On the Construction of China’s Image in D.C.Lau’s Translation of Tao Te Ching

Imagology is concerned with how identity and image are gradually formed, spread, strengthened and even shaped in a specific social and historical space. Translation is one of the important means and carriers of image construction and shaping. As a classic work of Taoism, Tao Te Ching contains infinite images of China. The English version of Tao Te Ching, translated by Chinese Sinologist D. C. Lau, is among the top 10 best-sellers on Amazon in the United States and has been widely praised by readers. D. C. Lau’s English translation of Tao Te Ching introduced Chinese Taoism to the West, and at the same time constructed an objective and true image of ancient China. Taking the English version of Tao Te Ching translated by D. C. Lau as an example, this paper analyzes the construction of China’s image in the English translation of Chinese classics from the perspective of Imagology on the level of intrinsic textual analysis. It is found that D.C. Lau’s translation constructed objective and true China’s image. Through his translation, a relatively serious, solemn and orthodox Chinese image, the ancient Chinese people’s wise image, ancient Chinese image of filial piety and fraternal kindness as well as the image of eagerness of peace and pursuit of harmonious and equal relationship between countries were re-presented

A Scientometirc Analysis on Translation Studies of Tao Te Ching in China Based on CiteSpace

Based on the data of research papers published between 1988 to 2023, which were retrieved from China National Knowledge Infrastructure (CNKI), this paper makes a scientometric analysis of the research focuses of English translations studies of Tao Te Ching in China by employing CiteSpace. The findings were presented in knowledge domains. Aiming to uncover the research development and hot-discussed topics, the paper probes into the number of published articles, the high-impact authors and institutions, high-frequency keywords. It is found that translator research and translation strategies are the focuses of this research field. Current research mainly focuses on three aspects: translators such as Roger T. Ames, Lin Yutang, D. C. Lau, and Legge; translation strategies such as domestication and foreignization; English translation of key words such as Tao

Cyberemotions in the Era of New Media

The introduction of new media has accelerated changes in the generation and delivery of information. Cyberemotions, as a new type of public opinion, can be more contagious and propagate in more diverse ways than traditional public sentiments. The emotions of internet users have a rising influence on the progression of recent significant social events. In order to spark additional discussion on online sentiment modulation and online public opinion tracking, this article presented an overview of cyberemotions definition and key analytical methodologies in existing online sentiment research, as well as a synopsis of major components of cyberemotions

The Effectiveness of English Writing Teaching in Junior Middle School Based on Production-Oriented Approach

Production-oriented Approach (POA) proposed by Chinese scholar Wen Qiufang has been widely used in English teaching in recent years, but there are few studies on its application in junior middle school English teaching. This study analyzed the impact of the application of Production-oriented Approach on junior middle school students’ English learning attitude and English writing performance. In the experimental design, both quantitative and qualitative methods were adopted. Writing tests, questionnaire, interview were used as instruments. A total of 116 Chinese students from Year 8 of Yangzhou Shiyan Junior Middle School, Jiangsu Province in China were invited to participate in an 8-week pre- and post-test experiment. By comparing the writing scores before and after the test, it is found that the English writing scores of the students in the experimental class are higher than those of the students in the control class. Through the analysis of the results of questionnaires and interview, it is found that the students’ attitude towards English writing teaching in the experimental class has improved significantly. The implications and suggestions for dissemination and implementation of POA for junior middle school students are discussed

Protein complex detection with semi-supervised learning in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.</p> <p>Results</p> <p>Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.</p> <p>Conclusions</p> <p>Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.</p

Doxorubicin@Bcl-2 siRNA core@shell nanoparticles for synergistic anticancer chemotherapy

Acquired drug resistance in malignant tumors seriously hinders effective chemotherapy against cancer. The main mechanisms of drug resistance include decreased drug influx, increased drug efflux, as well as antiapoptotic defense behavior in cancerous cells. To overcome these issues, we have designed a nanomedicine composed of pure doxorubicin (DOX) as the core and B-cell lymphoma-2 (Bcl-2) siRNA as the shell for synergistic cancer treatment. Between the core and shell, polyethylene glycol (PEG) and polyethylenimine (PEI) are employed to increase the stability of the core DOX NPs and facilitate siRNA coating, respectively. In this design, the siRNA is able to inhibit the expression of Bcl-2 protein which has a role of protecting cancer cells from apoptosis. DOX not only is for anticancer therapy but also acts as a nanocarrier for Bcl-2 siRNA delivery. Our studies show that Bcl-2 siRNA and DOX are efficiently delivered into tumor cells and tumor tissues, and such a codelivery nanosystem possesses synergistic effects on tumor inhibition, enabling significantly enhanced antitumor outcome. This work demonstrates that the codelivery of tumor-suppressive Bcl-2 siRNA and chemotherapeutic agents without using an excipient material as a drug carrier represents a promising therapy for enhanced cancer therapy

Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses