Yook, S. | bioRxiv Science

bioRxiv2025

Long-term innovative potential of genetic research and its suppression

Long-term innovation depends not only on discovering new elements but also on facilitating new recombination at the time of their co-availability. This notion of combinatorial innovation raises the question about whether scientific exploration and industry research persist over similar time horizons to meet innovative opportunities. Here, we analyze about 20 million papers and US, Chinese, and European patents to compare the courses of scientific and industrial activities in genetic research, a domain of far-reaching societal importance. We observe that research on new genes has declined since the early 2000s, but the exploration of novel gene combinations remains active for biotechnology innovation. Yet, fields of highly practical or commercial focus are less likely to test novel gene combinations. Furthermore, industry R&D on each gene tends to decline earlier than scientific interest in that gene. After this decline of the industrial momentum, continuous scientific research still sustains exploratory and innovative opportunities, involving novel gene combinations, clinical trials, and technologically-impactful work. Based on a hypothetical scenario of over-aligned science and industry, we estimate that up to 42.2-74.4% of the combinational opportunities would be unexplored if scientific research becomes hindered due to industrys retreat. This study suggests the potential complementary roles of scientific and industrial research with different temporal modes in innovation cycles, calling for balanced policies to foster innovation ecosystems.

scientific communication and education↗

bioRxiv2022

A novel neuroelectrophysiological age index implicates brain health and sleep disorders

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep electroencephalogram (EEG) based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index (BAI) with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and{vartheta} waves for sleep apnea vs. higher power in {beta} and{sigma} for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

neuroscience↗