Computer Science

Probing the active site of rhodanase with disulfide reagents Uses of gel electrophoresis to monitor detergent enzymes: application to safety assessments C-terminal sequence analysis of polypeptides containing C-terminal proline Exploiting automated protein sequence analysis: in situ cleavage and resequencing Study of a rearrangement at the C-terminus of peptides during the collision activated dissociation experiments Analysis of the microheterogeneity of recombinant Schistosoma mansoni antigen rSmp28 reveals N-formyl-alanine as a new post-translational modification in Saccharomyces cerevisiae Characterization of a recombinant cytosolic domain of CD45 phosphatase Preparation and characterization of the site-directed E211Q mutant of yeast enolase 1 Direct mass spectrometric analyses for protein chemistry studies Structure characterization of membrane bound and surface adsorbed protein p58, A membrane-and microfilament-associated gag-like protein from ascites tumor cell microvilli, binds SrC SH3 domain through a poly-proline motif 517 Direct determination of immobilized protein concentration Conformational flexibility of the Gly-Gly dipeptide within protein structures A highly conserved N-terminal sequence for teleost vitellogenins Rapid fingerprinting of proteins with immobilized protease columns Protein, farnesyi transferase. Evidence for an electrophilic substitution mechanism. Disulphide mapping of prolamins Conformational comparison in the snake toxin family Mapping and characterization of proteins associated with morphological transformation in the SHE cell assay Solution structure of a cyclic c~-MSH analogue--initial NMR studies Identification of modified PTH-amino acids in protein sequence analysis Crystallization of a thermal stable trypsin inhibitor from Phaseolus lunatus Characterization of recombinant porin by mass spectrometry Insights into the structure and active-site architecture of IPP:DMAPP isomerase Counting cysteine and cystine residues in proteins and peptides using MALDI-TOF mass spectrometry Detection of di-PTH-cys for assignment of disulfide linkages Mutagenesis of the cyclic AMP receptor protein (CRP) of Escherichia coli MPSA Abstract Listing 519 Reed J. Harris H5. Evaluating protein modification sites observed by N-terminal sequence analysis Detection of low levels of hydroxylysine in non-collagenous proteins Chemical examination of the diphenylphosphoroisothiocyanatidate/trimethylsilnolate method for protein carboxyl-terminal degradations Aptase and molecular chaperone activities of the maize endoplasmic reticulum-resident Stress-70 protein Micropreparation for sequence analysis of peptides of proteins obtained from old archived polyacrylamide gels by in situ trypsin digestion Identification and characterization of metal protein complexes of crude cell extracts by mass-spectrometry The structure and function of plant leginsulin Probing the Mg 2+ binding site of/3-galactosidase (E. coli) In-gel reduction and alkylation of proteins and enzymatic digestion: application to the sequence analysis of proteins separated by 1-D gel electrophoresis Analysis of the extracellular organization of the u-chain of mouse acetylcholine receptor on live muscle cells Probing of the phylogeny of the CCK/gastrin family by antisera directed against the conserved common C-terminus Separation and characterization of proieins with two electrophoresis An investigation of the bathochromic shift phenomenon in biology Microsequencing of proteins of the endoplasmic reticulum (ER) membrane N-isopropyliodoacetamide in the reduction and alkylation of proteins: Use in microsequence analysis Determination of the primary structure of a new milk-clotting protease by microsequencing techniques A pH dependence on the association properties of model coiled coil peptides C-terminal protein sequence analysis using the Hewlett-Packard C-terminal protein sequencing system 520 MPSA Abstract Listing Escherichia coli DMAPP-tRNA transferase expression and characterization Alternative reverse-phase supports for direct electroelution and in situ digestion on the Hewlett-Packard sequencing column A convenient procedure for determination of carboxyterminal amino acids of proteins immobilized on the membrane Quantitative evaluation of different multiple alignment programs Degradative modifications of rhIL-lra (recombinant human interleukin receptor antagonist) Automated preparation of complex biological samples prior to protein or peptide sequence analysis A new PTH amino acid separation system to improve performance at the low picomole level for older sequencers Arrestin topographic study using selective chemical modifications and hydrogen-deuterium exchange analyzed by mass spectrometry The use of maldron mass spectrometry and amino acid sequence analysis in characterizing small amounts on N-blocked protein Characterization of antigenic determinants of P64K a Neisseria rneningitidis membrane protein A straightforward method for amino-terminal sequencing of gamma-carboxyglutamic acid using standard Edman chemistry Characterization of individual N-and O-linked glycosylation sites using Edman degradation Identification of proteins recorded in human 2-D gel protein databases by mass spectrometric peptide mapping Localization of transglutaminase reactive glutamine and lysine residues in Alzheimer amyloid/3A4 peptide: Cross-linking to extracellular matrix proteins and c~2M receptor Different forms of apolipoprotein A and alcohol dehydrogenase studied by electrospray LC/MS Automated two-dimensional electrophoresis-liquid chromatography for the micropreparative isolation of proteins Determination of cysteine by amino acid analysis: Online oxidation and hydrolysis MPSA Abstract Listing 521 $2. Identification of an oxidative modification of glutamine synthetase using electrospray LC/MS and Edman sequencing techniques Characterization of a binding site of the human immunodeficiency virus type 1 RNA for the nucleocapsid protein P7 Functional and protein analytical studies on the major outer membrane protein of Campylobacter jejuni Relaxase (TraI) of IncP o~ Plasmid RP4 catalyzes a site-specific cleaving-joining reaction of single-stranded DNA High-speed chromatographic separation of proteins and peptides: Application to rapid peptide mapping of in-gel digested proteins Identification of posttranslational modifications in bovine osteopontin: localization of 28 phosphorylation sites, 30-glycosylation sites and 2 transglutaminase reactive glutamines Integration of MALDI mass spectrometry with in situ protease digestions on high retention PVDF membranes to obtain internal sequences at maximum sensitivity A thiol mediated autolytic cleavage of homocysteinyl prolyl bond Characterization of purified yeast protein geranylgeranyltransferase type I using a continuous fluorescence assay The DNA-topoisomerase I. Analysis by limited proteolysis of domain structure and conformational changes Preparation and characterization of a conjugate of annexin V and the B-chain of urokinase A novel C-terminal sequencing method using perfluoroacyl anhydrides Protein docking in the absence of detailed molecular structures Acetyl-CoA-carboxylase: registration of active polymer complex Structure determination by NMR of the nuclease inhibitor protein IM8 Local tight packing of hydrophobic groups in beta-structure Identification of distinct proteinase K cleavage sites in two strains of scrapie PRP by chemical isolation of unique sequence by orthophtalaldehyde and by comparison of N-terminal sequence maps of PRP fragments 52Z MPSA Abstract Listing W2.

