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Chronic non-bacterial osteomyelitis (CNO) is a rare and difficult-to-diagnose disease, especially when limited to the jaws. We report the case of a 5-year-old girl with spontaneous jaw pain and a large pre-auricular facial swelling overlying the angle of the mandible that was non-responsive to previous antibiotic and non-steroidal anti-inflammatory treatment. The diagnosis was confirmed as CNO following evaluation and workup by a multidisciplinary team. The patient exhibited a favourable response to oral alendronate sodium plus vitamin D3 combination treatment. We believe that alendronate sodium is an effective second-line drug for CNO.

Background Despite the high incidence and mortality of prostate cancer (PCa) in the Unites States, few risk factors have been consistently linked with these PCa outcomes. Assessing proxies of reproductive factors may offer insights into PCa pathogenesis. In this study, we examined fatherhood status as a proxy of fertility in relation to total, nonaggressive, aggressive, and fatal PCa. Methods We examined participants of two cohorts, the NIH‐AARP Diet and Health (NIH‐AARP) Study and Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. We used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals of associations between fatherhood status and number of children sired in relation to PCa incidence. Results Fatherhood status (one or more children vs. childless) was positively associated with total PCa risk in NIH‐AARP or PLCO, but was not statistically significant (p = 0.06 and 0.55, respectively). Number of children sired indicated a slightly elevated risk of total PCa, but HRs were rarely significant and were of a fairly constant magnitude with no discernable trend relative to the childless referent group. Associations were similar for nonaggressive and aggressive PCa. The trend test for fatal PCa was statistically significant in NIH‐AARP (p trend, Fatherhood status (one or more children vs. childless) was not associated with prostate cancer risk in NIH‐AARP or PLCO. However, in NIH‐AARP increased number of children sired was associated with an increased total prostate cancer, but this association did not present by disease aggressiveness.

Extracellular vesicles (EVs) play critical roles in many health and disease states, including ischemia, inflammation, and pain, which are major concerns in the perioperative period and in critically ill patients. EVs are functionally active, nanometer-sized, membrane-bound vesicles actively secreted by all cells. Cell signaling is essential to physiological and pathological processes, and EVs have recently emerged as key players in intercellular communication. Recent studies in EV biology have improved our mechanistic knowledge of the pathophysiological processes in perioperative and critical care patients. Studies also show promise in using EVs in novel diagnostic and therapeutic clinical applications. This review considers the current advances and gaps in knowledge of EVs in the areas of ischemia, inflammation, pain, and in organ systems that are most relevant to anesthesiology, perioperative medicine, critical care, and pain management. We expect the reader will better understand the relationship between EVs and perioperative and critical care pathophysiological states and their potential use as novel diagnostic and therapeutic modalities.

Background: BCR signaling mediated through Bruton's tyrosine kinase (BTK) plays a critical role in the development and maintenance of marginal zone lymphoma (MZL). BTK inhibitors have established activity in relapsed/refractory (R/R) MZL with the phase 2 study of ibrutinib demonstrating an objective response rate (ORR) of 48% (Noy et al. Blood. 2017;129:2224-2232).

Background: Excessive daytime sleepiness (EDS) in people with obstructive sleep apnoea (OSA) is associated with increased driving accident risk. Solriamfetol [Sunosi™] is a dopamine/norepinephrine reuptake inhibitor approved in the EU and US to treat EDS in adults with OSA (37.5–150mg/d). Aim: Evaluate solriamfetol’s effects on on-road driving performance in adults with EDS associated with OSA. Methods: In a randomised, double-blind, crossover study (NCT02806895/EudraCT 2015-003930-28), an on-road driving test was conducted at 2h and 6h postdose after 7d of treatment with solriamfetol (150mg/d×3d, then 300mg/d×4d) or placebo. Primary endpoint: standard deviation of lateral position (SDLP), a measure of weaving, at 2h postdose. Results: Baseline characteristics (N=34): 88% male; mean age, 52y; mean Epworth Sleepiness Scale, 14.4; Maintenance of Wakefulness Test, 14.3 min. At 2h and 6h, mean SDLP was significantly lower with solriamfetol vs placebo (Fig.). At 2h, 1 participant on solriamfetol and 4 on placebo had incomplete driving tests; at 6h, 3 and 7 participants, respectively, had incomplete driving tests. Common adverse events with solriamfetol: headache, nausea, insomnia, dizziness, and agitation. Conclusion: Solriamfetol (300mg/d) improved SDLP, an important measure of driving performance, at 2h and 6h postdose in participants with EDS associated with OSA.

