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  1. King B, Greenhill SJ, Reid LA, Ross M, Walworth M, Gray RD
    Sci Rep, 2024 Jun 28;14(1):14967.
    PMID: 38942799 DOI: 10.1038/s41598-024-65810-x
    The Philippines are central to understanding the expansion of the Austronesian language family from its homeland in Taiwan. It remains unknown to what extent the distribution of Malayo-Polynesian languages has been shaped by back migrations and language leveling events following the initial Out-of-Taiwan expansion. Other aspects of language history, including the effect of language switching from non-Austronesian languages, also remain poorly understood. Here we apply Bayesian phylogenetic methods to a core-vocabulary dataset of Philippine languages. Our analysis strongly supports a sister group relationship between the Sangiric and Minahasan groups of northern Sulawesi on one hand, and the rest of the Philippine languages on the other, which is incompatible with a simple North-to-South dispersal from Taiwan. We find a pervasive geographical signal in our results, suggesting a dominant role for cultural diffusion in the evolution of Philippine languages. However, we do find some support for a later migration of Gorontalo-Mongondow languages to northern Sulawesi from the Philippines. Subsequent diffusion processes between languages in Sulawesi appear to have led to conflicting data and a highly unstable phylogenetic position for Gorontalo-Mongondow. In the Philippines, language switching to Austronesian in 'Negrito' groups appears to have occurred at different time-points throughout the Philippines, and based on our analysis, there is no discernible effect of language switching on the basic vocabulary.
  2. King B, Yong HH, Borland R, Omar M, Ahmad AA, Sirirassamee B, et al.
    Tob Control, 2010 Dec;19(6):444-50.
    PMID: 20852322 DOI: 10.1136/tc.2009.034256
    OBJECTIVE: This study explored the extent to which Malaysian and Thai smokers believe "light" and menthol cigarettes are less harmful than "regular" cigarettes and the correlates of these beliefs.

    METHODS: The study used data from wave 1 of the International Tobacco Control Southeast Asia Survey. 2006 adult smokers (95.3% male) from Malaysia and 2000 adult smokers (94.5% male) from Thailand were interviewed face to face in 2005.

    RESULTS: 29% of Malaysian respondents reported currently smoking light cigarettes and 14% menthols, with 19% agreeing that lights are less harmful and 16% agreeing that menthols are less harmful. 38% of Thai respondents reported currently smoking light cigarettes and 19% menthols, with 46% agreeing that lights are less harmful and 35% agreeing that menthols are less harmful. Malaysian smokers reporting current use of light or menthol cigarettes were more likely to believe that they are less harmful. Reported use of lights did not relate to beliefs for Thai respondents. The belief that light and/or menthol cigarettes are less harmful was strongly related to the belief that they have smoother smoke.

    CONCLUSIONS: The experience of smoother smoke is likely to produce some level of belief in reduced harm, regardless of how brands are labelled and whether or not Federal Trade Commission FTC/International Organisation for Standardisation tar, nicotine and carbon monoxide yield figures are used.

  3. Arnaud E, Laporte MA, Kim S, Aubert C, Leonelli S, Miro B, et al.
    Patterns (N Y), 2020 Oct 09;1(7):100105.
    PMID: 33205138 DOI: 10.1016/j.patter.2020.100105
    Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.
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