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The Earth’s surface is a complex system involving mutual interactions of its many components, including mountains, rivers, forests, farmlands, lakes and grasses. The interaction and mutual feedback of chemical elements in Earth's surface layer can drive changes in chemical elemental distribution patterns. In this study, we evaluated the mechanisms and interactions driving the distribution patterns of macroelements, probiotics, halogens and heavy metals in soils in Southwest China, based on a systematic geochemical land-quality survey at a scale of 1:250000. The results showed that the parent material determines the natural state of chemical elements in land resources. Epigenetic geochemical dynamics reshapes the distribution patterns of chemical elements in top soil; biogeochemical processes drive the evolutionary trends of land quality; and human activities, such as mining, disrupt the natural evolution of chemical elemental distribution patterns. The establishment of an epigenetic geochemical dynamics theory allows the construction of a framework for understanding the Earth's surface layer and promoting technological innovations for the comprehensive geochemical investigation of land resources.

Additional file 1: Fig. S1. The UV–vis spectra of ciprofloxacin (CFL) and Ag from the AMPC. (A) Absorption peaks of CFL with different concentrations at 275 nm. The standard curves of (B) CFL and (C) AMPC. Fig. S2. The encapsulation efficiency of CFL into AMP. Fig. S3. Agarose gel electrophoresis of AMPC@siNC. Fig. S4. Cell viability after the incubation of AMP at different concentrations with RAW264.7 cells. BLK represents the untreated cells. The data represent as means ± SD, n = 6. Fig. S5. The hemolysis result of AMP at different concentrations. The data represent as means ± SD, n = 6. Fig. S6. Average fluorescence intensities for evaluating the siRNA delivery effect by flow cytometry. The data are mean ± SD, n = 3 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). “ns” represents no significant difference. Fig. S7. The expression of TNF-α mRNA in LPS-induced RAW 264.7 cells treated with different formulations by qRT-PCR. The data are mean ± SD, n = 3 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). “ns” represents no significant difference. Fig. S8 The turbidity observation of S. aureus in LB medium treated with different concentrations of formulations (AM, AMP, AMPC, and AMPC/siTNF-α). The MICs of the sample are marked with a red arrow. Fig. S9 The photographs of the S. aureus colony on agar plates with different treatments for 12 h. Fig. S10. Quantitative bacterial colonies densities based on Fig. S9 after different treatments for 12 h. The data represent mean ± SD, n = 3 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). “ns” represents no significant difference. Fig. S11. Growth curve of S. aureus in logarithmic growth period treated with different NPs. Fig. S12. Antibacterial ratio of S. aureus after different treatments for 12 h. The data represent mean ± SD, n = 3 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). “ns” represents no significant difference. Fig. S13. In vivo biosafety evaluation of NPs. H&E staining of histological sections including heart, liver, spleen, lung, and kidney of mice after 12 days of different treatments. LEVO represents levofloxacin. Scale bar = 100 μm. Table S1. MIC of different antibacterial nanoplatforms for S. aureus and the calculated CI value

