ORCID Profile
0000-0001-6718-7584
Current Organisations
Griffith University - Gold Coast Campus
,
Griffith University
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Image Processing | Radiology And Organ Imaging | Artificial Intelligence and Image Processing
Mental health | Nervous system and disorders | Diagnostic methods |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2005
Publisher: IGI Global
Date: 2020
Abstract: The electric network frequency (ENF) is recorded in the videos taken under the lights powered by grid and can be used for digital forensics. However, due to the lack of data caused by the low frame rate of the video, the ENF-based forensics methods always need a reference signal extracted from the grid, which limits the practical application of these methods. In this article, a new ENF-based time domain video forgery detection algorithm is proposed to solve the problem of data lack. The cubic spline interpolation is used to generate suitable data points of the ENF signal, and the detection sequence generated based on the correlation coefficient between data points in adjacent periods is used to catch the phase continuity interruption of the ENF signal and detect the exact position of forgery. The proposed algorithm can be used independently without any reference signals. The experimental results show that the proposed algorithm has good performance in detecting forgery videos with varying degrees of deletion, duplication and insertion of frames.
Publisher: Bentham Science Publishers Ltd.
Date: 03-2012
Publisher: Springer Science and Business Media LLC
Date: 31-03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2005
Publisher: Bentham Science Publishers Ltd.
Date: 02-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: IEEE
Date: 07-2015
Publisher: American Scientific Publishers
Date: 09-2013
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-026-4.CH121
Abstract: With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.
Publisher: Royal Society of Chemistry (RSC)
Date: 2023
DOI: 10.1039/D3RA00205E
Abstract: The impacts of six different types of vacancies on the electronic and mechanical properties of MX2 (M = Mo, W and X = S, Se) monolayers have been theoretically revealed.
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 18-07-2022
Publisher: Institution of Engineering and Technology (IET)
Date: 28-09-1995
DOI: 10.1049/EL:19951205
Publisher: Springer Science and Business Media LLC
Date: 24-04-2007
Publisher: Springer Science and Business Media LLC
Date: 11-2004
Publisher: Springer Netherlands
Date: 2008
Publisher: Elsevier BV
Date: 10-2013
DOI: 10.1016/J.COMPMEDIMAG.2013.06.001
Abstract: In neuroimage analysis, the automatic identification of symmetry plane has various applications. Despite the considerable amount of research, this remains an open problem. Most of the existing work based on image intensity is either sensitive to strong noise or not applicable to different imaging modalities. This paper presents a novel approach for identifying symmetry plane in three-dimensional brain magnetic resonance (MR) images based on the concepts of fractal dimension and lacunarity analysis which characterizes the complexity and homogeneity of an object. Experimental results, evaluation, and comparison with two other state-of-the-art techniques show the accuracy and the robustness of our method.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 03-2014
Publisher: IEEE
Date: 09-2015
Publisher: AME Publishing Company
Date: 09-2023
DOI: 10.21037/QIMS-23-98
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
DOI: 10.1109/TCBB.2011.53
Publisher: ACM
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2008
Publisher: IGI Global
Date: 31-07-2015
Publisher: IEEE
Date: 12-2019
Publisher: Inderscience Publishers
Date: 2005
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 08-2014
Publisher: Springer International Publishing
Date: 2014
Publisher: Wiley
Date: 05-02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11875741_4
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2004
Abstract: Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method.
Publisher: IEEE
Date: 08-2013
Publisher: Wiley
Date: 23-12-2010
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 2004
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-753-9.CH014
Abstract: Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.
Publisher: IEEE
Date: 2004
Publisher: MDPI AG
Date: 16-05-2022
DOI: 10.3390/NU14102086
Abstract: Huntington’s disease (HD) is a genetic, neurodegenerative illness that onsets in late adulthood as a series of progressive and terminal cognitive, motor, and psychiatric deficits. The disease is caused by a polyQ mutation in the Huntingtin gene (HTT), producing a polyglutamine expansion in the Huntingtin protein (HTT). HTT interacts with phospholipids in vitro however, its interactions are changed when the protein is mutated in HD. Emerging evidence suggests that the susceptibility of brain regions to pathological stimuli is influenced by lipid composition. This study aimed to identify where and how phospholipids are changed in human HD brain tissue. Phospholipids were extracted using a modified MTBE method from the post-mortem brain of 13 advanced-stage HD patients and 13 age- and sex-matched controls. Targeted precursor ion scanning mass spectrometry was used to detect phospholipid species. In the white cortex of HD patients, there was a significantly lower abundance of phosphatidylcholine (PC) and phosphatidylserine (PS), but no difference in phosphatidylethanolamine (PE). In HD putamen, ester-linked 22:6 was lower in all phospholipid classes promoting a decrease in the relative abundance of ester polyunsaturated fatty acids in PE. No differences in phospholipid composition were identified in the caudate, grey cortex or cerebellum. Ether-linked PE fatty acids appear protected in the HD brain, as no changes were identified. The nature of phospholipid alterations in the HD brain is dependent on the lipid (subclass, species, and bond type) and the location.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2003
Publisher: Elsevier BV
Date: 11-2005
Publisher: Elsevier BV
Date: 09-2012
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 12-2019
Publisher: IEEE
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 08-2020
Publisher: Springer Science and Business Media LLC
Date: 19-09-2017
Publisher: IEEE
Date: 07-2011
Publisher: Springer Science and Business Media LLC
Date: 16-11-2018
Publisher: IEEE
Date: 06-12-2022
Publisher: Institution of Engineering and Technology (IET)
Date: 2000
DOI: 10.1049/EL:20000931
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2002
Publisher: Elsevier BV
Date: 03-2008
DOI: 10.1016/J.JTBI.2007.11.030
Abstract: Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.
