• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.53-56
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• 2003

Ethernet is one of the most successful LAN technologies. Now gigabit ethernet is available in real network and some network interface cards(NIC) supports TCP segment offloading (TSO), IP checksum offloading(ICO), Jumbo frame and interrupt moderation. If we use this features appropriately, we obtain high throughput with low CPU utilization. This paper represents the network performance by varying above features.

• Biomolecules & Therapeutics
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• v.18 no.2
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• pp.152-158
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• 2010

Melanin concentrating hormone is a neuropeptide highly expressed in the brain that regulates several physiological functions mediated by receptors in the G-protein coupled receptor family, especially plays an important role in the complex regulation of energy balance and body weight mediated by the melanin concentrating hormone receptor subtype 1 (MCH1). Compelling pharmacological evidence implicating MCH1 signaling in the regulation of food intake and energy expenditure has generated a great deal of interest by pharmaceutical companies as MCH1 antagonists may have potential therapeutic benefit in the treatment of obesity and metabolic syndrome. Although fluorescence-based calcium mobilization assay platform has been one of the most widely accepted tools for receptor research and drug discovery, fluorescence interference and shallow assay window limit their application in high throughput screening and have led to a growing interest in alternative, luminescence-based technologies. Herein, a luminescence-based functional assay system for the MCH1 receptor was developed and validated with the mitochondrial targeted aequorin. Aequorin based functional assay system for MCH1 presented excellent Z' factor (0.8983) and high signal-to-noise ratio (141.9). The nonpeptide MCH1 receptor antagonist, SNAP 7941 and GSK 803430, exhibited $IC_{50}$ values of 0.62 ${\pm}$ 0.11 and 12.29 ${\pm}$ 2.31 nM with excellent correlation coefficient. These results suggest that the aequorin based assay system for MCH1 is a strong alternative to the traditional GPCR related tools such as radioligand binding experiments and fluorescence functional determinations for the compound screening and receptor research.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.155-158
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• 2001

A 640Gb/s high-speed ATM switching system that is based on the technologies of advanced MCM, 0.25um CMOS and optical WDM interconnection is fabricated for future N-ISDN services. A 40 layer, 160mm$\times$114mm ceramic MCM realizes the basic ATM switch module with 80Gbps throughput. The basic unit ATM switch module with 80Gb/s throughput. The basic unit ATM switch MCM consists of in 8 chip advanced 0.25um CMOS VLSI and 32 chip I/O Bipolar VLSIs. The MCM employs an 40 layer, very thin layer ceramic MCM and a uniquely structured closed loop type liquid colling system is adopted to cope with the MCM's high-power dissipation of 230w. The MCM is Mounted on a 32cm$\times$50cm mother board. A three stage ATM switch is realized by optical WDM interconnection between the high-performance MCM.

• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.513-517
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• 2013

Recently, maskless lithography (ML) systems have become popular in digital manufacturing technologies. To achieve high-throughput manufacturing processes, digital micromirror devices (DMD) in ML systems must be driven to their operational limits, often in harsh conditions. We propose an instrument and algorithm to detect DMD malfunctions to ensure perfect mask image transfer to the photoresist in ML systems. DMD malfunctions are caused by either bad DMD pixels or data transfer errors. We detect bad DMD pixels with $20{\times}20$ pixel by white and black image tests. To analyze data transfer errors at high frame rates, we monitor changes in the frame rate of a target DMD pixel driven by the input data with a set frame rate of up to 28000 frames per second (fps). For our data transfer error detection method, we verified that there are no data transfer errors in the test by confirming the agreement between the input frame rate and the output frame rate within the measurement accuracy of 1 fps.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• Journal of Crop Science and Biotechnology
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• v.11 no.2
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• pp.91-100
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• 2008