The L-type VGCC subtypes, including subtypes Cav1.1-1.4, have been shown to play critical roles in various cellular activities, including muscle contraction, hormone secretion, and neurotransmitter release. Recent research indicates the potential involvement of Cav1.3 in various neurological and psychiatric disorders, such as the early onset of Parkinson’s disease and substance abuse disorders. Non-selective L-VGCC subtype blockers such as dihydropyridines (DHPs) are used to treat hypertension and angina because they potently inhibit Cav1.2, but no selective Cav1.3 inhibitors have been developed yet. We resolved the molecular determinants to differentiate Cav1.2 and Cav1.3 in response to DHP nifedipine. Nifedipine IC50 for Cav1.2 and Cav1.3 are 22nM and 289nM determined by whole-cell patch-clamp. We identified two significant amino acids, Cav1.3/M1030 to Cav1.2/V1036 in the transmembrane IIIS5 and Cav1.3/S1100 Cav1.2/A1106 in the extracellular IIIS-3P loop, to differentiate the subtype affinity to nifedipine. We found that the Cav1.3/II-III loop fused to eGFP decreased glucose-activated action potential (GSAP) frequency by ~80% in the pancreatic β-cell. We introduced several synthetic peptides, and peptide P3-1 from C-terminal induced a -16mV shift in V1/2 inactivation with an EC50 of 231nM. P3-1 contains a protein kinase G (PKG) phosphorylation site (RRISE) required for PKG inhibition of Cav1.3 current but not conserved in Cav1.2. We found that the shift in V1/2 inactivation induced by co-expression of Cav1.3 with the Cav1.3/II-III loop/GFP requires the presence of a Cavβ subunit, and Cavβ3 also exhibits selectivity over other β subunits. Significantly, P3-1 shifts the Cav1.2 inactivation to a more positive voltage when co-expressed with either Cavβ2a or Cavβ3, demonstrating the ability of P3-1 to differentiate Cav1.2 and Cav1.3 in a Cavβ-dependent manner. Failure of pancreatic β-cells to secrete enough insulin to maintain glucose homeostasis is a hallmark of Type 2 diabetes. However, the consequences of the dysregulation of the endoplasmic reticulum (ER) Ca2+ channel ryanodine receptor-2 (RyR2) in pancreatic β-cells are not fully understood. Therefore, we characterized the electrical activity in INS-1 in which RyR2 has been deleted via CRISPR/Cas9 gene editing. We observed a decreased level of IP3 receptor binding protein (IRBIT) in RyR2KO INS-1 cells and generated IRBITKO INS-1 cells. VGCC current density in RyR2KO doubled compared to controls and was also elevated in IRBITKO compared to control cells. All HVA Ca2+ channels were upregulated, determined by fractional current blocked by nifedipine. We also found that GSAP frequency is doubled by RyR2 deletion due to failure to activate apamin sensitive SK (small conductance calcium-activated potassium) channels.

Additional file 1: Table S1. Coefficient of variation of the system measured with different concentration of CuSO4. Table S2 Physiologic correlation of skin cholesterol measurement as assessed by univariate analysis.

The Chang 6 reservoir in Hulangmao area is a lithologic reservoir controlled by Zhidan Jingbian delta depositional system in Northern Shanxi Province. The oil reservoir has entered the middle-high-water-cut stage. The reservoir is characterized by low porosity and low permeability, with strong heterogeneity and great development difficulty. Therefore, it is necessary to further study the distribution of remaining oil through fine single sand body division and correlation，so as to improve the level of the reservoir development. Based on the theory of high-resolution sequence stratigraphy, according to the characteristics of mudstone marker bed and logging combinations, the sedimentary cycles are studied by using the techniques of maximum entropy spectrum and wavelet transform, and the cycle characteristics of different levels are clarified. Chang 6 member is divided into 3 fourth-level cycles, 6 fifth-level cycles (small levels), 13 sixth-order cycles (single level), and 21 seventh-order cycles (single sand body). Based on the stratigraphic correlation analysis model of sand body equal thickness, channel down-cutting, lateral phase transformation and splitting of superimposed sand body, and combined with the core and logging characteristics of the target area, the correlation framework of single sand layer in the whole area is established.

The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the ‘Virtual Sampling Scheme (VSS)’. VSS is formed from two components: an algorithm for forming virtual clusters and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method.

Congestion in wireless sensor networks usually occurs when the traffic is heavy. Buffer management determines how to drop or buffer the arriving packet when congestion occurs. This paper presents a buffer management algorithm for wireless sensor nodes. The algorithm has four advantages: First, this algorithm is based on spatial correlation of sensing data, it can drop the most redundant data instead of useful data; Second, the algorithm ensure fairness among the stream on the view of spatiality; Third, the algorithm is an active buffer management algorithm, it can drop packet early in order to buffer burst application data; Finally, the algorithm only needs one floating-point calculation, it is fit for the sensor nodes.