DataSheet3_Significant nocturnal wakefulness after sleep onset in metabolic dysfunction–associated steatotic liver disease.PDF by Sofia Schaeffer (20379954)

“…Further, data from sleep questionnaires proved sleep-wake disruption in patients with MASLD. We objectively assessed sleep-wake rhythms in patients with biopsy-proven MASLD (n = 35) and healthy controls (HC, n = 16) using actigraphy 24/7 for 4 weeks. …”

DataSheet2_Significant nocturnal wakefulness after sleep onset in metabolic dysfunction–associated steatotic liver disease.PDF by Sofia Schaeffer (20379954)

“…Further, data from sleep questionnaires proved sleep-wake disruption in patients with MASLD. We objectively assessed sleep-wake rhythms in patients with biopsy-proven MASLD (n = 35) and healthy controls (HC, n = 16) using actigraphy 24/7 for 4 weeks. …”

DataSheet4_Significant nocturnal wakefulness after sleep onset in metabolic dysfunction–associated steatotic liver disease.PDF by Sofia Schaeffer (20379954)

“…Further, data from sleep questionnaires proved sleep-wake disruption in patients with MASLD. We objectively assessed sleep-wake rhythms in patients with biopsy-proven MASLD (n = 35) and healthy controls (HC, n = 16) using actigraphy 24/7 for 4 weeks. …”

This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

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

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

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

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

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

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

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

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

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

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Multilevel logistic regression analysis of individual and community level factors. by Mulugeta Wassie (10803155)

Subjects:

Socio- demographic characteristics of study participants. by Mulugeta Wassie (10803155)

Subjects:

Model comparison and random effect. by Mulugeta Wassie (10803155)

Subjects:

Sequence of <i>DpAP2</i> promoter. by Lingru Ruan (18995544)

“…It was speculated that MeJA significantly decreased expression of <i>DpAP2</i> gene, then the decreasing <i>DpAP2</i> expression significantly inhibited expression of some key enzyme genes such as <i>PSY</i>, <i>PDS</i> and <i>GGPS</i> in carotenoid biosynthesis pathway. …”

Predicted <i>cis</i>-acting elements in <i>DpAP2</i> promoter. by Lingru Ruan (18995544)

“…It was speculated that MeJA significantly decreased expression of <i>DpAP2</i> gene, then the decreasing <i>DpAP2</i> expression significantly inhibited expression of some key enzyme genes such as <i>PSY</i>, <i>PDS</i> and <i>GGPS</i> in carotenoid biosynthesis pathway. …”

S1 Graphical - by Agnieszka Murawska (11021928)

“…Administered alone, acetamiprid significantly decreased the level of urea in the hemolymph of worker honey bees. …”

Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

“…Moreover, we find that for individuals with schizophrenia, our model’s context embedding space is significantly correlated with both age and symptom severity. …”

Hyperparameter ranges by Eloy Geenjaar (21533195)

“…Moreover, we find that for individuals with schizophrenia, our model’s context embedding space is significantly correlated with both age and symptom severity. …”

Convolutional vs RNN context encoder by Eloy Geenjaar (21533195)

“…Moreover, we find that for individuals with schizophrenia, our model’s context embedding space is significantly correlated with both age and symptom severity. …”

<b>Data for s</b><b>easonal variations in coral lipids and their significance for energy maintenance in the </b><b>South China Sea</b> by Hongyan Mo (19721569)

“…The results show that ZD and the zooxanthellae photosynthesis intensity decreased in summer. Correspondingly,the content and δ13C of lipids are decreased significantly. …”

Participant characteristics by village. by Tyler M. Barrett (20846174)

“…Nearly all participants reported perceiving increases in temperature (94%) and decreases in rainfall (91%) in the previous five years, and most reported that they expect to have much less (57%) or somewhat less (35%) food to feed their families in the future due to changes in temperature and rainfall. …”