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Search Results: 1 - 10 of 122056 matches for " Yi-cheng LIU "
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Therapeutic effects of scalp-acupuncture in patients with vascular dementia induced by cerebral infarction: a randomized controlled trial
Yi-cheng LIU
Zhong Xi Yi Jie He Xue Bao , 2008,
Abstract: Objective: To investigate the effects of scalp-acupuncture on intelligence in patients with vascular dementia (VaD).Methods: A total of 108 VaD patients were randomly divided into two groups: scalp-acupuncture group and Western medicine group. The scores of Mini-Mental State Examination (MMSE), Blesse Dementia Scale (BDS), Hasegawa Dementia Scale (HDS), Ability of Daily Life (ADL) before and after treatment in the two groups were detected.Results: A total of 92 patients completed the treatment. The scores of MMSE, BDS and HDS in the two groups were significantly increased, and the ADL scores significantly declined (P<0.05). There was no difference in the scores of MMSE, BDS, HDS and ADL between the two groups before and after the treatment. There was no difference in the total effectiveness between the two groups (P>0.05).Conclusion: Scalp-acupuncture therapy could improve the clinical intelligence level of VaD patients.
The research of testing method for timing precision of seismic data acquisition systems

LIU Yi-Cheng,YI Bi-Jin,GONG Qi-Gang,

地球物理学进展 , 2009,
Abstract: The timing precision of telemetry seismic data acquisition systems is a very important performance index, which pases effects on stratum resolution of seismic prospecting directly. The timing precision of telemetry seismic data acquisition systems depends on the precision of sampling rates of data acquisition units. This paper gives the basic definition of timing precision first, based on analog-digital signal processing technique. It studies the single squire wave and double unit impulse methods of testing timing precision and compares their testing precisions,and presents test results using the method mentioned. At last it discusses test errors.
Contact Stress Analysis of Straight Concave Conical Involute Gear Pairs with Small Intersected Angles
Chia-Chang Liu,Yi-Cheng Chen,Shih-Hao Lin
Lecture Notes in Engineering and Computer Science , 2010,
Pulmonary Lymphangioleiomyomatosis: A Clinicopathological Analysis of Ten Cases.
Shir-Hwa Ueng,Hui-Ping Liu,Yi-Cheng Wu,Ying-Huang Tsai
Chang Gung Medical Journal , 2004,
Abstract: Background: Lymphangioleiomyomatosis (LAM) of the lung is a very rare disease ofunknown etiology which occurs particularly in women of childbearing age.So far, there have been very few reports of LAM in Taiwan.Methods: Data on all patients with a clinical or pathological diagnosis of LAM inChang Gung Memorial Hospital from 1990 to 2001 were collected from thepathology files or by consultation with thoracic surgeons, chest physicians,and radiologists of this institution. Cases were confirmed by lung biopsy andhigh-resolution computed tomographic scanning. Clinical data were obtainedfrom patients' charts. Additional immunostaining for HMB-45 antigen, estrogenreceptor, and progesterone receptor was carried out for cases with availableparaffin blocks.Results: In total, 10 patients confirmed with a diagnosis of LAM and adequate clinicalfollow-up information were found from 1991 to 2001. All were femalesof fertility age. The follow-up periods ranged from 18 to 167 months. Sixpatients received hormonal therapy. According to the pulmonary functiontests and clinical symptoms, 6 patients were in stable condition and 4 showedslow progression of disease by the time of the last follow-up. Two patientsdied after lung transplantation and abdominal surgery for retroperitonealLAM, respectively, at another medical center.Conclusions: The clinicopathological features of our series are similar to those reportedfrom other countries. The clinical course was slowly progressive with no significantresponse to hormonal therapy. Lung biopsies are important for adiagnosis, but the size and location of the biopsy specimens greatly affect thediagnostic reliability.
Minimally Invasive Surgery for Coronary Artery Disease with Associated Lung Cancer
Hung-I Lu,Yi-Cheng Wu,Ming-Ju Hsieh,Hui-Ping Liu
Chang Gung Medical Journal , 2002,
Abstract: Simultaneous surgical management of patients with co-existing ischemic heart diseaseand lung carcinoma remains controversial. Traditionally, these operations have been staged,with coronary artery revascularization first, followed by pulmonary resection at a later date.Under this procedure, tumor resection is delayed, and these staged procedures may increasemorbidity and cost. Our experience with minimally invasive surgery in the cardiac and thoracicfields suggests that both diseases can be resolved within the same operation. Thisreport presents a 65-year-old man with coronary artery disease (CAD), and left lung cancer.He received concomitant coronary artery bypass grafting for CAD and pulmonary resectionfor lung cancer via a left parasternal minithoracotomy. No complaints or clinical signs wereobserved during a 6 month follow-up. Therefore, major cardiac and thoracic procedures thatare performed via a minimally invasive approach using conventional instruments, can resultin a safer, quicker, and more economical procedure. The results of the a minimally invasiveapproach demonstrated the technical feasibility of treating a triple vessel cardiac diseasewith lung cancer.
