• The Korean Journal of Food And Nutrition
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• v.15 no.2
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• pp.112-118
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• 2002

Sporulation in the fission yeast Schizosaccharomyces pombe has been regarded as an important model of cellular development and differentiation. S. pombe cells proliferate by mitosis and binary fission on growth medium. Deprivation of nutrients especially nitrogen sources, causes the cessation of mitosis and initiates sexual reproduction by matting between two sexually compatible cell types. Meiosis is then followed in a diploid cell in the absence of nitrogen source. DNA fragment complemented with the mutations of sporulation gene was isolated from the S. pombe gene library constructed in the vector, pDB 248' and designated as pDB(spo5)1. We futher analyzed six recombinant plasmids, pDB(spo5)2, pDB(spo5)3, pDB(spo5)4, pDB(spo5)5, pDB (spo5)6, pDB(spo5)7 and found each of these plasmids is able to rescue the spo5-2, spo5-3, spo5-4, spo5-5, spo5-6, spo5-7 mutations, respectively. Mapping of the integrated plasmid into the homologous site of the S. pombe chromosomes demonstrated that pDB(spo5)1, and pDB(spu5)Rl contained the spo5 gene. Transcripts of spo5 gene were analyzed by Northern hybridization. Two transcripts of 3.2 kb and 2.5kb were detected with 5kb Hind Ⅲ fragment containing a part of the spo5 gene as a probe. The small mRNA(2.5kb) appeared only when a wild-type strain was cultured in the absence of nitrogen source in which condition the large mRNA (3.2kb) was produced constitutively. Appearance of a 2.5kb spo5-mRNA depends upon the function of the meil, mei2 and mei3 genes.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.41-52
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• 2022

As a cultivar of Korean wheat, 'Keumgang' wheat variety has a fast growth period and can be grown stably. Hexaploid wheat (Triticum aestivum) has moderately high salt tolerance compared to tetraploid wheat (Triticum turgidum L.). However, the molecular mechanisms related to salt tolerance of hexaploid wheat have not been elucidated yet. In this study, the candidate genes related to salt tolerance were identified by investigating the genes that are differently expressed in Keumgang variety and examining salt tolerant mutation '2020-s1340.'. A total of 85,771,537 reads were obtained after quality filtering using NextSeq 500 Illumina sequencing technology. A total of 23,634,438 reads were aligned with the NCBI Campala Lr22a pseudomolecule v5 reference genome (Triticum aestivum). A total of 282 differentially expressed genes (DEGs) were identified in the two Triticum aestivum materials. These DEGs have functions, including salt tolerance related traits such as 'wall-associated receptor kinase-like 8', 'cytochrome P450', '6-phosphofructokinase 2'. In addition, the identified DEGs were classified into three categories, including biological process, molecular function, cellular component using gene ontology analysis. These DEGs were enriched significantly for terms such as the 'copper ion transport', 'oxidation-reduction process', 'alternative oxidase activity'. These results, which were obtained using RNA-seq analysis, will improve our understanding of salt tolerance of wheat. Moreover, this study will be a useful resource for breeding wheat varieties with improved salt tolerance using molecular breeding technology.

• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.2
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• pp.72-81
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• 2024

Purpose: This study aimed to assess the marginal and internal fit of 3-unit monolithic zirconia fixed partial dentures (FPDs) fabricated via computer-aided design and computer-aided manufacturing (CAD/CAM) from solid working casts and removable die system. Materials and Methods: The tooth preparation protocol for a zirconia crown was executed on the mandibular right first premolar and mandibular right first molar, with the creation of a reference cast featuring an absent mandibular right second premolar. The reference cast was duplicated using polyvinyl siloxane impression, from which 20 working casts were fabricated following typical dental laboratory procedures. For comparative analysis, 10 FPDs were produced from a removable die system (RD group) and the remaining 10 FPDs from the solid working casts (S group). The casts were digitized using a dental desktop scanner to establish virtual casts and design the FPDs using CAD. The definitive 3-unit monolithic zirconia FPDs were fabricated via a CAM milling process. The seated FPDs on the reference cast underwent digital evaluation for marginal and internal fit. The Mann-Whitney U test was applied for statistical comparison between the two groups (α = 0.05). Results: The RD group showed significantly higher discrepancies in fit for both premolars and molars compared to the S group (P < 0.05), particularly in terms of marginal and occlusal gaps. Color mapping also highlighted more significant deviations in the RD group, especially in the marginal and occlusal regions. Conclusion: The study found that the discrepancies in marginal and occlusal fits of 3-unit monolithic zirconia FPDs were primarily associated with those fabricated using the removable die system. This indicates the significant impact of the fabrication method on the accuracy of FPDs.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.