Predicting Patent Value: A Data Mining Approach

Patents have long been recognized as a rich source of data for studying innovation, technical changes, and value creation. Patent data includes citations to previous patents, and patent citations allow one to create an indicator of patent value. Identifying valuable patents in a timely manner is essential for effectively harnessing the business value of inventions in the increasingly competitive global market. However, the existing methods of evaluating patent value suffer the issues of timeliness and accuracy. In this paper, we propose a data mining approach that utilizes the structural properties of patent citations networks to predict the value of patents while aiming to improve timeliness and accuracy

Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning

Digital human recommendation system has been developed to help customers find their favorite products and is playing an active role in various recommendation contexts. How to timely catch and learn the dynamics of the preferences of the customers, while meeting their exact requirements, becomes crucial in the digital human recommendation domain. We design a novel practical digital human interactive recommendation agent framework based on Reinforcement Learning(RL) to improve the efficiency of the interactive recommendation decision-making by leveraging both the digital human features and the superior flexibility of RL. Our proposed framework learns through real-time interactions between the digital human and customers dynamically through the state-of-art RL algorithms, combined with multimodal embedding and graph embedding, to improve the accuracy of personalization and thus enable the digital human agent to timely catch the attention of the customer. Experiments on real business data demonstrate that our framework can provide better personalized customer engagement and better customer experiences.Comment: 9 pages, 1 figure, 1 table, the paper has been accepted and this is the final camera-ready for NeurIPS 2022 Workshop on Human in the Loop Learning, https://neurips-hill.github.io

Schwann cell coculture improves the therapeutic effect of bone marrow stromal cells on recovery in spinal cord-injured mice

Studies of bone marrow stromal cells (MSCs) transplanted into the spinal cord-injured rat give mixed results: some groups report improved locomotor recovery while others only demonstrate improved histological appearance of the lesion. These studies show no clear correlation between neurological improvements and MSC survival. We examined whether MSC survival in the injured spinal cord could be enhanced by closely matching donor and recipient mice for genetic background and marker gene expression and whether exposure of MSCs to a neural environment (Schwann cells) prior to transplantation would improve their survival or therapeutic effects. Mice underwent a clip compression spinal cord injury at the fourth thoracic level and cell transplantation 7 days later. Despite genetic matching of donors and recipients, MSC survival in the injured spinal cord was very poor (~1%). However, we noted improved locomotor recovery accompanied by improved histopathological appearance of the lesion in mice receiving MSC grafts. These mice had more white and gray matter sparing, laminin expression, Schwann cell infiltration, and preservation of neurofilament and 5-HT-positive fibers at and below the lesion. There was also decreased collagen and chondroitin sulphate proteoglycan deposition in the scar and macrophage activation in mice that received the MSC grafts. The Schwann cell cocultured MSCs had greater effects than untreated MSCs on all these indices of recovery. Analyses of chemokine and cytokine expression revealed that MSC/Schwann cell cocultures produced far less MCP-1 and IL-6 than MSCs or Schwann cells cultured alone. Thus, transplanted MSCs may improve recovery in spinal cord-injured mice through immunosuppressive effects that can be enhanced by a Schwann cell coculturing step. These results indicate that the temporary presence of MSCs in the injured cord is sufficient to alter the cascade of pathological events that normally occurs after spinal cord injury, generating a microenvironment that favors improved recovery. © 2011 Cognizant Comm. Corp

A novel iterative learning approach for tracking control of high-speed trains subject to unknown time-varying delay

In this article, a novel iterative learning control scheme is proposed for high-speed trains, aiming to track the desired reference displacement and velocity, where the Krasovskii function is constructed to compensate for the negative influence of unknown time-varying speed delays. The main feature of the proposed approach is that the hyperbolic tangent function and the command filter are integrated into the learning controller to overcome the singularity problem that may occur during the control process and relax the requirement for the derivability of the desired velocity. The stability of control system is strictly proved through establishing the composite energy function, and the effectiveness is confirmed via numerical simulations. Compared with the existing works, the merits of the proposed control scheme lie in that more general nonlinear uncertainties are imposed on the dynamic model of train instead of the Lipschitz condition, and the reference acceleration assigned by the railway department is not required

Transcriptional landscape of myasthenia gravis revealed by weighted gene coexpression network analysis

Background: As one of the most common autoimmune diseases, myasthenia gravis (MG) severely affects the quality of life of patients. Therefore, exploring the role of dysregulated genes between MG and healthy controls in the diagnosis of MG is beneficial to reveal new and promising diagnostic biomarkers and clinical therapeutic targets.Methods: The GSE85452 dataset was downloaded from the Gene Expression Omnibus (GEO) database and differential gene expression analysis was performed on MG and healthy control samples to identify differentially expressed genes (DEGs). The functions and pathways involved in DEGs were also explored by functional enrichment analysis. Significantly associated modular genes were identified by weighted gene co-expression network analysis (WGCNA), and MG dysregulated gene co-expression modular-based diagnostic models were constructed by gene set variance analysis (GSVA) and least absolute shrinkage and selection operator (LASSO). In addition, the effect of model genes on tumor immune infiltrating cells was assessed by CIBERSORT. Finally, the upstream regulators of MG dysregulated gene co-expression module were obtained by Pivot analysis.Results: The green module with high diagnostic performance was identified by GSVA and WGCNA. The LASSO model obtained NAPB, C5orf25 and ERICH1 genes had excellent diagnostic performance for MG. Immune cell infiltration results showed a significant negative correlation between green module scores and infiltration abundance of Macrophages M2 cells.Conclusion: In this study, a diagnostic model based on the co-expression module of MG dysregulated genes was constructed, which has good diagnostic performance and contributes to the diagnosis of MG