The mammalian vestibular epithelium has a limited capacity for spontaneous hair cell regeneration. The mechanism underlying the regeneration is not well understood. Because the Notch signaling pathway mediates the formation of the sensory epithelial mosaic patterning during ear development, it may also play a role in hair cell regeneration in the mature mammalian vestibular epithelium after a lesion. To investigate the process of spontaneous regeneration in the vestibular epithelium vis-à-vis changes in Notch signaling, we induced a unilateral lesion by infusing streptomycin into the mouse posterior semicircular canal, and examined Notch signaling molecules and their mRNA expression levels by immunohistochemistry and quantitative real-time polymerase chain reaction (qRTPCR), respectively. We detected Jagged1 in supporting cells in both normal and lesioned utricles. Atoh1, a marker for early developing hair cells, was absent in the intact mature tissue, but re-appeared after the lesion. Many cells were either positive for both Atoh1 and myosin VIIa, or for one of them. qRTPCR data showed a post trauma decrease of Hes5 and an increase in Atoh1. Atoh1 up-regulation may either be a result of Hes5 down-regulation or mediated by another signaling pathway.

No abstract

ElsevierRovira Más, F.; Chatterjee, I.; Sáiz Rubio, V. (2015).

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

No abstract