Long-Term Effects of Ecological Restoration Projects on Ecosystem Services and Their Spatial Interactions: A Case Study of Hainan Tropical Forest Park in China.

Ecological restoration projects aim to comprehensively intervene in damaged or deteriorating ecosystems, restore them, improve the provision of ecosystem services, and achieve harmonious coexistence between humans and nature. Implementing ecological restoration projects leads to continuous changes in land use/land cover. Studying the long-term changes in land use/land cover and their impacts on ecosystem services, as well as the trade-off and synergy between these services, helps evaluate the long-term effectiveness of ecological restoration projects in restoring ecosystems. Therefore, this study analyzes the land use/land cover, and ecosystem services of the Hainan Tropical Forest Park in China to address this. Since 2000, the area has undergone multiple ecological restoration projects, divided roughly into two stages: 2003-2013 and 2013-2021. The InVEST model is used to quantify three essential ecosystem services in mountainous regions (water yield, carbon storage, and soil conservation), and redundancy analysis identifies the primary driving factors influencing their changes. We conducted spatial autocorrelation analysis to examine the interplay among ecosystem services under long-term land use/land cover change. The results indicate a decrease in the total supply of water yield (-5.14%) and carbon storage (-3.21%) in the first phase. However, the second phase shows an improvement in ecosystem services, with an increase in the total supply of water yield (11.45%), carbon storage (27.58%), and soil conservation (21.95%). The redundancy analysis results reveal that land use/land cover are the primary driving factors influencing the changes in ecosystem services. Furthermore, there is a shift in the trade-off and synergy between ecosystem services at different stages, with significant differences in spatial distribution. The findings of this study provide more spatially targeted suggestions for the restoration and management of tropical montane rainforests in the future.

[Determination of Gasoline Composition Based on Raman Spectroscopy].

For the purpose of the rapid prediction of every composition in gasoline, the Raman spectra of the gasoline brand 93 and 97, a batch of one-one mixtures with aromatic, olefin, ben, methanol and ethanol with different ratios are measured, 410 mixture samples were measured totally in this research. The obtained Raman spectra were preprocessed by a series of processing, they were data smoothing, baseline deduction and spectral normalized, etc. After that 33 characteristic peaks were extracted to be the eigenvalues for the whole Raman spectra. According to the current national standard test method, the values of every composition were measured by the gas chromatography. By using the eigenvalues as inputs, and actual contents of aromatic, olefin, ben, methanol and ethanol got from gas chromatography as outputs, two mathematical models of multi-output least squares support vector regression and partial least squares combination with multiple regression analysis were established to predict the values of the above compositions of a sample, respectively. The predicting results were compared with the values calculated from the gas chromatography measurement results and the mixture proportions, the multi-output least squares support vector regression has a better effects, and the obtained root mean square error of prediction for aromatic, olefin, ben, methanol and ethanol are 0.27%, 0.27%, 0.22%, 0.17%, 0.14%; the correlation coefficients are 0.999 3, 0.998 5, 0.998 6, 0.992 3, 0.993 5, respectively. This model is also applied to the detection of the unknown sample, the root mean square error of the prediction for the results does not exceed 0.5%, which can achieve the measurement requirements in the industry. Results show that the Raman spectra analysis technology based on multi-output least squares support vector regression can be a precise, fast and convenient new method for gasoline composition detection, and can be applied to the quality control of the gasoline production process, transportation, storage of the gasoline.

[Research on prediction method of fatty acid content in edible oil based on Raman spectroscopy and multi-output least squares support vector regression machine].

For the purpose of the rapid prediction of saturated fatty acid, oleic acid, linoleic acid content in edible vegetable oil, the Raman spectra of a batch of edible vegetable oils and their one-one mixtures with different ratios were measured in the range of 800-2 000 cm(-1), 91 samples were measured totally in this research, the obtained Raman spectra data were preprocessed by a new method proposed in this paper called auto-set fulcrums baseline fitting method based on peak-seeking algorithm, and 8 characteristic peak values (872 cm(-1) [v(C-C)], 972 cm(-1) [delta(C=C) trans], 1 082 cm(-1) [v(C-C)], 1 267 cm(-1) [delta(=C-H) cis], 1 303 cm(-1) [delta(CH2) twisting], 1 442 cm(-1) [delta(CH2) scissoring], 1 658 cm(-1) [v(C=C) cis], 1 748 cm(-1) [v(C=O)]) were extracted to be the eigenvalues for the whole spectra, among the 8 peaks there are three peaks (972, 1 267, 1 658 cm(-1)) that play an important role in the establishment of mathematical model, they are closely concerned with C=C band which distinguishes the three fatty acid types. By using these eigenvalues as inputs, and actual saturated fatty acid, oleic acid, linoleic acid contents of sample oils as outputs, a prediction mathematical model that predicts simultaneously the three fatty acid contents was established using multiple regression analysis: multi-output least squares support vector regression machine (MLS-SVR) and partial least squares (PLS). Results show that the MLS-SVR has better effects. The predicting results are compared with results of gas chromatography(GC), and the obtained root mean square error of prediction(RMSEP) for saturated fatty acid, oleic acid, linoleic acid are 0.496 7%, 0.840 0% and 1.019 9%, and the correlation coefficients (r) are 0.813 3, 0.999 2 and 0.998 1, respectively. When this model is applied in the detection of new unknown oil samples, the prediction error does not exceed 5%. Results show that the Raman spectra analysis technology based on MLS-SVR can be a convenient, fast, non-destructive, and precise new method for oil detection.

