Optical coherence tomography findings. by Eisuke Usui (12111855)

Metabolic incorporation of ¹⁴C-acetate into PE and CL in cellular lipids of log-phase <i>M</i>. <i>abscessus</i> is affected by NMP. by Timilehin Faboro (20175963)

Data_Sheet_1_IRE1α-XBP1 Affects the Mitochondrial Function of Aβ25–35-Treated SH-SY5Y Cells by Regulating Mitochondria-Associated Endoplasmic Reticulum Membranes.PDF by Bingcong Chu (10431125)

“…Increased levels of ATP, restoration of mitochondrial membrane potential, and decreased production of mitochondrial reactive oxygen species after Aβ treatment in the presence of 4μ8c showed that inhibiting the IRE1α-XBP1 axis effectively mitigated Aβ-induced mitochondrial dysfunction in SH-SY5Y cells. …”

NMP inhibits the incorporation of ¹⁴C-palmitic acid into glycopeptidolipids, phosphatidylethanolamine and cardiolipin in <i>M</i>. <i>abscessus</i>. by Timilehin Faboro (20175963)

Table_5_Change of mortality of patients with acute ischemic stroke before and after 2015.DOCX by Sang-Won Park (180226)

Image_5_B Cell and CD4 T Cell Interactions Promote Development of Atherosclerosis.tif by Christopher Tay (335988)

Preventing Protein Oxidation with Sugars: Scavenging of Hypohalous Acids by 5‑Selenopyranose and 4‑Selenofuranose Derivatives by Corin Storkey (1820602)

“…Rate constants for reaction of the seleno-sugars with HOBr are ∼8 times lower than those for HOCl (1.0–1.5 × 10⁷ M^–1 s^–1). …”

Additional questions on vaccination. by Sara Saleh (6733619)

“…The aim of this study was to assess the KAP of Lebanese mothers regarding childhood immunization and to identify potential barriers to vaccination in children.</p><p>Methods</p><p>A cross-sectional study was conducted among Lebanese mothers with children aged between zero days to 5 years old. …”

The raw data file. by Sara Saleh (6733619)

“…The aim of this study was to assess the KAP of Lebanese mothers regarding childhood immunization and to identify potential barriers to vaccination in children.</p><p>Methods</p><p>A cross-sectional study was conducted among Lebanese mothers with children aged between zero days to 5 years old. …”

DataSheet2_A comprehensive analysis of metabolomics and transcriptomics to reveal major metabolic pathways and potential biomarkers of human preeclampsia placenta.xlsx by Yan Feng (128912)

“…Furthermore, we found that CYP11A1, HSD3B2, and HSD17B6 are highly correlated with diseases.…”

Impact of hematocrit (1%, 2%, or 4%) on cellular response after UVA irradiation (2 J/cm²). by Marie Laulhé (6416483)

“…<p>(A) Percentage of Annexin-V+ cells measured daily up to 2 days after irradiation. …”

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

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

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

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Article data. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Overidentification test results. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Quantile regression results. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Instrumental variable regression results. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Other robust regression results. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Baseline regression results. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”

Results of propensity score matching. by Pu Miao (12886949)

“…Moreover, there is no interaction between talent subsidies and innovation incentives, or between talent subsidies and high-level talent innovation support, but there is an offsetting effect between the other policies. (3) The positive impact of science and technology talent policies exhibits a time lag, generally ranging from 4 to 5 years. …”