3D VOSNet: Segmentation of endoscopic images of the larynx with subsequent generation of indicators.

Video laryngoscope is available for visualizing the motion of vocal cords and aid in the assessment of analyzing the larynx-related lesion preliminarily. Laryngeal Electromyography (EMG) needs to be performed to diagnose the factors of vocal cord paralysis, which may cause patient feeling unwell. Thus, the problem is the lack of credible larynx indicators to evaluate larynx-related diseases in the department of otolaryngology. Therefore, this paper aims to propose a 3D VOSNet model, which has the characteristics of sequence segmentation to extract the time-series features in the video laryngoscope. The 3D VOSNet model can keep the time-series features of three images before and after of the specific image to achieve translation and occlusion invariance, which explicitly signifies that our model can segment and classify each item in the video of laryngoscopy not affected by extrinsic causes such as shaking or occlusion during laryngoscope. Numerical results revealed that the testing accuracy rates of the glottal, right vocal cord, and the left vocal cord are 89.91%, 94.63%, and 93.48%, respectively. Our proposed model can segment glottal and vocal cords from the sequence of laryngoscopy. Finally, using the proposed algorithm computes six larynx indicators, which are the area of the glottal, area of vocal cords, length of vocal cords, deviation of length of vocal cords, and symmetry of the vocal cords. In order to assist otolaryngologists in staying credible and objective when making decisions without any doubt during diagnosis and also explaining the clinical symptoms of the larynx such as vocal cord paralysis to patients after diagnosis, our proposed algorithm provides otolaryngologists with explainable indicators (X-indicators).

Honey bee sHSP are responsive to diverse proteostatic stresses and potentially promising biomarkers of honey bee stress.

The pollination services provided by the honey bee are critical in both natural and agricultural ecosystems. Honey bee colonies in the United States have suffered from an increased rate of die-off in recent years, stemming from a complex set of interacting stresses that remain poorly described. Defining specific common cellular processes and cellular stress responses impacted by multiple stressors represent a key step in understanding these synergies. Proteotoxic stresses negatively impact protein synthesis, folding, and degradation. Diverse proteotoxic stresses induce expression of genes encoding small heat shock proteins (sHSP) of the expanded lethal (2) essential for life (l(2)efl) gene family. In addition to upregulation by the Integrated Stress Response (ISR), the Heat Shock Response (HSR), and the Oxidative Stress Response (OSR), our data provide first evidence that sHSP genes are upregulated by the Unfolded Protein Response (UPR). As these genes appear to be part of a core stress response that could serve as a useful biomarker for cellular stress in honey bees, we designed and tested an RT-LAMP assay to detect increased l(2)efl gene expression in response to heat-stress. While this assay provides a powerful proof of principle, further work will be necessary to link changes in sHSP gene expression to colony-level outcomes, to adapt our preliminary assay into a Point of Care Testing (POCT) assay appropriate for use as a diagnostic tool for use in the field, and to couple assay results to management recommendations.