Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

“…While mean respiratory rate was not affected, midazolam resulted in a significant decrease in both VRR (ß = −0.071, 95% CI: −0.120 to −0.021) and VTV (ß = −0.117, 95% CI: −0.170 to −0.062). …”

Cohort characteristics. by Fernanda Talarico (807333)

“…</p><p>Results</p><p>The analysis reveals a significant decrease in all health services utilization from 2016 to 2019, followed by an increase until 2022. …”

Study-related adverse events. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”

Study flow chart. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”

Study CONSORT diagram. by Benjamin R. Lewis (22279166)

“…In a linear mixed model analysis (LMM), the MBSR + PAP arm evidenced a significantly larger decrease in QIDS-SR-16 score than the MBSR-only arm from baseline to 2-weeks post-intervention (between-groups effect = 4.6, 95% CI [1.51, 7.70]; <i>p</i> = 0.008). …”

Narrow- and broad-band spectral changes after hemispherotomy in an awake representative patient, displaying marked electroencephalography (EEG) slowing of the isolated cortex. by Michele Angelo Colombo (22446342)

Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”

Hyperparameter ranges by Eloy Geenjaar (21533195)

“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”

Convolutional vs RNN context encoder by Eloy Geenjaar (21533195)

“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”

Design of the D-trial. by Torsten Schober (20485754)

“…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Estimated mean values for light interception. by Torsten Schober (20485754)

“…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Raw data V-trial. by Torsten Schober (20485754)

“…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Raw data D-trial. by Torsten Schober (20485754)

“…An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Primer sequences used for RT-PCR. by Jingjing Chen (293564)

“…Notably, SIRT1 levels decrease with age in both mice and during cellular senescence, highlighting its significance in anti-aging processes. …”

Correlation in site-level DNA methylation estimates between TMS and RRBS data after permutation. by Amy Longtin (21409957)

Scores vs Skip ratios on single-agent task. by Hongjie Zhang (136127)

“…Inspired by human decision-making patterns, which involve reasoning only on critical states in continuous decision-making tasks without considering all states, we introduce the <i>LazyAct</i> algorithm. This algorithm significantly reduces the number of inferences while preserving the quality of the policy. …”

Time(s) and GFLOPs savings of single-agent tasks. by Hongjie Zhang (136127)

“…Inspired by human decision-making patterns, which involve reasoning only on critical states in continuous decision-making tasks without considering all states, we introduce the <i>LazyAct</i> algorithm. This algorithm significantly reduces the number of inferences while preserving the quality of the policy. …”

The source code of LazyAct. by Hongjie Zhang (136127)

“…Inspired by human decision-making patterns, which involve reasoning only on critical states in continuous decision-making tasks without considering all states, we introduce the <i>LazyAct</i> algorithm. This algorithm significantly reduces the number of inferences while preserving the quality of the policy. …”

Win rate vs Skip ratios on multi-agents tasks. by Hongjie Zhang (136127)

“…Inspired by human decision-making patterns, which involve reasoning only on critical states in continuous decision-making tasks without considering all states, we introduce the <i>LazyAct</i> algorithm. This algorithm significantly reduces the number of inferences while preserving the quality of the policy. …”

Visualization on SMAC-25m based on <i>LazyAct</i>. by Hongjie Zhang (136127)

“…Inspired by human decision-making patterns, which involve reasoning only on critical states in continuous decision-making tasks without considering all states, we introduce the <i>LazyAct</i> algorithm. This algorithm significantly reduces the number of inferences while preserving the quality of the policy. …”