scholarly journals Comparison of Soybean-Nodulating Bradyrhizobia Community Structures Along North Latitude Between Japan and USA

10.5772/57165 ◽  
2014 ◽  
Author(s):  
Yuichi Saeki ◽  
Sokichi Shiro
Keyword(s):  
Community Structures ◽  
North Latitude

scholarly journals An empirical characterization of community structures in complex networks using a bivariate map of quality metrics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vinh-Loc Dao ◽  
Cécile Bothorel ◽  
Philippe Lenca
Keyword(s):  
Complex Networks ◽  
Quality Metrics ◽  
Community Structures

scholarly journals Statistical inference of assortative community structures

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Lizhi Zhang ◽  
Tiago P. Peixoto
Keyword(s):  
Statistical Inference ◽  
Community Structures

scholarly journals Spatiotemporal Evolution and Determinant Factors of the Intra-Regional Trade Community Structures of the Indian Ocean Region

2021 ◽  
Vol 10 (4) ◽  
pp. 214
Author(s):  
Lihua Yuan ◽  
Xiaoqiang Chen ◽  
Changqing Song ◽  
Danping Cao ◽  
Hong Yi
Keyword(s):  
Indian Ocean ◽  
Explanatory Power ◽  
Community Structures ◽  
Indian Ocean Region ◽  
Spatiotemporal Evolution ◽  
Regional Trade ◽  
Determinant Factors ◽  
Ocean Region ◽  
The Indian Ocean ◽  
The Impact

The Indian Ocean Region (IOR) has become one of the main economic forces globally, and countries within the IOR have attempted to promote their intra-regional trade. This study investigates the spatiotemporal evolution of the community structures of the intra-regional trade and the impact of determinant factors on the formation of trade community structures of the IOR from 1996 to 2017 using the methods of social network analysis. Trade communities are groups of countries with measurably denser intra-trade ties but with extra-trade ties that are measurably sparser among different communities. The results show that the extent of trade integration and the trade community structures of the IOR changed from strengthening between 1996 and 2014 to weakening between 2015 and 2017. The largest explanatory power of the formation of the IOR trade community structures was the IOR countries’ economic size, indicating that market remained the strongest driver. The second-largest explanatory power was geographical proximity, suggesting that countries within the IOR engaged in intra-regional trade still tended to select geographically proximate trading partners. The third- and the fourth-largest were common civilization and regional organizational memberships, respectively. This indicates that sharing a common civilization and constructing intra-regional institutional arrangements (especially open trade policies) helped the countries within the IOR strengthen their trade communities.

Archaeal and bacterial community structures of rural household biogas digesters with different raw materials in Qinghai Plateau

2021 ◽  
Author(s):  
Rui Han ◽  
Li Liu ◽  
Yan Meng ◽  
Hairong Han ◽  
Rongbo Xiong ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Raw Materials ◽  
Rural Household ◽  
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 Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

Salt stress-induced changes in microbial community structures and metabolic processes result in increased soil cadmium availability

2021 ◽  
Vol 782 ◽  
pp. 147125
Author(s):  
Meng Wang ◽  
Shuwen Zhao ◽  
Lifu Wang ◽  
Shibao Chen ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Salt Stress ◽  
Microbial Community ◽  
Community Structures ◽  
Metabolic Processes ◽  
Microbial Community Structures ◽  
Soil Cadmium ◽  
Induced Changes

Urban community structure detection based on the OD of traffic analysis zones

2019 ◽  
Vol 33 (13) ◽  
pp. 1950164
Author(s):  
Qing-Feng Dong ◽  
Dian-Kun Chen ◽  
Ting Wang
Keyword(s):  
Community Structure ◽  
Quantitative Methods ◽  
Urban Community ◽  
Spatial Location ◽  
Traffic Analysis ◽  
Association Mining ◽  
Community Structures ◽  
City Community ◽  
Traffic Characteristics ◽  
Structure Detection

At present, the detection of urban community structures is mainly based on existing administrative divisions, and is performed using qualitative methods. The lack of quantitative methods makes it difficult to judge the rationality of urban community divisions. In this study, we used complex network association mining methods to detect a city community structure by using the Origin-Destinations (OD) at traffic analysis zone (TAZ) level, and successively assigned all the TAZs into different communities. Based on the community results, we calculated the community core degree of each TAZ within every community, and then calculated the Traffic Core Degree and Location Core Degree indicators of the community based on OD passenger flow and spatial location relationship between communities. Finally, we analyzed the correlation among three indicators to ensure the rationality of the community structure. We used the city of Zhengzhou in 2016 as an example case study. For Zhengzhou, we detected a total of six communities. We found a relatively low correlation between Traffic Core Degree and Location Core Degree. Within each group, the correlation between community core degree and Traffic Core Degree was higher than that between community core degree and Location Core Degree, indicating that the urban community structure is more reasonably based on traffic characteristics. The development of a quantitative approach for determining reasonable city community structures has important implications for transportation planning and industrial layout.

scholarly journals Microbial Population Changes during Bioremediation of an Experimental Oil Spill

1999 ◽  
Vol 65 (8) ◽  
pp. 3566-3574 ◽  
Author(s):  
Sarah J. MacNaughton ◽  
John R. Stephen ◽  
Albert D. Venosa ◽  
Gregory A. Davis ◽  
Yun-Juan Chang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Bacterial Community ◽  
Oil Spill ◽  
Phospholipid Fatty Acid ◽  
Community Structures ◽  
Gram Negative ◽  
Dgge Analysis ◽  
16S Rdna Pcr ◽  
Microbial Community Structures ◽  
Plfa Analysis

ABSTRACT Three crude oil bioremediation techniques were applied in a randomized block field experiment simulating a coastal oil spill. Four treatments (no oil control, oil alone, oil plus nutrients, and oil plus nutrients plus an indigenous inoculum) were applied. In situ microbial community structures were monitored by phospholipid fatty acid (PLFA) analysis and 16S rDNA PCR-denaturing gradient gel electrophoresis (DGGE) to (i) identify the bacterial community members responsible for the decontamination of the site and (ii) define an end point for the removal of the hydrocarbon substrate. The results of PLFA analysis demonstrated a community shift in all plots from primarily eukaryotic biomass to gram-negative bacterial biomass with time. PLFA profiles from the oiled plots suggested increased gram-negative biomass and adaptation to metabolic stress compared to unoiled controls. DGGE analysis of untreated control plots revealed a simple, dynamic dominant population structure throughout the experiment. This banding pattern disappeared in all oiled plots, indicating that the structure and diversity of the dominant bacterial community changed substantially. No consistent differences were detected between nutrient-amended and indigenous inoculum-treated plots, but both differed from the oil-only plots. Prominent bands were excised for sequence analysis and indicated that oil treatment encouraged the growth of gram-negative microorganisms within the α-proteobacteria andFlexibacter-Cytophaga-Bacteroides phylum. α-Proteobacteria were never detected in unoiled controls. PLFA analysis indicated that by week 14 the microbial community structures of the oiled plots were becoming similar to those of the unoiled controls from the same time point, but DGGE analysis suggested that major differences in the bacterial communities remained.

Effect of abiotic factors on understory community structures in moist deciduous forests of northern India

2013 ◽  
Vol 15 (4) ◽  
pp. 261-273 ◽  
Author(s):  
Ashish K. Mishra ◽  
Soumit K. Behera ◽  
Kripal Singh ◽  
R. M. Mishra ◽  
L. B. Chaudhary ◽  
...  
Keyword(s):  
Abiotic Factors ◽  
Deciduous Forests ◽  
Community Structures ◽  
Northern India
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