• Title/Summary/Keyword: Genetic Structure

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SEPT12 Interacts with SEPT6 and This Interaction Alters the Filament Structure of SEPT6 in Hela Cells

  • Ding, Xiangming;Yu, Wenbo;Liu, Ming;Shen, Suqin;Chen, Fang;Wan, Bo;Yu, Long
    • BMB Reports
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    • v.40 no.6
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    • pp.973-978
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    • 2007
  • Septins are a family of conserved cytoskeletal GTPase forming heteropolymeric filamentous structure in interphase cells, however, the mechanism of assembly are largely unknown. Here we described the characterization of SEPT12, sharing closest homology to SEPT3 and SEPT9. It was revealed that subcelluar localization of SEPT12 varied at interphase and mitotic phase. While SEPT12 formed filamentous structures at interphase, it was localized to the central spindle and to midbody during anaphase and cytokinesis, respectively. In addition, we found that SEPT12 can interact with SEPT6 in vitro and in vivo, and this interaction was independent of the coiled coil domain of SEPT6. Further, co-expression of SEPT12 altered the filamentous structure of SEPT6 in Hela cells. Therefore, our result showed that the interaction between different septins may affect the septin filament structure.

Optimization of a Composite Laminated Structure by Network-Based Genetic Algorithm

  • Park, Jung-Sun;Song, Seok-Bong
    • Journal of Mechanical Science and Technology
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    • v.16 no.8
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    • pp.1033-1038
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    • 2002
  • Genetic alsorithm (GA) , compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.

Multilocus Genotyping to Study Population Structure in Three Buffalo Populations of India

  • Tantia, M.S.;Vijh, R.K.;Mishra, Bina;Kumar, S.T. Bharani;Arora, Reena
    • Asian-Australasian Journal of Animal Sciences
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    • v.19 no.8
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    • pp.1071-1078
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    • 2006
  • Three buffalo populations viz. Bhadawari, Tarai and local buffaloes of Kerala were genotyped using 24 heterologous polymorphic microsatellite loci. A total of 140 alleles were observed with an average observed heterozygosity of 0.63. All the loci were neutral and 18 out of the 24 loci were in Hardy Weinberg Equilibrium. The $F_{IS}$ values (estimate of inbreeding) for 16 loci in all the three populations were negative. This indicated lack of population structure in the three populations. The effective number of immigrants was 5.88 per generation between the Tarai and Bhadawari populations which was quite high suggesting substantial gene flow. The genetic distances revealed closeness between the Tarai and Bhadawari populations which was expected from geographical contiguity. The FST values were not significantly different from zero showing no population differentiation. The Correspondence Analysis based on the allelic frequency data clustered the majority of the Tarai and Bhadawari individuals as an admixture.

A Study on Optimal Neural Network Structure of Nonlinear System using Genetic Algorithm (유전 알고리즘을 이용한 비선형 시스템의 최적 신경 회로망 구조에 관한 연구)

  • Kim, Hong-Bok;Kim, Jeong-Keun;Kim, Min-Jung;Hwang, Seung-Wook
    • Journal of Navigation and Port Research
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    • v.28 no.3
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    • pp.221-225
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    • 2004
  • This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

MOGA-Based Structural Design Method for Diagrid Structural Control System Subjected to Wind and Earthquake Loads

  • Kim, Hyun-Su;Kang, Joo-Won
    • International journal of steel structures
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    • v.18 no.5
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    • pp.1598-1606
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    • 2018
  • An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

Pelagic larval dispersal habits influence the population genetic structure of clam Gomphina aequilatera in China

  • Ye, Yingying;Fu, Zeqin;Tian, Yunfang;Li, Jiji;Guo, Baoying;Lv, Zhenming;Wu, Changwen
    • Genes and Genomics
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    • v.40 no.11
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    • pp.1213-1223
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    • 2018
  • Pelagic larval dispersal habits influence the population genetic structure of marine mollusk organisms via gene flow. The genetic information of the clam Gomphina aequilatera (short larval stage, 10 days) which is ecologically and economically important in the China coast is unknown. To determine the influence of planktonic larval duration on the genetic structure of G. aequilatera. Mitochondrial markers, cytochrome oxidase subunit i (COI) and 12S ribosomal RNA (12S rRNA), were used to investigate the population structure of wild G. aequilatera specimens from four China Sea coastal locations (Zhoushan, Nanji Island, Zhangpu and Beihai). Partial COI (685 bp) and 12S rRNA (350 bp) sequences were determined. High level and significant $F_{ST}$ values were obtained among the different localities, based on either COI ($F_{ST}=0.100-0.444$, P<0.05) or 12S rRNA ($F_{ST}=0.193-0.742$, P<0.05), indicating a high degree of genetic differentiation among the populations. The pairwise $N_m$ between Beihai and Zhoushan for COI was 0.626 and the other four pairwise $N_m$ values were >1, indicating extensive gene flow among them. The 12S rRNA showed the same pattern. AMOVA test results for COI and 12S rRNA indicated major genetic variation within the populations: 77.96% within and 22.04% among the populations for COI, 55.73% within and 44.27% among the populations for 12S rRNA. A median-joining network suggested obvious genetic differentiation between the Zhoushan and Beihai populations. This study revealed the extant population genetic structure of G. aequilatera and showed a strong population structure in a species with a short planktonic larval stage.

