Result of the phenotypic analysis. by Ashraf Attia Mahmoud (21262201)

Subjects:

Kaplan-Meier Survival Curve of Forced Stop Duration. by Fangyi Yang (18867853)

Subjects:

Out-of-sample Test Results of the Drop-off Decision Models: (a) ROC Curve of the First Forced Drop-off Decision Models; (b) ROC Curve for Second and More Forced Parking and Drop-of... by Fangyi Yang (18867853)

Subjects:

Oncogenic nature of mutations predicted using CScape and CScape-somatic. by Ashraf Attia Mahmoud (21262201)

Subjects:

(A) Pie chart showing the distribution of mutation types identified within the TP53 gene. by Ashraf Attia Mahmoud (21262201)

Subjects:

Structural clashes in TP53 mutants: (A) G266E mutation causes steric clashes with PHE109 and LEU257, destabilizing the structure. by Ashraf Attia Mahmoud (21262201)

Subjects:

(A) TP53 expression levels are elevated in TP53-mutant and nonmutant HNSC samples compared to normal tissues. by Ashraf Attia Mahmoud (21262201)

Subjects:

Type of Mutations. by Ashraf Attia Mahmoud (21262201)

Subjects:

Data Sheet 1_Prognostic value of left atrial strain in significant aortic valve disease: a systematic review and meta-analysis.pdf by Na Chen (153323)

“…Background<p>Previous studies on aortic valve disease have mainly focused on the left ventricle, but increasing evidence suggests that left atrial strain also has prognostic value in significant aortic valve disease.</p>Objective<p>To systematically evaluate the prognostic value of left atrial strain in significant aortic valve disease.…”

Certainty of affect predicted by positive and negative valence (study 2) Predicted certainty of knowing how you feel in a trial, as predicted by positive and negative valence ratin... by Anthony G. Vaccaro (5964368)

Differential gene sets associated with the transcriptional host response to SARS-CoV-2 infection across serially collected samples. by Vasanthi Avadhanula (259546)

Differential expression of innate immune genes in response to PAMPs in controls and BRD diagnosed calves is in response to TLR1/2 ligand Pam3CSK4. by Cian Reid (17274007)

Regression results of the Callaway method. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Regression results of crowding out effects. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Article data. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Overidentification test results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Quantile regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Instrumental variable regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Other robust regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Baseline regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”