Calcium-sensing receptor activation increases cell-cell adhesion and ß-cell function

Background/Aims: The extracellular calcium-sensing receptor (CaR) is expressed in pancreatic β-cells where it is thought to facilitate cell-to-cell communication and augment insulin secretion. However, it is unknown how CaR activation improves β-cell function. Methods: Immunocytochemistry and western blotting confirmed the expression of CaR in MIN6 β-cell line. The calcimimetic R568 (1µM) was used to increase the affinity of the CaR and specifically activate the receptor at a physiologically appropriate extracellular calcium concentration. Incorporation of 5-bromo-2’-deoxyuridine (BrdU) was used to measure cell proliferation, whilst changes in non-nutrient-evoked cytosolic calcium were assessed using fura-2-microfluorimetry. AFM-single-cell-force spectroscopy related CaR-evoked changes in epithelial (E)-cadherin expression to improved functional tethering between coupled cells. Results: Activation of the CaR over 48hr doubled the expression of E-cadherin (206±41%) and increased L-type voltage-dependent calcium channel expression by 70% compared to control. These changes produced a 30% increase in cell-cell tethering and elevated the basal-to-peak amplitude of ATP (50µM) and tolbutamide (100µM)-evoked changes in cytosolic calcium. Activation of the receptor also increased PD98059 (1-100µM) and SU1498 (1-100µM)-dependent β-cell proliferation. Conclusion: Our data suggest that activation of the CaR increases E-cadherin mediated functional tethering between β-cells and increases expression of L-type VDCC and secretagogue-evoked changes in [Ca2+]i. These findings could explain how local changes in calcium, co-released with insulin, activate the CaR on neighbouring cells to help ensure efficient and appropriate secretory function

Semantic segmentation on small datasets of satellite images using convolutional neural networks

This is the final version. Available from SPIE via the DOI in this recordSemantic segmentation is one of the most popular and challenging applications of deep learning. It refers to the process of dividing a digital image into semantically homogeneous areas with similar properties. We employ the use of deep learning techniques to perform semantic segmentation on high-resolution satellite images representing urban scenes to identify roads, vegetation, and buildings. A SegNet-based neural network with an encoder–decoder architecture is employed. Despite the small size of the dataset, the results are promising. We show that the network is able to accurately distinguish between these groups for different test images, when using a network with four convolutional layers

Factors influencing public attitudes toward paid online newspaper subscriptions- a field study

Despite the implementation of new business models in several Western media organizations, most Arab newspapers have not yet explored these models, and little is known about public attitudes towards their willingness to pay for online news. The study sought to identify factors that encourage the public to pay. It was applied to a sample of 530 newspaper consumers in the UAE, Saudi Arabia, and Oman. The study concluded that there are unfavorable trends in the public regarding their interest in following up on the news and did not show willingness for paying for online news. Most of them prefer to pay for entertainment materials. The study found that demographic variables such as age and income were important indicators of the public’s willingness to pay. Most respondents were not yet exposed to the paywall and anticipated difficulty in implementing the culture of paying for online news in Arab societies. They also expected that many print newspapers will disappear in the near future, and the idea of paying for online news was not seen favorably

Image Compression Using Tap 9/7 Wavelet Transform and Quadtree Coding Scheme

This paper is concerned with the design and implementation of an image compression method based on biorthogonal tap-9/7 discrete wavelet transform (DWT) and quadtree coding method. As a first step the color correlation is handled using YUV color representation instead of RGB. Then, the chromatic sub-bands are downsampled, and the data of each color band is transformed using wavelet transform. The produced wavelet sub-bands are quantized using hierarchal scalar quantization method. The detail quantized coefficient is coded using quadtree coding followed by Lempel-Ziv-Welch (LZW) encoding. While the approximation coefficients are coded using delta coding followed by LZW encoding. The test results indicated that the compression results are comparable to those gained by standard compression schemes

Detection of copy-move forgery in digital images using different computer vision approaches

