• Journal of Plant Biotechnology
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• v.50
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• pp.190-199
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• 2023

Date palm (Phoenix dactylifera L.: Arecaceae) is a dioecious species where only female trees bear fruits. In their natural state, date palms produce dates once a year. However, in Thailand, some trees were observed to produce dates during the off-season, despite no variations in morphology. The availability of such off-season fruits can significantly increase their market value. Interestingly, most female off-season date palms investigated in this study were obtained through micropropagation. Hence, there is an urgent need for genetic markers to distinguish female offseason flowering plantlets within tissue culture systems. In this study, we aimed to develop random amplification of polymorphic DNA-sequence characterized amplified region (RAPD-SCAR) markers for the identification of female off-season flowering date palms cultivated in Thailand. A total of 160 random decamer primers were employed to screen for specific RAPD markers in off-season flowering male and female populations. Out of these, only one primer, OPN-02, generated distinct genomic DNA patterns in female off-season flowering (FOFdp) individuals compared to female seasonal flowering genotypes. Based on the RAPD-specific sequence, specific SCAR primers denoted as FOFdpF and FOFdpR were developed. These SCAR primers amplified a single 517-bp DNA fragment, predominantly found in off-season flowering populations, with an accuracy rate of 60%. These findings underscore the potential of SCAR marker technology for tracking offseason flowering in date palms. Notably, a BLAST analysis revealed a substantial similarity between the SCAR marker sequence and the transcript variant mRNA from Phoenix dactylifera encoding the SET DOMAIN GROUP 40 protein. In Arabidopsis, this protein is involved in the epigenetic regulation of flowering time. The genetic potential of the off-season flowering traits warrants further elucidation.

• Clinical and Experimental Pediatrics
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• v.53 no.2
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• pp.178-183
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• 2010

Purpose : The aim of this study was to identify mutations associated with macrolide resistance in Mycoplasma pneumoniae (MP) and to establish a cultural method to determine antimicrobial susceptibility. Methods : Nasopharyngeal aspirates (NPAs) were collected from 62 children diagnosed with MP pneumonia by a serologic method or polymerase chain reaction. The 23S rRNA and L4 ribosomal protein genes of MP were amplified and sequenced. To identify mutations in these 2 genes, their nucleotide sequences were compared to those of the reference strain M129. MP cultivation was carried out for 32 (28 frozen and 5 refrigerated) NPAs and M129 strain using Chanock's glucose broth and agar plate in a 5% $CO_2$ incubator at $37^{\circ}C$ and examined at 2-3 day intervals for 6 weeks. Results : Among the 62 specimens, 17 had M144V mutations in ribosomal protein L4. The A2064G mutation was observed in 1 specimen; its 23S rRNA gene was successfully sequenced. Culture for MP was successful from the M129 strain and 2 of the 5 NPAs that were refrigerated for no longer than 3 days. However, MP did not grow from the 28 NPAs that were kept frozen at $-80^{\circ}C$ since 2003. Conclusion : We found the M144V mutation of L4 protein to be common and that of domain V of 23S rRNA gene was relatively rare among MP. Studies on the prevalence of macrolide-resistant MP and the relationship between the mutations of 23S rRNA gene and ribosomal protein L4 will aid in understanding the mechanism of macrolide resistance in MP.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.537-546
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• 2004

Adiponectin is adipocyte complement-related protein which is highly specialized to play important roles in metabolic and honnonal processes. This protein, called GBP-28, AdipoQ, and Acrp30, is encoded by the adipose most abundant gene transcript 1 (APM1) which locates on human chromosome 3q27 and mouse chromosome 16. In order to determine chromosomal localization of the porcine APM1, we carried out PCR analysis using somatic cell hybrid panel as well as porcine whole genome radiation hybrid (RH) panel. The result showed that the porcine APM1 located on chromosome 13q41 or 13q46-49. These locations were further investigated with the two point analysis of RH panel, revealed the most significant linked marker (LOD score 20.29) being SIAT1 (8 cRs away), where the fat-related QTL located. From the SSCP analysis of APM1 using 8 pig breeds, two distinct SSCP types were detected from K~ native and Korean wild pigs. The determined sequences in Korean native and Korean wild pigs showed that two nucleotide positions (T672C and C705G) were substituted. The primary sequence of the porcine APM1 has 79 to 87% identity with those of human, mouse, and bovine APM1. The domain structures of the porcine APM1 such as signal sequence, hypervariable region, collagenous region. and globular domain are also similar to those of mammalian genes.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.184-184
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• 2012

