291 research outputs found
Global dynamics in a chemotaxis model describing tumor angiogenesis with/without mitosis in any dimensions
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
We observed the classical interacting galaxy M51 with FAST and obtain high
sensitivity HI image with column density down to 3.8 10
cm. 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
s and a flux of 7.5 Jy km s. 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
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)
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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