Analysis of the interprovincial embodied carbon flow network of China's exports.

We calculate the embodied carbon emissions of China's through the multiregional input-output (MRIO) method, then we construct the interprovincial embodied carbon flow networks of China's exports based on the mean threshold, and the application of complex network analysis to conduct a detailed examination of the overall characteristics, key nodes and edges, and community structure of China's interprovincial embodied carbon flow network. We extended the embodied carbon flow network analysis at the provincial level. The results demonstrated the following: (1) The interprovincial embodied carbon flow network of China's exports has small-world and scale-free characteristics. The node degree probability distribution curves for the networks obviously conformed to a decreasing power law distribution, indicating that a few industrial sectors carry a large amount of embodied carbon and suggesting that reducing the embodied carbon of China's exports could yield twice the results with half the effort as long as attention is paid to a few sectors. (2) The key nodes and edges in the networks show that industrial sectors and production chains such as the power and heat production and supply industry, the petroleum processing, coking, and nuclear fuel processing industry, and the metal smelting and calendering industry play the role of key "bridges" in the entire network, among which Guangdong, Hebei, Jiangsu, Inner Mongolia, and Shanxi are important node provinces and the main flow paths for the generation of embodied carbon in national exports. These industrial sectors and production chains should bolster their policies to encourage the innovation of carbon emission reduction technologies and decrease carbon emissions, so as to reduce the embodied carbon of national exports on a large scale. (3) The number of communities firstly increased then decreased from 2007 to 2017, while the aggregation coefficient of the node and correlation density within first community displayed firstly downward then upward trends, reflecting firstly decentralization then centralization of the interprovincial embodied carbon flow.

Phellopterin cream exerts an anti-inflammatory effect that facilitates diabetes-associated cutaneous wound healing via SIRT1.

BACKGROUND: Diabetic ulcers, which are characterized by chronic nonhealing wounds with a long-lasting inflammatory state, are a typical symptom in individuals with diabetes, and there is still no effective treatment for these lesions. Angelica dahurica plays a critical role in inflammatory diseases. Among numerous monomeric compounds, phellopterin has been shown to have anti-inflammatory properties. PURPOSE: To research the bioactive constituents in Angelica dahurica and their mechanism of action in treating diabetic ulcers. STUDY DESIGN: Chemical research of Angelica dahurica led to the identification of a new coumarin, dahuricoumarin A (1), along with seven known compounds (2 - 8). All compounds were tested for anti-inflammatory activity, and phellopterin, compound (3), significantly decreased the expression of intercellular cell adhesion molecule-1 (ICAM-1), a representative indicator of inflammation. Phellopterin can also increase SIRT1 protein, a key target for inflammation. In our research, we confirmed the anti-inflammatory effects of phellopterin on diabetic ulcers and explored the underlying mechanism of action. METHODS: The expression of IFN-γ, SIRT1, and ICAM-1 in human diabetic ulcer tissues was studied using immunohistochemistry. Streptozotocin was used to induce a diabetic model in C57BL/6J mice, and ulcers were surgically introduced. After phellopterin treatment, the skin lesions of diabetic mice were observed over a period of time. The protein and mRNA expression levels of SIRT1 and ICAM-1 were measured using H&E, qRT-PCR and immunohistochemical staining. A HaCaT cell inflammatory model was induced by IFN-γ. Using a lentiviral packaging technique, MTT assay, and Western blotting, the effect of phellopterin on the proliferation of HaCaT cells and the expression of ICAM-1 was evaluated under normal and SIRT1 knockdown conditions. RESULTS: High levels of ICAM-1 and IFN-γ were identified, but low levels of SIRT1 were found in human diabetic ulcer tissues, and phellopterin showed therapeutic benefits in the healing process by attenuating chronic inflammation and promoting re-epithelialization, along with SIRT1 upregulation and ICAM-1 downregulation. However, inhibiting SIRT1 reversed its proliferative and anti-inflammatory effects. CONCLUSION: In vitro and in vivo, phellopterin exerts anti-inflammatory and proliferative effects that promote diabetic wound healing, and the potential mechanism depends on SIRT1.