Kaempferol promotes non-small cell lung cancer cell autophagy via restricting Met pathway.

BACKGROUND: Kaempferol is extracted from Hedyotis diffusa, exerting an obvious anti-cancer effect. Here in the present study, we explored the anti-cancer effects and mechanism of kaempferol in non-small cell lung cancer cell (NSCLC). PURPOSE: Our objective is to figure out the molecular mechanism by which kaempferol promotes autophagy in NSCLC cells. STUDY DESIGN: A549 and H1299 NSCLC cell lines were used for in vitro experiments. And BALB/c nude mice of NSCLC were used to perform in vivo experiments. METHODS: For in vitro experiments, CCK-8 and EdU assay was used to observe the effect of kaempferol on NSCLC cell proliferation. Confocal microscopy of mCherry-EGFR-LC3 assay and electron microscopy assay were used to detect NSCLC cell autophagy. Protein expression was determined using Western blot, and mRNA expression was determined using qRT-PCR. Flow cytometry was performed to detect the cell apoptosis. For in vivo experiments, a subcutaneously implanted tumor model in BALB/C nude mice was performed using human NSCLC cell line A549-Luc. The kaempferol effect on NSCLC mice model was detected by measuring the tumor weight and bioluminescence intensity. Immunohistochemistry was done to measure the key protein expression from mice tumor tissues. RESULTS: Our results confirmed that kaempferol inhibited NSCLC cell proliferation significantly. And it promoted NSCLC cell autophagy, leading to NSCLC cell death. Interestingly, Met-was greatly inhibited at both protein and mRNA levels. Meanwhile, PI3K/AKT/mTOR signaling pathway was inhibited accordingly. Furthermore, overexpressing Met-reversed the effect of kaempferol on NSCLC cell viability and cell autophagy with significance. Finally, the above effect and pathway were validated using the xenograft model. CONCLUSION: Kaempferol may exert its anti-NSCLC effect by promoting NSCLC cell autophagy. Mechanistically, Met-and its downstream PI3K/AKT/mTOR signaling pathway were involved in the process, which provides a novel mechanism how kaempferol functions in inhibiting NSCLC.

Positive Role of Chinese Herbal Medicine in Cancer Immune Regulation.

Complementary and alternative medicine (CAM) plays a critical role in treating cancer patients. Traditional Chinese Medicine (TCM) is the main component of CAM. TCM, especially Chinese Herbal Medicine (CHM), has been increasingly used in China, some other Asian countries and European countries. It has been proven to enhance the efficacy of chemotherapy, radiotherapy, targeted-therapy, and immunotherapy. It lessens the damage caused by these therapies. CHM functions on cancer by inhibiting tumor progression and improving an organism's immune system. Increasing evidence has shown that many CHM exert favorable effects on the immune regulation. We will summarize the role of CHM on patient's immune system when treating cancer patients. Our evidence reveals that single herbs, including their extracts, compound formulations, and preparations, will provide current advances on CHM study, especially from the perspective of immune regulation and novel insights for CHM application in clinic. The main herbs used to treat cancer patients are health-strengthening (Fu-Zheng) herbs and pathogen eliminating (Qu-Xie) herbs. The key mechanism is regulating the immune system of cancer patients. Firstly, health-strengthening herbs are mainly functioned as immune regulatory effectors on cancer. Secondly, some of the compound formulations mainly strengthen the health of patients by regulating the immune system of cancer patients. Lastly, some Chinese medicine preparations are widely used to treat cancer for their properties of spiriting vital energy and anti-cancer effects, mainly by improving immunity. CHM plays a positive role in regulating patients' immune system, which helps cancer patients to fight against cancer itself and finally improves patients' life quality.