A new synthetic route to molecularly imprinted ORMOSIL: Characterization and evaluation

A new strategy for synthesis of molecularly imprinted organically modified silica (MIORMOSIL) has been described. The MIORMOSIL was prepared by reacting vinyl acetate and 3-(trimethoxysilyl) propyl methacrylate, followed by condensation and hydrolysis with tetraethyl orthosilicate in the presence of caffeine. The ORMOSIL was characterized by SEM, FTIR, ²⁹Si MAS NMR, ¹³C MAS NMR, TGA, nitrogen adsorption/desorption and tested to extract analyte from coffee samples. The percentage recovery of caffeine 88 ± 5% (n = 9), limits of detection and quantification, 5.14 × 10^–4 and 1.71 × 10^?3 mmolL^?1 respectively, were achieved. Cross reactivity studies for theophylline and theobromine showed high recovery (77% and 82% respectively).

Abbreviations: HMIP: Molecularly Imprinted ORMOSIL; HNIP: non imprinted ORMOSIL; TMSPM: 3-(Trimethoxysilyl)propylmethacrylate     

Seed Oil Based Zinc Bioactive Polymers: Synthesis, Characterization and Biological Studies

Zinc containing oil based polyesteramide resins were synthesized by condensation polymerization reaction between castor/soyabean oil derived castor/soyabean fatty amide diol (HECA/HESA), Zn (OH)₂ and adipic acid. The conventional spectroscopic techniques such as FTIR, ¹HNMR, ¹³CNMR have been used to establish the structure of the polymers. Standard laboratory methods were used to study the physicochemical characteristics like acid value, hydroxyl value, saponification value, iodine value, specific gravity, and viscosity. The thermal behaviour of the polymers was analyzed by using thermo gravimetric analysis (TGA) and differential scanning calorimetry (DSC). In vitro antifungal (anticandidial) activity of the polymers was studied against C. albicans ATCC-10261, C. glabrata ATCC-90030 and C. tropicalis ATCC-750, respectively. Antibacterial activity against Gram positive (S. subtillis) and Gram negative bacteria (E. coli and S. typhi) was also examined. For more accuracy, growth curve studies were carried out with the polymer SZ showing higher biological activity against E. coli by using conventional spectrophotometer. The result showed that the polymers have potent anticandidial and antibacterial activities.     

Natural fibers and zinc hydroxystannate 3D microspheres based composite paper sheets for modern bendable energy storage application

For modern high-tech flexible energy storage devices, it becomes important to synthesize micro-/nanostructures as per the required shape and morphology with superior physical and electro-active characteristics. This work shares the fabrication and characterization of ZnSn(OH)₆ (Zinc hydroxystannate [ZHS]) prepared by facile microwave-assisted technique and furthermore converted into flexible sheets by employing lignocelluloses (LC) known as natural fibers, collected from Carica papaya leaf petiole as a substrate to provide the flexible matrix. X-ray diffraction measurements confirm the successful crystalline structure of ZHS. Scanning electron microscopy and transmission electron microscopy showed the solid spherical structure of ZHS microspheres. Fourier transform infrared spectrometry and Raman spectroscopy confirmed the composite formation of ZHS and LC-based composite sheets (ZHS/LC sheets). Electrochemical measurements that is, cyclic voltammetry (CV), Galvanostatic charge/discharge, and electrochemical impedance (EIS) spectroscopy revealed the electroactive behavior of ZHS/LC paper sheets as working electrode for energy storage applications. CV measurements revealed the specific capacitance of 100 F/g and EIS measurements confirmed the decrease in the resistance of LC fiber after the growth of ZHS microspheres. Presented flexible ZHS based paper sheets will be highly feasible for the modern bendable/flexible/disposable energy storage applications.     

A Coordinated Navigation Strategy for Multi-Robots to Capture a Target Moving with Unknown Speed

The paper proposes an algorithm for multi-robot coordination and navigation in order to intercept a target at a long distance. For this purpose, a limit cycle based algorithm using a neural oscillator with phase differences is proposed. The state of target is unknown, under the assumption that it is stationary or in motion with constant unknown speed along a straight line. Using the proposed algorithm, a group of robots is intended to move towards the target in such a way that the robots surround it. While moving to the target, self-collision between the robots is avoided. Moreover, a collision avoidance with static obstacles as well as dynamic target is realized. The robots reach the target at a desired distance, keeping uniformly distributed angles around the target. The algorithm is further extended so that a static interception point for the target can be estimated in place of pursuing a dynamic target, which is referred to as a virtual target in this paper. In other words, the robots move towards the virtual target instead of the actual target. The robots ultimately encircle the actual target when they arrive at the virtual target. The effectiveness of the proposed method is verified through simulation results.     

