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To reflect both fuzziness and hesitation in the evaluation of interactivity between attributes in the identification process of 2-order additive fuzzy measure, this work uses the hesitant fuzzy linguistic term set (HFLTS) to describe and depict the interactivity between attributes. Firstly, the interactivity between attributes is defined by the supermodular game theory. According to this definition, a linguistic term set is established to characterize the interactivity between attributes. Under the linguistic term set, the experts employ linguistic expressions generated by context-free grammar to qualitatively describe the interactivity between attributes. Secondly, through the conversion function, the linguistic expressions are transformed into the hesitant fuzzy linguistic term sets (HFLTSs). The individual evaluation results of all experts were further aggregated with the defined hesitant fuzzy linguistic weighted power average operator (HFLWPA). Thirdly, based on the standard Euclidean distance formula of the hesitant fuzzy linguistic elements (HFLEs), the hesitant fuzzy linguistic interaction degree (HFLID) between attributes is defined and calculated by constructing a piecewise function. As a result, a 2-order additive fuzzy measure identification method based on HFLID is proposed. Based on the proposed method, using the Choquet fuzzy integral as nonlinear integration operator, a multi-attribute decision making (MADM) process is then presented. Taking the credit assessment of the big data listed companies in China as an application example, the analysis results of application example prove the feasibility and effectiveness of the proposed method. This work successfully reflects both the fuzziness and hesitation in evaluating the interactivity between attributes in the identification process of 2-order additive fuzzy measure, enriches the theoretical framework of 2-order additive fuzzy measure, and expands the applicability and methodology of 2-order additive fuzzy measure in multi-attribute decision making. [ABSTRACT FROM AUTHOR]

Recent studies suggest that cell therapy may be an effective way to repair intervertebral disc degeneration. As a strong immune suppressor, TGF-β1 has been shown to inhibit inflammation respond effectively. The objective of this study was to explore the effects of TGF-β1 during bone marrow mesenchymal stem cells-based therapy for disc degeneration. In vitro assays demonstrated that co-culturing of nucleus pulposus cells with bone marrow mesenchymal stem cells resulted in significantly higher levels of TGF-βl secretion. This increase inhibited IκB phosphorylation and NF-κB activation, detected by western blot analysis. Meanwhile, in a rabbit model, MRI analysis revealed significant recovery of signal intensity in the degenerative discs of rabbits receiving cells transplantation, than receiving cells treated with a TGF-β1 inhibitor or saline. These findings indicated that enhanced TGF-β1 production recovered the degeneration of intervertebral disc. And also immunohistochemical staining detected enhanced collagen II expression in the rabbits treated with cell transplantation. However, the NF-κB positive cells were significantly less than other two control groups. Thus, cell therapy promoted TGF-β1 expression in nucleus pulposus, leading to anti-inflammatory effects via the inhibition of NF-κB, and the amelioration of disc degradation due to increased expression of collagen II and aggrecan in degenerative intervertebral disc. [ABSTRACT FROM AUTHOR]