Publisher: Elsevier BV
Date: 04-2020
Publisher: IEEE
Date: 10-2020
Publisher: Bentham Science Publishers Ltd.
Date: 08-2011
DOI: 10.2174/187221511796392097
Abstract: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits a consistent pattern over a subset of conditions. Although used extensively in gene expression data analysis, conventional clustering algorithms that consider the entire row or column in an expression matrix can therefore fail to detect useful patterns in the data. Recently, biclustering has been proposed as a powerful computational tool to detect subsets of genes that exhibit consistent pattern over subsets of conditions. In this article, we review several recent patents in bicluster analysis, and in particular, highlight a recent patent from our group about a novel geometric-based biclustering method that handles the class of bicluster patterns with linear coherent variation across the row and/or column dimension. This class of bicluster patterns is of particular importance since it subsumes all constant, additive, and multiplicative bicluster patterns normally used in gene expression data analysis.
Publisher: IEEE
Date: 12-2007
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 26-07-2011
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2018
Publisher: IEEE
Date: 10-2009
Publisher: American Scientific Publishers
Date: 06-2014
Publisher: Elsevier BV
Date: 11-2018
Publisher: IEEE
Date: 09-10-2022
Publisher: IEEE
Date: 04-12-2022
Publisher: Springer Science and Business Media LLC
Date: 07-2023
DOI: 10.1186/S40854-023-00486-2
Abstract: The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US) REIT market. The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method. To forecast the return on the US REIT index, this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity (GARCH) model. In this test, the training s les, testing s les, and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach. Corresponding experiments were performed using the GARCH model, and the accuracies of these two approaches were compared. Compared with GARCH, GMDH’s accuracy is much higher, indicating that the machine learning approach can provide a highly accurate prediction of REIT prices. The size of the training s les and the kernel functions in the GMDH model affect the accuracy of the prediction results. In particular, the kernel function has a significant impact on prediction accuracy. The linear and linear covariance kernel functions are simple to train and yield accurate predictions, whereas the quadratic function is difficult to train. Even with small training s les, GMDH can outperform GARCH in prediction accuracy. Although GMDH shows good performance in predicting the US REIT return, it is still a black-box model, and the algorithm is difficult for financial analysts to develop and customize. The data used in this study come from the US REIT market, which is the world’s largest and most liquid market. This research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns. Hence, investors can use the machine learning approach to make more accurate predictions of the target REITs’ returns and thus better investment decisions. Future investors and researchers may use GMDH to forecast the performance of REITs in other markets. This is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance. For ex le, this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network. It also includes short-term daily prediction returns that were not previously considered, making it a valuable reference for financial industry analysts.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 1997
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 07-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 1997
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 10-2020
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 02-2020
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 2004
Publisher: IGI Global
Date: 2009
Publisher: American Society for Microbiology
Date: 12-2000
DOI: 10.1128/AAC.44.12.3337-3343.2000
Abstract: Mutant prevention concentration (MPC) has been proposed as a new measure of antibiotic potency by which the ability to restrict selection of resistant mutants is evaluated. To determine whether MPC provides potency information unavailable from the more customary measurement of the MIC, 18 fluoroquinolones were examined for their ability to block the growth of Mycobacterium smegmatis and to select resistant mutants from wild-type populations. Both MPC and MIC were affected by changes in the moiety at the fluoroquinolone C-8 position and in alkyl groups attached to the C-7 piperazinyl ring. When eight resistant mutants, altered in the gyrase A protein, were tested with fluoroquinolones having either a methoxy or a hydrogen at the C-8 position, the MIC for the most resistant mutant correlated better with the MPC than did the MIC for wild-type cells. For C-8-fluorine derivatives, which were generally less active than the C-8-methoxy compounds but which were more active than C-8-hydrogen derivatives, the MICs for both the mutant and the wild type correlated well with the MPCs. Thus, measurement of the MICs for wild-type cells can reflect the ability of a quinolone to restrict the selection of resistance, but often it does not. With the present series of compounds, the most potent contained a C-8-methoxy and a small group attached to the C-7 ring.
