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The accumulation of misfolded proteins in the endoplasmic reticulum (ER) induces the unfolded protein response (UPR), which acts through various mechanisms to reduce ER stress. While the UPR has been well studied for its effects on the ER, its impact on the Golgi is less understood. The Golgi complex receives transport vesicles from the endosome through two types of tethering factors: long coiled-coil golgin and the multisubunit Golgi-associated retrograde protein (GARP) complex. Here, we report that ER stress increases the phosphorylation of golgin Imh1 to maintain the GARP-mediated recycling of the SNAREs Snc1 and Tlg1. We also identify a specific function of the Golgi affected by ER stress and elucidate a homeostatic response to restore this function, which involves both an Ire1-dependent and a MAP kinase Slt2/ERK2-dependent mechanism. Furthermore, our findings advance a general understanding of how two different types of tethers act cooperatively to mediate a transport pathway.

Hypoxia-inducible factor (HIF)-1α is a key transcription factor that contributes to aggressive and drug-resistant phenotypes in tumor cells under hypoxic conditions. Therefore, targeting HIF-1α represents a promising therapeutic strategy for cancer drug development. In the present study, we designed, synthesized, and evaluated a new series of biarylquinoline derivatives as potential HIF-1α inhibitors based on structure-activity relationship. Among these derivatives, compound 7f represents the optimal agent with IC

The yeast cell wall integrity (CWI) MAPK pathway is a signaling cascade function in maintaining cell wall integrity under stressful environmental conditions. Recently, the activity and signaling of Slt2p (Mpk1p) MAP kinase has been shown to control assembly of the processing body (P-body) upon cell wall stresses, implicating its posttranscriptional role in decay of cell wall mRNAs. However, how Slt2p MAP kinase directly regulates the stability of cell wall transcripts during cell wall stress remains unclear. Here, we reported that the RNA-binding protein Rbp1p (Ngr1p) is a downstream effector and target of Slt2p MAP kinase during activation of the cell wall stress signaling cascade. In addition to the well-defined target mitochondrial porin mRNA, we found that Rbp1p also negatively regulates the stability of a subset of Slt2p-regulated cell wall transcripts. Deletion ofRBP1increases the level of cell wall transcripts and partially suppresses the hypersensitivity of theslt2Δdeletion strain to cell wall damage. Slt2p is necessary for cell wall stress-induced stabilization of cell wall transcripts. Deletion ofRBP1compromises the destabilization of cell wall transcripts inslt2Δmutants under cell wall stress. Notably, C-terminal deleted Slt2p impairs its function in promoting turnover of the Rbp1p protein and fails to stabilize cell wall transcripts, although it can complement the growth defect of theslt2Δstrain upon cell wall stress. Altogether, our results demonstrate that MAP kinase Slt2p attenuates CWI mRNA decay in response to cell wall damage by downregulating the activity of the RNA-binding protein Rbp1p.

Background: A growing body of evidence suggests that Hsp70, which is overexpressed in human breast tumors, plays a role in tumorigenesis and tumor progression in breast cancer as well as in its aggressive phenotypes. Hsp70 constitutes a potential therapeutic target in the treatment of this disease. Methods: We developed a new series of rhodacyanine-based Hsp70 inhibitors, represented by compounds 1 and 6, in which the cationic pyridin-1-ium or thiazol-3-ium ring of existing Hsp70 inhibitors (e.g., JG-40 and JG-98) was replaced by a corresponding benzo- fused N-heterocycle. Results: Several lines of evidence suggest that these benzo-fused derivatives may exert their antitumor activities, in part, by targeting Hsp70. These putative inhibitors displayed differential antiproliferative efficacy against breast cancer cells (IC50 as low as 0.25 μM) versus nontumorigenic MCF-10A breast epithelial cells (IC50 ≥ 5 μM). This was correlated with the corresponding Hsp70 expression levels. Using a protein refolding assay, we confirmed that these agents effectively inhibited the chaperone activity of Hsp70. Moreover, these inhibitors effectively suppressed the expression of well-known oncogenic client proteins of Hsp70’s, including FoxM1, HuR, and Akt, which paralleled their antiproliferative efficacy. Supporting the established role of Hsp70 in regulating protein refolding, these derivatives induced autophagy, as manifested by the conversion of LC3B-I to LC3B-II. Notably, these putative Hsp70 inhibitors did not cause a compensatory elevation in Hsp90 expression, contrasting with the previously reported effects of Hsp90 inhibitors on Hsp70 upregulation. Conclusion: Together with the finding that compounds 1 and 6 showed improved microsomal stability, these results suggest the translational potential of these putative Hsp70 inhibitors to foster new strategies for cancer therapy. However, whether these benzo-fused rhodacyanines act on kinases or other targets remains unclear. It is currently under investigation.

Transdermal drug delivery is a convenient route to transport pharmaceuticals. However, its application is limited to a few compounds. Microneedle (MN) patches have demonstrated a high efficiency for delivering poorly permeable drugs and gained significant attention due to the advantages of being painless and convenient for patients as well as providing efficient delivery. This study presents dissolvable polyvinylpyrrolidone (PVP)-based MNs that do not require needle removal while providing the rapid release of encapsulated insulin. PVP with two molecular weights, PVP10 and PVP360, were used to fabricate the needle portion of the MNs, and PVP360/sodium carboxymethyl cellulose (CMC) was used for preparation of the backing layer. The detailed characteristics and in vitro skin penetration capability of a series of PVP360/PVP10 MNs were analyzed. The penetration depths of PVP MNs were evaluated using optical coherence tomography. The PVP360/PVP10 = 1:3 MN patch was chosen for ex vivo and in vivo studies. Ex vivo drug release profiles show that the permeation efficiency of Lissamine green B (LGB)- and fluorescein isothiocyanate-labeled BSA (FITC-BSA)-loaded PVP MN patches was much higher than the groups of patches without MNs. Furthermore, to estimate the feasibility of PVP MNs for diabetes treatment, insulin-loaded PVP MN patches were administered to diabetic mice to evaluate glycemic control. The relative pharmacologic availability revealed that the MN patch has an immediate and effective effect on hypoglycemic administration. This study demonstrates that dissolvable PVP MNs may serve as a promising device to deliver macromolecules or protein drugs for efficient transdermal drug delivery.

