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... حسب Mete Kara (21568148)

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

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

Major hyperparameters of RF-SVR. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب Resham B. Khatri (10949429)

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

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

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

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

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

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

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

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

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

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

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