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Entity Linking is the task of assigning entities from a Knowledge Base to textual mentions of such entities in a document. State-of-the-art approaches rely on lexical and statistical features which are abundant for popular entities but sparse for unpopular ones, resulting in a clear bias towards popular entities and poor accuracy for less popular ones. In this work, we present a novel approach that is guided by a natural notion of semantic similarity which is less amenable to such bias. We adopt a unified semantic representation for entities and documents-the probability distribution obtained from a random walk on a subgraph of the knowledge base-which can overcome the feature sparsity issue that affects previous work. Our algorithm continuously updates the semantic signature of the document as mentions are disambiguated, thus focusing the search based on context. Our experimental evaluation uses well-known benchmarks and different samples of a Wikipedia-based benchmark with varying entity popularity; the results illustrate well the bias of previous methods and the superiority of our approach, especially for the less popular entities.

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BackgroundFormer Detroit Red Wing Gordie Howe received stem cell (SC) treatment in Mexico in December 2014 for a stroke he suffered in October 2014. The news about his positive response to the SC treatment prompted discussion on social networks like Twitter. ObjectiveThis study aims to provide information about discussions that took place on Twitter regarding Howe’s SC treatment and SC treatment in general. In particular, this study examines whether tweets portrayed a positive or negative attitude towards Howe’s SC treatment, whether or not tweets mention that the treatment is unproven, and whether the tweets mention risks associated with the SC treatment.MethodsThis is an infodemiology study, harnessing big data published on the Internet for public health research and analysis of public engagement. A corpus of 2783 tweets about Howe’s SC treatment was compiled using a program that collected English-language tweets from December 19, 2014 at 00:00 to February 7, 2015 at 00:00. A content analysis of the corpus was conducted using a coding framework developed through a two-stage process.Results78.87% (2195/2783) of tweets mentioned improvements to Howe’s health. Only one tweet explicitly mentioned that Howe’s SC treatment was unproven, and 3 tweets warned that direct-to-consumer SC treatments lacked scientific evidence. In addition, 10.31% (287/2783) of tweets mentioned challenges with SC treatment that have been raised by scientists and researchers, and 3.70% (103/2783) of tweets either defined Howe as a “stem cell tourist” or claimed that his treatment was part of “stem cell tourism”. In general, 71.79% (1998/2783) of tweets portrayed a positive attitude towards Howe’s SC treatment.ConclusionsOur study found the responses to Howe’s treatment on Twitter to be overwhelmingly positive. There was far less attention paid to the lack of scientific evidence regarding the efficacy of the treatment. Unbalanced and uncritical discussion on Twitter regarding SC treatments is another example of inaccurate representations of SC treatments that may create unrealistic expectations that will facilitate the market for unproven stem cell therapies.

ABSTRACT. Verticillium wilt is one of the main diseases in cotton (Gossypium hirsutum), severely reduces yield and fiber quality, and is difficult to be controlled effectively. At present, the molecular mechanism that confers resistance to this disease is unclear. Transcriptome sequencing is an important method to detect resistance genes, explore metabolic pathways, and study resistance mechanisms. In this study, the transcriptome of a disease-resistant inbred cotton line inoculated with Verticillium dahliae was sequenced. A total of 126,402 unigenes were obtained using de novo assembly and data analysis, 99,712 (78.88%) of which were annotated into the Nr, Nt, Swiss-Prot, KEGG, COG, and GO databases. The expression patterns of 16 candidate disease-resistance genes showed that some genes were upregulated soon after V. dahliae inoculation and others were upregulated later, which may indicate instantaneous basal defense and lagged specific defense, respectively. We conducted a preliminary analysis of the transcriptome database, which will contribute to further research regarding the cloning of disease-resistance genes.