A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when ...the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.
Hao-Teng Chang 1; 2; 3 and Tatsuya Akutsu 4 and Oliver Ray 5 and Sorin Draghici 6 and Tun-Wen Pai 7; 8 1, Graduate Institute of Basic Medical Science, College of Medicine, China Medical University, ...Taichung, Taiwan 2, Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan 3, Department of Science Education, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China 4, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan 5, Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK 6, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA 7, Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan 8, Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan Received 27 December 2016; Accepted 27 December 2016; 28 February 2017 This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Due to mitogen-activated protein kinase providing greater oncolytic virus infection into cancer cells and limiting the replication of the virus, in order to provide the best dosage of MEK inhibitors and balance the positive and negative effects of the inhibitors, authors proposed an optimal control strategy regarding the inhibitor.
Background: Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. ...Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs). Methodology: A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs. Principal findings: We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined. Conclusion: Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.
The Genes journal retracts the article “Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test” ...
Due to high-density aquafarming in Taiwan, groupers are commonly infected with two different iridoviruses: Megalocytivirus (grouper iridovirus of Taiwan, TGIV) and Ranavirus (grouper iridovirus, ...GIV). Iridoviral diseases cause mass mortality, and surviving fish retain these pathogens, which can then be horizontally transferred. These viruses have therefore become a major challenge for grouper aquaculture. In this study, comparisons of the biological responses of groupers to infection with these two different iridoviruses were performed. A novel approach for transcriptomic analysis was proposed to enhance the discovery of differentially expressed genes and associated biological pathways. In this method, suitable and available reference species are selected from the NCBI taxonomy tree and the Ensembl and KEGG databases instead of either choosing only one model species or adopting the NCBI non-redundant dataset as references. Our results show that selection of multiple appropriate model species as references increases the efficiency and performance of analyses compared to those of traditional approaches. Using this method, 17 shared pathways and 5 specific pathways were found to be significantly differentially expressed following infection with the two iridoviruses, among which 11 pathways were additionally identified based on the proposed method of multiple reference species selection. Among the pathways responsive to infection with a specific iridovirus, the spliceosomal pathway (ko03040; p-value = 0.0011) was exclusively associated with TGIV infection, while the glycolysis/gluconeogenesis pathway (ko00010; p-value = 0.0032) was associated with GIV infection. These findings and designed corresponding biological experiments may facilitate a deeper understanding of the mechanisms by which both TGIV and GIV cause fatal infections, as well as the ways in which they induce different pathologies and symptoms. We believe that the proposed novel mechanism for de novo transcriptomic analysis provides superior and comprehensive functional annotations and that the resulting shared and specific pathways identified may help immunologists develop specific vaccines against various types of iridovirus in the near future.
•A novel approach for transcriptomic analysis through a multiple reference species selection mechanism.•The method was proposed to enhance the discovery of differentially expressed genes and associated significant biological pathways.•The real time PCR results were in accordance with RNA-seq results indicated iridovirus infection indeed reflect glucose metabolism in host.
Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential ...epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).
A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their ...complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.
We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen.
Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.
Personal health literacy is an important indicator for a national health status. Providing citizens with sufficient medical knowledge can help them understand their own health conditions. To achieve ...this goal, an integrated system is developed for evaluating the readability of healthcare documents by taking heart disease as a specific topic. The mechanism can be extended to other target diseases and languages by changing the corresponding word databank. The assessment system for examining document readability is based on patient-oriented aspects rather than professional aspects. Commonly used terms and professional medical terms extracted from a query document were utilized as fundamental elements for readability analysis, and the derived features included term frequency of professional medical terms, proportion of professional medical terms, and diversity indicator of medical terms. A five-fold cross validation is applied to measure the robustness of the proposed approach. The experimental results achieved a recall rate of 0.93, a precision rate of 0.97, and an accuracy rate of 0.95.
•Puzzle tile map is a static 2D representation for visualizing spatial-temporal data.•The mapping method of tiles considers not only geographic positions but also orientation of districts.•Puzzle ...connection points can represent not only the order of time-series data but also the change between the quantities of two adjacent pieces.•Generating tile maps is designed for non-contiguous geographical data specifically.
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Tile maps are a visualization tool to display geographic data without the accurate representation of geographic boundaries. Each region in a tile map is represented as a tile of identical shape and size. The tiles are fit in a regular grid at positions that approximate their geographic positions such that large regions do not dominate the map visualization, and information in small regions can be enhanced. In this study, the automatic generation of a tile map composed of puzzle tiles is proposed for spatial-temporal data visualization. A puzzle tile is an extension of a standard square tile. A sequence of connected and directional pieces in a puzzle tile is used to represent time-varying quantities in a geographic region. To generate a puzzle tile map, the proposed method includes algorithms for optimizing district-to-tile mapping according to not only geographic positions but also region orientations and for placing puzzle pieces in a tile. The proposed puzzle tile map can serve as a choropleth map in which the ordered pieces in a tile are shaded in proportion to the measurements of a statistical time variable, such as a time sequence of fertility rates, air pollution (PM2.5), or transfer of residential property, being displayed on a 2D map. Experimental demonstrations of various cases show that the proposed methods for district-to-tile mapping optimization and puzzle generation are feasible for automatic puzzle tile map generation. User studies show the capabilities of the puzzle tile map in terms of usability, readability, and comparability of spatial-temporal data visualization.
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