Postoperative hearing improvement is one of the main expectations for patients receiving tympanoplasty. The capacity to predict postoperative hearing may help to counsel a patient properly and avoid untoward expectations. It is difficult to predict postoperative hearing without knowing the disease process in the middle ear, which can only be assessed intraoperatively. However, the duration and extent of the underlying pathologies may represent in bone-conduction threshold and air-bone gap. Here in patients undergoing tympanoplasty without ossiculoplasty, we sorted and separated the surgery dates into the first group to build the predicting models and the second group to test the predictions. There were 87 and 30 ears, respectively. No specific enrollment or exclusion criteria were based on underlying pathologies such as the perforation size of the tympanic membrane or the middle ear conditions. The results show that bone-conduction threshold and air-bone gap together predicted air-conduction threshold after the surgery, including each frequency of 0.5k, 1k, 2k, and 4k Hz. The discrepancies between the predictions and recordings did not differ among these four frequencies. Of the variance in mean postoperative air-conduction threshold, 56.7% was linearly accounted for by these two preoperative predictors in this sample. The results suggest a trend that, the higher the frequency, the larger the part was accounted for by these two preoperative predictors. These together may help a surgeon to estimate frequency-specific hearing outcome after the surgery, answer patients’ questions with quantitative statistics, and counsel patients with proper expectations.

Web news provides a direct and efficient way to construct large text corpora. The creation of text data requires an understanding of HTML code and the preparation of customized parsing rules to identify text content in a webpage. Typically, parsing rules are written manually and cannot be applied to pages with different layouts. In this study, we present a web news extraction system that is based on a text detection framework. The proposed method scans the input HTML page and creates text statistics as a projection profile. Then, text block identification is applied to determine a set of content candidates. To filter noise, text verification determines whether a given text block can be included with content. We evaluate the proposed approach with the L3S-GN1 corpus and 3506 multilingual news data items randomly sampled from 325 websites (15 geographic regions and 11 distinct languages). We also compare the proposed method to 23 well-known state-of-the-art techniques. The experimental results show that the proposed method outperforms the second best method (NReadability) by 7.30% in the macro F-measure rate and is 16.91 times faster than NReadability. In terms of the perfect rate, the proposed method demonstrates 46.38% accuracy, whereas the Boilerpipe algorithm demonstrates only 21.54% accuracy. The proposed method is very useful for constructing a multilingual corpus because it requires no language-specific processing component.

Web news provides a quick and convenient means to create collections of large documents. The creation of a web news corpus has typically required the construction of a set of HTML parsing rules to identify content text. In general, these parsing rules are written manually and treat different web pages differently. We address this issue and propose a news content recognition algorithm that is language and layout independent. Our method first scans a given HTML document and roughly localizes a set of candidate news areas. Next, we apply a designed scoring function to rank the best content. To validate this approach, we evaluate the systems performance using 1092 items of multilingual web news data covering 17 global regions and 11 distinct languages. We compare these data with nine published content extraction systems using standard settings. The results of this empirical study show that our method outperforms the second-best approach (Boilerpipe) by 6.04 and 10.79 % with regard to the relative micro and macro F-measures, respectively. We also apply our system to monitor online RSS news distribution. It collected 0.4 million news articles from 200 RSS channels in 20 days. This sample quality test shows that our method achieved 93 % extraction accuracy for large news streams.

P-Glycoprotein (P-gp) overexpression is a major mechanism by which cancer cells acquire the multidrug resistance (MDR) phenotype, and is associated with poor clinical outcome in patients. In an effort to develop MDR-reversal agents, we synthesized and evaluated a series of thiophenylbenzofuran derivatives (4–31) as P-gp inhibitors, among which compounds 4, 10, and 14 represented the optimal agent in reversing the MDR phenotype. These P-gp inhibitors were dramatically effective than verapamil in sensitizing the human ABCB1-overexpressing ABCB1/Flp-In™-293 cells and MDR KBvin cells to a series of chemotherapeutic agents, including vincristine and paclitaxel, as manifested by multi-fold decreases in the respective IC50 values to therapeutically attainable levels.

With the growth of Virtual Reality (VR) and Augmented Reality (AR) in technologies such as artificial intelligence, wireless, 5G, big data, massive compute, industrial 4.0 and virtual stores. An improved secure mechanism which was previously still existed some shortcomings, was presented in this paper. Another new mechanism regards to achieve the decentralized environment access control is introduced to attain the needs of the non-specific internet. Furthermore, it is very important to look attentively to the administrative previleges in VR/AR clouds. The issues which gained from VR and AR could be tackled with the new mechanism. This new studies achieves a higher circumstance. Developer worker's duty can be distributed; the database system can be compatibly coordinates; moreover, the users' information security can be entirely secured.

