Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

BA attenuated the decrease in the expression of ZO-1 and Occludin induced by LPS in Caco2 cells. by Luqiong Liu (11537092)

Setting of experimental environment and model parameters. by Guozhu Sui (21672798)

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence. Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown. by Harry Saxton (19165441)

“…Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown.…”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Minimal data set. by Wei Huang (36889)

Comparison of WEI values for two scenarios. by Danuta Roman-Liu (8532645)

Y-27632 collaborated with BA to attenuate the increase in the integrity and decrease in the permeability of epithelial barrier injury induced by LPS in Caco2 monolayers. by Luqiong Liu (11537092)

“…<p>(<b>A)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on days 1–22. (<b>B)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on day 22. …”

Y-27632 synergized with BA to alleviate the decrease in the expression of and abnormal localization of ZO-1 and Occludin in LPS-induced Caco2 cells. by Luqiong Liu (11537092)

Class-wise performance on the test set. by Muhammad Zubair (728141)

Validation results for the different scenario parameter sets and the resulting KGE_mean value. by Fabian Mitze (21084251)

Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. by Leijie Wang (21485157)

Subjects:

Experimental parameter information setting table. by Wenhui Wang (34265)

Diagnostic value of the 8 hub genes in the GSE52093 dataset. by Wei He (131453)

Set-up of the optimized 90:20 Isometric Posterior Chain Test. by Dominic Michael Rasp (19959412)

Table 1_Food scarcity and decrease in income are associated with depression after COVID-19 pandemic in rural settings.docx by Dharmendra Gahwai (20987825)

Input parameter rankings for continuous measurements: Input parameters (P) and their corresponding influence values (E) calculated using eqn 11, displayed in decreasing order of influence for each continuous measurement set (from Set 1A to Set 4D).... by Harry Saxton (19165441)

“…<p>Input parameter rankings for continuous measurements: Input parameters (P) and their corresponding influence values (E) calculated using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0326112#pone.0326112.e114" target="_blank">eqn 11</a>, displayed in decreasing order of influence for each continuous measurement set (from Set 1A to Set 4D). …”

Confusion Metrics: Presenting classification results on the test set. by Muhammad Zubair (728141)

The MAE value of the model after STL decomposition. by Xiangjuan Liu (618000)

The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)