60 research outputs found

    Physical/mechanical properties and microstructure of dental lithium disilicate ceramics for chairside CAD/CAM restoration

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผ, 2014. 2. ์†ํ˜ธํ˜„.1. ๋ชฉ์  ์ตœ๊ทผ ํ•œ๋ฒˆ์˜ ๋‚ด์›์œผ๋กœ ์‹ฌ๋ฏธ์  ์ˆ˜๋ณต์น˜๋ฃŒ๋ฅผ ์™„๋ฃŒํ•  ์ˆ˜ ์žˆ๋Š” CAD/CAM์„ ์ด์šฉํ•œ ์ˆ˜๋ณต์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋Š˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋‘ ๊ฐ€์ง€ ์ƒ์šฉํ™”๋œ CAD/CAM์šฉ ๋ฆฌํŠฌ๋‹ค์ด์‹ค๋ฆฌ์ผ€์ดํŠธ ์„ธ๋ผ๋ฏน์˜ ๋ฌผ๋ฆฌ/๊ธฐ๊ณ„์  ํŠน์„ฑ์„ ๋น„๊ตํ•˜๊ณ  ๊ฒฐ์ •์˜ ๋ฏธ์„ธ ๊ตฌ์กฐ๋ฅผ ๊ด€์ฐฐํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 2. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• IPS e.max CAD (Ivoclar Vivadent)์™€ Rosetta SM (Hass)์˜ ์ดˆ์ŒํŒŒ ํƒ„์„ฑ ๊ณ„์ˆ˜ (n=5), ์ด์ถ• ๊ตด๊ณก ๊ฐ•๋„ (n=30), ํ‘œ๋ฉด ๊ฒฝ๋„ (n=10), ํŒŒ๊ดด ์ธ์„ฑ (n=3), ์—ดํŒฝ์ฐฝ ๊ณ„์ˆ˜ (n=2)๋ฅผ ์ธก์ •ํ•˜์—ฌ ๋น„๊ตํ•˜๊ณ , ๊ฒฐ์ •์˜ ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด ์ „๊ณ„ ๋ฐฉ์ถœ ์ฃผ์‚ฌ ์ „์ž ํ˜„๋ฏธ๊ฒฝ ์ดฌ์˜๊ณผ X์„  ํšŒ์ ˆ ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ์ด์ถ• ๊ตด๊ณก ๊ฐ•๋„์™€ ํ‘œ๋ฉด ๊ฒฝ๋„์˜ ํ‰๊ท ๊ฐ’ ๋ถ„์„์„ ์œ„ํ•ด ์ด์›๋ถ„์‚ฐ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ดํ›„ ์ด์ถ• ๊ตด๊ณก ๊ฐ•๋„๋Š” ์ฃผํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด Students t-test๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ํ‘œ๋ฉด ๊ฒฝ๋„๋Š” ์‚ฌํ›„ ๋ถ„์„์„ ์œ„ํ•ด Tukey HDS๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํƒ„์„ฑ ๊ณ„์ˆ˜์™€ ํŒŒ๊ดด ์ธ์„ฑ์˜ ํ‰๊ท ๊ฐ’ ๋ถ„์„์—๋Š” ์—ด์ฒ˜๋ฆฌ์— ๋”ฐ๋ฅธ ์ฐจ์ด ๋ถ„์„์„ ์œ„ํ•ด Wilcoxon signed rank test ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์žฌ๋ฃŒ์— ๋”ฐ๋ฅธ ์ฐจ์ด๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด Mann-Whitney U test๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํŒŒ๊ดด ์ธ์„ฑ์˜ ํ‰๊ท ๊ฐ’ ๋ถ„์„์„ ์œ„ํ•ด Mann-Whitney U test๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค (ฮฑ = 0.05). 