Background Global cerebral ischemia triggers neurodegeneration in the hippocampal CA1 region, but the mechanism of neuronal death remains elusive. The epsilon isoform of protein kinase C (PKCε) has recently been identified as a master switch that controls the nucleocytoplasmic trafficking of ATF2 and the survival of melanoma cells. It is of interest to assess the role of PKCε–ATF2 signaling in neurodegeneration. Results Phosphorylation of ATF2 at Thr-52 was reduced in the hippocampus of PKCε null mice, suggesting that ATF2 is a phosphorylation substrate of PKCε. PKCε protein concentrations were significantly reduced 4, 24, 48 and 72 h after transient global cerebral ischemia, resulting in translocation of nuclear ATF2 to the mitochondria. Degenerating neurons staining positively with Fluoro-Jade C exhibited cytoplasmic ATF2. Conclusions Our results support the hypothesis that PKCε regulates phosphorylation and nuclear sequestration of ATF2 in hippocampal neurons during ischemia-induced neurodegeneration.

Global cerebral ischemia that accompanies cardiac arrest is a major cause of morbidity and mortality. Protein Kinase C epsilon (PKCe) is a member of the novel PKC subfamily and plays a vital role in ischemic preconditioning. Pharmacological activation of PKCe before cerebral ischemia confers neuroprotection. The role of endogenous PKCe after cerebral ischemia remains elusive. Here we used male PKCe-null mice to assess the effects of PKCe deficiency on neurodegeneration after transient global cerebral ischemia (tGCI). We found that the cerebral vasculature, blood flow, and the expression of other PKC isozymes were not altered in the PKCe-null mice. Spatial learning and memory was impaired after tGCI, but the impairment was attenuated in male PKCe-null mice as compared to male wild-type controls. A significant reduction in Fluoro-Jade C labeling and mitochondrial release of cytochrome C in the hippocampus was found in male PKCe-null mice after tGCI. Male PKCe-null mice expressed increased levels of PKCδ in the mitochondria, which may prevent the translocation of PKCδ from the cytosol to the mitochondria after tGCI. Our results demonstrate the neuroprotective effects of PKCe deficiency on neurodegeneration after tGCI, and suggest that reduced mitochondrial translocation of PKCδ may contribute to the neuroprotective action in male PKCe-null mice.

This paper is the first to investigate the effect of industrial penetration (geographic concentration of industries) and internet intensity (the proportion of enterprises that uses the internet) for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations are based on a unique set of data, namely 153,081 manufacturing plants, and covers 26 two-digit industry categories and 358 geographical townships in Taiwan. The Heckman sample selection model is used to accommodate sample selectivity for unobservable data for firms that use the internet. The empirical results from Heckman’s two-stage estimation show that: (1) a higher degree of industrial penetration will not affect the probability that firms will use the internet, but it will affect the total expenditure on internet intensity; (2) for two-digit SIC (Standard Industrial Classification) industries, industrial penetration generally decreases the total expenditure on internet intensity; and, (3) industrial penetration and internet intensity are substitutes.

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. The authors used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, they present a modify indexing with terms in cluster. Their test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.

Polyelectrolyte multilayer films have been suggested as tunable substrates with flexible surface properties that can modulate cell behavior. However, these films’ biological effects on neural stem/progenitor cells have rarely been studied. Herein, biomimetic multilayer films composed of hyaluronic acid and poly-L-lysine were chosen to mimic the native extracellular matrix niche of brain tissue and were evaluated for their inductive effects, without the addition of chemical factors. Because neural stem/progenitor cells are sensitive to substrate properties, it is important that this system provides control over the surface charge, and slight stiffness variations are also possible. Both of these factors affect neural stem/progenitor cell differentiation. The results showed that neural stem/progenitor cells were induced to differentiate on the poly-L-lysine/hyaluronic acid multilayer films with 0.5–4 alternating layers. In addition, the neurite outgrowth length was regulated by the surface charge of the terminal layer but did not increase with the layer number. In contrast, the quantity of differentiated neurons was enhanced slightly as the number of layers increased but was not affected by the surface charge of the terminal layer. In sum, material pairs in the form of native poly-L-lysine/hyaluronic acid films achieved important targets for neural regenerative medicine, including enhancement of the neurite outgrowth length, regulation of neuron differentiation, and the formation of a network. These extracellular matrix–mimetic poly-L-lysine/hyaluronic acid multilayer films may provide a versatile platform that could be useful for surface modification for applications in neural engineering.

Modeling 3D objects has become one of the required technique in multimedia lectures. By the introduction of CAD software, most conceptual design can be implemented in the specific 3D view. To engaging studying, in this paper we design a novel teaching strategy that combines social activity and project-based learning as to develop news express in presentation. In the social activity, students are required to collect feedback and try to demonstrate to people online. In teaching project goal, learners work together and learn to establish big project as team work. In the final stage, each group should accomplish the news express design within 2 hours and publishing to social Web site. The proposed teaching strategy had been applied in real university course in two years. The empirical result showed that learners not only immersive in doing project, but also substantially improves the 3D modeling capability in course. The result of the satisfactory quiz shows that the teaching strategy is highly positive for learning.

In order to control differentiation of neural cells and guide the developed neurites to targets, polyelectrolyte multilayer (PEM) films were used because of their capability of modulation of electrical charged characteristics, thickness, and stiffness. In this work, we suggested that indium tin oxide (ITO) is an alternative surface to achieve the above-mentioned objectives. A microfluidic system with four culture chambers was developed and each chamber consisted of parallel ITO surfaces for the application of adjustable electrical field. Neural stem/progenitor cells (NSPCs) were respectively cultured on the ITO surfaces with and without PEM film, constructed by alternate adsorption of poly(L-lysine) (PLL) and poly(L-glutamic acid) (PLGA). Analyses of cell morphology, cytotoxicity, process outgrowth, differentiated cell types, and neuron functionality were compared between both surfaces. In this study, NSPCs successfully differentiated on ITO surface with electrical stimulation. The optimal electrical potential was found to be 80 mV that could stimulate the longest process, i.e.,300 μm, after 3 days culture. Cell differentiation, process development, and functionality of differentiated neuron on ITO surface were shown to be strongly controlled by the electrical stimulation that can be simply adjusted by external equipment. The electrically adjustable cell differentiation reported here could potentially be applied to neurochip for the study of neural signal transmission in a well-constructed network.

The key factors affecting the success of neural engineering using neural stem/progenitor cells (NSPCs) are the neuron quantity, the guidance of neurite outgrowth, and the induction of neurons to form functional synapses at synaptic junctions. Herein, a biomimetic material comprising a supported lipid bilayer (SLB) with adsorbed sequential polyelectrolyte multilayer (PEM) films was fabricated to induce NSPCs to form functional neurons without the need for serum and growth factors in a short-term culture. SLBs are suitable artificial substrates for neural engineering due to their structural similarity to synaptic membranes. In addition, PEM film adsorption provides protection for the SLB as well as the ability to vary the surface properties to evaluate the effects of physical and mechanical signals on NSPC differentiation. Our results revealed that NSPCs were inducible on SLB-PEM films consisting of up to eight alternating layers. In addition, the process outgrowth length, the percentage of differentiated neurons, and the synaptic function were regulated by the number of layers and the surface charge of the outermost layer. The average process outgrowth length was greater than 500 μm on SLB-PLL/PLGA (n = 7.5) after only 3 days of culture. Moreover, the quantity and quality of the differentiated neurons were obviously enhanced on the SLB-PEM system compared with those on the PEM-only substrates. These results suggest that the PEM films can induce NSPC adhesion and differentiation and that an SLB base may enhance neuron differentiation and trigger the formation of functional synapses.

Semi-supervised machine learning methods have the features of both, integrating labeled and unlabeled training data. In most structural problems, such as natural language processing and image processing, developing labeled data for a specific domain requires considerable amount of human resources. In this paper, we present a cluster-based method to fuse labeled training and unlabeled raw data. We design a top-down divisive clustering algorithm that ensures maximal information gain in the use of unlabeled data via clustering similar words. To implement this idea, we design a top-down iterative K -means clustering algorithm to merge word clusters. Differently, the derived term groups are then encoded as new features for the supervised learners in order to improve the coverage of lexical information. Without additional training data or external materials, this approach yields state-of-the-art performance on the shallow parsing and base-chunking benchmark datasets (94.50 and 93.12 in F ( β ) rates).

Linear kernel support vector machines (SVMs) using either $$L_{1}$$ -norm or $$L_{2}$$ -norm have emerged as an important and wildly used classification algorithm for many applications such as text chunking, part-of-speech tagging, information retrieval, and dependency parsing. $$L_{2}$$ -norm SVMs usually provide slightly better accuracy than $$L_{1}$$ -SVMs in most tasks. However, $$L_{2}$$ -norm SVMs produce too many near-but-nonzero feature weights that are highly time-consuming when computing nonsignificant weights. In this paper, we present a cutting-weight algorithm to guide the optimization process of the $$L_{2}$$ -SVMs toward a sparse solution. Before checking the optimality, our method automatically discards a set of near-but-nonzero feature weight. The final objects can then be achieved when the objective function is met by the remaining features and hypothesis. One characteristic of our cutting-weight algorithm is that it requires no changes in the original learning objects. To verify this concept, we conduct the experiments using three well-known benchmarks, i.e., CoNLL-2000 text chunking, SIGHAN-3 Chinese word segmentation, and Chinese word dependency parsing. Our method achieves 1–10 times feature parameter reduction rates in comparison with the original $$L_{2}$$ -SVMs, slightly better accuracy with a lower training time cost. In terms of run-time efficiency, our method is reasonably faster than the original $$L_{2}$$ -regularized SVMs. For example, our sparse $$L_{2}$$ -SVMs is 2.55 times faster than the original $$L_{2}$$ -SVMs with the same accuracy.

Modern information technologies and Internet services are suffering from the problem of selecting and managing a growing amount of textual information, to which access is often critical. Machine learning techniques have recently shown excellent performance and flexibility in many applications, such as artificial intelligence and pattern recognition. Question answering (QA) is a method of locating exact answer sentences from vast document collections. This paper presents a machine learning-based question-answering framework, which integrates a question classifier, simple document/passage retrievers, and the proposed context-ranking models. The question classifier is trained to categorize the answer type of the given question and instructs the context-ranking model to re-rank the passages retrieved from the initial retrievers. This method provides flexible features to learners, such as word forms, syntactic features, and semantic word features. The proposed context-ranking model, which is based on the sequential labeling of tasks, combines rich features to predict whether the input passage is relevant to the question type. We employ TREC-QA tracks and question classification benchmarks to evaluate the proposed method. The experimental results show that the question classifier achieves 85.60% accuracy without any additional semantic or syntactic taggers, and reached 88.60% after we employed the proposed term expansion techniques and a predefined related-word set. In the TREC-10 QA task, by using the gold TREC-provided relevant document set, the QA model achieves a 0.563 mean reciprocal rank (MRR) score, and a 0.342 MRR score is achieved after using the simple document and passage retrieval algorithms.

Recognizing textual entailment is to infer that a given text span follows from the meaning of a given hypothesis. To have better recognition capability, it is necessary to employ deep text processing units such as syntactic parsers and semantic taggers. However, these resources are not usually available in other non-English languages. In this paper, we present a light-weight Chinese textual entailment recognition system using part-of-speech information only. We designed two different feature models from training data and employed the well-known kernel method to learn to predict testing data. One feature set abstracts the generic statistics between the text pairs, while the other set directly models lexical features based on the traditional bag-of-words model. The ability of the proposed feature models not only brings additional statistical information from their datasets but also helps to enhance the prediction capability. To validate this, we conducted the experiments on the novel benchmark corpus - NTCIR-RITE-2011. The empirical results demonstrate that our method achieves the best results in comparison to the other competitors. In terms of accuracy, our method achieves 54.77% for the NTCIR RITE MC task.

