• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.8-19
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• 2008

Purpose : To investigate the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar nuclear Overhauser effect/enhancement (NOE) interaction through 2D- correlation spectroscopy (COSY) and 2D- NOE spectroscopy (NOESY) techniques. Materials and Methods : All 2D experiments were performed on Bruker Avance 500 (11.8 T) with the zshield gradient triple resonance cryoprobe at 298 K. Human brain metabolites were prepared with 10% $D_2O$. Two-dimensional spectra with 2048 data points contains 320 free induction decay (FID) averaging. Repetition delay was 2 sec. The Top Spin 2.0 software was used for post-processing. Total 7 metabolites such as N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), lutamine (Gln), glutamate (Glu), myo-inositol (Ins), and lactate (Lac) were included for major target metabolites. Results : Symmetrical 2D-COSY and 2D-NOESY pectra were successfully acquired: COSY cross peaks were observed in the only 1.0-4.5 ppm, however, NOESY cross peaks were observed in the 1.0-4.5 ppm and 7.9 ppm. From the result of the 2-D COSY data, cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methylene protons (CH2(3,$H{\alpha}$)) at 2.50ppm and methylene protons ($CH_2$,(3,$H_B$)) at 2.70 ppm were observed in NAA. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. From the result of 2-D NOESY data, cross peaks between the NH proton at 8.00 ppm and methyl protons ($CH_3$) were observed in NAA. Cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methyl protons (CH3) at 3.03 ppm and methylene protons (CH2) at 3.93 ppm were observed in Cr. Cross peaks between the methylene protons ($CH_2$(3)) at 2.11 ppm and methylene protons ($CH_2$(4)) at 2.35 ppm, and between the methylene protons($CH_2$ (3)) at 2.11 ppm and methine proton (CH(2)) at 3.76 ppm were observed in Glu. Cross peaks between the methylene protons (CH2 (3)) at 2.14 ppm and methine proton (CH(2)) at 3.79 ppm were observed in Gln. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. Conclusion : The present study demonstrated that in vitro 2D-COSY and NOESY represented the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar NOE interaction. This study could aid in better understanding the interactions between human brain metabolites in vivo 2DCOSY study.

• The Korean Journal of Physiology
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• v.8 no.2
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• pp.1-17
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• 1974

This experiment was designed to explore the specific functional interrelations between the vestibular semicircular canals and the extraocular muscles which may disclose the neural organization, connecting the vestibular canals and each ocular motor nuclei in the brain system, for vestibuloocular reflex mechanism. In urethane anesthetized rabbits, a fine wire insulated except the cut cross section of its tip was inserted into the canals closely to the ampullary receptor organs through the minute holes provided on the osseous canal wall for monopolar stimulation of each canal nerve. All extraocular muscles of both eyes were ligated and cut at their insertio, and the isometric tension and EMG responses of the extraocular muscles to the vestibular canal nerve stimulation were recorded by means of a physiographic recorder. Upon stimulation of the semicircular canal nerve, direction if the eye movement was also observed. The experimental results were as follows. 1) Single canal nerve stimulation with high frequency square waves (240 cps, 0. 1 msec) caused excitation of three extraocular muscles and inhibition of remaining three muscles in the bilateral eyes; stimulation of any canal nerve of a unilateral labyrinth caused excitation (contraction) of the superior rectus, superior oblique and medial rectus muscles and inhibition (relaxation) of the inferior rectus, inferior oblique and lateral rectos muscles in the ipsilateral eye, and it caused the opposite events in the contralateral eye. 2) By the overlapped stimulation of triple canal nerves of a unilateral labyrinth, unidirectional (excitatory or inhibitory) summation of the individual canal effects on a given extraocular muscles was demonstrated, and this indicates that three different canals of a unilateral vestibular system exert similar effect on a given extraocular muscles. 3) Based on the above experimental evidences, a simple rule by which one can define the vestibular excitatory and inhibitory input sources to all the extraocular muscles is proposed; the superior rectus, superior oblique and medial rectus muscles receive excitatory impulses from the ipsilateral vestibular canals, and the inferior rectus, inferior oblique and lateral rectus muscles from the contralateral canals; the opposite relationship applies for vestibular inhibitory impulses to the extraocular muscles. 4) According to the specific direction of the eye movements induced by the individual canal nerve stimulation, an extraocutar muscle exerting major role (a muscle of primary contraction) and two muscles of synergistic contraction could be differentiated in both eyes. 5) When these experimental results were compared to the well known observations of Cohen et al. (1964) made in the cats, extraocular muscles of primary contraction were the same but those of synergistic contraction were partially different. Moreover, the oblique muscle responses to each canal nerve excitation appeared to be all identical. However, the responnes of horizontal (medial and lateral) and vertical (superior and inferior) rectus muscles showed considerable differences. By critical analysis of these data, the author was able to locate theoretical contradictions in the observations of Cohen et al. but not in the author's results. 6) An attempt was also made to compare the functional observation of this experiment to the morphological findings of Carpenter and his associates obtained by degeneration experiments in the monkeys, and it was able to find some significant coincidence between there two works of different approach. In summary, the author has demonstrated that the well known observations of Cohen et al. on the vestibulo-ocular interrelation contain important experimental errors which can he proved by theoretical evaluation and substantiated by a series of experiments. Based on such experimental evidences, a new rule is proposed to define the interrelation between the vestibular canals and the extraocular muscles.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.