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Background: At present, there is a global pandemic of coronavirus disease 2019 (COVID-19) pneumonia. Two previous case series from China have suggested that cancer patients are at a higher risk of COVID-19 pneumonia, but the reports were limited by small numbers and few clinical information. Objective: To study clinical characteristics and outcomes of cancer patients infected with COVID-19. Design: Retrospective study. Setting: Four designated COVID-16 hospitals in Wuhan, Hubei province, China. Participants: Medical records of 67 cancer patients admitted to hospitals between Jan 5, 2020 to Feb 18, 2020 were included. Measurements: Demographic, clinical, laboratory, radiological and treatment data were collected. Survival data of the cohort was cut-off on Mar 10, 2020. Results: Of the 67 patients (median age: 66 years), the median age of patients who had severe illness was older than that of patients who had mild symptoms (P

Potato (Solanum tuberosum) late blight caused by oomycete Phytophthora infestans (Pi) is highly destructive to potato yield and cost huge losses each year. Regulation of a network of transcription machinery, controlled by transcription factors (TF’s), is required to overcome the susceptibility. WRKY TF’s are known to regulate transcription machinery upon biotic and abiotic stresses in different crop plants. We cloned and characterized a WRKY gene, StWRKY1, from potato cDNA synthesized from Pi infested leaves. StWRKY1 protein localized typically in the nucleus. Overexpression (OE) of StWRKY1 positively regulates Pi resistance as well as drought tolerance in transgenic potato. The elevated resistance in OE lines was co-related with higher accumulation of pathogenesis-related (PR) genes as compared to untransformed control plants. Interestingly, increased susceptibility of co-suppression (CSP) plants was associated with down regulation of PR genes expression. Moreover, transgenic lines overexpressing StWRKY1 showed tolerance in terms of less rate of water loss, during dehydration assay. Importantly, expression of StWRKY1 was upregulated upon treatment with plant hormones, suggesting its involvement in basal signal transduction pathway. Overall, our findings provided evidence that StWRKY1 positively regulate biotic and abiotic stress resistance thereby modulating plant basal defense networks, thus play a significant role for crop improvement.

In order to overcome the disadvantages of color, shape and texture-based features definition for medical images, this paper defines a new kind of semantic feature and its extraction algorithm. We firstly use kernel density estimation statistical model to describe the complicated medical image data, secondly, define some typical representative pixels of images as feature and finally, take hill-climbing strategy of Artificial Intelligence to extract those semantic features. Results of a content-based medial image retrieve system show that our semantic features have better distinguishing ability than those color, shape and texture-based features and can improve the ratios of recall and precision of this syste smartly.

Direct volume rendering is one of the methods for the visualization of 3D data set, It does not construct intermediate entity, directly generate 2D graphics on the screen by the 3D data set, which is better for parallel processing, but the amount of calculation is large, difficult to rendering with conventional graphics hardware. This paper mainly elaborated optimization and improvement about the algorithm of ray casting volume rendering for the visualization of 3D data set, within the framework of the CUDA, using the multi-core parallel computing ability of GPU. The vertex shader, pixel shader, calculation of the starting point of the sampling points of light, color and opacity tired and synthesis of image operations are completed by GPU. The vertex shader, pixel shader, calculation of the starting point of light, color and opacity of the sampling points accumulation and image synthesis operation are achieved by GPU. Compared with the GPU-based ray-casting algorithm, the algorithm takes full advantage of the characteristics of the CUDA parallel processing, can quickly draw a higher quality image, rendering speed has been raised about 15%.

In order to address the problems of discontinuity and block effect for the dehazing method based on dark channel prior, we improved this method using wavelet decomposition, fast kernel regression model, and bicubic interpolation. First, spatial resolution of the hazy image was reduced by the downsampling method with Haar wavelet decomposition. Second, the fast kernel regression model was proposed to smooth the central transmission with local neighbor transmissions. Last, the smoothed transmission for the approximation image was resized to the hazy image by the bicubic interpolation method. Experiments were carried out on synthetic hazy images with known ground truth and real-world hazy images without ground truth. The regions of sudden change of depth in the dehazed images by our method were more smooth and continuous than those of several state-of-the-art methods, and contrast of our method was higher than that of other methods. Indexes based on the concept of visibility level, mean squared error, and structural similarity of our method were better than those of other methods.