No abstract

No abstract

Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is much simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.

In recent years, video games have become one of the primary means of people's daily entertainment. With the diversification of video gaming platform and the enrichment of the contents, the impact of video games on people's mental health and behavior is increasing, and thus becomes a hot research area of psychology. Traditional psychology has done a lot of research on the negative impact of video games, but the advent of positive psychology offers a new research perspective of the video game. This paper presents a review of the research focusing on the video games on people's emotions, positive personal trait quality positive personal trait, relationships from the perspective of positive psychology, and offers suggestion for future video games design.

In this paper, we propose a new machine learning system that can predict the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate and measure both the cellular contractile force and the substrate wrinkles simultaneously. The cellular contractile forces are obtained using the traction force microscopy (TFM), while cells generate wrinkles thanks to our original plasma-irradiated substrates. Second, the wrinkle positions are extracted from the microscope images by using SW-UNet. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the force distributions and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images to the force distributions from the training. After sufficient training, the network can be utilized to predict the cellular forces just from the input images. Comparing with the TFM experiment (test data), our system has 33-35% errors in the force magnitude prediction and angle errors 19-20 o in the force direction. The system would be a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells, which is a way simpler method compared to the TFM experiment. We believe that our machine learning based system will be an useful method for other cellular assay applicants and researches in the future.

No abstract

H.L. and D.M developed the machine learning system, and K.I. supported the implementation. H.A. and T.S.M. designed and worked on the cell experiments. H.L., H.A. and D.M. analyzed the experimental data. D.M., A.D. and S.D. conceived the idea of simultaneous TFM with wrinkle extraction. D.M. and A.D. analyzed the physics. H.L., D.M., A.D. and S.D. wrote the article and designed the research.

Vibration of welding robot mechanism is an important factor affecting the welding quality. In this paper, a modal test scheme is designed depending on the characteristics of the modal change caused by the posture change during the welding robot operation. In the modal test scheme, the posture used for modal test selects from the dynamic process of continuous posture change. Then the selected posture is tested and the modal parameters are identified. On this basis, we analyze the modal frequency and modal vibration mode of the mechanism under different postures, propose the concept and method of modal interval division, obtain the modal test results of the dynamic continuous process of the welding robot linkage mechanism, and reveal the inherent dynamic characteristics of the welding robot mechanism.