3. ๊ฒฐ๊ณผ ์ดˆ์ŒํŒŒ ํƒ„์„ฑ ๊ณ„์ˆ˜๋Š” ์—ด์ฒ˜๋ฆฌ ์ „ํ›„ ๋ชจ๋‘ IPS e.max CAD๊ฐ€ Rosetta SM๋ณด๋‹ค ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ˆ˜์ค€์œผ๋กœ ๋†’์€ ์ˆ˜์น˜๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๊ตด๊ณก ๊ฐ•๋„๋Š” ์—ด์ฒ˜๋ฆฌ ์ „๊ณผ ํ›„์—์„œ ๋‘ ์žฌ๋ฃŒ ์‚ฌ์ด์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ์œผ๋‚˜, ์—ด์ฒ˜๋ฆฌ ์ „, ํ›„์˜ ๋น„๊ต์—์„œ๋Š” ๋‘ ์žฌ๋ฃŒ ๋ชจ๋‘ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ˆ˜์ค€์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ‘œ๋ฉด ๊ฒฝ๋„ ์ธก์ •๊ฐ’์€ IPS e.max CAD์˜ ๊ฒฝ์šฐ ์—ด์ฒ˜๋ฆฌ ํ›„ ์œ ์˜ํ•˜๊ฒŒ ์ค„์–ด๋“ค์—ˆ์œผ๋ฉฐ, ๋ฐ˜๋Œ€๋กœ Rosetta SM์€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋‘ ์žฌ๋ฃŒ๋ฅผ ๋น„๊ตํ•˜๋ฉด ์—ด์ฒ˜๋ฆฌ ์ „์—๋Š” IPS e.max CAD๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์—ด์ฒ˜๋ฆฌ ํ›„์—๋Š” Rosetta SM์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ ๊ตฌํ•œ ํŒŒ๊ดด ์ธ์„ฑ ํ‰๊ท ๊ฐ’์€ ๋‘ ์žฌ๋ฃŒ ๋ชจ๋‘ ์—ด์ฒ˜๋ฆฌ ํ›„ ํ˜„์ €ํžˆ ์ฆ๊ฐ€๋จ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, IPS e.max CAD๊ฐ€ ์•ฝ๊ฐ„์”ฉ ๋†’์€ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ์œผ๋‚˜ ํ†ต๊ณ„์  ์˜๋ฏธ๋ฅผ ์ฐพ๊ธฐ๋Š” ์–ด๋ ค์› ๋‹ค. ์ „๊ณ„ ๋ฐฉ์ถœ ์ฃผ์‚ฌ ์ „์ž ํ˜„๋ฏธ๊ฒฝ๊ณผ ์—‘์Šค์„  ํšŒ์ ˆ ๋ถ„์„ ๊ฒฐ๊ณผ ๋‘ ์ œํ’ˆ์˜ ๊ฒฐ์ •์˜ ์ข…๋ฅ˜๋‚˜ ํ˜•ํƒœ๋Š” ์œ ์‚ฌํ•˜์˜€์œผ๋‚˜ ํฌ๊ธฐ๋Š” ์—ด์ฒ˜๋ฆฌ ํ›„์— Rosetta SM์ด ์กฐ๊ธˆ ๋” ์ž‘๊ณ  ์กฐ๋ฐ€ํ•œ ๋ชจ์Šต์ด์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ์— ๊ธฐ์ดˆํ•˜์—ฌ ์ž„์ƒ๊ฐ€๋“ค์€ ์ง„๋ฃŒ์‹ค์—์„œ CAD/CAM์„ ์‚ฌ์šฉํ•œ ๋ฆฌํŠฌ ๋‹ค์ด์‹ค๋ฆฌ์ผ€์ดํŠธ ๊ธ€๋ผ์Šค ์„ธ๋ผ๋ฏน ์ˆ˜๋ณต๋ฌผ์„ ์ œ์ž‘ํ•  ๋•Œ ์žฌ๋ฃŒ ์„ ํƒ์˜ ํญ์„ ๋Š˜๋ฆด ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ๊ธฐ๋Œ€๋œ๋‹ค.์„œ๋ก  1 ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• ์žฌ๋ฃŒ ๋ฐ ์‹œํŽธ ์ œ์ž‘ 6 ์‹คํ—˜ ๋ฐฉ๋ฒ• 1) ์ดˆ์ŒํŒŒ ํƒ„์„ฑ ๊ณ„์ˆ˜ ์ธก์ • 7 2) ์ด์ถ• ๊ตด๊ณก ๊ฐ•๋„ ์ธก์ • 8 3) ํ‘œ๋ฉด ๊ฒฝ๋„ ์ธก์ • 9 4) ํŒŒ๊ดด ์ธ์„ฑ ์ธก์ • 10 5) ์ „๊ณ„ ๋ฐฉ์ถœ ์ฃผ์‚ฌ ์ „์ž ํ˜„๋ฏธ๊ฒฝ ๊ด€์ฐฐ 10 6) ์—‘์Šค์„  ํšŒ์ ˆ ๋ถ„์„ 10 7) ์—ดํŒฝ์ฐฝ ๊ณ„์ˆ˜ ์ธก์ • 11 8) ํ†ต๊ณ„ ๋ถ„์„ 11 ๊ฒฐ๊ณผ 12 ์ด๊ด„ ๋ฐ ๊ณ ์ฐฐ 16 ๊ฒฐ๋ก  20 ์ฐธ๊ณ ๋ฌธํ—Œ 21 Abstract 26Docto

