Journal: Npj Artificial Intelligence

Article Title: Exposure to content written by large language models can reduce stigma around opioid use disorder

doi: 10.1038/s44387-025-00049-z

Figure Lengend Snippet: Raw percentage change in attitudes toward MOUD (DV1), people with OUD (DV2), and OUD (DV3), averaged across participants, for the Control, Human, and LLM interventions after a single and b longitudinal exposure setups. Percentage change in attitudes was computed as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{({Y}_{post}-{Y}_{pre})}{{Y}_{pre}}\times 100$$\end{document} ( Y p o s t − Y p r e ) Y p r e × 100 ; where Y p o s t and Y p r e represent the aggregated DV score post and pre-intervention.

Article Snippet: Using a between-subjects study design, participants were randomly assigned to one of three interventions: (a) LLM, participants read LLM-generated responses to online queries on OUD (sourced from Reddit); (b) Human, participants read human-written responses (again, sourced from Reddit) to the same set of queries as the LLM intervention group; and (c) Control, participants were not provided any content to read.

Techniques: Control

Journal: Npj Artificial Intelligence

Article Title: Exposure to content written by large language models can reduce stigma around opioid use disorder

doi: 10.1038/s44387-025-00049-z

Figure Lengend Snippet: Responses read by participants within the LLM and Human intervention groups were evaluated for a , b emotional appeal, c readability, and d shared sense of belonging. Emotional appeal is reported using five relevant categories available in Empath , a lexicon-based tool; a higher score is indicative of a higher alignment to the category. Readability is reported using the Flesch–Kincaid Grade Level index ; a lower score is indicative of simpler, more readable text. Shared sense of belonging is reported using the identity social dimension classifier , which quantifies in-group or community forward linguistic cues; the higher the score, the better. Scores are averaged across all the responses read by participants during the single and longitudinal exposure setups. Mann–Whitney U-tests were performed to explore differences in score distributions for responses provided in the LLM and Human interventions. Statistically significant differences are noted with the test statistic and p values ( p ): * ( p < 0.05), ** ( p < 0.01), or *** ( p < 0.001). e At the end of our experiments, participants in the LLM and Human interventions rated the responses consumed during the respective interventions, using a 5-point Likert scale, as: (1) influential, responses offered a different approach to look at OUD ; (2) credible, responses were reasonable and trustworthy ; (3) informative, responses were knowledgeable ; (4) resourceful, likely to refer to such responses to gain information about OUD ; and (5) supportive, prefer to receive such responses if one had OUD . On finding no significant differences in ratings across single and longitudinal exposure setups, we combined participant ratings for the two setups and report a weighted average (weighted by the sample size). Mann–Whitney U tests were performed to examine differences in score distributions for ratings provided by participants in the LLM and Human intervention groups. Statistically significant differences are noted with the p values ( p ): * ( p < 0.05), ** ( p < 0.01), or *** ( p < 0.001).

Article Snippet: Using a between-subjects study design, participants were randomly assigned to one of three interventions: (a) LLM, participants read LLM-generated responses to online queries on OUD (sourced from Reddit); (b) Human, participants read human-written responses (again, sourced from Reddit) to the same set of queries as the LLM intervention group; and (c) Control, participants were not provided any content to read.

Techniques: MANN-WHITNEY

Journal: Frontiers in Plant Science

Article Title: Abiotic Stresses Cause Differential Regulation of Alternative Splice Forms of GATA Transcription Factor in Rice

doi: 10.3389/fpls.2017.01944

Figure Lengend Snippet: List of the OsGATA genes: chromosome no; locus ID of the genes as annotated in RGAP version 7; nomenclature of the putative GATA transcripts where genes are named according to Reyes et al. ( 2004 ) and a and b denotes the alternative splice products of the same gene; subfamily of the GATA gene member; domain composition of the putative GATA transcription factor as deduced by SMART and pfam databases; start and end of the CDS coordinates on the respective chromosome; orientation of the promoter; number of exons in the gene structure; number of introns in the gene; amino acid length; predicted pI and molecular weight of the OsGATA proteins.

Article Snippet: Additional domains like LLM and HAN present in the members of subfamily-II, have been well-studied and identified as functional component of plant growth in Arabidopsis , tomato, Brachypodium , and barley (Behringer et al., ).

Techniques: Molecular Weight

Journal: European Journal of Nuclear Medicine and Molecular Imaging

Article Title: Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

doi: 10.1007/s00259-021-05371-7

Figure Lengend Snippet: Clusterisation of relapsing/non-relapsing patients based on the best-performing prediction rule evidenced by the Rulex LLM analysis. On the left: three-dimensional scatter plot of patients experiencing (blue dots) or not (red dots) relapse. Patients were plotted based on the three genes’ expression levels evidenced by the Rulex LLM analysis (determining the first three conditions of rule number 4; see Table ). On the right: ROC curve for differentiating relapsing and non-relapsing patients based on a “score” including the expression levels of the CXXC4 , PAK3 , and GHR genes, as well as on the radiomic parameter LRHGE_PET. For each patient, the score was built summing, for each of the four conditions of the rule (Table ), 1 or 0 points. At the bottom rich corner of the ROC panel, the AUC value is reported

Article Snippet: We found a couple of rules through the Rulex LLM approach to correctly prognosticate the outcome in a good percentage of cases (61–67% of covering with 78–81% accuracy).

Techniques: Expressing