• Asian-Australasian Journal of Animal Sciences
• /
• v.20 no.9
• /
• pp.1342-1348
• /
• 2007

POU1F1 is a positive regulator for GH, PRL and TSH${\beta}$ and its mutations associate with production traits in ruminant animals. We described a DdeI PCR-RFLP method for detecting a silent allele in the goat POU1F1 gene: TCT (241Ser)>TCG (241Ser). Frequencies of $D_1$ allele varied from 0.600 to 1.000 in Chinese 801 goats. Significant associations of DdeI polymorphism with production traits were found in milk yield (*p<0.05), litter size (*p<0.05) and one-year-old weight (*p<0.05) between different genotypes. Individuals with genotype $D_1D_1$ had a superior performances when compared to those with genotype $D_1D_2$ (*p<0.05). Hence, the POU1F1 gene was suggested to the potential candidate gene for superior milk performance, reproduction trait and weight trait. Genotype $D_1D_1$, characterized by a DdeI PCR-RFLP detection, was recommended to geneticists and breeders as a molecular marker for better performance in the goat industry.

• The Plant Pathology Journal
• /
• v.37 no.5
• /
• pp.437-445
• /
• 2021

Tilletia laevis Kuhn (syn. Tilletia foetida (Wallr.) Liro.) causes wheat common bunt, which is one of the most devastating plant diseases in the world. Common bunt can result in a reduction of 80% or even a total loss of wheat production. In this study, the characteristics of T. laevis infection in compatible wheat plants were defined based on the combination of scanning electron microscopy, transmission electron microscopy and laser scanning confocal microscopy. We found T. laevis could lead to the abnormal growth of wheat tissues and cells, such as leakage of chloroplasts, deformities, disordered arrangements of mesophyll cells and also thickening of the cell wall of mesophyll cells in leaf tissue. What's more, T. laevis teliospores were found in the roots, stems, flag leaves, and glumes of infected wheat plants instead of just in the ovaries, as previously reported. The abnormal characteristics caused by T. laevis may be used for early detection of this pathogen instead of molecular markers in addition to providing theoretical insights into T. laevis and wheat interactions for breeding of common bunt resistance.

• Advances in nano research
• /
• v.14 no.4
• /
• pp.313-327
• /
• 2023

Zinc metal organic framework (MOF_Zn)-loaded goad nanoparticles (AuNPs) sol (Au@MOF_Zn), which was characterized by TEM, Mapping, FTIR, XRD, and molecular spectrum, was prepared conveniently by solvothermal method. The results indicated that Au@MOF_Zn had a very strong catalytic effect with the nanoreaction of AuNPs formation between sodium oxalate (SO) and HAuCl4. AuNPs in the new indicator reaction had a strong resonance Rayleigh scattering (RRS) signal at 370 nm. The indicator AuNPs generated by this reaction, which had the most intense surface enhanced Raman scattering (SERS) peak at 1621 cm -1. The new SERS/RRS indicator reaction in combination with specific aptamer (Apt) to fabricate a sensitive and selective Au@MOF_Zn catalytic amplification-aptamer SERS/RRS assay platform for carbendazim (CBZ), with SERS/RRS linear range of 0.025-0.5 ng/mL. The detection limit was 0.02 ng/mL. Similarly, this assay platform has been also utilized to detect oxytetracycline (OTC) and profenofos (PF).

• Journal of Breast Cancer
• /
• v.21 no.4
• /
• pp.363-370
• /
• 2018

Purpose: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. Methods: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. Results: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. Conclusion: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.12
• /
• pp.1114-1125
• /
• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• Journal of Industrial Convergence
• /
• v.20 no.5
• /
• pp.61-68
• /
• 2022

In recent years, malicious codes are being produced using the developing information and communication technology, and it is insufficient to detect them with the existing detection system. In order to accurately and efficiently detect and respond to such intelligent malicious code, an intelligent detection model is required, and in order to maximize detection performance, it is important to train with the main characteristic information set of the malicious code. In this paper, we proposed a technique for designing an intelligent detection model and generating the data required for model training as a set of key feature information through transformation, dimensionality reduction, and feature selection steps. And based on this, the main characteristic information was classified by malicious code. In addition, based on the classified characteristic information, we derived common characteristic information that can be used to analyze and detect modified or newly emerging malicious codes. Since the proposed detection model detects malicious codes by learning with a limited number of characteristic information, the detection time and response are fast, so damage can be greatly reduced and Although the performance evaluation result value is slightly different depending on the learning algorithm, it was found through evaluation that most malicious codes can be detected.

• Structural Engineering and Mechanics
• /
• v.91 no.5
• /
• pp.443-457
• /
• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Journal of Microbiology and Biotechnology
• /
• v.22 no.9
• /
• pp.1301-1306
• /
• 2012

We combined real-time RT-PCR and real-time PCR (R/P) assays using a hydrolysis probe to detect Mycobacterium tuberculosis complex (MTBC)-specific 16S rRNA and its rRNA gene (rDNA). The assay was applied to 28 non-respiratory and 207 respiratory specimens from 218 patients. Total nucleic acids (including RNA and DNA) were extracted from samples, and results were considered positive if the repeat RT-PCR threshold cycle was ${\leq}35$ and the ratio of real-time RT-PCR and real-time PCR load was ${\geq}1.51$. The results were compared with those from existing methods, including smear, culture, and real-time PCR. Following resolution of the discrepant results between R/P assay and culture, the overall sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of all samples (including non-respiratory and respiratory specimens) were 98.2%, 97.2%, 91.7%, and 99.4%, respectively, for R/P assay, and 83.9%, 89.9%, 72.3%, and 94.7%, respectively, for real-time PCR. Furthermore, the R/P assay of four patient samples showed a higher ratio before treatment than after several days of treatment. We conclude that the R/P assay is a rapid and accurate method for direct detection of MTBC, which can distinguish viable and nonviable MTBC, and thus may guide patient therapy and public health decisions.

• Journal of Internet Computing and Services
• /
• v.9 no.3
• /
• pp.169-178
• /
• 2008

In this paper, we propose a video summarization system which is based on activity in video acquired by multiple non-overlapping cameras for wide-area surveillance. The proposed system separates persons by time-independent background removal and detects activities of the segmented persons by their motions. In this paper, we extract eleven activities based on whose direction the persons move to and consider a key-frame as a frame which contains a meaningful activity. The proposed system summarizes based on activity-based key-frames and controls an amount of summarization according to an amount of activities. Thus the system can summarize videos by camera, time, and activity.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.8
• /
• pp.2732-2736
• /
• 2011

In pH 4.2 HAc-NaAc buffer solution, horseradish peroxidase (HRP) catalyzed $H_2O_2$ oxidation of nanosilver to form $Ag^+$. After centrifugation, $Ag^+$ in the supernatant can be measured by graphite furnace atomic absorption spectrophotometry (GFAAS) at the silver absorption wavelength of 328.1 nm. When HRP concentration increased, the $Ag^+$ concentration in the supernatant increased, and the absorption value enhanced. The HRP concentration in the range of 0.84-50 $ng{\cdot}mL^{-1}$ was linear to the enhanced absorption value (${\Delta}A$), with a regression equation of ${\Delta}A$=0.012C+0.11, correlation coefficient of 0.9988, and detection limit of 0.41 $ng{\cdot}mL^{-1}$ HRP. The proposed GFAAS method was used to detect HRP in waste water samples, with satisfactory results.