Table3_Remagnetization of Carboniferous Limestone in the Zaduo Area, Eastern Qiangtang Terrane, and Its Tectonic Implications.XLSX by Liang Yu (207992)

“…Based on both thermal and alternating field demagnetizations, the characteristic remanent magnetization directions for most samples were isolated: D_g = 6.3°, I_g = 50.1°, k_g = 54.9, α₉₅ = 6.2° in-situ, and D_s = 330.2°, I_s = 58.9°, k_s = 5.9, and α₉₅ = 20.5° after 2-step tilt correction. …”

Time-resolved UV-visible spectroscopy of bodipy-based materials by F Cucinotta (7836197)

“…The first set of materials is characterised by green luminescence that, as the dye loading increases from 1% to 50%, shows a decrease in quantum yields from 0.22 to 0.05 and a reduction of the excited state lifetime. …”

Igf signaling is required for cardiomyocyte proliferation during zebrafish heart development. by Ying Huang (53474)

“…E. A significant decrease (***<i>p</i><0.0001) in cardiomyocyte proliferation was detected in embryos treated with NVP-AEW541.…”

Synthesis and bioactivity of the γ-secretase modulator photo-probe AR243. by Thorsten Jumpertz (188948)

“…AR243 caused a dose-dependent decrease in Aβ42 levels with a concomitant increase in Aβ38 levels, confirming its bioactivity as a potent GSM with an IC₅₀ for Aβ42 reduction of 290 nM.…”

DataSheet_6_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.csv by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_1_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.pdf by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_5_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_7_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_4_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

DataSheet_3_Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.xlsx by Thomas Karl Atkins (17791715)

“…Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. …”

Rheological behavior of concentrated tucupi by Telma dos Santos COSTA (4618960)

“…Rheology at 25 °C indicated that the partial gelification of starch during concentration causes a decrease in the product’s viscosity and, if the concentration is carried out at a temperature that favors total starch gelification, the product’s viscosity increases. …”

Rheological behavior of concentrated tucupi by Telma dos Santos COSTA (4618960)

“…Rheology at 25 °C indicated that the partial gelification of starch during concentration causes a decrease in the product’s viscosity and, if the concentration is carried out at a temperature that favors total starch gelification, the product’s viscosity increases. …”

Enhanced electropermeabilization of peroxidized cells with both ultra-short and conventional electric pulse treatments. by P. Thomas Vernier (142483)

“…Peroxidized cells treated with a single 100 µs, 50 kV/m pulse show a similar increased susceptibility to electropermeabilization. …”

Table 6_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 3_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 1_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 4_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Data Sheet 1_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.docx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Table 5_Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.xlsx by Chengli Yu (1445185)

“…The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma.</p>Results<p>In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. …”

Simulated temporal dynamics in E2F activation using the stochastic Rb-E2F model. by Tae J. Lee (241477)

“…<p>(A) Stochastic simulations (25 events) exhibit variable time delays in E2F activation, as shown in gray lines. …”