A Critical Discourse Analysis of Cyberbullying Language Based on Text-mining Techniques——A Case Study of Prince Harry and Meghan Markle

With the development of AI,a large amount of cyberbullying language flooded into the Internet.Cyberbullying language damages the online environment and causes mental harm to the victims of online bullying.Many researchers,therefore,have paid much attention to the problem.This study collected online comments targeted at Prince Harry and Meghan Markle as a corpus and then analyzed text data based on Critical Discourse Analysis by using text-mining tools to explore the factors that contribute to the social ideological effects of the cyberbullying language.The research results show that cultural differences,prejudice,or social exclusion due to race or gender form cyberbullying on social media.

Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach

Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine,because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment approaches.One of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic development.Systematic accumulation of various types of biological data,including interactions between proteins,regulation of genes transcription,proteomics,and metabolomics experiments results,allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic targets.The aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components,as well as for identifying their roles in the development of various types of tumors.We automatically collect sets of abstract texts from the NCBI PubMed bibliographic database.For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach,while for all other proteins and genes machine learning method is used.For association extraction,we develop a set of semantic rules.We complete the results of the text analysis with the gene set enrichment analysis.The identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.

Melatonin modifies SOX2^+ cell proliferation in dentate gyrus and modulates SIRT1 and MECP2 in long-term sleep deprivation

Melatonin is a pleiotropic molecule that,after a short-term sleep deprivation,promotes the proliferation of neural stem cells in the adult hippocampus.However,this effect has not been observed in long-term sleep deprivation.The precise mechanism exerted by melatonin on the modulation of neural stem cells is not entirely elucidated,but evidence indicates that epigenetic regulators may be involved in this process.In this study,we investigated the effect of melatonin treatment during a 96-hour sleep deprivation and analyzed the expression of epigenetic modulators predicted by computational text mining and keyword clusterization.Our results showed that the administration of melatonin under sleep-deprived conditions increased the MECP2 expression and reduced the SIRT1 expression in the dentate gyrus.We observed that let-7 b,mir-132,and mir-124 were highly expressed in the dentate gyrus after melatonin administration,but they were not modified by sleep deprivation.In addition,we found more Sox2^+/5-bromo-2’-deoxyuridine(BrdU)^+cells in the subgranular zone of the sleep-deprived group treated with melatonin than in the untreated group.These findings may support the notion that melatonin modifies the expression of epigenetic mediators that,in turn,regulate the proliferation of neural progenitor cells in the adult dentate gyrus under long-term sleep-deprived conditions.All procedures performed in this study were approved by the Animal Ethics Committee of the University of Guadalajara,Mexico(approval No.CI-16610)on January 2,2016.

A Bootstrapping-based Method to Automatically Identify Data-usage Statements in Publications

Purpose： Our study proposes a bootstrapping-based method to automatically extract data- usage statements from academic texts. Design/methodology/approach： The method for data-usage statements extraction starts with seed entities and iteratively learns patterns and data-usage statements from unlabeled text. In each iteration, new patterns are constructed and added to the pattern list based on their calculated score. Three seed-selection strategies are also proposed in this paper. Findings： The performance of the method is verified by means of experiments on real data collected from computer science journals. The results show that the method can achieve satisfactory performance regarding precision of extraction and extensibility of obtained patterns. Research limitations： While the triple representation of sentences is effective and efficient for extracting data-usage statements, it is unable to handle complex sentences. Additional features that can address complex sentences should thus be explored in the future. Practical implications： Data-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation. Originality/value： To the best of our knowledge, this paper is among the first to address the important task of automatically extracting data-usage statements from real data.

Kaphta:Text mining web tool to extract information on the anticancer activity of polyphenols

In this paper,we describe the application of Kaphta architecture,a resource for text mining of the anticancer activity of polyphenols.The anticancer activity of these compounds against different types of cancer has been widely reported in the literature and they are one of the most promising molecules for the development of anticancer drugs.The architecture,which comprises four sequential and well-defined steps,uses a hybrid approach composed of a dictionary,rules and machine learning to identify abstracts containing sentences with associations between polyphenol,cancer and gene entities.The application of the architecture on 23826 PubMed abstracts generated a knowledge base of indexed abstracts with 172169 sentences containing,polyphenol-cancer and polyphenol-gene associations.A Web tool was implemented that allowed the user to search for information on 2006 polyphenols,240 cancers and 3121 genes entities,and 11750 polyphenol-cancer and 9160 polyphenol-gene associations indexed in the knowledge base.A ranking algorithm calculates scores for each indexed abstract considering the number and type of sentences with entities and rules recognized.A test with users demonstrated that the visualization resources on the web tool contributes to the understanding of the association between polyphenols,genes and cancers,in comparison with the PubMed Tool.The Kaphta architecture and web tool permits to extract knowledge on the anticancer activity of polyphenols and can thus contribute to the exploration of these molecules in the development of anticancer therapies.

HCSGD：An integrated database of human cellular senescence genes

Cellular senescence is an irreversible cell cycle arrest program in response to various exogenous and endogenous stimuli like telomere dysfunction and DNA damage.It has been widely accepted as an antitumor program and is also found closely related to embryo development,tissue repair,organismal aging and age-related degenerative diseases.In the past decades,numerous efforts have been made to uncover the gene regulatory mechanisms of cellular senescence.There is a strong demand to integrate these data from various resources into one open platform.To facilitate researchers on cellular senescence,we have developed Human Cellular Senescence Gene Database（HCSGD） by integrating multiple online published data sources into a comprehensive senescence gene annotation platform（http：//bioinfo.au.tsinghua.edu.cn/member/xwwang/HCSGD）.Potential Human Cellular Senescence Genes（HCSGS）were collected by combining information from published literatures,gene expression profiling data and Protein-Protein Interaction networks.Additionally,genes are annotated with gene ontology annotation and microRNA/drug/compound target information.HCSGD provides a valuable resource to visualize cellular senescence gene networks,browse annotated functional information,and retrieve senescenceassociated genes with a user-friendly web interface.