The aim of this review was to integrate the current literature into a comprehensive evaluation of pulmonary metastasizing ameloblastoma (MA). Related articles, published since January 2000, were reviewed. The PubMed, Embase, Cochrane Database of Systematic Reviews, and Web of Science databases were searched based on the PRISMA guidelines. Twenty-four studies, including 28 case reports, met the eligibility criteria. The mean ± standard deviation disease-free interval after primary treatment was 12.1 ± 9.3 years. Adjuvant therapy (surgery and chemoradiotherapy) is considered appropriate for pulmonary MA patients presenting with rapid progression or apparent pulmonary symptoms. The proportion of patients undergoing simple follow-up (observation) has increased rapidly in the past 10 years. Meanwhile, among the three treatment groups (observation, surgery, and adjuvant therapy), no significant difference was found in the efficacy (P = 0.081) or progression-free survival (P = 0.263). The 5-year and 10-year overall survival rates were 72.4% and 52.8%, respectively. This study provides insights that could help surgeons with the management of patients presenting with pulmonary MA.

Introduction The outbreak of coronavirus disease 2019 (COVID-19) has exerted a heavy burden on public health worldwide. We aimed to investigate the epidemiological and clinical characteristics of patients with COVID-19 in a designated hospital in Hangzhou, China. Methods This was a retrospective study that included laboratory-confirmed cases of COVID-19 in XiXi Hospital of Hangzhou from 15 January 2020 to 30 March 2020. We reviewed and analysed the epidemiological, demographic, clinical, radiological, and laboratory features involving these cases. Age-tratification analysis was also implemented. Results We analysed 96 confirmed cases. The patients had a mean age of 43 years, with six patients younger than 18 years and 14 patients older than 60 years. No significant gender difference was discovered. Co-morbidities were commonly observed in patients aged over 40 years. Twenty eight of the patients had travelled from Wuhan City, and 51 patients were infected through close contact. Familial clusters accounted for 48 of the cases. The mean incubation time was 7 days, and the symptoms were mainly fever, cough, fatigue, and sore throat. Lymphocytopenia was observed predominantly in patients aged over 60 years. Fifty five patients presented with bilateral pulmonary lesions. The radiological changes were typically distributed in the subpleural area, and pleural effusion rarely occurred. All patients were discharged successfully. Conclusion During the early stage of the COVID-19 outbreak, half of the patients from a designated hospital in Hangzhou City were discovered as familial clusters. Therefore, strict prevention and control measures during self-isolation should be implemented. Patients aged over 60 years who had underlying co-morbidities were prone to lymphocytopenia and severe infection.

Change detection is essential to keep 3D city models up-to-date. LiDAR data with high accuracy are used to create 3D city models. However, updating LiDAR data at state or nation level often takes around a decade. Very high resolution (VHR) stereo images, with often yearly updating rate and dense 3D information, provide an option for validating and updating LiDAR data. However, the 3D information in both data sources has quality problems. LiDAR point clouds are sparse and irregularly spaced, and have mixed returns near building edges, while 3D information extracted from stereo images are affected by shadow and low texture. This research proposes LiDAR-guided dense matching to address these problems explicitly for detecting accurate building changes. Data sparsity and irregular spacing is addressed by densifying LiDAR points in a form of a digital surface model (DSM). Instead of applying interpolation with associated edge problems due to mixed returns, three candidate DSMs are created by linking each DSM pixel to up to three planes as identified in segmented and triangulated LiDAR data. The candidate DSMs limit the disparity search space for dense matching, addressing low texture and shadow problems in images. Through edge-aware dense matching, the detailed building edge information in stereo pairs determine the optimal heights to address LiDAR edge problem. Changes are detected where corresponding pixels from dense matching have large color differences. Due to homogeneous surroundings and shadows, only partial changes are initially detected. A second hierarchical dense matching step is employed to complete changes and update 3D information by propagating initial partial changes iteratively. The proposed method is applied on data from two cities, Amersfoort and Assen, the Netherlands, with around 1200 existing buildings. In both areas, the method successfully verifies unchanged buildings while detecting minimum changes of 2×2×2m3. New and removed building detection in Amersfoort both have a F1 score of over 0.8, both in pixel and object evaluation, while F1 scores in Assen are over 0.9 for both categories. The experiments also show that the proposed method outperforms two well-known change detection methods in terms of verifying unchanged buildings and detecting small changes simultaneously.

Representation of scenes on the Earth surface by using voxels is gaining attention because of its suitability for integrating heterogeneous data sources in simulations and quantitative models. Computation of shadows in such models is needed, for example, to obtain crop suitability of agricultural fields in the presence of trees and buildings, or to analyze urban heat island causes and effects. We present an efficient algorithm to compute which of the voxels in a dataset receive direct sunlight, given the solar azimuth and elevation angles. The algorithm can work with multiple (sparse and dense) voxel storage strategies.

MAX IV Laboratory is the Swedish national particle accelerator that currently provides the world’s most brilliant X-rays for research. MAX IV Laboratory has two storage rings as well as a 300-meter linear accelerator with hundreds of components not only to be relatively aligned with tight tolerances but also to be absolutely aligned with respect to the global coordinate system of the facility. An advanced particle accelerator of this kind requires high-precision alignment of all its sensitive components through a multiple-stage process. We recognized the need for a full realignment of the linear accelerator due to the beam-based measurements done by our accelerator scientists as well as the expected alignment deflections since its initial alignment in 2015. Partial realignment of an operational facility – in this case, the realignment of the entire linear accelerator – involves serious risks, as it must precisely maintain its consistency with the storage rings and the beamlines of the facility that are already aligned and operational. This paper serves as a guide for large-scale realignment problems that involve surveying, outlier analysis, fiducialization, boundary conditions, solution/optimization of reference-networks, and physical realignment of the components. This work took place over 2 years, where the entire realignment process was successfully carried out and finally verified.

