Myeloid knockout <i>VANGL2</i> exacerbates the progression of DSS-induced colitis in mice. by Huaji Jiang (16907514)

Lys823 in NLRP3 is essential for its K27-linked ubiquitination and autophagic degradation. by Huaji Jiang (16907514)

Effects of clonal integration and <i>Bacillus subtilis</i> on ramet number (A, E, I), total stolon length (B, F, J), specific stolon length (C, G, K), and root-shoot ratio (D, H, L... by Bing-Nan Zhao (21553401)

Subjects:

VANGL2 specifically inhibits the activation of NLRP3 inflammasome. by Huaji Jiang (16907514)

pRPS6 and proliferation rates of starved Vasa2+/Piwi1+ cells were independent on the daytime of refeeding, and phospho-RPS6 was found in S- and M-phase Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

VANGL2 recruits NLRP3 to OPTN for selective autophagic degradation. by Huaji Jiang (16907514)

VANGL2 recruits MARCH8 to promote the K27-linked ubiquitination of NLRP3. by Huaji Jiang (16907514)

Effect of imidacloprid (Im) and rutin (Ru) administration on thoracic electrical impedance in Africanized honey bee foragers. by Juan P. Hernández (22216133)

Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry by Jiyao Wang (2121157)

“…Simulations and experiments demonstrate that precise voltage adjustments effectively minimize ion leakage, enhancing resolving power by 50% (reaching a maximum of 106), while the corresponding decrease in signal intensity follows the <i>I</i>_p–<i>R</i>_p linear relationship. …”

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. …”

STL Linear Combination Forecast Graph. 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. …”

LOSS curves for BWO-BiLSTM model training. 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. …”