Effect of NLRP3 inhibition on expression of phenotypic transition markers in CER treated VSMCs during Vascular Calcification. by Razia Rashid Rahil (22772495)

“…<p>(A) Representative images showed calcium deposition (red). …”

Effects of activators and inhibitors on TcPiezo channels. by Guozhong Huang (673424)

“…Downregulation of <i>TcPiezo2</i> expression (+Tet) showed a significant decrease of lysosomal Ca²⁺ release as in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1013105#ppat.1013105.g004" target="_blank">Fig 4A</a>. …”

β-catenin ablation suppresses YAP expression and odontogenic capacity. by Chang Chen (30907)

“…<p>(A) Immunofluorescence demonstrates that β-catenin knockdown not only abolishes OE-YAP-induced β-catenin upregulation but also reduces YAP expression, indicating β-catenin-dependent stabilization of YAP. …”

Generated spline library. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Correlation coefficient matrix. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Actual measurement of shape errors. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus learning rate. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus training parameters. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Proteolytic degradation of rhodopsin by recombinant archaeal PAN-T20S. by Celine Brooks (5416637)

“…<p><b><i>A</i></b>, GFP-ssrA protein is degraded by recombinant PAN-T20S resulting in decreasing fluorescence over time. …”

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

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

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

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

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

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Comprehensive prediction process of shape errors. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Shape error manual calculation process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Data Sheet 1_Temperature influences mood: evidence from 11 years of Baidu index data in Chinese provincial capitals.csv by Mengjiao Yin (10995604)

“…</p>Results<p>The results showed that for every 1°C increase in DMT, search indices for depression, anxiety, and loneliness increased significantly by 22.71%, 18.76%, and 19.59%, respectively (p < 0.01). Conversely, a 1°C increase in DTR led to decreases of 30.35%, 31.19%, and 15.41% in these indices (p < 0.05). …”

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

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Sliding window process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Original record form of error matrix. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Data Sheet 1_Use of the Temperament and Character Inventory to describe the effectiveness of Gestalt therapy.docx by Benjamin Calvet (11671588)

“…</p>Results<p>Statistically significant differences between the initial and final mean scores were observed for anxiety (t = 16.46; p < 0.0001), depression (t = 11.24; p < 0.0001), and harm avoidance (t = 8.82; p < 0.0001), and global psychological distress assessed by VAS (t = 18.7; p < 0.0001) (all showing decreased scores). …”

Form for machine recognition. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”