Change in drinking behaviour over time: number of visits. by M. Jordana Rivero (14610947)

Change in drinking behaviour over time: daily water intake. by M. Jordana Rivero (14610947)

δ¹⁵N in <i>Fucus spiralis</i> vs. <i>F. vesiculosus.</i> by Antonio Bode (1473376)

Change in drinking behaviour over time: water intake per visit. by M. Jordana Rivero (14610947)

Number of visits per clinic type, 2013-2015. by Jacob Dreiher (14856863)

“…During PE, statistically significant longer waiting times were found for surgery (+1.0 day) and imaging (+1.1 days), while a 2.4 days decrease was noted in pediatrics, controlled for age, sex, ethnicity and the daily number of visits. …”

Study Data. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Study outcomes. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Patient characteristics. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Thioflavin-T (ThT) fluorescence analysis of amyloid fibril formation in wild-type and p.R163C mutant αB-crystallin under thermochemical stress. by Aref Baharvand (21713886)

(A) FC and VIP bubble chart of Model.vs.WKY comparison group; (B) Hierarchical clustering results of differential metabolites in Model.vs.WKY comparison group (only the top 50 VIP... by Xiao Zhang (152326)

Table1_Neutrophil extracellular traps as immunofibrotic mediators in RA-ILD; pilot evaluation of the nintedanib therapy.docx by Aliki I. Venetsanopoulou (12197432)

“…Objective<p>Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a significant pulmonary complication of RA. This study tried to elucidate the mechanisms enhancing inflammation and causing lung injury in RA-ILD, focusing on the role of neutrophil extracellular traps (NETs). …”

Image1_Neutrophil extracellular traps as immunofibrotic mediators in RA-ILD; pilot evaluation of the nintedanib therapy.tif by Aliki I. Venetsanopoulou (12197432)

“…Objective<p>Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a significant pulmonary complication of RA. This study tried to elucidate the mechanisms enhancing inflammation and causing lung injury in RA-ILD, focusing on the role of neutrophil extracellular traps (NETs). …”

DataSheet1_The β2-adrenergic biased agonist nebivolol inhibits the development of Th17 and the response of memory Th17 cells in an NF-κB-dependent manner.docx by Mehri Hajiaghayi (19822665)

“…</p>Results<p>Nebivolol treatment decreased IL-17A and IFN-γ secretion by activated memory Th cells and elevated IL-4 levels. …”

Testing set error. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Flowchart of the STL. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

SARIMA predicts season components. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”