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Search Results: 1 - 10 of 27633 matches for " Tseng Yi-Jhan "
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Monocyte chemoattractant protein-1 affects migration of hippocampal neural progenitors following status epilepticus in rats
Hung Yu-Wen,Lai Ming-Tsong,Tseng Yi-Jhan,Chou Chien-Chen
Journal of Neuroinflammation , 2013, DOI: 10.1186/1742-2094-10-11
Abstract: Background Epilepsy is a common brain disorder characterized by a chronic predisposition to generate spontaneous seizures. The mechanisms for epilepsy formation remain unknown. A growing body of evidence suggests the involvement of inflammatory processes in epileptogenesis. In the present study, we investigated the involvement of monocyte chemoattractant protein-1 (MCP-1) in aberrant migration of hippocampal progenitors in rats after the insult of status epilepticus (SE). Methods SE was induced with pilocarpine in Sprague–Dawley rats. Transcriptional expression of MCP-1 in the dentate gyrus (DG) was measured using quantitative real-time PCR. From 1 to 28 days after SE, the temporal profiles of MCP-1 protein expression in DG were evaluated using enzyme-linked immunosorbent assay. Chemokine (C-C motif) receptor 2 (CCR2) expression in doublecortin-positive neuronal progenitors was examined using double-labeling immunohistochemistry. The involvement of MCP-1/CCR2 signaling in aberrant neuronal progenitor migration in the epileptic hippocampus was assessed in the SE rats using a CCR2 antagonist, RS102895, and the ectopic migration of neuronal progenitors was determined using Prox1/doublecortin double immunostaining. Results After SE, MCP-1 gene was significantly upregulated and its corresponding protein expression in the DG was significantly increased on days 1 and 3. Some hilar ectopic progenitor cells of SE rats expressed the MCP-1 receptor, CCR2. Notably, the ectopic migration of neuronal progenitors into hilus was attenuated by a blockade of the MCP-1/CCR2 interaction with a selective CCR2 inhibitor, RS102895. Conclusions An increase in dentate MCP-1 is associated with seizure-induced aberrant migration of neuronal progenitors through the interaction with CCR2. The upregulation of MCP-1 after an insult of SE may play a role in the generation of epilepsy.
Reduced Motor Cortex Deactivation in Individuals Who Suffer from Writer's Cramp
Yi-Jhan Tseng, Rou-Shayn Chen, Wan-Yu Hsu, Fu-Jung Hsiao, Yung-Yang Lin
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0097561
Abstract: This study investigated the neuromagnetic activities of self-paced finger lifting task and electrical median nerve stimulation in ten writer's cramp patients and fourteen control subjects. The event-related de/synchronizations (ERD/ERS) of beta-band activity levels were evaluated and the somatosensory cortical activity levels were analyzed using equivalent-current dipole modeling. No significant difference between the patients and control subjects was found in the electrical stimulation-induced beta ERS and electrical evoked somatosensory cortical responses. Movement-related beta ERD did not differ between controls and patients. Notably, the amplitude of the beta ERS after termination of finger movement was significantly lower in the patients than in the control subjects. The reduced movement-related beta ERS might reflect an impairment of motor cortex deactivation. In conclusion, a motor dependent dysregulation of the sensorimotor network seems to be involved in the functional impairment of patients with writer's cramp.
Analysis of the Multi-Relationships and Their Structures for Safety Culture  [PDF]
Teh-Sheng Su, Pei-Ru Lin, Yi-Liang Shu, Jo-Ming Tseng, Chen-Shan Kao
Open Journal of Safety Science and Technology (OJSST) , 2012, DOI: 10.4236/ojsst.2012.23012
Abstract: Studies on safety culture and safety performance have been increasing recently and several proposed models have been developed and evaluated to identify their key dimensions. However, many studies focus more on measuring the scales of the safety culture and safety performance dimensions. This study aims to seek a deeper understanding of the overall structures of safety culture, safety performance and their relationships by using exploratory and confirmatory factor analysis along with the structural equation modeling (SEM) statistical technique, because the above-mentioned concepts and technique have been seldom employed and are uncommon in manufacturing companies, in terms of Tai-wan. We first carry out a thorough literature review and propose an integrated model of safety culture and safety per-formance with eight dimensions and four dimensions, respectively. We then state a series of hypotheses and test the proposed model on a sample of 370 valid data from three manufacturing companies in Taiwan. Results show that the third-order safety culture associated with second-order safety performance model turns out to be the desired model, where both the measured variables and latent variables have good reliability. The proposed model meets many criteria of the goodness of fit as well.
Applying Backpropagation Neural Networks to Bankruptcy Prediction
Yi-Chung Hu,Fang-Mei Tseng
International Journal of Electronic Business Management , 2005,
Abstract: Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. In particular, the radial basis function network can be treated as a fuzzy neural network. Through examining their classification generalization abilities, the empirical results from the data resources consisting of bankrupt and nonbankrupt firms in England, demonstrated that the radial basis function network outperforms the other classification methods, including the multi-layer perceptron, the multivariate discriminant analysis, and the probit method.
