Table 4_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xlsx by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Image 4_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.jpeg by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 1_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xlsx by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 5_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xlsx by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 6_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xlsx by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 3_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xls by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Image 1_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.jpeg by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 2_Profiling of small RNAs derived from tomato brown rugose fruit virus in infected Solanum lycopersicum plants by deep sequencing.xls by Mangle Chen (20639051)

“…Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. …”

Table 1_Enhancement of oxaliplatin efficacy and amelioration of intestinal epithelial damage by Lactobacillus rhamnosus GG through modulation of gut microbiota.xlsx by Zijie Zhang (2543533)

“…Background<p>Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide, necessitating extensive research into effective treatment strategies. …”

Comparison of data after the experiment. by Neng Pan (5262032)

Figure 2 from Targeting PRMT1 Reduces Cancer Persistence and Tumor Relapse in <i>EGFR</i>- and <i>KRAS</i>-Mutant Lung Cancer by Xiaoxiao Sun (20589448)

“…<p>PRMT1 knockdown decreases persistence in STAT1-high <i>EGFR</i>^<i>mut</i> and <i>KRAS</i>^<i>G12C</i> lung cancer cell lines. …”

Generated spline library. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Correlation coefficient matrix. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

RMSE versus learning rate. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

RMSE versus training parameters. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Assembly process of machine recognition form. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Process of steel truss incremental launching. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

CGAN and AutoML stacking device. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

Sliding window process. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”