Data Sheet 3_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Image 1_Ketogenic diet–induced changes in methylation status and neuropeptide signaling: relationships between S-adenosylmethionine (AdoMet), orexin-A, and metabolic health.pdf by Laura Mosca (704904)

“…Circulating AdoMet (−75.7%) and Orexin-A (−7.2%) levels also decreased significantly. …”

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)

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)

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... by Shahryar Kargar (18880600)

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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The phenotype of the Δ<i>ace1</i> and (hemi)cellulase production of various mutants. by Zhongjiao Liu (11877464)

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PoAce1 enrichment in the specific regions of (hemi)cellulase genes assayed by ChIP-qPCR. by Zhongjiao Liu (11877464)

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