Genetic diversity and population genetic structure of Cambodian indigenous chickens

  • Ren, Theary;Nunome, Mitsuo;Suzuki, Takayuki;Matsuda, Yoichi
    • Animal Bioscience
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    • v.35 no.6
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    • pp.826-837
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    • 2022
  • Objective: Cambodia is located within the distribution range of the red junglefowl, the common ancestor of domestic chickens. Although a variety of indigenous chickens have been reared in Cambodia since ancient times, their genetic characteristics have yet to be sufficiently defined. Here, we conducted a large-scale population genetic study to investigate the genetic diversity and population genetic structure of Cambodian indigenous chickens and their phylogenetic relationships with other chicken breeds and native chickens worldwide. Methods: A Bayesian phylogenetic tree was constructed based on 625 mitochondrial DNA D-loop sequences, and Bayesian clustering analysis was performed for 666 individuals with 23 microsatellite markers, using samples collected from 28 indigenous chicken populations in 24 provinces and three commercial chicken breeds. Results: A total of 92 haplotypes of mitochondrial D-loop sequences belonging to haplogroups A to F and J were detected in Cambodian chickens; in the indigenous chickens, haplogroup D (44.4%) was the most common, and haplogroups A (21.0%) and B (13.2%) were also dominant. However, haplogroup J, which is rare in domestic chickens but abundant in Thai red junglefowl, was found at a high frequency (14.5%), whereas the frequency of haplogroup E was considerably lower (4.6%). Population genetic structure analysis based on microsatellite markers revealed the presence of three major genetic clusters in Cambodian indigenous chickens. Their genetic diversity was relatively high, which was similar to findings reported for indigenous chickens from other Southeast Asian countries. Conclusion: Cambodian indigenous chickens are characterized by mitochondrial D-loop haplotypes that are common to indigenous chickens throughout Southeast Asia, and may retain many of the haplotypes that originated from wild ancestral populations. These chickens exhibit high population genetic diversity, and the geographical distribution of three major clusters may be attributed to inter-regional trade and poultry transportation routes within Cambodia or international movement between Cambodia and other countries.

Genetic structure and population differentiation of endangered Scrophularia takesimensis (Scrophulariaceae) in Ulleung Island, Korea

  • Ma, Sunmi;Lim, Yongseok;Na, Sungtae;Lee, Jun;Shin, Hyunchur
    • Korean Journal of Plant Taxonomy
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    • v.41 no.3
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    • pp.182-193
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    • 2011
  • As a part of the on-going effort to conserve endangered Scrophularia takesimensis Nakai in Korea, its genetic structure and diversity from 3 population, consisted of 14 subpopulations in Ulleung Island were analyzed using RAPD band patterns. Out of 60 primers tested, 33 generated amplified bands with its genome, including 149 polymorphic and 67 monomorphic bands. The highest number (146) was found in northern population, especially, 64 in HY subpopulation; the smallest (40) in eastern population. An examination of its genetic structure with AMOVA revealed that about 60% of all variations could be assigned to among subpopulations within populations. Population differentiation among populations and subpopulations is seriously going now because of habitat fragmentation due to human activities, such as road and small port construction. Although the habitats of S. takesimensis in Ulleung Island, Korea are disappeared at an alarming rate, significant levels of genetic variation still exist at species level, and population level, especially northern population. Therefore, three conservation strategies should be needed urgently; 1) preservation of populations as it stands, 2) establishment of recovery plan to connect population and subpopulations genetically, and 3) long-term monitoring.

Genetic Diversity and Population Structure of Kaloula borealis (Anura, Microhylidae) in Korea

  • Yang, Suh-Yung;Kim, Jong-Bum;Min, Mi-Sook;Suh, Jae-Hwa;Kang, Young-Jin
    • Animal cells and systems
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    • v.4 no.1
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    • pp.39-44
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    • 2000
  • To assess the genetic diversity and population structure of Korean K. borealis, allozyme analysis was performed. The average genetic variability of Korean K. borealis populations was %P=13.2, Ho=0.048, and He=0.045. This value was the lowest in comparison with other Korean amphibian species studied. Also, the value was much lower than that of a reference population from Chinese K. borealis (%P=50, Ho=0.125, He=0.172). Wright's F-statistics showed that Korean K. borealis has distinctly low level of gene flow among regional populations (F$_{ST}$=0.339, Nm=0.487) in comparison with other Korean amphibian species studied. However, the average level of genetic divergence among Korean K. borealis populations was moderate (Nei's D=0.020). Therefore, it appeared that low levels of genetic diversity (He=0.045) and gene flow (Nm=0.487) among regional populations ave probably due to the results of decreasing population size and patchy distribution of this species in Korea.

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Fuzzy Logic Controller Design via Genetic Algorithm

  • Kwon, Oh-Kook;Wook Chang;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.612-618
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    • 1998
  • The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

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