Results of cost-effectiveness analysis of <i>Chuna</i> manual therapy compared with usual care^a. by Jae-Heung Cho (798319)

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NREM 1/f slopes calculated from Power Spectrum Densities (PSDs) have a robust nocturnal rhythm at low frequencies that are initially flattened during spontaneous oxycodone withdraw... by Michael Gulledge (20577135)

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Sphingolipid biosynthetic pathway. by Hala Tamim El Jarkass (16625309)

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<i>N. parisii</i> induces global changes to <i>C. elegans</i> lipidome. by Hala Tamim El Jarkass (16625309)

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All infection data from the study. by Hala Tamim El Jarkass (16625309)

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Prime sequences. by Qiaozhen Qin (13159201)

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Relationship between miR-129-5p and Mysm1 in depressed mice. by Qiaozhen Qin (13159201)

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Stereotactic injection of AAV-miR-129-5p attenuated microglia activation in depressed mice. by Qiaozhen Qin (13159201)

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Stereotactic injection of AAV-miR-129-5p suppressed astrocyte activation in depressed mice. by Qiaozhen Qin (13159201)

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Overexpression of miR-129-5p alleviated depression-like behaviors by increasing ATP content. by Qiaozhen Qin (13159201)

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Overexpression of miR-129-5p alleviated the depressive-like phenotypes of CRS and LPS treated mice. by Qiaozhen Qin (13159201)

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Gating strategy of experiment using 30 min EdU pulses experiments in mOr2-Piwi1 juvenile polyps. by Eudald Pascual-Carreras (12115380)

Dynamics and functional role of TOR signaling during cell cycle re-entry in Vasa2+/Piwi1+ cells after refeeding. by Eudald Pascual-Carreras (12115380)

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. …”