• Korean Journal Plant Pathology
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• v.13 no.2
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• pp.113-117
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• 1997

Low moleuclar weight (LMW) RNAs were isolated form Korean peonies which expressed symptoms of stunt and epinasty. The LMW plant RNAs were purified by Qiagen column chromatography which could separate viroid specific nucleic acid at differential salt concentration. After the inoculation of the purified RNAs from the peonies, the inoculated tomatoes (cv. Rutgers) expressed the symptoms of stunt and epinasty. Also the same molecular weight RNAs with viroid-like RNAs were isolated from the inoculated tomatoes. Double-stranded cDNA were synthesized by the methods of reverse transcription (RT) and polymerase chain reaction (PCR) with the purified RNA and primers. The same cDNAs associated with viroid-like RNAs wre cloned from the inoculated tomatoes. The cDNA has been sequenced and its 375-nucleotides were arranged into secondary structure. The cloned cDNA showed 47~54% homology compared with other viroids. The sequence homology of the cloned cDNA were partially high with plant genomic RNAs.

• Food Science of Animal Resources
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• v.29 no.2
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• pp.164-167
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• 2009

The lactoferrin from mongolian mare colostrum has been purified by gel filtration (Sephadex G-100), affinity chromatography (Toyopear1-AF-Heparin-650M) in two steps. Mare lactoferrin-containing fractions were identified in the first peak among 3 peaks on Sephadex G-100 as first step, and purified lactoferrin was eluted with a step gradient of 0.5M NaCl as a 3 step (gradient 0.1,0.3, 0.5M). Eluted fractions were analyzed by 12% SDS-PAGE, and showed a single protein. Its molecular weight was estimated to be 82kDa. N-terminal amino acid sequence was determined as APRKSVRWCTISPAEXAKXA.

• Annals of Fuzzy Mathematics and Informatics
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• v.16 no.3
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• pp.337-361
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• 2018

We introduce and study some basic properties of rough $I_{\lambda}$-statistical convergent of weight g (A), where $g:{\mathbb{N}}^3{\rightarrow}[0,\;{\infty})$ is a function statisying $g(m,\;n,\;k){\rightarrow}{\infty}$ and $g(m,\;n,\;k){\not{\rightarrow}}0$ as $m,\;n,\;k{\rightarrow}{\infty}$ and A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g (A) limits of a triple sequence of Bernstein-Stancu polynomials.

• Kyungpook Mathematical Journal
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• v.46 no.3
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• pp.337-343
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• 2006

In the present paper we shall prove that on a foundation *-semigroup S with an identity and with a locally bounded Borel measurable weight function ${\omega}$, the pointwise convergence and the uniform convergence of a sequence of ${\omega}$-bounded exponentially convex functions on S which are also continuous at the identity are equivalent.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.111-121
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• 1991

This paper considers a non preemtive single processor scheduling problem in which each set have the two common due dates. The objective of the problem is to minimize the weighted mean absolute deviation of job completion times about such two common due dates under the assumption that each job has a different weight. Such a job sequence is an W-shaped sequence. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. The performance comparison is made among three heuristic scheduling procedures.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• Composites Research
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• v.33 no.5
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• pp.288-295
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• 2020

Fiber-reinforced composite laminates have high specific stiffness and strength and are expected to be useful for weight reduction in weight-sensitive industries, such as automotive and aerospace. However, designing composite laminates is often dependent on designer's experience and intuition because of difficulties in determining the number of plies and stacking sequence, which tends to lead to over-design. In this study, optimal design of composite laminates was performed to minimize weight, while withstanding the given load. Based on the enumeration method, all combinations of stacking sequence satisfying the design guideline for composite laminates were considered. Composite laminates were discretized into panels. Optimal number of plies and stacking sequence for each panel were determined considering local load on each panel and contiguity across adjacent panels. Failure index from Tsai-Wu criteria was optimized for strength and buckling analysis was performed for compressive load. Stacking angles of 0, ±45 and 90° were used.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.3
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• pp.237-245
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• 2004

The deformities of micrognathia and glossoptosis in the newborn are frequently associated with a cleft palate, which is known as Pierre Robin sequence. Upper airway obstruction is the most serious problem in these patients. Treatment of Pierre Robin sequence includes either positional or surgical intervention. Mild cases are often managed in the prone position. However, when the patient fails to thrive due to chronic upper airway obstruction, or severe respiratory distress ensures despite positional treatment, surgical intervention is mandatory to relieve the obstruction. We experienced three infants with Pierre Robin sequence who showed a symptom triad of micrognathia, glossoptosis, and cleft palate. Intermittent cyanosis, depression of the chest, respiratory difficulty and feeding problems were also observed. To relieve severe upper airway obstruction caused by micrognathia and glossoptosis, we simultaneously performed modified tongue lip adhesion (TLA) and a subperiosteal release of the floor of the mouth (SRFM). Respiratory and feeding difficulties were relieved, the tongue positioned anteriorly, body weight increased, and mandibular growth improved. Simultaneous TLA and SRFM may constitute a simple and reliable method for surgical treatment of airway obstruction in patients with Pierre Robin sequence.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• BMB Reports
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• v.30 no.3
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• pp.205-210
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• 1997

p53 tumor suppressor plays an important role in the regulation of cellular proliferation. To identify proteins regulating the expression of p53 in rat liver, we analyzed p53 promoter by electrophoretic mobility shift assay (EMSA) and DNase I footprinting assay. We found that a protein binds the sequence CACGTG, bHLH consensus sequence in rat p53 promoter. Southwestern blotting analysis with oligonucleotides containing this sequence shows that the molecular weight of the protein is 100 kDa. This size is not compatible with the bHLH family such as USF or c-Myc/Max which is known to regulate the expression of the human and mouse p53 gene. Therefore this 100 kDa protein may be a new protein regulating basal transcription of rat p53. We purified this 100 kDa protein through sequence-specific DNA affinity chromatogaphy.