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Exploring Political Discourse Among High School Journalists with Web Scraping and AI Technology

Gong et al. | Jun 10, 2025

Exploring Political Discourse Among High School Journalists with Web Scraping and AI Technology

Here the authors provided greater coverage of adolescent stances by investigating the political perspectives and trends of high school journalists, utilizing web scraping methods and artificial intelligence (ChatGPT-4o) to analyze over 153,000 articles. They found that high school publications exhibit lower levels of political polarization compared to mainstream media and that journalists' views, while tending to lean moderately liberal, showed no significant correlation with local voting patterns.

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Validating DTAPs with large language models: A novel approach to drug repurposing

Curtis et al. | Mar 02, 2025

Validating DTAPs with large language models: A novel approach to drug repurposing
Image credit: Growtika

Here, the authors investigated the integration of large language models (LLMs) with drug target affinity predictors (DTAPs) to improve drug repurposing, demonstrating a significant increase in prediction accuracy, particularly with GPT-4, for psychotropic drugs and the sigma-1 receptor. This novel approach offers to potentially accelerate and reduce the cost of drug discovery by efficiently identifying new therapeutic uses for existing drugs.

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Lettuce seed germination in the presence of microplastic contamination

Kochar et al. | Dec 09, 2024

Lettuce seed germination in the presence of microplastic contamination

Microplastic pollution is a pressing environmental issue, particularly in the context of its potential impacts on ecosystems and human health. In this study, we explored the ability of plants, specifically those cultivated for human consumption, to absorb microplastics from their growing medium. We found no evidence of microplastic absorption in both intact and mechanically damaged roots. This outcome suggests that microplastics larger than 10 μm may not be readily absorbed by the root systems of leafy crops such as lettuce (L. sativa).

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The gender gap in STEM at top U.S. Universities: change over time and relationship with ranking

Kruus et al. | Jun 25, 2024

The gender gap in STEM at top U.S. Universities: change over time and relationship with ranking

Authors address the gender disparity in STEM fields, examining changes in gender diversity across male-dominated undergraduate programs over 19 years at 24 top universities. Analyzing data from NCES IPEDS, it identifies STEM as persistently male-dominated but notes increasing gender diversity in many disciplines, particularly in recent years. Results indicate that higher-ranked universities in disciplines like computer science and mechanical engineering show a weak correlation with improved gender diversity, suggesting effective initiatives can mitigate the gender gap in STEM, despite ongoing challenges.

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