52 research outputs found

    The Supreme People\u27s Court Annual Report on Intellectual Property Cases (2017) (China)

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    The Supreme People’s Court of China began publishing its Annual Report on Intellectual Property Cases in 2008. The Annual Report summarizes intellectual property cases, such as patent, trademark, trade secrets, copyright, and unfair competition cases. This 2017 Annual Report examines 42 cases and includes general guidelines for legal application. This summary reflects the Supreme People’s Court’s thoughts and approaches for ruling on new and complex IP and competition cases

    Numerical Simulation of Underwater Supersonic Jet of Vehicle with Shell-Shaped Flow Control Structure

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    When the underwater vehicle engine operates under the condition of over-expansion, the violent pulsation of the flow field pressure at the rear of the nozzle can cause violent fluctuations in engine thrust, leading to engine instability. In order to improve the engine's stability, this study drew inspiration from the wave attenuation characteristics of the shell-shaped surface texture structure and added a multi-layer shell-shaped texture structure to the rear wall to reduce pressure fluctuations in the flow field at the rear of the nozzle . Based on the numerical simulation method, the effects of different bionic shell-shaped structures on jet morphology, wall pressure and engine thrust were compared and analyzed. The results show that the multi-layer bionic shell-shaped texture structure can effectively inhibit the occurrence of periodic phenomena such as bulge, necking, and return stroke in the rear flow field, so as to effectively reduce the pressure fluctuation in the rear flow field of the nozzle. In addition, when the momentum thrust is almost unchanged, it is found through calculations that during the initial stage of the jet, the suppression of thrust is not significant. After 0.005 seconds, the oscillation amplitude of the combined force of pressure difference thrust and back pressure thrust decreased by 22%, and the oscillation amplitude of the total thrust decreased by 20%

    Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

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    Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only given a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in few-shot graph classification. Exiting methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines

    Galectin-3 alters the lateral mobility and clustering of beta 1-integrin receptors

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    Glycoprotein receptors are influenced by myriad intermolecular interactions at the cell surface. Specific glycan structures may interact with endogenous lectins that enforce or disrupt receptor-receptor interactions. Glycoproteins bound by multivalent lectins may form extended oligomers or lattices, altering the lateral mobility of the receptor and influencing its function through endocytosis or changes in activation. In this study, we have examined the interaction of Galectin-3 (Gal-3), a human lectin, with adhesion receptors. We measured the effect of recombinant Gal-3 added exogenously on the lateral mobility of the alpha 5 beta 1 integrin on HeLa cells. Using single-particle tracking (SPT) we detected increased lateral mobility of the integrin in the presence of Gal-3, while its truncated C-terminal domain (Gal-3C) showed only minor reductions in lateral mobility. Treatment of cells with Gal-3 increased beta 1-integrin mediated migration with no apparent changes in viability. In contrast, Gal-3C decreased both cell migration and viability. Fluorescence microscopy allowed us to confirm that exogenous Gal-3 resulted in reorganization of the integrin into larger clusters. We used a proteomics analysis to confirm that cells expressed endogenous Gal-3, and found that addition of competitive oligosaccharide ligands for the lectin altered the lateral mobility of the integrin. Together, our results are consistent with a Gal-3-integrin lattice model of binding and confirm that the lateral mobility of integrins is natively regulated, in part, by galectins
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