Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.Comment: AAAI-23 main trac

Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution

Current Scene text image super-resolution approaches primarily focus on extracting robust features, acquiring text information, and complex training strategies to generate super-resolution images. However, the upsampling module, which is crucial in the process of converting low-resolution images to high-resolution ones, has received little attention in existing works. To address this issue, we propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. Unlike previous graph attention mechanisms, our approach achieves 2-3 orders of magnitude improvement in efficiency and memory utilization by eliminating the dependency on sparse adjacency matrices and introducing a sliding window approach for efficient parallel computation. Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details. Comprehensive experiments on TextZoom demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy. For single-stage and multi-stage strategies, we achieved improvements of 0.7\% and 2.6\%, respectively, increasing the performance from 52.6\% and 53.7\% to 53.3\% and 56.3\%. The code is available at https://github.com/wenyu1009/RTSRN

Investigation on Natural Infection of Covert Mortality Nodavirus in Farmed Giant Freshwater Prawn (Macrobrachium rosenbergii)

Covert mortality nodavirus (CMNV), from the Nodaviridae family, is characterized by its unique cross-species transmission and wide epidemic distribution features. In this study, Macrobrachium rosenbergii was proved to be infected naturally by CMNV, which further expand the known host range of CMNV. Here, 61.9% (70/113) of the M. rosenbergii samples collected from Jiangsu Province were CMNV positive in the TaqMan RT-qPCR assay, which indicated the high prevalence of CMNV in M. rosenbergii. Meanwhile, the sequences of CMNV RdRp gene cloned from M. rosenbergii were highly identical to that of the original CMNV isolate from Penaeus vannamei. In situ hybridization (ISH) and histology analysis indicated that the intestine, gill, hepatopancreas and ovary were the targeted organs of CMNV infection in M. rosenbergii, and obvious histopathological damage including vacuolation and karyopyknosis were occurred in the above organs. Notably, the presence of CMNV in gonad alerted its potential risk of vertical transmission in M. rosenbergii. Additionally, numerous CMNV-like particles could be observed in tissues of hepatopancreas and gill under transmission electron microscopy. Collectively, our results call for concern of the potential negative impact of the spread and prevalence of CMNV in M. rosenbergii on its aquaculture, as well as providing a renewed orientation for further investigation and exploration of the diverse pathogenic factors causing M. rosenbergii diseases

Solution-processed quantum-dot light-emitting diodes combining ultrahigh operational stability, shelf stability, and luminance

Abstract The shelf-stability issue, originating from the ZnO-induced positive aging effect, poses a significant challenge to industrializing the display technology based on solution-processed quantum-dot light-emitting diodes (QLEDs). Currently, none of the proposed solutions can simultaneously inhibit exciton quenching caused by the ZnO-based electron-transporting layer (ETL) and retain other advantages of ZnO. Here in this work, we propose a bilayer design of ETL in which a buffer layer assembled of SnO2 nanoparticles (NPs) suppresses the QD-ETL exciton quenching and tunes charge balance while ZnO NPs provide high electron conductivity. As a result, the bottom-emitting QLED combining capped ZnO and SnO2 buffer exhibit a maximum luminance over 100,000 cd m−2 and a T 95 operational lifetime averaging 6200 h at 1000 cd m−2 on the premise of entirely inhibiting positive aging

The Protective Effect of Esculentoside A on Experimental Acute Liver Injury in Mice

<div><p>Inflammatory response and oxidative stress are considered to play an important role in the development of acute liver injury induced by carbon tetrachloride (CCl₄) and galactosamine (GalN)/lipopolysaccharides (LPS). Esculentoside A (EsA), isolated from the Chinese herb phytolacca esculenta, has the effect of modulating immune response, cell proliferation and apoptosis as well as anti-inflammatory effects. The present study is to evaluate the protective effect of EsA on CCl₄ and GalN/LPS-induced acute liver injury. In vitro, CCK-8 assays showed that EsA had no cytotoxicity, while it significantly reduced levels of TNF-α and cell death rate challenged by CCl₄. Moreover, EsA treatment up-regulated PPAR-γ expression of LO2 cells and reduced levels of reactive oxygen species (ROS) challenged by CCl₄. In vivo, EsA prevented mice from CCl₄-induced liver histopathological damage. In addition, levels of AST and ALT were significantly decreased by EsA treatment. Furthermore, the mice treated with EsA had a lower level of TNF-α, Interleukin (IL)-1β and IL-6 in mRNA expression. EsA prevented MDA release and increased GSH-Px activity in liver tissues. Immunohistochemical staining showed that over-expression of F4/80 and CD11b were markedly inhibited by EsA. The western bolt results showed that EsA significantly inhibited CCl₄-induced phosphonated IkBalpha (P-IκB) and ERK. Furthermore, EsA treatment also alleviated GalN/LPS-induced acute liver injury on liver enzyme and histopathological damage. Unfortunately, our results exhibited that EsA had no effects on CCl₄-induced hepatocyte apoptosis which were showed by TUNEL staining and Bax, Caspase-3 and cleaved Caspase-3 expression. Our results proved that EsA treatment attenuated CCl₄ and GalN/LPS-induced acute liver injury in mice and its protective effects might be involved in inhibiting inflammatory response and oxidative stress, but not apoptosis with its underlying mechanism associated with PPAR-γ, NF-κB and ERK signal pathways.</p></div

Effects of EsA on CCl₄-induced LO₂ cell injury and PPAR-γ expression.

<p>The treatment effects of EsA and protein expression of PPAR-γ were measured using western blot (A). Levels of ROS in LO2 cells challenged by CCl₄ were shown (B Magnification, 200×). The mRNA expression of PPAR-γ was measured using quantitative real-time PCR (C). The values presented are the means ± standard error of the mean (n = 5). *P<0.05, **P<0.01.</p