Inner ear morphology in wild versus laboratory house mice.

The semicircular canals of the inner ear are involved in balance and velocity control. Being crucial to ensure efficient mobility, their morphology exhibits an evolutionary conservatism attributed to stabilizing selection. Release of selection in slow-moving animals has been argued to lead to morphological divergence and increased inter-individual variation. In its natural habitat, the house mouse Mus musculus moves in a tridimensional space where efficient balance is required. In contrast, laboratory mice in standard cages are severely restricted in their ability to move, which possibly reduces selection on the inner ear morphology. This effect was tested by comparing four groups of mice: several populations of wild mice trapped in commensal habitats in France; their second-generation laboratory offspring, to assess plastic effects related to breeding conditions; a standard laboratory strain (Swiss) that evolved for many generations in a regime of mobility reduction; and hybrids between wild offspring and Swiss mice. The morphology of the semicircular canals was quantified using a set of 3D landmarks and semi-landmarks analyzed using geometric morphometric protocols. Levels of inter-population, inter-individual (disparity) and intra-individual (asymmetry) variation were compared. All wild mice shared a similar inner ear morphology, in contrast to the important divergence of the Swiss strain. The release of selection in the laboratory strain obviously allowed for an important and rapid drift in the otherwise conserved structure. Shared traits between the inner ear of the lab strain and domestic pigs suggested a common response to mobility reduction in captivity. The lab-bred offspring of wild mice also differed from their wild relatives, suggesting plastic response related to maternal locomotory behavior, since inner ear morphology matures before birth in mammals. The signature observed in lab-bred wild mice and the lab strain was however not congruent, suggesting that plasticity did not participate to the divergence of the laboratory strain. However, contrary to the expectation, wild mice displayed slightly higher levels of inter-individual variation than laboratory mice, possibly due to the higher levels of genetic variance within and among wild populations compared to the lab strain. Differences in fluctuating asymmetry levels were detected, with the laboratory strain occasionally displaying higher asymmetry scores than its wild relatives. This suggests that there may indeed be a release of selection and/or a decrease in developmental stability in the laboratory strain.

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography.

Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. Sleep is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform automated robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours of continuous data. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG time series. SleepPPG-Net was trained end-to-end and consists of a residual convolutional network for automatic feature extraction and a temporal convolutional network to capture long-range contextual information. We benchmarked the performance of SleepPPG-Net against models based on the best-reported state-of-the-art (SOTA) algorithms. When benchmarked on a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ( κ) score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good generalization performance to an external database, obtaining a κ score of 0.74 after transfer learning. Overall, SleepPPG-Net provides new SOTA performance. In addition, performance is high enough to open the path to the development of wearables that meet the requirements for usage in clinical applications such as the diagnosis and monitoring of obstructive sleep apnea.