Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

‘The only way is Essex’: Gender, union and mobilisation among fire service control room staff

This contribution to On the Front Line records a dialogue between two female Fire Brigades Union (FBU) representatives in the Essex Emergency Control Room who led industrial action over the imposition of a shift system that stretched their work–life balance to breaking point and constrained their ability to work full-time. Their testimony reveals how male members were mobilised in the interests of predominantly female control staff. Kate and Lynne’s discussion illuminates the interaction of gender and class interests and identities in the union and in the lives of its women members. It provides insight into the efficacy of trade unions for women’s collective action

Medical Therapies for Uterine Fibroids - A Systematic Review and Network Meta-Analysis of Randomised Controlled Trials

BACKGROUND: Uterine fibroids are common, often symptomatic and a third of women need repeated time off work. Consequently 25% to 50% of women with fibroids receive surgical treatment, namely myomectomy or hysterectomy. Hysterectomy is the definitive treatment as fibroids are hormone dependent and frequently recurrent. Medical treatment aims to control symptoms in order to replace or delay surgery. This may improve the outcome of surgery and prevent recurrence. PURPOSE: To determine whether any medical treatment can be recommended in the treatment of women with fibroids about to undergo surgery and in those for whom surgery is not planned based on currently available evidence. STUDY SELECTION: Two authors independently identified randomised controlled trials (RCT) of all pharmacological treatments aimed at the treatment of fibroids from a list of references obtained by formal search of MEDLINE, EMBASE, Cochrane library, Science Citation Index, and ClinicalTrials.gov until December 2013. DATA EXTRACTION: Two authors independently extracted data from identified studies. DATA SYNTHESIS: A Bayesian network meta-analysis was performed following the National Institute for Health and Care Excellence-Decision Support Unit guidelines. Odds ratios, rate ratios, or mean differences with 95% credible intervals (CrI) were calculated. RESULTS AND LIMITATIONS: A total of 75 RCT met the inclusion criteria, 47 of which were included in the network meta-analysis. The overall quality of evidence was very low. The network meta-analysis showed differing results for different outcomes. CONCLUSIONS: There is currently insufficient evidence to recommend any medical treatment in the management of fibroids. Certain treatments have future promise however further, well designed RCTs are needed

Examination of local movement and migratory behavior of sea turtles during spring and summer along the Atlantic Coast off the Southeastern United States : annual report.

Loggerhead sea turtles inhabiting coastal waters along the southeastern United States represent the progeny of multiple rookeries. Tagging studies of nesting female loggerheads suggest that most return to the same beaches in successive breeding seasons and it is widely accepted that most females return to their natal regions to nest. The focus of the in-water survey was modified to intensively target one small trawling area to: (1) examine the effect of intensive trawling on recapture rates and (2) quickly obtain an adequate sample size of turtles to outfit with satellite transmitters. This annual report highlights the major findings for research activities primarily carried out during 2005

Patient Specific Dosimetry Phantoms Using Multichannel LDDMM of the Whole Body

This paper describes an automated procedure for creating detailed patient-specific pediatric dosimetry phantoms from a small set of segmented organs in a child's CT scan. The algorithm involves full body mappings from adult template to pediatric images using multichannel large deformation diffeomorphic metric mapping (MC-LDDMM). The parallel implementation and performance of MC-LDDMM for this application is studied here for a sample of 4 pediatric patients, and from 1 to 24 processors. 93.84% of computation time is parallelized, and the efficiency of parallelization remains high until more than 8 processors are used. The performance of the algorithm was validated on a set of 24 male and 18 female pediatric patients. It was found to be accurate typically to within 1-2 voxels (2–4 mm) and robust across this large and variable data set

Native Texas Ornamental Bunchgrass Performance Under Water Restrictions

Growing human populations and increasing drought conditions compete with ornamental grassland landscapes for freshwater resources. With outdoor use as the largest consumer of municipal water, irrigation restrictions will likely be increasingly implemented, restricting ornamental municipal grasslands. Substituting irrigation-dependent exotic grasses with drought-adapted native bunchgrasses could help mitigate this problem. Greenhouse (GH) trials revealed exotic ornamental bunchgrasses declined faster than natives under progressive water stress, with natives performing best under moderate water with maximum water treatments decreasing aesthetic quality. There was wide variability among accessions, indicating promising genetic diversity from which to select drought resistance for ornamentals. Native grasses performed best in field trials with supplemental irrigation during warm-season growth and restricted irrigation during the cool season. In northcentral Texas, native little bluestem (LBS; Schizachyrium scoparium L.) accessions outperformed exotics in health and aesthetics across environments. Most response variables were species as well as accession dependent. Select LBS accessions are recommended for commercialization for municipal grasslands due to superior field performance under water restrictions. Replacing favored water-intensive exotic grasses with adapted native grasses could help reduce irrigation water use

Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation

Accurate 3D modeling of human organs plays a crucial role in building computational phantoms for virtual imaging trials. However, generating anatomically plausible reconstructions of organ surfaces from computed tomography scans remains challenging for many structures in the human body. This challenge is particularly evident when dealing with the large intestine. In this study, we leverage recent advancements in geometric deep learning and denoising diffusion probabilistic models to refine the segmentation results of the large intestine. We begin by representing the organ as point clouds sampled from the surface of the 3D segmentation mask. Subsequently, we employ a hierarchical variational autoencoder to obtain global and local latent representations of the organ's shape. We train two conditional denoising diffusion models in the hierarchical latent space to perform shape refinement. To further enhance our method, we incorporate a state-of-the-art surface reconstruction model, allowing us to generate smooth meshes from the obtained complete point clouds. Experimental results demonstrate the effectiveness of our approach in capturing both the global distribution of the organ's shape and its fine details. Our complete refinement pipeline demonstrates remarkable enhancements in surface representation compared to the initial segmentation, reducing the Chamfer distance by 70%, the Hausdorff distance by 32%, and the Earth Mover's distance by 6%. By combining geometric deep learning, denoising diffusion models, and advanced surface reconstruction techniques, our proposed method offers a promising solution for accurately modeling the large intestine's surface and can easily be extended to other anatomical structures

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities