Chondrogenic potential of human articular chondrocytes and skeletal stem cells: a comparative study

Regenerative medicine strategies have increasingly focused on skeletal stem cells (SSCs), in response to concerns such as donor site morbidity, dedifferentiation and limited lifespan associated with the use of articular chondrocytes for cartilage repair. The suitability of SSCs for cartilage regeneration, however, remains to be fully determined. This study has examined the chondrogenic potential of human STRO-1-immunoselected SSCs (STRO-1+ SSCs), in comparison to human articular chondrocytes (HACs), by utilising two bioengineering strategies, namely ‘‘scaffold-free’’ three-dimensional(3-D) pellet culture and culture using commercially available, highly porous, 3-D scaffolds with interconnected pore networks. STRO-1+ SSCs were isolated by magnetic-activated cell sorting from bone marrow samples of haematologically normal osteoarthritic individuals following routine hip replacement procedures. Chondrocytes were isolated by sequential enzymatic digestion of deep zone articular cartilage pieces dissected from femoral heads of the same individuals. After expansion in monolayer cultures, the harvested cell populations were centrifuged to form high-density 3-D pellets and also seeded in the 3-D scaffold membranes, followed by culture in serum-free chondrogenic media under static conditions for 21 and 28 days, respectively. Chondrogenic differentiation was determined by gene expression,histological and immunohistochemical analyses. Robust cartilage formation and expression of hyaline cartilage-specific markers were observed in both day-21 pellets and day-28 explants generated using HACs. In comparison, STRO-1+ SSCs demonstrated significantly lower chondrogenic differentiation potential and a tendency for hypertrophic differentiation in day-21 pellets. Culture of STRO-1+ SSCs in the 3-D scaffolds improved the expression of hyaline cartilage-specific markers in day-28 explants, however, was unable to prevent hypertrophic differentiation of the SSC population. The advantages of application of SSCs in tissue engineering are widely recognised; the results of this study, however, highlight the need for further development of cell culture protocols that may otherwise limit the application of this stem cell population in cartilage bioengineering strategies

Effects of laser fluence on silicon modification by four-beam laser interference

This paper discusses the effects of laser fluence on silicon modification by four-beam laser interference. In this work, four-beam laser interference was used to pattern single crystal silicon wafers for the fabrication of surface structures, and the number of laser pulses was applied to the process in air. By controlling the parameters of laser irradiation, different shapes of silicon structures were fabricated. The results were obtained with the single laser fluence of 354 mJ/cm, 495 mJ/cm, and 637 mJ/cm, the pulse repetition rate of 10 Hz, the laser exposure pulses of 30, 100, and 300, the laser wavelength of 1064 nm, and the pulse duration of 7-9 ns. The effects of the heat transfer and the radiation of laser interference plasma on silicon wafer surfaces were investigated. The equations of heat flow and radiation effects of laser plasma of interfering patterns in a four-beam laser interference distribution were proposed to describe their impacts on silicon wafer surfaces. The experimental results have shown that the laser fluence has to be properly selected for the fabrication of well-defined surface structures in a four-beam laser interference process. Laser interference patterns can directly fabricate different shape structures for their corresponding applications

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201