Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. Based on the idea of coarse-grained description and grouping in physics, a new feature extraction method with grouped weight for protein sequence is presented, and applied to apoptosis protein subcellular localization prediction associated with support vector machine. For the same training dataset and the same predictive algorithm, the overall prediction accuracy of our method in Jackknife test is 13.2% and 15.3% higher than the accuracy based on the amino acid composition and instability index. Especially for the else class apoptosis proteins, the increment of prediction accuracy is 41.7 and 33.3 percentile, respectively. The experiment results show that the new feature extraction method is efficient to extract the structure information implicated in protein sequence and the method has reached a satisfied performance despite its simplicity. The overall prediction accuracy of EBGW_SVM model on dataset ZD98 reach 92.9% in Jackknife test, which is 8.2-20.4 percentile higher than other existing models. For a new dataset ZW225, the overall prediction accuracy of EBGW_SVM achieves 83.1%. Those implied that EBGW_SVM model is a simple but efficient prediction model for apoptosis protein subcellular location prediction.

Knee osteoarthritis is a chronic degenerative disease. Cartilage and subchondral bone degeneration, as well as synovitis, are the main pathological changes associated with knee osteoarthritis. Mechanical overload, inflammation, metabolic factors, hormonal changes, and aging play a vital role in aggravating the progression of knee osteoarthritis. The main treatments for knee osteoarthritis include pharmacotherapy, physiotherapy, and surgery. However, pharmacotherapy has many side effects, and surgery is only suitable for patients with end-stage knee osteoarthritis. Exercise training, as a complementary and adjunctive physiotherapy, can prevent cartilage degeneration, inhibit inflammation, and prevent loss of the subchondral bone and metaphyseal bone trabeculae. Increasing evidence indicates that exercise training can improve pain, stiffness, joint dysfunction, and muscle weakness in patients with knee osteoarthritis. There are several exercise trainings options for the treatment of knee osteoarthritis, including aerobic exercise, strength training, neuromuscular exercise, balance training, proprioception training, aquatic exercise, and traditional exercise. For Knee osteoarthritis (KOA) experimental animals, those exercise trainings can reduce inflammation, delay cartilage and bone degeneration, change tendon, and muscle structure. In this review, we summarize the main symptoms of knee osteoarthritis, the mechanisms of exercise training, and the therapeutic effects of different exercise training methods on patients with knee osteoarthritis. We hope this review will allow patients in different situations to receive appropriate exercise therapy for knee osteoarthritis, and provide a reference for further research and clinical application of exercise training for knee osteoarthritis.

In future high-capacity wavelength division multiplexed (WDM) optical networks, the failure of a network component such as a fiber link can lead to severe disruption in the networks' traffic. Hence, it is imperatively important to provide fast and full protection in WDM optical networks. In this paper, we propose a new approach, called shared preconfigured protection cycles (shared-p-cycles), for the design of survivable WDM networks. We develop an integer linear program (ILP) formulation to solve the problem of shared-p-cycles design for WDM networks with and without wavelength conversion. Numerical results show that the shared-p-cycles design is more efficient in the use of spare capacity and requires much less spare capacity than the conventional pcycles design.