Dataset. Dataset. by Michael R. Lawrence (21369854)

“…Serum levels of nerve growth factor (NGF), a biomarker of brain plasticity, were significantly lower in the long COVID group, which was significantly more likely than controls to have serum levels of inflammatory marker (interleukin (IL)-10) values greater than or equal to the median (p = 0.015). …”

Self-Report Questionnaires. by Michael R. Lawrence (21369854)

“…Serum levels of nerve growth factor (NGF), a biomarker of brain plasticity, were significantly lower in the long COVID group, which was significantly more likely than controls to have serum levels of inflammatory marker (interleukin (IL)-10) values greater than or equal to the median (p = 0.015). …”

Change in temperature from baseline. by Katharyn L. Flickinger (4759323)

Hemodynamic and respiratory changes over time. by Katharyn L. Flickinger (4759323)

<b>Data for s</b><b>easonal variations in coral lipids and their significance for energy maintenance in the </b><b>South China Sea</b> by Hongyan Mo (19721569)

“…<p dir="ltr">In recent years, the intensification of global warming and extreme climate have led to an increase in the frequency and severity of coral bleaching. Coral bleaching means a decrease in symbiotic zooxanthellae density (ZD). …”

Percentage regional changes in decadal regional hydrology. by Nic Gedney (19762301)

Projected decadal changes in seasonality in the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

Boxplots display the median (as the central tendency) and the interquartile range (25th to 75th percentiles) as the bottom and top edges of the box. by Manal Ali Ahmad (16868808)

RT-qPCR evaluation of MYD88, NLRP3, and NFκβ in GA 18.5 pup brains. by Adrienne J. Bradley (20861948)

Box plot of queens and males in the young colony (left) and old colony (right) A-B compares the queens and males distribution in the young and old colonies, respectively. by Moïse Pierre Exélis (21578515)

Data cleaning and preparation algorithm. by Ayana Ablayeva (22103708)

“…<div><p>Background</p><p>Liver cancer is the third-deadliest cancer globally, significantly affecting the Asian population, which accounts for 70% of cases. …”

Renal outcomes of both treatment groups. by Marwan El-Deyarbi (21688492)

“…Participants in the multifactorial group achieved a significant mean difference in low-density lipoprotein cholesterol levels (mean difference = −0.14, 95% CI: −0.27–0.001, P < 0.03), and significant adjusted mean difference of eGFR levels difference (3.93 mL/min/1.73 m², 95% CI: 1.27–6.58, P < 0.01) at study completion compared to those in the control group. …”

Data_GDP/ Ndvi. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Flow chart of the study. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Example of manual identification. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Data_soil. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Data_road. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Excel_ESs and transfer matrix. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Data sources and descriptions. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”

Coupling coordination types. by Qianhong Mao (22305184)

“…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”