SummaryObjectives Obesity is associated with significant morbidity and mortality rates. Even modest weight loss may be associated with health benefits. Alpha-lipoic acid (ALA) is a naturally occurring antioxidant. Studies have suggested anti-obesity properties of ALA; however, results are inconsistent. The purpose of this study is to conduct a meta-analysis of the effect of ALA on weight and body mass index (BMI). Methods A comprehensive, systematic literature search identified 10 articles on randomized, double-blind, placebo-controlled studies involving ALA. We conducted a meta-analysis of mean weight and BMI change differences between ALA and placebo treatment groups. Results Alpha-lipoic acid treatment coincided with a statistically significant 1.27 kg (confidence interval = 0.25 to 2.29) greater mean weight loss compared with the placebo group. A significant overall mean BMI difference of −0.43 kg/ m2 (confidence interval = −0.82 to −0.03) was found between the ALA and placebo groups. Meta-regression analysis showed no significance in ALA dose on BMI and weight changes. Study duration significantly affected BMI change, but not weight change. Conclusions Alpha-lipoic acid treatment showed small, yet significant short-term weight loss compared with placebo. Further research is needed to examine the effect of different doses and the long-term benefits of ALA on weight management.

Introduction: Inhaled toxins from tobacco smoking, cannabis leaf smoking as well as vaping/e-cigarette products use are known causes of cardio-respiratory injury. While tobacco smoking has decreased among Canadian adults, there are now several other forms of legal inhalant products. While legal, the evidence of benefit and safety of vaping is limited. Of concern, cases of e-cigarette or vaping products use associated lung injury (EVALI) have been accumulating in the U.S. and now in Canada. Despite this, very little is known about the inhalation exposure of emergency department (ED) patients; this study was designed to explore lung health in the ED. Methods: We investigated the prevalence of exposure to vaping, tobacco and cannabis among patients presenting to a Canadian ED from July to November 2019. Ambulatory (CTAS 2 to 5), stable, adult (≥ 17 years) patients were prospectively identified and invited to complete a survey addressing factors related to lung health (previous diagnosis of respiratory conditions and respiratory symptoms at the ED presentation) and information on current exposure to vaping, tobacco and cannabis smoking. Categorical variables are reported as frequencies and percentages; continuous variables are reported as medians with interquartile range (IQR). The study was approved by the Health Research Ethics Board. Results: Overall, 1024 (71%) of 1433 eligible patients completed the survey. The median age was 43.5 (IQR: 29, 60), and 51% were female. A total of 351 (31%) participants reported having been previously diagnosed with ≥1 respiratory conditions, and 177 (17%) were visiting the ED as a result of ≥1 respiratory symptoms (e.g., cough, shortness of breath, wheezing). Daily tobacco smoking was reported by 190 (19%), and 83 (8%) reported using vaping/e-cigarette products. Cannabis use within 30 days was described by 80 (15%) respondents. Exposure to tobacco and vaping products was reported by 39 (4%) participants, 63 (6%) reported using tobacco in combination with cannabis smoking, and 3% reported combining vaping and cannabis use. Conclusion: Patients seeking care in the ED are exposed to a large quantity of inhaled toxins. Vaping products, considered the cause of the most recent epidemic of severe lung injury, are used in isolation and in combination with other smoking products in Canada. These exposures should be documented and may increase the risk of lung health injuries and exacerbations of chronic respiratory conditions.

On-site safety is the most crucial issue that the construction industry focuses on, and safety helmet is one piece to on-site personnel protection and accident reduction. Construction safety managers expect to communicate and locate the on-site personnel in-time, thus an upgrade of traditional safety helmet is established. This paper proposes an intelligent safety helmet that works more than a personal protection equipment. It effectively takes the dynamic nature of site operatives and the needs of safety management into account, using a platform to deal with safety management issues on construction sites. Through the combinations of ergonomics, radio frequency identification technology and wireless communication, the intelligent safety helmet can be used to record the attendance, locate the on-site personnel, alarm the security and communicate in-time.

