Super Resolution Using a Single Image Dictionary

To perform super resolution of low resolution images, state-of-the-art methods are based on learning a pair of lowresolution and high-resolution dictionaries from multiple images. These trained dictionaries are used to replace patches in lowresolution image with appropriate matching patches from the high-resolution dictionary. In this paper we propose using a single common image as dictionary, in conjunction with approximate nearest neighbour fields (ANNF) to perform super resolution (SR). By using a common source image, we are able to bypass the learning phase and also able to reduce the dictionary from a collection of hundreds of images to a single image. By adapting recent developments in ANNF computation, to suit super-resolution, we are able to perform much faster and accurate SR than existing techniques. To establish this claim, we compare the proposed algorithm against various state-of-the-art algorithms, and show that we are able to achieve b etter and faster reconstruction without any training.

Sparse coding based VLAD for efficient image retrieval

Representing images and videos in the form of compact codes has emerged as an important research interest in the vision community, in the context of web scale image/video search. Recently proposed Vector of Locally Aggregated Descriptors (VLAD), has been shown to outperform the existing retrieval techniques, while giving a desired compact representation. VLAD aggregates the local features of an image in the feature space. In this paper, we propose to represent the local features extracted from an image, as sparse codes over an over-complete dictionary, which is obtained by K-SVD based dictionary training algorithm. The proposed VLAD aggregates the residuals in the space of these sparse codes, to obtain a compact representation for the image. Experiments are performed over the `Holidays' database using SIFT features. The performance of the proposed method is compared with the original VLAD. The 4% increment in the mean average precision (mAP) indicates the better retrieval performance of the proposed sparse coding based VLAD.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

Effect of solvent; enhancing the wettability and engineering the porous structure of a calcium phosphate/agarose composite for drug delivery

Tissue engineering deals with the regeneration of tissues for bone repair, wound healing, drug delivery, etc., and a highly porous 3D artificial scaffold is required to accommodate the cells and direct their growth. We prepared 3D porous calcium phosphate ((hydroxyapatite/beta-tricalcium phosphate)/agarose, (HAp/beta-TCP)/agarose) composite scaffolds by sol-gel technique with water (WBS) and ethanol (EBS) as solvents. The crystalline phases of HAp and beta-TCP in the scaffolds were confirmed by X-ray diffraction (XRD) analysis. The EBS had reduced crystallinity and crystallite size compared to WBS. WBS and EBS revealed interconnected pores of 1 mu m and 100 nm, respectively. The swelling ratio was higher for EBS in water and phosphate buffered saline (PBS). An in vitro drug loading/release experiment was carried out on the scaffolds using gentamicin sulphate (GS) and amoxicillin (AMX). We observed initial burst release followed by sustained release from WBS and EBS. In addition, GS showed more extended release than AMX from both the scaffolds. GS and AMX loaded scaffolds showed greater efficacy against Pseudomonas than Bacillus species. WBS exhibited enhanced mechanical properties, wettability, drug loading and haemocompatibility compared to EBS. In vitro cell studies showed that over the scaffolds, MC3T3 cells attached and proliferated and there was a significant increase in live MC3T3 cells. Both scaffolds supported MC3T3 proliferation and mineralization in the absence of osteogenic differentiation supplements in media which proves the scaffolds are osteoconducive. Microporous scaffolds (WBS) could assist the bone in-growth, whereas the presence of nanopores (EBS) could enhance the degradation process. Hence, WBS and EBS could be used as scaffolds for tissue engineering and drug delivery. This is a cost effective technique to produce scaffolds of degradable 3D ceramic-polymer composites.

Mesoporous silica-chondroitin sulphate hybrid nanoparticles for targeted and bio-responsive drug delivery

This work proposes the fabrication of a novel targeted drug delivery system based on mesoporous silica-biopolymer hybrids that can release drugs in response to biological stimuli present in cancer cells. The proposed system utilizes mesoporous silica nanoparticles as a carrier to host the drug molecules. A bio-polymer cap is attached onto these particles which serves the multiple functions of drug retention, targeting and bio-responsive drug release. The biopolymer chondroitin sulphate used here is a glycosaminoglycan that can specifically bind to receptors over-expressed in cancer cells. This molecule also possesses the property of disintegrating upon exposure to enzymes over-expressed in cancer cells. When these particles interact with cancer cells, the chondroitin sulphate present on the surface recognizes and attaches onto the CD44 receptors facilitating the uptake of these particles. The phagocytised particles are then exposed to the degradative enzymes, such as hyaluronidase present inside the cancer cells, which degrade the cap resulting in drug release. By utilizing a cervical cancer cell line we have demonstrated the targetability and intracellular delivery of hydrophobic drugs encapsulated in these particles. It was observed that the system was capable of enhancing the anticancer activity of the hydrophobic drug curcumin. Overall, we believe that this system might prove to be a valuable candidate for targeted and bioresponsive drug delivery.

