No abstract

Purpose Chronic opioid use is a significant public health burden. Orthopaedic trauma is one of the main indications for opioid prescription. We aimed to assess the risk for long-term opioid use in a healthy patient cohort. Methods In this matched cohort study, bicycle trauma patients from a Swedish Level-I-Trauma Centre in 2006–2015 were matched with comparators on age, sex, and municipality. Information about dispensed opioids 6 months prior until 18 months following the trauma, data on injuries, comorbidity, and socioeconomic factors were received from national registers. Among bicycle trauma patients, the associations between two exposures (educational level and injury to the lower extremities) and the risk of long-term opioid use (> 3 months after the trauma) were assessed in multivariable logistic regression models. Results Of 907 bicycle trauma patients, 419 (46%) received opioid prescriptions, whereof 74 (8%) became long-term users. In the first quarter after trauma, the mean opioid use was significantly higher in the trauma patients than in the comparators (253.2 mg vs 35.1 mg, p < 0.001) and fell thereafter to the same level as in the comparators. Severe injury to the lower extremities was associated with an increased risk of long-term opioid use [OR 4.88 (95% CI 2.34–10.15)], whereas high educational level had a protecting effect [OR 0.42 (95% CI 0.20–0.88)]. Conclusion The risk of long-term opioid use after a bicycle trauma was low. However, opioids should be prescribed with caution, especially in those with injury to lower extremities or low educational level.

We applied computational style transfer, specifically coloration and brush stroke style, to achromatic images of a ghost painting beneath Vincent van Gogh's Still life with meadow flowers and roses. Our method is an extension of our previous work in that it used representative artworks by the ghost painting's author to train a Generalized Adversarial Network (GAN) for integrating styles learned from stylistically distinct groups of works. An effective amalgam of these learned styles is then transferred to the target achromatic work.

We discuss the problem of computationally generating images resembling those of lost cultural patrimony, specifically two-dimensional artworks such as paintings and drawings. We view the problem as one of computing an estimate of the image in the lost work that best conforms to surviving information in a variety of forms: works by the source artist, including preparatory works such as cartoons for the target work; copies of the target by other artists; other works by these artists that reveal aspects of their style; historical knowledge of art methods and materials; stylistic conventions of the relevant era; textual descriptions of the lost work and images associated with the target's title. Some of the general information linking images and text can be learned from large corpora of natural photographs and accompanying text scraped from the web. We present some preliminary proof-of-concept simulations for recovering lost artworks with a special focus on textual information about target artworks. We outline our future directions, such as methods for assessing the contributions of different forms of information in the overall task of recovering lost artworks.