Facile preparation of agarose-chitosan hybrid materials and nanocomposite ionogels using an ionic liquid via dissolution, regeneration and sol-gel transition

We report simultaneous dissolution of agarose (AG) and chitosan (CH) in varying proportions in an ionic liquid (IL), 1-butyl-3-methylimidazolium chloride [C4mim][Cl]. Composite materials were constructed from AG-CH-IL solutions using the antisolvent methanol, and IL was recovered from the solutions. Composite materials could be uniformly decorated with silver oxide (Ag2O) nanoparticles (Ag NPs) to form nanocomposites in a single step by in situ synthesis of Ag NPs in AG-CH-IL sols, wherein the biopolymer moiety acted as both reducing and stabilizing agent. Cooling of Ag NPs-AG-CH-IL sols to room temperature resulted in high conductivity and high mechanical strength nanocomposite ionogels. The structure, stability and physiochemical properties of composite materials and nanocomposites were characterized by several analytical techniques, such as Fourier transform infrared (FTIR), CD spectroscopy, differential scanning colorimetric (DSC), thermogravimetric analysis (TGA), gel permeation chromatography (GPC), and scanning electron micrography (SEM). The result shows that composite materials have good thermal and conformational stability, compatibility and strong hydrogen bonding interactions between AG-CH complexes. Decoration of Ag NPs in composites and ionogels was confirmed by UV-Vis spectroscopy, SEM, TEM, EDAX and XRD. The mechanical and conducting properties of composite ionogels have been characterized by rheology and current-voltage measurements. Since Ag NPs show good antimicrobial activity, Ag NPs -AG-CH composite materials have the potential to be used in biotechnology and biomedical applications whereas nanocomposite ionogels will be suitable as precursors for applications such as quasi-solid dye sensitized solar cells, actuators, sensors or electrochromic displays

DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

A TMS320C54 system for effective online Signature Verification using Hidden Markov Models

In this paper we present a scheme for real time implementation of a Hidden Markov Model based Signature Verification System on a TMS320C54 processor. Here we explain in detail our overall methodology and the subsequent DSP implementation. We also propose two new algorithms which would further facilitate real-time operation. We use the Baum-We1ch Algorithm for training the HMM and the Viterbi Algorithm for the testing of our proposed system. It may be noted that the technique of HMMs have hitherto been applied for speech modelling and only recently has its application to the field of Signature Verification been considered. Our proposed system has an overall accuracy of 11.64% FAR and 0.64% FRR

Computer-Aided Detection of Pathologically Enlarged Lymph Nodes On Non-Contrast CT In Cervical Cancer Patients For Low-Resource Settings

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily CT images, which are widely available in LMICs(1). A region of interest which is likely to contain the nodal volumes relevant for cervical cancer was defined on a single patient CT(2). This region was deformed onto new patients using an in-house, demons-based deformation software. Edge detection and erosion filtering were used to distinguish potential positive nodes from normal structures. Regions on adjacent slices were then connected into a potential nodal 3D-structure. To differentiate these 3D structures from normal tissues, eighty-six features were generated based on the shape and mean pixel values of the structures, and four classification ensemble methods were tested to differentiate the positive nodes from normal tissues. A cohort of fifty-eight MD Anderson cervical cancer patients with pathologically enlarged lymph nodes were used as a training-test set. Similarly, twenty MD Anderson cervical cancer patients were obtained as a validation set. They contained 154 and 35 pathologically enlarged lymph nodes, respectively. Model comparison led to the selection of the Adaboost ensemble model, utilizing 17 features. In the validation set, 60% of the clinically significant positive cervical cancer nodes were identified along with a false/true positive ratio of ~4:1. The entire process takes approximately 10/number-of-cores-minutes. Our findings demonstrated that our computer-aided detection model can assist in the identification of metastatic nodal disease where high quality diagnostic imaging is not readily available. By identifying these nodes, radiation treatment fields can be modified to include pathologically enlarged lymph nodes, which is an essential element to providing potentially curative radiotherapy for cervical cancer