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In times of steadily increasing traffic loads and extreme weather phenomena, the safe maintenance of infrastructure poses a difficult challenge to operators, especially when a vast number ...

The public health policies and sanitary measures taken by governments in various countries to stem the spread of the COVID-19 pandemic (e.g. lockdown, social distancing) have major implications for athletes. The radical changes are challenging and risk causing significant career disruption to athletes, with subsequent negative psychological effects. Thus, the ways athletes cope with such adversity is of critical importance. The present study aimed to identify athletes' coping profiles using a person-centred approach, based on their reported use of multiple coping strategies in response to the impact of the COVID-19 outbreak, and to compare levels of anxiety, stress appraisals, interpersonal coping strategies, and availability and appreciation of the major sources of support across profiles. A total of 526 French athletes competing at national to elite levels answered an online questionnaire during the lockdown. Latent profile analysis results yielded four distinct coping profiles (i.e. self-reliant, engaged, avoidant, active and social). The MANOVA showed that athletes belonging to the four profiles differed on anxiety, stress appraisals, social support, and interpersonal coping. In particular, avoidant copers reported high levels of anxiety, threat, and uncontrollability, and appeared less able to regulate responses to the impact of the COVID-19 outbreak. Using a person-centred approach, the findings could inform the development of more adequate care, support, and intervention for athletes, especially avoidant copers, who were characterized by the least effective coping skills and resources. Accordingly, stress reappraisal and stress mindset interventions could be promising approaches to effectively manage pandemic-related impact during and after the COVID-19 crisis.Highlights The COVID-19 outbreak has major implications for athletes and is causing significant disruption to their careers. Using a person-centred approach, four coping profiles emerge showing athletes' preferred use of several coping strategies in response.The four coping profiles (i.e. self-reliant, engaged, avoidant, active and social) differentiate distinct groups of athletes in relation to anxiety, stress appraisals, social support, and interpersonal coping.Avoidant copers were characterized by the least effective coping skills and social context of coping. Management of the COVID-19 situation may be more problematic for them than other in mitigating its negative psychological effects.Using a person-centred approach, the findings could inform the development of more adequate care, support, and intervention for athletes, especially avoidant copers, who were characterized by the least effective coping skills and resources.

Deformation monitoring and structural reliability assessment are key components in modern conventional tunneling. The state-of-the-art monitoring design is usually based on displacement measurements of geodetic targets using total stations paired with pointwise geotechnical sensors inside the tunnel lining. In recent years, distributed fiber optic sensing (DFOS) has become more popular in tunneling applications. DFOS measurements basically deliver internal strain and temperature distributions, but no direct relation to the tunnel shape’s behavior. This paper introduces a novel sensing and evaluation concept, which combines DFOS strain measurements and geodetic displacement readings for distributed shape assessment along curved structures, such as tunnel cross-sections. The designed system was implemented into shotcrete tunnel cross-sections as well as shaft linings and enables the determination of displacement profiles with high spatial resolution in the range of centimeters. Evaluations of continuous monitoring campaigns over several weeks as well as epoch-wise measurements performed by different DFOS sensing units in combination with stochastic analysis demonstrate the high potential of the developed approach and its capability to extend traditional monitoring methods in tunneling.

Prophylaxis treatment is considered as the reference approach for children with severe haemophilia A or B. However, no consensus about the best prophylaxis protocol has yet been identified in term of dosage and timing of infusions. Guidelines were drawn up in France in the early 2000s by an expert group. The objective of this 16-year study (2001 to 2016) was to describe the clotting factor concentrates (CFCs) use in haemophiac outpatients. This is a retrospective monocentric study. Pharmaceutical and clinical data were captured using medical records. Main outcome measures are CFCs use and clinical data in paediatrics. Eighty haemophiliacs A or B under 12 years old with a factor level less than 2% were included (74% of HA), from pharmaceutical outpatient data. Global use of CFCs followed the evolution of the patients' number and regimen type introduced: increase of prophylaxis and decrease of on demand regimen. The average age at the prophylaxis introduction is significantly different according to the birth year. Prophylaxis introduction was made earlier with an increase of prophylactic regimen joined to an increase of CFCs use. The significant reduction of haemarthrosis in our cohort can be linked to a first infusion age and a prophylaxis introduction much earlier.

Predictive maintenance and safety assessment during the construction and operational phase are becoming more and more important in modern tunnelling. However, traditional measurement methods are often time-consuming, expensive and partially require an interruption of the tunnel traffic. In this article, we present a tunnel monitoring approach based on distributed fibre optic sensing (DFOS), which delivers hundreds of strain and temperature sensing points inside the structure and gives completely new information about the behaviour of the tunnel lining. Measurements can be performed automatically without the need of access to the tunnel and hence, monitoring can be made without disturbing the tunnel construction or operation. The developed system was installed within the shotcrete tunnel lining of a railway tunnel under construction in Austria. In addition to the critical installation process, this article discusses the main monitoring results and compares them to conventional measurements.

