Development, in vitro and in vivo characterization of zoledronic acid functionalized hydroxyapatite nanoparticle based formulation for treatment of osteoporosis in animal model

We investigated the potential of using novel zoledronic acid (ZOL)-hydroxyapatite (HA) nanoparticle based drug formulation in a rat model of postmenopausal osteoporosis. By a classical adsorption method, nanoparticles of HA loaded with ZOL (HNLZ) drug formulation with a size range of 100-130 nm were prepared. 56 female Wistar rats were ovariectomized (OVX) or sham-operated at 3 months of age. Twelve weeks post surgery, rats were randomized into seven groups and treated with various doses of HNLZ (100, 50 and 25 mu g/kg, intravenous single dose), ZOL (100 mu g/kg, intravenous single dose) and HA nanoparticle (100 mu g/kg, intravenous single dose). Untreated OVX and sham OVX served as controls. After three months treatment period, we evaluated the mechanical properties of the lumbar vertebra and femoral mid-shaft. Femurs were also tested for trabecular microarchitecture. Sensitive biochemical markers of bone formation and bone resorption in serum were also determined. With respect to improvement in the mechanical strength of the lumbar spine and the femoral mid-shaft, the therapy with HNLZ drug formulation was more effective than ZOL therapy in OVX rats. Moreover, HNLZ drug therapy preserved the trabecular microarchitecture better than ZOL therapy in OVX rats. Furthermore, the HNLZ drug formulation corrected increase in serum levels of bone-specific alkaline phosphatase, procollagen type I N-terminal propeptide, osteocalcin, tartrate-resistant acid phosphatase 5b and C-telopeptide of type 1 collagen better than ZOL therapy in OVX rats. The results strongly suggest that HNLZ novel drug formulation appears to be more effective approach for treating severe osteoporosis in humans. (C) 2014 Elsevier B.V. All rights reserved.

Combined inhalation and oral supplementation of Vitamin A and Vitamin D: A possible prevention and therapy for tuberculosis

Tuberculosis is continuing as a problem of mankind. With evolution, MDR and XDR forms of tuberculosis have emerged from drug sensitive strain. MDR and XDR strains are resistant to most of the antibiotics, making the management more difficult. BCG vaccine is not providing complete protection against tuberculosis. Therefore new infections are spreading at a tremendous rate. At the present moment there is experimental evidence to believe that Vitamin A and Vitamin D has anti-mycobacterial property. It is in this context, we have hypothesized a host based approach using the above vitamins that can cause possible prevention and cure of tuberculosis with minimal chance of resistance or toxicity. (C) 2015 Elsevier Ltd. All rights reserved.

Zero-Crossings Based Spectrum Sensing Under Noise Uncertainties

In this work, the hypothesis testing problem of spectrum sensing in a cognitive radio is formulated as a Goodness-of-fit test against the general class of noise distributions used in most communications-related applications. A simple, general, and powerful spectrum sensing technique based on the number of weighted zero-crossings in the observations is proposed. For the cases of uniform and exponential weights, an expression for computing the near-optimal detection threshold that meets a given false alarm probability constraint is obtained. The proposed detector is shown to be robust to two commonly encountered types of noise uncertainties, namely, the noise model uncertainty, where the PDF of the noise process is not completely known, and the noise parameter uncertainty, where the parameters associated with the noise PDF are either partially or completely unknown. Simulation results validate our analysis, and illustrate the performance benefits of the proposed technique relative to existing methods, especially in the low SNR regime and in the presence of noise uncertainties.

Soft Real Time Implementation of a Cognitive Radio Testbed for Frequency Hopping Primary Satisfying QoS Requirements

The Cognitive Radio (CR) is a promising technology which provides a novel way to subjugate the issue of spectrum underutilization caused due to the fixed spectrum assignment policies. In this paper we report the design and implementation of a soft-real time CR MAC, consisting of multiple secondary users, in a frequency hopping (Fit) primary scenario. This MAC is capable of sensing the spectrum and dynamically allocating the available frequency bands to multiple CR users based on their QoS requirements. As the primary is continuously hopping, a method has also been implemented to detect the hop instant of the primary network. Synchronization usually requires real time support, however we have been able to achieve this with a soft-real time technique which enables a fully software implementation of CR MAC layer. We demonstrate the wireless transmission and reception of video over this CR testbed through opportunistic spectrum access. The experiments carried out use an open source software defined radio package called GNU Radio and a basic radio hardware component USRP.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Multi-dimensional Goodness-of-Fit Tests for Spectrum Sensing Based on Stochastic Distances

In this paper, we study two multi-dimensional Goodness-of-Fit tests for spectrum sensing in cognitive radios. The multi-dimensional scenario refers to multiple CR nodes, each with multiple antennas, that record multiple observations from multiple primary users for spectrum sensing. These tests, viz., the Interpoint Distance (ID) based test and the h, f distance based tests are constructed based on the properties of stochastic distances. The ID test is studied in detail for a single CR node case, and a possible extension to handle multiple nodes is discussed. On the other hand, the h, f test is applicable in a multi-node setup. A robustness feature of the KL distance based test is discussed, which has connections with Middleton's class A model. Through Monte-Carlo simulations, the proposed tests are shown to outperform the existing techniques such as the eigenvalue ratio based test, John's test, and the sphericity test, in several scenarios.

