<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. …”

Significantly Enriched Pathways. by Jiyong Zhang (2498740)

“…Pathway analysis revealed a marked decrease in expression within certain key metabolic pathways (such as the one-carbon pool by folate) in the NAFLD group, while expression in DNA repair-related pathways (such as non-homologous end joining) was significantly increased. …”

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Flow diagram of participants selection. by Wei Huang (36889)

“…Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Minimal data set. by Wei Huang (36889)

“…Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Weighted comparison of baseline characteristics. by Wei Huang (36889)

“…Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Trend coefficients and AUC statistics across four control populations (“Pop’n 1” to “Pop’n 4”) and four warming populations (“Pop’n 5” to “Pop’n 8”), as calculated from empirical time series during the approach to criticality. Each statistical metric was calculated within fifteen-day sliding windows, throughout the pre-critical interval. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. Here, we show analyses of empirical time series performed within the sixty-day pre-critical interval. See analyses within the forty- and thirty-day pre-critical intervals in S7 Fig. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval. Negative values indicate a decreasing trend prior to local bifurcation, while positiv by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval. Negative values indicate a decreasing trend prior to local bifurcation, while positiv</p>…”

Demographic and ocular features. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

Machine learning model to diagnose PCG. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

ROC curves of TAS + SOD + MDA to diagnose PCG. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

Infection-specific cytokines produced during <i>L. infantum</i>-infection in macrophages derived from men and women. by Annika Bea (17686341)

Table 1_The diagnostic and prognostic value of CXCL13, CXCL10, and CXCL8 in patients with neurosyphilis.docx by Hongjing Guan (172193)

“…To enhance the effectiveness of differential diagnosis, we employed logistic regression analysis to screen variables and developed a predictive model MODEL1. The results showed that the AUC value of MODEL1 was 0.888, and the calibration curve and DCA curve demonstrated good accuracy and clinical benefits of the model, demonstrating good predictive performance. …”

Image 1_The diagnostic and prognostic value of CXCL13, CXCL10, and CXCL8 in patients with neurosyphilis.jpeg by Hongjing Guan (172193)

“…To enhance the effectiveness of differential diagnosis, we employed logistic regression analysis to screen variables and developed a predictive model MODEL1. The results showed that the AUC value of MODEL1 was 0.888, and the calibration curve and DCA curve demonstrated good accuracy and clinical benefits of the model, demonstrating good predictive performance. …”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…A random selection of 15% of these patients (n=1,508) was utilized for external validation. …”

Supplementary Material for: The value of circulating tumor DNA in the prognostic diagnosis of bladder cancer: a systematic review and meta-analysis by figshare admin karger (2628495)

“…PLR and NLR were 2.8 (95% CI: 1.24-6.35) and 0.43 (95% CI: 0.28-0.65), respectively, with a DOR of 6.56 (95% CI: 2.12-20.33).Fagan plot analysis showed that the posterior probability of a positive result rose to 74% and the posterior probability of a negative result decreased to 30% when the prior probability was 50%. …”

Characteristics of women at admission. by Guiyou Yang (10259597)

“…The AUC-PRC values (0.2–0.4) of the fullPIERS model remained low (i.e., close to the daily fraction of adverse outcomes, indicating low discriminative capacity). …”

Supplementary file 1_Analysis of the diagnostic value of peripheral blood immune inflammatory indicators of female bladder pain syndrome.zip by Yinglei Wang (3844756)