SummaryHuman embryonic stem cells (hESCs) are promising in regenerative medicine. Although several hESC-based clinical trials are under way, a widely accepted standard of clinical-grade cells remains obscure. To attain a completely xeno-free clinical-grade cell line, the system must be free of xenogenic components, the cells must have a comprehensive set of functions, and good manufacturing practice conditions must be used. In this study, following these criteria, we successfully derived two hESC lines, which were thereby considered “clinical-grade embryonic stem cells”. In addition to the primary capacity for pluripotency, these two cell lines were efficiently differentiated into various types of clinical-grade progeny. Importantly, the cells were recognized by the National Institutes for Food and Drug Control of China for further eligible accreditation. These data indicate that we have established completely xeno-free clinical-grade hESC lines and their derivatives, which will be valuable for the foundation of an international standard for clinical-grade cells for therapy.

Background The efficiency of conventional English teaching quality evaluation is comparatively small, and evaluation statistics are challenging. To investigate the use of artificial intelligence (AI) technology in teacher teaching assessment, a machine learning algorithm is proposed to create a teaching evaluation model suitable for the current educational model to assist colleges and universities in overcoming existing teaching challenges. Objectives The proposed Machine learning‐based Gaussian process model (MLGPM) improves the student's language skills. The proposed model uses Gaussian mixed model to express the circulation features of samples and enhances the support vector machine. Therefore, this paper suggests an active learning algorithm that, in association with Gaussian mixed model and sparse Bayesian learning, strategically chooses and labels samples to construct a classifier that syndicates the distribution characteristics of the samples. As a result, the accuracy of a considered quality index for English classrooms is verified, and the quality and control of English as a foreign language can be enhanced. Results The experiment results show that the model presented in this study is effective and beneficial when assessing the efficiency of teaching in universities and analyzing big data sets. Conclusion The simulation analysis with student performance improvement in English teaching quality using machine learning high fluency rate of 95.3, high accuracy ratio of 98.1%, improve vocabulary prediction ratio of 94.6%, improve passage prediction ratio of 92.7%, enhance learning rate of 95.2%, reduce the error rate of 24.1%, F1‐score of 91.5% and assessment score of 92.1% when compared with other methods.

In this paper, confocal micro-Raman spectrum (CMR) was used to investigate the distribution of main components (ferulic acid and nicotinic acid) in Radix Angelicae sinensis (RAS). The ferulic acid and nicotinic acid were scanned to obtain their Raman spectra of single component; the RAS from different origins were scanned to obtain the Raman spectrum of RAS, and the Raman spectrum was compared with single component. The Raman spectrum of a single component was introduced into the map review to localize ferulic acid and nicotinic acid in RAS. In the results, the RAS from different origins showed strong similarity, with obvious characteristic peaks at 120 cm–1 140 cm–1210 cm–1,480 cm–1 and 1050 cm–1. The characteristic peaks of ferulic acid are mainly concentrated in the spectrum range of 100-1100 cm1 with obvious characteristic peaks around 160 cm150 cm–1 and 1800 cm–1 while the peaks of nicotinic acid mainly concentrated in the spectrum range of 100-1100 cm–1, with obvious characteristic peaks around 120 cm–1, 140 cm–1,810 cm–1and 1050 cm–1 The red and the green color represent the locations of ferulic acid and nicotinic acid, respectively. In conclusion, CMR can be used to establish the Raman fingerprint library of RAS for a quick and accurate identify, and locate the known active components.

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