Skeletal and dental indicators of health in the late mediaeval (12-15th century) population from Nin, southern Croatia

A comprehensive bioarchaeological study of the late mediaeval (12-15th century) skeletal sample from Nin was carried out in order to test the historically documented hypothesis that during the late mediaeval period Nin sustained a period of rapid development that resulted in it becoming one of the major urban centres on the eastern Adriatic coast. The analysed pathological changes (alveolar bone disease, dental caries, dental enamel hypoplasia, cribra orbitalia, periostitis, tuberculosis, Schmorl's nodes, vertebral osteoarthritis, and bone fractures) indicate a relatively good quality of life for the majority of the population from this late mediaeval site. A low prevalence of dental pathologies suggests an adequate diet while a low frequency of long bone trauma testifies to a relatively peaceful life for the inhabitants of mediaeval Nin. Increased urban development during this period resulted in a worsening of sanitary conditions most likely caused by overcrowding, which is reflected in the presence of tuberculosis and the relatively high frequencies of dental enamel hypoplasia and cribra orbitalia. An additional health concern for the late mediaeval inhabitants of Nin may have been the presence of malaria, as recorded in numerous historical sources. Comparison with other Croatian mediaeval skeletal samples suggests that the inhabitants of late mediaeval Nin experienced somewhat better living conditions than their contemporaries from other parts of Croatia

Link prediction on Twitter.

With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags