H&E staining of the sternocleidomastoid muscle. by Rie Asayama (693361)

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The relationship between dietary niacin intake and COPD stratified using different characteristics. by Yushan Shi (16440272)

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Descriptive statistics for categorical variables. by Junhui Zhao (626062)

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List of triathlon events analyzed. by Junhui Zhao (626062)

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Association between dietary niacin intake and COPD odds ratio. by Yushan Shi (16440272)

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Quantile regression results of female triathletes. by Junhui Zhao (626062)

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Descriptive statistics for numerical variables. by Junhui Zhao (626062)

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Videofluoroscopic swallowing study parameters. by Rie Asayama (693361)

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Flow diagram of study participants. by Yushan Shi (16440272)

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Interventions for Psychological Stress in Pregnant African American Women: A Scoping Review by Taleah A. Frazier (22423294)

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Setting up for VFSS image. by Rie Asayama (693361)

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Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

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Demographics of the study population. by Sohyun Park (78358)

“…</p><p>Results</p><p>Based on the final clinical diagnosis, 79 patients with iPD and 16 disease controls were included. The mean OBH was significantly smaller in iPD than in disease controls (<i>p</i> < 0.0001). …”

Table 3 - by Sohyun Park (78358)

“…</p><p>Results</p><p>Based on the final clinical diagnosis, 79 patients with iPD and 16 disease controls were included. The mean OBH was significantly smaller in iPD than in disease controls (<i>p</i> < 0.0001). …”

Mean GHQ scores (2019- September 2021). by Mhairi Webster (20454888)

“…Loneliness accounted for a share of the mental health gender gap, and a more decrease in mental health was recorded for young women experiencing loneliness, compared to older age groups. …”

Top 10 significant functional annotations of up-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

Top 10 significant functional annotations of down-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

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. …”