Editorial: Aging, neurogenesis and neuroinflammation in hearing loss and protection

Exacerbated age-related hearing loss in mice lacking the p43 mitochondrial T3 receptor

BACKGROUND

Age-related hearing loss (ARHL), also known as presbycusis, is the most common sensory impairment seen in elderly people. However, the cochlear aging process does not affect people uniformly, suggesting that both genetic and environmental (e.g., noise, ototoxic drugs) factors and their interaction may influence the onset and severity of ARHL. Considering the potential links between thyroid hormone, mitochondrial activity, and hearing, here, we probed the role of p43, a N-terminally truncated and ligand-binding form of the nuclear receptor TRα1, in hearing function and in the maintenance of hearing during aging in p43-/- mice through complementary approaches, including in vivo electrophysiological recording, ultrastructural assessments, biochemistry, and molecular biology.

RESULTS

We found that the p43-/- mice exhibit no obvious hearing loss in juvenile stages, but that these mice developed a premature, and more severe, ARHL resulting from the loss of cochlear sensory outer and inner hair cells and degeneration of spiral ganglion neurons. Exacerbated ARHL in p43-/- mice was associated with the early occurrence of a drastic fall of SIRT1 expression, together with an imbalance between pro-apoptotic Bax, p53 expression, and anti-apoptotic Bcl2 expression, as well as an increase in mitochondrial dysfunction, oxidative stress, and inflammatory process. Finally, p43-/- mice were also more vulnerable to noise-induced hearing loss.

CONCLUSIONS

These results demonstrate for the first time a requirement for p43 in the maintenance of hearing during aging and highlight the need to probe the potential link between human THRA gene polymorphisms and/or mutations and accelerated age-related deafness or some adult-onset syndromic deafness.

Isolated Sign Language Recognition with Grassmann Covariance Matrices

In this article, to utilize long-term dynamics over an isolated sign sequence, we propose a covariance matrix--based representation to naturally fuse information from multimodal sources. To tackle the drawback induced by the commonly used Riemannian metric, the proximity of covariance matrices is measured on the Grassmann manifold. However, the inherent Grassmann metric cannot be directly applied to the covariance matrix. We solve this problem by evaluating and selecting the most significant singular vectors of covariance matrices of sign sequences. The resulting compact representation is called the Grassmann covariance matrix . Finally, the Grassmann metric is used to be a kernel for the support vector machine, which enables learning of the signs in a discriminative manner. To validate the proposed method, we collect three challenging sign language datasets, on which comprehensive evaluations show that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost.