Extracorporeal shockwave (ESW) has been shown of great potential in promoting the osteogenesis of bone marrow mesenchymal stem cells (BMSCs), but it is unknown whether this osteogenic promotion effect can also be achieved in other MSCs (i.e., tendon-derived stem cells (TDSCs) and adipose-derived stem cells (ADSCs)). In the current study, we aimed not only to compare the osteogenic effects of BMSCs induced by ESW to those of TDSCs and ADSCs; but also to investigate the underlying mechanisms. We show here that ESW (0.16 mj/mm2) significantly promoted the osteogenic differentiation in all the tested types of MSCs, accompanied with the downregulation of miR-138, but the activation of FAK, ERK1/2, and RUNX2. The enhancement of osteogenesis in these MSCs was consistently abolished when the cells were pretreated with one of the following conditions: overexpression of miR-138, FAK knockdown using specific siRNA, and U0126, implying that all of these elements are indispensable for mediating the effect of ESW. Moreover, our study provides converging genetic and molecular evidence that the miR-138-FAK-ERK1/2-RUNX2 machinery can be generally activated in ESW-preconditioned MSCs, suggesting that ESW may be a promising therapeutic strategy for the enhancement of osteogenesis of MSCs, regardless of their origins.
Although great efforts are being made using growth factors and gene therapy, the repair of bone defects remains a major challenge in modern medicine that has resulted in an increased burden on both healthcare and the economy. Emerging tissue engineering techniques that use of combination of biodegradable poly-lactic-co-glycolic acid (PLGA) and mesenchymal stem cells have shed light on improving bone defect healing; however, additional growth factors are also required with these methods. Therefore, the development of novel and cost-effective approaches is of great importance. Our in vitro results demonstrated that ESW treatment (10 kV, 500 pulses) has a stimulatory effect on the proliferation and osteogenic differentiation of bone marrow-derived MSCs (BMSCs). Histological and micro-CT results showed that PLGA scaffolds seeded with ESW-treated BMSCs produced more bone-like tissue with commitment to the osteogenic lineage when subcutaneously implanted in vivo, as compared to control group. Significantly greater bone formation with a faster mineral apposition rate inside the defect site was observed in the ESW group compared to control group. Biomechanical parameters, including ultimate load and stress at failure, improved over time and were superior to those of the control group. Taken together, this innovative approach shows significant potential in bone tissue regeneration.
A new method of logo detection in document images is proposed in this paper. It is based on the boundary extension of feature rectangles of which the definition is also given in this paper. This novel method takes advantage of a layout assumption that logos have background (white spaces) surrounding it in a document. Compared with other logo detection methods, this new method has the advantage that it is independent on logo shapes and very fast. After the logo candidates are detected, a simple decision tree is used to reduce the false positive from the logo candidate pool. We have tested our method on a public image database involving logos. Experiments show that our method is more precise and robust than the previous methods and is well qualified as an effective assistance in document retrieval.
We present an offline signature verification system using three different pseudo-dynamic features, two different classifier training approaches and two datasets. One of the most difficult problems of off-line signature verification is that the signature is just a static image while losing a lot of useful dynamic information. Three separate pseudo-dynamic features based on gray level: local binary pattern (LBP), gray level cooccurrence matrix (GLCM) and histogram of oriented gradients (HOG) are used. The classification is performed using writerdependent Support Vector Machine (SVMs) classifiers and Global Real Adaboost method, where two different approaches to train the classifier. In the first mode, each SVM is trained with the feature vectors obtained from the reference signatures of the corresponding user and those random forgeries for each signer while the global Adaboost classifier is trained using genuine and random forgery signatures of signers that are excluded from the test set. The fusion of all features achieves the best result of 7.66% and 9.94% equal error rate in GPDS while 7.55% and 11.55% equal error rate in CSD respectively.
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