Additional file 2: of Microbial community structure and the relationship with soil carbon and nitrogen in an original Korean pine forest of Changbai Mountain, China

Correction between tPLFA and MBC. (PNG 13 kb

Heavy metals pollution and potential ecological risk assessment in farmland soils from typical mining area: a case study

The research aimed to investigate HMS, utilizing the Pearson correlation coefficient for speciation distribution analysis, PCA for assessing pollution characteristics and identifying sources, the Muller index to evaluate ecological risk level, and the Hakanson potential ecological risk index to determine the order of risk from heavy metals. The topsoil near SA was collected, and the contents of seven kinds of HMS, As, Cd, Pb, Zn, Ni, Cu and Cr were determined, so as to evaluate the types of high-risk heavy metal pollution further accurately. The research recorded valuable data showing that the concentration values of all seven HMS in the investigated area exceeded prescribed agricultural soil contamination limits. The concentrations of As, Cd, and Pb were found to be 8.30, 46.20, and 6.08 times higher than the screening values in Hunan Province, respectively. In the GYB sampling area, the coefficient of variation (CV) values for Cu, Pb, As, Zn, and Cd are all between 0.50 and 1.00. Notably, the CV value for Cd reaches 0.82, indicating a significant variation. Significant correlations were found between Cd and Zn (Cd-Zn), Pb and Zn (Pb-Zn), Ni and Cr (Ni-Cr) in the tested soils. The ecological risk index (Eri) results showed that Cd was the primary pollutant in the study area, with the potential ecological hazards in the tested soils ranked as Cd>As>Pb>Cu>Zn>Ni>Cr. Combining both evaluation methods, the study area’s potential ecological risk order is SZY>GYB>CTL.</p

Additional file 1: of Microbial community structure and the relationship with soil carbon and nitrogen in an original Korean pine forest of Changbai Mountain, China

Vegetation distribution of the sampling areas. (DOCX 19 kb

DataSheet1_Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.docx

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.</p

Table_1_Soil bacterial community in a photovoltaic system adopted different survival strategies to cope with small-scale light stress under different vegetation restoration modes.docx

Solar photovoltaic (PV) power generation is a major carbon reduction technology that is rapidly developing worldwide. However, the impact of PV plant construction on subsurface microecosystems is currently understudied. We conducted a systematic investigation into the effects of small-scale light stress caused by shading of PV panels and sampling depth on the composition, diversity, survival strategy, and key driving factors of soil bacterial communities (SBCs) under two vegetation restoration modes, i.e., Euryops pectinatus (EP) and Loropetalum chinense var. rubrum (LC). The study revealed that light stress had a greater impact on rare species with relative abundances below 0.01% than on high-abundance species, regardless of the vegetation restoration pattern. Additionally, PV shadowing increased SBCs’ biomass by 20–30% but had varying negative effects on the numbers of Operational Taxonomic Unit (OTU), Shannon diversity, abundance-based coverage estimator (ACE), and Chao1 richness index. Co-occurrence and correlation network analysis revealed that symbiotic relationships dominated the key SBCs in the LC sample plots, with Chloroflexi and Actinobacteriota being the most ecologically important. In contrast, competitive relationships were significantly increased in the EP sample plots, with Actinobacteriota having the most ecological importance. In the EP sample plot, SBCs were found to be more tightly linked and had more stable ecological networks. This suggests that EP is more conducive to the stability and health of underground ecosystems in vulnerable areas when compared with LC. These findings offer new insights into the effects of small-scale light stress on subsurface microorganisms under different vegetation restoration patterns. Moreover, they may provide a reference for optimizing ecological restoration patterns in fragile areas.</p

Pseudo-first-order kinetics of MC-LR degradation under different initial Ag₃PO₄ concentration.

<p>Pseudo-first-order kinetics of MC-LR degradation under different initial Ag₃PO₄ concentration.</p

XRD patterns of the Ag₃PO₄ powders.

<p>XRD patterns of the Ag₃PO₄ powders.</p

Possible interactions between Arg and MeAsp on the cyclic structure of MC-LR.

<p>Possible interactions between Arg and MeAsp on the cyclic structure of MC-LR.</p

UV−vis diffuse reflectance spectra of the as-synthesized Ag₃PO₄ photocatalyst.

<p>UV−vis diffuse reflectance spectra of the as-synthesized Ag₃PO₄ photocatalyst.</p

Pseudo-first-order kinetics of MC-LR degradation under 4 cycling runs.

<p>Pseudo-first-order kinetics of MC-LR degradation under 4 cycling runs.</p