157 research outputs found

    Differential expression of decorin, EGFR and cyclin D1 during mammary gland carcinogenesis in TA2 mice with spontaneous breast cancer

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>The Tientsin Albino 2 (TA2) mouse is an inbred strain originating from the Kunming strain. It has a high incidence of spontaneous breast cancer without the need for external inducers or carcinogens. Until now, the mechanism of carcinogenesis has remained unclear. In this study, we investigate differential gene expression, especially the expression of decorin, EGFR and cyclin D1, during mammary gland epithelial cell carcinogenesis in TA2 mice.</p> <p>Methods</p> <p>Gene expression profiles of spontaneous breast cancer and matched normal mammary gland tissues in TA2 mice were ascertained using an Affymetrix Mouse 430 2.0 array. Twelve mammary tissue samples from five month-old female TA2 mice (Group A), as well as 28 samples from mammary (Group B) and cancer tissues (Group C) of spontaneous breast cancer-bearing TA2 mice, were subsequently used to detect the expression of decorin, EGFR and cyclin D1 by real-time PCR and immunohistochemical methods.</p> <p>Results</p> <p>Several imprinted genes, oncogenes and tumor suppressor genes were differentially expressed between normal mammary gland tissues and breast cancer tissues of TA2 mice. The imprinted gene decorin and the oncogene EGFR were down-regulated in tumor tissues, while the oncogene cyclin D1 was up-regulated. Immunohistochemistry showed that samples in Group A showed high decorin expression more frequently than those in Group B (<it>P </it>< 0.05). More tissue samples in Group B than Group A were positive for nuclear EGFR, and tissue samples in Group B more frequently showed high nuclear EGFR expression than those in Group A or Group C (<it>P </it>< 0.05). The labeling index for cyclin D1 in Group C was significantly higher than in Group B. Mammary tissues of Group A expressed the highest level of decorin mRNA (<it>P </it>< 0.05), and mammary tissues of Group B expressed the highest level of EGFR mRNA (<it>P </it>< 0.05), while cancer tissues expressed the highest level of cyclin D1 mRNA (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>The expression of decorin, EGFR and cyclin D1 in mammary epithelial cells changes with increasing age. The abnormal expression of them may partly contribute to the genesis of spontaneous breast cancer in TA2 mice.</p

    Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.</p> <p>Methods</p> <p>Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.</p> <p>Results</p> <p>A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).</p> <p>Conclusion</p> <p>Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.</p

    The relationship between the interactive behavior of industry–university–research subjects and the cooperative innovation performance: The mediating role of knowledge absorptive capacity

    Get PDF
    IntroductionIndustry–university–research cooperation innovation, which is often characterized by resource complementarity and the sharing technology, has become one of the most preferred innovation cooperation methods for enterprises. However, various problems still occur in the process of industry–university–research cooperations, such as poor innovation performance and difficulty in sustaining cooperation. Existing studies mostly focus on the macroscopic perspectives of geographic location, cooperation scale, concentration, and diversification of industry–university–research cooperation subjects, and fail to explore the microscopic behavioral mechanisms.MethodsTherefore, this paper establishes the interactive behavior of industry–university–research subjects and defines its concepts and dimensions in an attempt to provide a mechanism for improving the cooperative innovation performance of industry–university–research from the micro-behavioral perspective. On the basis of theoretical analysis, this paper develops a model of the relationship between cooperative trust, cooperative communication, and cooperative innovation performance for interactive behavior, while exploring the mediating role of knowledge absorptive capacity. The model was validated by stepwise regression using data from 325 questionnaires.ResultsThe paper found that cooperative trust and cooperative communication in the cooperative interactive behavior of industry–university–research positively contribute to the improvement of cooperative innovation performance. Knowledge absorptive capacity plays a partially mediating role between the interactive behaviors and cooperative innovation performance. More specifically, knowledge absorptive capacity partially mediates cooperative communication in cooperative innovation performance and completely mediates cooperative trust in cooperative innovation performance. The results are largely consistent with the results of the heterogeneity analysis of the sample.DiscussionThis paper not only explains why the cooperative innovation performance of industry–university–research is poor from the perspective of interactive behavior, but also enriches the research perspective of industry–university–research and provides theoretical support for enterprises to optimize the relationship between industry, university, and research institutes

    REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

    Full text link
    [EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, J.I.O is supported by WWTF (Medical University of Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12- 009). Team Masker is supported by Natural Science Foundation of Guangdong Province of China (Grant 2017A030310647). Team BUCT is partially supported by the National Natural Science Foundation of China (Grant 11571031). The authors would also like to thank REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10(4), 87. doi:10.3390/sym10040087Almazroa, A., Burman, R., Raahemifar, K., & Lakshminarayanan, V. (2015). Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. Journal of Ophthalmology, 2015, 1-28. doi:10.1155/2015/180972Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology, 135(11), 1170. doi:10.1001/jamaophthalmol.2017.3782Carmona, E. J., Rincón, M., García-Feijoó, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243-259. doi:10.1016/j.artmed.2008.04.005Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., … Zangwill, L. M. (2018). Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Scientific Reports, 8(1). doi:10.1038/s41598-018-35044-9De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231. doi:10.5566/ias.1155DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3), 837. doi:10.2307/2531595European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Part 1Supported by the EGS Foundation. (2017). British Journal of Ophthalmology, 101(4), 1-72. doi:10.1136/bjophthalmol-2016-egsguideline.001Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 1-10. doi:10.1145/1360612.1360666Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018). Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation. IEEE Transactions on Medical Imaging, 37(7), 1597-1605. doi:10.1109/tmi.2018.2791488Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., … Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express, 10(2), 892. doi:10.1364/boe.10.000892Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Hagiwara, Y., Koh, J. E. W., Tan, J. H., Bhandary, S. V., Laude, A., Ciaccio, E. J., … Acharya, U. R. (2018). Computer-aided diagnosis of glaucoma using fundus images: A review. Computer Methods and Programs in Biomedicine, 165, 1-12. doi:10.1016/j.cmpb.2018.07.012Haleem, M. S., Han, L., van Hemert, J., & Li, B. (2013). Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Computerized Medical Imaging and Graphics, 37(7-8), 581-596. doi:10.1016/j.compmedimag.2013.09.005Holm, S., Russell, G., Nourrit, V., & McLoughlin, N. (2017). DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. Journal of Medical Imaging, 4(1), 014503. doi:10.1117/1.jmi.4.1.014503Joshi, G. D., Sivaswamy, J., & Krishnadas, S. R. (2011). Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 30(6), 1192-1205. doi:10.1109/tmi.2011.2106509Kaggle, 2015. Diabetic Retinopathy Detection. https://www.kaggle.com/c/diabetic-retinopathy-detection. [Online; accessed 10-January-2019].Kumar, J. R. H., Seelamantula, C. S., Kamath, Y. S., & Jampala, R. (2019). Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening. Scientific Reports, 9(1). doi:10.1038/s41598-019-43385-2Lavinsky, F., Wollstein, G., Tauber, J., & Schuman, J. S. (2017). The Future of Imaging in Detecting Glaucoma Progression. Ophthalmology, 124(12), S76-S82. doi:10.1016/j.ophtha.2017.10.011Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125(8), 1199-1206. doi:10.1016/j.ophtha.2018.01.023Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. doi:10.1016/j.media.2017.07.005Liu, S., Graham, S. L., Schulz, A., Kalloniatis, M., Zangerl, B., Cai, W., … You, Y. (2018). A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs. Ophthalmology Glaucoma, 1(1), 15-22. doi:10.1016/j.ogla.2018.04.002Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L. (2004). Optic Nerve Head Segmentation. IEEE Transactions on Medical Imaging, 23(2), 256-264. doi:10.1109/tmi.2003.823261Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., … Kopp-Schneider, A. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications, 9(1). doi:10.1038/s41467-018-07619-7Miri, M. S., Abramoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., & Garvin, M. K. (2015). Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach. IEEE Transactions on Medical Imaging, 34(9), 1854-1866. doi:10.1109/tmi.2015.2412881Niemeijer, M., van Ginneken, B., Cree, M. J., Mizutani, A., Quellec, G., Sanchez, C. I., … Abramoff, M. D. (2010). Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 29(1), 185-195. doi:10.1109/tmi.2009.2033909Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., … Angelopoulou, E. (2013). Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database. IET Image Processing, 7(4), 373-383. doi:10.1049/iet-ipr.2012.0455Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16-27. doi:10.1109/tbme.2016.2535311Park, S. J., Shin, J. Y., Kim, S., Son, J., Jung, K.-H., & Park, K. H. (2018). A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training. Journal of Korean Medical Science, 33(43). doi:10.3346/jkms.2018.33.e239Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., … Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164. doi:10.1038/s41551-018-0195-0Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3(3), 25. doi:10.3390/data3030025Prokofyeva, E., & Zrenner, E. (2012). Epidemiology of Major Eye Diseases Leading to Blindness in Europe: A Literature Review. Ophthalmic Research, 47(4), 171-188. doi:10.1159/000329603Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49. doi:10.1016/j.ins.2018.01.051Reis, A. S. C., Sharpe, G. P., Yang, H., Nicolela, M. T., Burgoyne, C. F., & Chauhan, B. C. (2012). Optic Disc Margin Anatomy in Patients with Glaucoma and Normal Controls with Spectral Domain Optical Coherence Tomography. Ophthalmology, 119(4), 738-747. doi:10.1016/j.ophtha.2011.09.054Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-ySchmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018). Artificial intelligence in retina. Progress in Retinal and Eye Research, 67, 1-29. doi:10.1016/j.preteyeres.2018.07.004Sevastopolsky, A. (2017). Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, 27(3), 618-624. doi:10.1134/s1054661817030269Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging, 15(1). doi:10.1186/s12880-015-0068-xThakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Johnson, S. S., Wang, J.-K., Islam, M. S., Thurtell, M. J., Kardon, R. H., & Garvin, M. K. (2018). Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography. Lecture Notes in Computer Science, 277-284. doi:10.1007/978-3-030-00949-6_33Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., … Dhillon, B. (2013). Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal. Investigative Opthalmology & Visual Science, 54(5), 3546. doi:10.1167/iovs.12-10347Vergara, I. A., Norambuena, T., Ferrada, E., Slater, A. W., & Melo, F. (2008). StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-265Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. doi:10.1016/j.patcog.2019.01.006Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated «Disease/No Disease» Grading of Age-Related Macular Degeneration by an Image Mining Approach. Investigative Opthalmology & Visual Science, 53(13), 8310. doi:10.1167/iovs.12-957

    A Combined SERS and MCBJ Study on Molecular Junctions on Silicon Chips

    Get PDF
    We have developed a combined Surface-enhanced raman spectroscopy (SERS) and mechanically controllable break junction (MCBJ) method to detect and characterize molecular junctions formed by two electrochemically nanofabricated electrodes on silicon chips. The method allows us to obtain vibrational spectra of the molecular junction and perform electron transport measurement on the molecules simultaneously. The preliminary IN characterization and SERS measurement on an asymmetric molecule, OPE-NO(2), and a symmetric molecule, OPE, were conducted. This approach may provide new insights into not only electron transport in molecules, but also the enhancement mechanism in single-molecule SERS

    Investigation of Losses in Industrial Enterprises in Liaoning Province

    No full text
    Liaoning is an area where heavy industry is relatively concentrated, making up 69 percent of the provincial industrial total, while its light industry only makes up 31 percent. In the province's economic development, however, enterprise losses are a conspicuous problem. In June 1987, we investigated losses in some enterprises in a few industries, focusing on operational losses suffered by industrial enterprises included in the state budget. On the basis of this investigation and study, we present below some views and suggestions.

    Global existence and blow-up of solution to a class of fourth-order equation with singular potential and logarithmic nonlinearity

    No full text
    In this paper, we consider the well-posedness and asymptotic behavior of Dirichlet initial boundary value problem for a fourth-order equation with strong damping and logarithmic nonlinearity. We establish the local solvability by the technique of cut-off combining with the method of Faedo–Galerkin approximation. By means of potential well method and Rellich inequality, we obtain the global existence and the decay estimate of global solutions under some appropriate conditions. Furthermore, we prove the finite time blow-up results of weak solutions, and establish the upper and lower bounds for blow-up time

    Application of Pt/CdS for the Photocatalytic Flue Gas Desulfurization

    Get PDF
    A photocatalytic flue gas desulfurization technology was designed to control emissions of SO2 from the combustion of fossil fuels. With the photocatalytic technology, we cannot only achieve the purpose of solving the problem of SO2 emissions but also realize the desire of hydrogen production from water. CdS loaded with Pt were selected as the model photocatalyst for the photocatalytic flue gas desulfurization. The factors influencing the rate of hydrogen production and ammonia sulfite solution oxidation were detected
    corecore