    A Solution Methodology for DistributedInformation System Configuration Problem Simultaneously Considering File Allocation and Computer Location Assignment

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    We have undertaken to develop an efficient solution methodology to adress distributed information system configuration problems through mathematical programming. We have simultaneously considered file allocation and computer location assignment problems which are two aspects of the design tighly coupled in a distributed computer system. A model for solving the problem is shown to be a class of nonlinearinteger programming problems and procedures are developed for computing its lower bound. A heuristic algorithm is also developed and some results are obtained. Numerical results yield practical low cost solutions with substantial savings in computer processing time

    An Efficient Workflow Management Scheme with Explicit Business Rules

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    In this paper, we have identified and classified various workflow operational rules. There are many business rules involved in the operation of workflow systems within the enterprise business environments. The rules are defined as ECA (Event-Condition- Action) rules and integrated with workflow systems with the active DB technology. Operational rules are categorized into task dispatching rules, dynamic process adaptation rules, exception handling rules, event-based monitoring rules, and external domain business rules. By adopting rule-based approach, the modification of business rules for process management can be easier. With the explicit management of business rules, the reasoning process of organizations can be formalized and managed transparently, which enables rapid and clear decision-making

    Resource Allocation Algorithm for Differentiated Multimedia Services Using Game Theory

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    ๋ณธ ๋…ผ๋ฌธ์€ 2008๋…„๋„ ํ•œ๊ตญ๊ฒฝ์˜๊ณผํ•™ํšŒ ์ถ”๊ณ„ํ•™์ˆ ๋Œ€ํšŒ ๊ฒฝ์Ÿ๋ถ€๋ฌธ(์ด๋ก ) ์ˆ˜์ƒ๋…ผ๋ฌธ์œผ๋กœ ์†Œ์ •์˜ ์‹ฌ์‚ฌ๊ณผ์ •์„ ๊ฑฐ์ณ ๊ฒŒ์žฌ ์ถ”์ฒœ๋˜์—ˆ์Œ.Game theory is adapted to a variety of domains such as economics, biology, engineering, political science, computer science, and philosophy in order to analyze economic behaviors. This research is an application of game theory to wireless communication. In particular, in terms of bargaining game we dealt with a multimedia resource allocation problem in wireless communication, which is rapidly spreading such as Wibro, WCDML, IPTV, etc. The algorithm is assumed to allocate multimedia resources to users who can choose and access differentiated media services. For this purpose, a utility function of users is devised to reflect quality of service (QoS) and price. We illustrated experimental results with synthesis data which were made to mimic real multimedia data, and analyzed differentiated service providing and the effect of the utility function.๋ณธ ์—ฐ๊ตฌ๋Š” ์ง€์‹๊ฒฝ์ œ๋ถ€ ๋ฐ ์ •๋ณดํ†ต์‹  ์—ฐ๊ตฌ์ง„ํฅ์›์˜ IT์›์ฒœ๊ธฐ์ˆ ๊ฐœ๋ฐœ์‚ฌ์—…(IITA-2008-F-005-01)์˜ ์ง€์›์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ์Œ

