Orientational distribution function in nematic liquid crystals by x-rays: Fourier method

The existing methods for the determination of the orientational distribution function f(beta) in the nematic liquid crystals using X-rays have been reviewed. A simple Fourier method which gives f(beta) in terms of the measured intensity is analysed. Using this distribution function, the accuracy with which the order parameters could be evaluated is discussed and the results show the elegance of the Fourier method used here

HIF- and Non-HIF-Regulated Hypoxic Responses Require the Estrogen-Related Receptor in Drosophila melanogaster

Low-oxygen tolerance is supported by an adaptive response that includes a coordinate shift in metabolism and the activation of a transcriptional program that is driven by the hypoxia-inducible factor (HIF) pathway. The precise contribution of HIF-1a in the adaptive response, however, has not been determined. Here, we investigate how HIF influences hypoxic adaptation throughout Drosophila melanogaster development. We find that hypoxic-induced transcriptional changes are comprised of HIF-dependent and HIF-independent pathways that are distinct and separable. We show that normoxic set-points of carbohydrate metabolites are significantly altered in sima mutants and that these animals are unable to mobilize glycogen in hypoxia. Furthermore, we find that the estrogen-related receptor (dERR), which is a global regulator of aerobic glycolysis in larvae, is required for a competent hypoxic response. dERR binds to dHIFa and participates in the HIF-dependent transcriptional program in hypoxia. In addition, dERR acts in the absence of dHIFa in hypoxia and a significant portion of HIF-independent transcriptional responses can be attributed to dERR actions, including upregulation of glycolytic transcripts. These results indicate that competent hypoxic responses arise from complex interactions between HIF-dependent and -independent mechanisms, and that dERR plays a central role in both of these programs

Purification and biochemical characterization of a novel secretory dipeptidyl peptidase IV from porcine serum

Purification of DPP-IV enzyme from porcine serum, is presented in this study for the first time. The high molecular weight DPP-IV from porcine serum was fractioned using Sephadex G-75 gel filtration followed by DEAE Sephadex anion exchange and Sephadex G-100 gel filtration chromatography columns with a final yield of 11.25%. The SDS-PAGE of the purified sample showed a single band of molecular mass nearing 160 kDa. Distinct single band was observed after PAS staining confirmed it to be a glycoprotein. The purified enzyme showed an optimum pH and temperature of 8 and 37 degrees C, respectively. The enzyme effectively cleaved fluorogenic substrate Gly-Pro-AMC with Km and Vmax of 4.578 mu M and 90.84 nmoles/min, respectively. Purified DPP-IV activity was inhibited by Diprotin A with an IC(50)value of 8.473 mu M. Among the three plant extracts used to study DPP-IV inhibition, the aqueous hot extract ofTerminalia chebulashowed the highest inhibition of 87.19%, followed by the aqueous cold extract ofMomordica carantia, ( 31.6%) andAzadirachta indica(34.16%) at the concentration of 25 mu g

Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and ({\delta}-)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2 , Reluplex, and Reluval

Grocery Shopping Assistant Using OpenCV

In this paper we present an android mobile application that allows user to keep track of food products and grocery items bought during each grocery shopping along with its nutrient information. This application allows user to get nutrient information of products and grocery by just taking a photo. Product matching is performed using SURF feature detection followed by FLANN feature matching. We extract the table from the nutrient fact table image using concepts of erosion, dilation and contour detection. Classifying the grocery is done using Object Categorization through the concepts of Bag of Words (BOW) and SVM machine learning. This application includes three main subsystems: client (Android), server (Node.js) and image processing (OpenCV)