Deep learning-based method for analyzing the optically trapped sperm rotation.

Optical tweezers exert a strong trapping force on cells, making it crucial to analyze the movement of trapped cells. The rotation of cells plays a significant role in their swimming patterns, such as in sperm cells. We proposed a fast deep-learning-based method that can automatically determine the projection orientation of ellipsoidal-like cells without additional optical design. This method was utilized for analyzing the planar rotation of trapped sperm cells using an optical tweezer, demonstrating its feasibility in extracting the rotation of the cell head. Furthermore, we employed this method to investigate sperm cell activity by examining variations in sperm rotation rates under different conditions, including temperature and laser output power. Our findings provide evidence for the effectiveness of this method and the rotation analysis method developed may have clinical potential for sperm quality evaluation.

Noise analysis in Stokes parameter reconstruction for division-of-focal-plane polarimeters.

The division-of-focal-plane (DoFP) polarimeter can quickly and effectively obtain the polarization information of light in real time, where Stokes parameter reconstruction is a critical issue. Many reconstruction methods have been proposed to address this; however, their performance tends to degrade in the presence of noise. Thus, it is significant to clarify the noise-induced error in Stokes parameter reconstruction. In this work, we investigate the link between the noise-introduced error and the reconstruction method and develop a simple and effective way to evaluate the noise robustness of reconstruction methods. Furthermore, a novel experimental scheme of noise measurement, to the best of our knowledge, is designed to verify the theory. Based on the criterion, our scheme guides the selection of reconstruction methods and further promotes the practical application of the DoFP technique.