To determine the ratio of IGFBP3:IGF-1 in normal and diabetic human tears, and in telomerase-immortalized human corneal epithelial cells (hTCEpi) cultured under elevated glucose conditions and to correlate these changes with total and phosphorylated levels of IGF-1R. Tear samples were collected noninvasively from diabetic subjects and non-diabetic controls; corneal sensitivity was assessed using a Cochet-Bonnet Aesthesiometer. Conditioned media were collected following culture of hTCEpi cells in normal (5 mM) and elevated (25 mM) glucose conditions; mannitol was used as an osmotic control. IGFBP3, IGF-1, and phosphorylated IGF-1R levels were assessed by ELISA. IGFBP3 and IGF-1R mRNA were assessed by real-time polymerase chain reaction (PCR). Total and phosphorylated IGF-1R expression in whole cell lysates was assessed by western blot. There was a 2.8-fold increase in IGFBP3 in diabetic tears compared to non-diabetic controls (P=0.006); IGF-1 levels were not significantly altered. No difference in corneal sensitivity was detected between groups. The concentration of IGFBP3 in tears was independent of IGF-1. Consistent with human tear measurements in vivo, IGFBP3 secretion was increased 2.2 fold (P

It is claimed that online news holds valuable contents of information to some extent; as a matter of fact, it reflects the polarity of investigators from time to time. Especially, such information in the stock market outlines an event usually hold on to unobserved effects, e.g. law problem, which might not be immediately and completely price-revealed. Instead of traditional pure price-based indicators, this paper is to apply some sentiment technical indicators at hand into working out a news-based box through the fuzzy design. It pays to notice that the sentiment indicators automatically come out from the contents of online news. It comes out a weight on keywords in text through a combination of natural language processing techniques and membership functions together. For example, given a topic of target firm, the box collects news and mines patterns and then infers the degree of sentiment. To examine the feasibility of such concept, an experiment is built to test the rate of accuracy, using the data fully quoted on the online news took place for 3 month long. It results that the proposed fuzzy inference algorithm achieve 88% rate of accuracy in inferring the polarity of the whole news. This method is also feasible for the other languages, e.g. English and Japanese.

This study characterized the expression and subcellular localization of the IGF-1R in human corneal epithelial cells. Using a human telomerase-immortalized corneal epithelial cell line, IGF-1R expression and localization was assayed by immunofluorescence and subcellular fractionation followed by western blot. IGF-1R expression was confirmed in primary cultured human corneal epithelial cells. Nuclear localization was assessed under basal and IGF-1 stimulated culture conditions; phosphorylation status of the receptor in response to IGF-1 was demonstrated by western blot. IGF-1R:E-cadherin interactions were detected by immunofluorescence and co-immunoprecipitation of whole cell lysates. The results of this study demonstrated that IGF-1R localized predominantly to the nucleus and in a perinuclear cap pattern which co-localized with the Golgi complex in proliferating corneal epithelial cells. There was no difference in nuclear localization between primary or telomerized cell lines. Subcellular fractionation confirmed IGF-1Rα- and β-subunit localization in soluble and chromatin-bound nuclear fractions. Neither growth factor withdrawal nor IGF-1 stimulation altered nuclear IGF-1R. At points of cell-cell contact, IGF-1R co-localized with E-cadherin; co- immunoprecipitation assays confirmed the presence of an IGF-1R:E-cadherin complex. Importantly, this is the first report to identify IGF-1R in the nucleus and complexed with E-cadherin at points of cell-cell contact in corneal epithelial cells. Nuclear trafficking appeared to be independent of ligand-mediated events at the plasma membrane. The identification of IGF-1R in the nucleus and complexed with E-cadherin suggests novel regulatory functions outside the canonical ligand-induced endocytosis signaling pathway.

Automatic Chinese text classification is an important and a well-known technology in the field of machine learning. The first step for solving Chinese text categorization problems is to tokenize the Chinese words from a sequence of non-segmented sentences. However, previous literatures often employ a Chinese word tokenizer that was trained with different sources and then perform the conventional text classification approaches. However, these taggers are not perfect and often provide incorrect word boundary information. In this paper, we propose an N-gram-based language model which takes word relations into account for Chinese text categorization without Chinese word tokenizer. To prevent from out-of-vocabulary, we also propose a novel smoothing approach based on logistic regression to improve accuracy. The experimental result shows that our approach outperforms traditional methods at least 11% on micro-average F-measure.

In this paper, we present a robust passage retrieval algorithm to extend the conventional text question answering (Q/A) to videos. Users interact with our videoQ/A system through natural language queries, while the top-ranked passage fragments with associated video clips are returned as answers. We compare our method with five of the high-performance ranking algorithms that are portable to different languages and domains. The experiments were evaluated with 75.3 h of Chinese videos and 253 questions. The experimental results showed that our method outperformed the second best retrieval model (language models) in relatively 1.43% in mean reciprocal rank (MRR) score and 11.36% when employing a Chinese word segmentation tool. By adopting the initial retrieval results from the retrieval models, our method yields an improvement of at least 5.94% improvement in MRR score. This makes it very attractive for the Asia-like languages since the use of a well-developed word tokenizer is unnecessary.

