Vaccinia virus infection (MOI = 3) of A549 cells in media supplemented with glucose/galactose. by Tyron Chang (9922533)

Glucose/galactose regulation of IFN-γ induced IRF1 protein is conserved in mammals. by Tyron Chang (9922533)

Characterization of the impact of glucose/galactose on infection outcomes. by Tyron Chang (9922533)

Glucose/galactose selectively alters ISG protein not RNA levels in interferon-primed cells. by Tyron Chang (9922533)

Glucose but not galactose culturing of IFN-γ primed cells reduces VACV and HSV-1 replication. by Tyron Chang (9922533)

<i>IRF1</i> impacts VACV replication in glucose/IFN-γ primed but not galactose conditions. by Tyron Chang (9922533)

Prisma flow diagram of study selection. by Hattapark Dejakaisaya (22238613)

“…Additionally, watching ≥6 hours of television per day was associated with a significant decrease in cognitive score (standardized beta coefficient = −0.09; 95% CI: −0.17, −0.003; I² = 71.8%; seven studies). …”

Proportion of time spent in the Start Room or Avoidance Room or showing a behaviour (Stimulus Contact) presented across stimuli and separately for piglets and gilts. by Sara Hintze (3216885)

Subjects:

Frequencies of different behaviours (Compartment Changes, Escape Attempts, Stimulus Orientation, Stimulus Approach, Stimulus Contact, Grunting, Barking, Excretion), the minimum dis... by Sara Hintze (3216885)

Subjects:

Medicare clozapine data analysis. by Luke R. Cavanah (19022435)

“…We observed a steady decrease in clozapine use adjusted for population (−18.0%) and spending (−24.9%) over time. …”

Interaction panel of Treat × Period effect (T × P) for ether extract intake (g/d) as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein cal... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... by Shahryar Kargar (18880600)

Effect of different doses of the defatted extract (DE) of the leaves of <i>S. malaccense</i> and <i>S. samarangense</i> on urea and creatinine levels. Data represented as mean±SEM... by Fatma A. Moharram (9276104)

A T-test mean comparison of pre and post COVID-19 differences for selected variables. by Beshir Bedru Nasir (8218863)

Comparison of the studied complete blood count parameters between <i>Anaplasma</i> spp. and <i>A. ovis</i> positive and negative goats enrolled from Dera Ghazi Khan district in Pun... by Sumaira Sumaira (21453523)

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. …”