Image forgery detection approaches are many and varied, but they generally all serve the same objectives: detect and localize the forgery. Copy-move forgery detection (CMFD) is widely spread and must challenge approach. In this thesis, We first investigate the problems and the challenges of the existed algorithms to detect copy-move forgery in digital images and then we propose integrating multiple forensic strategies to overcome these problems and increase the efficiency of detecting and localizing forgery based on the same image input source. Test and evaluate our copy-move forgery detector algorithm presented the outcome that has been enhanced by various computer vision field techniques. Because digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy for forgers, we propose strategies and applications based on the PatchMatch algorithm and deep neural network learning (DNN). We further focus on the convolutional neural network (CNN) architecture approach in a generative adversarial network (GAN) and transfer learning environment. The F-measure score (FM), recall, precision, accuracy, and efficiency are calculated in the proposed algorithms and compared with a selection of literature algorithms using the same evaluation function in order to make a fair evaluation. The FM score achieves 0.98, with an efficiency rate exceeding 90.5% in most cases of active and passive forgery detection tasks, indicating that the proposed methods are highly robust. The output results show the high efficiency of detecting and localizing the forgery across different image formats for active and passive forgery detection. Therefore, the proposed methods in this research successfully overcome the main investigated issues in copy-move forgery detection as such: First, increase efficiency in copy-move forgery detection under a wide range of manipulation process to a copy-moved image. Second, detect and localized the copy-move forgery patches versus the pristine patches in the forged image. Finally, our experiments show the overall validation accuracy based on the proposed deep learning approach is 90%, according to the iteration limit. Further enhancement of the deep learning and learning transfer approach is recommended for future work

A Weighted Linear Combining Scheme for Cooperative Spectrum Sensing

AbstractCooperative spectrum sensing exploits spatial diversity of secondary-users (SUs), to reliably detect the availability of a spectrum. Soft energy combining schemes have optimal detection performance at the cost of high cooperation overhead, since actual sensed data is required at the fusion center. To reduce cooperation overhead, in hard combining only local decisions are shared; however the detection performance is suboptimal due to the loss of information. In this paper, a weighted linear combining scheme is proposed in which a SU performs a local sensing test based on two threshold levels. If local test result lies between the two thresholds then the SU report neither its local decision nor sequentially estimated unknown SNR parameter values, to the fusion center. Thereby, uncertain decisions about the presence/absence of the primary-user signal are suppressed. Simulation results suggest that the detection performance of the proposed scheme is close to optimal soft combining schemes yet its overhead is similar to hard combining techniques

Multifidelity Computing for Coupling Full and Reduced Order Models

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes

Interface learning of multiphysics and multiscale systems

Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm and put forth a data-driven closure approach based on memory embedding to provide physically correct boundary conditions at the interface. To enable the interface learning for hyperbolic systems by considering the domain of influence and wave structures into account, we put forth the concept of upwind learning towards a physics-informed domain decomposition. The promise of the proposed approach is shown for a set of canonical illustrative problems. We highlight that high-performance computing environments can benefit from this methodology to reduce communication costs among processing units in emerging machine learning ready heterogeneous platforms toward exascale era

Trapping cold atoms using surface-grown carbon nanotubes

We present a feasibility study for loading cold atomic clouds into magnetic traps created by single-wall carbon nanotubes grown directly onto dielectric surfaces. We show that atoms may be captured for experimentally sustainable nanotube currents, generating trapped clouds whose densities and lifetimes are sufficient to enable detection by simple imaging methods. This opens the way for a novel type of conductor to be used in atomchips, enabling atom trapping at sub-micron distances, with implications for both fundamental studies and for technological applications

DESIGN AND EVALUATION OF DOMPERIDONE SUBLINGUAL TABLETS

Objective: The aim of this work was to enhance the bioavailability of poorly soluble, anti-emetic drug; domperidone (DMP) having a poor oral bioavailability (13-17%) due to extensive first pass metabolism. The goal of this study was achieved through solubilization of DMP using solid dispersion technology followed by incorporation of solid dispersions into sublingual tablets to bypass pre-systemic metabolism.Methods: Solid dispersions of DMP with Pluronic F-68 were prepared in different weight ratios by fusion method and they were evaluated for their in vitro dissolution rate to select the best ratio for final formulation. Then, solid dispersions were formulated into sublingual tablets in combination with various soluble excipients. Sublingual tablets were prepared by direct compression technique and evaluated for their physical properties, in vitro dissolution rate and kinetics of drug release. The best formulae were selected for in vivo studies in rabbits in comparison with marketed oral tablets; Motinorm®.Results: Solid dispersions of DMP with Pluronic F-68 in a weight ratio of 1:7 (w/w) showed the highest dissolution rate and were selected for sublingual tablets formulation. Sublingual tablets formulae S16 (containing Fructose and 10% w/w Ac-Di-Sol) and S20 (containing Fructose and 10% w/w Explotab) showed the best results and were selected for in vivo studies in rabbits. The selected formulae showed marked enhancement of DMP bioavailability compared with the commercial oral tablets; Motinorm®, with relative bioavailability values of 432.49±10.13% and 409.32±11.59 % for S16 and S20, respectively.Conclusion: The results confirmed that sublingual tablets were an effective tool for DMP delivery with marked enhancement of bioavailability.Keywords: Domperidone, Solubility, Solid dispersions, Sublingual tablets, First-pass metabolism, Bioavailabilit