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 18-07-2022
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 05-2003
Publisher: Institution of Engineering and Technology (IET)
Date: 2000
Publisher: Elsevier BV
Date: 05-2023
Publisher: IEEE
Date: 2003
Publisher: AIP
Date: 2007
DOI: 10.1063/1.2816637
Publisher: ACM
Date: 14-12-2021
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-59904-849-9.CH045
Abstract: Important insights into gene function can be gained by gene expression analysis. For ex le, some genes are turned on (expressed) or turned off (repressed) when there is a change in external conditions or stimuli. The expression of one gene is often regulated by the expression of other genes. A detail analysis of gene expression information will provide an understanding about the inter-networking of different genes and their functional roles. DNA microarray technology allows massively parallel, high throughput genome-wide profiling of gene expression in a single hybridization experiment [Lockhart & Winzeler, 2000]. It has been widely used in numerous studies over a broad range of biological disciplines, such as cancer classification (Armstrong et al., 2002), identification of genes relevant to a certain diagnosis or therapy (Muro et al., 2003), investigation of the mechanism of drug action and cancer prognosis (Kim et al., 2000 Duggan et al., 1999). Due to the large number of genes involved in microarray experiment study and the complexity of biological networks, clustering is an important exploratory technique for gene expression data analysis. In this article, we present a succinct review of some of our work in cluster analysis of gene expression data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 10-2006
Publisher: Wiley
Date: 02-02-2016
DOI: 10.1002/JCC.24314
Abstract: Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: © 2016 Wiley Periodicals, Inc.
Publisher: Oxford University Press (OUP)
Date: 29-01-2004
DOI: 10.1093/BIOINFORMATICS/BTG479
Abstract: Motivation: Microarray time-series data provides us a possible means for identification of transcriptional regulation relationships among genes. Currently, the most commonly used method in determining whether or not two genes have a potential regulatory relationship is to measure their expressional similarity using Pearson's correlation coefficient. Although this traditional correlation method has been successfully applied to find functionally correlated genes, it does have many limitations. In the hope of overcoming such circumstances and getting more insights into the transcriptional regulatory issue, we propose an autoregressive (AR)-based technique for detection of potential regulated gene pairs from time-series microarray measurements. Results: We use the well-known AR modeling technique to characterize temporal gene expression data from the Spellman's α-synchronized yeast cell-cycle experiment. In this method, time-series expression profiles are decomposed into spectral components and correlations between profiles are then computed in a component-wise sense. We show how these component-wise correlations reveal possible regulatory relationships. Our technique is applied on known transcriptional regulations and is able to identify many of those missed by the traditional correlation method.
Publisher: Wiley
Date: 08-2005
Abstract: One of the major challenges facing protein analysis is the dynamic range of protein expression within massively complex s les (Corthals, G. L. et al.., Electrophoresis 2000, 21, 1104-1115). In plasma this difference is as great as ten orders of magnitude, and this is currently beyond the range of detection achievable by any of the analytical techniques. Plasma has the additional challenge of having a few highly abundant proteins, such as albumin, which mask the detection of lower abundance and biologically significant proteins. The use of the Gradiflow BF400 as a fractionation tool to deplete highly abundant albumin from human plasma is reported here. A sequential three-step protocol was performed on five plasma s les as part of the International Plasma Proteome Project organised by the HUPO four containing different anticoagulants: EDTA, citrate, heparin and a control s le (NIBSC) and a serum s le. Plasma from an alternate source also underwent fractionation and served as an in-house control. Time modulation between 1 and 7 h was observed for the depletion of albumin from these s les. Following albumin depletion, each fraction was trypsin-digested and the peptides were fractionated further using a 2-D LC-MS/MS. Differences in the total number of proteins identified for each s le were also noted.
