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      Simulation and Prediction of Fungal Community Evolution Based on RBF Neural Network

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          Abstract

          Simulation and prediction of the scale change of fungal community. First, using the experimental data of a variety of fungal decomposition activities, a mathematical model of the decomposition rate and the relationship between the bacterial species was established, thereby revealing the internal mechanism of fungal decomposition activity in a complex environment. Second, based on the linear regression method and the principle of biodiversity, a model of fungal decomposition rate was constructed, and it was concluded that the interaction between mycelial elongation and moisture resistance could increase the fungal decomposition rate. Third, the differential equations are used to quantify the competitive relationship between different bacterial species, divide the boundaries of superior and inferior species, and simulate the long-term and short-term evolution trends of the community under the same initial environment. And an empirical analysis is made by taking the sudden change of the atmosphere affecting the evolution of the colony as an example. Finally, starting from summer, combining soil temperature, humidity, and fungal species data in five different environments such as arid and semiarid, a three-dimensional model and RBF neural network are introduced to predict community evolution. The study concluded that under given conditions, different strains are in short-term competition, and in the long-term, mutually beneficial symbiosis. Biodiversity is important for the biological regulation of nature.

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          An amplification-selection model for quantified rhizosphere microbiota assembly

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            A trait-based understanding of wood decomposition by fungi

            As the primary decomposers of organic material in terrestrial ecosystems, fungi are critical agents of the global carbon cycle. Yet our ability to link fungal community composition to ecosystem functioning is constrained by a limited understanding of the factors accounting for different wood decomposition rates among fungi. Here we examine which traits best explain fungal decomposition ability by combining detailed trait-based assays on 34 saprotrophic fungi from across North America in the laboratory with a 5-y field study comprising 1,582 fungi isolated from 74 decomposing logs. Fungal growth rate (hyphal extension rate) was the strongest single predictor of fungal-mediated wood decomposition rate under laboratory conditions, and accounted for up to 27% of the in situ variation in decomposition in the field. At the individual level, decomposition rate was negatively correlated with moisture niche width (an indicator of drought stress tolerance) and with the production of nutrient-mineralizing extracellular enzymes. Together, these results suggest that decomposition rates strongly align with a dominance-tolerance life-history trade-off that was previously identified in these isolates, forming a spectrum from slow-growing, stress-tolerant fungi that are poor decomposers to fast-growing, highly competitive fungi with fast decomposition rates. Our study illustrates how an understanding of fungal trait variation could improve our predictive ability of the early and midstages of wood decay, to which our findings are most applicable. By mapping our results onto the biogeographic distribution of the dominance-tolerance trade-off across North America, we approximate broad-scale patterns in intrinsic fungal-mediated wood decomposition rates.
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              THE HOSOYA INDEX OF GRAPHS FORMED BY A FRACTAL GRAPH

              The computational complexity of the Hosoya index of a given graph is NP-Complete. Let [Formula: see text] be the graph constructed from [Formula: see text] by a triangle instead of all vertices of the initial graph [Formula: see text]. In this paper, we characterize the Hosoya index of the graph [Formula: see text]. To our surprise, it shows that the Hosoya index of [Formula: see text] is thoroughly given by the order and degrees of all the vertices of the initial graph [Formula: see text].
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                Author and article information

                Contributors
                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                cmmm
                Computational and Mathematical Methods in Medicine
                Hindawi
                1748-670X
                1748-6718
                2021
                8 October 2021
                : 2021
                : 7918192
                Affiliations
                1School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China
                2School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China
                Author notes

                Academic Editor: Hui Ding

                Author information
                https://orcid.org/0000-0001-9231-1137
                https://orcid.org/0000-0001-8040-1901
                https://orcid.org/0000-0001-9730-8017
                https://orcid.org/0000-0002-9620-7692
                https://orcid.org/0000-0001-9419-9433
                Article
                10.1155/2021/7918192
                8519688
                34659448
                5f9015bf-7187-4637-a4a2-f4518495c13b
                Copyright © 2021 Xiao-Wei Cai et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 August 2021
                : 20 September 2021
                Funding
                Funded by: Anhui University
                Award ID: acjyyb2020011
                Award ID: acjyyb2019109
                Award ID: ANJYYB2019053
                Award ID: acylkc202008
                Funded by: Anhui Department of Education
                Award ID: 2020jyxm0017
                Award ID: 2019jyxm0186
                Categories
                Research Article

                Applied mathematics
                Applied mathematics

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