Correlation of Anaplastic Lymphoma Kinase Overexpression and the EML4-ALK Fusion Gene in Non-Small Cell Lung Cancer by Immunohistochemical Study
Tai-Di Chen,Il-Chi Chang,Hui-Ping Liu,Yi-Cheng Wu
Chang Gung Medical Journal , 2012,
Abstract: Background: Recently the echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) fusion gene with transforming activity was identified in non-small cell lung cancer (NSCLC). In addition, NSCLC patients with the EML4-ALK fusion gene had a dramatic response and longer progression free survival after ALK inhibitor treatment than those without this fusion gene. However, the incidence and clinical and molecular characteristics of the EML4-ALK fusion gene in NSCLC patients of Taiwan are still unclear.Methods:Sixty-four fresh frozen tumor specimens were obtained from the tissue bank of Chang Gung Memorial Hospital for RNA extraction and EML4-ALK fusion gene detection. Paraffin sections of lung tumors from all of these patients were available and were analyzed for ALK protein expression by immunohistochemical (IHC) study. The results were correlated with clinical and molecular biomarkers.Results: Three of the 64 tumors (4.7%) had the EML4-ALK fusion gene. Two were adenocarcinomas, and one was adenosquamous carcinoma. Twenty patients with non-squamous cell carcinomas had epidermal growth factor receptor (EGFR) mutations, so the EML4-ALK fusion gene was found in 14.3% of EGFR wild type non-squamous cell carcinomas. Two tumors were variant 3 (3a+3b with 3b predominant) and had strong staining (3+) for ALK by IHC stains. One tumor was variant 1 and had moderate staining (2+) for ALK. None of the ALK wild type tumors had strong staining for ALK. When compared with other clinical and molecular features, only the IHC stain for ALK was significantly correlated with the EML4-ALK fusion gene (p = 0.0002).Conclusions: ALK overexpression detected by IHC study could be a promising detection method for the EML4-ALK fusion gene and is worth further confirmation with more samples.
Ultra accurate personalized recommendation via eliminating redundant correlations
Tao Zhou,Riqi Su,Runran Liu,Luoluo Jiang,Bing-Hong Wang,Yi-Cheng Zhang
Physics , 2008, DOI: 10.1088/1367-2630/11/12/123008
Abstract: In this paper, based on a weighted projection of bipartite user-object network, we introduce a personalized recommendation algorithm, called the \emph{network-based inference} (NBI), which has higher accuracy than the classical algorithm, namely \emph{collaborative filtering}. In the NBI, the correlation resulting from a specific attribute may be repeatedly counted in the cumulative recommendations from different objects. By considering the higher order correlations, we design an improved algorithm that can, to some extent, eliminate the redundant correlations. We test our algorithm on two benchmark data sets, \emph{MovieLens} and \emph{Netflix}. Compared with the NBI, the algorithmic accuracy, measured by the ranking score, can be further improved by 23% for \emph{MovieLens} and 22% for \emph{Netflix}, respectively. The present algorithm can even outperform the \emph{Latent Dirichlet Allocation} algorithm, which requires much longer computational time. Furthermore, most of the previous studies considered the algorithmic accuracy only, in this paper, we argue that the diversity and popularity, as two significant criteria of algorithmic performance, should also be taken into account. With more or less the same accuracy, an algorithm giving higher diversity and lower popularity is more favorable. Numerical results show that the present algorithm can outperform the standard one simultaneously in all five adopted metrics: lower ranking score and higher precision for accuracy, larger Hamming distance and lower intra-similarity for diversity, as well as smaller average degree for popularity.
Effect of user tastes on personalized recommendation
Jian-Guo Liu,Tao Zhou,Qiang Guo,Bing-Hong Wang,Yi-Cheng Zhang
Physics , 2009, DOI: 10.1142/S0129183109014825
Abstract: In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the users' tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score, more importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the users' tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.
Highly accurate recommendation algorithm based on high-order similarities
Jian-Guo Liu,Tao Zhou,Bing-Hong Wang,Yi-Cheng Zhang
Physics , 2008, DOI: 10.1016/j.physa.2009.10.027
Abstract: In this Letter, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the standard Pearson coefficient, the user-user similarities are obtained by a diffusion process. Furthermore, by considering the second order similarities, we design an effective algorithm that depresses the influence of mainstream preferences. The corresponding algorithmic accuracy, measured by the ranking score, is further improved by 24.9% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the algorithm based on second order similarity can outperform the MCF simultaneously in all three criteria.
Adaptive information filtering for dynamic recommender systems
Ci-Hang Jin,Jian-Guo Liu,Yi-Cheng Zhang,Tao Zhou
Mathematics , 2009,
Abstract: The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.
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