To compare febrile neutropenia (FN) incidence and hospitalization among breast cancer patients on docetaxel with no granulocyte colony-stimulating factors (GCSF) primary prophylaxis (PP), 4/5-day PP, or 7-day PP. We identified 3916 breast cancer patients using docetaxel-cyclophosphamide (TC), doxorubicin-cyclophosphamide then docetaxel (AC-T), fluorouracil-epirubicin-cyclophosphamide then docetaxel (FEC-T), docetaxel-carboplatin-trastuzumab (TJH), or docetaxel-doxorubicin-cyclophosphamide (TAC) from a hospital pharmacy dispensing database in Hong Kong between 2014 and 2016. Patients were offered GCSF within 5 days since administering docetaxel. Outcomes included FN incidence, time to first hospitalization, hospitalization rate, and duration. In TC regimen, FN incidence (with odds ratio, OR) of patients with no PP, 4/5-day PP, and 7-day PP was 21.69%, 7.95% (OR 0.31, p < 0.001), and 5.33% (OR 0.20, p < 0.001), respectively. In TJH regimen, FN incidence of patients with no PP, 4/5-day PP, and 7-day PP was 38.26%, 8.33% (OR 0.15, p < 0.001), and 8.57% (OR 0.15, p < 0.001), respectively. FN incidence of patients on AC-T regimen with no PP and 4/5-day PP was 20.93% and 6.84%, respectively (OR 0.28, p = 0.005); with FEC-T regimen, the incidence was 9.91% and 4.77%, respectively (OR 0.46, p = 0.035). Only 3.27% FN cases were not hospitalized. Mean (±standard deviation, SD) time to first hospitalization was 8.21 ± 2.44 days. Mean (±SD) duration of hospitalization for patients with no PP, 4/5-day PP, and 7-day PP was 4.66 ± 2.60, 4.37 ± 2.85, and 5.12 ± 2.97 days, respectively. GCSF prophylaxis in breast cancer patients on docetaxel could reduce FN incidence and hospitalization. 4/5-day PP demonstrated similar efficacy to 7-day PP with superior saving benefits on healthcare expenditure.

Up-to-date 3D building models are important for many applications. Airborne very high resolution (VHR) images often acquired annually give an opportunity to create an up-to-date 3D model. Building segmentation is often the first and utmost step. Convolutional neural networks (CNNs) draw lots of attention in interpreting VHR images as they can learn very effective features for very complex scenes. This paper employs Mask R-CNN to address two problems in building segmentation: detecting different scales of building and segmenting buildings to have accurately segmented edges. Mask R-CNN starts from feature pyramid network (FPN) to create different scales of semantically rich features. FPN is integrated with region proposal network (RPN) to generate objects with various scales with the corresponding optimal scale of features. The features with high and low levels of information are further used for better object classification of small objects and for mask prediction of edges. The method is tested on ISPRS benchmark dataset by comparing results with the fully convolutional networks (FCN), which merge high and low level features by a skip-layer to create a single feature for semantic segmentation. The results show that Mask R-CNN outperforms FCN with around 15% in detecting objects, especially in detecting small objects. Moreover, Mask R-CNN has much better results in edge region than FCN. The results also show that choosing the range of anchor scales in Mask R-CNN is a critical factor in segmenting different scale of objects. This paper provides an insight into how a good anchor scale for different dataset should be chosen.

Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images with rich geometric and spectral information and a high update rate are increasingly applied for the purpose of updating 3D models. Shadow detection is the primary step for image interpretation, as shadow causes radiometric distortions. In addition, shadow itself is valuable geometric information. However, shadows are often complicated and environment-dependent. Supervised learning is considered to perform well in detecting shadows when training samples selected from these images are available. Unfortunately, manual labeling of images is expensive. Existing 3D models have been used to reconstruct shadows to provide free, computer-generated training samples, i.e., samples free from intensive manual labeling. However, accurate shadow reconstruction for large-scene 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, mislabeled samples affect classification results, but are unavoidable, due to inaccuracy and incompleteness of the model, and different acquisition time between 3D models and images, mislabeling refers to training samples that are shadows but labeled as non-shadows and vice versa. We propose a ray-tracing approach with an effective KD tree construction to feasibly reconstruct accurate shadows for a large 3D scene model. An adaptive erosion approach is first provided to remove mislabeling effects near shadow boundaries. Next, a comparative study considering four classification methods, quadratic discriminant analysis (QDA) fusion, support vector machine (SVM), K nearest neighbors (KNN) and Random forest (RF), is performed to select the best classification method with respect to capturing the complicated properties of shadows and robustness to mislabeling. The experiments are performed on Dutch Amersfoort data with around 20% mislabels and the Toronto benchmark by simulating mislabels from inverting shadows to non-shadows. RF is tested to give robust and best results with 95.38% overall accuracy (OA) and a value of 0.9 for kappa coefficient (KC) for Amersfoort and around 96% OA and 0.92 KC for Toronto benchmarks until when no more than 50% of shadows are inverted. QDA fusion and KNN are tested to be robust to mislabels but their capability to capture complicated properties of shadows is worse than RF. SVM is tested to have a good capability to separate shadow and non-shadows but is largely affected by mislabeled samples. It is shown that RF with free-training samples from existing 3D models is an automatic, effective, and robust approach for shadow detection from VHR images.

