Rabbit Anti-Human Vinculin Monoclonal Antibody, clone B3863

The aims of this study were to investigate (a) the effects of single or multiple sperm collection with catheter, and (b) impacts of multiple sampling method (catheter or syringe) on sperm quality in pikeperch (Sander lucioperca). To compare the effects of sampling method, sperm was collected repeatedly from the same fish using catheter (n = 6) or syringe (n = 5) at 48, 52 and 56 h post-hormonal injection. For the single collection group as control, sperm was collected with catheter from 12 fish at 48 h post-injection. The results showed that multiple sperm collection with catheter does not significantly (P> 0.05) influence sperm volume and density compared to single sperm collection. However, multiple collection with syringe led to drastic reduction of sperm density at 56 h post-injection. Moreover, sperm volume was influenced by post-injection time where the highest volume was obtained at 48 h post-injection. Furthermore, sperm density and osmolality of seminal plasma were significantly affected by sampling method and post-injection time where higher values were found for the samples collected with catheter at 48 h post-injection. Number of males producing milt with motile spermatozoa decreased along with multiple sperm collection. Sperm motility at 60 s post-activation was affected by the interaction of sampling method and post-injection time, and the highest value was obtained for the sperm collected with syringe at 48 h post-injection. Sperm curvilinear velocity (VCL) at 15 and 45 s post-activation was affected by sampling method and post-injection time, respectively. These findings contribute to the pikeperch hatchery practices, as multiple sperm collection could be applied during artificial reproduction if limited number of males are available.

Region-based Fully Convolutional Network (R-FCN) is recently developed to detect objects and has been successfully used to detect various kinds of semantic objects such as humans and dogs. We investigate the ability of R-FCN for detecting unusual objects in this paper. In detail, based on R-FCN we present an efficient method for vertical corner line (VCL) detection on buildings. Traditionally, VCLs are treated as one kind of image features. In this work, however, they are treated as a class of symbolic objects. Experimental results show that R-FCN, when employed to detect VCLs, could perform potentially better than traditional feature detection algorithms.