• 한국동물위생학회지
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• 제45권1호
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• pp.19-30
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• 2022

In this study, a simple loop-mediated isothermal amplification (LAMP) combined with visual detection method (vLAMP) assay was developed for the rapid and specific detection of African swine fever virus (ASFV), overcoming the shortcomings of previously described LAMP assays that require additional detection steps or pose a cross-contamination risk. The assay results can be directly detected by the naked eye using hydroxynaphthol blue after incubation for 40 min at 62℃. The assay specifically amplified ASFV DNA and no other viral nucleic acids. The limit of detection of the assay was <50 DNA copies/reaction, which was ten times more sensitive than conventional polymerase chain reaction (cPCR) and comparable to real-time PCR (qPCR). For clinical evaluation, the ASFV detection rate of vLAMP was higher than cPCR and comparable to OIE-recommended qPCR, showing 100% concordance, with a κ value (95% confidence interval) of 1 (1.00~1.00). Considering the advantages of high sensitivity and specificity, no possibility for cross-contamination, and being able to be used as low-cost equipment, the developed vLAMP assay will be a valuable tool for detecting ASFV from clinical samples, even in resource-limited laboratories.

• Journal of Veterinary Science
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• 제22권3호
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• pp.40.1-40.12
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• 2021

Background: The microsporidian parasite Nosema ceranae is a global problem in honeybee populations and is known to cause winter mortality. A sensitive and rapid tool for stable quantitative detection is necessary to establish further research related to the diagnosis, prevention, and treatment of this pathogen. Objectives: The present study aimed to develop a quantitative method that incorporates ultra-rapid real-time quantitative polymerase chain reaction (UR-qPCR) for the rapid enumeration of N. ceranae in infected bees. Methods: A procedure for UR-qPCR detection of N. ceranae was developed, and the advantages of molecular detection were evaluated in comparison with microscopic enumeration. Results: UR-qPCR was more sensitive than microscopic enumeration for detecting two copies of N. ceranae DNA and 24 spores per bee. Meanwhile, the limit of detection by microscopy was 2.40 × 10⁴ spores/bee, and the stable detection level was ≥ 2.40 × 10⁵ spores/bee. The results of N. ceranae calculations from the infected honeybees and purified spores by UR-qPCR showed that the DNA copy number was approximately 8-fold higher than the spore count. Additionally, honeybees infected with N. ceranae with 2.74 × 10⁴ copies of N. ceranae DNA were incapable of detection by microscopy. The results of quantitative analysis using UR-qPCR were accomplished within 20 min. Conclusions: UR-qPCR is expected to be the most rapid molecular method for Nosema detection and has been developed for diagnosing nosemosis at low levels of infection.

• Molecules and Cells
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• 제45권7호
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• pp.502-511
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• 2022

Bacterial volatile compounds (BVCs) exert beneficial effects on plant protection both directly and indirectly. Although BVCs have been detected in vitro, their detection in situ remains challenging. The purpose of this study was to investigate the possibility of BVCs detection under in situ condition and estimate the potentials of in situ BVC to plants at below detection limit. We developed a method for detecting BVCs released by the soil bacteria Bacillus velezensis strain GB03 and Streptomyces griseus strain S4-7 in situ using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS). Additionally, we evaluated the BVC detection limit in the rhizosphere and induction of systemic immune response in tomato plants grown in the greenhouse. Two signature BVCs, 2-nonanone and caryolan-1-ol, of GB03 and S4-7 respectively were successfully detected using the soil-vial system. However, these BVCs could not be detected in the rhizosphere pretreated with strains GB03 and S4-7. The detection limit of 2-nonanone in the tomato rhizosphere was 1 µM. Unexpectedly, drench application of 2-nonanone at 10 nM concentration, which is below its detection limit, protected tomato seedlings against Pseudomonas syringae pv. tomato. Our finding highlights that BVCs, including 2-nonanone, released by a soil bacterium are functional even when present at a concentration below the detection limit of SPME-GC-MS.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.454-456
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• 2002

The evaluation of interval changes between temporally sequential chest radiographs is necessary for the detection of new abnormalities or interval changes, such as pulmonary nodules and interstitial disease. For interstitial lung disease, the interval changes are very important for diagnosis and treatment. Especially, interstitial lung disease may show rapid changes in the radiographs, show changes in the entire lung field in minute detail, or show changes in multiple parts depending on the type. It is therefore difficult to have an accurate grasp of the condition of the disease only with conventional radiographs. The temporal subtraction technique which was developed at the University of Chicago, provides a subtraction image of the current warped image and the previous image. A temporal subtraction image, shows only differences and changes between the two images, can be very useful for a diagnosis of interstitial lung disease. However, the evaluation of the temporal subtraction technique for interstitial lung disease using receiver operating characteristic(ROC) studies has not been reported yet. Therefore, we have evaluated the clinical usefulness of a temporal subtraction technique for detection of interval changes of interstitial lung disease by ROC analysis.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2781-2784
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• 2007

Early detection of a disease is important to tackle treatment issues in a better manner. Several diagnostic techniques are in use, these days; for such purpose and tremendous research is going on to develop newer and newer methods. However, more work is required to be done to develop cheap and reliable early detection techniques. Micro-fluidic chips are also playing key role to deliver new devices for better health care. The present study focuses on a review of recent developments in the interrogation of different techniques and present state-of-the-art of microfluidic sensor for better, quick, easy, rapid, early, inexpensive and portable POCT (Point of Care testing device) device for a particular study, in this case, bone disease called osteoporosis. Some simulations of the microchip are also made to enable feasibility of the development of a blood-chip-based system. The proposed device will assist in early detection of diseases in an effective and successful manner.

• ETRI Journal
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• 제46권3호
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.17-24
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• 2020

Rice (Oryza Sativa) is an important source of food for the people of our country, even though of world also .It is also considered as the staple food of our country and we know agriculture is the main source country's economy, hence the crop of Rice plays a vital role over it. For increasing the growth and production of rice crop, ground-breaking technique for the detection of any type of disease occurring in rice can be detected and categorization of rice crop diseases has been proposed in this paper. In this research paper, we perform comparison between two classifiers namely MDC and Bayes' classifiers Survey over different digital image processing techniques has been done for the detection of disease in rice crops. The proposed technique involves the samples of 200 digital images of diseased rice leaf images of five different types of rice crop diseases. The overall accuracy that we achieved by using Bayes' Classifiers and MDC are 69.358 percent and 81.06 percent respectively.

• 스마트미디어저널
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• 제13권6호
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• The Plant Pathology Journal
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• 제39권4호
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.