Trending Research

Background: Modern indoor-centric lifestyles reduce direct contact with natural surfaces, potentially impairing autonomic regulation and psychological wellbeing. Anecdotal and emerging scientific evidence suggest that grounding (earthing)—direct physical contact with the Earth's surface—may benefit heart rate variability (HRV) and mood. Objective: To evaluate the effects of daily barefoot grass exposure each morning on HRV and subjective wellbeing in office workers. Methods: In this two-arm, parallel-group randomized controlled trial, 60 healthy adults (25–55 years, full-time indoor employees) were randomized to a grounding intervention (20 minutes barefoot on grass each morning for two weeks) or a control group maintaining their usual routine. Primary outcome was HRV (RMSSD) recorded via a WHOOP Strap 4.0 wearable device. Secondary outcomes included Perceived Stress Scale (PSS-10), Positive and Negative Affect Schedule (PANAS), and daily energy ratings (1–10 Likert scale). Assessments occurred at baseline and Day 14. Compliance was monitored via a mobile app and SMS reminders. Results: The grounding group showed a significant increase in mean daily RMSSD (p = 0.03), a reduction in perceived stress (mean decrease 4.2 PSS-10 points, p < 0.01), increased positive affect (p < 0.01), decreased negative affect (p = 0.04) and higher daily energy ratings (p = 0.03). No significant changes were observed in the control group. Compliance averaged 93%, with no adverse events reported. Conclusion: A simple, accessible intervention of 20 minutes of morning barefoot grass exposure over two weeks significantly improved autonomic balance and subjective wellbeing in office workers. These findings support inclusion of nature-based grounding practices in occupational wellness programs.

We demonstrate Cas13-based RNA editing to correct disease-causing mutations in ALS and Huntington’s models with 60-85 % efficiency.

Quantum error correction is crucial for the development of fault-tolerant quantum computers. We present a machine-learning approach that reduces error rates by up to 45 %.