The investigation of learners’ interlanguage could greatly contribute to the teaching of English as a foreign language and the development of teaching materials. The present study investigates the collocational profiles of large-scale written production by English learners with varied L1 backgrounds and different proficiency levels. Using the British National Corpus as reference corpus, learners’ collocation use was extracted by corpus query language and further identified by t-score via Python programming language. The collocation list consists of 2,501 make/take + noun (the direct object) collocations. Findings show that proficient learners tend to use collocations containing more semantically complicated and abstract noun elements for varied communication tasks. Moreover, advanced learners are inclined to use collocations comprised of more difficult and longer noun elements.
The antioxidant potential of Actinidia macrosperma C. F. Liang (Actinidiaceae) was investigated in vitro for total phenolic content, along with total antioxidant activity (TAA), 1,1-diphenyl 2-picryl hydrazyl (DPPH), and lipid peroxidation (LP). The results indicated that different polarity extracts of A. macrosperma exhibit different biological activities, which depends mainly on the presence of phenolic compounds. The antioxidant activity was in the following decreasing order: MeOH extract > EtOAc extract > aqueous extract > CHCl3 extract > Hexane extract. Moreover, the cytotoxic activity of this plant by MTT dye assay using SMMC-7721 has been determined also. The hexane, EtOAc, and CHCl3 extracts showed cytotoxicity in a dose-dependent manner. Methanol and aqueous extracts, however, showed weak activities in this test. And a very significant cytotoxic activity, not significantly different from the positive control of quercetin, was observed in CHCl3 extract.
The rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-translation (MT) with human translation (HT), considering the metadiscoursal interactional features. The study uses a parallel corpus consisting of 79 texts translated from Chinese to English by professional human translators and machine translations (Baidu translate & Google translate) and a comparable reference corpus of non-translated English text. The statistical analysis revealed no statistically significant difference between Baidu and Google translate regarding all types of metadiscoursal indicators. However, the findings of this study demonstrate significant disparities in the interactional characteristics of various HT and MT groups. Compared to the metadiscourse features in non-translated English political texts, human translators were found to outperform machine translations in the use of attitude markers. In contrast, the distribution of directives in machine-translated texts is more native-like. In addition, MT and HT have utilized a significantly smaller number of hedges, self-mention, and readers than non-translated texts. Our results indicate that the MT systems, though still calling for further improvement, have shown tremendous growth potential and may complement human translators.
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