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The present study examined the mediating role of anxiety, depression, sleepiness, insomnia, and sleep quality in the association between problematic social media use and quality of life (QoL) among patients with cancer. This cross-sectional survey study recruited 288 patients with cancer to respond to measures on anxiety, depression, sleepiness, insomnia, sleep quality, problematic social media use, and QoL. Structural Equation Modeling was used for the mediation analysis. There were significant relationships between all of the variables used in the study. It was revealed that problematic social media use did not directly influence the QoL of patients with cancer except via anxiety, depression, sleepiness, and insomnia. Sleep quality did not mediate the association between problematic social media use and QoL. Healthcare workers managing cancer should pay attention to the mental health needs of their patients even as they treat their cancer so as to improve their quality of life. Future studies may examine other variables that affect the QoL of patients with cancer as well as other mediating and moderating variables. [ABSTRACT FROM AUTHOR]

Background: Single Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpose of extracting mutations and diseases from texts. However, there is no available corpus, for extracting associations from texts, that is annotated with linguistic-based negation, modality markers, neutral candidates, and confidence level of associations. Method: In this research, different steps were presented so as to produce the SNPPhenA corpus. They include automatic Named Entity Recognition (NER) followed by the manual annotation of SNP and phenotype names, annotation of the SNP-phenotype associations and their level of confidence, as well as modality markers. Moreover, the produced corpus was annotated with negation scopes and cues as well as neutral candidates that play crucial role as far as negation and the modality phenomenon in relation to extraction tasks. Result: The agreement between annotators was measured by Cohen's Kappa coefficient where the resulting scores indicated the reliability of the corpus. The Kappa score was 0.79 for annotating the associations and 0.80 for the confidence degree of associations. Further presented were the basic statistics of the annotated features of the corpus in addition to the results of our first experiments related to the extraction of ranked SNP-Phenotype associations. The prepared guideline documents render the corpus more convenient and facile to use. The corpus, guidelines and inter-annotator agreement analysis are available on the website of the corpus: http://nil.fdi.ucm.es/?q=node/639. Conclusion: Specifying the confidence degree of SNP-phenotype associations from articles helps identify the strength of associations that could in turn assist genomics scientists in determining phenotypic plasticity and the importance of environmental factors. What is more, our first experiments with the corpus show that linguistic-based confidence alongside other non-linguistic features can be utilized in order to estimate the strength of the observed SNP-phenotype associations. Trial Registration: Not Applicable. [ABSTRACT FROM AUTHOR]

Wordnets are an effective resource for natural language processing and information retrieval, especially for semantic processing and meaning related tasks. So far, wordnets have been constructed for many languages. However, the automatic development of word- nets for low-resource languages has not been well studied. In this paper, an Expectation- Maximization algorithm is used to create high quality and large scale wordnets for poor- resource languages. The proposed method benefits from possessing cross-lingual word sense disambiguation and develops a wordnet by only using a bi-lingual dictionary and a mono- lingual corpus. The proposed method has been executed with Persian language and the resulting wordnet has been evaluated through several experiments. The results show that the induced wordnet has a precision score of 90% and a recall score of 35%. [ABSTRACT FROM AUTHOR]