• The Plant Pathology Journal
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• v.17 no.2
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• pp.67-70
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• 2001

Medicago truncatula is a diploid legume plant related to the forage crop alfalfa. Recently, it has been chosen as a model species for genomic studies due to its small genome, self-fertility, short generation time, and high transformation efficiency. M. truncatula engages in symbiosis with nitrogen-fixing soil bacterium Rhizobium meliloti. M. truncatula mutants that are defective in nodulation and developmental processes have been generated. Some of these mutants exhibited altered phenotypes in symbiotic responses such as root hair deformation, expression of nodulin genes, and calcium spiking. Thus, the genes controlling these traits are likely to encode functions that are required for Nod-factor signal transduction pathways. To facilitate genome analysis and map-based cloning of symbiotic genes, a bacterial artificial chromosome library was constructed. An efficient polymerase chain reaction-based screening of the library was devised to fasten physical mapping of specific genomic regions. As a genomics approach, comparative mapping revealed high levels of macro- and microsynteny between M. truncatula and other legume genomes. Expressed sequence tags and microarray profiles reflecting the genetic and biochemical events associated with the development and environmental interactions of M. truncatula are assembled in the databases. Together, these genomics programs will help enrich our understanding of the legume biology.

• Journal of Preventive Medicine and Public Health
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• v.42 no.6
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• pp.356-359
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• 2009

In the more than 100 genome wide association studies (GWAS) conducted in the past 5 years, more than 250 genetic loci contributing to more than 40 common diseases and traits have been identified. Whilst many genes have been linked to a trait, both their individual and combined effects are small and unable to explain earlier estimates of heritability. Given the rapid changes in disease incidence that cannot be accounted for by changes in diagnostic practises, there is need to have well characterized exposure information in addition to genomic data for the study of gene-environment interactions. The case-control and cohort study designs are most suited for studying associations between risk factors and occurrence of an outcome. However, the case control study design is subject to several biases and hence the preferred choice of the prospective cohort study design in investigating geneenvironment interactions. A major limitation of utilising the prospective cohort study design is the long duration of follow-up of participants to accumulate adequate outcome data. The GWAS paradigm is a timely reminder for traditional epidemiologists who often perform one- or few-at-a-time hypothesis-testing studies with the main hallmarks of GWAS being the agnostic approach and the massive dataset derived through large-scale international collaborations.

• Molecules and Cells
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• v.37 no.1
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• pp.36-42
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• 2014

The tomato (Solanum lycopersicum L.) is a model plant for genome research in Solanaceae, as well as for studying crop breeding. Genome-wide single nucleotide polymorphisms (SNPs) are a valuable resource in genetic research and breeding. However, to do discovery of genome-wide SNPs, most methods require expensive high-depth sequencing. Here, we describe a method for SNP calling using a modified version of SAMtools that improved its sensitivity. We analyzed 90 Gb of raw sequence data from next-generation sequencing of two resequencing and seven transcriptome data sets from several tomato accessions. Our study identified 4,812,432 non-redundant SNPs. Moreover, the workflow of SNP calling was improved by aligning the reference genome with its own raw data. Using this approach, 131,785 SNPs were discovered from transcriptome data of seven accessions. In addition, 4,680,647 SNPs were identified from the genome of S. pimpinellifolium, which are 60 times more than 71,637 of the PI212816 transcriptome. SNP distribution was compared between the whole genome and transcriptome of S. pimpinellifolium. Moreover, we surveyed the location of SNPs within genic and intergenic regions. Our results indicated that the sufficient genome-wide SNP markers and very sensitive SNP calling method allow for application of marker assisted breeding and genome-wide association studies.

• Proceedings of the KIEE Conference
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• summer
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• pp.292-294
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• 2004

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing TCSC-PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership Auction and control rules. Simulation results show that the proposed control technique is superior to a conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of Veterinary Clinics
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• v.37 no.1
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• pp.50-52
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• 2020

Twenty-four cinereous vultures that had been taken to a wildlife center due to starvation and exhaustion were studied to evaluate approaches for determining sex. Coelioscopy was performed to identify sexes of two vultures, whereas, DNA testing was performed to identify the sexes of the 24 vultures. Testes and ovaries could be unambiguously identified with an endoscope and DNA analyses could identify sex sex in most, but not all of the specimens. Although the coelioscopy examination can unambiguously confirm sex, the approach is invasive and requires anesthesia. Thus, coelioscopic examination should only be performed when sex cannot be determined through DNA analysis.

• Journal of Radiation Industry
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• v.9 no.4
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• pp.181-185
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• 2015

Today the biosafety evaluation, a common problem of vital importance, is based on internationally proved test-systems, standards and techniques. The paradigm of biosafety includes multidisciplinary approach, a combination of physical, chemical and biological tests to monitor the environmental level of pollutants and needs to be improved by modern approaches. The genetic risk of environmental pollutions has long been studied by many researchers. In this study, used was the known sensitive plant test-system, clones of plant Tradescantia (spiderwort) able to detect gene mutations (frequency of mutational events and formation of micronuclei) in combination with chemical and, in some instances, with radiological measurements. In addition, male gametophyte generation of fruit trees was applied as bioindicators of genotoxicity. The obtained results did not show any significant increase along with wind direction. As for the male gametophyte assay, the fertility of the investigated fruit-trees near to NPP did not significantly differ from that of the control point. The influence of the NPP on the male generative system of the investigated taxa of fruit trees for the investigated year was not revealed. The system described needs to be expanded by species of interest (human) as there is a difficulty to transfer the revealed dose correlations to humans. The development of this idea includes various levels: population (epidemiological studies), individual, cellular, molecular (DNA), etc.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.1
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• pp.1-8
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• 2008

Numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm(NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis is presented in this work. Reynolds-averaged Navier-Stokes(RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• Journal of Integrative Natural Science
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• v.2 no.4
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• pp.243-251
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• 2009

This paper surveys some researches to accomplish on bioinformatics. These researches wish to propose a database architecture combining a general view of bioinformatics data as a graph of data objects and data relationships, with the efficiency and robustness of data management and query provided by indexing and generic programming techniques. Here, these invert the role of the index, and make it a first-class citizen in the query language. It is possible to do this in a structured way, allowing users to mention indexes explicitly without yielding to a procedural query model, by converting functional relations into explicit functions. In the limit, the database becomes a graph, in which the edges are these indexes. Function composition can be specified either explicitly or implicitly as path queries. The net effect of the inversion is to convert the database into a hyperdatabase: a database of databases, connected by indexes or functions. The inversion approach was motivated by their work in biological databases, for which hyperdatabases are a good model. The need for a good model has slowed progress in bioinformatics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2732-2753
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• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.