BackgroundAirway surface liquid, often referred to as mucus, is a thin layer of fluid covering the luminal surface that plays an important defensive role against foreign particles and chemicals entering the lungs. Airway mucus contains various macromolecules, the most abundant being mucin glycoproteins, which contribute to its defensive function. Airway epithelial cells cultured in vitro secrete mucins and nonmucin proteins from their apical surface that mimics mucus production in vivo. The current study was undertaken to identify the polypeptide constituents of human airway epithelial cell secretions to gain a better understanding of the protein composition of respiratory mucus.ResultsFifty-five proteins were identified in the high molecular weight fraction of apical secretions collected from in vitro cultures of well-differentiated primary human airway epithelial cells and isolated under physiological conditions. Among these were MUC1, MUC4, MUC5B, and MUC16 mucins. By proteomic analysis, the nonmucin proteins could be classified as inflammatory, anti-inflammatory, anti-oxidative, and/or anti-microbial.ConclusionsBecause the majority of the nonmucin proteins possess molecular weights less than that selected for analysis, it is theoretically possible that they may associate with the high molecular weight and negatively charged mucins to form a highly ordered structural organization that is likely to be important for maintaining the proper defensive function of airway mucus.

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An active matrix organic light emitting diode pixel circuit and its driving scheme for high frame frequency are proposed for implementation of a 3D display. The proposed pixel circuit can compensate the threshold voltage distribution of low temperature poly silicon-thin film transistors at high-speed operation of 240Hz or more. According to the simulation, current deviation of 1.73% and 3.94% are obtained at frame rates of 240Hz and 480Hz when V th distribution is ±0.5 V.

Compressive sensing (CS) based estimation technique utilizes a sparsity promoting constraint and solves the direction-of-arrival (DOA) estimation problem efficiently with high resolution. In this paper a grid free CS based DOA estimation technique is proposed, which uses sequential multiple snapshot data. Conventional CS technique suffers from a basis mismatch issue, while grid free CS technique is relieved of basis mismatch problem. Moreover, when the DOAs are stationary, multiple snapshot processing provides stable estimates over fluctuating single snapshot processing results. For multiple snapshot processing, the generalized version of total variation norm (group total variation norm) is implemented to impose a common sparsity pattern of multiple snapshot solution vectors in a continuous angular domain. Furthermore, an extended version is proposed using the singular value decomposition technique to mitigate computational complexity resulting from a large number of multiple snapshots. Data from SWellEx-96 are used to examine the proposed method. From the experimental data, it was observed that the present method not only offers high resolution even when the sources are coherent, but also the basis mismatch in the conventional CS method can be avoided.

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This paper describes a time delay estimation (TDE) technique using compressive sensing (CS) off the grid, which estimates the channel impulse response in a continuous time domain. The TDE can be formulated into a sparse signal reconstruction problem where the CS technique can be applied. Previous works have used standard finite dimensional CS with evenly discretized grids. However, the actual time delays will not always lie on the discrete grid, and this mismatch between the actual and discretized time delays results in reconstruction degradation. To overcome the basis mismatch, a TDE technique using an off the grid CS framework is proposed by modifying the scheme in the off the grid direction of arrival (DOA) estimation [Xenaki and Gerstoft, J. Acoust. Soc. Am. 137(4), 1923-1935 (2015)]. The effectiveness of the suggested method is demonstrated on real data from a water tank experiment. The off the grid CS TDE is shown to have super-resolution, which enables close arrivals to be distinguished.

This paper presents methods for the estimation of the time-varying directions of arrival (DOAs) of signals emitted by moving sources. Following the sparse Bayesian learning (SBL) framework, prior information of unknown source amplitudes is modeled as a multi-variate Gaussian distribution with zero-mean and time-varying variance parameters. For sequential estimation of the unknown variance, we present two sequential SBL-based methods that propagate statistical information across time to improve DOA estimation performance. The first method heuristically calculates the parameters of an inverse-gamma hyperprior based on the source signal estimate from the previous time step. In addition, a second sequential SBL method is proposed, which performs a prediction step to calculate the prior distribution of the current variance parameter from the variance parameter estimated at the previous time step. The SBL-based sequential processing provides high-resolution DOA tracking capabilities. Performance improvements are demonstrated by using simulated data as well as real data from the SWellEx-96 experiment.