International audience; The aim of this study was to identify young elite athletes’ personality profiles using a person-centered approach, and to investigate whether the profiles significantly differ in stress and sleep. 260 athletes from a variety of sports completed a questionnaire package to assess neuroticismand conscientiousness traits, stress appraisals (i.e. intensity and directional interpretation of stress, challenge and threat appraisals), and various indicators of sleep (i.e. sleep quality, social jet lag, Ford insomnia response to stress test (FIRST)). A latent profile analysis (LPA) approach was used to identify personality profiles based on the scores of neuroticism and conscientiousness. A multivariate analysis of variance was performed to examine if the athletes belonging to different personality profiles differ on stress appraisals and indicators of sleep.Three profiles emerged: Maladaptive profile (high levels of conscientiousness and neuroticism); Highly adaptive profile (moderate level of conscientiousness and low level of neuroticism); Adaptive profile (high level of conscientiousness and moderate level of neuroticism). Results showed that athletes from the adaptive profile reported significantly lower scores of stress intensity and threat appraisal than those from other profiles. Athletes from the maladaptive profile reported significantly higher levels of FIRST than those from other profiles as well as worse sleep quality and lower levels of challenge appraisal than the athletes from the highly adaptive profile. These results suggest that investigating personality profile may be useful in identifying athletes at higher risk of stress sensitivity and worsening sleep that are likely to benefit from preventive actions (e.g. cognitive behavioural therapy interventions).

GPS and more recently multi-GNSS carrier phase measurements have been used to measure the dynamic displacements of large structures, such as long-span bridges, in both the time and frequency domains. Such measurements can be used as part of a structural health monitoring system. Additionally, fibre optic-based systems have been used to measure long-term deformations of structures, such as tunnels and roads. The research presented in this paper brings together the ideas and technologies in the two aforementioned areas of research, resulting in dynamic displacement measurements of the hull of a ship at high frequencies, and indoor environments. Field trials using kinematic GPS and FBG sensors on the 138-m long Smyril passenger and vehicle ferry operating in the North Atlantic Ocean on the Faroe Islands are presented. FBG sensors were in the bow and engine room of the ship, gathering data at 1 kHz. The configuration of the surveys and the results from the FBG and GPS sensors are presented, in both the time and frequency domains. Various frequencies were measured, due to the movements of the ship in the ocean as well as the vibrations caused through the ship mainly due to the engine. One dominant frequency was of 12.25 Hz, measured at all the FBG locations, due to the engines’ operating speed of 735 RPM. Common frequencies were evident for both the FBG and GPS results for lower frequency displacement, caused by the movements in the sea. Such measurements could be used to monitor the long-term displacement characteristics and changes, in both the time and frequency domains, and used to help understand the health characteristics of the ship. Further, such measurements could be used to analyse the noise characteristics of the ship for both operational and environmental reasons.

Bovine babesiosis, caused by Babesia divergens, is in general a rare disease in Europe. Nonetheless, local outbreaks can cause severe economic damage, and postmortem identification represents a diagnostic challenge. During a recent outbreak in May 2018 in northern Germany, 21 animals of a herd of 150 cattle died within 40 days having had clinical signs of fever and hemoglobinuria. Gross examination of 4 of the 21 deceased animals revealed a tick infestation, jaundice, and dark brown staining of urine and kidneys. Histologically, there were iron-positive deposits, hyperplasia of the red pulp of the spleen, and centrilobular necrosis of hepatocytes. In several locations, small basophilic granules suggestive of intraerythrocytic parasites were visible in hematoxylin-eosin- and Giemsa-stained sections. Peripheral blood smears from a living cow from the herd and polymerase chain reaction (PCR) of feeding ticks revealed B. divergens infection. In situ hybridization (ISH) was applied on formalin-fixed, paraffin-embedded (FFPE) tissue of the necropsied cattle to confirm babesiosis in these animals postmortem. Digoxigenin-labeled DNA probes were generated based on a specific nucleotide sequence for B. divergens, obtained by PCR and sequencing of DNA isolates from infected Ixodes ricinus ticks from deceased cattle. ISH using these probes allowed postmortem diagnosis of B. divergens infection in routinely fixed FFPE tissues.

Introduction With current molecular diagnosis, about 1 to 5% of haemophilia A (HA) patients remain genetically unresolved. In these cases, deep intronic variation or structural variation disrupting the F8 gene could be causal. Aim To identify the causal variation in four genetically unresolved mild-to-severe HA patients using an F8 mRNA analysis approach. Methods Ectopic F8 mRNA analysis was performed in four unrelated HA patients. An in vitro minigene assay was performed in order to confirm the deleterious splicing impact of each variation identified. Results In all probands, mRNA analysis revealed an aberrant splicing pattern, and sequencing of the corresponding intronic region found a deep intronic substitution. Two of these were new variations: c.2113+601G>A and c.1443+602A>G, while the c.143+1567A>G, found in two patients, has previously been reported. The c.1443+602A>G and the c.143+1567A>G variants both led to the creation of a de novo acceptor or donor splice site, respectively. Moreover, the c.143+1567A>G was found in 3/6 patients with genetically unresolved moderate HA registered in our laboratory. Haplotype analysis performed in all patients carrying the c.143+1567A>G variation suggests that this variation could be a recurrent variation. The c.2113+601G>A led to the exonization of a 122-bp antisense AluY element by increasing the strength of a pre-existing cryptic 5' splice site. For each point variation, in vitro splicing analysis confirmed its deleterious impact on splicing of the F8 transcript. Conclusion We identified three deep intronic variations, leading to an aberrant mRNA splicing process as HA causing variation.

