SMR reveals common molecular patterns. by Daniela Fusco (10027975)

Summary of the program evaluation. by Anton Kurapov (11346019)

“…Participants reported significant pre-post reductions in the severity of symptoms, with sleep problems decreasing by 22.60% (Cohen’s <i>d </i>= 0.53), insomnia by 35.08% (<i>d</i> = 0.69), fear of sleep by 32.43% (<i>d</i> = 0.25), anxiety by 27.72% (<i>d</i> = 0.48), depression by 28.67% (<i>d</i> = 0.52), PTSD by 32.41% (<i>d</i> = 0.51), somatic symptoms by 24.52% (<i>d</i> = 0.51), and perceived stress by 17.90% (<i>d</i> = 0.39). …”

Primers for qPCR. by Kaitao Zhao (3617825)

“…Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Antibodies used for western blotting. by Kaitao Zhao (3617825)

“…Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Target sequences of siRNAs. by Kaitao Zhao (3617825)

“…Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Plasmids information. by Kaitao Zhao (3617825)

“…Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Raw data. by Kaitao Zhao (3617825)

“…Transiently or stably knockdown of MRE11, RAD50 or NBS1 in hepatocytes before HBV infection significantly decreased viral markers, including cccDNA, while reconstitution reversed the effect. …”

Shell colour luminance variation of three phenotypic groups according to background and sutural band colour categories for yellow shells of <i>Polymita picta</i>. by Mario Juan Gordillo-Pérez (20565248)

Shell colour luminance variation of five phenotypic groups according to background colour categories of <i>Polymita picta</i>. by Mario Juan Gordillo-Pérez (20565248)

Shell colour luminance variation of six phenotypic groups according to background colour and spiral band categories of <i>Polymita muscarum</i>. by Mario Juan Gordillo-Pérez (20565248)

Fig 1 - by Mario Juan Gordillo-Pérez (20565248)

Comparison of average glutathione reductase. by Zohreh Ghotbeddin (20108032)

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 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 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)

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)

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. …”