MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations

Mathematical formulas serve as the means of communication between humans and nature, encapsulating the operational laws governing natural phenomena. The concise formulation of these laws is a crucial objective in scientific research and an important challenge for artificial intelligence (AI). While traditional artificial neural networks (MLP) excel at data fitting, they often yield uninterpretable black box results that hinder our understanding of the relationship between variables x and predicted values y. Moreover, the fixed network architecture in MLP often gives rise to redundancy in both network structure and parameters. To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction. This adaptive network employs the PANGU meta function as its activation function, which is a unique type capable of evolving into various basic functions during training to compose mathematical formulas tailored to specific needs. We then evolve the neural network into a concise, interpretable mathematical expression. To evaluate MetaSymNet's performance, we compare it with four state-of-the-art symbolic regression algorithms across more than 10 public datasets comprising 222 formulas. Our experimental results demonstrate that our algorithm outperforms others consistently regardless of noise presence or absence. Furthermore, we assess MetaSymNet against MLP and SVM regarding their fitting ability and extrapolation capability, these are two essential aspects of machine learning algorithms. The findings reveal that our algorithm excels in both areas. Finally, we compared MetaSymNet with MLP using iterative pruning in network structure complexity. The results show that MetaSymNet's network structure complexity is obviously less than MLP under the same goodness of fit.Comment: 16 page

Endophytic Bacteria Isolated from Panax ginseng Improves Ginsenoside Accumulation in Adventitious Ginseng Root Culture

Ginsenoside is the most important secondary metabolite of ginseng. Natural sources of wild ginseng have been overexploited. Although root culture could reduce the length of the growth cycle of ginseng, the number of ginsenosides is fewer and their contents are lower in adventitious roots of ginseng than that in ginseng cultivated in the field. In this study, we investigated the effects of endophytic bacterial elicitors on biomass and ginsenoside production in adventitious roots cultures of Panax ginseng. Endophyte LB 5-3 as an elicitor could increase biomass and ginsenoside accumulation in ginseng adventitious root culture. After 6 days elicitation with a 10.0 mL of strain LB 5-3, the content of total ginsenoside was 2.026 mg g−1 which was four times more than that in unchallenged roots. The combination of methyl jasmonate and strain LB 5-3 had a negative effect on ginseng adventitious root growth and ginsenoside production. The genomic DNA of strain LB 5-3 was sequenced, and was found to be most closely related to Bacillus altitudinis (KX230132.1). The challenged ginseng adventitious root extracts exerted inhibitory effect against the HepG2 cells, which IC50 value was 0.94 mg mL−1

Sub-Inhibitory Concentrations of Mupirocin Strongly Inhibit Alpha-Toxin Production in High-Level Mupirocin-Resistant MRSA by Down-Regulating agr, saeRS, and sarA

Mupirocin, a topical antibiotic, has been utilized for decades to treat Staphylococcus aureus skin infections, as well as to decolonize patients at risk of methicillin-resistant S. aureus (MRSA) infection. The aims of this study were to investigate the expression of α-toxin (encoded by the hla gene) in ten clinical MRSA strains (MIC = 1024 μg/ml) in response to a sub-inhibitory concentration of mupirocin (1/32 minimum inhibitory concentration [MIC]) (32 μg/ml) by using α-toxin activity determination and enzyme-linked immune sorbent assay (ELISA). Subsequently, real-time polymerase chain reaction (RT-PCR) was used to examine the expression of saeR, agrA, RNAIII, and sarA genes under sub-inhibitory concentration of mupirocin in order to investigate the mechanism of action of this treatment regarding its strong inhibition of α-toxin expression. For all the strains tested, mupirocin dramatically reduced mRNA levels of α-toxin. The results indicated that α-toxin activity in mupirocin-treated groups was significantly lower than that in untreated groups. The results show that the levels of agrA, RNAIII, saeR, and sarA expression significantly decrease by 11.82- to 2.23-fold (P < 0.01). Moreover, we speculate that mupirocin-induced inhibition of α-toxin expression may be related to the inhibition of regulatory loci, such as agr, sarA and saeRS. More specifically, we found that the mechanism involves inhibiting the expression of agrA and RNAIII. In conclusion, sub-inhibitory concentrations of mupirocin strongly inhibit alpha-toxin production in high-level mupirocin-resistant MRSA by down-regulating agr, saeRS and sarA, which could potentially be developed as a supplemental treatment to control high-level mupirocin-resistant MRSA infection and reduce the risk of infection and colonization