287 research outputs found

    Global dynamics in a chemotaxis model describing tumor angiogenesis with/without mitosis in any dimensions

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    In this work, we study the Neumann initial boundary value problem for a three-component chemotaxis model in any dimensional bounded and smooth domains; this model is used to describe the branching of capillary sprouts during angiogenesis. First, we find three qualitatively simple sufficient conditions for qualitative global boundedness, and then, we establish two types of global stability for bounded solutions in qualitative ways. As a consequence of our findings, the underlying system without chemotaxis and the effect of ECs mitosis can not give rise to pattern formations. Our findings quantify and extend significantly previous studies, which are set in lower dimensional convex domains and are with no qualitative information.Comment: 43 pages, under review in a journa

    High sensitivity HI image of diffuse gas and new tidal features in M51 observed by FAST

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    We observed the classical interacting galaxy M51 with FAST and obtain high sensitivity HI image with column density down to 3.8 ×\times 1018^{18} cm2^{-2}. In the image we can see a diffuse extended envelope around the system and several new tidal features. We also get a deeper look at M51b's probable gas, which has an approximated velocity range of 560 to 740 km s1^{-1} and a flux of 7.5 Jy km s1^{-1}. Compared to the VLA image, we observe more complete structures of the Southeast Tail, Northeast Cloud and Northwest Plume, as well as new features of the Northwest Cloud and Southwest Plume. M51's most prominent tidal feature, the Southeast Tail, looks very long and broad, in addition with two small detached clouds at the periphery. Due to the presence of optical and simulated counterparts, the Northwest cloud appears to be the tail of M51a, while the Northwest Plume is more likely a tidal tail of M51b. The large mass of the Northwest Plume suggests that M51b may have been as gas-rich as M51a before the interaction. In addition, the formation process of the Northeast Cloud and Southwest Plume is obscured by the lack of optical and simulated counterparts. These novel tidal features, together with M51b's probable gas, will inspire future simulations and provide a deeper understanding of the evolution of this interacting system.Comment: 11 pages, 9 figures, accepted for publication in MNRA

    Prevalence and genotyping of Norovirus in environment and food handlers of catering services and hotels

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    Objective To investigate the prevalence and genotyping of Norovirus in environment and food handlers in catering services and hotels. Methods A total of 40 catering services and 10 hotels were selected as the sampling sites in this study and 4 environment samples and 2 food-handler fecal samples were collected from each site. RNA was extracted and preliminary analyzed for Norovirus by real-time polymerase chain reaction (PCR). Partial opening reading frames 1 (ORF1) sequences were amplified by reverse transcription-polymerase chain reaction (RT-PCR), followed by sequence and phylogenetic analysis. Results One mop sink swab out of 200 environment samples (0.5%, 1/200) and 3 out of 100 food handlers fecal samples (3.0%, 3/100) were positive for Norovirus. The genotyping of Norovirus revealed that one belonged to GII. 17 genotype and two belonged to GI. 3 genotype. Conclusion The transmission risk of Norovirus in catering services and hotels should be paid more attention to and hygienic management should be strengthened. Health education of food handlers to prevent the transmission of Norovirus should be strengthened

    Label-free Node Classification on Graphs with Large Language Models (LLMS)

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    In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN
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