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Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1–3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7–9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete’s perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

Introduction There is epidemiological evidence to suggest that events in childhood influence lung growth and constitute a significant risk for adult COPD. The aim of the study is to evaluate for an association between childhood asthma and adult COPD. Methods This longitudinal, prospective study of 6–7-year-old children with asthma has been regularly reviewed every 7 years to the current analysis at 50 years of age. Participants completed respiratory questionnaires and lung function spirometry with postbronchodilator response. At the age of 50, subjects were classified to the following subgroups: non-asthmatics, asthma remission, current asthma and COPD which was defined by FEV1 to FVC ratio postbronchodilator of less than 0.7. Results Of the remaining survivors, 346 participated in the current study (participation rate of 76%) of whom 197 completed both questionnaire and lung function testing. As compared with children without symptoms of wheeze to the age of 7, (non-asthmatics) children with severe asthma had an adjusted 32 times higher risk for developing COPD (95% CI 3.4 to 269). In this cohort, 43% of the COPD group had never smoked. There was no evidence of a difference in the rate of decline in FEV1 (mL/year, 95th CI) between the COPD group (17, 10 to 23) and the other groups: non-asthmatics (16, 12 to 21), asthma remission (20, 16 to 24) and current asthma (19, 13 to 25). Conclusions Children with severe asthma are at increased risk of developing COPD.

Snow water equivalent (SWE), the amount of water generated when a snowpack melts, has been used to study the impacts of climate change on the cryosphere processes and snow cover dynamics during the winter season. In most analyses, high-temporal-resolution SWE and SD data are aggregated into monthly and yearly averages to detect and characterize changes. Aggregating snow measurements, however, can magnify the modifiable aerial unit problem, resulting in differing snow trends at different temporal resolutions. Time series analysis of gridded SWE data holds the potential to unravel the impacts of climate change and global warming on daily, weekly, and monthly changes in snow during the winter season. Consequently, this research presents a high-temporal-resolution analysis of changes in the SWE across the cold regions of Canada. A Siamese UNet (Si-UNet) was developed by modifying the model’s last layer to incorporate the structural similarity (SSIM) index. The similarity values from the SSIM index are passed to a contrastive loss function, where the optimization process maximizes SSIM index values for pairs of similar SWE images and minimizes the values for pairs of dissimilar SWE images. A comparison of different model architectures, loss functions, and similarity metrics revealed that the SSIM index and the contrastive loss improved the Si-UNet’s accuracy by 16%. Using our Si-UNet, we found that interannual SWE declined steadily from 1979 to 2018, with March being the month in which the most significant changes occurred (R2 = 0.1, p-value < 0.05). We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.

Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Convolutional neural networks (CNNs) are known for their ability to learn shape and texture descriptors useful for object detection, pattern recognition, and classification problems. Deeper layer filters of CNN generally learn global image information vital for whole-scene or object discrimination. In landscape pattern comparison, however, dense localized information encoded in shallow layers can contain discriminative information for characterizing changes across image local regions but are often lost in the deeper and non-spatial fully connected layers. Such localized features hold potential for identifying, as well as characterizing, process–pattern change across space and time. In this paper, we propose a simple yet effective texture-based CNN (Tex-CNN) via a feature concatenation framework which results in capturing and learning texture descriptors. The traditional CNN architecture was adopted as a baseline for assessing the performance of Tex-CNN. We utilized 75% and 25% of the image data for model training and validation, respectively. To test the models’ generalization, we used a separate set of imagery from the Aerial Imagery Dataset (AID) and Sentinel-2 for model development and independent validation. The classical CNN and the Tex-CNN classification accuracies in the AID were 91.67% and 96.33%, respectively. Tex-CNN accuracy was either on par with or outcompeted state-of-the-art methods. Independent validation on Sentinel-2 data had good performance for most scene types but had difficulty discriminating farm scenes, likely due to geometric generalization of discriminative features at the coarser scale. In both datasets, the Tex-CNN outperformed the classical CNN architecture. Using the Tex-CNN, gradient-based spatial attention maps (feature maps) which contain discriminative pattern information are extracted and subsequently employed for mapping landscape similarity. To enhance the discriminative capacity of the feature maps, we further perform spatial filtering, using PCA and select eigen maps with the top eigen values. We show that CNN feature maps provide descriptors capable of characterizing and quantifying landscape (dis)similarity. Using the feature maps histogram of oriented gradient vectors and computing their Earth Movers Distances, our method effectively identified similar landscape types with over 60% of target-reference scene comparisons showing smaller Earth Movers Distance (EMD) (e.g., 0.01), while different landscape types tended to show large EMD (e.g., 0.05) in the benchmark AID. We hope this proposal will inspire further research into the use of CNN layer feature maps in landscape similarity assessment, as well as in change detection.

