Detraining after tumor-bearing accelerates tumor growth while continuous training decreases tumor growth in mice

Objective:Previous studies have shown that exercise suppresses tumor growth.However,the effects of exercise with different intensities and exercise detraining after tumor-bearing on tumor progression remain unclear.The purpose of this study was to investigate the effects of continuous and disrupted free and exhausted swimming training after tumor-bearing on tumor progression in melanoma B16-F10-bearing C57BL/6 mice.Methods:C57BL/6 mice were subjected to free or exhausted swimming exercise training for 4 weeks prior to the injection of melanoma B16-F10 cells.Subsequently,the B16-F10-bearing mice were maintained with training consisting of free or exhausted swimming or without exercise for 2 weeks during the tumor challenge.Results:The tumor weight was increased by 42%and 109%in mice with 4-week exhausted swimming prior to B16-F10 tumor cells inoculation followed by 2-week training cessation compared with the tumor-bearing control(P<.05)and continuous training groups(P<.01).Tumor weights in groups with exercise detraining after tumor cell inoculation tended to be increased,while the proliferation of splenic T lymphocytes tended to be decreased compared with the group that maintained exercise intensity.After 6-weeks continuous free or exhausted swimming training,the tumor weight of mice was decreased and the proliferation of splenic T lymphocytes was increased compared with the tumor-bearing control group.The frequency of natural killer cells in tumors was increased in all exercise training groups of mice.Conclusions:These results suggest that maintaining exercise intensity after tumor-bearing slows tumor growth in mice,possibly because of the enhanced proliferative activity of splenic lymphocytes rather than natural killer cell infiltration.However,detraining after tumor-bearing might accelerate tumor progression because of the reduced proliferation of splenic T lymphocytes.

A Generic Object Detection Using a Single Query Image Without Training

A method was developed to detect generic objects using a single query image. The query image could be a typical real image, a virtual image, or even a hand-drawn sketch of the object. Without a training process, the key problem is how to describe the object class from only one query image with no pre-segmentation or other pre-processing procedures. The method introduces densely computed Scale-lnvariant Feature Transform （SIFT） as the descriptor to extract ＂gradient distribution＂ features of the image. The descriptor emphasizes the edge parts and their distribution structures, which are very representative of the object class, so it is very robust and can deal with virtual images or hand-drawn sketches. Tests on car detection, face detection, and generic object detection demonstrate that the method is effective, robust, and widely applicable. The results using queries of real images compare well with other training-free methods and state-of-the-art training-based methods.