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Bijie, China

Gui G.-F.,Southwest University | Gui G.-F.,Bijie University | Zhuo Y.,Southwest University | Chai Y.-Q.,Southwest University | And 2 more authors.
Analytical Chemistry | Year: 2014

This work described a new electrogenerated chemiluminescence (ECL) aptasensor for ultrasensitive detection of thrombin (TB) based on the in situ generating self-enhanced luminophore by β-lactamase catalysis for signal amplification. Briefly, a ruthenium complex (Ru-Amp), including two regions of [Ru(phen)2(cpaphen)]2+ and ampicillin (Amp), was synthesized as a self-enhanced ECL luminophore, which can produce an ECL signal through intramolecular interactions. Then, carbon nanotubes (CNTs) were used for immobilization of Ru-Amp via π-π stacking interactions to form the Ru-Amp@CNTs nanocomposite. Using poly(ethylenimine) (PEI) as a linkage reagent, Au nanocages (AuNCs), owing to their electronic property and large surface areas, were decorated to the CNTs to form the Ru-Amp@CNTs-PEI-AuNCs nanocomposites, which were further used to immobilize thrombin binding aptamer II (TBA II) to form a signal probe (Ru-Amp@CNTs-PEI-AuNCs-TBA II). Through "sandwich" tactics, TBA II bioconjugates, TB and TBA I were immobilized onto the gold nanoparticles modified electrode. Then, with the enzyme catalysis of β-lactamase, a novel self-enhanced ECL luminophore (Ru-AmpA) was in situ produced, which could exhibit a significant enhancement of ECL signal, due to the structure transformation of an amide bond into a secondary amine. A sandwich ECL assay for TB detection was developed with excellent sensitivity of a concentration variation from 1.0 fM to 1.0 pM and a detection limit of 0.33 fM. Therefore, the self-enhanced ECL luminophore, combining the further enhancement by in situ enzymatic reaction, is expected to have potential applications in biotechnology and clinical diagnosis. © 2014 American Chemical Society.


Yang J.-Y.,Nanyang Technological University | Peng Z.-L.,Bijie University | Chen X.,Nanyang Technological University
BMC Bioinformatics | Year: 2010

Background: Prediction of protein structural classes (α, β, α + β and α/β) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.Results: We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/.Conclusion: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of α helices and β strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences. © 2010 Yang et al; licensee BioMed Central Ltd.


Peng Z.-L.,University of Alberta | Peng Z.-L.,Bijie University | Yang J.-Y.,Nanyang Technological University | Chen X.,Nanyang Technological University
BMC Bioinformatics | Year: 2010

Background: G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.Results: We propose a new method called PCA-GPCR, to predict GPCRs using a comprehensive set of 1497 sequence-derived features. The principal component analysis is first employed to reduce the dimension of the feature space to 32. Then, the resulting 32-dimensional feature vectors are fed into a simple yet powerful classification algorithm, called intimate sorting, to predict GPCRs at five levels. The prediction at the first level determines whether a protein is a GPCR or a non-GPCR. If it is predicted to be a GPCR, then it will be further predicted into certain family, subfamily, sub-subfamily and subtype by the classifiers at the second, third, fourth, and fifth levels, respectively. To train the classifiers applied at five levels, a non-redundant dataset is carefully constructed, which contains 3178, 1589, 4772, 4924, and 2741 protein sequences at the respective levels. Jackknife tests on this training dataset show that the overall accuracies of PCA-GPCR at five levels (from the first to the fifth) can achieve up to 99.5%, 88.8%, 80.47%, 80.3%, and 92.34%, respectively. We further perform predictions on a dataset of 1238 GPCRs at the second level, and on another two datasets of 167 and 566 GPCRs respectively at the fourth level. The overall prediction accuracies of our method are consistently higher than those of the existing methods to be compared.Conclusions: The comprehensive set of 1497 features is believed to be capable of capturing information about amino acid composition, sequence order as well as various physicochemical properties of proteins. Therefore, high accuracies are achieved when predicting GPCRs at all the five levels with our proposed method. © 2010 Peng et al; licensee BioMed Central Ltd.


Liu Y.,Bijie University
Journal of Applied Optics | Year: 2014

The complex amplitude distribution of diffraction light was analyzed by stationary phase integral method, when the point light source was passing through the system composed of two parallel placed transmission gratings with finite width. When the bi-grating imaging condition was satisfied, the comparison between the diffraction image of finite-width bi-grating and that of infinitely wide bi-grating was conducted. It is concluded that the position of bi-grating diffraction image has no effects from the finite-width grating; however, the size of diffraction image is affected by the relative distance and diffraction orders between two gratings. The size of diffraction image was calculated, which was enlarged by 1.22 times, when the condition of bi-grating imaging and some experimental parameters were satisfied. Furthermore, the study on the color of white light ordinary transmission hologram was done based on its principles.


In this paper, a new maximal element theorem is established in product GFC-spaces. As application, a new existence theorem of solutions for systems of generalized mixed vector quasiequilibrium problems is obtained. © (2013) Trans Tech Publications, Switzerland.

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