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This study aimed to develop a method based on hyperspectral imaging (400–1 000 nm) combined with chemometrics to identify the diverse geographical origins of Lycium barbarum fruit. Hyperspectral images of L. barbarum fruit from Ningxia, Gansu, Inner Mongolia, Qinghai and Xinjiang were acquired, and spectral data was extracted from the region of interest (ROI) by threshold segmentation method. Multiple preprocessing methods were employed to eliminate the interference information from the spectra, and the results showed that the discriminant model based on normalized reflectance spectrum (NR) exhibited better performance. Furthermore, the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), particle swarm optimization (PSO), iteratively retaining informative variables (IRIV), and CARS + IRIV were used to select characteristic wavelengths. The results showed that the simplified model based on the wavelengths selected by CARS + IRIV had the best performance. In the models ranging from binary to quintuple classifications, the selected characteristic wavelengths accounted for only 15.6% to 27.7% of the full spectra. The prediction accuracy was 97.7%, 90.9%, 89.2%, and 87.1%, respectively. In addition, a confusion matrix was employed to visualize the optimal simplified classification model in order to intuitively distinguish the classification categories. Satisfactory sensitivity, specificity and Kappa coefficients were obtained in classifying L. barbarum. The results illustrated that hyperspectral imaging technology combined with chemometric methods could effectively identify the geographical origin of L. barbarum and provide crucial technical support for the development of the L. barbarum industry.

Background and purpose: Exogenous bone morphogenetic protein 9 (BMP9) inhibits the malignant progression of human breast cancer, but its expression is often abnormally low in breast cancer. In this study, we intended to explore the expression and role of epigenetically-modified histone lysine-specific demethylase 4A (KDM4A) in breast cancer, and to investigate the relationship between KDM4A and BMP9 and its possible regulatory mechanism. Methods: The expression of KDM4A in breast cancer and its relationship with BMP9 were analyzed by bioinformatics and verified by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and Western blot. Chromatin immunoprecipitation (ChIP) verified the regulatory role of KDM4A on BMP9, and RNA stability experiments and CHX protein stability experiments verified the effect of KDM4A in BMP9 expression. Exogenous recombinant MDA-MB-231 cells transfected with KDM4A small interfering RNA (siKDM4A) or infected with siBMP9 adenovirus (Ad-siBMP9) were constructed using RNA interference technology and adenoviruses knocking down BMP9, and the migratory and invasive abilities of the cells were detected by scratch healing assay and transwell assay, respectively. Results: Bioinformatics analysis showed that the expression of KDM4A was significantly higher in breast cancer than in normal tissues, and there was a negative correlation between the expression of KDM4A and that of BMP9 in breast cancer; RTFQ-PCR and Western blot showed that KDM4A was highly expressed in different breast cancer cell lines, and the knockdown of KDM4A significantly up-regulated BMP9. ChIP experiment confirmed that KDM4A could be significantly enriched in the promoter region of BMP9 gene, reducing its histone lysine 36 position instead of position 4 methyl status, thus silencing the expression of BMP9. RNA stability assay and CHX protein stability assay confirmed that KDM4A had no significant effect on the mRNA of BMP9, but could affect its protein degradation. After knocking down KDM4A, the migration and invasion abilities of breast cancer cells MDA-MB-231 were significantly inhibited, and this effect could be partially reversed by knocking down BMP9. Conclusion: KDM4A is highly expressed in breast cancer and breast cancer cell MDA-MB-231, and can silence its expression by down-regulating the level of histone methylation in the promoter region of the BMP9 gene, as well as affecting the stability of BMP9 at the protein level rather than at the level of mRNA, and promoting the migration and invasion of breast cancer.

This paper uses hierarchical analysis and gray comprehensive evaluation to quantitatively evaluate the level of integration of innovation education in colleges and universities, and for this purpose, the evaluation system is first designed. The B/S structure model is employed to construct the system, which is divided into seven modules. Within the evaluation module, the evaluation index system is first constructed, combined with questionnaires and other means, the weights are calculated using the hierarchical analysis method, and then the level of innovative education in colleges and universities is quantitatively evaluated using the gray comprehensive evaluation algorithm. Three schools were tested for empirical analysis after the system design was completed. The trial situation of each school shows that the indicators of publicity report and achievement transformation each have 20%~40% poor evaluations, in addition to the indicators of grade ranking and the status of relatives’ business enterprises have 40% very poor evaluations. The innovation and entrepreneurship competition in the ‘process quality’ competition received a score of approximately 62 points, which is generally low. The average score for ‘quality of results’ is typically lower than 70, with a lower average score for ‘innovation and entrepreneurship’ at 63.95 points. The system is functioning well and able to examine the challenges in the school’s innovation education.

Big data analysis of education management in colleges and universities plays an important role in the improvement of education levels. In this paper, a triangular fuzzy algorithm based on association rule mining is proposed to realize a better data mining effect. Firstly, the relative similarity in WBARM algorithm is used to find out the similarity difference index of attributes from decision-making data, and then the association rule mining algorithm is used to find out the influence factor of attributes, and finally, the weight of attributes is obtained by combining the above results, and the generated association factor is evaluated, and the research is carried out in terms of enrollment and admission, learning behavior and teaching evaluation. The results show that: in terms of college enrollment and admission, the three colleges and universities in M can increase the promotion of candidates with scores between 451-580 on the college entrance examination, and expand the number of enrollment in the majors of visual communication design, software engineering, and mechanics. In terms of teachers’ teaching quality, the overall teaching level of teachers in the 3 colleges and universities is at a medium level (3.06), among which G1 has the best teaching level, with an average score of 4.13. By communicating more with teachers from G1, teachers from the other two schools can enhance their teaching level. Developing the educational management of colleges and universities towards precision can be accomplished through the results of big data analysis.

While global climate risk is increasing substantially with greenhouse gas emission, energy transition is a key approach to alleviate this concern. This paper constructs the difference in difference model (DID) and the difference-in-differences based propensity score matching model (PSM-DID) to estimate the effects of China’s Environmental Protection Tax Law on energy transition and its transmission path. The empirical results show that (1) China’s Environmental Protection Tax Law can promote energy transition through electrification, and this conclusion is still valid after a series of robustness tests. (2) China’s Environmental Protection Tax Law can facilitate energy transition by improving both the quantity and the quality of industrial structure upgrading. The findings of this paper not only enrich the literature on the energy transition and environmental tax designs in developing countries but also provide an empirical reference for the government to promote energy transition orderly by implementing environmental tax.