Structure diagram of the SE attention mechanism. by Junyan Wang (4738518)

“…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …”

Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 3 level. by P.G.J. ter Horst (21743830)

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S1 Data - Medication, age and abstinence days associated with low semen quality: A cross-sectional study in more than 7000 men visiting the centre for reproductive medicine by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age-adjusted), concerning a selection of medicaments p < 0.1 and n ≥ 10. by P.G.J. ter Horst (21743830)

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Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 7 level. by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning medicament(s) use and Low Semen Quality (LSQ). by P.G.J. ter Horst (21743830)

Subjects:

A. Proportion of men with Low Semen Quality (LSQ) including 95% confidence interval (CI) versus prevalence, overall and for men on medication(s) (M); B: Point estimates with 95% CI... by P.G.J. ter Horst (21743830)

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Prevalence of low semen quality including 95% confidence interval (CI) for medication at the ATC 5 level. by P.G.J. ter Horst (21743830)

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Univariable and multivariable analyses (age- and abstinence days-adjusted), concerning a selection of medicaments with n ≥ 10 medicament users and p < 0.1 by univariate analysis.... by P.G.J. ter Horst (21743830)

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Top 10 significant functional annotations of up-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

Top 10 significant functional annotations of down-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Voxel-based whole-brain analysis shows regional and dose-dependent effects of netoglitazone in decreasing plaque mean size. by Francesca Catto (21253435)

“…However, there is only a minimal effect on decreasing plaque size. (C) Short-term-treatment with a high dose of netoglitazone reveals a significant reduction in plaque size, especially observed in the hippocampus, striatum, thalamus, hypothalamus, midbrain, and hindbrain. …”

nsP4-C483Y or Q192L mutations decrease sensitivity to 4′-FlU. by Peiqi Yin (6533747)

Box plots representing comparisons of PVS volumes across different variables with statistical comparison annotations for TP(a), DOSE(b), Shunt(c), Anesthesia(d), and Sex(e). by Ruitian Song (11045127)

PVS ratio comparisons across different variables using box plots with statistical comparison annotations for TP(a), DOSE(b), Shunt(c), Anesthesia(d), and Sex(e). by Ruitian Song (11045127)