Publisher: Springer Science and Business Media LLC
Date: 16-08-2008
Publisher: Elsevier BV
Date: 11-2022
Publisher: IEEE
Date: 2003
Publisher: Wiley
Date: 16-10-2021
DOI: 10.1002/CPE.6667
Publisher: Oxford University Press (OUP)
Date: 06-03-2006
DOI: 10.1093/NAR/GKL047
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 08-2005
Publisher: IEEE
Date: 10-2021
Publisher: Institution of Engineering and Technology (IET)
Date: 2000
Publisher: IEEE
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2008
Publisher: IGI Global
Date: 31-07-2015
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 09-2015
Publisher: Springer Science and Business Media LLC
Date: 05-07-2014
Publisher: IEEE
Date: 11-2015
Publisher: International Press of Boston
Date: 2010
Publisher: Elsevier BV
Date: 2016
Publisher: IEEE
Date: 09-2019
Publisher: Oxford University Press (OUP)
Date: 14-12-2010
DOI: 10.1093/BIB/BBQ080
Abstract: Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.
Publisher: IEEE
Date: 08-2012
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 06-2019
Publisher: Inderscience Publishers
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2003
Publisher: IEEE
Date: 09-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 1997
DOI: 10.1049/EL:19971338
Publisher: Elsevier BV
Date: 04-2016
Publisher: Elsevier BV
Date: 12-2023
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: American Chemical Society (ACS)
Date: 22-09-2016
Abstract: Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at erver/SPRINT-CBH .
Publisher: Elsevier BV
Date: 06-2019
Publisher: Edward Elgar Publishing
Date: 26-02-2010
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 2005
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Abstract: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only [1]. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis. We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm. We have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.
Publisher: IEEE
Date: 2007
Publisher: Springer International Publishing
Date: 2014
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2023
Abstract: Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the litude of malicious perturbations along certain prescribed directions to cause DoS, we propose a flexible model poisoning attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks.
Publisher: Springer-Verlag
Date: 2005
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2014
Publisher: Informa UK Limited
Date: 27-05-2015
Publisher: Public Library of Science (PLoS)
Date: 03-08-2012
Publisher: Oxford University Press (OUP)
Date: 26-09-2018
DOI: 10.1093/BIOINFORMATICS/BTX614
Abstract: Protein–peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processes, functional mechanisms, and drug discovery. Protein–peptide interactions can be analyzed by studying the structures of protein–peptide complexes. However, only a small portion has known complex structures and experimental determination of protein–peptide interaction is costly and inefficient. Thus, predicting peptide-binding sites computationally will be useful to improve efficiency and cost effectiveness of experimental studies. Here, we established a machine learning method called SPRINT-Str (Structure-based prediction of protein–Peptide Residue-level Interaction) to use structural information for predicting protein–peptide binding residues. These predicted binding residues are then employed to infer the peptide-binding site by a clustering algorithm. SPRINT-Str achieves robust and consistent results for prediction of protein–peptide binding regions in terms of residues and sites. Matthews’ Correlation Coefficient (MCC) for 10-fold cross validation and independent test set are 0.27 and 0.293, respectively, as well as 0.775 and 0.782, respectively for area under the curve. The prediction outperforms other state-of-the-art methods, including our previously developed sequence-based method. A further spatial neighbor clustering of predicted binding residues leads to prediction of binding sites at 20–116% higher coverage than the next best method at all precision levels in the test set. The application of SPRINT-Str to protein binding with DNA, RNA and carbohydrate confirms the method‘s capability of separating peptide-binding sites from other functional sites. More importantly, similar performance in prediction of binding residues and sites is obtained when experimentally determined structures are replaced by unbound structures or quality model structures built from homologs, indicating its wide applicability. erver/SPRINT-Str Supplementary data are available at Bioinformatics online.
Publisher: Institution of Engineering and Technology (IET)
Date: 22-06-1995
DOI: 10.1049/EL:19950749
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: Cambridge University Press (CUP)
Date: 07-2011
DOI: 10.1177/103530461102200207
Abstract: Since 1993 and the removal of the separate award system for the Australian State of Victoria, many Victorian workers have been on five minimum conditions and on pay levels well below that of employees in other States. Despite attempts to rectify the situation (with Victorian common rule awards), issues of coverage and employer compliance remained. The implementation of WorkChoices legislation in 2006 posed a further challenge to Victorian low-paid workers. Our research found that the impact of WorkChoices on the Victorian low-paid has been largely insidious, surfacing primarily as an increased wage-effort ratio, with people working more unpaid hours and at an increased pace. The implications of this are that these hidden effects are more likely to linger, even with the replacement of WorkChoices with the Fair Work Act , 2009. Furthermore, it appears that employer compliance with minimum conditions requires more adequate enforcement by the Federal Government.
Publisher: Elsevier BV
Date: 04-2009
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Abstract: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis.