[Improved generalized regression neural network for quantitative analysis of crude oil density by gas chromatography].

In the field of oil and gas exploration and development, the quick identification of reservoir crude oil properties has a guiding significance for engineers and technicians. Geochemical logging technology is a conventional method to evaluate the properties of crude oil in reservoirs, and it can provide professional knowledge for comprehensive evaluation of reservoirs. In this study, the principles of rock pyrolysis and gas chromatographic analyses in geochemical logging are studied. Moreover, a new method for quantitative analysis of crude oil density by chromatogram is proposed. Combined with the division standard of crude oil property density, the properties of reservoir crude oil can be quickly evaluated. In the experiment, first, the chromatogram was standardized and normalized using computer image processing software. The curve characteristic law of rock pyrolysis gas chromatogram was analyzed, and the corresponding characteristic parameter extraction method was proposed. The chromatogram was converted into a characteristic parameter matrix. Second, three types of artificial intelligence prediction and classification models were studied. The latest meta-heuristic optimization algorithm (sparrow search optimization algorithm) was used to optimize the hyperparameters of the generalized regression neural network, and the accuracy and convergence speed of the model were improved. To study the influence of different positions of rock samples on the experimental results, two groups of samples were utilized: cuttings samples and wall core samples. Based on a comprehensive comparison of the prediction results of the three models, it was found that the generalized regression neural network prediction model optimized by sparrow search algorithm provided the best effect, being a stable model, with small prediction density error, and strong generalization ability. The prediction error coincidence rate (absolute error < 0.02) of this model for cuttings and wall core samples was 95% and 100%, respectively. The root mean square errors were 0.0079 and 0.0069 respectively. The classification accuracy of crude oil properties was 95%. The analysis of the two groups of parallel experimental data indicated that the rock samples from the wall center can more accurately reflect the crude oil properties of the reservoir. Therefore, the method proposed in this study can provide reliable data support for reservoir comprehensive evaluation and on-site construction.

Plasma miR-126 expression correlates with risk and severity of psoriasis and its high level at baseline predicts worse response to Tripterygium wilfordii Hook F in combination with acitretin.

BACKGROUND: Treatment of psoriasis is always difficult, which requires intensive scientific research. OBJECTIVE: Tripterygium wilfordii Hook F (TwHF) with acitretin(TwHF + acitretin) is normally used in treating psoriasis. This study aimed to investigate the correlation of plasma miR-126 expression with risk and severity of psoriasis, and its predictive value of response to TwHF + acitretin treatment in psoriasis. METHODS: MiRNA-126(MiR-126) expression in plasma was analyzed in psoriasis patients at month 0 (M0), M1, M3 and M6 and in health controls (HCs) at enrollment by qPCR. Psoriasis-affected body surface area (BSA) and Psoriasis Area and Severity Index (PASI) score were used to assess severity and treatment response. RESULTS: Plasma miR-126 levels were decreased in psoriasis patients compared with HCs (P < 0.001), with area under the curve (AUC) of 0.771. MiR-126 expression was negatively correlated with PASI score (P = 0.001), and negatively associated with psoriasis-affected BSA (P = 0.825). At M6, 65.3% and 36.1% patients achieved PASI 50 and 75, respectively. MiR-126 increased at M1, M3 and M6 after TwHF + acitretin treatment when comparing with M0 (all P < 0.001). Meanwhile, miR-126 expression baseline in PASI 50 group declined when comparing with non-PASI 50 group (P < 0.001). Additionally, data revealed that the cause of high miR-126 baseline level was due to unsuccessfully achieving PASI 50 at M6 after TwHF + acitretin treatment (P < 0.001). However, miR-126 baseline expression was not a predictive factor for PASI 75 achievement (P > 0.05). CONCLUSION: Plasma miR-126 expression is negatively correlated with psoriasis risk and severity, and its high baseline level can be used as a biomarker to predict worse clinical response to TwHF + acitretin treatment in psoriasis.