Fabrication,characterization and evaluation of the effect of PLGA and PLGA–PEG biomaterials on the proliferation and neurogenesis potential of human neural SH-SY5Y cells

In recent years with regard to the development of nanotechnology and neural stem cell discovery, the combinatorial therapeutic strategies of neural progenitor cells and appropriate biomaterials have raised the hope for brain regeneration following neurological disorders. This study aimed to explore the proliferation and neurogenic effect of PLGA and PLGA–PEG nanofibers on human SH-SY5Y cells in in vitro condition. Nanofibers of PLGA and PLGA–PEG biomaterials were synthesized and fabricated using electrospinning method. Physicochemical features were examined using HNMR, FT-IR, and water contact angle assays. Ultrastructural morphology, the orientation of nanofibers, cell distribution and attachment were visualized by SEM imaging. Cell survival and proliferation rate were measured. Differentiation capacity was monitored by immunofluorescence staining of Map-2. HNMR, FT-IR assays confirmed the integration of PEG to PLGA backbone. Water contact angel assay showed increasing surface hydrophilicity in PLGA–PEG biomaterial compared to the PLGA substrate. SEM analysis revealed the reduction of PLGA–PEG nanofibers' diameter compared to the PLGA group. Cell attachment was observed in both groups while PLGA–PEG had a superior effect in the promotion of survival rate compared to other groups (p < .05). Compared to the PLGA group, PLGA–PEG increased the number of Ki67⁺ cells (p < .01). PLGA–PEG biomaterial induced neural maturation by increasing protein Map-2 compared to the PLGA scaffold in a three-dimensional culture system. According to our data, structural modification of PLGA with PEG could enhance orientated differentiation and the dynamic growth of neural cells.     

Plasma Spray Coatings of Ni-Al-SiC Composite

Ni-Al-SiC powder mixture containing 12 wt.% SiC was prepared by conventional ball milling. Morphological and microstructural investigations showed that powder particles after 15 h of milling time had the optimum characteristics with respect to their size and microstructure. X-ray diffraction patterns of powder particles included only the elemental Ni, Al, and SiC peaks without any traces of oxides or intermetallic phases. The powder mixture was then deposited onto a steel substrate by atmospheric plasma spray (APS) process under different conditions. The results showed that under APS conditions used here, the coatings were composed of various intermetallics including Ni-Al and Ni₂Al₃. The mean hardness of coating was found to be about 567 HV. It was also found that by increasing current density of APS, the coating/substrate adhesive strength was increased.     

Synthesis,structure and optical properties of x(CuO)/(1−x)Ni(OH)2 [x=0, 0.1 and 0.3] nanocomposites

The x(CuO)/(1−x)Ni(OH)₂ [x=0, 0.1 and 0.3] nanocomposites were prepared by the hydrothermal method in the presence of the surfactant polyethylenglycol-10000 (PEG-10000). X-ray diffraction (XRD), infrared (IR) spectroscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to characterize the as-prepared samples. The increase of the CuO content led to the increase of the crystallite size of both, the β-Ni(OH)₂ and the CuO. The increase in the crystallite size greatly affects the band gap energy of the as-prepared nanocomposites. The band gap energies of the x(CuO)/(1−x)Ni(OH)₂ nanocomposites were estimated by UV–vis spectroscopic method. UV–vis spectroscopic results showed an apparent decrease in the direct band gap energies. The x(CuO)/(1−x)Ni(OH)₂ [x=0, 0.1 and 0.3] nanocomposites show low band gap energies compared to the Ni(OH)₂ bulk materials. The enhanced optical properties lead to their possible use in photocatalytic and photovoltaic applications.     

Electrophoretic deposition of bioactive glass nanopowders on magnesium based alloy for biomedical applications

To slow down the initial biodegradation rate of magnesium (Mg) alloy, crystalline nano-sized bioactive glass coating was used to deposit on micro-arc oxidized AZ91 samples via electrophoretic deposition (EPD). Zeta potential and conductivity of the bioactive glass suspension were characterized at various pH values to identify the most stable dispersion conditions. The bone-bonding properties of bioactive glass coated samples were evaluated in terms of apatite-forming ability during the immersion in simulated body fluid (SBF) solution. Results revealed that the ability to form a bioactive glass coating via EPD was influenced by the degree of its crystalline phase composition. Moreover, the potentiodynamic polarization tests recorded significant drops in corrosion current density and corrosion rate of the coated samples which implies a good level of corrosion protective behavior. These preliminary results show that this process will enable the development of Mg implants in the later stage of bone healing.     

Natural Rubber Bearing Incorporated with Steel Ring Damper (NRB-SRD)

The present paper is aimed to investigate the behavior of Natural Rubber Bearing incorporated with steel ring damper (NRB-SRD). These types of dampers are integrated of several steel rings which are considered with two configurations namely, continual steel ring damper and separate steel ring damper and are inserted between top and bottom plates. The performance characteristics of the system such as effective horizontal stiffness, energy dissipation, equivalent viscous damping and residual deformation are calculated and then compared with the results of high damping rubber bearings and also shape memory alloy (SMA)-lead core rubber bearing (SMA-LRB). The results show that the energy dissipation in steel rings are mainly based on plastic deformation due to flexural behavior of the rings. NRB-SRD shows better performance in energy dissipation comparing to SMA-LRB and HDRB. These additional dampers show higher stability and energy dissipation in low shear strains due to developing of link between structure and substructure having desirable initial stiffness under weak earthquakes and wind loads and also in higher shear strains due to creation of higher energy dissipation, stability and secondary stiffening.