Publisher: Springer International Publishing
Date: 2019
Publisher: Edward Elgar Publishing
Date: 26-02-2010
Publisher: Edward Elgar Publishing
Date: 26-02-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 12-2003
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2010
Publisher: Springer International Publishing
Date: 09-07-2017
Publisher: Elsevier BV
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2004
Publisher: American Chemical Society (ACS)
Date: 25-11-2014
DOI: 10.1021/CI500673Z
Abstract: Kinesin is a walking motor protein that shuttles cellular cargoes along microtubules (MTs). This protein is considered as an information processor capable of sensing cellular inputs and transforming them into mechanical steps. Here, we propose a computational model to describe the mechanochemical kinetics underlying forward and backward stepping behavior of kinesin motor as a digital circuit designed based on an adenosine triphosphate (ATP)-driven finite state machine. Kinetic analysis suggests that the backward stepping of kinesin is mainly driven by ATP hydrolysis, whereas ATP synthesis rises the duration of this stepping. It is shown that kinesin pausing due to waiting for ATP binding at limiting ATP concentration ([ATP]) and low backward loads could be longer than that caused by low rate of ATP synthesis under high backward loads. These findings indicate that the pausing duration of kinesin in MT-bound (M·K) kinetic state is affected by [ATP], which in turn affects its velocity at fixed loads. We show that the proposed computational model accurately simulates the forward and backward stepping behavior of kinesin motor under different [ATP] and loads.
Publisher: AIP
Date: 2007
DOI: 10.1063/1.2816619
Publisher: ACM
Date: 13-07-2019
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 07-2019
Publisher: IEEE
Date: 11-2017
Publisher: IEEE Computer. Soc
Date: 2001
Publisher: Cambridge University Press (CUP)
Date: 02-2012
DOI: 10.1177/103530461202300109
Abstract: Neoliberal policies of industrial relations decentralisation and privatisation have transformed the economic landscape of Australia in the last 20 years. The primary objective of these policies has been to enhance wealth and prosperity by improving productivity and flexibility of the workforce and competition and accountability in the market. Yet the evidence suggests that precarious workers are not benefiting from this increased prosperity, indeed they suffer by comparison with all other workers. Cleaners are a subset of precarious workers who have been hard hit by the dual impacts of labour market decentralisation and privatisation. This study finds quantitative evidence of an increasing gap in earnings between cleaners and other workers in Australia since the onset of workplace relations decentralisation and the proliferation of privatisation in the mid 1990s. We locate our argument in recent debates about the nature of variegated neoliberalism, the emergence of the networked economy, and the implications of these developments for the nature of work and employment.
Publisher: Elsevier BV
Date: 12-2022
Publisher: American Chemical Society (ACS)
Date: 27-03-2018
Publisher: AIP
Date: 2007
DOI: 10.1063/1.2816614
Publisher: American Physical Society (APS)
Date: 12-04-2007
Publisher: Elsevier BV
Date: 12-2007
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 11-2021
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 12-2013
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 12-2002
Publisher: Elsevier BV
Date: 08-2003
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-026-4.CH284
Abstract: Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For ex le, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 11-2016
Publisher: Wiley
Date: 31-03-2021
DOI: 10.1002/CPE.6286
Abstract: Genetic algorithms are a practical approach for finding near‐optimal solutions for nondeterministic polynomial‐hard problems. In this work we exploit the parallel processing capability of graphics processing units and Nvidia's CUDA programming platform to accelerate the island model genetic algorithm by modifying the evolutionary operations to fit the hardware architecture and have successfully achieved significant computational speedups.
Publisher: IEEE
Date: 2004
Publisher: Cambridge University Press (CUP)
Date: 14-01-2005
DOI: 10.1017/S0022029904000561
Abstract: The rheological properties of the CMT gel were analysed. Data are presented to demonstrate that the gel is a non-homogenous, visco-elastic, non-Newtonian fluid with rheopectic, and rheodestructive behaviour. The fundamental chemistry of the CMT is reviewed and a modified theory of gel formation is presented. The implications of the rheological properties and modified theory of gel formation for an automatic sensor are discussed.
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 09-2014
DOI: 10.1016/J.SLEEP.2014.04.011
Abstract: Cerebral structural changes related to obstructive sleep apnoea (OSA) have been reported in adult OSA patients however, similar data and their associations with neurocognitive dysfunction are scarce in childhood OSA. To compare neurocognitive function, regional grey matter density and cerebral volume in children with and without OSA. Fifty OSA cases and 27 normal controls underwent a panel of neurocognitive tests. High resolution 3-dimensional magnetic resonance images of the brain were obtained from 23 OSA cases and 15 gender and age matched controls. Total cerebral volume and regional grey matter density were analyzed using voxel-based morphometry technique and compared between the two groups. In iduals with an obstructive apnoea hypopnoea index (OAHI) > 5 were defined as having moderate-to-severe OSA. Children with OSA showed significantly reduced attention and visual-fine motor coordination scores compared with controls. Grey matter volume deficit was observed in prefrontal and temporal regions of cases with moderate-to-severe OSA only. Significant negative correlations were found between the visual-fine motor coordination score and the ratio of grey matter volume over total brain volume. Children with OSA had impaired attention and visual-fine motor coordination. Regional grey matter reduction was evident in children with more severe OSA.