Distributed Intrusion Detection of Byzantine Attacks in Wireless Networks with Random Linear Network Coding
Jen-Yeu Chen,Yi-Ying Tseng
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/758340
Abstract:
Algorithms for Optimally Arranging Multicore Memory Structures
Wei-Che Tseng,Jingtong Hu,Qingfeng Zhuge,Yi He
EURASIP Journal on Embedded Systems , 2010, DOI: 10.1155/2010/871510
Abstract: As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental research to explore the effects of memory sharing on energy in a multicore processor. We study the Memory Arrangement (MA) Problem. We prove that the general case of MA is NP-complete. We present an optimal algorithm for solving linear MA and optimal and heuristic algorithms for solving rectangular MA. On average, we can produce arrangements that consume 49% less energy than an all shared memory arrangement and 14% less energy than an all private memory arrangement for randomly generated instances. For DSP benchmarks, we can produce arrangements that, on average, consume 20% less energy than an all shared memory arrangement and 27% less energy than an all private memory arrangement.
Algorithms for Optimally Arranging Multicore Memory Structures
Tseng Wei-Che,Hu Jingtong,Zhuge Qingfeng,He Yi
EURASIP Journal on Embedded Systems , 2010,
Abstract: As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental research to explore the effects of memory sharing on energy in a multicore processor. We study the Memory Arrangement (MA) Problem. We prove that the general case of MA is NP-complete. We present an optimal algorithm for solving linear MA and optimal and heuristic algorithms for solving rectangular MA. On average, we can produce arrangements that consume 49% less energy than an all shared memory arrangement and 14% less energy than an all private memory arrangement for randomly generated instances. For DSP benchmarks, we can produce arrangements that, on average, consume 20% less energy than an all shared memory arrangement and 27% less energy than an all private memory arrangement.
Distributed Intrusion Detection of Byzantine Attacks in Wireless Networks with Random Linear Network Coding
Jen-Yeu Chen,Yi-Ying Tseng
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/758340
Abstract: Network coding (NC) can be applied to achieve the maximal information flow in a network. In energy-constraint wireless networks such as wireless sensor networks, applying NC can further reduce the number of wireless transmissions and hence prolong the life time of sensor nodes. Although applying NC in wireless networks is obviously beneficial, it is possible that a malicious node (Byzantine attacker) can take advantage of the inherent vulnerability of error propagation in the NC scheme to corrupt all the information transmissions. In the NC scheme, an intermediate node linearly combines several incoming messages as its outgoing messages. Thus a data error injected in any intermediate nodes will corrupt the information reaching a destination. Recent research efforts have shown that NC can be combined with classical error control codes and cryptography for secure communications or misbehavior detections. Nevertheless, when it comes to Byzantine attacks, these results have limited effects. In this paper, a distributed algorithm is developed to effectively detect, locate, and isolate the Byzantine attackers in a wireless ad hoc network with random linear network coding (RLNC). To the best of our knowledge, our work is the first to address the problem of Byzantine failures in a wireless network with RLNC. 1. Introduction 1.1. Network Coding Network coding has become a paradigm shift in information transmission, it is first brought up by Ahlswede et al. [1]. Instead of traditional information transmission method, simply storing and forwarding, network coding allows intermediate nodes to mix received information together and transmit new information generated by the received information in terms of encoding. Due to encoding operation at intermediate nodes, data can be regarded as information flowing through a network, which is a sense of data compression. Therefore, throughput and bandwidth efficiency can be increased and delay can be decreased via network coding. In [1], it has showed that network capacity with network coding can be bounded by min-cut max-flow theory, which is larger than traditional storing-and-forwarding method. 1.2. Random Linear Network Coding Recent research proving throughput gain of network coding in a variety of application makes network coding an attractive topic. With algebraic approaches, such as [2], a communication pattern with network coding of a network can be designed and achieve its promised capacity, which is the min-cut from the source to the sinks in a network graph [1]. However, the requirement of global topology
Graded index and randomly oriented core-shell silicon nanowires with broadband and wide angle antireflection for photovoltaic cell applications
P. Pignalosa,H. Lee,L. Qiao,M. Tseng,Yasha Yi
Physics , 2011,
Abstract: Antireflection with broadband and wide angle properties is important for a wide range of applications on photovoltaic cells and display. The SiOx shell layer provides a natural antireflection from air to the Si core absorption layer. In this work, we have demonstrated the random core-shell silicon nanowires with both broadband (from 400nm to 900nm) and wide angle (from normal incidence to 60\degree) antireflection characteristics within AM1.5 solar spectrum. The graded index structure from the randomly oriented core-shell (Air/SiOx/Si) nanowires may provide a potential avenue to realize a broadband and wide angle antireflection layer.
Modeling Perceived Relevance for Tail Queries without Click-Through Data
Changsung Kang,Xiaotong Lin,Xuanhui Wang,Yi Chang,Belle Tseng
Computer Science , 2011,
Abstract: Click-through data has been used in various ways in Web search such as estimating relevance between documents and queries. Since only search snippets are perceived by users before issuing any clicks, the relevance induced by clicks are usually called \emph{perceived relevance} which has proven to be quite useful for Web search. While there is plenty of click data for popular queries, very little information is available for unpopular tail ones. These tail queries take a large portion of the search volume but search accuracy for these queries is usually unsatisfactory due to data sparseness such as limited click information. In this paper, we study the problem of modeling perceived relevance for queries without click-through data. Instead of relying on users' click data, we carefully design a set of snippet features and use them to approximately capture the perceived relevance. We study the effectiveness of this set of snippet features in two settings: (1) predicting perceived relevance and (2) enhancing search engine ranking. Experimental results show that our proposed model is effective to predict the relative perceived relevance of Web search results. Furthermore, our proposed snippet features are effective to improve search accuracy for longer tail queries without click-through data.
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