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Pneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition. In all, 13,292 frontal chest X-rays (3,107 with pneumothorax) were visually annotated by radiologists. This dataset was used to train and evaluate multiple network architectures. Images showing large- or moderate-sized pneumothorax were considered positive, and those with trace or no pneumothorax were considered negative. Images showing small pneumothorax were excluded from training. Using an internal validation set (n = 1,993), we selected the 2 top-performing models; these models were then evaluated on a held-out internal test set based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV). The final internal test was performed initially on a subset with small pneumothorax excluded (as in training; n = 1,701), then on the full test set (n = 1,990), with small pneumothorax included as positive. External evaluation was performed using the National Institutes of Health (NIH) ChestX-ray14 set, a public dataset labeled for chest pathology based on text reports. All images labeled with pneumothorax were considered positive, because the NIH set does not classify pneumothorax by size. In internal testing, our \"high sensitivity model\" produced a sensitivity of 0.84 (95% CI 0.78-0.90), specificity of 0.90 (95% CI 0.89-0.92), and AUC of 0.94 for the test subset with small pneumothorax excluded. Our \"high specificity model\" showed sensitivity of 0.80 (95% CI 0.72-0.86), specificity of 0.97 (95% CI 0.96-0.98), and AUC of 0.96 for this set. PPVs were 0.45 (95% CI 0.39-0.51) and 0.71 (95% CI 0.63-0.77), respectively. Internal testing on the full set showed expected decreased performance (sensitivity 0.55, specificity 0.90, and AUC 0.82 for high sensitivity model and sensitivity 0.45, specificity 0.97, and AUC 0.86 for high specificity model). External testing using the NIH dataset showed some further performance decline (sensitivity 0.28-0.49, specificity 0.85-0.97, and AUC 0.75 for both). Due to labeling differences between internal and external datasets, these findings represent a preliminary step towards external validation. We trained automated classifiers to detect moderate and large pneumothorax in frontal chest X-rays at high levels of performance on held-out test data. These models may provide a high specificity screening solution to detect moderate or large pneumothorax on images collected when human review might be delayed, such as overnight. They are not intended for unsupervised diagnosis of all pneumothoraces, as many small pneumothoraces (and some larger ones) are not detected by the algorithm. Implementation studies are warranted to develop appropriate, effective clinician alerts for the potentially critical finding of pneumothorax, and to assess their impact on reducing time to treatment.

Despite the great advances in synthesis and structural determination of atomically precise, thiolate-protected metal nanoclusters, our understanding of the driving forces for their colloidal stabilization is very limited. Currently there is a lack of models able to describe the thermodynamic stability of these ‘magic-number’ colloidal nanoclusters as a function of their atomic-level structural characteristics. Herein, we introduce the thermodynamic stability theory, derived from first principles, which is able to address stability of thiolate-protected metal nanoclusters as a function of the number of metal core atoms and thiolates on the nanocluster shell. Surprisingly, we reveal a fine energy balance between the core cohesive energy and the shell-to-core binding energy that appears to drive nanocluster stabilization. Our theory applies to both charged and neutral systems and captures a large number of experimental observations. Importantly, it opens new avenues for accelerating the discovery of stable, atomically precise, colloidal metal nanoclusters. The thermodynamic stability of atomically precise, liganded metal nanoclusters remains poorly understood. Here, the authors use first-principles calculations to derive a new theory that rationalizes the stability of these nanoclusters as a function of their composition and morphology.

Purpose To systematically review the aetiological factors associated with molar incisor hypomineralisation (MIH). To this day, the aetiology remains unknown. Determining risk factors would allow risk assessment and enhance early diagnosis of MIH in young patients. The aim was to assess, evaluate and summarise the relationship between MIH and reported aetiological hypotheses. Methods Electronic database searches of MEDLINE, EMBASE, EBSCO, LILACS and Cochrane Library were conducted. Authors conformed to PRISMA guidelines. Studies were screened, data extracted, assessment of risk of bias and calibration was completed by two independent reviewers. Meta-analyses with heterogeneity calculations were performed. Results Of the potential 8949 studies, 64 studies were included in the qualitative analysis whilst 45 were included in the quantitative analysis. Prenatal factors: results are inconclusive as only unspecified maternal illnesses appear to be linked to MIH. Perinatal factors: prematurity (OR 1.45; 95% CI 1.24–1.70; p  = 0.0002) and caesarean delivery (OR 1.45; 95% CI 1.09, 1.93; p  < 0.00001) are associated with an increased risk of developing MIH. Birth complications are also highlighted. These three factors can lead to hypoxia, and children with perinatal hypoxia are more likely to develop MIH (OR 2.76; 95% CI 2.09–3.64; p  < 0.0001). Postnatal factors: measles, urinary tract infection, otitis media, gastric disorders, bronchitis, kidney diseases, pneumonia and asthma are associated with MIH. Fever and antibiotic use, which may be considered as consequences of childhood illnesses, are also associated with MIH. Genetic factors: an increasing number of studies highlight the genetic and epigenetic influences in the development of MIH. Conclusion Several systemic and genetic and/or epigenetic factors acting synergistically or additively are associated with MIH, revealing a multifactorial aetiology model. Peri- and postnatal aetiological factors are more likely to increase the odds of causing MIH than prenatal factors.

Purpose – Mobile applications, or apps, are an increasingly important part of omnichannel retailing. While the adoption and usage of apps for marketing purposes has grown exponentially over the past few years, there is little academic research in this area. The purpose of this paper is to examine how the mobile phone platform (Android vs Apple iOS), interest in the app and recency of store visit affect consumers’ likelihood to use the apps for purchasing and information-sharing activities. Design/methodology/approach – The paper tests a model by analysing survey data collected from customers of a major US retailer using partial least squares regression. Findings – The analysis finds that the level of interest in a retail app is positively related to the consumer's intention to engage in both purchasing and information-sharing activities. In addition, the recency of the consumer's last visit to the retail store has a moderating effect on both types of activities; the more recent the last visit, the larger the effect-size of interest in the app on intention to share information and make a purchase. Practical implications – While marketing and advertising managers may have suspected that Apple iOS users are more receptive to retail mobile apps, this study provides empirical support for the proposition. In addition, the moderating effect of recency of visit suggests that in-store promotions may be effective in increasing usage of the retailer's mobile apps. Originality/value – This study is among the first in the academic literature to examine predictors of mobile app usage for purchasing and information sharing. It fills a gap in the literature, while at the same time providing actionable information for practitioners.

The international Rosetta mission was launched in 2004 and consists of the orbiter spacecraft Rosetta and the lander Philae. The aim of the mission is to map the comet 67P/Churyumov-Gerasimenko by remote sensing, and to examine its environment in situ and its evolution in the inner Solar System. Rosetta was the first spacecraft to rendezvous with and orbit a comet, accompanying it as it passes through the inner Solar System, and to deploy a lander, Philae, and perform in situ science on the comet's surface. The primary goals of the mission were to: characterize the comet's nucleus; examine the chemical, mineralogical and isotopic composition of volatiles and refractories; examine the physical properties and interrelation of volatiles and refractories in a cometary nucleus; study the development of cometary activity and the processes in the surface layer of the nucleus and in the coma; detail the origin of comets, the relationship between cometary and interstellar material and the implications for the origin of the Solar System; and characterize asteroids 2867 Steins and 21 Lutetia. This paper presents a summary of mission operations and science, focusing on the Rosetta orbiter component of the mission during its comet phase, from early 2014 up to September 2016. This article is part of the themed issue ‘Cometary science after Rosetta’.