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The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Cao et al. | Jun 17, 2013

The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil

Environmental disasters such as the Deepwater Horizon oil spill can be devastating to ecosystems for long periods of time. Safer, cheaper, and more effective methods of oil clean-up are needed to clean up oil spills in the future. Here, the authors investigate the ability of natural ocean algae to process crude oil into less toxic chemicals. They identify Coccochloris elabens as a particularly promising algae for future bioremediation efforts.

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Understanding the correlation between various pollutants and cancer across geographical clusters in the U.S.

Naik et al. | Sep 12, 2026

Understanding the correlation between various pollutants and cancer across geographical clusters in the U.S.

Here the authors investigate the relationship between environmental pollutants and cancer incidence rates across various geographical clusters in the United States from 2018 to 2020. By calculating Pearson correlation coefficients and t-statistics on CDC and EPA data, they demonstrated a strong correlation between specific pollutants and various cancers, offering insights that could help explain regional disparities in cancer rates and aid in preventing premature deaths.

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Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff

Venkat et al. | Sep 12, 2026

Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff
Image credit: Liz Harrell

Here the authors investigated the effectiveness of algal pretreatment in reducing nitrate toxicity and extending the survival of fish in contaminated river water. Their findings demonstrate that treating nitrate-polluted water with algae extended fish survival by up to 50%, suggesting that algae could serve as a valuable tool for mitigating the destructive impacts of agricultural and industrial runoff on aquatic ecosystems.

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Mining social media posts: An alternative approach to understanding home health care workers’ experiences

Tang et al. | Jun 15, 2026

Mining social media posts: An alternative approach to understanding home health care workers’ experiences
Image credit: Markus Winkler

This study developed an effective method for extracting valuable information from social media data and employed computer-assisted human coding alongside the AI-powered tool to analyze social media posts. The findings provide a deep understanding of the major challenges that home health care workers are experiencing and offer important implications regarding how to improve home healthcare workers' well-being, and provide suggestions on optimizing the home health care service experience.

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Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring

Mahatara et al. | May 25, 2026

Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring
Image credit: JonTyson

This study investigated perceptions of the emerging workforce toward the use of artificial intelligence in hiring, specifically for assessing subjective "culture fit." Through a mixed-methods survey of 150 high school and early-college students in Nepal, we found a significant disconnect between organizational adoption of AI and the profound skepticism of young job candidates, who express deep concerns about fairness, transparency, and the potential for AI to perpetuate systemic discrimination.

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