Simonsen Voss (skiingbanana90)

The main causes of failure of orthopedic implants are infection and poor bone ingrowth. Surface modification of the implants to allow for long-term antibacterial and osteogenic functions is an effective solution to prevent failure of the implants. We developed silver-rich TiN/Ag nano-multilayers on the surface of titanium alloy with different doses of Ag+ . The antibacterial stability and osteogenesis of the silver-rich surface were determined by evaluating the adhesion and proliferation of Staphylococcus epidermidis, and the adhesion, proliferation, alkaline phosphatase activity, extracellular matrix mineralization, and the expression level of genes involved in osteogenic differentiation of rat bone mesenchymal stem cells (BMSCs). The results demonstrated that the antibacterial rates (Ra) of 5 × 1016 -Ag, 1 × 1017 -Ag, 5 × 1017 -Ag, and 1 × 1018 -Ag were respectively 46.21%, 85.66%, 94.99%, 98.48%, and 99.99%. After subcutaneous implantation in rats or immersion in phosphate buffered saline for up to 12 weeks, the silver-rich surface of the titanium alloy showed long-term stable inhibition of Staphylococcus epidermidis. Furthermore, in vitro and in vivo studies indicated that the Ag-implanted titanium did not show apparent cytotoxicity and that lower Ag+ implanted groups (5 × 1016 -Ag, 1 × 1017 -Ag) had better viability and biological safety when compared with higher Ag+ implanted groups. In addition, when compared with the Ti6Al4V-group, all Ag-implanted groups exhibited enhanced osteogenic indicators in rat BMSCs. Regarding osteogenic indicators, the surfaces of the 5 × 1017 -Ag group had better osteogenic effects than those of other groups. Therefore, the proper dose of Ag+ implanted TiN/Ag nano-multilayers may be one of the options for the hard tissue replacement materials with antibacterial activity and osteogenic functions.In many computer vision applications, an object can be represented by multiple different views. Due to the heterogeneous gap triggered by the different views' inconsistent distributions, it is challenging to exploit these multiview data for cross-view retrieval and classification. Motivated by the fact that both labeled and unlabeled data can enhance the relations among different views, this article proposes a deep cross-view learning framework called deep semisupervised classes- and correlation-collapsed cross-view learning (DSC³L) for cross-view retrieval and classification. Different from the existing methods which focus on the two-view problems, the proposed method learns U (generally U≥2) view-specific deep transformations to gradually project U different views into a shared space in which the projection embraces the supervised learning and the unsupervised learning. We propose collapsing the instances of the same class from all views into the same point, with the instances of different classes into distinct points simultaneously. Second, to exploit the abundant unlabeled U-wise multiview data, we propose to collapse-correlated data into the same point, with the uncorrelated data into distinct points. Specifically, these two processes are formulated to minimize the two Kullback-Leibler (KL) divergences between the conditional distribution and a desirable one, for each instance. Finally, the two KL divergences are integrated into a joint optimization to learn a discriminative shared space. The experimental results on five widely used public datasets demonstrate the effectiveness of the proposed method.The epidemic caused by COVID-19 has been highly concerned by the international community including World Health Organization (WHO). This is an ongoing battle for human life and health. We should always remember and learn lessons from the past, which could be promoted to all over the country, even the world. Many phenomena and problems in the work of epidemic prevention, control and treatment are worthy of our deep reflection. We should use scientific approach and dialectical materialism to make a p