Posttranslational modification of lysine to hydroxylysine (Hyl) occurs in collagen and collagen-like domains of a few other proteins (2). Recently, we reported the unexpected presence of Hyl at solvent-accessible sites in some noncollagenous proteins, including Lys-277 of rtPA and Lys 46 of rCD4 (5) while performing routine tryptic map analysis. Trypsin cleaves peptide bonds on the C-terminal side of Lys or Arg residues, except in the presence of adjacent prolines or multiple acidic residues (1).

With the outbreak of social networking platforms such as Facebook, Twitter, Instagram, etc., there has been a trend in big data community researches to engage themselves in social network studies, as these networks have proven to be the excellent sources of hidden information patterns [1-3]. Extensive studies have been conducted on identifying characteristics and heuristics of these network types, by transforming these datasets into a network graph. Undoubtedly, network theory enabled researchers to address many real time complex problems such as product recommendations in e-commerce, friendship in social networks, computing the personalized PageRank between two nodes in quick time, web page ranking for efficient information retrieval, etc. Of these, one of the major concerns for network theory researchers is to find the optimal solutions to maximize node interactions and develop algorithms with minimal time complexity. For example, instead of sending news about an offer to all the actors in the network, a set of precisely chosen actors capable of efficiently spreading the information, can be identified as information propagators to ingest the offer message into the network. For this purpose, finding popular or most influential nodes in the network has proven to be helpful [4]. Such

Graphs and Networks have been the most researched topics with applications ranging from theoretical to practical fields, such as social media, genetics, and education. In many competitive environments, the most productive activities may be interacting with high-profile people, reading a much-cited article, or researching a wide range of fields such as the study on highly connected proteins. This thesis proposes two methods to deal with top-K nodes identification: centrality-based and activity-based methods for identifying top-K nodes. The first method is based on the topological structure of the network and uses the centrality measure called Katz Centrality; a path based ranking measure that calculates the local influence of a node as well as its global influence. It starts by filtering out the top-K nodes from a pool of network data using Katz centrality. By providing a means to filter out unnecessary nodes based on their centrality values, one can focus more on the most important ...

