Study area habitat quality LISA clustering map. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Spato-temporal changes in land use types. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Pattern indices of landscape levels. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Type level landscape index changes in 1990-2020. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Location map of the study area. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Data source. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Research Technology Flow Chart. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Robustness of the computational results. by Farhad Razi (22076077)

Table 1_The mitochondrial function of peripheral blood cells in cognitive frailty patients.docx by Li Qin (205731)

“…</p>Conclusion<p>Age, lower educational attainment, malnutrition, and depression are significant risk factors for CF. Moreover, mitochondrial dysfunction, characterized by decreased mtDNAcn, impaired respiratory function and increased ROS levels appears to be a critical phenotype of CF.…”

miR-184 does not downregulate target pSJ and TCJ mRNA levels but changes Nrv2 expression. by Sravya Paluri (5475260)

Overexpression of miR-184 disrupts the blood-brain barrier and larval locomotion. by Sravya Paluri (5475260)

Characterization of purified esAEVs based on protein content and the localization of Mif on the surface. by Safia A. Essien (20108379)

Principal coordinates analysis (PCoA). by Wararak Choovanichvong (22110371)

“…Analysis of bacterial abundance revealed a shift in trends as the disease combined from control to NSESKD and SESKD group, respectively, across 7 genera: <i><i>Actinobacillus</i></i>, <i>TM7x</i>, <i><i>Capnocytophaga</i></i>, <i><i>Neisseria</i></i>, and <i><i>Leptotrichia</i></i> increased in abundance, while <i><i>Actinomyces</i></i> and <i><i>Atopobium</i></i> decreased. Linear discriminant analysis effect size (LEfSe) identified <i><i>Leptotrichia</i></i> as a potential biomarker for ESKD (both with and without sarcopenia).…”

The effect of EDF derived from <i>E</i>. <i>coli</i> (in mid-logarithmic growth phase, OD = 0.6) on the MRSA and MSSA. by Shahriar Bakhti (20391188)

“…In all the analyses, p<0.05 was considered statistically significant. The data were expressed as the mean value plus–minus the standard error of the mean (mean ± SEM).…”

Overexpression of miR-184 disrupts pleated septate junctions in peripheral nerves. by Sravya Paluri (5475260)

Table 1_The knowledge paradox: an inverted U-shaped association between HIV knowledge and stigma among older men in Sichuan Province, Southwest China.docx by Zhihua Ye (3133311)

“…Stigma initially increased with increasing knowledge (linear β = 1.71, p < 0.001), peaked at a knowledge score of 4.14, and subsequently decreased with increasing knowledge gain (quadratic β = −0.21, p < 0.001). …”

Rotated component matrix. by Jiaqiang Sun (176863)

“…Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. …”

Conceptual framework of this study. by Jiaqiang Sun (176863)

“…Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. …”

The robustness verification result. by Jiaqiang Sun (176863)

“…Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. …”

KMO and Bartlett’s test. by Jiaqiang Sun (176863)

“…Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. …”