Faster dynamic spatial partitioning in OpenSimulator

OpenSimulator has emerged as one of the leading tools to help researchers, developers, and practitioners working in the field of virtual worlds since it is an open-source alternative to second life, the state of the art in virtual worlds. The grid mode of OpenSimulator is highly scalable, and it places no restriction on the number of cooperating OpenSimulator instances, each of which may simulate activity in an arbitrary number of regions. However, like second life, it suffers from both over-provision and under-provision of resources due to static allocation of regions to instances and the lack of an expansion and contraction model which adjusts resource allocation according to workload. We have used OpenSimulator to implement dynamic scalability which is an integral part of our novel infrastructure presented in earlier work. This paper reports timing analysis of the basic capabilities of OpenSimulator that are used to re-locate regions to additional simulators. The focus has been on a conservative extension to OpenSimulator using existing methods. To overcome serious performance issues during reassignment of a region, we present two extended region removal methods. Comparison of timing information for both existing and extended strategies is provided on both a network of Windows systems and a cluster of Linux nodes.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

Novel poly(arylene ethynylene) derivatives containing main chain triphenylamine and pendent quinoxaline moieties: synthesis and elementary characterization

BACKGROUND: Various poly(arylene ethynylene)s (PAEs) have been prepared and applied as molecular wires, in sensors, in nonlinear optics and as electroluminescent materials. But, to our knowledge, there has been no attention paid to the investigation of conjugated PAEs containing both triarylamine and quinoxaline groups. The influence imparted by the introduction of triarylamine and quinoxaline on the photophysical and electrochemical properties of PAEs is of interest. RESULTS: Two kinds of novel PAE derivatives, with electron‐donating triphenylamine groups in the backbone and electron‐accepting pendent quinoxaline moieties and bearing side chains of different lengths, were successfully synthesized with the Sonogashira coupling reaction. These polymers are soluble in common organic solvents and exhibit good film‐forming ability and thermal stability. UV‐visible investigations indicate that the ground states of these materials are unaffected by the polarity of their medium. An efficient intramolecular charge transfer effect is observed from an investigation of their photoluminescence properties in different solvents. Cyclic voltammetry study reveals that these polymers possess relatively high highest occupied molecular orbital levels due to the incorporation of triphenylamine segments into the polymer backbones. CONCLUSION: Primary characterization of these novel PAE derivatives shows that they might serve as potential active materials in optoelectronic devices. Copyright © 2009 Society of Chemical Industry     

Small unilamellar vesicles: a platform technology for molecular imaging of brain tumors

Molecular imaging enables the non-invasive investigation of cellular and molecular processes. Although there are challenges to overcome, the development of targeted contrast agents to increase the sensitivity of molecular imaging techniques is essential for their clinical translation. In this study, spontaneously forming, small unilamellar vesicles (sULVs) (30 nm diameter) were used as a platform to build a bimodal (i.e., optical and magnetic resonance imaging (MRI)) targeted contrast agent for the molecular imaging of brain tumors. sULVs were loaded with a gadolinium (Gd) chelated lipid (Gd-DPTA-BOA), functionalized with targeting antibodies (anti-EGFR monoclonal and anti-IGFBP7 single domain), and incorporated a near infrared dye (Cy5.5). The resultant sULVs were characterized in vitro using small angle neutron scattering (SANS), phantom MRI and dynamic light scattering (DLS). Antibody targeted and nontargeted Gd loaded sULVs labeled with Cy5.5 were assessed in vivo in a brain tumor model in mice using time domain optical imaging and MRI. The results demonstrated that a spontaneously forming, nanosized ULVs loaded with a high payload of Gd can selectively target and image, using MR and optical imaging, brain tumor vessels when functionalized with anti-IGFBP7 single domain antibodies. The unique features of these targeted sULVs make them promising molecular MRI contrast agents.