Surface characterization and biological properties of regular dentin,demineralized dentin,and deproteinized dentin

Bone autografts are often used for reconstruction of bone defects; however, due to the limitations of autografts, researchers have been in search of bone substitutes. Dentin is of particular interest for this purpose due to high similarity to bone. This in vitro study sought to assess the surface characteristics and biological properties of dentin samples prepared with different treatments. This study was conducted on regular (RD), demineralized (DemD), and deproteinized (DepD) dentin samples. X-ray diffraction and Fourier transform infrared spectroscopy were used for surface characterization. Samples were immersed in simulated body fluid, and their bioactivity was evaluated under a scanning electron microscope. The methyl thiazol tetrazolium assay, scanning electron microscope analysis and quantitative real-time polymerase chain reaction were performed, respectively to assess viability/proliferation, adhesion/morphology and osteoblast differentiation of cultured human dental pulp stem cells on dentin powders. Of the three dentin samples, DepD showed the highest and RD showed the lowest rate of formation and deposition of hydroxyapatite crystals. Although, the difference in superficial apatite was not significant among samples, functional groups on the surface, however, were more distinct on DepD. At four weeks, hydroxyapatite deposits were noted as needle-shaped accumulations on DemD sample and numerous hexagonal HA deposit masses were seen, covering the surface of DepD. The methyl thiazol tetrazolium, scanning electron microscope, and quantitative real-time polymerase chain reaction analyses during the 10-day cell culture on dentin powders showed the highest cell adhesion and viability and rapid differentiation in DepD. Based on the parameters evaluated in this in vitro study, DepD showed high rate of formation/deposition of hydroxyapatite crystals and adhesion/viability/osteogenic differentiation of human dental pulp stem cells, which may support its osteoinductive/osteoconductive potential for bone regeneration.     

NKG2C+ NK Cells for Immunotherapy of Glioblastoma Multiforme

In glioblastoma, non-classical human leucocyte antigen E (HLA-E) and HLA-G are frequently overexpressed. HLA-E loaded with peptides derived from HLA class I and from HLA-G contributes to inhibition of natural killer (NK) cells with expression of the inhibitory receptor CD94/NKG2A. We investigated whether NK cells expressing the activating CD94/NKG2C receptor counterpart were able to exert anti-glioma effects. NKG2C+ subsets were preferentially expanded by a feeder cell line engineered to express an artificial disulfide-stabilized trimeric HLA-E ligand (HLA-E*spG). NK cells expanded by a feeder cell line, which facilitates outgrowth of conventional NKG2A+, and fresh NK cells, were included for comparison. Expansion via the HLA-E*spG feeder cells selectively increased the fraction of NKG2C+ NK cells, which displayed a higher frequency of KIR2DL2/L3/S2 and CD16 when compared to expanded NKG2A+ NK cells. NKG2C+ NK cells exhibited increased cytotoxicity against K562 and KIR:HLA-matched and -mismatched primary glioblastoma multiforme (GBM) cells when compared to NKG2A+ NK cells and corresponding fresh NK cells. Cytotoxic responses of NKG2C+ NK cells were even more pronounced when utilizing target cells engineered with HLA-E*spG. These findings support the notion that NKG2C+ NK cells have potential therapeutic value for treating gliomas.     

An ultra low-voltage low power PSK backscatter modulator for passive UHF RFID tags compatible with C1 G2 EPC standard protocol

Reducing the power consumption of a passive radio frequency identification (RFID) tag is the key in many applications. As the modulator is usually the most power-hungry block in an RFID tag, this paper proposes a power-saving modulator. The proposed modulator uses phase shift keying (PSK) backscatter modulation which allows tag to communicate data from its memory to a reader by PSK modulation. The proposed modulator uses a MOSCAP as a variable impedance and is designed in a new one-inverter structure in compare to the conventional varactor-based modulators designed in two-inverter structure, as this modulator needs just a low voltage swing to drive its MOSCAP. Using MOSCAP as the variable capacitance leads to a low voltage design. Also, the fundamental equations required for determination of the capacitive impedance seen by the antenna is presented. This impedance is the master key in modulator design. The modulator has been designed, simulated and optimized in 0.18 μm CMOS technology. All possible simulation results are presented to approve its compatible operation with C1 G2 EPC global standard. The power consumption of less than 46 nW is achieved in all process corner cases at 0.8 V power supply.     

