Overview of the study area. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Single factor detection results. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Impact of mobility in a three-region system with heterogeneous epidemic conditions. by Mousumi Roy (8869412)

“…From left to right, we consider the scenarios with decreasing outgoing mobility and increasing non-commuting population. …”

Types of interaction between two factors. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Research frameworks. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Model index comparison. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Datasheet1_Global, regional, and national time trends in ischaemic heart disease incidence over three decades (1990–2019): an age-period-cohort analysis of the global burden of dis... by Juan Tang (437969)

“…Age effects across all countries and genders demonstrated an increasing trend over time, indicating age as a significant risk factor for IHD. …”

Table1_Trp-574-Leu and the novel Pro-197-His/Leu mutations contribute to penoxsulam resistance in Echinochloa crus-galli (L.) P. Beauv..docx by Penglei Sun (18343673)

“…In vitro ALS activity assays demonstrated a significantly higher activity in AH-R compared to the susceptible population (YN-S). …”

Table2_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table13_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table12_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table3_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table9_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Image1_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.tif by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table7_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table6_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table11_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table8_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table5_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”

Table10_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. …”