TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

TITAN decreaser macroinvertebrate heatmap Heatmap of sensitive (i.e., <i>z-</i>) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of numbe... by Brent J. Bellinger (21156150)

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

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Explanation of condition nodes and decision nodes in the SCM, ordered by decreasing centrality. by Stella D. Juventia (19961247)

TITAN increaser macroinvertebrate heatmap Heatmap of tolerant (i.e., <i>z</i>+) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of number... by Brent J. Bellinger (21156150)

<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)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". …”

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

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

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TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5^th and 95^th percentile change points are determined from 500 bootstrap replicate runs. Sensitive taxa change points derived from decreaser sum <i>z</i>- relative to increasing impervious cover (I.C.; %), specific conductivity (SpCond; μS cm^-1), total nitrogen (TN; μg L^-1), and total phosphorus (TP; μg L^-1).... by Brent J. Bellinger (21156150)

“…Sensitive taxa change points derived from decreaser sum <i>z</i>- relative to increasing impervious cover (I.C.; %), specific conductivity (SpCond; μS cm^-1), total nitrogen (TN; μg L^-1), and total phosphorus (TP; μg L^-1). …”

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence. Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown. by Harry Saxton (19165441)

“…Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown.…”

Calculation of peak force and impulse values from trial with overlap. by Leena Inkilä (20614460)

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Changes in blood pressure, HbA1c, blood lipid and liver function-related indicator values compared with baseline values in all patients over time. by Jia Song (495396)

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Calculation of peak force and impulse values from force plate data. by Leena Inkilä (20614460)

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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>…”

Changes in inflammatory cytokine levels compared with baseline values in all patients over time. by Jia Song (495396)

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

Changes in the UACR and urinary β2-MG levels compared with baseline values over time. by Jia Song (495396)

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Changes in the eGFRs and serum potassium levels compared with values at the end of 1 month of treatment in all patients over time. by Jia Song (495396)

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