Papillomavirus E2 proteins are critical regulatory proteins that function in replication, genome segregation, and viral transcription, including control of expression of the viral oncogenes, E6 and E7. Sumoylation is a post-translational modification that has been shown to target and modulate the function of many transcription factors, and we now demonstrate that E2 proteins are sumoylated. Both bovine and human papillomavirus E2 proteins bind to the SUMO conjugation enzyme, Ubc9, and using in vitro and E. coli sumoylation systems, these E2 proteins were readily modified by SUMO proteins. In vivo experiments further confirmed that E2 can be sumoylated by SUMO1, SUMO2, or SUMO3. Mapping studies identified lysine 292 as the principal residue for covalent conjugation of SUMO to HPV16 E2, and a lysine 292 to arginine mutant showed defects for both transcriptional activation and repression. The expression levels, intracellular localization, and the DNA-binding activity of HPV16 E2 were unchanged by this K292R mutation, suggesting that the transcriptional defect reflects a functional contribution by sumoylation at this residue. This study provides evidence that sumoylation has a role in the regulation of papillomavirus E2, and identifies a new mechanism for the modulation of E2 function at the post-translational level.

Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, and noun phrase (NP)-chunking. Even though they yield very accurate results, they are not efficient for online applications, which need to handle hundreds of thousand words in a limited time. In this paper, we firstly re-examine five typical multiclass SVM methods and the adaptation to phrase chunking. However, most of them were inefficient when the number of phrase types scales. We thus introduce the proposed two new multiclass SVM models that make the system substantially faster in terms of training and testing while keeps the SVM accurate. The two methods can also be applied to similar tasks such as named entity recognition and Chinese word segmentation. Experiments on CoNLL-2000 chunking and Chinese base-chunking tasks showed that our method can achieve very competitive accuracy and at least 100 times faster than the state-of-the-art SVM-based phrase chunking method. Besides, the computational time complexity and the time cost analysis of our methods were also given in this paper.

Video question answering aims to pinpoint answers in response to user's specified questions. However, most question answering technologies involve in integrating rich specific external knowledge such as syntactic parsers, which are often unavailable for many languages. In this paper, we present a new string pattern matching-based passage ranking algorithm for extending traditional text Q/A toward videoQ/A. Users interact with our videoQ/A system through natural language questions whereas our system returns three sentence-length passages with corresponding video clips as answers. We collect 45GB Discovery videos and 253 Chinese questions for evaluation. The experimental results showed that our method outperformed six top-performed ranking models. It is 7.39% better than the second best method (language model-based) in relatively MRR score and 6.12% in precision rate. Besides, we also show that the use of a trained Chinese word segmentation tool did decrease the overall videoQ/A performance where most ranking algorithms dropped at least 10% in relatively MRR, precision, and answer pattern recall rates.

Automatic text chunking aims to recognize grammatical phrase structures in natural language text. Text chunking provides downstream syntactic information for further analysis, which is also an important technology in the area of text mining (TM) and natural language processing (NLP). Existing chunking systems make use of external knowledge, e.g. grammar parsers, or integrate multiple learners to achieve higher performance. However, the external knowledge is almost unavailable in many domains and languages. Besides, employing multiple learners does not only complicate the system architecture, but also increase training and testing time costs. In this paper, we present a novel phrase chunking model based on the proposed mask method without employing external knowledge and multiple learners. The mask method could automatically derive more training examples from the original training data, which significantly improves system performance. We had evaluated our method in different chunking tasks and languages in comparison to previous studies. The experimental results show that our method achieves state of the art performance in chunking tasks. In two English chunking tasks, i.e., shallow parsing and base-chunking, our method achieves 94.22 and 93.23 in F"("@b"="1") rates. When porting to Chinese, the F"("@b"="1") rate is 92.30. Also, our chunker is quite efficient. The complete chunking time of a 50K-words is less than 10s.

In this paper, we proposed an efficient and accurate text chunking system using linear SVM kernel and a new technique called masked method. Previous researches indicated that systems combination or external parsers can enhance the chunking performance. However, the cost of constructing multi-classifiers is even higher than developing a single processor. Moreover, the use of external resources will complicate the original tagging process. To remedy these problems, we employ richer features and propose a masked-based method to solve unknown word problem to enhance system performance. In this way, no external resources or complex heuristics are required for the chunking system. The experiments show that when training with the CoNLL-2000 chunking dataset, our system achieves 94.12 in F"("@b") rate with linear. Furthermore, our chunker is quite efficient since it adopts a linear kernel SVM. The turn-around tagging time on CoNLL-2000 testing data is less than 50s which is about 115 times than polynomial kernel SVM.

Cancer stem cells (CSCs) represent a subpopulation of tumor cells that exhibit capacities for self-renewal, tumor initiation, disease relapse or metastasis, and resistance to chemotherapy and radiotherapy. However, the major obstacle associated with the use of CSCs is the difficulty in their isolation and enrichment. According to recent studies, CSCs share similar properties with normal stem cells, and it has been observed that hyaluronan (HA) plays a key factor in CSCs niches and that HA-mediated CD44 interaction promotes tumor progression. Therefore, HA-based multilayer films were used to fabricate sequential surface properties variation and to mimic CSC niches. A quartz crystal microbalance was used to investigate the layer-by-layer adsorption of PAH/HA multilayer films. Colony formation was observed on a series of poly(allylamine hydrochloride) PAH/HA multilayer films, and cytotoxicity and cell viability were evaluated by MTT, LDH and live/dead assay. It was observed that the cells isolated from (PAH/HA)3 displayed the best colony formation ability and that the expression of CD133/CD44 double positive cells was up-regulated to approximately 70% after 7 days of culture. Furthermore, the cells isolated from (PAH/HA)3 displayed higher chemo-resistance than the control group. The stem-cell-related genes expression of selected cells from (PAH/HA)3 after 7 days of culture was significantly different from that of the control group. In conclusion, this study provides a label-free selection and enrichment system that could serve as a new strategy for the future development of CSC selection and drug evaluation in cancer therapy.

Taiwan has been hailed as a world leader in the development of global innovation and industrial clusters for the past decade. This paper investigates the effects of industrial agglomeration on the use of the internet and internet intensity for Taiwan manufacturing firms, and analyses whether the relationships between industrial agglomeration and total expenditure on internet usage for industries are substitutes or complements. The sample observations are based on 153,081 manufacturing plants, and covers 26 2-digit industry categories and 358 geographical townships in Taiwan. The Heckman selection model is used to adjust for sample selectivity for unobservable data for firms that use the internet. The empirical results from two-stage estimation show that: (1) for the industry overall, a higher degree of industrial agglomeration will not affect the probability that firms will use the internet, but will affect the total expenditure on internet usage; and (2) for 2-digit industries, industrial agglomeration generally decreases the total expenditure on internet usage, which suggests that industrial agglomeration and total expenditure on internet usage are substitutes.

Neural stem/progenitor cells (NSPCs) are a possible candidate for advancing development and lineage control in neural engineering. Differentiated protocols have been developed in this field to generate neural progeny and to establish neural networks. However, continued refinement is required to enhance differentiation specificity and prevent the generation of unwanted cell types. In this study, we fabricated a niche-modulated system to investigate surface effects on NSPC differentiation by the formation of polyelectrolyte multilayer (PEM) films governed by electrostatic interactions of poly-l-glutamine acid as a polyanion and poly-l-lysine as a polycation. The serum- and chemical agent-free system provided a clean and clear platform to observe in isolation the interaction between surface niche and stem cell differentiation. We found that NSPCs were inducible on PEM films of up to eight alternating layers. In addition, neurite outgrowth, neuron percentage, and synaptic function were regulated by layer number and the surface charge of the terminal layer. The average process outgrowth length was over 500μm on PLL/PLGA(n=7.5) only after 3 days of culture. Moreover, the quantity and quality of the differentiated neurons were enhanced as the number of layers increased, especially when the terminal layer was poly-l-lysine. Our results achieve important targets of neural engineering, including long processes, large neural network size, and large amounts of functional neurons. Our methodology for nanoscale control of material deposition can be successfully applied for surface modification, neural niche modulation, and neural engineering applications.

SUMOylation of viral proteins is widespread and serves to modify or regulate the properties of those proteins. Papillomaviruses are a large group of small DNA viruses that infect the skin, leading to benign lesions (warts) that in some cases can progress to malignancy. The papillomavirus life cycle is intimately connected with the differentiation process of stratified epithelium, and several viral early proteins function to modulate the host cell environment. One of the critical early proteins is the E2 protein, which functions in both viral replication and transcription. In the present paper, we demonstrate that E2 proteins are SUMOylated and that overexpression of SUMOylation results in a dramatic increase in intracellular levels of the E2 protein. We have shown previously that there is increased SUMOylation during keratinocyte differentiation, suggesting that the levels of E2 protein may be tied to changes in the cellular SUMOylation state during differentiation. In addition to itself being regulated by SUMOylation, E2 appears to influence the SUMOylation state of one of its binding partners, the cellular transcription factor, C/EBP (CCAAT/enhancer-binding protein). Overall, these observations indicate a complex interplay between this viral protein and the host SUMOylation system.

Reading news is one of the most important learning activities in the education of the communication schools. Learners learn to improve the awareness and writing essay ability through observing contents inside the news. How to create the learning materials in a short time is still a gap between traditional communication education and information technology. In addition, the most challenge is that there embeds a huge amount of commercial ads and external links within a news page which usually mislead learners to read and explore further topics. To solve this, in this paper, we proposed a clean and pure news reading environment which makes it possible to touch up-to-date global news in our news corpus. It automatically crawls the daily news through the web without additional human effort. To preserve the news content, we also design a content extraction algorithm to keep the news content in the database is clean to read. Instructors can easily prepare and organize a set of news topics in this platform.

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. We used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, we present a modify indexing with terms in cluster. Our test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.

Purpose The aim of this study was to establish a relationship between hypoxic stress and the expression of ΔNp63α in an established rabbit contact lens model and in cultured corneal epithelial cells. Methods New Zealand white rabbits were fit in one eye with either a non-oxygen transmissible or hyper-oxygen-permeable rigid contact lens for 24 hrs of wear; the contralateral eye was used as a control. All the rabbits underwent a bilateral nictitating membranectomy to facilitate lens retention. ΔNp63α expression was analyzed by immunofluorescence and western blot. Telomerase-immortalized human corneal epithelial cells (hTCEpi) were grown in serum-free media and treated with the hypoxia-mimetic cobalt chloride to simulate hypoxia for 6 hrs (short term) or 24 hrs (prolonged). Transcriptional activity and protein levels were assessed using luciferase reporter assays, reverse transcription polymerase chain reaction, and western blot. Cell viability was assessed by live/dead assay. Results Compared with the non-lens wearing eye, 24 hrs of non-oxygen transmissible lens wear in vivo decreased ΔNp63α protein levels in both the limbal and central corneal epithelium; this decrease was not found in the hyper-oxygen transmissible lens group. In hTCEpi cells in vitro, hypoxia increased the activity of the ΔN promoter but reduced the levels of ΔNp63α mRNA after 24 hrs of prolonged culture. Similarly, ΔNp63α expression levels were unaffected from short-term exposure but decreased after 24 hrs. Live/dead assay confirmed the presence of viable cells after CoCl2 treatment at 6- and 24-hr time points. Cells treated for 24 hrs were viable but were smaller and rounded with signs of membrane blebbing, consistent with early stages of apoptosis. Conclusions Hypoxic stress induced by either prolonged wear of a nonoxygen transmissible lens in vivo or hypoxic-mimic conditions by cobalt chloride in vitro downregulates ΔNp63α in the corneal epithelium. The loss of ΔNp63α in response to hypoxic stress may contribute to the disruption of normal renewal mechanisms reported with low oxygen transmissible contact lens wear and prolonged eyelid closure.