B-Spline potential function for maximum a-posteriori image reconstruction in fluorescence microscopy

An iterative image reconstruction technique employing B-Spline potential function in a Bayesian framework is proposed for fluorescence microscopy images. B-splines are piecewise polynomials with smooth transition, compact support and are the shortest polynomial splines. Incorporation of the B-spline potential function in the maximum-a-posteriori reconstruction technique resulted in improved contrast, enhanced resolution and substantial background reduction. The proposed technique is validated on simulated data as well as on the images acquired from fluorescence microscopes (widefield, confocal laser scanning fluorescence and super-resolution 4Pi microscopy). A comparative study of the proposed technique with the state-of-art maximum likelihood (ML) and maximum-a-posteriori (MAP) with quadratic potential function shows its superiority over the others. B-Spline MAP technique can find applications in several imaging modalities of fluorescence microscopy like selective plane illumination microscopy, localization microscopy and STED. (C) 2015 Author(s).

Graphene oxide-Fe3O4 nanoparticle composite with high transverse proton relaxivity value for magnetic resonance imaging

The potential of graphene oxide-Fe3O4 nanoparticle (GO-Fe3O4) composite as an image contrast enhancing material in magnetic resonance imaging has been investigated. Proton relaxivity values were obtained in three different homogeneous dispersions of GO-Fe3O4 composites synthesized by precipitating Fe3O4 nanoparticles in three different reaction mixtures containing 0.01 g, 0.1 g, and 0.2 g of graphene oxide. A noticeable difference in proton relaxivity values was observed between the three cases. A comprehensive structural and magnetic characterization revealed discrete differences in the extent of reduction of the graphene oxide and spacing between the graphene oxide sheets in the three composites. The GO-Fe3O4 composite framework that contained graphene oxide with least extent of reduction of the carboxyl groups and largest spacing between the graphene oxide sheets provided the optimum structure for yielding a very high transverse proton relaxivity value. It was found that the GO-Fe3O4 composites possessed good biocompatibility with normal cell lines, whereas they exhibited considerable toxicity towards breast cancer cells. (C) 2015 AIP Publishing LLC.

Enhancing the mechanical and biological performance of a metallic biomaterial for orthopedic applications through changes in the surface oxide layer by nanocrystalline surface modification

Nanostructured metals are a promising class of biomaterials for application in orthopedics to improve the mechanical performance and biological response for increasing the life of biomedical implants. Surface mechanical attrition treatment (SMAT) is an efficient way of engineering nanocrystalline surfaces on metal substrates. In this work, 316L stainless steel (SS), a widely used orthopedic biomaterial, was subjected to SMAT to generate a nanocrystalline surface. Surface nanocrystallization modified the nature of the oxide layer present on the surface. It increased the corrosion-fatigue strength in saline by 50%. This increase in strength is attributed to a thicker oxide layer, residual compressive stresses, high strength of the surface layer, and lower propensity for intergranular corrosion in the nanocrystalline layer. Nanocrystallization also enhanced osteoblast attachment and proliferation. Intriguingly, wettability and surface roughness, the key parameters widely acknowledged for controlling the cellular response remained unchanged after nanocrystallization. The observed cellular behavior is explained in terms of the changes in electronic properties of the semiconducting passive oxide film present on the surface of 316L SS. Nanocrystallization increased the charge carrier density of the n-type oxide film likely preventing denaturation of the adsorbed cell-adhesive proteins such as fibronectin. In addition, a net positive charge developed on the otherwise neutral oxide layer, which is known to facilitate cellular adhesion. The role of changes in the electronic properties of the oxide films on metal substrates is thus highlighted in this work. This study demonstrates the advantages of nanocrystalline surface modification by SMAT for processing metallic biomaterials used in orthopedic implants.

Protamine-carboxymethyl cellulose magnetic nanocapsules for enhanced delivery of anticancer drugs against drug resistant cancers