    A Rich Web Search Mechanism using Linkage Information

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    ๊ธฐ์กด์˜ ์›น ๊ฒ€์ƒ‰ ์‹œ์Šคํ…œ์€ ์ฃผ์–ด์ง„ ํ‚ค์›Œ๋“œ๋“ค์„ ๋ชจ๋‘ ํฌํ•จํ•œ ์›น ํŽ˜์ด์ง€์˜ ๋„์ถœ์„ ๋ชฉํ‘œ๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ฒ€ ์ƒ‰์–ด์˜ popularity๊ฐ€ ๋–จ์–ด์ง€๋Š” ๊ฒฝ์šฐ๋‚˜, ํ‚ค์›Œ๋“œ์˜ ํ•œ์ • ์ •๋„๊ฐ€ ๊ณผ๋„ํ•˜๊ฑฐ๋‚˜ ๋งŽ์•„์ง€๋Š” ๊ฒฝ์šฐ, ๋˜๋Š” ํ‚ค์›Œ๋“œ๊ฐ€ ๊ธธ ์–ด์ง€๋Š” ๊ฒฝ์šฐ ๋“ฑ์—๋Š” ๊ฒ€์ƒ‰๊ฒฐ๊ณผ๊ฐ€ ํฌ๊ฒŒ ์ค„์–ด๋“œ๋Š” ๊ฒฐ๊ณผํฌ์†Œ์„ฑ ๋ฌธ์ œ(scarcity problem)๋ฅผ ๊ฒช๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์— ์„œ๋Š” ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ฒ€์ƒ‰๊ฒฐ๊ณผ๋ฅผ ๊ฐœ๋ณ„ ์›นํŽ˜์ด์ง€ ์ง‘ํ•ฉ์ด ์•„๋‹Œ ๊ด€๋ จ ์›นํŽ˜์ด์ง€๋“ค๊ฐ„์˜ ๋งํฌ ๊ตฌ์กฐ๋กœ ๋„ ์ถœํ•ด์ฃผ๋Š” ํ™•์žฅ๋œ ๊ฒ€์ƒ‰ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ํ™•์žฅ๋œ ์›น ๊ฒ€์ƒ‰์€ ๋งํฌ๊ธฐ๋ฐ˜ ๋ถ„์„์„ ํ™œ์šฉํ•˜์—ฌ ๊ฐœ๋ณ„ ์งˆ์˜๋“ค์— ๋Œ€ ํ•œ ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ ํŽ˜์ด์ง€๋“ค๊ฐ„์˜ ๋งํฌ ๊ด€๊ณ„๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ , ๋„์ถœ๋œ ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ์˜ ์ˆœ์œ„ ์ธก์ •์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ํ™•์žฅ๋œ ์›น ๊ฒ€์ƒ‰ ๋ฐฉ๋ฒ•์€ ๊ฒฐ๊ณผํฌ์†Œ์„ฑ ๋ฌธ์ œ์— ํšจ๊ณผ์ ์ž„์„ ๋ณด์˜€๋‹ค

    ๋‹ค์ค‘๋ถ„๋ฅ˜ ๋ฌธ์ œ๋ฅผ ์œ„ํ•œ ์ด์งˆ์  ์•™์ƒ๋ธ” ํ•™์Šต

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฐ์—…๊ณตํ•™๊ณผ, 2015. 8. ์กฐ์„ฑ์ค€.In data mining, classification is a type of supervised learning task that involves predicting output variables consisting of a finite number of categories called classes. When the number of classes is larger than two, a classification problem is called a multi-class classification problem. Multi-class classification provides more informative predictions, and is more related to real-world scenarios. In practice, the performance for a multi-class classification problem is typically measured according to the following three perspectives: accurate, reliable, and fast classification. In order to achieve the better performance for the three perspectives, this dissertation proposes to use heterogeneous ensemble learning that exploits multiple classifiers from various classification algorithms, where each classifier plays a different role to accomplish the desired functionality. For accurate multi-class classification, Diversified One-Against-One (DOAO) and Optimally Diversified One-Against-One (ODOAO) are proposed. Their main idea is to decompose the original problem into several binary sub-problems based on the one-against-one approach. DOAO finds the best classification algorithm for each class pair from the set of heterogeneous base classifiers, thereby makes various classification algorithms to complement each other. Since the best classification algorithm for each class pair is different, DOAO enables better classification accuracy. ODOAO, an extension of DOAO, construct an ensemble where a meta-classifier effectively combines the outputs from all the heterogeneous base classifiers. Heterogeneous Ensemble of One-class Classifiers (HEOC) is also proposed for accurate classification based on decomposition of the original problem into several one-class sub-problems. HEOC constructs an ensemble consisting of one-class classifiers from various one-class classification algorithms. HEOC addresses the normalization of heterogeneous base classifiers via stacking. For reliable multi-class classification, a hybrid reject option is proposed to reject ambiguous instances instead of predicting for all instances. The hybrid reject option constructs a filter classifier and a predictor classifier separately, where the filter decides whether to predict using the predictor based on the confidence for an instance, and the predictor predicts the class of the instance. Each component is trained using the best respective classification algorithm to maximize the capability of its role, thereby improve reject option performance as providing better prediction accuracy for the same degree of rejection. For fast multi-class classification, Neural Network Approximator (NNA) is proposed to reduce computational time in the test phase. NNA approximates a classifier by adopting a multiple-outputs artificial neural network as a function approximator, where each output node corresponds to a decision function in the classifier. This approximator enables fast classification speed without compromising accuracy. The effectiveness of the proposed heterogeneous ensemble methods is demonstrated through experiments on benchmark datasets and real-world applications.1. Introduction 2. Literature Review 3. Heterogeneous Ensemble for Accurate Classification: Binary Classifier Approach 4. Heterogeneous Ensemble for Accurate Classification: One-class Classifier Approach 5. Heterogeneous Ensemble for Reliable Classification 6. Heterogeneous Ensemble for Fast Classification 7. ConclusionDocto

