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. …”

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

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Alteration of Ubiquitination in the Brain of ENOPH1 Knockout Mice after Early Ischemic Stroke by Yike Wu (20982007)

“…Our results showed that 4000 ubiquitination-modified sites in 1613 proteins were quantified, with 772 ubiquitinated sites in 464 proteins significantly decreasing or increasing after ENOPH1 knockout (fold change >1.5 or <1/1.5, <i>p</i> < 0.05). …”

Alteration of Ubiquitination in the Brain of ENOPH1 Knockout Mice after Early Ischemic Stroke by Yike Wu (20982007)

“…Our results showed that 4000 ubiquitination-modified sites in 1613 proteins were quantified, with 772 ubiquitinated sites in 464 proteins significantly decreasing or increasing after ENOPH1 knockout (fold change >1.5 or <1/1.5, <i>p</i> < 0.05). …”

Effect of dilution factor and replicate number on the reproducibility of αSyn^D seed concentration estimates in PD (A-D) and DLB (E-H) brain homogenates by ED RT-QuIC ass... by Ankit Srivastava (742990)

“…<p>(A) & (E) Percent positive wells for triplicate assays versus sample dilution with 10-, 5-, and 2-fold dilution factors (as indicated in color legend). …”

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

Prediction of individual spatial patterns of nodal module overlap from structural brain features in children. by Tianyuan Lei (19683861)

Feature engineering. by Youli Jiang (15249070)

Univariate relationship in spatial patterns between nodal entropy and structural brain features within each scan in children. by Tianyuan Lei (19683861)

Loss of <i>Rab23</i> decreases ciliation in MEFs and Tbr1 + intermediate progenitors. by Wan Ying Leong (22075947)

Comparison of altered gene expression across groups of saline/vehicle-, KA/vehicle-, and KA/compound 4f-treated mice. by Naoto Arimura (416442)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…This type of monitoring often relies on the use of navigation systems such as the Automatic Identification System (AIS). AIS data has been used to support the defense teams when identifying equipment defects, locating suspicious activity, ensuring ship collision avoidance, and detecting hazardous events. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

The gut-brain axis underlying hepatic encephalopathy in liver cirrhosis by Xiaolong He (19696763)

“…Here, through constructing gut-brain modules to assess bacterial neurotoxins from metagenomic datasets, we found phenylalanine decarboxylase (PDC) genes, mainly from Ruminococcus gnavus (R. gnavus), increased ~10-folds in cirrhosis and higher in HE patients. …”

Presents the bibliometric search results. by Yan She (8639796)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)