Model test results. by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”

Explanatory variable definition. by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”

Spatial Dubin panel model estimation results. by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”

S1 Dataset - by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”

A) RT-qPCR analysis of <i>Yap</i>, <i>Taz</i>, and <i>Tead2</i> shows a significant decrease in transcript levels over postnatal stages from P0 to P28. by Heather L. Dingwall (14573796)

IR-induced mutation clusters in DR, BER and RecQ helicase mutants. by Bettina Meier (274833)

Connectivity between the left nucleus accumbens and two clusters. by Karel Joineau (21799908)

“…After intervention there was a significant decrease in the FR group (mean at V1: 0.249 ± 0.094, mean at V5: 0.130 ± 0.101, difference: −0.119 ± 0.097, p < 0.001) and a significant increase in the SM group (mean at V1: 0.156 ± 0.113, mean at V5: 0.287 ± 0.107, difference: 0.131 ± 0.066, p < 0.001). …”

Percentage of US adults with health insurance coverage by income-level, age 60 to 70 years. by Rahul Aggarwal (4428457)

Subjects:

Association of Medicare eligibility and enrollment with prescription medications by income level¹. by Rahul Aggarwal (4428457)

Subjects:

Baseline characteristics of the US population ages 64 and 66 years by income level¹. by Rahul Aggarwal (4428457)

Subjects:

Association of Medicare eligibility and enrollment with financial strain due to medical bills by income level¹. by Rahul Aggarwal (4428457)

Subjects:

Repeated measures ANOVA analysis of selected variables by the clustering method. by Luoying Yang (5767502)

Unsupervised hierarchical clustering for benign samples only (nontreatment, STT, and LTT groups). by Jinho Lee (587173)

The correlation value of gene expression related to areas of GM decrease (ASD < TDCs) in Yeo’s [57] and Brainnetome’s [58] resting-state functional networks. by Alessia Camasio (14185864)

Subjects:

Association between FF Proximity and BMI by sex. by Kimberly Yuin Y’ng Wong (22766265)

“…An exponential decrease in FF proximity was associated with 0.7 kg/m² (p < 0.001) increase in BMI among males and 0.4 kg/m² (p < 0.05) decrease among females. …”

Change in log health expenditures for most recent care and in the preceding month^a. by Emily Lyles (3174990)

Subjects:

Sample demographic characteristics and living conditions at baseline and endline. by Emily Lyles (3174990)

Subjects:

Household economy and receipt of humanitarian assistance. by Emily Lyles (3174990)

Subjects:

Change in health utilization measures by group and difference in magnitude of change between groups. by Emily Lyles (3174990)

Subjects:

Reasons and timeframes for care-seeking. by Emily Lyles (3174990)

Subjects: