(A) Number of crossings, (B) Number of rearings, (C) Latency time for novel object, (D) Familiar object exploration time, (E) Novel object exploration time for novel object recogni... by Hammad Ismail (4227988)

Predictors in ordinal regression model for GDS. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Classification of hand grip strength. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Rating scale for functional severity [28]. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Regression model coefficients. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

ICOPE screening positive participant’s responses. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

WHO BMI classification for adults. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Data_Sheet_2_Urolithin A alleviates cell senescence by inhibiting ferroptosis and enhances corneal epithelial wound healing.docx by Xiao-Xiao Guo (6092141)

“…The results of RNA-seq of HS-induced corneal epithelial cells showed that the ferroptosis pathway was significantly dysregulated. Further investigation revealed that UA decreased the level of oxidative stress in HCE-T cells, including the levels of LPO and MDA (p < 0.05). …”

Data_Sheet_1_Urolithin A alleviates cell senescence by inhibiting ferroptosis and enhances corneal epithelial wound healing.zip by Xiao-Xiao Guo (6092141)

“…The results of RNA-seq of HS-induced corneal epithelial cells showed that the ferroptosis pathway was significantly dysregulated. Further investigation revealed that UA decreased the level of oxidative stress in HCE-T cells, including the levels of LPO and MDA (p < 0.05). …”

Changes in the active H3K27ac and repressive H3K27me3 histone marks among Vasa2+/Piwi1+ and all cells in fed, starved, and refed juvenile polyps. by Eudald Pascual-Carreras (12115380)

“…Between fed, T_5ds and T_20ds timepoints, MFI levels of H3K27ac progressively and significantly decreased while levels H3K27me3 (M) did not change significantly (N). …”

Combination of intraperitoneal and intratumoral administration of vitamin D3 is more effective in reducing the EAC tumor volume compared to just i.p. administration: by Vidya G. Bettada (22208808)

“…Ki67 on the other hand showed a significant reduction in the expression in the i.p & i.t treated vitamin D3 group. 7D. …”

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. …”

BWO-BiLSTM model 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. …”

Bi-LSTM architecture diagram. 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. …”