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The papers in this special section is to broadly engage the machine learning and multimedia communities on the emerging yet challenging interpretable machine learning. Multimedia is increasingly becoming the “biggest big data,” among the most important and valuable source for insight and information. Many powerful machine learning algorithms, especially deep learning models such as convolutional neural networks (CNNs), have recently achieved outstanding predictive performance in a wide range of multimedia applications, including visual object classification, scene understanding, speech recognition, and activity prediction. Nevertheless, most deep learning algorithms are generally conceived as blackbox methods, and it is difficult to intuitively and quantitatively understand the results of their prediction and inference. Since this lack of interpretability is a major bottleneck in designing more successful predictive models and exploring wider-range useful applications, there has been an explosion of interest in interpreting the representations learned by these models, with profound implications for research into interpretable machine learning in the multimedia community.

Efficient and economic foundations are essential to ensure the long-term integrity of structures. Driven ductile piles offer a safe and quick solution for foundations, which can be individually customized to changing soil conditions. Geotechnical load tests on a small subset of piles can be performed at large construction sites to examine the bearing capacity for optimization purposes. Arising deformations during these statical tests are usually measured using electrical sensors at the top, which, however, do not deliver information about the stress distribution along the pile. This paper presents a fiber optic monitoring approach, which provides distributed strain profiles with a spatial resolution of up to 10 mm along driven ductile piles. The high measurement resolution of about $$1~{\mu}m/m$$ 1 μ m / m enables the detection of local effects in the load transfer from the pile to the surrounding grout and soil. The critical sensor installation on-site as well as results of various field applications with pile lengths of up to 25 m are presented. Verification measurements at the pile’s head and internal measurements of strain gauges prove the suitability of the developed monitoring approach and demonstrate the high potential of distributed fiber optic sensing for applications in soil mechanics.

Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection Challenge., 17 pages, 3 figures, informations about challenge submission

Health of people with severe haemophilia (PwSH) improves thanks to the advancements in haemophilia care, giving them more opportunities in occupational integration. However, there is little literature on the occupational integration of PwSH.The main objective of our study was to assess the occupational integration of PwSH and to compare it with that of the general population. The secondary objective was to study the association between individual characteristics (sociodemographic, clinical and psycho-behavioural) and occupational integration of PwSH.A multicentre, non-interventional, cross-sectional study was conducted in 2018-2020 on PwSH, aged over 18 and under 65 years and included in the FranceCoag registry. Measurements included indicators of occupational integration, sociodemographic, clinical and psycho-behavioural characteristics. The indicators of occupational integration were compared with those of the general population, using indirect standardization. The data of the general population were available from the National Institute of Statistics and Economic Studies (INSEE). Determinants of occupational integration were explored using structural equation modelling.Of 1262 eligible people, 588 were included. PwSH had a lower employment rate than the general population (standardized ratio, .85; 95% CI, .77-.94). There were more PwSH at tertiary education level than expected (standardized ratio, 1.38; 95% CI, 1.17-1.61). HIV infection, poor physical health and mental health concerns were associated with a higher risk of unemployment in PwSH.Employment rate of PwSH is lower than that of the general population despite their higher education level. Target interventions focusing on determinants of difficult occupational integration could be helpful for PwSH.

Unsupervised domain adaptation is a promising technique for computer vision tasks, especially when annotating large amounts of data is very costly and time-consuming, as in semantic segmentation. Here it is attractive to train neural networks on simulated data and fit them to real data on which the models are to be used. In this paper, we propose a consistency regularization method for domain adaptation in semantic segmentation that combines pseudo-labels and strong per- turbations. We analyse the impact of two simple perturbations, dropout and image mixing, and show how they contribute enormously to the final performance. Experiments and ablation studies demonstrate that our simple approach achieves strong results on relevant synthetic-to-real domain adaptation benchmarks.

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the Vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs., Comment: accepted at CVSports (CVPR 2022 Workshops)

This paper presents a practical and efficient workflow for deformation monitoring of transport infrastructure. We propose using commercially available mobile laser scanning (MLS) systems to scan civil infrastructure while driving by in a car or rail vehicle. Our processing pipeline corrects for MLS-specific systematic deviations and models deformations from point clouds of two epochs. Following the concept of rigorous deformation analysis, we statistically test the deformations for significance. The required point cloud uncertainty may be obtained in two ways. First option is empirically by multiple passes and, secondly, by prediction with a learned stochastic model. We apply the method to three retaining structures and evaluate results based on ground truth geodetic surveys. The deviations did not exceed 10 mm, even for complex object surfaces or when traveling at 80 km/h. We demonstrate that the method is capable of revealing displacements in the centimeter range without relying on any installations on the structure. The approach shows great potential as a novel, efficient tool for detecting and quantifying defective structures in a road and railway network.