Recently, InGaN/GaN multi-quantum well grown on GaN pyramid structures have attracted much attention due to their hybrid characteristics of quantum well, quantum wire, and quantum dot. This gives us broad band emission which will be useful for phosphor-free white light emitting diode. On the other hand, by using quantum dot emission on top of the pyramid, site selective single photon source could be realized. However, these structures still have several limitations for the single photon source. For instance, the quantum efficiency of quantum dot emission should be improved further. As detection systems have limited numerical aperture, collection efficiency is also important issue. It has been known that micro-cavities can be utilized to modify the radiative decay rate and to control the radiation pattern of quantum dot. Researchers have also been interested in nano-cavities using localized surface plasmon. Although the plasmonic cavities have small quality factor due to high loss of metal, it could have small mode volume because plasmonic wavelength is much smaller than the wavelength in the dielectric cavities. In this work, we used localized surface plasmon to improve efficiency of InGaN qunatum dot as a single photon emitter. We could easily get the localized surface plasmon mode after deposit the metal thin film because lnGaN/GaN multi quantum well has the pyramidal geometry. With numerical simulation (i.e., Finite Difference Time Domain method), we observed highly enhanced decay rate and modified radiation pattern. To confirm these localized surface plasmon effect experimentally, we deposited metal thin films on InGaN/GaN pyramid structures using e-beam deposition. Then, photoluminescence and time-resolved photoluminescence were carried out to measure the improvement of radiative decay rate (Purcell factor). By carrying out cathodoluminescence (CL) experiments, spatial-resolved CL images could also be obtained. As we mentioned before, collection efficiency is also important issue to make an efficient single photon emitter. To confirm the radiation pattern of quantum dot, Fourier optics system was used to capture the angular property of emission. We believe that highly focused localized surface plasmon around site-selective InGaN quantum dot could be a feasible single photon emitter.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.33-42
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• 2020

There has been difficulties in identifying objects by relying on the naked eye in various surveillance systems. There is a growing need for automated surveillance systems to replace soldiers in the field of military surveillance operations. Even though the object detection technology is developing rapidly in the civilian domain, but the research applied to the military is insufficient due to a lack of data and interest. Thus, in this paper, we applied one of deep learning algorithms, Convolutional Neural Network-based binary classification to develop an autonomous identification model of both friend and foe helicopters (AH-64, Mi-17) among the military weapon systems, and evaluated the model performance by considering accuracy, precision, recall and F-measure. As the result, the identification model demonstrates 97.8%, 97.3%, 98.5%, and 97.8 for accuracy, precision, recall and F-measure, respectively. In addition, we analyzed the feature map on convolution layers of the identification model in order to check which area of imagery is highly weighted. In general, rotary shaft of rotating wing, wheels, and air-intake on both of ally and foe helicopters played a major role in the performance of the identification model. This is the first study to attempt to classify images of helicopters among military weapons systems using CNN, and the model proposed in this study shows higher accuracy than the existing classification model for other weapons systems.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.5
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• pp.704-711
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• 2020

Objective: Muscle fiber types, numbers and area are crucial aspects associated with meat production and quality. However, there are few studies of pig muscle fibre traits in terms of the detection power, false discovery rate and confidence interval precision of whole-genome quantitative trait loci (QTL). We had previously performed genome scanning for muscle fibre traits using 183 microsatellites and detected 8 significant QTLs in a White Duroc×Erhualian F₂ population. The confidence intervals of these QTLs ranged between 11 and 127 centimorgan (cM), which contained hundreds of genes and hampered the identification of QTLs. A whole-genome sequence imputation of the population was used for fine mapping in this study. Methods: A whole-genome sequences association study was performed in the F₂ population. Genotyping was performed for 1,020 individuals (19 F₀, 68 F₁, and 933 F₂). The whole-genome variants were imputed and 21,624,800 single nucleotide polymorphisms (SNPs) were identified and examined for associations to 11 longissimus dorsi muscle fiber traits. Results: A total of 3,201 significant SNPs comprising 7 novel QTLs showing associations with the relative area of fiber type I (I_RA), the fiber number per square centimeter (FN) and the total fiber number (TFN). Moreover, one QTL on pig chromosome 14 was found to affect both FN and TFN. Furthermore, four plausible candidate genes associated with FN (kinase non-catalytic C-lobe domain containing [KNDC1]), TFN (KNDC1), and I_RA (solute carrier family 36 member 4, contactin associated protein like 5, and glutamate metabotropic receptor 8) were identified. Conclusion: An efficient and powerful imputation-based association approach was utilized to identify genes potentially associated with muscle fiber traits. These identified genes and SNPs could be explored to improve meat production and quality via marker-assisted selection in pigs.

• Journal of Embryo Transfer
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• v.23 no.4
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• pp.251-255
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• 2008

The aim of the present recent study was to compare the protein patterns in the vaginal mucus of Hanwoo cattles during spontaneous and CIDR induced-estrus. Ten cattles, who had been observed in estrus, received no treatment and served as the group of cattles with normal spontaneous estrus. Thirteen cattles in the CIDR received an CIDR insert on day 14 were removed and cattles were injected GnRH on day 15. Vaginal mucus samples were collected from all cattles at the same time the single AI in cattles with spontaneous estrus and the AI in cattles with induced estrus. Spontaneous and CIDR-induced estrus vaginal mucus samples were analyzed on two different array surfaces: cation-exchange (CM10), anion-exchange (Q10). In addition, using the NaCl solution by which the proteins combined after washing are 0.5, 1 and 2 M, it was fractionated and a protein was collected successively. The results are summarized as follows: 1) Ionic surfaces chemistries (Q10 and CM10) gave the best results in terms of detectable protein peaks, with more than 100 protein peaks in the two fractions and under each condition. 2) Protein mass spectrometer using 11 different proteins in protein identification of 7 were able to determine the protein. List of identified proteins as follows; Ribosome-binding protein 1, GRIP 1-associated protein 1, Katanin p60 ATPase-containing subunit A-like 1, Protein FAM44A, DUF729 domain-containing protein 1, Prolactin precursor, Dihydrofolate erductase. Conclusively, on the basis of this study, protein expression in the vaginal mucus could be used as an indicator for time of estrus manifestation in order to increase conception rates by applying AI at an optional time.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.