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Therapeutic modulation of LUBAC activity may be crucial to improve outcomes during severe influenza virus infection, as it functions as a molecular rheostat of the host response. Here we review the evidence for modulating inflammation to ameliorate influenza virus infection-induced lung injury, data on current anti-inflammatory strategies, and potential new avenues to target viral inflammation and improve outcomes.<br /><br /> Exacerbations are crucial events during bronchiectasis progression.<br /><br /> To explore the associations between bacterial, viral, and bacterial plus viral isolations and bronchiectasis exacerbations.<br /><br /> In this prospective study, we enrolled 108 patients who were followed up every 3-6 months and at onset of exacerbations between March 2017 and November 2018. Spontaneous sputum was split for detection of bacteria (routine culture) and viruses (quantitative polymerase chain reaction). Symptoms and lung function were assessed during exacerbations.<br /><br /> The median exacerbation rate was 2.0 (interquartile range 1.0-2.5) per patient-year. At any visit, viral isolations (V+) occurred more frequently during onset of exacerbations [odds ratio (OR) 3.28, 95% confidence interval (95%CI) 1.76-6.12], as did isolation of new bacteria (NB+) (OR 2.52, 95%CI 1.35-4.71) and bacterial plus viral isolations (OR 2.24, 95%CI 1.11-4.55). Whilst coryza appeared more common in exacerbations with V+ than in exacerbations with no pathogen isolations and those with NB+, lower airway symptoms were more severe in exacerbations with NB+ (<br /><br /> &lt;.05). Sputum interleukin-1β levels were higher in exacerbations with NB+ than in exacerbations with no pathogen isolations and those with V+ (both <br /><br /> &lt;.05). Significantly more coryza symptoms correlated with bacterial plus viral isolations at exacerbations (<br /><br /> =.019). Compared with V+ alone, bacterial with and without viral isolations tended to yield more severe lower airway symptoms, but not sputum cytokine levels at exacerbations.<br /><br /> Viral isolations, isolation of new bacteria and bacterial plus viral isolation are associated with bronchiectasis exacerbations. Symptoms at exacerbations might inform clinicians the possible culprit pathogens.<br /><br />Viral isolations, isolation of new bacteria and bacterial plus viral isolation are associated with bronchiectasis exacerbations. Symptoms at exacerbations might inform clinicians the possible culprit pathogens.Body posture influences human and robot performance in manipulation tasks, as appropriate poses facilitate motion or the exertion of force along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control, and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or applying a specific force. In this context, this article presents a novel manipulability transfer framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive-definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Accurately segmenting organs in abdominal computed tomography (CT) scans is crucial for clinical applications such as pre-operative planning and dose estimation. With the recent advent of deep learning algorithms, many robust frameworks have been proposed for organ segmentation in abdominal CT images. However, many of these frameworks require large amounts of training data in order to achieve high segmentation accuracy. Pediatric abdominal CT images containing reproductive organs are particularly hard to obtain since these organs are extremely sensitive to ionizing radiation. [https://www.selleckchem.com/products/enfortumab-vedotin-ejfv.html Enfortumab vedotin-ejfv supplier] Hence, it is extremely challenging to train automatic segmentation algorithms on organs such as the uterus and the prostate. To address these issues, we propose a novel segmentation network with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditionally generates additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) is trained on a single loss function which combines adversarial loss, reconstruction loss, auxiliary classifier loss and segmentation loss. 2.5D segmentation experiments are performed on a custom data set containing 24 female CT volumes containing the uterus and 40 male CT volumes containing the prostate. CFG-SegNet achieves an average segmentation accuracy of 0.929 DSC (Dice Similarity Coefficient) on the prostate and 0.724 DSC on the uterus with 4-fold cross validation. The results show that our network is high-performing and has the potential to precisely segment difficult organs with few available training images.The role of digital technologies (DTs) in humanitarian supply chains (HSC) has become an increasingly researched topic in the operations literature. While numerous publications have dealt with this convergence, most studies have focused on examining the implementation of individual DTs within the HSC context, leaving relevant literature, to date, dispersed and fragmented. This study, through a systematic literature review of 110 articles on HSC published between 2015 and 2020, provides a unified overview of the current state-of-the-art DTs adopted in HSC operations. The literature review findings substantiate the growing significance of DTs within HSC, identifying their main objectives and application domains, as well as their deployment with respect to the different HSC phases (i.e., Mitigation, Preparedness, Response, and Recovery). Furthermore, the findings also offer insight into how participant organizations might configure a technological portfolio aimed at overcoming operational difficulties in HSC endeavours.