DataSheet1_Hydroxysafflor Yellow A and Anhydrosafflor Yellow B Protect Against Cerebral Ischemia/Reperfusion Injury by Attenuating Oxidative Stress and Apoptosis via the Silent Inf... by Yijia Fangma (11503942)

“…Although HSYA and AHSYB have different chemical structures, both of them exert similar neuroprotective properties against I/R injury in vitro and in vivo, which means that AHSYB is also a non-negligible component in safflower.…”

DataSheet3_Hydroxysafflor Yellow A and Anhydrosafflor Yellow B Protect Against Cerebral Ischemia/Reperfusion Injury by Attenuating Oxidative Stress and Apoptosis via the Silent Inf... by Yijia Fangma (11503942)

“…Although HSYA and AHSYB have different chemical structures, both of them exert similar neuroprotective properties against I/R injury in vitro and in vivo, which means that AHSYB is also a non-negligible component in safflower.…”

Image1_Hydroxysafflor Yellow A and Anhydrosafflor Yellow B Protect Against Cerebral Ischemia/Reperfusion Injury by Attenuating Oxidative Stress and Apoptosis via the Silent Informa... by Yijia Fangma (11503942)

“…Although HSYA and AHSYB have different chemical structures, both of them exert similar neuroprotective properties against I/R injury in vitro and in vivo, which means that AHSYB is also a non-negligible component in safflower.…”

DataSheet2_Hydroxysafflor Yellow A and Anhydrosafflor Yellow B Protect Against Cerebral Ischemia/Reperfusion Injury by Attenuating Oxidative Stress and Apoptosis via the Silent Inf... by Yijia Fangma (11503942)

“…Although HSYA and AHSYB have different chemical structures, both of them exert similar neuroprotective properties against I/R injury in vitro and in vivo, which means that AHSYB is also a non-negligible component in safflower.…”

Distribution of the permutated Area under the curves (AUCs) in comparison to normal distribution and the significance of the real AUC for the best performed marker panel of (A) Pri... by Sakuni Rankothgedera (11457923)

Features on GMSC dataset used in NATE. by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

Searching space for hyperparameters in Table 7. by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

GMSC dataset (IR: Imbalance Ratio). by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

Under-sampled dataset. by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

The system architecture of NATE. by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

Over-sampled dataset. by Seongil Han (20490232)

“…Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency; however, its predictive power decreases when applied to complex or non-linear datasets, resulting in reduced accuracy. …”

Marginal means for postural effects on cerebral oxygenation. by Nhu N. Tran (10776772)

“…<p>This figure demonstrates the direction of effects for cerebrovascular stability in each group. rcSO₂ values are least square means estimated from the generalized estimating equation that tested our a priori hypotheses and are adjusted for postconceptional age, sex, ethnicity, and SpO₂. …”

The effect of repeating k-means clustering ten times is presented with respect to the change in cluster allocation and the respective mean Silhouette value. by Gregor Kuntze (5892437)

Countermovement jump force-time metric definitions and classifications. by Nicolas M. Philipp (13272198)

Age-specific incidence, treatment, and survival trends in esophageal cancer: a Dutch population-based cohort study by Ali Al-Kaabi (7437800)

“…Treatment trends and relative survival rates (RSR) were estimated by age and stage grouping.</p> <p>A total 59,584 patients were included. In adults <50 years, EAC incidence tripled (mean increase per year: males 1.5%, females 3%), while the incidence of ESCC decreased (mean decrease per year: males −5.3%, females −4.3%). …”

Performance enhancements of classification models using the undersampling method (NearMiss). by Seongil Han (20490232)

Descriptive statistics for CMJ and DJ force-time metrics across the three time-points. by Nicolas M. Philipp (13272198)

Performance enhancements of classification models using the oversampling method (SMOTE). by Seongil Han (20490232)

Depletion of TPX2 causes defects in 53BP1 ionizing radiation-induced foci formation. by Gernot Neumayer (77179)

“…<p>(A) The protein level of 53BP1 is not affected by miRNA-mediated depletion of TPX2. …”

Descriptive statistics for CMJ and DJ force-time metrics across the three time-points and by RSA-classification. by Nicolas M. Philipp (13272198)