Data Sheet 2_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 7_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 5_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 4_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 3_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 8_Effects of aging on otolith morphology and functions in mice.xlsx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

Data Sheet 1_Effects of aging on otolith morphology and functions in mice.docx by Keita Ueda (19855536)

“…Background<p>Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. …”

DataSheet1_The OsGAPC3 mutation significantly affects grain quality traits and improves the nutritional quality of rice.docx by Bo Peng (273834)

“…The number and volume of type-II protein bodies in the endosperm of the OsGAPC3 mutants, and GPC increase significantly. We report significant increases in chalkiness area and degree, and decreases for starch content, gel consistency, and taste value. …”

Raw data. by Jia Zhu (135506)

“…Biomechanical results revealed that the ROM of the surgical segment had decreased significantly under six basic working conditions following NZ implantation. …”

This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

Table 1_Body mass index influences Antimüllerian Hormone and inhibin B in adult males.xls by Wen Zhou (49050)

“…However, due to the low effect size of BMI/AMH, caution is needed in interpreting its clinical significance. Although we found a non-linear relationship and key thresholds between these variables, further studies with larger sample sizes are needed to confirm these findings.…”

Effect of supplementation of oocyte maturation medium (OMM) with follicular fluid (FF) from small or large follicles on development of embryos to the blastocyst stage. by Verónica M. Negrón-Pérez (20691412)

Effect of follicular fluid (FF) supplementation and time of maturation on development of embryos to the blastocyst stage. by Verónica M. Negrón-Pérez (20691412)

Effect of follicular fluid (FF) supplementation during the maturation period on cumulus cell expansion. by Verónica M. Negrón-Pérez (20691412)

Clustering analysis of total and active RNAP II at genes during the transition from G1 to G2. by Douglas Maya-Miles (7024967)

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. …”