scholarly journals Community structures of phytoplankton and their relationships with environmental factors in the Jinshahe Reservoir, Hubei Province

2015 ◽  
Vol 27 (5) ◽  
pp. 902-910 ◽  
Author(s):  
ZHANG Yun ◽  
◽  
MA Xufa ◽  
GUO Feifei ◽  
LI Jianzhu ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Hubei Province ◽  
Community Structures

scholarly journals Denitrifying bacterial communities in surface-flow constructed wetlands during different seasons: characteristics and relationships with environment factors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jia-ming Wei ◽  
Li-juan Cui ◽  
Wei Li ◽  
Yun-mei Ping ◽  
Wan Li
Keyword(s):  
Environmental Factors ◽  
High Throughput Sequencing ◽  
Bacterial Abundance ◽  
Flow Direction ◽  
Denitrifying Bacteria ◽  
Surface Flow ◽  
Strongly Correlated ◽  
Community Structures ◽  
Oxidation Reduction ◽  
Different Seasons

AbstractDenitrification is an important part of the nitrogen cycle and the key step to removal of nitrogen in surface-flow wetlands. In this study, we explored space–time analysis with high-throughput sequencing to elucidate the relationships between denitrifying bacteria community structures and environmental factors during different seasons. Our results showed that along the flow direction of different processing units, there were dynamic changes in physical and chemical indicators. The bacterial abundance indexes (ACEs) in May, August, and October were 686.8, 686.8, and 996.2, respectively, whereas the Shannon-Weiner indexes were 3.718, 4.303, and 4.432, respectively. Along the flow direction, the denitrifying bacterial abundance initially increased and then decreased subsequently during the same months, although diversity tended to increase. The abundance showed similar changes during the different months. Surface flow wetlands mainly contained the following denitrifying bacteria genus: unclassified Bacteria (37.12%), unclassified Proteobacteria (18.16%), Dechloromonas (16.21%), unranked environmental samples (12.51%), unclassified Betaproteobacteria (9.73%), unclassified Rhodocyclaceae (2.14%), and Rhodanobacter (1.51%). During different seasons, the same unit showed alternating changes, and during the same season, bacterial community structures were influenced by the second genus proportion in different processing units. ACEs were strongly correlated with temperature, dissolved oxygen, and pH. Bacterial diversity was strongly correlated with temperature, electrical conductivity, pH, and oxidation reduction potential. Denitrifying bacteria are greatly affected by environmental factors such as temperature and pH.

scholarly journals An Ecological Comparison Study On The Causal Association Between Leptospirosis and Moyamoya Disease in Hubei, China, 2017-2019

2021 ◽  
Author(s):  
Ma Zhiyang ◽  
Xiong Wang ◽  
Meifang Li ◽  
Da Zhou ◽  
Jianjian Zhang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Geographic Distribution ◽  
Moyamoya Disease ◽  
Hot Spots ◽  
Rice Field ◽  
Hubei Province ◽  
Comparison Study ◽  
Cold Spots ◽  
Adjusted Incidence ◽  
Epidemiological Characteristics

Abstract Background and PurposeThe importance of environmental factors (especially leptospirosis) in moyamoya disease (MMD) has not been clarified. Here we investigated the epidemiological characteristics of MMD under perspective of ecology in Hubei province, China.MethodsWe conducted a population-based study to describe the epidemiologic characteristics of MMD in Hubei province between 2017 and 2019. The regional clusters of the hot spots (high incidence) and cold spots (low incidence) of MMD were identified using the spatial statistical method. To evaluate the role of leptospirosis in MMD, we performed an ecological comparison study to evaluate whether the socioeconomic and environmental variables of hot spots are more suitable for leptospirosis spread. Results The average annual age-adjusted incidence of MMD was 2.85 per 100 000 person-years from 2017 to 2019. The middle-aged had higher incidence of MMD than the children. There existed an obvious geographic distribution of MMD at county level that the average annual age-adjusted incidence of hot spots was about 8 times than the cold spots. The hot spots were identified mainly in the low mountainous and hilly terrain, while cold spots were located in the Jianghan Plains. Compared to cold spots, the hot spots had larger cattle density (28.9 vs 7.7, P=0.002), higher percentages of rice field (80.3% vs 35.7%, P=0.002), and lower elevation (33.6 vs 157.4, P=0.001)Conclusions We identified the obvious geographic distribution of MMD in the province, which initially strengthened the importance of environmental factors of this disease. Moreover, we preliminarily identified that people who lived in the low elevation regions with close contact to the cattle and rice field has a high risk of MMD. Future studies are needed to explore the potential environmental factors in MMD, especially for early-life exposure to leptospirosis.

scholarly journals Lack of Congruence in Species Diversity Indices and Community Structures of Planktonic Groups Based on Local Environmental Factors

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e69594 ◽  
Author(s):  
Hideyuki Doi ◽  
Kwang-Hyeon Chang ◽  
Yuichiro Nishibe ◽  
Hiroyuki Imai ◽  
Shin-ichi Nakano
Keyword(s):  
Species Diversity ◽  
Environmental Factors ◽  
Diversity Indices ◽  
Community Structures ◽  
Species Diversity Indices

scholarly journals Epidemiological Characteristics of COVID-19 Deaths In China: An Analysis of Environmental Factors

