Phagocytosis of fluorescent latex beads (2 μm) by uninfected and infected PEMs, BMMs, and THP-1 (C) in static culture system. by Alaa Riezk (7037297)

Macropinocytosis of pHrodo Red dextran by uninfected and infected PEMs, BMMs and THP-1) in static culture system. by Alaa Riezk (7037297)

Fig 7 - by Varun Tiwari (9258130)

Phagocytosis of fluorescent latex beads (2 μm) by infected PEMs, BMMs and THP-1 in the three culture systems (static, slow flow rate 1.45 x 10− − 9 m/s and fast flow rate 1.23 x 10... by Alaa Riezk (7037297)

Macropinocytosis of pHrodo Red dextran by infected PEMs, BMMs and THP-1 at the three culture systems (static, slow flow rate 1.45 x 10− − 9 m/s and fast flow rate 1.23 x 10− − 7 m/... by Alaa Riezk (7037297)

Mean uncertainty of <i>boro</i> rice yields estimated by random forest model from 2002–2021. by Varun Tiwari (9258130)

Fig 5 - by Varun Tiwari (9258130)

RICTOR silencing inhibits cell proliferation via UGCG regulation. by Mohammad Nafees Ansari (22232505)

“…(<b>C</b>) Cell proliferation studies show a decrease in the proliferation of MCF-7_RICTOR^SH cells (mean ± SEM, <i>n</i> = 4) compared to MCF-7_SCRAM^SH cells. …”

Accuracy of the random forest regression model with various input variables (all inputs vs. top 30 inputs). by Varun Tiwari (9258130)

Random forest top 30 variable importance utilizing all input variables, including the MODIS bands, spectral indices, and land surface temperature. by Varun Tiwari (9258130)

Indices calculated using MODIS bands (B1-B7) which were used to estimate <i>boro</i> rice yields, where MODIS bands relate to indices as follows: B1 = RED, B2 = NIR1, B3 = BLUE, B4... by Varun Tiwari (9258130)

Changes in (A) Deaths and (B) DALYs of pancreatitis due to alcohol use according to aging, population growth and epidemiological change from 1990 to 2021 at global level by SDI qui... by Letai Li (19229848)

A proposed model on the impact of DHA and ARA supplementation on <i>L. vannamei</i> health. by Virak Visudtiphole (11574169)

Effects of the observed significant angular changes at individual joints on the leading toe-obstacle clearance in healthy older adults during dual-task obstacle crossing (black sti... by Shiuan-Huei Lu (22430628)

Characteristics of water footprint by sector at the regional level in China. by Jianqin Ma (20328414)

Effects of the observed significant angular changes at individual joints on the trailing toe-obstacle clearance in healthy older adults during dual-task obstacle crossing (black st... by Shiuan-Huei Lu (22430628)

Trends in spatial beta diversity over time. by Zoë J. Kitchel (21688386)

“…A lack of significant trend is shown in blue. The average linear trend across surveys (black line with 95% confidence interval in gray) is also plotted from a linear mixed effect model with a random slope and intercept for survey. …”

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. …”