[Application of passive image-free navigation system for total knee arthroplasty].

Total knee arthroplasty(TKA) has been the final clinical treatment of knee osteoarthritis at the final stage, postoperative limb and prosthesis alignment restoration directly affect clinical effect. In recent years, computer-assisted surgery has been used in TKA and obtained satisfied results. There paper has investigated that the use of computer-assisted systems could improve soft tissue balance after TKA, improve accuracy of installation of prosthesis, recover limb alignment and decrease intro-blood loss, postoperative fat embolism. Although computer-assisted navigation is not mainstream, with the continuous improvement of technology and innovation, the computer-assisted surgery could exert a more important role in TKA, and considerably beneficial effect on improvement of postoperative clinical effects.

Synergistic Effects of Gold Nanocages in Hyperthermia and Radiotherapy Treatment.

Gold nanocages (GNCs) are a promising material that not only converts near infrared (NIR) light to heat for the ablation of tumors but also acts as a radiosensitizer. The combination of hyperthermia and radiotherapy has a synergistic effect that can lead to significant tumor cell necrosis. In the current study, we synthesized GNCs that offered the combined effects of hyperthermia and radiotherapy. This combination strategy resulted in increased tumor cell apoptosis and significant tumor tissue necrosis. We propose that GNCs can be used for clinical treatment and to potentially overcome resistance to radiotherapy by clearly increasing the antitumor effect.

[Automatic classification method of star spectra data based on manifold fuzzy twin support vector machine].

Support vector machine (SVM) with good leaning ability and generalization is widely used in the star spectra data classification. But when the scale of data becomes larger, the shortages of SVM appear: the calculation amount is quite large and the classification speed is too slow. In order to solve the above problems, twin support vector machine (TWSVM) was proposed by Jayadeva. The advantage of TSVM is that the time cost is reduced to 1/4 of that of SVM. While all the methods mentioned above only focus on the global characteristics and neglect the local characteristics. In view of this, an automatic classification method of star spectra data based on manifold fuzzy twin support vector machine (MF-TSVM) is proposed in this paper. In MF-TSVM, manifold-based discriminant analysis (MDA) is used to obtain the global and local characteristics of the input data and the fuzzy membership is introduced to reduce the influences of noise and singular data on the classification results. Comparative experiments with current classification methods, such as C-SVM and KNN, on the SDSS star spectra datasets verify the effectiveness of the proposed method.