scholarly journals Temporal and Spatial Distribution of Clonal Complexes of Streptococcus pneumoniae Isolates Resistant to Multiple Classes of Antibiotics in Belgium, 1997 to 2004

2008 ◽  
Vol 52 (9) ◽  
pp. 3216-3220 ◽  
Author(s):  
Heather Amrine-Madsen ◽  
Johan Van Eldere ◽  
Robertino M. Mera ◽  
Linda A. Miller ◽  
James A. Poupard ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Streptococcus Pneumoniae ◽  
Pneumococcal Conjugate Vaccine ◽  
Multilocus Sequence Typing ◽  
Invasive Disease ◽  
Temporal And Spatial Distribution ◽  
Time Period ◽  
Temporal And Spatial ◽  
The Relationship ◽  
Over Time

ABSTRACT We performed multilocus sequence typing on 203 invasive disease isolates of Streptococcus pneumoniae to assess the clonal compositions of isolates from two provinces in Belgium and to determine the relationship between clones and antibiotic nonsusceptibility, particularly nonsusceptibility to two or more classes of antibiotics. The frequency of multiclass nonsusceptibility (MCNS) was higher in the province of West Flanders (38%) than in Limburg (21%). This difference was largely attributable to five clonal complexes (CC156, CC81, CC143, CC193, and CC1848), which contained high proportions of isolates with MCNS (>47%) and which were circulating at higher frequencies in West Flanders. The S. pneumoniae population changed over time, as CC156 and CC81 declined in frequency from 1997 to 1999 to 2001 to 2004. Over the same time period, the frequency of pneumococcal conjugate vaccine 7 (PCV7) serotypes dropped from 69% to 41%. In contrast, the nonvaccine serotype 19A increased in frequency from 2.1% to 6.6%. None of these changes can be attributed to PCV7 vaccine, as it was not in use in Belgium during the time period studied. There was evidence that MCNS clones flowed from West Flanders to Limburg.

scholarly journals Temporal and Spatial Evolution Features of Precipitable Water in China during a Recent 65-Year Period (1951–2015)

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hao Wang ◽  
Jianxin He
Keyword(s):  
Spatial Distribution ◽  
Water Vapor ◽  
Transition Period ◽  
Precipitable Water ◽  
Distribution Characteristics ◽  
Temporal And Spatial Distribution ◽  
Spatial Distribution Characteristics ◽  
Distribution Features ◽  
Temporal And Spatial ◽  
The Relationship

Water vapor in the atmosphere is not only an important greenhouse gas, but also an important factor that significantly affects the variations of global climate and water circulation. This study utilized the National Centers for Environmental Prediction (NCEP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) reanalysis data to probe the temporal and spatial distribution features of atmospheric precipitable water (PW) in China during a recent 65-year period (1951–2015), and the relationship between PW and actual precipitation was also studied. The temporal and spatial distribution characteristics of PW in China presented an overall decreasing spatial trend from the southeast to northwest direction. The spatial distribution pattern of the first eigenvector demonstrated that the PW in China shows nationwide variation features with a varying amount of PW across different regions. The year 1967 was further identified as an important transition period for the temporal and spatial distribution characteristics of the PW. We also found that the PW had inherent variability of around 30 years. Regarding the relationship with precipitation, PW was most closely correlated with precipitation in the northeastern region and the upper northwestern region in China. Different regions displayed different efficiencies for converting PW to precipitation. The conclusions are useful for understanding the long-term water vapor evolution and its potential effects on precipitation in China.

scholarly journals Distribution characteristics of nitrogen and the related microbial community in the surface sediments of the Songhua River

RSC Advances ◽  
2021 ◽  
Vol 11 (43) ◽  
pp. 26721-26731
Author(s):  
Congyu Li ◽  
Zhen Zhong ◽  
Wenfu Wang ◽  
Haiyan Wang ◽  
Guokai Yan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Microbial Community ◽  
Surface Sediments ◽  
River Sediments ◽  
Distribution Characteristics ◽  
Temporal And Spatial Distribution ◽  
Songhua River ◽  
Temporal And Spatial ◽  
The Relationship ◽  
The Songhua River

In this study, temporal and spatial distribution of nitrogen in the Songhua River sediments and distribution characteristics of related microbes as well as the relationship between them were investigated.

scholarly journals Research on the Coordination Characteristics and Interaction Between Innovation-driven Development and Green Development of the Yangtze River Economic Belt in China

2021 ◽  
Author(s):  
wei wang ◽  
lei zhou ◽  
wei chen ◽  
chao Wu
Keyword(s):  
Sustainable Development ◽  
Spatial Distribution ◽  
Yangtze River ◽  
The Yangtze River ◽  
Temporal And Spatial Distribution ◽  
Development Index ◽  
Green Development ◽  
Regional Sustainable Development ◽  
Temporal And Spatial ◽  
The Relationship

Abstract Innovation-driven development and green development are both important ways to achieve regional sustainable development. Many studies have focused on innovation-driven dynamic factors and green development impact factors, yet most have paid little attention to the relationship between the two types of factors. This study considers the innovation-driven development and green development evaluation systems of 130 cities in the Yangtze River Economic Belt. Through expert group evaluation, the three dimensions of green production, green life and green ecology are selected to represent the green development index. Innovation input, innovation performance, and innovation potential reflect the innovation-driven development index. The entropy TOPSIS method is used to measure the innovation-driven development index and the green development index of 130 cities in the Yangtze River Economic Belt. Then, a coupling coordination evaluation model and a spatiotemporal heterogeneity analysis model are constructed to discuss the coupling coordination index of regional innovation-driven development and green development in the Yangtze River Economic Belt and to determine its temporal and spatial distribution characteristics. Finally, we choose a spatial panel regression model to explore the relationship between the innovation-driven development index and the green development index of the Yangtze River Economic Belt. The research results show that there is a significant difference between the innovation-driven development index and the green development index of the 130 cities in the Yangtze River Economic Belt in terms of the temporal and spatial distribution. The coordination index of the two has an imbalanced distribution feature, and there is a significant direct or indirect relationship between the two structural indicators in a mathematical sense. This study enhances the academic community's understanding of the interaction between innovation-driven development and green development, provides scientifically based support for green development, offers guidance for the implementation of innovation capabilities, and ultimately supports a policy design facilitating regional sustainable development.

