Quick response code secure: a cryptographically secure anti-phishing tool for QR code attacks.

The two-dimensional quick response (QR) codes can be misleading due to the difficulty in differentiating a genuine QR code from a malicious one. Since, the vulnerability is practically part of their design, scanning a malicious QR code can direct the user to cloned malicious sites resulting in revealing sensitive information. In order, to evaluate the vulnerabilities and propose subsequent countermeasures, we demonstrate this type of attack through a simulated experiment, where a malicious QR code directs a user to a phishing site. For our experiment, we cloned Google's web page providing access to their email service (Gmail). Since, the URL is masqueraded into the QR code the unsuspecting user who opens the URL is directed to the malicious site. Our results proved that hackers could easily leverage QR codes into phishing attack vectors targeted at smartphone users, even bypassing web browsers safe browsing feature. In addition, the second part of our paper presents adequate countermeasures and introduces QRCS (Quick Response Code Secure). QRCS is a universal efficient and effective solution focusing exclusively on the authenticity of the originator and consequently, the integrity of QR code by using digital signatures

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Novel Inhibitor Design for Hemagglutinin against H1N1 Influenza Virus by Core Hopping Method

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area

Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses

Identification of Potent EGFR Inhibitors from TCM Database@Taiwan

Overexpression of epidermal growth factor receptor (EGFR) has been associated with cancer. Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells. Hence, we employed Traditional Chinese Medicine Database (TCM Database@Taiwan) (http://tcm.cmu.edu.tw) to identify potential EGFR inhibitor. Multiple Linear Regression (MLR), Support Vector Machine (SVM), Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarities Indices Analysis (CoMSIA) models were generated using a training set of EGFR ligands of known inhibitory activities. The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid, and all had higher binding affinities than the control Iressa®. The TCM candidates had interactions with Asp855, Lys716, and Lys728, all which are residues of the protein kinase binding site. Validated MLR (r² = 0.7858) and SVM (r² = 0.8754) models predicted good bioactivity for the TCM candidates. In addition, the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship (3D-QSAR) map derived from the CoMFA (q² = 0.721, r² = 0.986) and CoMSIA (q² = 0.662, r² = 0.988) models. The steric field, hydrophobic field, and H-bond of the 3D-QSAR map were well matched by each TCM candidate. Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain. Based on the different structures, H-bonds were formed at either Asp855 or Lys716/Lys728. The compounds remained stable throughout molecular dynamics (MD) simulation. Based on the results of this study, 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University (CMU98-TCM)China Medical University (CMU99-TCM)China Medical University (CMU99-S-02)China Medical University (CMU99-ASIA-25)China Medical University (CMU99-ASIA-26)China Medical University (CMU99-ASIA-27)China Medical University (CMU99-ASIA-28)Asia UniversityTaiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005

Multiplexing of optical fiber gas sensors with a frequency-modulated continuous-wave technique

Author name used in this publication: W. JinAuthor name used in this publication: H. L. HoAuthor name used in this publicaiton: M. S. Demokan2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic

A Novel Fibronectin Binding Motif in MSCRAMMs Targets F3 Modules

BBK32 is a surface expressed lipoprotein and fibronectin (Fn)-binding microbial surface component recognizing adhesive matrix molecule (MSCRAMM) of Borrelia burgdorferi, the causative agent of Lyme disease. Previous studies from our group showed that BBK32 is a virulence factor in experimental Lyme disease and located the Fn-binding region to residues 21-205 of the lipoprotein.Studies aimed at identifying interacting sites between BBK32 and Fn revealed an interaction between the MSCRAMM and the Fn F3 modules. Further analysis of this interaction showed that BBK32 can cause the aggregation of human plasma Fn in a similar concentration-dependent manner to that of anastellin, the superfibronectin (sFn) inducing agent. The resulting Fn aggregates are conformationally distinct from plasma Fn as indicated by a change in available thermolysin cleavage sites. Recombinant BBK32 and anastellin affect the structure of Fn matrices formed by cultured fibroblasts and inhibit endothelial cell proliferation similarly. Within BBK32, we have located the sFn-forming activity to a region between residues 160 and 175 which contains two sequence motifs that are also found in anastellin. Synthetic peptides mimicking these motifs induce Fn aggregation, whereas a peptide with a scrambled sequence motif was inactive, suggesting that these motifs represent the sFn-inducing sequence.We conclude that BBK32 induces the formation of Fn aggregates that are indistinguishable from those formed by anastellin. The results of this study provide evidence for how bacteria can target host proteins to manipulate host cell activities