(Semi)-automatic facade reconstruction from terrestrial LiDAR point clouds is often affected by both quality of point cloud itself and imperfectness of object recognition algorithms. In this paper, we employ regularities, which exist on façades, to mitigate these problems. For example, doors, windows and balconies often have orthogonal and parallel boundaries. Many windows are constructed with the same shape. They may be arranged at the same lines and distance intervals, so do different windows. By identifying regularities among objects with relatively poor quality, these can be applied to calibrate the objects and improve their quality. The paper focuses on the regularities among the windows, which is the majority of objects on the wall. Regularities are classified into three categories: within an individual window, among similar windows and among different windows. Nine cases are specified as a reference for exploration. A hierarchical clustering method is employed to identify and apply regularities in a feature space, where regularities can be identified from clusters. To find the corresponding features in the nine cases of regularities, two phases are distinguished for similar and different windows. In the first phase, ICP (iterative closest points) is used to identify groups of similar windows. The registered points and a number of transformation matrices are used to identify and apply regularities among similar windows. In the second phase, features are extracted from the boundaries of the different windows. When applying regularities by relocating windows, the connections, called chains, established among the similar windows in the first phase are preserved. To test the performance of the algorithms, two datasets from terrestrial LiDAR point clouds are used. Both show good effects on the reconstructed model, while still matching with original point cloud, preventing over or under-regularization.

The squeal noise occurring from the disc brakes of passenger cars has been analyzed by using the complex eigenvalue method numerically. The contact between a disc and two pads was analytically modeled as many linear springs and dampers in an effort to develop the improved equation of motion derived on the basis of Lagrange’s equation and the assumed mode method. The finite element modal analysis results for disc brake components constitute an eigenvalue matrix in the analytical equation of motion. The complex eigenvalue analyses based on the equations of motion are able to examine the dynamic instability of a brake system, which is an onset of squeal, by considering the disc rotational effect. Numerical analyses showed that the modes unstable in an undamped analysis became stable in a damped case, which illustrates the important effect of damping on the squeal instability in a brake squeal simulation. Then several modified brake models were suggested and investigated how effectively they suppressed the occurrence of squeal noise. The brake parts such as a pad chamfer and a disc vane were modified and the influence of pad chamfer and vane shapes on squeal occurrence was proved to be significant. The numerical results showed that proper structural modification of a disc brake system can suppress the brake squeal to some extent.

The Rankine cycle system for waste heat recovery of heavy-duty vehicle diesel engines has been regarded as a promising technique to reduce fuel consumption. Its heat dissipation in the condensation process, however, should be taken away in time, which is an energy-consuming process. A fan-assisted auxiliary water-cooling system is employed in this paper. Results at 1300 r/min and 50% load indicate that the cooling pump and cooling fan together consume 7.66% of the recovered power. What’s worse for the heavy load, cooling accessories may deplete of all the recovered power of the Rankine cycle system. Afterwards, effects of the condensing pressure and water feeding temperature are investigated, based on which a cooling power consumption model is established. Finally, an overall efficiency optimization is conducted to balance the electric power generation and cooling power consumption, taking condensing pressure, pressure ratio and exhaust bypass valve as major variables. The research suggests that the priority is to increase condensing pressure and open exhaust bypass valve appropriately at high speed and heavy load to reduce the cooling power consumption, while at low speed and light load, a lower condensing pressure is favored and the exhaust bypass valve should be closed making the waste heat recovered as much as possible. Within the sub-critical region, a larger pressure ratio yields higher overall efficiency improvement at medium-low speed and load. But the effects taper off at high speed and heavy load. For a given vehicular heavy-duty diesel engine, the overall efficiency can be improved by 3.37% at 1300 r/min and 25% load using a Rankine cycle system to recover exhaust energy. The improvement becomes smaller as engine speed and load become higher.

Introduction: Despite being legal in Canada, the vaporized liquid of vaping systems contains several chemicals presenting unknown risks to lung health and little is known about their use in patients visiting the emergency department (ED). This study explores associations between exposure to inhaled products and respiratory presentations in the ED. Methods: A cross-sectional lung health survey among patients presenting to a Canadian ED was conducted, exploring the association between inhaled toxic exposures (e.g., vaping, cannabis and cigarette smoking) and visits related to respiratory symptoms. Eligible participants were ambulatory (CTAS 2 to 5), stable, adults (≥ 17 years) visiting the ED from July to November, 2019. Results are described as frequencies and percentages (categorical variables) and medians with interquartile range (IQR, continuous variables). Univariate and multivariate logistic regression models report associations as odds ratios (OR) with 95% confidence intervals (CIs). The Health Research Ethics Board approved the study. Results: From 1433 eligible patients, 1024 (71%) completed the survey. The median age was 43.5 (IQR: 29, 60), and 51% were female. Overall, 177 (17%) reported ≥1 respiratory symptoms and 83 (8%) reported using any vaping products. In a univariate regression analysis, exposure to vaping was positively associated with ED visits related to respiratory symptoms (OR 2.11, 95% CI: 1.26 to 3.54). In the multivariate model, vaping and a previous diagnosis of ≥1 respiratory conditions showed positive association with respiratory-related ED visits (OR 1.86, 95% CI: 1.03 to 3.33; and OR 2.13, 95% CI: 1.50 to 3.02, respectively). There was evidence of an additive effect of the combined exposure to cigarettes and vaping and respiratory-related ED visits (OR 3.22, 95% CI: 1.61 to 6.43). Smoking cannabis and cigarettes alone were not associated with increased risk of respiratory-related visits. Conclusion: Using vaping products increased the occurrence of respiratory-related ED visits, particularly in people with pre-existing lung conditions. A dose-response relationship exists where the risk is highest in patients inhaling a combination of toxins. Contrary to previous assumptions, the use of vaping products has a negative impact on lung health.

