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_Transcriptomic analysis of ROS1+ non-small cell lung cancer reveals an upregulation of nucleotide synthesis and cell adhesion pathways.xlsx by Marc Terrones (18094462)

“…When comparing ROS1+ tumor versus cell line transcriptomes, an upregulation of MYC and MET was found in cell lines together with a significantly decreased expression of HER3, HER4 and BRAF. …”

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

Flow chart of experimental protocol. by Eun-Seon Yang (19751340)

“…SBP showed an increasing trend, but this was not statistically significant (<i>p</i> = 0.108). Interestingly, we observed significant decreases in arterial stiffness and PWV in POST_VM when comparing with PRE_VM (<i>p</i><0.001 and <i>p</i><0.001, respectively). …”

A representative ultrasound axis image. by Eun-Seon Yang (19751340)

“…SBP showed an increasing trend, but this was not statistically significant (<i>p</i> = 0.108). Interestingly, we observed significant decreases in arterial stiffness and PWV in POST_VM when comparing with PRE_VM (<i>p</i><0.001 and <i>p</i><0.001, respectively). …”

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.…”

Assembly error angle of a single spline. 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.…”

Original record form of error 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.…”

Form for machine recognition. 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 architectural 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.…”

Kalman 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.…”