Electrogenic transport and K(+) ion channel expression by the human endolymphatic sac epithelium.

The endolymphatic sac (ES) is a cystic organ that is a part of the inner ear and is connected to the cochlea and vestibule. The ES is thought to be involved in inner ear ion homeostasis and fluid volume regulation for the maintenance of hearing and balance function. Many ion channels, transporters, and exchangers have been identified in the ES luminal epithelium, mainly in animal studies, but there has been no functional study investigating ion transport using human ES tissue. We designed the first functional experiments on electrogenic transport in human ES and investigated the contribution of K(+) channels in the electrogenic transport, which has been rarely identified, even in animal studies, using electrophysiological/pharmacological and molecular biological methods. As a result, we identified functional and molecular evidence for the essential participation of K(+) channels in the electrogenic transport of human ES epithelium. The identified K(+) channels involved in the electrogenic transport were KCNN2, KCNJ14, KCNK2, and KCNK6, and the K(+) transports via those channels are thought to play an important role in the maintenance of the unique ionic milieu of the inner ear fluid

Unintended complication of intracranial subdural hematoma after percutaneous epidural neuroplasty.

Percutaneous epidural neuroplasty (PEN) is a known interventional technique for the management of spinal pain. As with any procedures, PEN is associated with complications ranging from mild to more serious ones. We present a case of intracranial subdural hematoma after PEN requiring surgical evacuation. We review the relevant literature and discuss possible complications of PEN and patholophysiology of intracranial subdural hematoma after PEN

An Exploratory Study of the Effects of Price Decreases on Online Product Reviews: Focusing on Amazon’s Kindle 2

As online shopping proliferates, online product reviews (OPRs) play a crucial role in online consumers’ purchasing decisions. Although prior research on the effects of price changes on consumer reactions has provided insightful implications, little is known about the impact of price changes on the characteristics of OPRs. With the growing importance of OPRs as a key social recommendation system for potential consumers’ decision-making, it is important to understand the dynamics of OPRs around price changes. We select the Kindle 2 from Amazon.com as our focal product and conduct an exploratory case study. By analyzing 6,714 reviews on the Kindle 2, we examine how consumers respond to price decreases using OPRs. The results show that all four characteristics of OPRs (star-rating, review depth, positive emotion, and negative emotion) are significantly influenced by price decreases. Moreover, we found that the impacts of price decreases on OPRs’ characteristics are different between the first and the second attempts at price reduction. Interestingly, the number of reviews per day significantly soars immediately after the first price decrease, while there is no significant change in the number of reviews after the second price cut. We conclude the paper with a discussion of our findings

Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network

The mechanical properties of lightweight aggregate concrete (LWAC) depend on the mixing ratio of its binders, normal weight aggregate (NWA), and lightweight aggregate (LWA). To characterize the relation between various concrete components and the mechanical characteristics of LWAC, extensive studies have been conducted, proposing empirical equations using regression models based on their experimental results. However, these results obtained from laboratory experiments do not provide consistent prediction accuracy due to the complicated relation between materials and mix proportions, and a general prediction model is needed, considering several mix proportions and concrete constituents. This study adopts the artificial neural network (ANN) for modeling the complex and nonlinear relation between constituents and the resulting compressive strength and elastic modulus of LWAC. To construct a database for the ANN model, a vast amount of detailed and extensive data was collected from the literature including various mix proportions, material properties, and mechanical characteristics of concrete. The optimal ANN architecture is determined to enhance prediction accuracy in terms of the numbers of hidden layers and neurons. Using this database and the optimal ANN model, the performance of the ANN-based prediction model is evaluated in terms of the compressive strength and elastic modulus of LWAC. Furthermore, these prediction accuracies are compared to the results of previous ANN-based analyses, as well as those obtained from the commonly used linear and nonlinear regression models