Isaacs syndrome: long-term improvement with intravenous polyvalent immunoglobulins

A 35 year-old man developed a syndrome with muscle cramp, myokimia, generalized, fasciculations, excessive sweating, sleep disorders and severe impairment. It was a syndrome of continuous muscle fiber activity--or Isaacs syndrome--with central disorders (this may be called "Maladie de Morvan"). Previous reports have suggested that Isaac's syndrome might be an autoimmune disorder. Moreover, high doses intravenous immunoglobulins were given resulting in a substantial improvement six months after the onset of this treatment.     

A cluster prediction model-based data collection for energy efficient wireless sensor network

The Journal of Supercomputing - Wireless sensor networks (WSN) are expected to cover the major portion of the earth’s surface in the coming years. In the era of IoT, the WSN is the major data...     

Output tracking control of switched nonlinear systems with multiple time-varying delays

In this paper, the output tracking control problem for a class of switched nonlinear systems with multiple time-varying delays is studied based on equivalent-input-disturbance (EID) approach. More precisely, with the use of suitable Lyapunov-Krasovskii functional together with average dwell-time technique, an output feedback tracking controller is designed which makes that the states of resulting system can asymptotically track the desired trajectory. Further, the EID estimator is implemented to reject both matched and unmatched disturbances effectively without requiring any prior knowledge of the disturbances. Simulation results are presented to illustrate the effectiveness and potential of the developed EID-based output tracking control design technique. The results reveal the fact that the tracking controller based on EID provides a better tracking performance than the feedback controller based on sliding mode technique.     

Enhancing multi-document summarization using concepts

In this paper we propose a methodology to mine concepts from documents and use these concepts to generate an objective summary of all relevant documents. We use the conceptual graph (CG) formalism as proposed by Sowa to represent the concepts and their relationships in the documents. In the present work we have modified and extended the definition of the concept given by Sowa. The modified and extended definition is discussed in detail in section 2 of this paper. A CG of a set of relevant documents can be considered as a semantic network. The semantic network is generated by automatically extracting CG for each document and merging them into one. We discuss (i) generation of semantic network using CGs and (ii) generation of multi-document summary. Here we use restricted Boltzmann machines, a deep learning technique, for automatically extracting CGs. We have tested our methodology using MultiLing 2015 corpus. We have obtained encouraging results, which are comparable to those from the state of the art systems.     

Metal ion sensing and light activated antimicrobial activity of aloe-vera derived carbon dots

Carbon dots (CDs) have emerged out as a potential material amongst the carbon family for a wide range of applications including chemical/biological sensing, photocatalysis, bioimaging, etc. The green synthesis of these CDs from natural sources is gaining the significant interest of peer community for their wide utility. Herein, we present a facile one-step pyrolysis method for CDs synthesis from Aloe-Vera extract, which show bright blue luminescence under UV light with a quantum yield of 12.3%. Further, ex-situ morphological, structural and optical characterizations reveal their high quality and excitation independent emission behavior with the presence of carboxyl, hydroxyl functional groups. Furthermore, these CDs were studied for Fe(III) sensing in water without any surface modifications and assessed for their light activated antibacterial activity against E.Coli and Staphylococcus aureus.     

Surface topography of polylactic acid nanofibrous mats: influence on blood compatibility