     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Model Misspecification of Generalized Gamma Distribution for Accelerated Lifetime-Censored Data

Abstract

The performance of reliability inference strongly depends on the modeling of the product’s lifetime distribution. Many products have complex lifetime distributions whose optimal settings are not easily found. Practitioners prefer to use simpler lifetime distribution to facilitate the data modeling process while knowing the true distribution. Therefore, the effects of model mis-specification on the product’s lifetime prediction is an interesting research area. This article presents some results on the behavior of the relative bias (RB) and relative variability (RV) of pth quantile of the accelerated lifetime (ALT) experiment when the generalized Gamma (GG₃) distribution is incorrectly specified as Lognormal or Weibull distribution. Both complete and censored ALT models are analyzed. At first, the analytical expressions for the expected log-likelihood function of the misspecified model with respect to the true model is derived. Consequently, the best parameter for the incorrect model is obtained directly via a numerical optimization to achieve a higher accuracy model than the wrong one for the end-goal task. The results demonstrate that the tail quantiles are significantly overestimated (underestimated) when data are wrongly fitted by Lognormal (Weibull) distribution. Moreover, the variability of the tail quantiles is significantly enlarged when the model is incorrectly specified as Lognormal or Weibull distribution. Precisely, the effect on the tail quantiles is more significant when the sample size and censoring ratio are not large enough. Supplementary materials for this article are available online.     

Iron oxide/gold nanoparticles‐decorated reduced graphene oxide nanohybrid as the thermo‐radiotherapy agent

The main focus of the current study is the fabrication of a multifunctional nanohybrid based on graphene oxide (GO)/iron oxide/gold nanoparticles (NPs) as the combinatorial cancer treatment agent. Gold and iron oxide NPs formed on the GONPs via the in situ synthesis approach. The characterisations showed that gold and iron oxide NPs formed onto the GO. Cell toxicity assessment revealed that the fabricated nanohybrid exhibited negligible toxicity against MCF‐7 cells in low doses (<50 ppm). Temperature measurement showed a time and dose‐dependent heat elevation under the interaction of the nanohybrid with the radio frequency (RF) wave. The highest temperature was recorded using 200 ppm concentration nanohybrid during 40 min exposure. The combinatorial treatments demonstrated that the maximum cell death (average of 53%) was induced with the combination of the nanohybrid with RF waves and radiotherapy (RT). The mechanistic study using the flow cytometry technique illustrated that early apoptosis was the main underlying cell death. Moreover, the dose enhancement factor of 1.63 and 2.63 were obtained from RT and RF, respectively. To sum up, the authors’ findings indicated that the prepared nanohybrid could be considered as multifunctional and combinatorial cancer therapy agents.Inspec keywords: radiation therapy, toxicology, gold, biomedical materials, nanofabrication, nanoparticles, iron compounds, cancer, nanomedicine, cellular biophysics, tumours, graphene compounds, biothermicsOther keywords: graphene oxide nanohybrid, combinatorial cancer treatment agent, cell toxicity assessment, MCF‐7 cells, dose‐dependent heat elevation, multifunctional cancer therapy agents, thermoradiotherapy agent, graphene oxide‐iron oxide‐gold nanoparticles, temperature measurement, radiofrequency wave, flow cytometry, time 40.0 min, CO‐FeO‐Au     

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.     

Time‐domain hybrid formulations for wave simulations in three‐dimensional PML‐truncated heterogeneous media

We are concerned with the numerical simulation of wave motion in arbitrarily heterogeneous, elastic, perfectly‐matched‐layer‐(PML)‐truncated media. We extend in three dimensions a recently developed two‐dimensional formulation, by treating the PML via an unsplit‐field, but mixed‐field, displacement‐stress formulation, which is then coupled to a standard displacement‐only formulation for the interior domain, thus leading to a computationally cost‐efficient hybrid scheme. The hybrid treatment leads to, at most, third‐order in time semi‐discrete forms. The formulation is flexible enough to accommodate the standard PML, as well as the multi‐axial PML. We discuss several time‐marching schemes, which can be used à la carte, depending on the application: (a) an extended Newmark scheme for third‐order in time, either unsymmetric or fully symmetric semi‐discrete forms; (b) a standard implicit Newmark for the second‐order, unsymmetric semi‐discrete forms; and (c) an explicit Runge–Kutta scheme for a first‐order in time unsymmetric system. The latter is well‐suited for large‐scale problems on parallel architectures, while the second‐order treatment is particularly attractive for ready incorporation in existing codes written originally for finite domains. We compare the schemes and report numerical results demonstrating stability and efficacy of the proposed formulations. Copyright © 2014 John Wiley & Sons, Ltd.