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The autoimmune regulator gene Aire shows predominant expression in thymus and other immunologically relevant tissues, and is assigned the major function of programming autoreactive T-cell deletion. However, the expression of this gene in tissues outside the immune system raises a question about its possible function beyond the T-cell deletion dogma. We detected Aire in mouse testis, and the expression of AIRE protein was remarkably high in postmeiotic germ cells. Sequencing results indicate that testis expressed Aire variant 1a. AIRE could be detected in spermatozoa, with heavy localization on the principal acrosomal domains. Mouse oocytes stained negatively for AIRE before fertilization, but stained positively for AIRE 30 min after fertilization. In the zygote, the levels of AIRE correlated negatively with cyclin B2 levels. Goat testicular lysates spiked with recombinant human AIRE exhibited augmented cyclin B2 degradation in the presence of protease inhibitors, which was inhibited by MG-132, indicating the operation of proteasomal pathways. Thus, this study identifies a correlation between the presence of AIRE and proteasomal breakdown of cyclin B2, which leads us to speculate that cyclin B2 could be a target of AIRE's E3-ubiquitin ligase activity.

Objective: This study provides a novel approach for an automated system using a machine learning algorithm to predict the predominant site of upper airway collapse into four classes (‘lateral wall’, ‘palate’, ‘tongue-base’ related collapse or ‘multi-level’ site-of-collapse) in obstructive sleep apnoea (OSA) patients from the audio signal recorded during normal sleep. Approach: Snore sounds from 58 patients were recorded simultaneously with full-night polysomnography during sleep with a ceiling mounted microphone. The probable site-of-airway collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea. Time and frequency features of the audio signal were extracted from each hypopnoea event to classify the audio signal into ‘lateral wall’, ‘palate’ and ‘tongue-base’ related collapse according to prior research. The data was divided into two sets. The Learning Set contained the data of the first 45 patients and was used for building the model. The Hidden Set contained the data from the remaining 13 patients and was used for testing the performance of the model. Feature selection was employed to boost the classification performance. The classification was carried out with a multi-class linear discriminant analysis classifier to classify the predominant site-of-collapse for a patient into the four classes. Performance was evaluated by comparing the automatic and manually labelled data based on the predominant site-of-collapse and calculating the accuracy. Main results: The model achieved an overall accuracy on the Hidden Set of 77% for discriminating tongue/non-tongue collapse and an accuracy of 62% accuracy for all site-of-collapse classes. Significance: Our results demonstrate that the audio signal recorded during sleep can successfully identify the site-of-collapse in the upper airway. The additional information regarding the obstruction site may assist clinicians in deciding the most appropriate treatment for OSA.

Study objectives Acoustic analysis of isolated events and snoring by previous researchers suggests a correlation between individual acoustic features and individual site of collapse events. In this study, we hypothesised that multi-parameter evaluation of snore sounds during natural sleep would provide a robust prediction of the predominant site of airway collapse. Methods The audio signals of 58 OSA patients were recorded simultaneously with full night polysomnography. The site of collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea events and corresponding audio signal segments containing snore were manually extracted and processed. Machine learning algorithms were developed to automatically annotate the site of collapse of each hypopnoea event into three classes (lateral wall, palate and tongue-base). The predominant site of collapse for a sleep period was determined from the individual hypopnoea annotations and compared to the manually determined annotations. This was a retrospective study that used cross-validation to estimate performance. Results Cluster analysis showed that the data fits well in two clusters with a mean silhouette coefficient of 0.79 and an accuracy of 68% for classifying tongue/non-tongue collapse. A classification model using linear discriminants achieved an overall accuracy of 81% for discriminating tongue/non-tongue predominant site of collapse and accuracy of 64% for all site of collapse classes. Conclusions Our results reveal that the snore signal during hypopnoea can provide information regarding the predominant site of collapse in the upper airway. Therefore, the audio signal recorded during sleep could potentially be used as a new tool in identifying the predominant site of collapse and consequently improving the treatment selection and outcome.

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