scholarly journals Measurement of spatial and temporal fine sediment dynamics in a small river

2011 ◽  
Vol 8 (6) ◽  
pp. 11315-11355 ◽  
Author(s):  
Y. Schindler Wildhaber ◽  
C. Michel ◽  
P. Burkhardt-Holm ◽  
D. Baenninger ◽  
C. Alewell
Keyword(s):  
Spatial Distribution ◽  
Water Level ◽  
Low Cost ◽  
Spatial Dynamics ◽  
Fine Sediment ◽  
Sediment Dynamics ◽  
Published Data ◽  
Temporal And Spatial Distribution ◽  
Time Period ◽  
Temporal And Spatial

Abstract. Empirical measurements on fine sediment dynamics, infiltration and accumulation have been conducted worldwide, but it is difficult to compare the results because the applied methods differ widely. We compared established methods to capture temporal and spatial dynamics of suspended sediment (SS), fine sediment infiltration and accumulation and tested them for their suitability in small, canalized rivers of the Swiss Plateau. Suitability was assessed by data comparison, relation to hydrological data and in the context of previously published data. SS were assessed by optical backscatter (OBS) sensors and SS samplers. The former exhibit a better temporal resolution but were associated with calibration problems. Due to the relatively low cost and easy mounting of SS samplers, they can provide a higher spatial distribution in the cross section. This results in a better correlation between sediment infiltration and SS assessed by SS samplers than with OBS sensors. Sediment infiltration baskets and bedload traps were able to resolve the temporal and spatial distribution of fine sediment infiltration. Data obtained by both methods were positively correlated with water level and SS. In contrast, accumulation baskets can not assess the temporal behavior of fine sediment but the accumulation over a certain time period. Results indicate that less fine sediment accumulated in upwelling zones and within areas of higher mean water level due to scouring of fine sediment. Even though there was an increase of SS and sediment assessed with the bedload traps from up- to downstream, less fine sediment accumulated downstream. This is probably also due to more scouring downstream.

scholarly journals The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China

2020 ◽  
Author(s):  
Shengnan Lin ◽  
Yaman Li ◽  
Jia Rui ◽  
Yao Wang ◽  
Ruixin Wang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Social Factors ◽  
Exponential Model ◽  
Temporal And Spatial Distribution ◽  
Medical Institutions ◽  
Health Practitioners ◽  
Temporal And Spatial ◽  
Novel Coronavirus ◽  
The Relationship

Abstract Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China. Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model. Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant. Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.

scholarly journals The Xixuthrus species of Fiji (Coleoptera: Cerambycidae:) Prioninae

Zootaxa ◽  
2004 ◽  
Vol 777 (1) ◽  
pp. 1 ◽  
Author(s):  
DOUGLAS YANEGA ◽  
DAVID OLSON ◽  
SHARON SHUTE ◽  
ZIRO KOMIYA
Keyword(s):  
Spatial Distribution ◽  
Digital Images ◽  
New Guinea ◽  
Type Specimens ◽  
Species Identity ◽  
Temporal And Spatial Distribution ◽  
Temporal And Spatial ◽  
Over Time

There have been four species of the cerambycid genus Xixuthrus described from the Fijian archipelago; X. heros (Heer), X. terribilis Thomson, X. ganglbaueri Lameere, and X. heyrovskyi Tippmann. Over time, X. terribilis had been reduced to synonymy, and it had been suggested that X. ganglbaueri was probably from New Guinea rather than Fiji. We here re-establish the validity of X. terribilis, place X. heyrovskyi in synonymy with it, and designate a neotype for X. ganglbaueri to resolve both the species identity and its geographic provenance. Diagnoses of the three confirmed Fijian species are presented, including digital images of type specimens, and notes on temporal and spatial distribution.

Research on Temporal and Spatial Distribution of PM10 in Yantai Urban Areas from 2006 to 2010

2012 ◽  
Vol 178-181 ◽  
pp. 737-740
Author(s):  
Ming Chang ◽  
Jian Guo Song ◽  
Xin Zhi Wang ◽  
Wei Liu
Keyword(s):  
Spatial Distribution ◽  
Urban Areas ◽  
Seasonal Changes ◽  
Interannual Variation ◽  
Average Concentration ◽  
Temporal And Spatial Distribution ◽  
Yantai City ◽  
Temporal And Spatial ◽  
Two Stages ◽  
Over Time

The purpose of this thesis is to study the characteristics of PM10 concentrations in the atmosphere over time granularity and spatial distribution in Yantai city. The concentration of PM10 is investigated in the daily from 2006 to 2010. The result shows that the characteristics of PM10 concentration in each time granularity are different: the interannual variation can be divided into two stages and the highest is approximately equal twice times of the lowest. The seasonal changes is distinct and the PM10 average concentration presents a feature that spring > winter > autumn > summer. And the highest month of PM10 is April, followed in March and May. The geographical distribution of PM10 is the center of pollution has been located in region A. Each region getting lowest in summer and the minimum one is region H where the concentration is 0.052mg/m3. The function regions can be divided into two categories: most regions show same as the seasonal changes but the region A and G’s characteristics change dramatically and the concentration of PM10 has come to 0.156 mg/m3 in the winter.

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