Melting curve analysis of fluorescently labeled DNA fragments is used extensively for genotyping single nucleotide polymorphism(SNP). Here, we evaluated a SNP genotyping method by melting curve analysis with the two probe chemistries in a 384-well plate format on a Roche LightCycler 480. The HybProbe chemistry is based on the fluorescence resonance energy transfer(FRET) and the SimpleProbe chemistry uses a terminal self-quenching fluorophore. We evaluated FRET HybProbes and SimpleProbes for two SNP sites closely linked to two quantitative trait loci(QTL) for southern root-knot nematode resistance. These probes were used to genotype the two parents and 94 $F_2$ plants from the cross of PI 96354$\times$Bossier. The SNP genotypes of all samples determined by the LightCycler software agreed with previously determined SSR genotypes and the SNP genotypes determined on a Luminex 100 flow cytometry instrument. Multiplexed HybProbes for the two SNPs showed a 98.4% success rate and 100% concordance between repeats two of the same 96 DNA samples. Also, we developed a HybProbe assay for the Rcs3 gene conditioning broad resistance to the frogeye leaf spot(FLS) disease. The LightCycler 480 provides rapid PCR on 384-well plate and allows simultaneous amplification and analysis in approximately 2 hours without any additional steps after amplification. This allowed for a reduction of the potential contamination of PCR products, simplicity, and enablement of a streamlined workflow. The melting curve analysis on the LightCycler 480 provided high-throughput and rapid SNP genotyping and appears highly effective for marker-assisted selection in soybean.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.96-102
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• 2013

In recent years, a variety of ITS services are available such as driving information, road conditions, V2X messages as well as navigation and traffic jams notification. The development of ITS services is accelerating by V2X communication technologies for high-speed vehicles. In this paper, WAVE communication devices based on the IEEE802.11p standard is introduced as a solution of V2X communication technologies. The H/W and S/W structures of the WAVE communication device and the characteristics of RF/antenna are described. The performance is evaluated in the test road by measuring throughput, PER and latency. The implemented WAVE communication device has 6~7 Mbps throughput with 10% PER at 1km coverage. The packet latency is less than 3ms for the whole test road. It is shown that the implemented WAVE technology is satisfactory to provide ITS services and Internet video-streaming services.

• Korean Journal of Clinical Laboratory Science
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• v.44 no.4
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• pp.167-177
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• 2012

Next-Generation Sequencing (NGS) is a term that means post-Sanger sequencing methods with high-throughput sequencing technologies. NGS parallelizes the sequencing process, producing thousands or millions of sequences at once. The latest NGS technologies use even single DNA molecule as a template and measures the DNA sequence directly via measuring electronic signals from the extension or degradation of DNA. NGS is making big impacts on biomedical research, molecular diagnosis and personalized medicine. The hospitals are rapidly adopting the use of NGS to help to patients understand treatment with sequencing data. As NGS equipments are getting smaller and affordable, many hospitals are in the process of setting up NGS platforms. In this review, the progress of NGS technology development and action mechanisms of representative NGS equipments of each generation were discussed. The key technological advances in the commercialized platforms were presented. As NGS platforms are a great concern in the healthcare area, the latest trend in the use of NGS and the prospect of NGS in the future in diagnosis and personalized medicine were also discussed.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.313-318
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• 2005

Estimating the reliability of protein-protein interaction data sets obtained by high-throughput technologies such as yeast two-hybrid assays and mass spectrometry is of great importance. We develop a maximum likelihood estimation method that uses both protein localization and gene expression data to estimate the reliability of protein interaction data sets. By integrating protein localization data and gene expression data, we can obtain more accurate estimates of the reliability of various interaction data sets. We apply the method to protein physical interaction data sets and protein complex data sets. The reliability of the yeast two-hybrid interactions by Ito et al. (2001) is 27%, and that by Uetz et at.(2000) is 68%. The reliability of the protein complex data sets using tandem affinity purification-mass spec-trometry (TAP) by Gavin et at. (2002) is 45%, and that using high-throughput mass spectrometric protein complex identification (HMS-PCI) by Ho et al. (2002) is 20%. The method is general and can be applied to analyze any protein interaction data sets.