    Syntheses, Structures and Properties of Novel Discotic Liquid Crystals

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    ์ •์ˆ˜๊ณ„ํš๋ฒ•์„ ์ด์šฉํ•œ ํ”„๋กœ์ ํŠธ ํ™•์žฅ์ˆœ์„œ๊ฒฐ์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Planning for the expansion of production capacity is of vital importance in many applications within the private and public sectors. This paper considers a sequencing expansion problem in which capacity can be added only at discrete points in time. Given the demand forecast of each period, capacity and cost of each expansion project, we are to determine the sequence of expansion necessary to provide sufficient capacity to meet the demand in all periods at minimum cost. This problem is formulated as a pure integer programming and solved by branch and bound method using Lagrangian relaxation. At first. simple sequencing expansion problem is presented, and in the latter part, extension to include precedence between projects is suggested

    Studies on the intracellular distribution of myocardial catecholamines of normal animals

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] [์˜๋ฌธ] Accumulated evidence indicates that the catecholamines are stored as granule within the celles of adrenal medulla or sympathetic nervous tissues. Myocardial catecholmines have been shown to be present in the particulate fraction as well as in the soluble fraction obtained by differential centrifugation of cell free suspension from homogenates of cardiac muscle (Potter and Axelrod, 1962; Wegman and Kako, 1962; Campos Shideman, 1962). Howere, according to these investigatore the ratio to that in the soluble fraction of myocardium varies considerably. This may be attributed to a species and fraction procedure difference. Present experiment was designed to determine the intracellular distribution of myocardial catecholamines, with special reference to the animal species. The heart was rapidly removed from animals under ether anesthesia and 5 volumes of 0.38 M ice-cold sucose in a waring blender. The resultant suspension was passed through a double layer of gauze to remove the fibrous tissue. The homogenate was then centrifuged at 3-5โ„ƒ for 10 minutes at 600 xg (low-speed centrifugation), which brought down incompletely fragmented cells and nucli. A low-speed supernatant was thus obtained, and was again centrifuged at 3-5โ„ƒ for 30 minutes at 15,000 xg (high-speed centrifugation), which fractionated into supernatant and sediment. The concentration of catecholamines in each fraction obtained by the above differential centrifugation, was determined spectrophotofluormetrically by the modified procedure of the method described by Shore and Olin (1958). 1. Examination of the intracellular distribution of the myocardial catecholamines of normal rabbits showed that a higher concentration of the aminos was present in the high-speed supernatant fraction (0.40ug/gm) and the ratio of supernatant/sediment was 4. The catecholamines present in the low-speed supernatant was almost completely recovered in both fractions obtained by high-speed centrifugation. 2. In the cardiac homogenate of normal cats, a similar intracellular distribution of catecholamines was observed. Thus, the concentrations of amines in high-speed supernatant and sediment were 0.48 and 0.09ug/gm respectively. 3.In the experiment with cardiac homogenate of normal rate, it was also found that higher concentration of catecholamines was present in the high-speed supernatant and the ratio of supernatant/sediment was 4.3 which was not significantly different from those observed in the cardiac homogenates of normal rabbits or cats. The concentrations of catecholamines in the high-speed supernatant and sediment fractions would not represent the actual distribution in vivo. However, these values are reproducible and relatively constant under the described experimental conditions. Therefore, these may be considered useful as a criteria for the study of intracellular distribution of myocardial catecholamines. From the above results, it may be concluded that there is no significant difference in the intracellular distribution of myocardial catecholamines of normal rabbits, cats and rats.restrictio
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