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Objective To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province. Methods Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest-neighbor analysis and Spearman correlation analysis. Results O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm2, including 1 230.77 hm2 emerging snail habitats and 3 089.87 hm2 reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran’s I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran’s I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high-high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high-high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong’an. Kernel density estimation showed that there were high-, secondary high- and medium-density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high- and medium-density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = −0.108 9, P < 0.05). Conclusions There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.
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Objective To analyze the spatial point pattern distribution characteristics of hemorrhagic fever with renal syndrome (HFRS) in Jingzhou city, Hubei province during the two seasons spring- summer and autumn-winter of 2017, to discuss its high incidence area and reason, and to provide basis for the resource allocation of public health. Methods The analytical data was collected from Infectious Disease Reporting Information System in China, and the spring-summer season was from March to August of 2017, while the autumn-winter was from the September of 2017 to the February of 2018. The Ripley's K-function and kernel density estimation were applied to analyze the spatial point pattern distribution and compare the distribution characteristics of spatial point pattern between the two seasons. Results In 2017, 133 cases of HFRS were reported in Jingzhou city, including the spring- summer and autumn-winter two pick incidences. The strongest aggregation distance was 17.77km in spring-summer season, and 14.40 km in autumn-winter season. The spatial gathering center was located in the north of Jianli County in spring-summer, and it moved to the south of Jiangling County and Shashi District in autumn-winter. Conclusions The key areas for the prevention and control of HFRS in Jingzhou City are Jiangling County, the southern part of Shashi District and the northern part of jianli county. The key groups are the residents of the urban-rural junction in the southern part of Shashi City, residents along the route of large-scale projects, and farmers engaged in agricultural planting or crayfish breeding in the gathering areas.
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Objective To develop an automatic algorithm to predict the dose-volume histogram (DVH) and implement it in clinical practice.Methods Based on the prior information in the existing plan,such as dosimetric results of organs at risk (OARs) and OAR-target spatial relationship,a two-dimensional kernel density estimation was implemented to predict the DVH of OARs.The predicted DVH curves were converted into objective functions that would be implemented in the Pinnacle treatment planning system.Comparisons between predicted and actual values and between Auto-plan and manual planning were made by paired t test.Results We applied this algorithm to 10 rectal cancer patients,10 breast cancer patients,and 10 nasopharyngeal carcinoma patients.The predicted DVH of OARs showed that the deviation between the actual and predicted values at important clinical dose points were within 5%(P>0.05).The re-planning for the 10 breast cancer patients using Auto-plan showed that the heart dose was significantly reduced and the target coverage was increased,which was consistent with the predicted results.Conclusions The method proposed in this study allows for accurat DVH prediction,and,combined with Auto-plan,can be used to generate clinically accepted treatment plans.
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Objective A major component of flow cytometry data analysis involves gating , which is the process of identifying homogeneous groups of cells .As manual gating is error-prone, non-reproducible, nonstandardized, and time-consuming , we propose a time-efficient and accurate approach to automated analysis of flow cytometry data .Methods Unlike manual analysis that successively gates the data projected onto a two-dimensional filed, this approach, using the K-means clustering results , directly analyzed multidimensional flow cytometry data via a similar subpopulations-merged algorithm.In order to apply the K-means to analysis of flow cytometric data , kernel density estimation for selecting the initial number of clustering and k-d tree for optimizing efficiency were proposed .After K-means clustering , results closest to the true populations could be achieved via a two-segment line regression algorithm .Results The misclassification rate (MR) was 0.0736 and time was 2 s in Experiment One, but was 0.0805 and 1 s respectively in Experiment Two. Conclusion The approach we proposed is capable of a rapid and direct analysis of the multidimensional flow cytometry data with a lower misclassification rate compared to both nonprobabilistic and probabilistic clustering methods .
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Objective To detect the spatial point pattern distribution rules of neural tube defects.Methods The kernel density estimation and Ripley's K-function were used to analyze the spatial point pattern of the neural tube birth defects in Heshun county in 1998-2001.Results The kernel density estimation result showed that there was two clusters' distribution in central area and southeastern area respectively.In addition,the result by the Ripley's K-function presented that the location of neural tube birth defects had a significant cluster tendency in the spatial distance from 3.17 to 10.41 kilometers in the investigated area.Conclusion These results can provide an important clue for identifying the relations between environment risk factors and birth defects in this area in the future.