The acrosome cap allows sperm to penetrate the egg membrane and produce male pronuclei within female chicken eggs, facilitating successful fertilization. Given this, it is important to establish practical methods for evaluating the integrity of the acrosome cap and thus the quality of the rooster’s sperm. There are several established methods for evaluating the acrosomes of mammalian sperm, but none of these methods are suitable for evaluating the acrosome status of rooster spermatozoa. Therefore, a simplified method for evaluating the rooster acrosome is needed. Here we evaluated the usefulness of CBB (coomassie brilliant blue) staining of the acrosome at concentrations of 0.04%, 0.08%, and 0.3% CBB solutions. Our data revealed a clear staining pattern for intact acrosome caps at 0.04% and 0.08% CBB but not at 0.3% CBB. This protocol revealed differences in acrosome integrity between fresh and frozen rooster sperm smears suggesting that CBB staining may facilitate easier semen evaluation in roosters. This protocol allows for the accurate differential staining of acrosome cap in rooster spermatozoa.

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.