Kanazawa, Y. | Search bioRxiv

bioRxiv2022

Three-dimensional analysis and in vivo imaging for sperm release and transport in the murine seminiferous tubule

IntroductionSpermatozoa released from Sertoli cells must be transported to the epididymis. However, the contribution of the peristaltic motion in the seminiferous tubule to sperm release and transport remains unclear. We, therefore, investigated luminal flow and movements in the seminiferous tubules by three-dimensional analysis and in vivo imaging. Materials and MethodsSerial testicular sections were cut in 5-m-thick and 50-m-interval and stained by PAS-hematoxylin. After the three-dimensional reconstruction of the seminiferous tubules, the localization of the flowing spermatozoa and stages observed in the sections were recorded in each reconstructed tubule. The luminal movements in the seminiferous tubule were observed by in vivo imaging using a fluorescent-reporter mouse and two-photon excitation microscopy system. ResultsFlowing spermatozoa were mainly scattered in the lumina at stage VII/VIII, and clustered spermatozoa were also found in some regions. The clustered spermatozoa were observed at zero to two regions in each seminiferous tubule. Flowing spermatozoa were also found in the opposite direction to the rete testis. The flagellum direction of the spermatozoa attached to the seminiferous epithelium was reversed within a few seconds to a few tens of seconds when observed by in vivo imaging. The epithelium at the inner curve of the seminiferous tubule moved more actively and attached fewer spermatozoa compared to that at the outer curve. DiscussionThis study revealed the presence of repeatedly reversed luminal flow in the seminiferous tubule. Such movements are suggested to help the sperm release from the Sertoli cells and the following aggregation of the released spermatozoa.

cell biology↗

bioRxiv2021

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for delineation of individual crowns and classification to determine species identity. This competition included data from multiple sites to assess the methods ability to generalize learning across multiple sites simultaneously, and transfer learning to novel sites where the methods were not trained. Six teams, representing 4 countries and 9 individual participants, submitted predictions. Methods from a previous competition were also applied and used as the baseline to understand whether the methods are changing and improving over time. The best delineation method was based on an instance segmentation pipeline, closely followed by a Faster R-CNN pipeline, both of which outperformed the baseline method. However, the baseline (based on a growing region algorithm) still performed well as did the Faster R-CNN. All delineation methods generalized well and transferred to novel forests effectively. The best species classification method was based on a two-stage fully connected neural network, which significantly outperformed the baseline (a random forest and Gradient boosting ensemble). The classification methods generalized well, with all teams training their models using multiple sites simultaneously, but the predictions from these trained models generally failed to transfer effectively to a novel site. Classification performance was strongly influenced by the number of field-based species IDs available for training the models, with most methods predicting common species well at the training sites. Classification errors (i.e., species misidentification) were most common between similar species in the same genus and different species that occur in the same habitat. The best methods handled class imbalance well and learned unique spectral features even with limited data. Most methods performed better than baseline in detecting new (untrained) species, especially in the site with no training data. Our experience further shows that data science competitions are useful for comparing different methods through the use of a standardized dataset and set of evaluation criteria, which highlights promising approaches and common challenges, and therefore advances the ecological and remote sensing field as a whole.

ecology↗