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QT interval dispersion in the patients with central serous chorioretinopathy

International Journal of Ophthalmology(English edition) 2015年第1期8卷 61-65页

作者： Necati Dagli Burak Turgut Rumeysa Tanyildizi Sabiha Kobat Mehmet Ali Kobat Orhan dogdu Department of Cardiology School of MedicineFirat University Department of Ophthalmology School of MedicineFirat University Elazig Education and Research Hospital

AIM: To evaluate QT dispersion(QTD) in patients with central serous chorioretinopathy(CSC).METHODS: This clinical, comperative, case-control study included 30 patients with CSC at acute phase(Group 1) and 30 age- and sex-matched healthy subjects(Group 2, the control group). From all subjects, a 12-lead surface electrocardiography was obtained. The heart rate(HR), QT maximum(QT max), QT minimum(QT min), QT corrected(QT c), QTD and T mean were manually measured and analyzed. Student’s t-test and Pearson’s method of correlation were used for statistical analysis.· RESULTS: The patient and control groups were matched for age, smoking status(rate and duration) and gender. There were no significant differences with regard to these among the groups(P >0.05). The participants included 19 men(63.3%) and 11 women(36.7%) in Group1, 20 men(66.7%) and 10 women(33.3%) in Group *** max, QTD and QT c were significantly higher than those of healthy controls(P 0.05 for HR). No significant correlation was found between duration of the disorder and QTD values(r =0.13, P >0.05).CONCLUSION: These findings suggest that CSC may be associated with an increase in QTD and that the patients might be at risk for ventricular arrhythmia.

关键词： central serous chorioretinopathy electrocardiography QT dispersion ventricular arrhythmia

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Tagging Accuracy Analysis on Part-of-Speech Taggers

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Journal of Computer and Communications 2014年第4期2卷 157-162页

作者： Semih Yumusak Erdogan dogdu Halife Kodaz Computer Engineering Department KTO Karatay University Konya Turkey Computer Engineering Department Selcuk University Konya Turkey Computer Engineering Department TOBB University of Economics and Technology Ankara Turkey

Part of Speech (POS) Tagging can be applied by several tools and several programming languages. This work focuses on the Natural Language Toolkit (NLTK) library in the Python environment and the gold standard corpora installable. The corpora and tagging methods are analyzed and com- pared by using the Python language. Different taggers are analyzed according to their tagging ac- curacies with data from three different corpora. In this study, we have analyzed Brown, Penn Treebank and NPS Chat corpuses. The taggers we have used for the analysis are;default tagger, regex tagger, n-gram taggers. We have applied all taggers to these three corpuses, resultantly we have shown that whereas Unigram tagger does the best tagging in all corpora, the combination of taggers does better if it is correctly ordered. Additionally, we have seen that NPS Chat Corpus gives different accuracy results than the other two corpuses.

关键词： POS Tagger Brown Corpus Penn Treebank Corpus NPS Chat Corpus

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