In general, video-based learning contains rich media information, but displaying an entire video linearly is time-consuming. As an alternative, video summarization techniques extract important content provides short but informative fragments. In this paper, a video learning platform (KVSUM: Keyword-based Video Summarization) is presented that integrates image processing, text summarization, and keyword extraction techniques. Without human annotators, the learning platform can process an input video and transform it into online learning materials automatically. The video frames are first split from a given video while the transcription is used to generate the text summary and keywords. In the current study, the video surrogates are composed of extracted keywords, text and video summaries, and video frames. In other words, KVSUM is able to provide both visual and verbal surrogates. In order to validate the effect of surrogates in the KVSUM, a comparison with another video surrogate, the fast forward (FF), to evaluate learnersi¦ comprehension to video contents. Sixty undergraduate students took part in examining two different video surrogates. The experimental results show that KVSUM had a more positive effect than FF in comprehension to videos. In terms of system usage and satisfaction, KVSUM is significantly more attractive than FF to learners.

Video-based concordancer can be used to provide a scenario that engage context information of studying novel words. Usually, a fixed number of contextual sentences are retrieved accompanying the keywords. However, there may be a lack of complete context for learners to comprehend the keywords in the videos. Few studies have discussed about how the videos are presented to assist learners to use the keywords appropriately, and lead learners to find relevant knowledge effectively. In this paper, a keyword-in-scene video concordance (KWIS), which recognizes the scenes in the videos and provides the scene-based clips, is proposed. Each video clip is tagged actual scene type information. Learners are able to query the KWIS system with keywords, phrases, or natural language sentences, and watch relevant scenario clips to understand where the conversation can be carried on. A pilot study was conducted to evaluate the proposed system. The result shows that there is a positive effect on students' comprehension of English phrases while using the system.

Isothiocyanates (ITCs) derived from cruciferous vegetables induce apoptosis in cancer cells. We demonstrate that certain naturally occurring ITCs selectively deplete mutant p53 but not the wild-type and do so via a transcription-independent mechanism. Direct p53 binding followed by conformational changes appears to be a mechanism by which mutant p53 is depleted. Structure-activity relationship studies (SARs) using naturally occurring and synthetic ITCs show that depletion is influenced by the ITC side-chain moiety. Furthermore, we show that cells with p53 mutations are more sensitive to cytotoxicity induced by phenethyl isothiocyanate (PEITC) than those with the wild-type protein. 2,2-Diphenylethyl ITC, a synthetic ITC, is one of the most potent depletors of mutant p53 studies and induces apoptosis to the greatest extent in mutant p53 breast cancer cells. Collectively, this study shows that mutant p53 depletion may be an important novel target for cancer chemoprevention and therapy by natural and synthetic ITCs.

In general, watch and view the whole video is often time-consuming which involves displaying the video film linearly. In this paper, we propose a webpage-based and video summarization-based learning platform (WVSUM). The learning platform provides not only video lecture preview but also combines image, text, and video information as learning materials. The input videos can be automatically transformed into online lectures without human annotators through the subtitle extraction techniques. By means of the extracted caption words, the designed text summarization method is applied to generate the summary. Our summarization approach can generate variant length of video summary by setting up the time constraint. To validate the effectiveness, we compare with the existing fast forwards (one kind of video surrogate interface) and evaluate users' comprehension to video content in the limited viewing time. Thirty undergraduate students were invited to examine the video learning platforms. The experimental results showed that our WVSUM had better effect than fast forwards on comprehension to videos. In terms of system usage and satisfaction, our WVSUM achieved significantly better scores than the classic fast forwards surrogate.

It is conceivable that stimulating proteasome activity for rapid removal of misfolded and oxidized proteins is a promising strategy to prevent and alleviate aging-related diseases. Sulforaphane (SFN), an effective cancer preventive agent derived from cruciferous vegetables, has been shown to enhance proteasome activities in mammalian cells and to reduce the level of oxidized proteins and amyloid β-induced cytotoxicity. Here, we report that SFN activates heat shock transcription factor 1-mediated heat shock response. Specifically, SFN-induced expression of heat shock protein 27 (Hsp27) underlies SFN-stimulated proteasome activity. SFN-induced proteasome activity was significantly enhanced in Hsp27-overexpressing cells but absent in Hsp27-silenced cells. The role of Hsp27 in regulating proteasome activity was further confirmed in isogenic REG cells, in which SFN-induced proteasome activation was only observed in cells stably overexpressing Hsp27, but not in the Hsp27-free parental cells. Finally, we demonstrated that phosphorylation of Hsp27 is irrelevant to SFN-induced proteasome activation. This study provides a novel mechanism underlying SFN-induced proteasome activity. This is the first report to show that heat shock response by SFN, in addition to the antioxidant response mediated by the Keap1-Nrf2 pathway, may contribute to cytoprotection.

Linear support vector machines (SVMs) have become one of the most prominent classification algorithms for many natural language learning problems such as sequential labeling tasks. Even though the L 2-regularized SVMs yields slightly more superior accuracy than L 1-SVM, it produces too much near but non zero feature weights. In this paper, we present a cutting-weight algorithm to guide the optimization process of L 2-SVM into sparse solution. To verify the proposed method, we conduct the experiments with three well-known sequential labeling tasks and one dependency parsing task. The result shows that our method achieved at least 400% feature parameter reduction rates in comparison to the original L 2-SVM, with almost no change in accuracy and training times. In terms of run time efficiency, our method is faster than the original L 2-regularized SVMs at least 20% in all tasks.