Effect of imidacloprid (Im) and rutin (Ru) administration on thoracic electric currents in Africanized honey bee foragers. by Juan P. Hernández (22216133)

Effect of imidacloprid (Im) and rutin (Ru) administration on evaporation dynamics of extracellular fluid (ECF) extracted from thoraxes of Africanized honey bee foragers. by Juan P. Hernández (22216133)

Feeding and starvation affect the proportion and cell cycle activity of Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

AO/EtBr Staining of Cancer cell lines: <i>To check whether vitamin D3 promoted cell death in cancer cells, a dual staining protocol was implemented as detailed in the methods secti... by Vidya G. Bettada (22208808)

pRPS6 and proliferation rates of starved Vasa2+/Piwi1+ cells were independent on the daytime of refeeding, and phospho-RPS6 was found in S- and M-phase Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

The effect of feeding and starvation on the proliferative competence and onset of cell cycle re-entry in all cell cycle-gated cells. by Eudald Pascual-Carreras (12115380)

Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry by Jiyao Wang (2121157)

“…In Ion Mobility Spectrometry (IMS), ion gates are essential for controlling ion flow, significantly impacting detection sensitivity and resolution. …”

The TOR inhibitors Rapamycin and AZD-8055 strongly reduce RPS6 phosphorylation and cell proliferation in Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

“…<i>n</i> = 2–4 biological replicates per condition, with 15 individuals per replicate. Significance levels for Student <i>t</i> test are indicated for adjusted <i>p</i> values: *<i>p</i> < 0.05, ***<i>p</i> < 0.001, ***<i>p</i> < 0.0001. d: day(s), n.s.: non-significant. …”

Gating strategy of experiment using 30 min EdU pulses experiments in mOr2-Piwi1 juvenile polyps. by Eudald Pascual-Carreras (12115380)

The effect of feeding and starvation on the proliferative competence and cell cycle re-entry dynamics of Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

Effect of 0.1mM salicylic acid (SA) or hydrogen peroxide (H₂O₂) and salt treatment (0 and 75 mM NaCl) on in the guaicaolperoxydase activity (GPOX) in shoots (... by Amal Bouallegue (21512298)

Effect of 0.1mM salicylic acid (SA) or hydrogen peroxide (H₂O₂) and salt treatment (0 and 75 mM NaCl) on shoots (A) and roots (B) DW of lentil plants. by Amal Bouallegue (21512298)

Effect of 0.1mM salicylic acid (SA) or hydrogen peroxide (H₂O₂) and salt treatment (0 and 75 mM NaCl) on the superoxide dismutase activity (SOD) in shoots (A)... by Amal Bouallegue (21512298)

Effect of 0.1mM salicylic acid (SA) or hydrogen peroxide (H₂O₂) and salt treatment (0 and 75 mM NaCl) on malondialdehyde (MDA) level in shoots (A) and roots (... by Amal Bouallegue (21512298)

Effect of 0.1mM salicylic acid (SA) or hydrogen peroxide (H₂O₂) and salt treatment (0 and 75 mM NaCl) on hydrogen peroxide (H₂O₂) conten... by Amal Bouallegue (21512298)

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. …”