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BackgroundBased on extensive mitochondrial DNA (mtDNA) sequence data, we previously showed that the model of speciation among species of herring gull (Larus argentatus) complex was not that of a ring species, but most likely due more complex speciation scenario's. We also found that two species, herring gull and glaucous gull (L. hyperboreus) displayed an unexpected biphyletic distribution of their mtDNA haplotypes. It was evident that mtDNA sequence data alone were far from sufficient to obtain a more accurate and detailed insight into the demographic processes that underlie speciation of this complex, and that extensive autosomal genetic analysis was warranted.ResultsFor this reason, the present study focuses on the reconstruction of the phylogeographic history of a limited number of gull species by means of a combined approach of mtDNA sequence data and 230 autosomal amplified fragment length polymorphism (AFLP) loci. At the species level, the mtDNA and AFLP genetic data were largely congruent. Not only for argentatus and hyperboreus, but also among a third species, great black-backed gull (L. marinus) we observed two distinct groups of mtDNA sequence haplotypes. Based on the AFLP data we were also able to detect distinct genetic subgroups among the various argentatus, hyperboreus, and marinus populations, supporting our initial hypothesis that complex demographic scenario's underlie speciation in the herring gull complex.ConclusionsWe present evidence that for each of these three biphyletic gull species, extensive mtDNA introgression could have taken place among the various geographically distinct subpopulations, or even among current species. Moreover, based on a large number of autosomal AFLP loci, we found evidence for distinct and complex demographic scenario's for each of the three species we studied. A more refined insight into the exact phylogeographic history within the herring gull complex is still impossible, and requires detailed autosomal sequence information, a topic of our future studies.

As a key service of the future 6G network, healthcare digital twin is the virtual replica of a person, which employs Internet of Things (IoT) technologies and AI-powered models to predict the state of health and provide suggestions to a range of clinical questions. To support healthcare digital twins, the right cyber resilience technologies and policies must be applied and maintained to preserve cyber resilience. Vulnerability detection is a fundamental technology for cyber resilience in healthcare digital twins. Recently, deep learning (DL) has been applied to address the limitations of traditional machine learning in vulnerability detection. However, it is important to consider code context relationships and pay attention on the vulnerability related keywords for searching an IoT vulnerability in healthcare digital twins. Due to massive software and complexity of healthcare digital twin, a full automatic solution is really needed for assisting cyber resilience check in the real-world scenarios. This paper presents a novel scheme for recognising potential vulnerable functions to support healthcare digital twins. We develop a new deep neural model to capture bi-directional context relationships among the risky code keywords. A number of well-designed experiments are carried out on a large ground truth, which consists of tens of thousands of vulnerable and non-vulnerable functions from IoT related software. The results show our new scheme outperforms the state-of-the-art DL-based methods for vulnerability detection. INDEX TERMSInternet of Things (IoT), healthcare, digital twin, cyber resilience, lung cancer VOLUME xxx, 20xx