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The article describes the revolutionary new ways of communicating the written word when moving from the Medieval to Modern period in Europe, primarily the use of paper and moveable typeface printing and how these catalysed important cultural developments. A similar revolution has taken place in the last 50 years with the development of the internet. The article looks at how scientific publishing has changed with electronic publishing, including the development of Open Access. The journal Heritage Science is placed into this context. Especially important for scientific journals in an era when anyone can post anything on a webpage, is maintaining standards through high quality refereeing which distinguishes formal scientific literature from informal websites. [ABSTRACT FROM AUTHOR]

Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines. [ABSTRACT FROM AUTHOR]

Objective: To assess the association between Cannabis use and bladder cancer. Methods: A systematic literature review was performed using studies published in electronic databases including PubMed, MEDLINE, and Google Scholar. Due to the scarcity of literature on this topic, the search was not limited to a specific design, year of publication, or human studies. The studies were screened by two reviewers in the following steps; first, the studies were discovered according to the predetermined search strategy; second, the unrelated studies and duplicates were eliminated by screening the abstracts, titles, and keywords; third, the full text of relevant and eligible papers were critically appraised and assessed for the risk of bias using the respective tool. The two review authors independently assessed the risk of bias and outcome levels using the Newcastle-Ottawa Scale for the outcomes in observational studies. Any disagreements were settled by a third party. Results: The search strategy yielded 39 research articles. After removing 21 duplicates, 18 publications were eligible for title and abstract review. Thirteen studies were found to be irrelevant and subsequently excluded. Only three full-text articles were evaluated and included in the qualitative synthesis. Conclusions: The role of Cannabis in bladder cancer has been seldom studied. The small number of studies show contradictory findings; potential carcinogenic versus protective effect. The growing interest in Cannabis use after legalization necessitates further investigations with a robust design to assess the long-term effect of Cannabis on bladder cancer. [ABSTRACT FROM AUTHOR]