Introduction Excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) is associated with an increased risk of driving accidents. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved in the US (Sunosi®) for EDS associated with OSA (37.5-150 mg/day). This study evaluated solriamfetol’s effects on on-road driving performance in participants with EDS associated with OSA. Methods In each period of this randomized, double-blind, placebo-controlled, crossover study (NCT 02806895; EudraCT 2015-003930-28), driving performance during an on-road driving test was assessed at 2 hours and 6 hours postdose following 7 days of treatment with solriamfetol (150mg/day × 3, then 300mg/day × 4) or placebo. The primary endpoint—standard deviation of lateral position (SDLP), a measure of “weaving,” at 2 hours postdose—was compared between solriamfetol and placebo per time point using a repeated mixed-effects analysis of variance model. Results The study included 34 participants. Baseline characteristics reflected the broader OSA population (88% male; mean age=52 years; mean Epworth Sleepiness Scale score=14.4). SDLP at 2 hours postdose was statistically significantly lower following solriamfetol (least squares [LS] mean [standard error; SE], 18.83cm [0.63]) compared with placebo (19.92cm [0.63]): LS mean difference, -1.08cm; 95% confidence interval (CI), -1.85, -0.32; P=0.0062 (incomplete driving tests: solriamfetol, n=1; placebo, n=4), indicating better performance with solriamfetol. At 6 hours postdose, SDLP following solriamfetol (LS mean[SE], 19.24cm [0.63]) was statistically significantly lower compared with placebo (20.04cm [0.63]): LS mean difference, -0.80cm; 95% CI, -1.58, -0.03; P=0.0432 (incomplete driving tests: solriamfetol, n=3; placebo, n=7). Common adverse events (≥5%) with solriamfetol were headache, nausea, insomnia, dizziness, and agitation. Conclusion Solriamfetol (300mg/day) improved SDLP, an important measure of driving performance, at 2 and 6 hours in participants with EDS associated with OSA. Support Jazz Pharmaceuticals

Introduction Patients with narcolepsy have an increased risk of automobile accidents. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved in the US (Sunosi®) for adults with excessive daytime sleepiness (EDS) associated with narcolepsy (75-150 mg/day). This study evaluated the effects of solriamfetol on on-road driving performance in participants with narcolepsy. Methods In each period of this randomized, double-blind, placebo-controlled, crossover study (NCT 02806908; EudraCT 2015-003931-36), driving performance during an on-road driving test (a 1-hour drive on a public highway) was assessed at 2 hours and 6 hours postdose following 7 days of treatment with solriamfetol (150 mg/day × 3, then 300 mg/day × 4) or placebo. For assessment of driving performance, the primary endpoint was standard deviation of lateral position (SDLP), a measure of “weaving,” at 2 hours postdose. Comparisons (solriamfetol vs placebo) used a Wilcoxon signed-rank test. Results The study included 24 participants (54% male; mean age, 40 years); 22 were included in the analyses of SDLP data. At 2 hours postdose, SDLP for solriamfetol (median, 19.08 cm) was statistically significantly lower than that for placebo (median, 20.46 cm; P=0.0022; incomplete driving tests: solriamfetol, n=4; placebo, n=7), indicating a better performance with solriamfetol. At 6 hours postdose, SDLP for solriamfetol (median, 19.59 cm) was not statistically significantly different from that for placebo (median, 19.78 cm; P=0.1245; incomplete driving tests: solriamfetol, n=3; placebo, n=10). Common adverse events (≥5%) were headache, decreased appetite, somnolence, sleep disorder, agitation, nausea, and palpitations. Conclusion Solriamfetol (300 mg/day) improved SDLP, an important measure of driving performance, at 2 hours after administration in participants with narcolepsy. Support Jazz Pharmaceuticals

BACKGROUND: Research in treatment-naïve, first episode patients with severe mental illnesses (SMIs) suggest a pre-diabetic condition at the onset of illness. However, we have limited knowledge on glucose metabolism differences across diagnostic categories of SMIs. AIMS: To compare the differences of glucose metabolism outcomes between treatment-naïve, patients with first episode psychosis (FEP) and mood disorders. METHOD: We conducted a systematic review and meta-analysis of glucose intolerance in treatment-naïve, first episode patients with SMIs. RESULTS: We identified 31 eligible studies. Compared to healthy controls, FEP group have higher insulin and insulin resistance levels, and both groups have higher glucose tolerance test results. No significant differences were found in glucose metabolism outcomes between FEP and mood disorder groups. CONCLUSIONS: Our results highlight impaired glucose metabolism at the onset of SMIs, suggesting both patients with psychosis and mood disorders are high-risk groups for diabetes development.

Change detection is an essential step to locate the area where an old model should be updated. With high density and accuracy, LiDAR data is often used to create a 3D city model. However, updating LiDAR data at state or nation level often takes years. Very high resolution (VHR) images with high updating rate is therefore an option for change detection. This paper provides a novel and efficient approach to derive pixel-based building change detection between past LiDAR and new VHR images. The proposed approach aims notably at reducing false alarms of changes near edges. For this purpose, LiDAR data is used to supervise the process of finding stereo pairs and derive the changes directly. This paper proposes to derive three possible heights (so three DSMs) by exploiting planar segments from LiDAR data. Near edges, the up to three possible heights are transformed into discrete disparities. A optimal disparity is selected from a reasonable and computational efficient range centered on them. If the optimal disparity is selected, but still the stereo pair found is wrong, a change has been found. A Markov random field (MRF) with built-in edge awareness from images is designed to find optimal disparity. By segmenting the pixels into plane and edge segments, the global optimization problem is split into many local ones which makes the optimization very efficient. Using an optimization and a consecutive occlusion consistency check, the changes are derived from stereo pairs having high color difference. The algorithm is tested to find changes in an urban areas in the city of Amersfoort, the Netherlands. The two different test cases show that the algorithm is indeed efficient. The optimized disparity images have sharp edges along those of images and false alarms of changes near or on edges and occlusions are largely reduced.

Various kinds of urban applications require true orthophotos. True orthophoto generation requires a DSM (Digital Surface Model) to project the photo orthogonally and minimize geometric distortion due to topographic variance. DSMs are often generated from airborne laser scan data. In urban scenes, DSM data may fail to deliver sharp and straight building roof edges. This will affect the quality of the resulting orthophotos. Therefore, it is necessary to incorporate good quality building outlines as breaklines during DSM interpolation. This study proposes a data-driven approach to construct building roof outlines from LiDAR point clouds by a workflow consisting of the following steps: given roof segments, roof boundary points are extracted using a concave hull algorithm. Straight edges may be difficult to find in complex roof configurations. Therefore, two ingredients are combined. First, RanSAC corner point preselection, and second, DBSCAN-based clustering of edge points. The method is demonstrated on an area of ±1.2 km2 containing 42 buildings of different characteristics. A quality assessment shows that the proposed method is able to deliver 92 % of building lines with acceptable geometric accuracy in comparison to a building line in the base map.

A superconducting accelerator module is developed for the CAEP THz-FEL facility, which contains two 1.3GHz 4-cell TESLA-type cavities. This paper presents the microwave properties study of the 4-cell cavity. The microwave parameters of the cavity were simulated, such as frequencies, R/Q values, and HOM passbands etc. The most harmful mode types have been identified according to simulation results. And the frequency and field flatness of the fundamental mode were adjusted to satisfy the operation requirement. The Qe of the pickup port and the HOM coupler ports were measured by microwave measurement at room temperature. The measurement techniques and results are revealed in this paper.

Our network meta-analysis analyzed the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on fracture risk. By combining data from randomized controlled trials, we found that GLP-1 RAs were associated with a decreased bone fracture risk, and exenatide is the best option agent with regard to the risk of fracture. This study is registered with PROSPERO (CRD42018094433). Data on the effects of GLP-1 RAs on fracture risk are conflicted. This study aimed to analyze the available evidence on the effects of GLP-1 RAs on fracture risk in type 2 diabetes mellitus patients. Electronic databases were searched for relevant published articles, and unpublished studies presented at ClinicalTrials.gov were searched for relevant clinical data. All analyses were performed with STATA 12.0 and R software (Version 3.4.4). We estimated the risk ratio (RR) and 95% confidence interval (CI) by combining RRs for fracture effects of included trials. There were 54 eligible random control trials (RCTs) with 49,602 participants, including 28,353 patients treated with GLP-1 RAs. Relative to placebo, exenatide (RR, 0.17; 95% CI 0.03–0.67) was associated with lowest risk of fracture among other GLP-1 RAs. Exenatide had the highest probability to be the safest option with regard to the risk of fracture (0.07 ‰), followed by dulaglutide (1.04%), liraglutide (1.39%), albiglutide (5.61%), lixisenatide (8.07%), and semaglutide (18.72%). A statistically significant inconsistency was observed in some comparisons. The Bayesian network meta-analysis suggests that GLP-1 RAs were associated with a decreased bone fracture risk compared to users of placebo or other anti-hyperglycemic drugs in type 2 diabetes mellitus patients, and exenatide is the best option agent with regard to the risk of fracture.

