Predicting affinity ties in a surname network.
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ABSTRACT:
SUBMITTER: Mendoza M
PROVIDER: S-EPMC8412287 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveWe investigate surname affinities among areas of modern-day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions.Materials and methodsWe acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities.ResultsWe obtain a 10-layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter-distances and intra-distances of the communities and obtain meaningful regional ethnicity classification.DiscussionThe visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo-genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population.
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Project description:Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.
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Project description:The biological behavior of the Y chromosome, which is paternally inherited, implies that males sharing the same surname may also share a similar Y chromosome. However, socio-cultural factors, such as polyphyletism, non-paternity, adoption, or matrilineal surname transmission, may prevent the joint transmission of the surname and the Y chromosome. By genotyping 17 Y-STRs and 68 SNPs in ~2500 male samples that each carried one of the 50 selected Catalan surnames, we could determine sets of descendants of a common ancestor, the population of origin of the common ancestor, and the date when such a common ancestor lived. Haplotype diversity was positively correlated with surname frequency, that is, rarer surnames showed the strongest signals of coancestry. Introgression rates of Y chromosomes into a surname by non-paternity, adoption, and transmission of the maternal surname were estimated at 1.5-2.6% per generation, with some local variation. Average ages for the founders of the surnames were estimated at ~500 years, suggesting a delay between the origin of surnames (twelfth and thirteenth centuries) and the systematization of their paternal transmission. We have found that, in general, a foreign etymology for a surname does not often result in a non-indigenous origin of surname founders; however, bearers of some surnames with an Arabic etymology show an excess of North African haplotypes. Finally, we estimate that surname prediction from a Y-chromosome haplotype, which may have interesting forensic applications, has a ~60% sensitivity but a 17% false discovery rate.
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Project description:BackgroundeHealth literacy is defined as the ability to seek, find, understand, and appraise health information from electronic sources and apply knowledge gained to addressing or solving a health problem. Previous research has shown high reliance on both online and face-to-face interpersonal sources when sharing and receiving health information.ObjectiveIn this paper, we examine these interpersonal sources and their interplay with respondents' eHealth literacy and perceived health outcomes. Specifically, we look at how the relationship between eHealth literacy and health outcomes is moderated by (1) finding help while performing online activities, (2) finding others with similar health concerns online, and (3) the importance of finding others with similar health concerns for people from ethnic minorities, specifically Palestinian citizens of Israel versus Israeli Jews.MethodsWe used a nationally representative random-digit dial telephone household survey of an Israeli adult population (age ?21 years, N=819). The collected data were analyzed using two regression models. The first examined how the correlation between eHealth literacy and perceived outcomes was moderated by the availability of help. The second examined how the correlation between eHealth literacy and perceived outcomes was moderated by finding others with similar health concerns and by ethnicity.ResultsRespondents with low eHealth literacy who were able to recruit help when performing online activities demonstrated higher perceived health outcomes compared to similar respondents who did not find help. Respondents with low eHealth literacy, who were able to find others with similar health concerns (online), demonstrated higher perceived health outcomes when compared to similar respondents who did not find others with similar health concerns. Finally, finding similar others online was more helpful in enhancing health outcomes for ethnic minorities; Palestinian citizens of Israel gained more health benefits by finding similar others compared to Israeli Jews.ConclusionsAlthough the availability of help and the notion of ethnicity have been discussed extensively within the context of social capital and health, our findings offer initial evidence for the relevancy of these concepts when studying individuals' eHealth literacy. Specifically, our findings enable a better understanding of the role of social ties and ethnicity in moderating the interplay between eHealth literacy and perceived health outcomes. Given the increased importance of eHealth information, our findings enhance understanding of how social ties can potentially compensate for low eHealth literacy.
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