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 12-2016
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 2001
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Cambridge University Press (CUP)
Date: 03-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IOP Publishing
Date: 02-06-2023
Abstract: Halogenated MXenes have been experimentally demonstrated to be promising two-dimensional materials for a wide range of applicability. However, their physicochemical properties are largely unknown at the atomic level. In this study, we applied density functional theory (DFT) to theoretically investigate the halogenation effects on the structural, electronic, and mechanical characteristics of Ti 3 C 2 , which is the most studied MXene material. Three atomic configurations with different adsorption sites for four kinds of halogen terminals (fluorine, chlorine, bromine, and iodine) were considered. Our DFT results reveal that the adsorption site of terminals has a considerable impact on the properties of MXene. This can be ascribed to the different coordination environments of the surface Ti atoms, which change d-orbital splitting configurations of surface Ti atoms and the stabilities of systems. According to the density of states, crystal orbital Hamilton population, and charge analyses, all the considered halogenated MXenes are metallic. The electronic and mechanical properties of the halogenated MXenes are strongly dependent on the electronegativity of the halogen terminal group. The Ti–F bond has more ionic characteristics, which causes Ti 3 C 2 F 2 mechanically behave in a more ductile manner. Our DFT results, therefore, suggest that the physicochemical properties of MXenes can be tuned for practical applications by selecting specific halogen terminal groups.
Publisher: Wiley
Date: 16-05-2022
DOI: 10.1002/CB.2055
Abstract: Adopting the conservation of resources theory, this research explores the joint influence of multiple factors affecting the choice of housing, an instance of a high involvement product. We develop an innovative Machine Learning approach to identify the most significant set of factors affecting consumers' housing choices. It was found that housing choices were primarily bound by energy resources and secondarily by other personal, conditional, and object resources. The study identified 25 key factors, many of which previous studies have not explored. The new factors include government payments, vehicle possession, time living with children, marriage duration, current location duration, and health conditions. The results suggest that joint effects of factors are more prominent than in idual effects in influencing the choice of high‐involvement products. Further, the study suggests that ML methods are more robust than traditional methods and can be applied to analyse other types of high involvement products. The findings can assist real estate investors, policymakers and other stakeholders to understand sophisticated market behaviours to develop better and tailored housing strategies.
Publisher: SAGE Publications
Date: 06-11-2009
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 09-10-2022
Publisher: Wiley
Date: 03-2003
Abstract: Analysis of complex protein s les by two-dimensional electrophoresis (2-DE) is often more difficult in the presence of a few predominant proteins. In plasma, proteins such as albumin mask proteins of lower abundance, as well as significantly limiting the amount of protein that can be loaded onto the immobilized pH gradient strip. In this paper the Gradiflow, a preparative electrophoresis system, has been used to deplete human plasma of the highly abundant protein albumin under native and denatured conditions. A three step protocol incorporating a charge separation to collect proteins with an isoelectric point greater than albumin and two size separations to isolate proteins larger and smaller than albumin, was used. When the albumin depleted fractions were analysed on pH 3-10 2-DE gels, proteins that were masked by albumin were revealed and proteins not seen in the unfractionated plasma s le were visualised. Matrix-assisted laser desorption/ionisation-time of flight mass spectrometry analysis confirmed the identification of the protein that lies beneath albumin to be C4B-binding protein alpha chain. The liquid fractions from the Gradiflow separations were also analysed by liquid chromatography-tandem mass spectrometry to confirm the proteins were separated according to their size and charge mobility in an electric field.