Error exponent analysis of energy-based Bayesian decentralized spectrum sensing under fading

This paper considers decentralized spectrum sensing, i.e., detection of occupancy of the primary users' spectrum by a set of Cognitive Radio (CR) nodes, under a Bayesian set-up. The nodes use energy detection to make their individual decisions, which are combined at a Fusion Center (FC) using the K-out-of-N fusion rule. The channel from the primary transmitter to the CR nodes is assumed to undergo fading, while that from the nodes to the FC is assumed to be error-free. In this scenario, a novel concept termed as the Error Exponent with a Confidence Level (EECL) is introduced to evaluate and compare the performance of different detection schemes. Expressions for the EECL under general fading conditions are derived. As a special case, it is shown that the conventional error exponent both at individual sensors, and at the FC is zero. Further, closed-form lower bounds on the EECL are derived under Rayleigh fading and lognormal shadowing. As an example application, it answers the question of whether to use pilot-signal based narrowband sensing, where the signal undergoes Rayleigh fading, or to sense over the entire bandwidth of a wideband signal, where the signal undergoes lognormal shadowing. Theoretical results are validated using Monte Carlo simulations. (C) 2015 Elsevier B.V. All rights reserved.

Trans-dichlorooxovandium (IV) complex as a novel photoinducible DNA interstrand crosslinker for cancer therapy

Although DNA interstrand crosslinking (ICL) agents such as mitomycin C, cisplatin and psoralen serve as potent anticancer drugs, these agents are known to have dose-limiting toxic effects on normal cells. Moreover, tumor resistance to these agents has been reported. Here, we show that trans-dichlorooxovanadium (IV) complex of pyrenyl terpyridine (VDC) is a novel photoinducible DNA crosslinking agent. By a combination of in vitro and ex vivo experiments including plasmid-based assays, we find that VDC forms monoadducts on the DNA and can be activated by UV-A and visible light to generate DNA interstrand crosslinks. VDC efficiently activates Fanconi anemia (FA) pathway of DNA interstrand crosslink repair. Strikingly, photoinduction of VDC induces prolonged activation of cell cycle checkpoint and a high degree of cell death in homologous recombination (HR)/ICL repair defective cells. Moreover, VDC specifically targets cells that express pathological RAD51C mutants. These data imply that VDC can be potentially used for cancer therapy and suggest that tumors arising in patients with gene mutations in FA and HR repair pathway can be specifically targeted by a photoactivatable VDC.

Design,optimization and in vivo evaluation of non-viral vectors for gene therapy. Applications in ocular disorders

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Dextran and protamine-based solid lipid nanoparticles as potential vectors for the treatment of X-linked juvenile retinoschisis

This is a copy of an article published in the Human gene therapy © 2012 copyright Mary Ann Liebert, Inc.; Human gene therapy is available online at: http://online.liebertpub.com.

Structural Integrity of the Contralesional Hemisphere Predicts Cognitive Impairment in Ischemic Stroke at Three Months

After stroke, white matter integrity can be affected both locally and distally to the primary lesion location. It has been shown that tract disruption in mirror's regions of the contralateral hemisphere is associated with degree of functional impairment. Fourteen patients suffering right hemispheric focal stroke (S) and eighteen healthy controls (HC) underwent Diffusion Weighted Imaging (DWI) and neuropsychological assessment. The stroke patient group was divided into poor (SP; n = 8) and good (SG; n = 6) cognitive recovery groups according to their cognitive improvement from the acute phase (72 hours after stroke) to the subacute phase (3 months post-stroke). Whole-brain DWI data analysis was performed by computing Diffusion Tensor Imaging (DTI) followed by Tract Based Spatial Statistics (TBSS). Assessment of effects was obtained computing the correlation of the projections on TBSS skeleton of Fractional Anisotropy (FA) and Radial Diffusivity (RD) with cognitive test results. Significant decrease of FA was found only in right brain anatomical areas for the S group when compared to the HC group. Analyzed separately, stroke patients with poor cognitive recovery showed additional significant FA decrease in several left hemisphere regions; whereas SG patients showed significant decrease only in the left genu of corpus callosum when compared to the HC. For the SG group, whole brain analysis revealed significant correlation between the performance in the Semantic Fluency test and the FA in the right hemisphere as well as between the performance in the Grooved Pegboard Test (GPT) and theTrail Making Test-part A and the FA in the left hemisphere. For the SP group, correlation analysis revealed significant correlation between the performance in the GPT and the FA in the right hemisphere. Palabras clave

The COMT Val158 Met polymorphism as an associated risk factor for Alzheimer disease and mild cognitive impairment in APOE 4 carriers

Background: The aim of this study is to examine the influence of the catechol-O-methyltranferase (COMT) gene (polymorphism Val158 Met) as a risk factor for Alzheimer's disease (AD) and mild cognitive impairment of amnesic type (MCI), and its synergistic effect with the apolipoprotein E gene (APOE). A total of 223 MCI patients, 345 AD and 253 healthy controls were analyzed. Clinical criteria and neuropsychological tests were used to establish diagnostic groups. The DNA Bank of the University of the Basque Country (UPV-EHU) (Spain) determined COMT Val158 Met and APOE genotypes using real time polymerase chain reaction (rtPCR) and polymerase chain reaction (PCR), and restriction fragment length polymorphism (RFLPs), respectively. Multinomial logistic regression models were used to determine the risk of AD and MCI. Results: Neither COMT alleles nor genotypes were independent risk factors for AD or MCI. The high activity genotypes (GG and AG) showed a synergistic effect with APOE epsilon 4 allele, increasing the risk of AD (OR = 5.96, 95% CI 2.74-12.94, p < 0.001 and OR = 6.71, 95% CI 3.36-13.41, p < 0.001 respectivily). In AD patients this effect was greater in women. In MCI patients such as synergistic effect was only found between AG and APOE epsilon 4 allele (OR = 3.21 95% CI 1.56-6.63, p = 0.02) and was greater in men (OR = 5.88 95% CI 1.69-20.42, p < 0.01). Conclusion: COMT (Val158 Met) polymorphism is not an independent risk factor for AD or MCI, but shows a synergistic effect with APOE epsilon 4 allele that proves greater in women with AD.