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Search Results: 1 - 10 of 39270 matches for " Sio-Hong Lam "
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Aqueous Extract of Paeonia suffruticosa Inhibits Migration and Metastasis of Renal Cell Carcinoma Cells via Suppressing VEGFR-3 Pathway
Shih-Chin Wang,Sai-Wen Tang,Sio-Hong Lam,Chung-Chieh Wang,Yu-Huei Liu,Hsuan-Yuan Lin,Shoei-Sheng Lee,Jung-Yaw Lin
Evidence-Based Complementary and Alternative Medicine , 2012, DOI: 10.1155/2012/409823
Abstract: Renal cell carcinoma (RCC) cells are characterized by strong drug resistance and high metastatic incidence. In this study, the effects of ten kinds of Chinese herbs on RCC cell migration and proliferation were examined. Aqueous extract of Paeonia suffruticosa (PS-A) exerted strong inhibitory effects on cancer cell migration, mobility, and invasion. The results of mouse xenograft experiments showed that the treatment of PS-A significantly suppressed tumor growth and pulmonary metastasis. We further found that PS-A markedly decreased expression of VEGF receptor-3 (VEGFR-3) and phosphorylation of FAK in RCC cells. Moreover, the activation of Rac-1, a modulator of cytoskeletal dynamics, was remarkably reduced by PS-A. Additionally, PS-A suppressed polymerization of actin filament as demonstrated by confocal microscopy analysis and decreased the ratio of F-actin to G-actin in RCC cells, suggesting that PS-A inhibits RCC cell migration through modulating VEGFR-3/FAK/Rac-1 pathway to disrupt actin filament polymerization. In conclusion, this research elucidates the effects and molecular mechanism for antimigration of PS-A on RCC cells and suggests PS-A to be a therapeutic or adjuvant strategy for the patients with aggressive RCC.
Characteristics of Corrosion Product Layer Formed on Weathering Steel Exposed to the Tropical Climate of Vietnam  [PDF]
Le Thi Hong Lien, Hoang Lam Hong
Materials Sciences and Applications (MSA) , 2013, DOI: 10.4236/msa.2013.47A1002

The weathering steel (Corten B) was exposed to out-door atmosphere of Hanoi (urban site) and Donghoi (marine site). The results showed the protective ability of corrosion product layer formed on weathering steel in the initial stage. The SEM-EDX analysis detected the presence of chromium and copper in the inner layers of corrosion product formed on weathering steel. These elements improved corrosion resistance of corrosion product layers. In addition, the dense α-FeOOH phase were appeared early in corrosion product which is detected by X-ray diffraction and Micro Raman investigations. The results of polarization and EIS measurements also demonstrated the protectiveness of the corrosion product of weathering steel.

A Review of Machine Learning Algorithms for Text-Documents Classification
Baharum Baharudin,Lam Hong Lee,Khairullah Khan
Journal of Advances in Information Technology , 2010, DOI: 10.4304/jait.1.1.4-20
Abstract: With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and knowledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing techniques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing literature.
Genetically Improved BarraCUDA
W. B. Langdon,Brian Yee Hong Lam
Quantitative Biology , 2015,
Abstract: BarraCUDA is a C program which uses the BWA algorithm in parallel with nVidia CUDA to align short next generation DNA sequences against a reference genome. The genetically improved (GI) code is up to three times faster on short paired end reads from The 1000 Genomes Project and 60percent more accurate on a short BioPlanet.com GCAT alignment benchmark. GPGPU Barracuda running on a single K80 Tesla GPU can align short paired end nextgen sequences up to ten times faster than bwa on a 12 core CPU.
Review: Results of studying atmospheric corrosion in Vietnam 1995–2005
Le Thi Hong Lien, Pham Thy San and Hoang Lam Hong
Science and Technology of Advanced Materials , 2007,
Abstract: Vietnam is situated in the wet tropical zone; thus, atmospheric conditions are characterized by high temperatures and a long time of wetness (TOW). In addition, the salt air coming in from the sea causes a high chloride concentration in coastal areas. Furthermore, Vietnam is a developing country, which means that air pollution is increasing with the development of industry. These factors result in significant damage to materials by atmospheric corrosion. In this report, the results of a recent study on the corrosion of carbon steel and zinc-galvanized steel at 6–8 testing sites in Vietnam over 10 recent years (1995–2005) are focused on as well as the effects of environmental factors on atmospheric corrosion. The results showed that the corrosion of carbon steel is dominated by TOW, whereas zinc-galvanized-steel corrosion strongly depends on the chloride ion concentration in the air. The corrosion losses of both carbon- and zinc-galvanized steel fit the power model well with high correlation coefficients. In addition, the characteristics of the Vietnamese climate are introduced in the form of distribution maps of temperature (T), relative humidity (RH), total rainfall and TOW. A relationship between TOW, T and RH was found that enabled the calculation of TOW from T and RH data, which are available at meteorological stations. Finally, atmospheric corrosivity is determined on the basis of data on TOW, Cl and SO2 concentrations, and the carbon steel corrosion rate. It is shown that in Vietnam, TOW is so long that the corrosion rate of carbon steel is in the C3 category; nevertheless, Cl and SO2 concentrations in the atmosphere are not high.
Porosity Estimation from High Resolution CT SCAN Images of Rock Samples by Using Housfield Unit  [PDF]
Nguyen Lam Quoc Cuong, Nguyen Hong Minh, Hoang Manh Cuong, Phan Ngoc Quoc, Ngo Hoang Van Anh, Nguyen Van Hieu
Open Journal of Geology (OJG) , 2018, DOI: 10.4236/ojg.2018.810061
Abstract: Computer Automated Tomography has been shown to be a valuable tool in production research because it provides a non-destructive method to identify and evaluate the internal structural characteristics of reservoir rock. In CT scan, Hounsfield Unit (HU) is proportional to the degree of X-ray attenuation by the tissue. The aim of the present study was to introduce the method to estimate porosity which is one of physical parameters of reservoir rock though HU data. In this study, an Image J software was used to extract Hounsfield Unit data and calibrate by standard material’s density. This method provides the ability of using CT Scanner in advanced reservoir characterization and flow test experiments.
A Sub-dimension Based Probabilistic Neural Network for Occlusion Classification
Yi Wang,Benson S. Y. Lam,Hong Yan
Lecture Notes in Engineering and Computer Science , 2008,
Text Document Pre-Processing Using the Bayes Formula for Classification Based on the Vector Space Model
Dino Isa,Lee Lam Hong,V. P. Kallimani,R. Rajkumar
Computer and Information Science , 2009, DOI: 10.5539/cis.v1n4p79
Abstract: This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the na ve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data.
Genome-Wide Identification of Molecular Pathways and Biomarkers in Response to Arsenic Exposure in Zebrafish Liver
Hongyan Xu, Siew Hong Lam, Yuan Shen, Zhiyuan Gong
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0068737
Abstract: Inorganic arsenic is a worldwide metalloid pollutant in environment. Although extensive studies on arsenic-induced toxicity have been conducted using in vivo and in vitro models, the exact molecular mechanism of arsenate toxicity remains elusive. Here, the RNA-SAGE (serial analysis of gene expression) sequencing technology was used to analyse hepatic response to arsenic exposure at the transcriptome level. Based on more than 12 million SAGE tags mapped to zebrafish genes, 1,444 differentially expressed genes (750 up-regulated and 694 down-regulated) were identified from a relatively abundant transcripts (>10 TPM [transcripts per million]) based on minimal two-fold change. By gene ontology analyses, these differentially expressed genes were significantly enriched in several major biological processes including oxidation reduction, translation, iron ion transport, cell redox, homeostasis, etc. Accordingly, the main pathways disturbed include metabolic pathways, proteasome, oxidative phosphorylation, cancer, etc. Ingenity Pathway Analysis further revealed a network with four important upstream factors or hub genes, including Jun, Kras, APoE and Nr2f2. The network indicated apparent molecular events involved in oxidative stress, carcinogenesis, and metabolism. In order to identify potential biomarker genes for arsenic exposure, 27 out of 29 up-regulated transcripts were validated by RT-qPCR analysis in pooled RNA samples. Among these, 14 transcripts were further confirmed for up-regulation by a lower dosage of arsenic in majority of individual zebrafish. Finally, at least four of these genes, frh3 (ferrintin H3), mgst1 (microsomal glutathione S-transferase-like), cmbl (carboxymethylenebutenolidase homolog) and slc40a1 (solute carrier family 40 [iron-regulated transporter], member 1) could be confirmed in individual medaka fish similarly treated by arsenic; thus, these four genes might be robust arsenic biomarkers across species. Thus, our work represents the first comprehensive investigation of molecular mechanism of asenic toxicity and genome-wide search for potential biomarkers for arsenic exposure.
Selection of Reliable Biomarkers from PCR Array Analyses Using Relative Distance Computational Model: Methodology and Proof-of-Concept Study
Chunsheng Liu, Hongyan Xu, Siew Hong Lam, Zhiyuan Gong
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0083954
Abstract: It is increasingly evident about the difficulty to monitor chemical exposure through biomarkers as almost all the biomarkers so far proposed are not specific for any individual chemical. In this proof-of-concept study, adult male zebrafish (Danio rerio) were exposed to 5 or 25 μg/L 17β-estradiol (E2), 100 μg/L lindane, 5 nM 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or 15 mg/L arsenic for 96 h, and the expression profiles of 59 genes involved in 7 pathways plus 2 well characterized biomarker genes, vtg1 (vitellogenin1) and cyp1a1 (cytochrome P450 1A1), were examined. Relative distance (RD) computational model was developed to screen favorable genes and generate appropriate gene sets for the differentiation of chemicals/concentrations selected. Our results demonstrated that the known biomarker genes were not always good candidates for the differentiation of pair of chemicals/concentrations, and other genes had higher potentials in some cases. Furthermore, the differentiation of 5 chemicals/concentrations examined were attainable using expression data of various gene sets, and the best combination was the set consisting of 50 genes; however, as few as two genes (e.g. vtg1 and hspa5 [heat shock protein 5]) were sufficient to differentiate the five chemical/concentration groups in the present test. These observations suggest that multi-parameter arrays should be more reliable for biomonitoring of chemical exposure than traditional biomarkers, and the RD computational model provides an effective tool for the selection of parameters and generation of parameter sets.
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