Background
Cancer is one of the leading causes of death and a main economic burden in China. Investigating the differences in cancer patterns and control strategies between China and developed ...countries could provide reference for policy planning and contribute to improving cancer control measures. In this study, we reviewed the rates and trends of cancer incidence and mortality and disability‐adjusted life year (DALY) burden in China, and compared them with those in the United States (US) and the United Kingdom (UK).
Methods
Cancer incidence, mortality, and DALY data for China, US and UK were obtained from the GLOBOCAN 2020 online database, Global Burden of Disease (GBD) 2019 study, and Cancer Incidence in Five Continents plus database (CI5 plus). Trends of cancer incidence and mortality in China, US, and UK were analyzed using Joinpoint regression models to calculate annual percent changes (APCs) and identify the best‐fitting joinpoints.
Results
An estimated 4,568,754 newly diagnosed cancer cases and 3,002,899 cancer deaths occurred in China in 2020. Additionally, cancers resulted in 67,340,309 DALYs in China. Compared to the US and UK, China had lower cancer incidence but higher cancer mortality and DALY rates. Furthermore, the cancer spectrum of China was changing, with a rapid increase incidence and burden of lung, breast, colorectal, and prostate cancer in addition to a high incidence and heavy burden of liver, stomach, esophageal, and cervical cancer.
Conclusions
The cancer spectrum of China is changing from a developing country to a developed country. Population aging and increase of unhealthy lifestyles would continue to increase the cancer burden of China. Therefore, the Chinese authorities should adjust the national cancer control program with reference to the practices of cancer control which have been well‐established in the developed countries, and taking consideration of the diversity of cancer types by of different regions in China at the same time.
The cancer spectrum of China is changing, with a rapidly increase incidence and burden of "cancers of the rich" (lung, breast, colorectal, and prostate cancer) in addition to a high incidence and heavy burden of “cancers of the poor” (liver, stomach, esophageal, and cervical cancer).
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
With the recent advancement of deep convolutional neural networks, significant progress has been made in general face recognition. However, the state-of-the-art general face recognition models do not ...generalize well to occluded face images, which are exactly the common cases in real-world scenarios. The potential reasons are the absences of large-scale occluded face data for training and specific designs for tackling corrupted features brought by occlusions. This article presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks. In addition, we construct massive occluded face images to train FROM effectively and efficiently. FROM is simple yet powerful compared to the existing methods that either rely on external detectors to discover the occlusions or employ shallow models which are less discriminative. Experimental results on the LFW, Megaface challenge 1, RMF2, AR dataset and other simulated occluded/masked datasets confirm that FROM dramatically improves the accuracy under occlusions, and generalizes well on general face recognition.
Cross View Fusion for 3D Human Pose Estimation Qiu, Haibo; Wang, Chunyu; Wang, Jingdong ...
2019 IEEE/CVF International Conference on Computer Vision (ICCV),
10/2019
Conference Proceeding
Open access
We present an approach to recover absolute 3D human poses from multi-view images by incorporating multi-view geometric priors in our model. It consists of two separate steps: (1) estimating the 2D ...poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses. First, we introduce a cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple views. Consequently, the 2D pose estimation for each view already benefits from other views. Second, we present a recursive Pictorial Structure Model to recover the 3D pose from the multi-view 2D poses. It gradually improves the accuracy of 3D pose with affordable computational cost. We test our method on two public datasets H36M and Total Capture. The Mean Per Joint Position Errors on the two datasets are 26mm and 29mm, which outperforms the state-of-the-arts remarkably (26mm vs 52mm, 29mm vs 35mm).
Pan et al discuss their single-center observational study on lung recruitability in COVID-19-associated acute respiratory distress syndrome. COVID-19 outbreak was declared a public health emergency ...by the World Health Organization on January 30, 2020. A majority of critically ill patients who were admitted to an ICU with a confirmed infection of severe acute respiratory syndrome coronavirus 2 developed acute respiratory distress syndrome. The results revealed that ung recruitability can be assessed at the bedside even in a very constrained environment and was low in our patients with COVID-19-nduced ARDS.
Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, more than 153 million cases have been confirmed, and more than 3.20 million individuals have died worldwide. The strategies of ...widespread testing, strict social distancing and isolation, information technology-based tracing, and widespread face mask use rapidly controlled the COVID-19 outbreak in China. Rapid responses and such strategies led to effective control in 1–2 months for several later localized outbreaks. On April 29th, there were only 324 confirmed cases in China; most of the cases were imported. Numerous lessons and experiences from this pandemic could impact and change critical care delivery in the future.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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