Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M 2 UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M 2 UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.The COVID-19 pandemic has affected many countries, posing a threat to human health and safety, and putting tremendous pressure on the medical system. This paper proposes a novel SLAM technology using RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19. Most current visual SLAM researches assume that the environment is stationary, which makes handling real-world scenarios such as hospitals a challenge. This paper proposes a method that effectively deals with SLAM problems for scenarios with dynamic objects, e.g., people and movable objects, based on the semantic descriptor extracted from images with help of a knowledge graph. Specifically, our method leverages a knowledge graph to construct a priori movement relationship between entities and establishes high-level semantic information. Built upon this knowledge graph, a semantic descriptor is constructed to describe the semantic information around key points, which is rotation-invariant and robust to illumination. The seamless integration of the knowledge graph and semantic descriptor helps eliminate the dynamic objects and improves the accuracy of tracking and positioning of robots in dynamic environments. Experiments are conducted using data acquired from healthcare facilities, and semantic maps are established to meet the needs of robots for delivering medical services. In addition, to compare with the state-of-the-art methods, a publicly available dataset is used in our evaluation. Compared with the state-of-the-art methods, our proposed method demonstrated great improvement with respect to both accuracy and robustness in dynamic environments. The computational efficiency is also competitive.Kokon Rohrbach-Berg is the first Austrian rehabilitation clinic exclusively built for children and teenagers aged 0-18.  https://www.selleckchem.com/products/triptolide.html  There are 77 beds for patients available at kokon Rohrbach-Berg, with an additional 67 beds for accompanying chaperones.We are specialized in the fields of mobilisation, cardiologic and pulmologic disorders as well as in mental health rehabilitation. Our focus lies on an integrative perception of children and teenagers' chronic illnesses.The rehabilitation program is designed around the specific needs of each child or teenager.Our interdisciplinary teams create individual therapy plans, offer child-friendly incentives to further develop independence and strive to improve participation in daily life.We are living an extraordinary season of uncertainty and danger, which is caused by SARS-Cov-2 infection and consequent COVID-19 infection. This preliminary study comes from both a mix of entrepreneurial experience and scientific research. It is aimed by the exigency to reach a new and more effective analysis of the risks on the filed and to reduce them inside a necessary cooperation process which may regard both research and some of the economic activities which are damaged by passive protection measures such as indiscriminate lockdowns. This global emergency requires specific efforts by any discipline that regards specific problems which need to be solved urgently. The characteristic airborne diffusion patterns of COVID-19 shows that the airborne presence of viruses depends on multiple factors which include the dimension of microdroplets emitted by a contagious person, the atmospheric temperature and humidity, the presence of atmospheric particulate and pollution, which may act as a transport vehicle for thc risks of new generalized lockdowns.
  A high incidence of pulmonary embolism has been described during the coronavirus pandemic.
 
  This work is a single-center retrospective study which reviewed computed tomography pulmonary angiograms ordered due to suspected pulmonary embolism during two periods from March 1, 2020 to May 31, 2020 (pandemic) and during the same interval in 2019 (control).
 
  Twenty-two pulmonary embolism were diagnosed during the control period and 99 in the pandemic, 74 of which were associated with COVID-19. Of all patients hospitalized with COVID-19, 5.3% had a pulmonary embolism, with a delay between the two diagnoses of 9.1+/-8.4 days. During the pandemic, patients with pulmonary embolism had fewer predisposing conditions (previous pulmonary embolism 5.1% vs. 18.2%, p = .05; previous surgery 2% vs. 35.4%, p = .0001; deep vein thrombosis 11.1% vs. 45.5%, p=.0001); peripheral pulmonary embolisms were the most frequent (73.5% vs. 50%, p =. 029).
 
  There is an increased risk of having a pulmonary embolism during the SARS-CoV-2 pandemic, which affects patients with a different clinical profile and more often causes distal pulmonary embolisms.