Readability scores for Chatgpt-4o, Gemini, and Perplexity responses to the most frequently asked Ankylosing spondylitis -related questions, and a statistical comparison of the text... by Mete Kara (21568148)

Analysis of stress-related gene expression in cherry shrimp (<i>N</i>. <i>davidi</i> var. red). by Wei-Wei Hou (3405947)

Correlations between feed additives and color-related genes. by Wei-Wei Hou (3405947)

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. …”

Schematic diagram of the basic principles of 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. …”

Determinants of childbirth in private HF among women who had a live birth one year before the survey, NDHS 2022. by Resham B. Khatri (10949429)

The model in which the PoFbx23 is required in activating PoAce1 to control the expression of target genes. by Zhongjiao Liu (11877464)

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Data that underlies the TAP-MS of three Ace1-TAP samples. by Zhongjiao Liu (11877464)

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Data that underlies the ChIP-qPCR in this paper. by Zhongjiao Liu (11877464)

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The results of TAP-MS using transcription factor PoAce1 as bait. by Zhongjiao Liu (11877464)

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The top proteins interacting with PoAce1 identified through TAP-MS. by Zhongjiao Liu (11877464)

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Ubiquitination of PoAce1 and effect of proteasome inhibitor MG132 on PoAce1. by Zhongjiao Liu (11877464)

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The result of TAP-MS using PoAce1 as the bait. by Zhongjiao Liu (11877464)

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Saturation analysis of transcriptome data for the WT and Δ<i>fbx23</i>. by Zhongjiao Liu (11877464)

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Construction strategies for GFP strains Ace1/Fbx23-GFP and BiFC strain Ace1-YFP-Fbx23. by Zhongjiao Liu (11877464)

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Construction strategy and verification of strains Ace1/Fbx23-TAP, Δ<i>fbx23</i>, and Re<i>fbx23</i>. by Zhongjiao Liu (11877464)

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Strains used in this study. by Zhongjiao Liu (11877464)

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