• Title, Summary, Keyword: negative selection

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Modeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm

  • Sim, Kwee-Bo;Lee, Dong-Wook
    • International Journal of Control, Automation, and Systems
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    • v.1 no.4
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    • pp.453-458
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    • 2003
  • The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

Negative Selection Algorithm for DNA Pattern Classification

  • Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.190-195
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    • 2004
  • We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

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Negative Selection Algorithm for DNA Sequence Classification

  • Lee, Dong Wook;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.231-235
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    • 2004
  • According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

The Effect of Selection Attribute of HMR Product on the Consumer Purchasing Intention of an Single Household - Centered on the Regulation Effect of Consumer Online Reviews - (HMR 상품의 선택속성이 1인 가구의 소비자 구매의도에 미치는 영향 - 소비자 온라인 리뷰의 조절효과 중심으로 -)

  • Kim, Hee-Yeon
    • Culinary science and hospitality research
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    • v.22 no.8
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    • pp.109-121
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    • 2016
  • This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

Single-Base Genome Editing in Corynebacterium glutamicum with the Help of Negative Selection by Target-Mismatched CRISPR/Cpf1

  • Kim, Hyun Ju;Oh, Se Young;Lee, Sang Jun
    • Journal of Microbiology and Biotechnology
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    • v.30 no.10
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    • pp.1583-1591
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    • 2020
  • CRISPR/Cpf1 has emerged as a new CRISPR-based genome editing tool because, in comparison with CRIPSR/Cas9, it has a different T-rich PAM sequence to expand the target DNA sequence. Single-base editing in the microbial genome can be facilitated by oligonucleotide-directed mutagenesis (ODM) followed by negative selection with the CRISPR/Cpf1 system. However, single point mutations aided by Cpf1 negative selection have been rarely reported in Corynebacterium glutamicum. This study aimed to introduce an amber stop codon in crtEb encoding lycopene hydratase, through ODM and Cpf1-mediated negative selection; deficiency of this enzyme causes pink coloration due to lycopene accumulation in C. glutamicum. Consequently, on using double-, triple-, and quadruple-base-mutagenic oligonucleotides, 91.5-95.3% pink cells were obtained among the total live C. glutamicum cells. However, among the negatively selected live cells, 0.6% pink cells were obtained using single-base-mutagenic oligonucleotides, indicating that very few single-base mutations were introduced, possibly owing to mismatch tolerance. This led to the consideration of various target-mismatched crRNAs to prevent the death of single-base-edited cells. Consequently, we obtained 99.7% pink colonies after CRISPR/Cpf1-mediated negative selection using an appropriate single-mismatched crRNA. Furthermore, Sanger sequencing revealed that single-base mutations were successfully edited in the 99.7% of pink cells, while only two of nine among 0.6% of pink cells were correctly edited. The results indicate that the target-mismatched Cpf1 negative selection can assist in efficient and accurate single-base genome editing methods in C. glutamicum.

Introduction to a Novel Optimization Method : Artificial Immune Systems (새로운 최적화 기법 소개 : 인공면역시스템)

  • Yang, Byung-Hak
    • IE interfaces
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    • v.20 no.4
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    • pp.458-468
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    • 2007
  • Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

Approaching the Negative Super-SBM Model to Partner Selection of Vietnamese Securities Companies

  • NGUYEN, Xuan Huynh;NGUYEN, Thi Kim Lien
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.3
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    • pp.527-538
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    • 2021
  • The purpose of the study is to determine the efficiency, position, and partner selection of securities companies via the negative super-SBM model used in data envelopment analysis (DEA). This model utilizes a variety of inputs, including current assets, non-current assets, fixed assets, liabilities, owner's equity and charter capital, and outputs including net revenue, gross profit, operating profit, and net profit after tax collected from the financial reports (Vietstock, 2020) of 32 securities companies, operating during the period from 2016 to 2019, negative data are collected as well. Empirical results determined both efficient and inefficient terms, and then further determined the position of each securities firm under consideration of every term. The overall score arrived at discovered a large performance change realizing a maximum score able to reach 20.791. In the next stage, alliancing inefficient companies was carried out based on the 2019 scores to seek out optimal partners for the inefficient companies. The tested result indicated that AAS was the best partner selection when its partners received a good result after alliancing, as with FTS (11.04469). The partner selection is deemed as a solution helpful to inefficient securities companies in order to improve their future efficiency scores.

Fault Phase Selection Algorithm using Unit Vector of Sequence Voltages for Transmission Line Protection (대칭분 전압 단위 벡터를 이용한 송전선로 보호용 고장상 선택 알고리즘)

  • Lee, Myeong-Su;Lee, Jae-Gyu;Kim, Su-Nam;Yu, Seok-Gu
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.51 no.9
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    • pp.460-466
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    • 2002
  • A reliable fault phase selection algorithm plays a very important role in transmission line protection, Particularly in Extra High Voltage (EHV) networks. The conventional fault phase selection algorithm used the phase difference between positive and negative sequence current excluding load current. But, it is difficult to pick out only fault current since we can not know when a fault occurs and select the fault phase in weak-infeed conditions that dominate zero-sequence current in phase current. The proposed algorithm can select the accurately fault phase using the sum of unit vectors which are calculated by positive-sequence voltage and negative-sequence voltage.

Identification of Chinese Event Types Based on Local Feature Selection and Explicit Positive & Negative Feature Combination

  • Tan, Hongye;Zhao, Tiejun;Wang, Haochang;Hong, Wan-Pyo
    • Journal of information and communication convergence engineering
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    • v.5 no.3
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    • pp.233-238
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    • 2007
  • An approach to identify Chinese event types is proposed in this paper which combines a good feature selection policy and a Maximum Entropy (ME) model. The approach not only effectively alleviates the problem that classifier performs poorly on the small and difficult types, but improve overall performance. Experiments on the ACE2005 corpus show that performance is satisfying with the 83.5% macro - average F measure. The main characters and ideas of the approach are: (1) Optimal feature set is built for each type according to local feature selection, which fully ensures the performance of each type. (2) Positive and negative features are explicitly discriminated and combined by using one - sided metrics, which makes use of both features' advantages. (3) Wrapper methods are used to search new features and evaluate the various feature subsets to obtain the optimal feature subset.

Artificial and Natural Selection for Phototactic Behavior in Drosophila melanogaster (초파리의 走光性行動에 대한 人爲淘汰와 自然淘汰)

  • Choo, Jong-Kil
    • The Korean Journal of Zoology
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    • v.20 no.1
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    • pp.1-8
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    • 1977
  • Several hundred flies of Drosophila melanogaster collected in Anyang City were selected for positive, negative and neutral directions during 15 generations. The population responded effectively to the artificial selection. The realized heritability estimated for the first 10 generations was $2 \\sim 4%$ per generation in the positive and negative phototaxis. The results of diallel crosses among selected populations indicated that some polygenes showing a negative phototaxis were partially dominant over polygenes controlled the positive phototaxis, and the dominant effect became greater in later generations. The populations selected for positive and negative phototaxis were relaxed after 15 generations of selection, and their phototactic responses were completely returned to their original states. Such phenomena would be explained by the genetic homeostasis resulted from an action of natural selection. It seems reasonable to assume that the phototactic neutrality of a natural population was maintained as an adaptive trait under natural environment.

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