The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

Transepithelial resistance of NHBE decreases significantly after PAA exposure. by Ellie S. Burns (21246537)

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

“…<div><p>Bed regulation within Brazil’s National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. …”

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

Subjects:

Conventional agriculture increases global warming while decreasing system sustainability by Ahmed I. Abdo (13269798)

BA attenuated the decrease in the expression of ZO-1 and Occludin induced by LPS in Caco2 cells. by Luqiong Liu (11537092)

The effect of HA digestion and HA replenishment alone or with CS on barrier function measured by TEER. by Charlotte J. van Ginkel (20790466)

“…PS decreased TEER. (C) Different treatments (treatment groups n = 8)with HA and/or CS did not affect TEER recovery after PS treatment, full recovery was seen in all groups after 24 hours. …”

BA attenuated the decrease in the integrity and increase in the permeability of the epithelial barrier injury induced by LPS in Caco2 cell monolayers. by Luqiong Liu (11537092)

“…<p>(<b>A)</b> Changes in TEER with increasing culture time on days 1–22. (<b>B)</b> BA alleviated the LPS-induced decrease in TEER in Caco2 cells after treatment for 24 h. …”

Y-27632 collaborated with BA to attenuate the increase in the integrity and decrease in the permeability of epithelial barrier injury induced by LPS in Caco2 monolayers. by Luqiong Liu (11537092)

“…<p>(<b>A)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on days 1–22. (<b>B)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on day 22. …”

Y-27632 synergized with BA to alleviate the decrease in the expression of and abnormal localization of ZO-1 and Occludin in LPS-induced Caco2 cells. by Luqiong Liu (11537092)

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

<b>Decreasing storage of dissolved inorganic carbon in global lakes</b> by Yao Yan (22508279)

Statistical analysis of the AUC measurement. by Esra Ayan (21156015)

“…<p>Analysis indicated that INSv application significantly changed spontaneous calcium activity (IAsp <i>p</i>-value: 0.1290, INSv <i>p</i>-value: 0.0002). The mean AUC decreased in both samples.…”

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

Youthful systemic milieu in young recipients decreased apoptosis and oxidative stress damage in allografts, and promoted proliferation. by Chengjun Yu (12136227)

Masking distance decreases pupillary responses. by Inmaculada Márquez (19681346)

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), 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 trends. …”

Uniaxial testing system. by Feng Wang (44414)

Noise decrease rate comparison. by Xianlin Ren (22783589)

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)

“…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</p>…”