2021 ◽  
Author(s):  
Xixi Feng ◽  
Peipei Du ◽  
Guobao Li ◽  
Peihua Cao ◽  
Jiaohua Luo ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Descriptive Analysis ◽  
Mainland China ◽  
Case Fatality ◽  
Temporal Distribution ◽  
Hubei Province ◽  
Environmental Data ◽  
Mortality Data ◽  
Lower Temperature ◽  
Epidemiological Characteristics

Abstract Background: As of April 2020, most of the confirmed cases outside Hubei province have been cured or confirmed dead in China. We aimed to understand environmental factors leading to COVID-19-related mortality in non-Hubei region.Methods: We collected spatial-temporal and environmental data of 99 cases of COVID-19-related deaths outside of Hubei province in Mainland China between January 22, 2020 and April 6, 2020. A descriptive analysis, including a spatial-temporal distribution of daily reported diagnosed cases and related deaths, was conducted. We analyzed the possible environmental factors that affect the provincial-level case fatality rate (CFR) of COVID-19 outside Hubei, China.Results: Among the 99 reported deaths, 59 (59.6%) were male and 40 (40.4%) were female. The mean age at death was 71.30 (SD 12.98) years and 74 deaths were among those 65 years or older. The CFR was negatively correlated with temperature (r=-0.679, P<0.001) and humidity (r=-0.607, P=0.002), while latitude was positively correlated with the CFR (r=0.636, P=0.001). There were no statistically significant associations between CFR and the social environment factors.Conclusion: Higher CFR of COVID-19 was associated with lower temperature, lower humidity, and higher latitude. Continual analysis of daily reported diagnoses and mortality data can help healthcare professionals and policy makers understand the trends within a country in order to better prepare nationwide prevention and care guidelines, along with adequately appropriate funds accordingly.

scholarly journals Epidemiological Characteristics of COVID-19 Deaths in China: An Analysis of Environmental Factors

2021 ◽  
Author(s):  
Xixi Feng ◽  
Peipei Du ◽  
Guobao Li ◽  
Peihua Cao ◽  
Jiaohua Luo ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Descriptive Analysis ◽  
Mainland China ◽  
Temporal Distribution ◽  
Hubei Province ◽  
Environmental Data ◽  
Mortality Data ◽  
Containment Measures ◽  
Lower Temperature ◽  
Epidemiological Characteristics

Abstract Background Since December 2019, China has carried out dramatic containment measures to control the spread of COVID-19. As of April 6, 2020, most of the confirmed cases outside Hubei province have been cured or confirmed dead. In this study, we aimed to understand environmental factors leading to COVID-19-related mortality outside of Hubei province, in mainland China. Methods We collected spatial-temporal and environmental data of 99 cases of COVID-19-related deaths outside of Hubei province in Mainland China between January 22, 2020 and April 6, 2020. A descriptive analysis, including a spatial-temporal distribution of daily reported diagnosed cases and related deaths, was conducted. We analyzed the possible environmental factors that affect the provincial-level CFR of COVID-19 outside Hubei, China. Results Among the 99 reported deaths, 59 (59.6%) were male and 40 (40.4%) were female. The mean age at death was 71.30 (SD 12.98) years and 74 deaths were among those 65 years or older. The CFR was negatively correlated with temperature (r=-0.679, P < 0.001) and humidity (r=-0.607, P = 0.002), while latitude was positively correlated with the CFR (r = 0.636, P = 0.001). There were no statistically significant associations between CFR and the social environment factors. Conclusion Higher CFR of COVID-19 was associated with lower temperature, lower humidity, and higher latitude. Continual analysis of daily reported diagnoses and mortality data can help healthcare professionals and policy makers understand the trends within a country in order to better prepare nationwide prevention and care guidelines, along with adequately appropriate funds accordingly.

Community structures of phytoplankton and its relationship with environmental factors in the Lhasa River

2019 ◽  
Vol 39 (3) ◽  
Author(s):  
君珊 JUN Shan ◽  
王东波 WANG Dongbo ◽  
周健华 ZHOU Jianhua ◽  
白晓宇 BAI Xiaoyu ◽  
白凯 BAI Kai
Keyword(s):  
Environmental Factors ◽  
Community Structures ◽  
Lhasa River

Reductionist thinking and animal models in neuropsychiatric research

2019 ◽  
Vol 42 ◽  
Author(s):  
Nicole M. Baran
Keyword(s):  
Animal Models ◽  
Mental Disorders ◽  
Environmental Factors ◽  
Neuropsychiatric Disorders ◽  
Model Organisms ◽  
Developmental Genetic ◽  
Term Study ◽  
Genetic And Environmental Factors ◽  
Mechanisms Of Plasticity

AbstractReductionist thinking in neuroscience is manifest in the widespread use of animal models of neuropsychiatric disorders. Broader investigations of diverse behaviors in non-model organisms and longer-term study of the mechanisms of plasticity will yield fundamental insights into the neurobiological, developmental, genetic, and environmental factors contributing to the “massively multifactorial system networks” which go awry in mental disorders.

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