Formation Mechanism of Microbial Diversity in Artificial Intelligence Devices due to Intermediate Disturbance by Low-Dose UV Radiation for Complementary Medicine

Abstract

The development of artificial intelligence devices in the complementary medicine field is rapid and the surface microbial diversity pollution was found with periodic low-dose ultraviolet radiation (LDUVR). Since artificial intelligence devices do not have enough different types of substrates for microbial communities, it is unclear how the great microbial diversity can emerge and persist, as this clearly defies the competitive exclusion principle of ecology. In this study, the 5 most common genera in the artificial intelligence devices, Escherichia, Pseudomonas, Streptococcus, Staphylococcus and Aeromonas have been sampled without and with periodic LDUVR, respectively. A new hypothesis was put up to clarify the construction and maintenance process of high microbiological diversity in artificial intelligence devices by comparing and evaluating the variations between the dynamic response characteristics of their relative abundances in the two scenarios as follows: the periodic LDUVR can be regarded as an adverse factor with intermediate disturbance, causing stronger microbial stochastic growth responses (SGR) which would inevitably give rise to stronger random variation of the other important processes tightly correlated with SGR, such as intra- and interspecific competition process, and substrates production and consumption process, which could effectively diminish the auto- and cross-correlation of stochastic processes of microbial populations, alleviating the intra- and inter-specific competitions. In artificial intelligence devices with LDUVR, these crucial succession processes can propel the microbial communities to generate and sustain a high species diversity. Finally, thorough Monte Carlo simulations were used to thoroughly confirm the idea. This research can build the theoretical groundwork, offer fresh viewpoints, and suggest potential microbial prevention strategies for the succession of microbial communities in LDUVR.

Figure 1

Stochastic processes of 5 genera abundances and the Simpson Index of microbial community respectively sampled from the controlled room and UV disinfection room.

Figure 2

Autocorrelation function of the relative abundances stochastic process of each microbial genus sampled from the controlled room.

Figure 3

Autocorrelation function of the relative abundances stochastic process of each microbial genus sampled from UV disinfection room.

Figure 4

Cross-correlation function between two microbial genus-relative abundance stochastic processes from the controlled room.

Figure 5

Cross-correlation function between two microbial genus-relative abundance stochastic processes from the UV disinfection room.

Figure 6

Part of the simulation model of microbial community succession in artificial intelligence devices.

Figure 7

Microbial community succession pattern in artificial intelligence devices with periodic LDUVR.

Figure 8

Microbial community succession pattern in artificial intelligence devicess without LDUVR.