Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag's accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows.

To improve the vitrification efficiency of bovine germinal vesicle (GV) oocytes, the use of hydroxyapatite (HA) nanoparticles as a novel cryopreservation additive represents a promising approach. This study aimed to investigate the effects of HA nanoparticles and permeable cryoprotective agents (CPAs) on the ultrastructure, developmental competence, and gene expression of bovine GV oocytes following vitrification. Oocytes were vitrified in vitrification solutions containing HA nanoparticles of different sizes (20, 40, or 60 nm) and concentrations (0.01%, 0.05%, or 0.1%) to determine the optimal conditions based on survival rate, mitochondrial membrane potential (MMP) level, and developmental competence. Subsequently, the synergistic effects of HA nanoparticles and permeable CPAs (VS: 20% EG + 20% DMSO; VS1: 17.5% EG + 17.5% DMSO) were further evaluated. The optimal treatment (40 nm 0.05% HA nanoparticles) significantly increased MMP level, and improved developmental competence compared with the vitrified control group (p < 0.05). Among the vitrified groups, vitrified oocytes in the VS1-HA group (combining HA nanoparticles with reduced concentrations of permeable CPAs) exhibited the highest MMP level (1.89), maturation rate (50.39%), cleavage rate (27.07%), and blastocyst rate (10.53%) (p < 0.05). Ultrastructural analysis further revealed that the VS1-HA group maintained more intact zona pellucida structures and showed reduced mitochondrial swelling compared with the vitrified control group. Moreover, the expression levels of genes associated with zona pellucida formation (ZP3), mitochondrial fusion (MFN1), and chromatin remodeling (NPM2) were significantly upregulated in the VS1-HA group relative to the vitrified control group. Overall, these findings indicate that the combination of HA nanoparticles with lower concentrations of permeable CPAs enhances MMP level, alleviates vitrification-induced ultrastructural damage, and upregulates the expression of key developmental genes, thereby improving the developmental competence of vitrified bovine GV oocytes.