C Wright Mills, power and the power elites ? a reappraisal

This paper revisits and presents a critical appraisal of Mills's analysis of power and the power elite. There are signs of a revival of interest in Mills, but recent commentators have shown little interest in the intellectual, social or political context of his analysis. Setting Mills's thesis in its historical context, we consider an element of his project that has been particularly neglected in recent discussion: Mills's search for possible ways of redistributing power and his attempt to forge an ethico-political stance. Reflecting on recent discussion of contemporary elite formations, we comment on what critics might take from Mills in our own time in relation to the analysis of elites and the politics of critical management studies

"The dots just don't join up": understanding the support needs of families of children on the autism spectrum

Much research has documented the elevated levels of stress experienced by families of autistic children. Yet remarkably little research has examined the types of support that these families perceive to be beneficial to their lives. This study, co-produced by researchers and school-based professionals, sought to establish these families’ support needs from their own perspectives. In total, 139 parents of autistic children with additional intellectual disabilities and limited spoken communication, all attending an inner-city London school, participated in an initial survey examining parental wellbeing, self-efficacy and the extent to which they felt supported. Semi-structured interviews were conducted with a subgroup of parents (n = 17), some of whom reported in the survey that they felt unsupported, in order to gain their in-depth perspectives. The results from both the survey and the interviews suggested that existing support (particularly from formal support services) was not meeting parents’ needs, which ultimately made them feel isolated and alienated. Parents who were interviewed called for service provision that adopted a relational, family-centred approach – one that understands the specific needs of the whole family, builds a close working relationship with them and ensures that they are supported at times when the parents and families feel they need it most

Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks

Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-ba sed algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures

Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deeplearning- based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the GI tract (esophagus, stomach, duodenum) and surrounding organs (liver, spleen, left kidney, gallbladder). We directly compared the segmentation accuracy of the proposed method to existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 vs. 0.71, 0.74 and 0.74 for the pancreas, 0.90 vs 0.85, 0.87 and 0.83 for the stomach and 0.76 vs 0.68, 0.69 and 0.66 for the esophagus. We conclude that deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures

Mitochondrial junction region as genotyping marker for cyclospora cayetanensis

© 2019, Centers for Disease Control and Prevention (CDC). All rights reserved. Cyclosporiasis is an infection caused by Cyclospora cayetanensis, which is acquired by consumption of contaminated fresh food or water. In the United States, cases of cyclosporiasis are often associated with foodborne outbreaks linked to imported fresh produce or travel to disease-endemic countries. Epidemiologic investigation has been the primary method for linking outbreak cases. A molecular typing marker that can identify genetically related samples would be helpful in tracking outbreaks. We evaluated the mitochondrial junction region as a potential genotyping marker. We tested stool samples from 134 laboratory-confirmed cases in the United States by using PCR and Sanger sequencing. All but 2 samples were successfully typed and divided into 14 sequence types. Typing results were identical among samples within each epidemiologically defined case cluster for 7 of 10 clusters. These findings suggest that this marker can distinguish between distinct case clusters and might be helpful during cyclosporiasis outbreak investigation

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

MOTIVATION: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. METHOD: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. RESULTS: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. CONCLUSION: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance

A pragmatic randomised trial of stretching before and after physical activity to prevent injury and soreness

OBJECTIVE: To determine the effects of stretching before and after physical activity on risks of injury and soreness in a community population. DESIGN: Internet-based pragmatic randomised trial conducted between January 2008 and January 2009. SETTING: International. PARTICIPANTS: A total of 2377 adults who regularly participated in physical activity. INTERVENTIONS: Participants in the stretch group were asked to perform 30 s static stretches of seven lower limb and trunk muscle groups before and after physical activity for 12 weeks. Participants in the control group were asked not to stretch. MAIN OUTCOME MEASUREMENTS: Participants provided weekly on-line reports of outcomes over 12 weeks. Primary outcomes were any injury to the lower limb or back, and bothersome soreness of the legs, buttocks or back. Injury to muscles, ligaments and tendons was a secondary outcome. RESULTS: Stretching did not produce clinically important or statistically significant reductions in all-injury risk (HR=0.97, 95% CI 0.84 to 1.13), but did reduce the risk of experiencing bothersome soreness (mean risk of bothersome soreness in a week was 24.6% in the stretch group and 32.3% in the control group; OR=0.69, 95% CI 0.59 to 0.82). Stretching reduced the risk of injuries to muscles, ligaments and tendons (incidence rate of 0.66 injuries per person-year in the stretch group and 0.88 injuries per person-year in the control group; HR=0.75, 95% CI 0.59 to 0.96). CONCLUSION: Stretching before and after physical activity does not appreciably reduce all-injury risk but probably reduces the risk of some injuries, and does reduce the risk of bothersome soreness. TRIAL REGISTRATION: anzctr.org.au 12608000044325

The impact of measurement error in modelled ambient particles exposures on health effect estimates in multi-level analysis: a simulation study.

Background: Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the “true” underlying daily exposure surfaces for PM10 and PM2.5 for 2009–2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from −11% (underestimate) to 20% (overestimate) for PM10 and of −20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments