Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins

<p>Abstract</p> <p>Background</p> <p>While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites.</p> <p>Results</p> <p>By investigating glycosylated sites that contain various motifs between Transmembrane (TM) and non-Transmembrane (non-TM) proteins, this work presents a novel method, GlycoRBF, that implements radial basis function (RBF) networks with significant amino acid pairs (SAAPs) for identifying O-linked glycosylated serine and threonine on TM proteins and non-TM proteins. Additionally, a membrane topology is considered for reducing the false positives on glycosylated TM proteins. Based on an evaluation using five-fold cross-validation, the consideration of a membrane topology can reduce 31.4% of the false positives when identifying O-linked glycosylation sites on TM proteins. Via an independent test, GlycoRBF outperforms previous O-linked glycosylation site prediction schemes.</p> <p>Conclusion</p> <p>A case study of Cyclic AMP-dependent transcription factor ATF-6 alpha was presented to demonstrate the effectiveness of GlycoRBF. Web-based GlycoRBF, which can be accessed at <url>http://GlycoRBF.bioinfo.tw</url>, can identify O-linked glycosylated serine and threonine effectively and efficiently. Moreover, the structural topology of Transmembrane (TM) proteins with glycosylation sites is provided to users. The stand-alone version of GlycoRBF is also available for high throughput data analysis.</p

EXPLORING E-LEARNING BEHAVIOR THROUGH LEARNING DISCOURSES

As many studies predict e-learning behaviors through intention, few of them investigate user’s learning behaviors directly. In addition to intention, individual’s e-learning behaviors may be influenced by technology readiness and group influences, such as social identity and social bond. This research-in-progress study explores how e-learning behaviors vary with intention, technology readiness, social identity and social bond. Our investigation was based on analyzing the speech acts embedded in fourteen learners’ online discourses in an eighteen-week e-learning course. We then compared how speech acts varied among groups with different degree of intention, technology readiness, social identity, and social bond. Our findings contribute e-learning research by clarifying how intention, technology readiness, social identity, and social bond influence learning behaviors in e-learning context

Glycine tomentella Hayata inhibits IL-1β and IL-6 production, inhibits MMP-9 activity, and enhances RAW264.7 macrophage clearance of apoptotic cells

<p>Abstract</p> <p>Background</p> <p>To assess the effects of <it>Glycine tomentella </it>Hayata (GTH), a traditional herbal medicine for treatment of rheumatic diseases on the expression of the proinflammatory cytokines and on the clearance of apoptotic cells by macrophages.</p> <p>Methods</p> <p>RAW264.7 cells were cultured with lipopolysaccharide (LPS) in the presence or absence of ethanol extract of GTH. The expression of proinflammatory cytokines IL-1β, IL-6, and TNF-α, and inducible nitric oxide synthase (iNOS) and transglutaminase 2 (TG2) were assayed by reverse transcriptase-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA). Matrix metalloproteinase (MMP)-2 and MMP-9 were assayed by gelatin zymography. For detecting uptake of apoptotic cells, RAW264.7 cells were cultured with carboxyfluorescein diacetate (CFDA)-stained apoptotic cells and assayed by flow cytometry.</p> <p>Results</p> <p>The major components of GTH analyzed by high-performance liquid chromatography (HPLC) chromatogram were daidzein (42.5%), epicatechin (28.8%), and naringin (9.4%).</p> <p>GTH treatment inhibited the expression of proinflammatory cytokines IL-1β, IL-6 and MMP-9 but did not affect the expression of TNF-α and iNOS. GTH significantly enhanced the expression of TG2 and the clearance of apoptotic cells by RAW264.7 macrophages.</p> <p>Conclusions</p> <p>GTH inhibits proinflammatory cytokine secretion and MMP-9 activity, enhances apoptotic cell uptake and up-regulates TG2 expression. Our data show that GTH might have beneficial effects on rheumatic diseases.</p

How E-Servqual Affects Customer\u27s Online Purchase Intention?

With the boom of Internet, Internet has become one of the consumers’ online shopping channels. However, there is different in online shopping situation is because of consumers in different cultures and countries have different online shopping behavior is worth to discuss. This study is to explore the Internet users’ online shopping situation in developing country, Malaysia, and 118 questionnaire respondents were collected. Statistical analysis software SPSS 17.0 and AMOS 6.0 were used to analyze the impact on e-service quality, satisfaction, trust, and purchase intention. The model fit of this study was in an acceptable level, and this indicates that the theoretical model of this study supports the description of e-service quality for e-retailers that online shopping situation will be effected by trust and satisfaction. The result of this study will be available for those who interested in developing a transnational e-retailer as a reference, as well as academic research on cross-cultural comparative analysis

High Improvement in Conversion Efficiency of μc-SiGe Thin-Film Solar Cells with Field-Enhancement Layers

The improved performance for hydrogenated microcrystalline silicon-germanium (μc-Si1−xGex:H, x~0.1) p-i-n single solar cells with hydrogenated microcrystalline silicon (μc-Si:H) field-enhancement layers (FELs) is demonstrated for the first time. The fill factor (FF) and conversion efficiency (η) increase by about 19% and 28% when the thickness of the μc-Si FEL is increased from 0 to 200 nm, it is attributed to the longer hole life-time and enhanced electric field in the μc-Si0.9Ge0.1:H layer. Therefore, we can successfully manufacture high-performance μc-SiGe:H solar cells with the thickness of absorbers smaller than 1 μm by conducting FELs. Moreover, the simulation tool is used to simulate the current-voltage (J-V) curve, thus we can investigate the carrier transport in the absorber of μc-Si0.9Ge0.1:H solar cells with different EFLs

Impact of microbiota in colorectal carcinogenesis: lessons from experimental models

A role of gut microbiota in colorectal cancer (CRC) growth was first suggested in germ-free rats almost 50 years ago, and the existence of disease-associated bacteria (termed pathobionts) had becoming increasingly evident from experimental data of fecal transplantation, and microbial gavage or monoassociation. Altered bacterial compositions in fecal and mucosal specimens were observed in CRC patients compared to healthy subjects. Microbial fluctuations were found at various cancer stages; an increase of bacterial diversity was noted in the adenoma specimens, while a reduction of bacterial richness was documented in CRC samples. The bacterial species enriched in the human cancerous tissues included Escherichia coli, Fusobacterium nucleatum, and enterotoxigenic Bacteroides fragilis. The causal relationship of gut bacteria in tumorigenesis was established by introducing particular bacterial strains in in situ mouse CRC models. Detailed experimental protocols of bacterial gavage and the advantages and caveats of different experimental models are summarized in this review. The microbial genotoxins, enterotoxins, and virulence factors implicated in the mechanisms of bacteria-driven tumorigenesis are described. In conclusion, intestinal microbiota is involved in colon tumorigenesis. Bacteria-targeting intervention would be the next challenge for CRC

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201