15‐Methylene‐Eburnamonine Kills Leukemic Stem Cells and Reduces Engraftment in a Humanized Bone Marrow Xenograft Mouse Model of Leukemia

Recent studies suggest that leukemia stem cells (LSCs) play a critical role in the initiation, propagation, and relapse of leukemia. Herein we show that (?)‐15‐methylene‐eburnamonine, a derivative of the alkaloid (?)‐eburnamonine, is cytotoxic against acute and chronic lymphocytic leukemias (ALL and CLL) and acute myelogenous leukemia (AML). The agent also decreases primary LSC frequency in vitro. The cytotoxic effects appear to be mediated via the oxidative stress pathways. Furthermore, we show that the compound kills AML, ALL, and CLL stem cells. By the use of a novel humanized bone marrow murine model of leukemia (huBM/NSG), it was found to decrease progenitor cell engraftment.     

Development of information collection scheme in internet of things environment for intelligent radiation monitoring systems

The new revolution in computing and wireless communications technologies led to Internet of Things (IoT). Information collection scheme performs an important role for energy efficient utilization and latency awareness in IoT environments. The enhancement of an effectual information collection scheme is crucial to improve the overall performance of the internet of things applications. In this paper, the proposed information collection scheme aimed to enhance the confidence regarding any captured measurements under IoT environment. The scheme can verify the selection of optimal information collection routes through using the Dijkstra algorithm. It depends on selecting the preferable IoT devices (collectors) with optimal paths in efficient energy utilization. The IoT devices (sources) have to elect whichever a preferable collector can deliver the captured information with an endeavour to sense the latest contextual information. They elect the optimal collectors through implicit and explicit solutions. Also, it considers different failure conditions to specify the optimal collection time for furtherance scalability of IoT environment. The simulation results show the ability of our scheme to improve the performance in terms of network lifetime, energy consumption, and reliability.

     

Spectral width reduction using apodized cascaded fiber Bragg grating for post-dispersion compensation in WDM optical networks

This paper proposes a system that aims to reduce the spectral width, Δλ, of the optical signal at transmitter for WDM system over distance 100 km. Also, a chirped fiber Bragg grating (CFBG) at the receiver is used to compensate dispersion. The proposed system consists of four cascaded FBGs connected between light source and optical fiber. Many apodization functions are investigated to enhance the performance of the FBG in the proposed system, and Δλ is obtained at every stage and apodization function. The Q-factor and bit error rate (BER) are obtained at distances 30, 40 and 50 km. It is found that Cauchy apodization function is the best one that reduces the reflective spectral width, Δλ, and achieves a maximum Q-factor and minimum BER at distances 30, 40 and 50 km at the last stage.

     

Neural Networks Pipeline for Offline Machine Printed Arabic OCR

In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.     

A novel deep learning framework for copy-moveforgery detection in images

Multimedia Tools and Applications - In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of...     

Secure image sharing method over unsecured channels

Image sharing has become a prominent field in large and small networks. However, in order to deliver the shares to their respective shareholders, we need a secure channel to protect the shares from potential attacks by other users on the network. These users can locate the shares and retrieve the image using the Lagrange interpolation method if they are able to locate the minimum required number of shares. This paper proposes a method to protect the privacy and security of the shares when sending them through unsecured channels. The proposed method is divided into three phases. In the first phase, image pixels are changed using the column and row indices, then the shares are created using Shamir’s secret sharing (SSS) method. Then a relationship is created between the shares using a linear independence function. In the second phase, the first part of the image is encrypted to hide the solver data. Finally, in the third phase, the data is shared using the SSS method. Participants can retrieve the whole image by applying the reverse order of the proposed method using only the information from the sender through the same channel. The simulation results show that the proposed method is efficient and robust against different types of attacks and can be used to send the shares over unsecured channels.     

Digital image scrambling based on elementary cellular automata

Image scrambling is the process of converting an image to an unintelligible format, mainly for security reasons. The scrambling is considered as a pre-process or a post-process of security related applications such as watermarking, information hiding, fingerprinting, and encryption. Cellular automata are parallel models of computation that prove an interesting concept where a simple configuration can lead to a complex behavior. Since there are a lot of parameters to configure, cellular automata have many types and these types differ in terms of complexity and behavior. Cellular automata were previously used in scrambling different types of multimedia, but only complex two-dimensional automata were explored. We propose a scheme where the simplest type of cellular automata is used that is the elementary type. We test the scrambling degree for different cellular automata rules that belong to classes three and four of Wolfram’s classification which correspond to complex and chaotic behavior; we also check the effect of other parameters such as the number of generations and the boundary condition. Experimental results show that our proposed scheme outperforms other schemes based on cellular automata in terms of scrambling degree.     

Parallelizing exact motif finding algorithms on multi-core

The motif finding problem is one of the important and challenging problems in bioinformatics. A variety of sequential algorithms have been proposed to find exact motifs, but the running time is still not suitable due to high computational complexity of finding motifs. In this paper we parallelize three efficient sequential algorithms which are HEPPMSprune, PMS5 and PMS6. We implement the algorithms on a Dual Quad-Core machine using openMP to measure the performance of each algorithm. Our experiment on simulated data show that: (1) the parallel PMS6 is faster than the other algorithms in case of challenging instances, while the parallel HEPPMSprune is faster than the other algorithms in most of solvable instances; (2) the scalability of parallel HEPPMSprune is linear for all instances, while the scalability of parallel PMS5 and PMS6 is linear in case of challenging instances only; (3) the memory used by HEPPMSprune is less than that of the other algorithms.     

Hall effect on unsteady couette flow with heat transfer under exponential decaying pressure gradient

The unsteady Couette flow of an electrically conducting, viscous, incompressible fluid bounded by two parallel non-conducting porous plates is studied with heat transfer taking the Hall effect into consideration. An external uniform magnetic field and a uniform suction and injection are applied perpendicular to the plates while the fluid motion is subjected to an exponential decaying pressure gradient. The two plates are kept at different but constant temperatures while the Joule and viscous dissipations are included in the energy equation. The effect of the ion slip and the uniform suction and injection on both the velocity and temperature distributions is examined.