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
Exploitable vulnerabilities in software have attracted tremendous attention in recent years because of their potentially high severity impact on computer security and information safety. Many vulnerability detection methods have been proposed to aid code inspection. Among these methods, there is a line of studies that apply machine learning techniques and achieve promising results. This paper reviews 22 recent studies that adopt deep learning to detect vulnerabilities, aiming to show how they utilize state-ofthe-art neural techniques to capture possible vulnerable code patterns. Among reviewed studies, we identify four game changers that significantly impact the domain of deep learning-based vulnerability detection and provide detailed reviews of the insights, ideas, and concepts that the game changers have brought to this field of interest. Based on the four identified game changers, we review the remaining studies, presenting their approaches and solutions which either build on or extend the game changers, and sharing our views on the future research trends. We also highlight the challenges faced in this field and discuss potential research directions. We hope to motivate the readers to conduct further research in this developing but fast-growing field.INDEX TERMS deep learning, vulnerability detection.
A Gram-negative, non-spore-forming, yellow-pigmented bacterium, strain LQY-7 T , was isolated from activated sludge treating synthetic pyrethroid-manufacturing wastewater. The taxonomic status of the strain was determined using a polyphasic taxonomic approach. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain LQY-7 T was a member of the genus Flavobacterium but had low similarities with other species of this genus (95.0 % similarity with Flavobacterium indicum GPTSA100-9 T and ,94 % similarities with other Flavobacterium species). On the basis of phenotypic, genetic and phylogenetic data, strain LQY-7 T should be classified as a representative of a novel species of the genus Flavobacterium, for which the name Flavobacterium haoranii sp. nov. is proposed; the type strain is LQY-7 T (5ACCC 05409 T 5KCTC 23008 T ).The genus Flavobacterium was proposed by Bergey et al. (1923) and emended by Bernardet et al. (1996) to include Gram-negative, aerobic, rod-shaped, yellow-pigmented bacteria that are usually motile by gliding and have a DNA G+C content of 30-41 mol% (Bernardet & Bowman, 2006;Park et al., 2006). At the time of writing, the genus comprised about 60 recognized species isolated from diverse habitats such as fresh-and salt-water, diseased fish, soil, sediment and micromats.Cypermethrin is one of the most important synthetic pyrethroid pesticides and is widely used to control pests in cotton and vegetable crops. However, cypermethrin affects the central nervous system, causes allergic skin reactions, lymph node and spleen damage, and eye irritation. In addition, cypermethrin is highly toxic to fish and other aquatic organisms, as well as to bees. It has been classified as 'moderately hazardous' (Class II) by the World Health Organization and considered as a possible human carcinogen by the US Environmental Protection Agency. Microbes play significant roles in degrading and detoxifying cypermethrin residues in the environment (Kaufman et al., 1981;Roberts & Standen, 1981). In this paper, a cypermethrin-degrading bacterial strain, designated LQY-7 T , was isolated from activated sludge in a synthetic pyrethroid-manufacturing wastewater treatment facility (Yangnong Chemical Group Co., Jiangsu Province, China). The taxonomic status of this strain was determined using a polyphasic taxonomic approach. The data obtained suggest that the isolate represents a novel species of the genus Flavobacterium.For investigation of morphological features, strain LQY-7 T was cultivated aerobically on trypticase soy agar (TSA; Difco) at 30 u C. Cell morphology and dimensions were examined by light microscopy (BH-2; Olympus) and transmission electron microscopy (H-7650; Hitachi) using cells from an exponentially growing culture. Gram-staining was performed according to the classical Gram procedure (Buck, 1982). Gliding motility, production of flexirubintype pigments and adsorption of Congo red by colonies were investigated by the methods of Bernardet et al. (2002). Growth at various temperatures (4, 10, 15...
Background: Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyse the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems.
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