Publisher: Oxford University Press (OUP)
Date: 09-10-2014
DOI: 10.1093/BIOINFORMATICS/BTU664
Abstract: Motivation: Cellular interactions of kinesin-1, an adenosine triphosphate (ATP)-driven motor protein capable of undergoing multiple steps on a microtubule (MT), affect its mechanical processivity, the number of steps taken per encounter with MT. Even though the processivity of kinesin has been widely studied, a detailed study of the factors that affect the stepping of the motor along MT is still lacking. Results: We model the cellular interactions of kinesin as a probabilistic timed automaton and use the model to simulate the mechanical processivity of the motor. Theoretical analysis suggests: (i) backward stepping tends to be powered by ATP hydrolysis, rather than ATP synthesis, (ii) backward stepping powered by ATP synthesis is more likely to happen with limiting ATP concentration ([ATP]) at high loads and (iii) with increasing load the frequency of backward stepping powered by ATP hydrolysis at high [ATP] is greater than that powered by ATP synthesis at limiting [ATP]. Together, the higher frequency of backward stepping powered by ATP hydrolysis than by ATP synthesis is found to be a reason for the more dramatic falling of kinesin processivity with rising load at high [ATP] compared with that at low [ATP]. Simulation results further show that the processivity of kinesin can be determined by the number of ATP hydrolysis and synthesis kinetic cycles taken by the motor before becoming inactive. It is also found that the duration of a backward stepping cycle at high loads is more likely to be less than that of a forward stepping cycle. Contact: h.r.khataee@griffithuni.edu.au or a.liew@griffith.edu.au .
Publisher: Elsevier BV
Date: 2016
Publisher: Wiley
Date: 14-05-2018
DOI: 10.1002/JCC.25353
Abstract: Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half-sphere exposure provides additional improvement to the prediction performance. SPRINT-Mal trained on mouse data yields robust performance for 10-fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews' Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT-Mal achieved comparable performance when testing on H. sapiens proteins without species-specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT-Mal is freely available as an online server at: erver/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc.
Publisher: Oxford University Press (OUP)
Date: 30-11-2013
DOI: 10.1093/BIOINFORMATICS/BTT698
Abstract: Motivation: Kinesin is a smart motor protein that steps processively forward and backward along microtubules (MTs). The mechanical kinetics of kinesin affecting its stepping behavior is not fully understood. Here, we propose a mathematical model to study the mechanical kinetics of forward and backward stepping of kinesin motor based on the four-state discrete stochastic model of the motor. Results: Results show that the probabilities of forward and backward stepping can be modeled using the mean probabilities of forward and backward kinetic transitions, respectively. We show that the backward stepping of kinesin motor starts when the probability of adenosine diphosphate (ADP) binding to the motor is much higher than that of adenosine triphosphate (ATP) binding. Furthermore, our results indicate that the backward stepping is related to both ATP hydrolysis and synthesis with rate limiting factor being ATP synthesis. Low rate of ATP synthesis under high backward loads above 10 pN is also suggested as a reason for the detachment of kinesin motor from MT in the kinetic state MTċKinesinċADPċPi. Availability and implementation: The code for this work is written in Visual C# and is available by request from the authors. Contact: a.liew@griffith.edu.au
Publisher: The Royal Society
Date: 08-10-2014
Abstract: Various time-series decomposition techniques, including wavelet transform, singular spectrum analysis, empirical mode decomposition and independent component analysis, have been developed for non-linear dynamic system analysis. In this paper, we describe a symplectic geometry spectrum analysis (SGSA) method to decompose a time series into a set of independent additive components. SGSA is performed in four steps: embedding, symplectic QR decomposition, grouping and diagonal averaging. The obtained components can be used for de-noising, prediction, control and synchronization. We demonstrate the effectiveness of SGSA in reconstructing and predicting two noisy benchmark nonlinear dynamic systems: the Lorenz and Mackey-Glass attractors. Ex les of prediction of a decadal average sunspot number time series and a mechanomyographic signal recorded from human skeletal muscle further demonstrate the applicability of the SGSA method in real-life applications.
Publisher: Springer-Verlag
Date: 2005
Publisher: IEEE
Date: 2000
Publisher: Elsevier BV
Date: 2003
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 07-2015
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11427469_113
Publisher: Bentham Science Publishers Ltd.
Date: 11-2013
Publisher: PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.
