Comparison of Development and Antioxidative Ability in Fertilized Crossbred (Yorkshire × Landrace × Duroc) Oocytes Using Duroc and Landrace Sperm.

Pig production through crossbreeding methods is a pillar of the swine industry; however, research on the fertilization ability of male pigs in crossbreeds is lacking. Therefore, this study investigated the effects of Duroc sperm (DS) and Landrace sperm (LS) on fertility in Yorkshire × Landrace × Duroc (YLD) oocytes. Sperm were collected from the Duroc and Landrace species, and sperm characteristics, viability, and acrosome reactions were analyzed using flow cytometry.

Deep learning framework for bovine iris segmentation.

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results.

Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.

The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception-ResNet v2 architectures.