Multidrug resistance is a major therapeutic challenge faced in the conventional chemotherapy. Nanocarriers are beneficial in the transport of chemotherapeutics by their ability to bypass the P-gp efflux in cancers. Most of the P-gp inhibitors under phase II clinical trial are facing failures and hence there is a need to develop a suitable carrier to address P-gp efflux in cancer therapy. Herein, we prepared novel protamine and carboxymethyl cellulose polyelectrolyte multi-layered nanocapsules modified with Fe3O4 nanoparticles for the delivery of doxorubicin against highly drug resistant HeLa cells. The experimental results revealed that improved cellular uptake, enhanced drug intensity profile with greater percentage of apoptotic cells was attained when doxorubicin loaded magnetic nanocapsules were used in the presence of external magnetic field. Hence, we conclude that this magnetic field assisted nanocapsule system can be used for delivery of chemotherapeutics for potential therapeutic efficacy at minimal dose in multidrug resistant cancers. From the Clinical Editor: Many cancer drugs fail when cancer cells become drug resistant. Indeed, multidrug resistance (MDR) is a major therapeutic challenge. One way that tumor cells attain MDR is by over expression of molecular pumps comprising of P-glycoprotein (P-gp) and multidrug resistant proteins (MRP), which can expel chemotherapeutic drugs out of the cells. In this study, the authors prepared novel protamine and carboxymethyl cellulose polyelectrolyte multi-layered nanocapsules modified with Fe3O4 nanoparticles for the delivery of doxorubicin. The results show that there was better drug delivery and efficacy even against MDR tumor cells. (C) 2015 Elsevier Inc. All rights reserved.

Synthetic image super resolution using FeatureMatch

In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.

Real-time maximum a-posteriori image reconstruction for fluorescence microscopy

Rapid reconstruction of multidimensional image is crucial for enabling real-time 3D fluorescence imaging. This becomes a key factor for imaging rapidly occurring events in the cellular environment. To facilitate real-time imaging, we have developed a graphics processing unit (GPU) based real-time maximum a-posteriori (MAP) image reconstruction system. The parallel processing capability of GPU device that consists of a large number of tiny processing cores and the adaptability of image reconstruction algorithm to parallel processing (that employ multiple independent computing modules called threads) results in high temporal resolution. Moreover, the proposed quadratic potential based MAP algorithm effectively deconvolves the images as well as suppresses the noise. The multi-node multi-threaded GPU and the Compute Unified Device Architecture (CUDA) efficiently execute the iterative image reconstruction algorithm that is similar to 200-fold faster (for large dataset) when compared to existing CPU based systems. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

S center dot center dot center dot O chalcogen bonding in sulfa drugs: insights from multipole charge density and X-ray wavefunction of acetazolamide

Experimental charge density analysis combined with the quantum crystallographic technique of X-ray wavefunction refinement (XWR) provides quantitative insights into the intra-and intermolecular interactions formed by acetazolamide, a diuretic drug. Firstly, the analysis of charge density topology at the intermolecular level shows the presence of exceptionally strong interaction motifs such as a DDAA-AADD (D-donor, A-acceptor) type quadruple hydrogen bond motif and a sulfonamide dimer synthon. The nature and strength of intra-molecular S center dot center dot center dot O chalcogen bonding have been characterized using descriptors from the multipole model (MM) and XWR. Although pure geometrical criteria suggest the possibility of two intra-molecular S center dot center dot center dot O chalcogen bonded ring motifs, only one of them satisfies the ``orbital geometry'' so as to exhibit an interaction in terms of an electron density bond path and a bond critical point. The presence of `s-holes' on the sulfur atom leading to the S center dot center dot center dot O chalcogen bond has been visualized on the electrostatic potential surface and Laplacian isosurfaces close to the `reactive surface'. The electron localizability indicator (ELI) and Roby bond orders derived from the `experimental wave function' provide insights into the nature of S center dot center dot center dot O chalcogen bonding.

Digital image analysis around isotropic points for photoelastic pattern recognition

Fringe tracking and fringe order assignment have become the central topics of current research in digital photoelasticity. Isotropic points (IPs) appearing in low fringe order zones are often either overlooked or entirely missed in conventional as well as digital photoelasticity. We aim to highlight image processing for characterizing IPs in an isochromatic fringe field. By resorting to a global analytical solution of a circular disk, sensitivity of IPs to small changes in far-field loading on the disk is highlighted. A local theory supplements the global closed-form solutions of three-, four-, and six-point loading configurations of circular disk. The local theoretical concepts developed in this paper are demonstrated through digital image analysis of isochromatics in circular disks subjected to three-and four-point loads. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Cadmium Sulfide Nanocrystal Sensitized Vertically Aligned Titanium Dioxide Rods for Large Area Image Sensors on 3-D Substrates

We discuss here a semiconductors assembly comprising of titanium dioxide (TiO2) rods sensitized by cadmium sulfide (CdS) nanocrystals for potential applications in large area electronics on three dimensional (3-D) substrates. Vertically aligned TiO2 rods are grown on a substrate using a 150 degrees C process flow and then sensitized with CdS by SILAR method at room temperature. This structure forms an effective photoconductor as the photo-generated electrons are rapidly removed from the CdS via the TiO2 thereby permitting a hole rich CdS. Current-voltage characteristics are measured and models illustrate space charge limited photo-current as the mechanism of charge transport at moderate voltage bias. The stable assembly and high speed are achieved. The frequency response with a loading of 10 pF and 9 M Omega shows a half power frequency of 100 Hz. (C) 2015 The Electrochemical Society. All rights reserved.