Figure 1: Example output from our system, generated in real-time with a handheld Kinect depth camera and no other sensing infrastructure. Normal maps (colour) and Phong-shaded renderings (greyscale) from our dense reconstruction system are shown. On the left for comparison is an example of the live, incomplete, and noisy data from the Kinect sensor (used as input to our system). ABSTRACTWe present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.

Figure 1: Example output from our system, generated in real-time with a handheld Kinect depth camera and no other sensing infrastructure. Normal maps (colour) and Phong-shaded renderings (greyscale) from our dense reconstruction system are shown. On the left for comparison is an example of the live, incomplete, and noisy data from the Kinect sensor (used as input to our system). ABSTRACTWe present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.

We argue that the field of extracellular vesicle (EV) biology needs more transparent reporting to facilitate interpretation and replication of experiments. To achieve this, we describe EV-TRACK, a crowdsourcing knowledgebase (http://evtrack.org) that centralizes EV biology and methodology with the goal of stimulating authors, reviewers, editors and funders to put experimental guidelines into practice.

Transforming growth factor–β (TGFβ) signaling drives aneurysm progression in multiple disorders, including Marfan syndrome (MFS), and therapies that inhibit this signaling cascade are in clinical trials. TGFβ can stimulate multiple intracellular signaling pathways, but it is unclear which of these pathways drives aortic disease and, when inhibited, which result in disease amelioration. Here we show that extracellular signal–regulated kinase (ERK) 1 and 2 and Smad2 are activated in a mouse model of MFS, and both are inhibited by therapies directed against TGFβ. Whereas selective inhibition of ERK1/2 activation ameliorated aortic growth, Smad4 deficiency exacerbated aortic disease and caused premature death in MFS mice. Smad4-deficient MFS mice uniquely showed activation of Jun N-terminal kinase–1 (JNK1), and a JNK antagonist ameliorated aortic growth in MFS mice that lacked or retained full Smad4 expression. Thus, noncanonical (Smad-independent) TGFβ signaling is a prominent driver of aortic disease in MFS mice, and inhibition of the ERK1/2 or JNK1 pathways is a potential therapeutic strategy for the disease.

We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For 50 targets with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 domains, and improved over the best starting model for 43 domains. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in 7 cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.

ObjectivesTo estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective during the early phases of the pandemic.DesignSystematic review.MethodsTwo parallel searches of academic bibliographic databases and grey literature were undertaken until 8 May 2020. Governments were also contacted for further information where possible. There were no restrictions on language, information sources used, publication status and types of sources of evidence. The AACODS checklist or the National Institutes of Health study quality assessment tools were used to appraise each source of evidence.Outcome measuresPublication characteristics, country-specific data points, COVID-19-specific data, demographics of affected HCWs and public health measures employed.ResultsA total of 152 888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%, n=14 058) and nurses (38.6%, n=10 706), but deaths were mainly in men (70.8%, n=550) and doctors (51.4%, n=525). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.2 deaths reported per 100 infections for HCWs aged over 70 years. Europe had the highest absolute numbers of reported infections (119 628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7).ConclusionsCOVID-19 infections and deaths among HCWs follow that of the general population around the world. The reasons for gender and specialty differences require further exploration, as do the low rates reported in Africa and India. Although physicians working in certain specialities may be considered high risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs.

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