104 research outputs found
Propaganda visualizations of Chinese communist party in posters and magazine covers during 1989-2009
Get PDFThis research-based thesis is to discover the development of visualizations of political ideology and the utilization of visual language in contemporary Chinese propaganda posters and magazine covers during 1989-2009 (including 1989). The chosen set of poster cases contains posters that were published only by the party’s propaganda organs. The set of magazine cover cases contains a Chinese state-level magazine “China Pictorial” aimed for commercial circulation. It can be purchased by every Chinese citizen in book stores in China. In general, the author aims to discover how visual language is applied in political propaganda in two different media and to discover what kind of visual rhetoric is used in contemporary Chinese political propaganda.
The author has applied content analysis, semiology and Marja Seliger’s visual rhetoric theory (2008) as research methods to conduct the visual research on 210 visual cases in total including both propaganda posters and covers of “China Pictorial”.
Through the visual content analysis, the author finds out that there are three types of visual signs applied in research material. They are “iconic sign”, “indexical sign” and “symbolic sign”. Moreover, the author also discovers that the Chinese Communist Party’s propaganda organ has applied different symbolic actions in posters and magazine covers to construct various visual arguments. These visual arguments can be concluded in five reflexive themes. The author finds out that the five themes are ‘China’s modernization’, ‘China’s technological progression and competence’, ‘the excellence of the Chinese Communist Party’, ‘happy Chinese people’ and ‘the glories of the socialist China’. In addition to that, the author discovers “brand rhetoric”, “personalized rhetoric” and “poetic rhetoric” in the five reflexive themes
Prediction of amyloid fibril-forming segments based on a support vector machine
Get PDFAbstract Background Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets. Results We propose a method, named Pafig (Prediction of amyloid fibril-forming segments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids – alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids – aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies. Conclusion The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.</p
Predicting changes in protein thermostability brought about by single- or multi-site mutations
No full textAbstract Background An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔG) of thermal denaturation. Results We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations. Conclusions The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.</p
News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model
No full textThis paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted
Enhanced and preferential optical trapping in a slot-graphite photonic crystal (Conference Presentation)
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Presyncodon, a Web Server for Gene Design with the Evolutionary Information of the Expression Hosts
No full textIn the natural host, most of the synonymous codons of a gene have been evolutionarily selected and related to protein expression and function. However, for the design of a new gene, most of the existing codon optimization tools select the high-frequency-usage codons and neglect the contribution of the low-frequency-usage codons (rare codons) to the expression of the target gene in the host. In this study, we developed the method Presyncodon, available in a web version, to predict the gene code from a protein sequence, using built-in evolutionary information on a specific expression host. The synonymous codon-usage pattern of a peptide was studied from three genomic datasets (Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae). Machine-learning models were constructed to predict a selection of synonymous codons (low- or high-frequency-usage codon) in a gene. This method could be easily and efficiently used to design new genes from protein sequences for optimal expression in three expression hosts (E. coli, B. subtilis, and S. cerevisiae). Presyncodon is free to academic and noncommercial users; accessible at http://www.mobioinfor.cn/presyncodon_www/index.html
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