The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels. The potential surveillance value in online sources of information about emergent health events include early warning, situational awareness, risk perception and evaluation of health messaging among others. The challenge in harnessing these sources of data is the vast number of potential sources to monitor and developing the tools to translate dynamic unstructured content into actionable information. In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America. We collected AI-related messages over a five-month period and compared these to official surveillance records of AI outbreaks. A fully automated data extraction and analysis pipeline was developed to acquire, structure, and analyze social media messages in an online context. Two methods of outbreak detection; a static threshold and a cumulative-sum dynamic threshold; based on a time series model of normal activity were evaluated for their ability to discern important time periods of AI-related messaging and media activity. Our findings show that peaks in activity were related to real-world events, with outbreaks in Nigeria, France and the USA receiving the most attention while those in China were less evident in the social media data. Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous. Further analyses of these data might focus on quantifying the bias in coverage and relation between outbreak characteristics and detectability in social media data. Finally, while the analyses here focused on broad themes and trends, there is likely additional value in developing methods for identifying low-frequency messages, operationalizing this methodology into a comprehensive system for visualizing patterns extracted from the Internet, and integrating these data with other sources of information such as wildlife, environment, and agricultural data.

Background Certain foods may increase or decrease the risk of developing asthma, rhinoconjunctivitis and eczema. We explored the impact of the intake of types of food on these diseases in Phase Three of the International Study of Asthma and Allergies in Childhood. Methods Written questionnaires on the symptom prevalence of asthma, rhinoconjunctivitis and eczema and types and frequency of food intake over the past 12 months were completed by 13–14-year-old adolescents and by the parents/guardians of 6–7-year-old children. Prevalence ORs were estimated using logistic regression, adjusting for confounders, and using a random (mixed) effects model. Results For adolescents and children, a potential protective effect on severe asthma was associated with consumption of fruit ≥3 times per week (OR 0.89, 95% CI 0.82 to 0.97; OR 0.86, 95% CI 0.76 to 0.97, respectively). An increased risk of severe asthma in adolescents and children was associated with the consumption of fast food ≥3 times per week (OR 1.39, 95% CI 1.30 to 1.49; OR 1.27, 95% CI 1.13 to 1.42, respectively), as well as an increased risk of severe rhinoconjunctivitis and severe eczema. Similar patterns for both ages were observed for regional analyses, and were consistent with gender and affluence categories and with current symptoms of all three conditions. Conclusions If the association between fast foods and the symptom prevalence of asthma, rhinoconjunctivitis and eczema is causal, then the findings have major public health significance owing to the rising consumption of fast foods globally.

Abstract Rationale Better understanding of evolution of lung function in infants with cystic fibrosis (CF) and its association with pulmonary inflammation and infection is crucial in informing both early intervention studies aimed at limiting lung damage and the role of lung function as outcomes in such studies. Objectives To describe longitudinal change in lung function in infants with CF and its association with pulmonary infection and inflammation. Methods Infants diagnosed after newborn screening or clinical presentation were recruited prospectively. FVC, forced expiratory volume in 0.5 seconds (FEV0.5), and forced expiratory flows at 75% of exhaled vital capacity (FEF75) were measured using the raised-volume technique, and z-scores were calculated from published reference equations. Pulmonary infection and inflammation were measured in bronchoalveolar lavage within 48 hours of lung function testing. Measurements and Main Results Thirty-seven infants had at least two successful repeat lung function measurements. Mean (SD) z-scores for FVC were −0.8 (1.0), −0.9 (1.1), and −1.7 (1.2) when measured at the first visit, 1-year visit, or 2-year visit, respectively. Mean (SD) z-scores for FEV0.5 were −1.4 (1.2), −2.4 (1.1), and −4.3 (1.6), respectively. In those infants in whom free neutrophil elastase was detected, FVC z-scores were 0.81 lower (P = 0.003), and FEV0.5  z-scores 0.96 lower (P = 0.001), respectively. Significantly greater decline in FEV0.5  z-scores occurred in those infected with Staphylococcus aureus (P = 0.018) or Pseudomonas aeruginosa (P = 0.021). Conclusions In infants with CF, pulmonary inflammation is associated with lower lung function, whereas pulmonary infection is associated with a greater rate of decline in lung function. Strategies targeting pulmonary inflammation and infection are required to prevent early decline in lung function in infants with CF.

BackgroundCross-sectional studies implicate neutrophilic inflammation and pulmonary infection as risk factors for early structural lung disease in infants and young children with cystic fibrosis (CF). However, the longitudinal progression in a newborn screened population has not been investigated.AimTo determine whether early CF structural lung disease persists and progresses over 1 year and to identify factors associated with radiological persistence and progression.Methods143 children aged 0.2–6.5 years with CF from a newborn screened population contributed 444 limited slice annual chest CT scans for analysis that were scored for bronchiectasis and air trapping and analysed as paired scans 1 year apart. Logistic and linear regression models, using generalised estimating equations to account for multiple measures, determined associations between persistence and progression over 1 year and age, sex, severe cystic fibrosis transmembrane regulator (CFTR) genotype, pancreatic sufficiency, current respiratory symptoms, and neutrophilic inflammation and infection measured by bronchoalveolar lavage.ResultsOnce detected, bronchiectasis persisted in 98/133 paired scans (74%) and air trapping in 178/220 (81%). The extent of bronchiectasis increased in 139/227 (63%) of paired scans and air trapping in 121/264 (47%). Radiological progression of bronchiectasis and air trapping was associated with severe CFTR genotype, worsening neutrophilic inflammation and pulmonary infection.DiscussionCT-detected structural lung disease identified in infants and young children with CF persists and progresses over 1 year in most cases, with deteriorating structural lung disease associated with worsening inflammation and pulmonary infection. Early intervention is required to prevent or arrest the progression of structural lung disease in young children with CF.

Background. Child tuberculosis contact screening and management can enhance case finding and prevent tuberculosis disease. It is universally recommended but rarely implemented in tuberculosis-endemic settings. The World Health Organization (WHO)–recommended symptom-based screening approach could improve implementation but has not been prospectively evaluated. Methods. We conducted a cohort study of children who were close contacts of pulmonary tuberculosis patients in Indonesia from August 2010 to December 2012. We performed clinical assessment, tuberculin skin test, and chest radiography in all eligible children irrespective of symptoms at baseline. Mycobacterial culture and Xpert MTB/RIF assay were performed on sputum from children with persistent symptoms of suspected tuberculosis. Children were managed according to WHO guidelines and were prospectively followed for 12 months. Results. A total of 269 child contacts of 140 index cases were evaluated. At baseline, 21 (8%) children had tuberculosis diagnosed clinically; an additional 102 (38%) had evidence of infection without disease. Of children with any tuberculosis-related symptoms at baseline, 21% had tuberculosis diagnosed compared with none of the asymptomatic children (P < .001). After 12 months of follow-up, none of the 99 eligible young child contacts (<5 years) who received isoniazid preventive therapy (IPT) had developed disease compared with 4 of 149 (2.6%) asymptomatic older children who did not receive IPT. Conclusions. Symptom-based screening is an effective and simple approach to child tuberculosis contact management that can be implemented at the primary healthcare level.