With data mining techniques one can easily disclose other's sensitive information or knowledge. So, preserving privacy for sensitive knowledge has become an important aspect. Privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. DSRRC (Decrease Support of R.H.S. item of Rule Clusters) algorithm is used to preserve privacy for sensitive association rules in database. This algorithm clusters the sensitive association rules based on certain criteria by modifying fewer transactions and hides many rules at a time. Moreover it provides privacy for sensitive rules at certain level while ensuring data quality.

The page rank (PR) algorithm was first used by Google to rank web pages. Because today's complex social networks need a fast and scalable algorithm for their searching needs, a great many new PR algorithms utilize improved versions of the initial PR mechanism. PR has found usage in a variety of applications, including such social networks as Twitter recommendation systems, and scientific data bases that search for the relative importance of publications, for example . This algorithm relies on the findings of the very first repository of web pages, called WebBase, which was an experimental prototype used as a proof of concept for PR . Other studies included research on URL's and web search engines by Chakrabati et al. ; as well as work on popularity-oriented PR and the prototype search engine by Bharat and Mihaila [7].

—In the era of Big Data , reduced models capable of reducing big data graph to estimate Personalized PageRank are limited. Personalized PageRank is a page rank calculation where random jumps are only allowed to a subset of start nodes. The resources of current process of calculation of Personalized PageRank is highly prohibitive, in this paper, we propose a novel fast accurate and less resource intensive algorithm to the Personalized PageRank problem. FAST Personalized PageRank is utilized to find the target node set. Using the mentioned target set, the algorithm gives an estimation of the closeness of any pair of nodes in the graph. As the time taken by the estimation of Personalized PageRank is directly proportional to the network size. In this study,a node reduction method is used to prune the graph. In this pruning model, most popular nodes also known as hubs are fond using Personalized Page Vector. To decrease the entropy and reduce the number of alternate paths to the target nodes, popular nodes are identified and flagged. The flagged nodes are, then, given a lower priority in the computation. This way the redundant path will being ignored in the computation process. After pruning the graph, estimation results achieve an improved time complexity. In our experiment we compare our result with the benchmark FAST Personalized PageRank approach. Our algorithm significantly reduces the computation time and out performed the benchmark FAST Personalized PageRank algorithm in highly dense graphs.

In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.

In the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.

In the past decade, dietary assessment has been one of the most popular topics of research in the food industry, which has resulted in developing several automatic or semi-automatic dietary assessment systems using visible spectrum images for food recognition. However, the main shortcoming of visible spectrum image-based systems is its inability to differentiate foods of similar color. Researchers have added additional features such as shape, size and texture to the color model to improve the overall accuracy. However, the shape and size features are rendered inefficient when recognizing food in the mixed or cooked form. The aim of this research is to show the capability of hyperspectral bands for accurate food recognition based on individual spectral bands. In this work we use a hyperspectral imaging system of 240 spectral bands with the wavelength range between 400 nm to 900 nm. The ReliefF and PCA methods select/extract less, but the most informative features which are important to learn Logistic Regression and Support Vector Machine (SVM) as binary and multiple classifiers, respectively. A total of 20 different food samples in various forms (uncut and cut), shapes, and sizes were used in this study. The prediction results indicate that the hyperspectral images have the advantages of being able to recognize different food items from a mixed form with similar color and similar food types with different colors. In our experiments the highest classification accuracy of 0.6874 with 20 different food samples is produced by SVM multiclassification of ReliefF data with the top 110 hyperspectral features. We are able to obtain approximately 0.90 accuracy, using binary classification on a specific subset of food samples.