Semantic segmentation, especially for buildings, from the very high resolution (VHR) airborne images is an important task in urban mapping applications. Nowadays, the deep learning has significantly improved and applied in computer vision applications. Fully Convolutional Networks (FCN) is one of the tops voted method due to their good performance and high computational efficiency. However, the state-of-art results of deep nets depend on the training on large-scale benchmark datasets. Unfortunately, the benchmarks of VHR images are limited and have less generalization capability to another area of interest. As existing high precision base maps are easily available and objects are not changed dramatically in an urban area, the map information can be used to label images for training samples. Apart from object changes between maps and images due to time differences, the maps often cannot perfectly match with images. In this study, the main mislabeling sources are considered and addressed by utilizing stereo images, such as relief displacement, different representation between the base map and the image, and occlusion areas in the image. These free training samples are then fed to a pre-trained FCN. To find the better result, we applied fine-tuning with different learning rates and freezing different layers. We further improved the results by introducing atrous convolution. By using free training samples, we achieve a promising building classification with 85.6 % overall accuracy and 83.77 % F1 score, while the result from ISPRS benchmark by using manual labels has 92.02 % overall accuracy and 84.06 % F1 score, due to the building complexities in our study area.

Objective Diabetes mellitus (DM) and diabetic retinopathy (DR) are associated with oxidative stress and carotenoids have antioxidant properties. This study aimed to test the relationship between serum carotenoid concentrations and the risk for DM and DR. Methods This is a cross-sectional study of the Chinese urban population. A total of 747 subjects, consisting of 272 DR patients, 190 diabetic patients without retinopathy, and 285 non-diabetes mellitus healthy controls, were recruited to this study. Demographic and lifestyle characteristics were ascertained by questionnaire. General physical and ophthalmic examinations were completed for all participants. Serum carotenoids were measured by high-performance liquid chromatography (HPLC). The associations of serum carotenoids with DM and DR were assessed by logistic regression with adjustment of known risk factors. The correlation analyses of serum carotenoids with the candidate influence factors were assessed using the single variable linear regression. Results Both pro-vitamin A (PVA) carotenoids and non-PVA carotenoids in the serum were measured and compared between different groups. Levels of α-carotene were significantly lower in DR patients and β-carotene were significantly lower in DM patients as compared to non DM healthy control group. In contrast, levels of β-cryptoxanthin, lycopene, lutein and zeaxanthin were comparable among different groups. After adjusting for confounding factors, β-carotene concentration was associated with reduced risk for DM (OR (95%CI): 0.56 (0.34, 0.91), P=0.02) and α-carotene was associated with reduced risk for DR in non-smokers (OR (95%CI): 0.41 (0.17, 0.99), P=0.048). No significant association was found between hemoglobin A1c and any carotenoids (P>0.05). Significantly associations with serum carotenoids were found in age, sex, BMI, smoking, and exercise (P Conclusion Serum β-carotene may have a protective effect on DM and α-carotene may be a protective factor for DR in non-smokers.

Palliative care can play an important role in the management of heart failure. We conducted a systematic review and meta-analysis to compare the efficacy and safety of palliative care in patients with heart failure. PubMed, Embase, Web of Science, EBSCO, and the Cochrane Library databases were systematically searched. Randomized controlled trials (RCTs) on the impact of palliative care on heart failure were included. Two investigators independently searched the articles, extracted data, and assessed the quality of included studies. The primary outcome was mortality. Seven RCTs were included in the meta-analysis. Compared with usual care for heart failure, palliative care was associated with a significantly increased quality of life (standardized mean difference = 1.46; 95% confidence interval [CI] = 0.12 to 2.79; p = 0.03) and reduced depression scores (standardized mean difference = −0.62; 95% CI = −0.99 to −0.25; p = 0.03), but demonstrated no impact on mortality (risk ratio [RR] = 1.28; 95% CI = 0.86 to 1.92; p = 0.22) and rehospitalization (RR = 0.84; 95% CI = 0.66 to 1.07; p = 0.16). Palliative care can improve the quality of life and reduce the occurrence of depression in patients with heart failure.

We report on the design, fabrication, measurement, and analysis of a dynamically tunable ultra-broadband flexible microwave absorber. The absorber uses an active frequency selective surface based on PIN diode. The results show that the absorber can alter the bias voltage of PIN diode arrays to realize the tunable reflectivity through power control system, including below −6 dB between 2.65 and 18 GHz and −10 dB between 2.86 and 14.2 GHz. By analyzing the input impedance of the proposed absorber, it is indicated that the absorber and free space are impedance matched at three resonant frequencies, including 4.4, 11.2, and 16.3 GHz. The transmission line equivalent circuit model is built, it turns out that fixed point of impedance matching exists when it’s at low frequency.

A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTM-SOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cmsup-1/supresolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10sup-3/sup mW/cmsup2/sup/sr/cmsup-1/supand the relative error is typically less than 0.2%.