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1.
Plant Dis ; 2022 Feb 08.
Article in English | MEDLINE | ID: mdl-35133183

ABSTRACT

Polygonatum odoratum var. pluriflorum, called "Dunggulle", is cultivated in East Asia to obtain rhizomes. In Korea and China, these rhizomes are used in traditional teas, health beverages, and herbal medicines (Zhao and Li, 2015). In 2019, Dunggulle was cultivated in 47 hectares, with an annual output of 120M/T in Korea. In November 2020, Dunggulle rhizomes with symptoms of blue mold rot were observed at a Dunggulle farm storage (36°06'01''N, 127°29'20''E) in Geuman, Korea, where the temperature ranged from 9 to 13°C, with an average humidity of 70%. The disease incidence was 2 to 3% out of 200 rhizomes across all markets surveyed. The disease begins with a greenish blue mold covering the rhizome surface (30 to 60%), followed by rhizome rot with a dark brown color as the disease progresses. Leading edges of the rotten rhizome pieces were sterilized with 1% NaOCl and 70% ethanol and placed on MEA (Malt Extract Agar) with penicillin G and streptomycin (both 50 µg/mL). After 7 days of incubation at 25°C, greenish-blue colonies appeared, from which a monospore was isolated. A representative isolated strain was deposited in the Korean Agricultural Culture Collection (KACC, Wanju, Korea) with Acc. No. KACC 49832. The strain grew slowly on MEA at 25°C (8 to 18 mm diam. after 7 days), producing greenish blue conidia. The conidiophores were hyaline and mostly terverticillate; the branches were appressed against the main axis; the stipes were smooth-walled; and the metulae were cylindrical, 10 to 13 × 2.7 to 3.2 µm, with 6 to 10 flask-shaped phialides, measured 9 to 12 × 2.7 to 3.1 µm. The conidia were globose or subglobose and 2.8 to 4.1 µm diam. These morphological characteristics fit well with the description of Penicillium expansum (Frisvad & Samson, 2004). Genomic DNA was extracted from the mycelia of the KACC 49832 MEA plate. ITS rDNA, beta-tubulin (BenA), and calmodulin (CaM) gene regions were sequenced for identification (Houbraken et al., 2020), and the rsulting sequences were deposited in GenBank (Acc. Nos. MZ189258, MZ226688, and MZ226689, respectively). Comparison with the GenBank sequences revealed that the Korean isolate was highly similar to P. expansum (ITS rDNA 99.8%, BenA 98.6%, and CaM 97.8%). Phylogenetic results based on the maximum-likelihood analysis revealed that KACC 49832 was grouped with P. expansum. To demonstrate pathogenicity, 10 µL of conidial suspension (1.3 × 105 conidia/mL) was dropped on the surface of three Dunggulle rhizomes scratched with a syringe needle. For the control, sterile water was applied on three control rhizomes. All rhizomes were surface-sterilized as referred above before being sprayed and dried. All inoculated and control rhizomes were kept in incubator at 25°C and 90-95% relative humidity. After a week, the inoculated points showed symptoms similar to those of the initial infection, whereas controls remained symptomless. The re-isolated fungus matched KACC 49832 based on morphological and sequence analyses, thereby fulfilling Koch's postulates. The experiment was performed three times. To our knowledge, this is the first report of P. expansum as a Dunggulle rhizome pathogen in Korea. As this pathogen is known to produce patulin, a carcinogenic fungal metabolite, further studies on the mycotoxicity of the infected rhizomes are required. This report might help manage the storage conditions of Dunggulle rhizomes to prevent the blue mold rot.

2.
Sensors (Basel) ; 19(5)2019 Feb 27.
Article in English | MEDLINE | ID: mdl-30818806

ABSTRACT

In this research, we present a differential evolution approach to optimize the weights of dynamic time warping for multi-sensory based gesture recognition. Mainly, we aimed to develop a robust gesture recognition method that can be used in various environments. Both a wearable inertial sensor and a depth camera (Kinect Sensor) were used as heterogeneous sensors to verify and collect the data. The proposed approach was used for the calculation of optimal weight values and different characteristic features of heterogeneous sensor data, while having different effects during gesture recognition. In this research, we studied 27 different actions to analyze the data. As finding the optimal value of the data from numerous sensors became more complex, a differential evolution approach was used during the fusion and optimization of the data. To verify the performance accuracy of the presented method in this study, a University of Texas at Dallas Multimodal Human Action Datasets (UTD-MHAD) from previous research was used. However, the average recognition rates presented by previous research using respective methods were still low, due to the complexity in the calculation of the optimal values of the acquired data from sensors, as well as the installation environment. Our contribution was based on a method that enabled us to adjust the number of depth cameras and combine this data with inertial sensors (multi-sensors in this study). We applied a differential evolution approach to calculate the optimal values of the added weights. The proposed method achieved an accuracy 10% higher than the previous research results using the same database, indicating a much improved accuracy rate of motion recognition.

3.
Sensors (Basel) ; 17(8)2017 Aug 17.
Article in English | MEDLINE | ID: mdl-28817094

ABSTRACT

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user's location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

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