A Framework on the Performance Analysis of Cooperative Wireless Body Area Networks

Wireless body area networks (WBANs) are deal with wireless networks in the human body. We describe the performance analysis of dual-hop cooperative relaying systems employing amplify-and-forward (AF) technique in WBANs over independent and nonnecessary identically distributed Gamma fading channels. More specifically, we present closed-form derivations of the outage probabilities (OP), symbol error probabilities (SEP) and ergodic capacity (EC) for fixed gain and channel state information (CSI)-assisted relaying techniques at arbitrary signal-to-noise-ratios (SNRs). We also deduce novel expressions in the high SNR region. By doing so, we can quantify the performance of system by the diversity and coding gains. Using the derived expressions as a starting point and for the case of Exponential fading, we consider three practical optimization scenarios. They are optimal relay position with fixed power allocation, power allocation under the fixed location of the relay and joint optimization of power allocation and relay position under a transmit power constraint. The Monte Carlo simulations are used to validate the accuracy of our derivations, where it is demonstrated that the proposed adaptive allocation method significantly outperforms the fixed allocation method.

     

Volumetric quantification of atherosclerotic plaque in CT considering partial volume effect

Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation maximization (MEM) of a statistical model for the calcified and background material is used to estimate the partial calcium content of the voxels. The algorithm limits the region over which MEM is performed. By using MEM, the statistical properties of the model are iteratively updated based on the calculated resultant calcium distribution from the previous iteration. The estimated statistical properties are used to generate a map of the partial calcium content in the calcified region. The volume of calcium in the calcified region is determined based on the map. The experimental results on a cardiac phantom, scanned 90 times using 15 different protocols, demonstrate that the proposed method is less sensitive to partial volume effect and noise, with average error of 9.5% (standard deviation (SD) of 5-7mm(3)) compared with 67% (SD of 3-20mm(3)) for conventional techniques. The high reproducibility of the proposed method for 35 patients, scanned twice using the same protocol at a minimum interval of 10 min, shows that the method provides 2-3 times lower interscan variation than conventional techniques.     

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.     

New methods for enhancing fine acquisition in dual folding algorithm of long pseudo noise codes

Long code Direct Sequence Spread Spectrum is a good choice for applications where security and antispoofing ability of a spread spectrum connection is concerned. Due to long period of spreading code, acquisition is a challenge for these systems because a trade‐off between detection probability and acquisition time must be made. For confronting this challenge, various algorithms were proposed in the literature but almost all of them focus on expediting the coarse acquisition. In this paper, we consider the efficient dual folding algorithm for coarse acquisition level and propose 3 methods for enhancing the fine acquisition level leading to faster execution of overall acquisition algorithm. The methods are based on estimations from coarse acquisition level that are not used in conventional algorithms for fine acquisition, ie, zero padding. Theoretical expressions of 2 main comparison criteria in acquisition algorithms, ie, detection probability and mean acquisition time, are derived for conventional zero padding and each of the proposed method. Besides, a coarse estimate of resource consumption is provided by the number of floating point operations for each algorithm to make a useful comparison. Considering these 3 parameters, in comparison, the proposed methods surpass zero padding in 1, 2, or all of the 3 aspects. Simulation agrees well with analytical results.     

Hybrid Paper–Plastic Microchip for Flexible and High‐Performance Point‐of‐Care Diagnostics

A low‐cost and easy‐to‐fabricate microchip remains a key challenge for the development of true point‐of‐care (POC) diagnostics. Cellulose paper and plastic are thin, light, flexible, and abundant raw materials, which make them excellent substrates for mass production of POC devices. Herein, a hybrid paper–plastic microchip (PPMC) is developed, which can be used for both single and multiplexed detection of different targets, providing flexibility in the design and fabrication of the microchip. The developed PPMC with printed electronics is evaluated for sensitive and reliable detection of a broad range of targets, such as liver and colon cancer protein biomarkers, intact Zika virus, and human papillomavirus nucleic acid amplicons. The presented approach allows a highly specific detection of the tested targets with detection limits as low as 10² ng mL^?1 for protein biomarkers, 10³ particle per milliliter for virus particles, and 10² copies per microliter for a target nucleic acid. This approach can potentially be considered for the development of inexpensive and stable POC microchip diagnostics and is suitable for the detection of a wide range of microbial infections and cancer biomarkers.     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.