Table 4_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Table 6_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Table 5_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Table 1_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Image 1_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.tif by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Table 3_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Table 2_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

“…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”

Thirty-day inpatient survival by admission serum sodium concentration: Kaplan-Meier analysis stratified by pre- and during-COVID-19 pandemic periods. by Rajkumar Rajendram (22505442)

Parameters of VMR separated by light or dark periods run at 6 dpf. by Morgan Barnes (7876373)

“…(E-H) VMR parameters in dnVDRa induced zebrafish at 48 hpf. There is a significant decrease in distance moved in the dark (p < 0.01) and light (p < 0.05) periods, a significant decrease in velocity in the dark (p < 0.05) and light (p < 0.01) periods, a significant decrease in activity state in the dark (p < 0.05) and light (p < 0.01) periods and a significant increase in distance to point in the light period (p < 0.01) in the 48 + fish. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCAECG_GAN. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Experimental hardware and software environment. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Classification model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”