The complete mitochondrial genomes of intermediate host snails for Schistosoma in China were sequenced, including the sub-species Oncomelania hupensis hupensis in two types, and O. hupensis robertsoni, intermediate hosts for S. japonicum, and Tricula hortensis, the intermediate host of S. sinensium. Four genomes have completely the same gene order as in other caenogastropods, containing 13 protein-coding genes and 22 transfer RNAs. The gene size, start codon and termination codon are mostly the same for all protein-coding genes. However, pairwise sequence alignments revealed quite different degrees of variation. The ribbed-shelled O. hupensis hupensis and the smooth-shelled but with varix O. hupensis hupensis had a lower level of genetic distance (3.1% for protein-coding genes), but the coden usages differed obviously in the mitochondrial genomes of these two types of snails, implying that their genetic difference may be larger than previously recognized. The mean genetic distance between O. hupensis hupensis and O. hupensis robertsoni was 12% for protein-coding genes, indicating a higher degree of genetic difference. In consideration of the difference in morphology and distribution, we considered that O. hupensis hupensis and O. hupensis robertsoni can be considered as separate species. The ribbed-shelled O. hupensishupensis and smooth-shelled O. hupensis robertsoni were phylogenetically clustered together within a same clade, which was then clustered with T. hortensis, confirming their close relationship. However, species or sub-species in the Oncomelania from southeastern Asian countries should be included in future study in order to resolve the phylogenetic relationship and origination of all snails in the genus.
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http://dx.doi.org/10.1016/j.ympev.2010.05.026 | DOI Listing |
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
June 2024
Anqing Municipal Institute of Schistosomiasis Control, Anqing, Anhui 246001, China.
Objective: To investigate the distribution of snails in different water systems in Anqing City from 2016 to 2022, so as to provide insights into snail control in the city.
Methods: Snail survey data and distribution of water systems in snail-infested environments were collected from schistosomiasis-endemic areas of Anqing City from 2016 to 2022. The vector maps of towns and water systems in Anqing City were downloaded from National Geomatics Center of China.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
November 2024
Suzhou Center for Disease Prevention and Control, Suzhou, Jiangsu 215000, China.
Objective: To investigate the snails spread and its spatio-temporal clustering characteristics in Suzhou City, Jiangsu Province from 2016 to 2023, so as to provide insights into precision control of snails in the City.
Methods: snail surveillance data in Suzhou City from 2016 to 2023 were collected, and the areas of snail spread and areas of emerging and re-emerging snail habitats were retrieved. The spatial distribution characteristics and clustering types and locations of environments with snail spread were investigated using global and local spatial auto correlation analyses with the software ArcGIS 10.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Anhui Provincial Center for Disease Control and Prevention, Hefei, Anhui 230601, China.
Objective: To predict the areas of snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of snail spread, so as to provide insights into investigating the trends in areas of snail spread.
Methods: Data pertaining to snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination ().
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, Yunnan 671000, China.
Objective: To predict the potential geographic distribution of in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into surveillance and control in Yunnan Province.
Methods: The snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population).
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
January 2025
School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Objective: To construct a visual intelligent recognition model for in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of .
Methods: A total of 400 and 400 snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 and 300 snails. A total of 925 images and 1 062 snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 and 354 images from the remaining 100 snails served as an external test set.
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