Fabricating nanofibrous scaffolds with robust blood compatibility remains an unmet challenge for cardiovascular applications since anti-thrombogenic surface coatings did not withstand physiological shear force. Hence, the present study envisages the influence of smooth and porous topographies of poly(lactic acid) (PLA) nanofibers on hemocompatibility as it could offer time-independent blood compatibility. Further, recent studies have evolved to integrate various contrasting agents for augmenting the prognostic properties of tissue engineered scaffolds; an attempt was also made to synthesize Curcumin–superparamagnetic iron oxide nanoparticle complex (Cur–SPION) as a contrasting agent and impregnated into PLA nanofibers for evaluating the blood compatibility. Herein, electrospun nanofibers of PLA with different topographies (smooth and porous) were fabricated and characterized for surface morphology, zeta potential, fluorescence, and crystallinity. The scaffolds with smooth, porous and rough surface topographies were thoroughly investigated for its hemocompatibility by evaluating hemolysis percentage, platelet adhesion, in vitro kinetic clotting time, serum protein adsorption, plasma recalcification time (PRT), capture and release of erythrocytes. Although the nanofibers of all three groups showed acceptable hemolytic percentage (HP?<?5%), the adhered RBCs on Cur–SPION based fibers undergo morphological transformation from biconcave discocytes to echinocytes with cube-like protrusions. On the contrary, no morphological changes were observed in RBCs cultured on smooth and porous nanofibers. Porous fibers exhibited excellent anti-thrombogenic property and adhered lesser platelets and maintained the discoidal morphology of native platelets. Cur–SPION integrated PLA nanofibers showed inactivated platelets with anti-thrombogenic activity compared to smooth nanofibers. In conclusion, PLA nanofibers porous topography did not affect the RBC membrane integrity and maintained discoidal morphology of platelets with superior anti-thrombogenic activity. However, smooth and Cur–SPION integrated PLA nanofibers were found to activate the platelets and deform the RBC membrane integrity, respectively. Hence, the nanofibers with porous structures provide an ideal topography for time-independent hemocompatibility.

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Synthesis,defect characterization and photocatalytic degradation efficiency of Tb doped CuO nanoparticles

Monoclinic undoped and Tb doped CuO are prepared by solution combustion method and annealed at different temperatures. The effect of annealing and doping on their structural and optical properties of CuO are examined using XRD, FTIR and DRS. The surface and lattice defects in CuO and Tb doped CuO is analyzed qualitatively and quantitatively using positron lifetime and Doppler broadening spectroscopy. The average positron lifetime and electron momentum (energy) S parameter increases owing to the number of vacancies in the CuO lattice upon doping and decreases with increasing temperature. The migration of vacancies from grain to grain boundary region is observed at 600 °C annealed samples. At 800 °C, the overall behavior of lifetime value denotes that the vacancy type defect is recovered, cluster vacancy and microvoids exists with reducing size. The photocatalytic performance of undoped and Tb doped CuO on degradation of methylene blue (MB) and methyl orange (MO) is investigated under visible light for two different lamp power and dye concentration. The influence of annealing temperature and dopant ion on the efficiency is also elaborated. Enhanced photocatalytic efficiency in Tb doped CuO is observed upon annealing. X-ray photoelectron spectroscopy (XPS) result indicates that the valence states of Cu, O and Tb ions exist at the surface of the particles. Brunauer–Emmett–Teller N₂ adsorption–desorption analyses were employed to characterize specific surface area and porosity of Tb doped CuO. The doped CuO with pore size of about ～34 nm have a surface area of 16–28 m²/g. The surface area effect plays an important role in the enhanced catalytic performance on Tb doped catalysts.     

Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy. Therefore, we aimed to develop a novel integrated approach to increase the accuracy of detection. This approach utilized both convolutional neural networks and signal processing techniques. In this proposed method, the biological electro retinogram (ERG) sensor network (BSN) and deep convolution neural network (DCNN) were developed to detect and classify DR. In the BSN system, electrodes were used to record ERG signal, which was pre-processed to be noise-free. Processing was performed in the frequency domain by the application of fast Fourier transform (FFT) and mel frequency cepstral coefficients (MFCCs) were extracted. Artificial neural network (ANN) classifier was used to classify the signals of eyes with DR and normal eye. Additionally, fundus images were captured using a fundus camera, and these were used as the input for DCNN-based analysis. The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images, non-proliferative DR (NPDR) or early-stage DR images, and proliferative DR (PDR) or advanced-stage DR images. Furthermore, it classified NPDR according to microaneurysms, hemorrhages, cotton wool spots, and exudates, and the presence of new blood vessels indicated PDR. The accuracy, sensitivity, and specificity of the ANN classifier were found to be 94%, 95%, and 93%, respectively. Both the accuracy rate and sensitivity rate of the DCNN classifier was 96.5% for the images acquired from various hospitals as well as databases. A comparison between the accuracy rates of BSN and DCNN approaches showed that DCNN with fundus images decreased the error rate to 4%.     

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.