Lycorine reduces mortality of human enterovirus 71-infected mice by inhibiting virus replication

Human enterovirus 71 (EV71) infection causes hand, foot and mouth disease in children under 6 years old and this infection occasionally induces severe neurological complications. No vaccines or drugs are clinical available to control EV71 epidemics. In present study, we show that treatment with lycorine reduced the viral cytopathic effect (CPE) on rhabdomyosarcoma (RD) cells by inhibiting virus replication. Analysis of this inhibitory effect of lycorine on viral proteins synthesis suggests that lycorine blocks the elongation of the viral polyprotein during translation. Lycorine treatment of mice challenged with a lethal dose of EV71 resulted in reduction of mortality, clinical scores and pathological changes in the muscles of mice, which were achieved through inhibition of viral replication. When mice were infected with a moderate dose of EV71, lycorine treatment was able to protect them from paralysis. Lycorine may be a potential drug candidate for the clinical treatment of EV71-infected patients

INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE

With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings

Imaging-based amplitude laser beam shaping for material processing by 2D reflectivity tuning of a spatial light modulator

We have demonstrated an imaging-based amplitude laser-beam-shaping technique for material processing by 2D reflectivity tuning of a spatial light modulator. Intensity masks with 256 gray levels were designed to shape the input laser beam in the outline profile and inside intensity distribution. Squared and circular flattop beam shapes were obtained at the diffractive near-field and then reconstructed at an image plane of a

Ultrafast laser beam shaping for material processing at imaging plane by geometric masks using a spatial light modulator

We have demonstrated an original ultrafast laser beam shaping technique for material processing using a spatial light modulator (SLM). Complicated and time-consuming diffraction far-field phase hologram calculations based on Fourier transformations are avoided, while simple and direct geometric masks are used to shape the incident beam at diffraction near-field. Various beam intensity shapes, such as square, triangle, ring and star, are obtained and then reconstructed at the imaging plane of an f-theta lens. The size of the shaped beam is approximately 20 µm, which is comparable to the beam waist at the focal plane. A polished stainless steel sample is machined by the shaped beam at the imaging plane. The shape of the ablation footprint well matches the beam shape

Learning Global-aware Kernel for Image Harmonization

Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references. Specifically, GKNet includes two parts, \ie, harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, \eg, achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB $\uparrow$; decreasing fMSE/MSE by 11.5\%$\downarrow$/6.7\%$\downarrow$ compared with the SoTA method. Code will be available at \href{https://github.com/XintianShen/GKNet}{here}.Comment: 10 pages, 10 figure

Iterative Few-shot Semantic Segmentation from Image Label Text

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet.Comment: ijcai 202