scholarly journals A Rapid Recognition Method for Rice False Smut based on HOG Features and SVM Classification

2020 ◽  
Vol 1576 ◽  
pp. 012018
Author(s):  
Naila S ◽  
Yu JJ ◽  
Yang N ◽  
Kashif H ◽  
Tang J ◽  
...  
Keyword(s):  
Recognition Method ◽  
Svm Classification ◽  
Rice False Smut ◽  
False Smut

scholarly journals A novel transcription factor UvCGBP1 regulates development and virulence of rice false smut fungus Ustilaginoidea virens

Virulence ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 1563-1579
Author(s):  
Xiaoyang Chen ◽  
Pingping Li ◽  
Hao Liu ◽  
Xiaolin Chen ◽  
Junbin Huang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Smut Fungus ◽  
Ustilaginoidea Virens ◽  
Rice False Smut ◽  
False Smut

scholarly journals Quantitative Proteomics Analysis Reveals the Function of the Putative Ester Cyclase UvEC1 in the Pathogenicity of the Rice False Smut Fungus Ustilaginoidea virens

2021 ◽  
Vol 22 (8) ◽  
pp. 4069
Author(s):  
Xiaoyang Chen ◽  
Zhangxin Pei ◽  
Pingping Li ◽  
Xiabing Li ◽  
Yuhang Duan ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Fungal Pathogens ◽  
Protein Localization ◽  
Proteomics Data ◽  
Ustilaginoidea Virens ◽  
Rice False Smut ◽  
Oxidative Stresses ◽  
Tandem Mass Tag ◽  
Cyclase Domain ◽  
False Smut

Rice false smut is a fungal disease distributed worldwide and caused by Ustilaginoidea virens. In this study, we identified a putative ester cyclase (named as UvEC1) as being significantly upregulated during U. virens infection. UvEC1 contained a SnoaL-like polyketide cyclase domain, but the functions of ketone cyclases such as SnoaL in plant fungal pathogens remain unclear. Deletion of UvEC1 caused defects in vegetative growth and conidiation. UvEC1 was also required for response to hyperosmotic and oxidative stresses and for maintenance of cell wall integrity. Importantly, ΔUvEC1 mutants exhibited reduced virulence. We performed a tandem mass tag (TMT)-based quantitative proteomic analysis to identify differentially accumulating proteins (DAPs) between the ΔUvEC1-1 mutant and the wild-type isolate HWD-2. Proteomics data revealed that UvEC1 has a variety of effects on metabolism, protein localization, catalytic activity, binding, toxin biosynthesis and the spliceosome. Taken together, our findings suggest that UvEC1 is critical for the development and virulence of U. virens.

The role of Ustilaginoidea virens sclerotia in increasing incidence of rice false smut disease in the subtropical zone in China

2017 ◽  
Vol 150 (3) ◽  
pp. 669-677 ◽  
Author(s):  
Mingli Yong ◽  
Qide Deng ◽  
Linlin Fan ◽  
Jiankun Miao ◽  
Chaohui Lai ◽  
...  
Keyword(s):  
Ustilaginoidea Virens ◽  
Subtropical Zone ◽  
Rice False Smut ◽  
False Smut

scholarly journals Characterization of mating-type loci in rice false smut fungus Villosiclava virens

2015 ◽  
Vol 362 (9) ◽  
Author(s):  
Jun-jie Yu ◽  
Wen-xian Sun ◽  
Mi-na Yu ◽  
Xiao-le Yin ◽  
Xiang-kun Meng ◽  
...  
Keyword(s):  
Mating Type ◽  
Smut Fungus ◽  
Rice False Smut ◽  
Villosiclava Virens ◽  
False Smut

Genetic diversity and population structure of the rice false smut pathogen Ustilaginoidea virens in Sichuan-Chongqing region

Plant Disease ◽  
2021 ◽  
Author(s):  
Anfei Fang ◽  
Zhuangyuan Fu ◽  
Zexiong Wang ◽  
Yuhang Fu ◽  
Yubao Qin ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
Genetic Variation ◽  
Genetic Recombination ◽  
Resistance Breeding ◽  
Ustilaginoidea Virens ◽  
Breeding Lines ◽  
Rice False Smut ◽  
Geographical Populations ◽  
False Smut

Rice false smut caused by Ustilaginoidea virens is currently one of the most devastating fungal diseases of rice panicles worldwide. In this study, two novel molecular markers derived from SNP-rich genomic DNA fragments and a previously reported molecular marker were used for analyzing the genetic diversity and population structure of 167 U. virens isolates collected from nine areas in Sichuan-Chongqing region, China. A total of 62 haplotypes were identified, and a few haplotypes with high frequency were found and distributed in two to three areas, suggesting gene flow among different geographical populations. All isolates were divided into six genetic groups. The groups Ⅰ and Ⅵ were the largest including 61 and 48 isolates, respectively. The pairwise FST values showed significant genetic differentiation among all compared geographical populations. AMOVA showed that intergroup genetic variation accounted for 40.17% of the total genetic variation, while 59.83% of genetic variation came from intragroup. The UPGMA dendrogram and population structure revealed that the genetic composition of isolates collected from ST (Santai), NC (Nanchong), YC (Yongchuan), and WS (Wansheng) dominated by the same genetic subgroup was different from those collected from other areas. In addition, genetic recombination was found in a few isolates. These findings will help to improve the strategies for rice false smut management and resistance breeding, such as evaluating breeding lines with different isolates or haplotypes at different elevations and landforms.

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Rice false smut and its management in major rice growing areas in Ashanti region of Ghana

2017 ◽  
Vol 12 (43) ◽  
pp. 3129-3136
Author(s):  
Dokie Tokpah Daniel ◽  
Kwoseh Charles ◽  
Saye Tokpah Eric ◽  
Kolleh David
Keyword(s):  
Rice False Smut ◽  
Ashanti Region ◽  
False Smut
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