI provision requires the workforce of mammogram readers to learn to effectively interpret abMRI. The purpose of this study was to examine the diagnostic accuracy of mammogram readers to interpret abMRI after a single day of standardised small-group training and to compare diagnostic performance of mammogram readers experienced in full-protocol breast MRI (fpMRI) interpretation (Group 1) with that of those without fpMRI interpretation experience (Group 2). Methods Mammogram readers were recruited from six NHS Breast Screening Programme sites. Small-group hands-on workstation training was provided, with subsequent prospective, independent, blinded interpretation of an enriched dataset with known outcome. A simplified form of abMRI (first post-contrast subtracted images (FAST MRI), displayed as maximum-intensity projection (MIP) and subtracted slice stack) was used. Per-breast and per-lesion diagnostic accuracy analysis was undertaken, with comparison across groups, and double-reading simulation of a consecutive screening subset. Results 37 readers (Group 1: 17, Group 2: 20) completed the reading task of 125 scans (250 breasts) (total = 9250 reads). Overall sensitivity was 86% (95% confidence interval (CI) 84–87%; 1776/2072) and specificity 86% (95%CI 85–86%; 6140/7178). Group 1 showed significantly higher sensitivity (843/952; 89%; 95%CI 86–91%) and higher specificity (2957/3298; 90%; 95%CI 89–91%) than Group 2 (sensitivity = 83%; 95%CI 81–85% (933/1120) p < 0.0001; specificity = 82%; 95%CI 81–83% (3183/3880) p < 0.0001). Inter-reader agreement was higher for Group 1 (kappa = 0.73; 95%CI 0.68–0.79) than for Group 2 (kappa = 0.51; 95%CI 0.45–0.56). Specificity improved for Group 2, from the first 55 cases (81%) to the remaining 70 (83%) ( p = 0.02) but not for Group 1 (90–89% p = 0.44), whereas sensitivity remained consistent for both Group 1 (88–89%) and Group 2 (83–84%). Conclusions Single-day abMRI interpretation training for mammogram readers achieved an overall diagnostic performance within benchmarks published for fpMRI but was insufficient for diagnostic accuracy of mammogram readers new to breast MRI to match that of experienced fpMRI readers. Novice MRI reader performance improved during the reading task, suggesting that additional training could further narrow this performance gap.

Acute kidney injury (AKI) is a common complication of hospitalisations. This national audit assessed the care received by patients with AKI in hospital Trusts in England and Wales. Twenty four hospital Trusts across England and Wales took part. Patients with AKI stage2/3 were identified using the UK Renal Registry AKI master patient index. Data was returned through a secure portal with linkage to hospital episode statistic mortality and hospitalisation data. Completion rates of AKI care standards and regional variations in care were established. 989 AKI episodes were included in the analyses. In-hospital 30-day mortality was 31-33.1% (AKI 2/3). Standard AKI interventions were completed in >80% of episodes. Significant inter-hospital variation remained in attainment of AKI care standards after adjustment for age and sex. Recording of urinalysis (41.9%) and timely imaging (37.2%) were low. Information on discharge summaries relating to medication changes/re-commencement and follow-up blood tests associated with reduced mortality. No quality indicators relating to clinical management associated with mortality. Better communication on discharge summaries associated with reduced mortality. Outcomes for patients with AKI in hospital remain poor. Regional variation in care exists. Work is needed to assess whether improving and standardising care improves patient outcomes.

Background Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. Methods A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. Study design Prospective, blinded interpretation of an enriched dataset by multiple readers. Results 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81–84%; 1994/2408), specificity 94% (95%CI 93–94%; 7806/8338), readers’ agreement with the true outcome kappa = 0.75 (95%CI 0.74–0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59–81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p  = 0.14), but slightly higher specificity (94% v. 93%, p  = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task ( p  = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p  = 0.02). Concordance with the ground truth was significantly associated with reading batch size ( p  = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8–47,466) to interpret each FAST MRI scan compared with 78 (14–22,830, p < 0.0001) for Group 2. Conclusions Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019) : ISRCTN16624917.

Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch.

The recently proposed System Identification via Validation and Adaptation (SIVA) method allows system identification, uncertainty quantification, and model validation directly from data. Inspired by generative modeling, SIVA employs a neural network that converts random noise to physically meaningful parameters. The known equation of motion utilizes these parameters to generate fake accelerations, which are compared to real training data using a mean square error loss. For concurrent parameter validation, independent datasets are passed through the model, and the resulting signals are classified as real or fake by a discriminator network, which guides the parameter-generator network. In this work, we apply SIVA to simulated vibration data from a cantilever beam that contains a lumped mass and a nonlinear end attachment, demonstrating accurate parameter estimation and model updating on complex, highly nonlinear systems.