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Integrins are a family of transmembrane proteins, involved in substrate recognition and cell adhesion in cross-talk with the extra cellular matrix. In this study, we investigated the influence of integrin alpha 2 beta 1 on tendons, another collagen type I-rich tissue of the musculoskeletal system. Morphological, as well as functional, parameters were analyzed in vivo and in vitro, comparing wild-type against integrin alpha 2 beta 1 deficiency. Tenocytes lacking integrin alpha 2 beta 1 produced more collagen in vitro, which is similar to the situation in osseous tissue. Fibril morphology and biomechanical strength proved to be altered, as integrin alpha 2 beta 1 deficiency led to significantly smaller fibrils as well as changes in dynamic E-modulus in vivo. This discrepancy can be explained by a higher collagen turnover: integrin alpha 2 beta 1-deficient cells produced more matrix, and tendons contained more residual C-terminal fragments of type I collagen, as well as an increased matrix metalloproteinase-2 activity. A greatly decreased percentage of non-collagenous proteins may be the cause of changes in fibril diameter regulation and increased the proteolytic degradation of collagen in the integrin-deficient tendons. The results reveal a significant impact of integrin alpha 2 beta 1 on collagen modifications in tendons. Its role in tendon pathologies, like chronic degradation, will be the subject of future investigations.

Integrins are a family of transmembrane proteins, involved in substrate recognition and cell adhesion in cross-talk with the extra cellular matrix. In this study, we investigated the influence of integrin alpha 2 beta 1 on tendons, another collagen type I-rich tissue of the musculoskeletal system. Morphological, as well as functional, parameters were analyzed in vivo and in vitro, comparing wild-type against integrin alpha 2 beta 1 deficiency. Tenocytes lacking integrin alpha 2 beta 1 produced more collagen in vitro, which is similar to the situation in osseous tissue. Fibril morphology and biomechanical strength proved to be altered, as integrin alpha 2 beta 1 deficiency led to significantly smaller fibrils as well as changes in dynamic E-modulus in vivo. This discrepancy can be explained by a higher collagen turnover: integrin alpha 2 beta 1-deficient cells produced more matrix, and tendons contained more residual C-terminal fragments of type I collagen, as well as an increased matrix metalloproteinase-2 activity. A greatly decreased percentage of non-collagenous proteins may be the cause of changes in fibril diameter regulation and increased the proteolytic degradation of collagen in the integrin-deficient tendons. The results reveal a significant impact of integrin alpha 2 beta 1 on collagen modifications in tendons. Its role in tendon pathologies, like chronic degradation, will be the subject of future investigations.

Several anomalies in the semileptonic B-meson decays such as $\mathcal{R}(D^{(*)})$ have been reported by $BABAR$, Belle, and LHCb collaborations recently. In this paper, we investigate the contributions of the charged Higgs bosons from the 3-3-1 models to the $\mathcal{R}(D^{(*)})$ anomalies. We find that, in a wide range of parameter space, the 3-3-1 models might give reasonable explanations to the $\mathcal{R}(D^{(*)})$ anomalies and other analogous anomalies of the B meson's semileptonic decays., Comment: Accpeted by Physical Review D

An effective anti-jamming system is significant in improving the working reliability of hydraulic drilling rigs. An anti-jamming valve is a core component of an anti-jamming system. A back-propagation (BP) neural network model was established for an anti-jamming valve by analyzing the structure and working principle of an anti-jamming valve on a drilling rig and by utilizing the theory of neural network. The established model was employed and applied with the raw information to a stone pit to perform structural topology optimization and obtain the optimal model. The theoretical feed pressure was determined through calculation and analysis after the rock drill became completely jammed. A comparison between the relative error of computed results and the experimental values shows that the forecast data obtained with the BP model (8%) are closer to the experimental values than those obtained with the theoretical formula (14%). The highly nonlinear characteristic of a BP neural network provides a fresh insight into the intellectualization of a drilling rig anti-jamming system.

An effective anti-jamming system is significant in improving the working reliability of hydraulic drilling rigs. An anti-jamming valve is a core component of an anti-jamming system. A back-propagation (BP) neural network model was established for an anti-jamming valve by analyzing the structure and working principle of an anti-jamming valve on a drilling rig and by utilizing the theory of neural network. The established model was employed and applied with the raw information to a stone pit to perform structural topology optimization and obtain the optimal model. The theoretical feed pressure was determined through calculation and analysis after the rock drill became completely jammed. A comparison between the relative error of computed results and the experimental values shows that the forecast data obtained with the BP model (8%) are closer to the experimental values than those obtained with the theoretical formula (14%). The highly nonlinear characteristic of a BP neural network provides a fresh insight into the intellectualization of a drilling rig anti-jamming system.