The unknowing presence of pits in olives is an undesirable and even dangerous experience that affects overall quality of products contain olives or serving dishes in salad bars, airline meals, and cocktail lounges. Therefore, detection for removal of pits and pit fragments are very important for maintaining a high quality of products/services. The contributions of the paper are a) new hyperspectral data captured in a specific range of the wavelengths that are suitable for detecting olives with pits inside; b) developing a classification method, that is the CNN classifier that yields a high accuracy of pit detection. We have collected spectral responses of Manzanilla (black and green) and Gemlik (black) olives in the 400 nm-900 nm spectrum. Olives have been classified using 1D convolutional neural networks (1D-CNN) with 99.5% and 97.69% of classification accuracies of pitted and whole olives for training and test sets, respectively. Further boosting of the accuracy up to 98.27% on the test dataset has been attained by CNN with a dropout layer. As expected, the CNN attained a better performance than those of the Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LA) classifiers. INDEX TERMS Olive pit, hyperspectral imaging, CNN, food safety, non-destructive test, RGB. the Electrical and Computer Engineering Department, University of Nevada, Las Vegas. She has been working and publishing her research performed for numerous funded projects on the topics in image processing, computed tomography and compressed sensing, remote sensing and hyperspectral image analysis, computer assisted medical diagnostics, biomedical research, data compression, and coding, intelligent transportation systems, neutron and photon imaging for security and inspection, digital system design, and embedded DSP.

The unknowing presence of pits in olives is an undesirable and even dangerous experience that affects overall quality of products contain olives or serving dishes in salad bars, airline meals, and cocktail lounges. Therefore, detection for removal of pits and pit fragments are very important for maintaining a high quality of products/services. The contributions of the paper are a) new hyperspectral data captured in a specific range of the wavelengths that are suitable for detecting olives with pits inside; b) developing a classification method, that is the CNN classifier that yields a high accuracy of pit detection. We have collected spectral responses of Manzanilla (black and green) and Gemlik (black) olives in the 400 nm-900 nm spectrum. Olives have been classified using 1D convolutional neural networks (1D-CNN) with 99.5% and 97.69% of classification accuracies of pitted and whole olives for training and test sets, respectively. Further boosting of the accuracy up to 98.27% on the test dataset has been attained by CNN with a dropout layer. As expected, the CNN attained a better performance than those of the Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LA) classifiers. INDEX TERMS Olive pit, hyperspectral imaging, CNN, food safety, non-destructive test, RGB. the Electrical and Computer Engineering Department, University of Nevada, Las Vegas. She has been working and publishing her research performed for numerous funded projects on the topics in image processing, computed tomography and compressed sensing, remote sensing and hyperspectral image analysis, computer assisted medical diagnostics, biomedical research, data compression, and coding, intelligent transportation systems, neutron and photon imaging for security and inspection, digital system design, and embedded DSP.

In this paper, a fused global saliency-based multiscale multiresolution multistructure local binary pattern (salM 3 LBP) feature and local codebookless model (CLM) feature is proposed for high-resolution image scene classification. First, two different but complementary types of descriptors (pixel intensities and differences) are developed to extract global features, characterizing the dominant spatial features in multiple scale, multiple resolution, and multiple structure manner. The micro/macrostructure information and rotation invariance are guaranteed in the global feature extraction process. For dense local feature extraction, CLM is utilized to model local enrichment scale invariant feature transform descriptor and dimension reduction is conducted via joint low-rank learning with support vector machine. Finally, a fused feature representation between salM 3 LBP and CLM as the scene descriptor to train a kernel-based extreme learning machine for scene classification is presented. The proposed approach is extensively evaluated on three challenging benchmark scene datasets (the 21-class land-use scene, 19-class satellite scene, and a newly available 30-class aerial scene), and the experimental results show that the proposed approach leads to superior classification performance compared with the state-of-the-art classification methods.

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNN’s and CRNN’s). Interestingly, CRNN’s did not appear to perfo...

A signed graph G is a graph where each edge is assigned a + (positive edge) or a (negative edge). The signed degree of a vertex v in a signed graph, denoted by sdeg(v), is the number of positive edges incident to v subtracted by the number of negative edges incident to v. Finally, we say G realizes the set D if: D = {sdeg(v) : v ∈ V (G)}. The topic of signed degree sets and signed degree sequences has been studied from many directions. In this paper, we study properties needed for signed trees to have a given signed degree set. We start by proving that D is the signed degree set of a tree if and only if 1 ∈ D or −1 ∈ D. Further, for every valid set D, we find the smallest diameter that a tree must have to realize D. Lastly, for valid sets D with nonnegative numbers, we find the smallest order that a tree must have to realize D.