NREM 1/f slopes calculated from Power Spectrum Densities (PSDs) have a robust nocturnal rhythm at low frequencies that are initially flattened during spontaneous oxycodone withdraw... by Michael Gulledge (20577135)

δ¹⁵N in <i>Fucus spp. vs. Bifurcaria bifurcata.</i> by Antonio Bode (1473376)

PLE inhibits IMQ-induced inflammatory in mice, and the contents of TNF-α (A) and IL-17A (B). by Xiaoqiang Huang (1457860)

Stable isotopes in algae and mussels. by Antonio Bode (1473376)

Seasonality of δ¹⁵N in <i>Mytilus galloprovincialis.</i> by Antonio Bode (1473376)

PLE inhibits IMQ-induced oxidative stress in mice, and the contents of SOD (A, C) and MDA (B, D) in mouse serum and back skin lesions were measured. by Xiaoqiang Huang (1457860)

Seasonality of δ¹⁵N in <i>Fucus spiralis.</i> by Antonio Bode (1473376)

Search strategy. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Fig 1 - by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

GMT by age cohort for PWH and PWoH. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Fig 4 - by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Difference in GMT for HPV16 and HPV18 in PWH. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Fig 3 - by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Summary of characteristics of studies reviewed. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Cumulative meta-analysis. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

Summary of immunogenicity information. by Alvine M. Akumbom (20460907)

“…A random effects meta-analysis was performed comparing geometric mean titer (GMT) in PWH to PWoH. Twenty-eight studies out of 988 were eligible for inclusion in our study, and qualitatively synthesized. …”

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. …”