Date: 2007
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 07-2013
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 11-2017
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-026-4.CH388
Abstract: In recent years, there is a growing interest in using visual information for automatic lipreading (Kaynak, Zhi, Cheok, Sengupta, Jian, & Chung, 2004) and visual speaker authentication (Mok, Lau, Leung, Wang, & Yan, 2004). It has been shown that visual cues, such as lip shape and lip movement, would greatly improve the performance of these systems. Various techniques have been proposed in the past decades to extract speech/speaker relevant information from lip image sequences. One approach is to extract the lip contour from lip image sequences. This generally involves lip region segmentation and lip contour modeling (Liew, Leung, & Lau, 2002 Wang, Lau, Leung, & ALiew, 2004), and the performance of the visual speech recognition and visual speaker authentication systems depends much on the accuracy and efficiency of these two procedures. Lip region segmentation aims to label the pixels in the lip image into lip and non-lip. The accuracy and robustness of the lip segmentation process is of vital importance for subsequent lip extraction. However, large variations caused by different speakers, lighting condition, or make-ups make the task difficult. The low color contrast between lip and facial skin, and the presence of facial hair, further complicate the problem. Given a correctly segmented lip region, the lip extraction process then involves fitting a lip model to the lip region. A good lip model should be compact, that is, with a small number of parameters, and should adequately represent most valid lip shapes while rejecting most invalid shapes. As most lip extraction techniques involve iterative model fitting, the efficiency of the optimization process is another important issue.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Daedalus Enterprises
Date: 31-07-2018
Abstract: Common among patients with COPD is declining health-related quality of life (HRQOL). Although results of research identified some factors associated with HRQOL, resilience factors are yet to be fully investigated. This study examined resilience and demographic factors associated with HRQOL. Participants >40 y old were recruited from community health programs and hospitals in South East Queensland. Self-administered questionnaires were used to query subjects' HRQOL and levels of resilience. A decision tree examined the factors important to HRQOL in 159 subjects with COPD. Factors of importance in the HRQOL of subjects with COPD were found in 3 domains of the St George Respiratory Questionnaire. Of importance on the breathlessness domain was marital status, defensive coping, coping, number of comorbidities, relationships, decision-making, self-esteem, self-efficacy, and professional support of health and well-being. Of the symptoms domain, self-efficacy, recruitment location, anxiety/depression, decision-making, self-esteem, coping, relationships, professional support of health and well-being, and risks were important. The cough domain found recruitment location, anxiety/depression, professional support of health and well-being, coping, and defensive coping to be important for subjects' HRQOL. Resilience and confounding factors were of importance in the HRQOL of subjects with COPD. Thus, consultation with a medical professional, especially at discharge, who identifies, encourages, and approves of the patient's disease management abilities will enhance both resilience and HRQOL.
Publisher: IEEE
Date: 11-2016
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-186-5.CH005
Abstract: As the first step of many visual speech recognition and visual speaker authentication systems, robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down when dealing with lip images with complex and inhomogeneous background region such as mustaches and beards. In order to solve this problem, a Multi-class, Shapeguided FCM (MS-FCM) clustering algorithm is proposed in this chapter. In the proposed approach, one cluster is set for the lip region and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. With the spatial distribution of the lip cluster, a spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 04-2020
Publisher: World Scientific Publishing Company
Date: 10-2005
Publisher: Elsevier BV
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IGI Global
Date: 31-07-2015
Publisher: ACM
Date: 25-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2005
Publisher: Elsevier BV
Date: 02-2019
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer International Publishing
Date: 2020
Publisher: SAGE Publications
Date: 02-2013
Publisher: Frontiers Media SA
Date: 16-12-2021
DOI: 10.3389/FNINS.2021.778767
Abstract: Cerebral microbleeds (CMB) are increasingly present with aging and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI, such as susceptibility imaging, but are challenging to train because of the limited availability of ground truth and many confounding imaging features, such as vessels or infarcts. In this study, we present a novel generative adversarial network (GAN) that has been trained to generate three-dimensional lesions, conditioned by volume and location. This allows one to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state-of-the-art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high ersity and without needing laboriously marked ground truth.
Publisher: IEEE
Date: 2003
Publisher: IEEE Comput. Soc
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Informa UK Limited
Date: 09-2012
Publisher: IEEE
Date: 11-2017
Publisher: American Physical Society (APS)
Date: 27-06-2003
Publisher: Elsevier BV
Date: 09-2018
Publisher: IEEE
Date: 1995
Publisher: Wiley
Date: 07-11-2018
Abstract: The ability to predict the cellular dynamics of intracellular transport has enormous potential to impact human health. A key transporter is kinesin-1, an ATP-driven molecular motor that shuttles cellular cargos along microtubules (MTs). The dynamics of kinesins depends critically on their unbinding rate from MT, which varies depending on the force direction applied on the motor, i.e. the force-unbinding rate relation is asymmetric. However, it remains unclear how changing the force direction from resisting (applied against the motion direction) to assisting (applied in the motion direction) alters the kinesin's unbinding and stepping. Here, we propose a theoretical model for the influence of the force direction on the stepping dynamics of a single kinesin. The model shows that the asymmetry of the force-unbinding rate relation is independent of ATP concentration. It also reveals that the synthesis of ATP from backward stepping under assisting forces is less likely than under resisting forces. It then finds that the unbinding of kinesin in the strongly MT-bound kinetic states enhances under assisting forces.
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 06-2019
Publisher: Wiley
Date: 08-10-2008
Start Date: 2010
End Date: 12-2014
Amount: $150,000.00
Funder: Australian Research Council
View Funded Activity