On Solutions of Variational Inequality Problems via Iterative Methods

We investigate an algorithm for a common point of fixed points of a finite family of Lipschitz pseudocontractive mappings and solutions of a finite family of γ-inverse strongly accretive mappings. Our theorems improve and unify most of the results that have been proved in this direction for this important class of nonlinear mappings

Strong Convergence Theorems for Quasi-Bregman Nonexpansive Mappings in Reflexive Banach Spaces

We study a strong convergence for a common fixed point of a finite family of quasi-Bregman nonexpansive mappings in the framework of real reflexive Banach spaces. As a consequence, convergence for a common fixed point of a finite family of Bergman relatively nonexpansive mappings is discussed. Furthermore, we apply our method to prove strong convergence theorems of iterative algorithms for finding a common solution of a finite family equilibrium problem and a common zero of a finite family of maximal monotone mappings. Our theorems improve and unify most of the results that have been proved for this important class of nonlinear mappings

Patient perception and attitudes toward magnetic resonance imaging safety

BackgroundMagnetic resonance imaging (MRI) scanners use strong, static and fast magnetic fields to form images. Due to rapid developments in MRI technology, several accidents have been recorded in hospitals worldwide as a result of insufficient knowledge about the dangers of MRI on the part of the patient or a failure to follow safety guidelines. This study evaluates patients’ perception and attitudes about MRI safety.AimsThis is a cross sectional study to evaluate the perception and attitudes of patients regarding MRI safety procedures.MethodsA 21 items questionnaire was collected from 119 patients in the MRI waiting area before the commencement of examination. Data were analysed using Statistical Package for the Social Sciences (SPSS) software (version 22.0, IBM Corp, Armonk, New York). The odds (OR) and 95 per cent confidence interval (CI) were used for analysis, the level of significance was set at p=0.05 using Chi-Square test to evaluate the relationship among the variables in the questionnaire.ResultsThe responses were collected from the patients and their relatives (46 male (38.6 per cent) and 73 female (61.4 per cent)). Approximately 71 per cent of the participants have already read or heard about MRI and the related safety aspects. 76 per cent of overall participants stated that they are aware of the need for preparation before an MRI exam with more awareness of MRI safety issues among younger patients (88 per cent). In this instance, females showed a higher level of knowledge (26 per cent) compared to males (11 per cent) with p=0.035.ConclusionPatients reported insufficient information about MRI safety which may increase the potential for accidents

Catalytic effectiveness of azobisisobutyronitrile/[SiMes)Ru(PPH3)(Ind)Cl2 initiating system in the polymerization of methyl methacrylate and other vinylic monomers

The catalytic system of azo-bis-isobutyronitrile (AIBN) combined with (SiMes)Ru(PPH3)(Ind)Cl-2 [M-20] was investigated for the controlled radical polymerization of methyl methacrylate (MMA) in solution. Various factors that may influence the catalytic polymerization process, such as the aging time of the initiating system, AIBN/M-20 ratio, concentration of monomer, polymerization time, temperature, and the nature of solvent were examined. The results showed that the yield, molecular weight, and molecular distribution are practically unaffected by these parameters; however, the syndiotactic stereo-structure tendency that characterizes the produced poly(methyl methacrylate) (PMMA) varied with temperature. The optimum conditions for PMMA synthesis were determined to produce an essentially syndiotactic material with uniformly high molecular weights. It was also revealed that the kinetics of MMA polymerization is of first order with respect to the concentration of monomer. A comparison was also made for some vinylic polymers synthesized either with the AIBN alone or with the AIBN/M-20 initiating system under the same conditions

Interaction Analysis of MRP1 with Anticancer Drugs Used in Ovarian Cancer: In Silico Approach

Multidrug resistance (MDR) is one of the major therapeutic challenges that limits the efficacy of chemotherapeutic response resulting in poor prognosis of ovarian cancer (OC). The multidrug resistance protein 1 (MRP1) is a membrane-bound ABC transporter involved in cross resistance to many structurally and functionally diverse classes of anticancer drugs including doxorubicin, taxane, and platinum. In this study, we utilize homology modelling and molecular docking analysis to determine the binding affinity and the potential interaction sites of MRP1 with Carboplatin, Gemcitabine, Doxorubicin, Paclitaxel, and Topotecan. We used AutoDock Vina scores to compare the binding affinities of the anticancer drugs against MRP1. Our results depicted Carboplatin \u3c Gemcitabine \u3c Topotecan \u3c Doxorubicin \u3c Paclitaxel as the order of binding affinities. Paclitaxel has shown the highest binding affinity whereas Carboplatin displayed the lowest affinity to MRP1. Interestingly, our data showed that Carboplatin, Paclitaxel, and Topotecan bind specifically to Asn510 residue in the transmembrane domains 1 of the MRP1. Our results suggest that Carboplatin could be an appropriate therapeutic choice against MRP1 in OC as it couples weakly with Carboplatin. Further, our findings also recommend opting Carboplatin with Gemcitabine as a combinatorial chemotherapeutic approach to overcome MDR phenotype associated with recurrent OC. View Full-Tex

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

A staircase-shaped quasi-fractal antenna is presented to meet the requirements of compact electronics operating in UWB or E-UWB spectrum. A conventional broadband monopole antenna is converted into UWB antenna utilizing three iterations of fractal patches. The resultant antenna offers wide impedance bandwidth ranges 2.3–17.8 GHz, having a notch band at 6.1–7.2 GHz. Afterwards, a two-port MIMO antenna is created by placing the second element orthogonally with an edge-to-edge distance of 8.5 mm, that is λ/15 where λ corresponds to free space wavelength at the lowest cut-off frequency. Hereafter, a meandered line-shaped stub is inserted to reduce the mutual coupling between closely spaced MIMO elements to less than −25 dB. As the intended application of the proposed work is On-body, Specific Absorption Rate (SAR) analyses are carried out at 2.4, 5.8 and 8 GHz, showing an acceptable range for both 1-g and 10-g averaged tissues standards. Moreover, various parameters of the MIMO antenna are studied, and a comparison is made between simulated and measured results as well as those of the state of the art

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder