MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Some works are inspired by $k$-WL/FWL (Folklore WL) and design the corresponding neural versions. Despite the high expressive power, there are serious limitations in this line of research. In particular, (1) $k$-WL/FWL requires at least $O(n^k)$ space complexity, which is impractical for large graphs even when $k=3$; (2) The design space of $k$-WL/FWL is rigid, with the only adjustable hyper-parameter being $k$. To tackle the first limitation, we propose an extension, $(k,t)$-FWL. We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k,t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem. To tackle the second problem, we propose $k$-FWL+, which considers any equivariant set as neighbors instead of all nodes, thereby greatly expanding the design space of $k$-FWL. Combining these two modifications results in a flexible and powerful framework $(k,t)$-FWL+. We demonstrate $(k,t)$-FWL+ can implement most existing models with matching expressiveness. We then introduce an instance of $(k,t)$-FWL+ called Neighborhood$^2$-FWL (N$^2$-FWL), which is practically and theoretically sound. We prove that N$^2$-FWL is no less powerful than 3-WL, and can encode many substructures while only requiring $O(n^2)$ space. Finally, we design its neural version named N$^2$-GNN and evaluate its performance on various tasks. N$^2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%. Moreover, N$^2$-GNN achieves new SOTA results on the BREC dataset (71.8%) among all existing high-expressive GNN methods.Comment: Accepted to NeurIPS 202

Biocontrol and growth promotion potential of Bacillus velezensis NT35 on Panax ginseng based on the multifunctional effect

The Bacillus velezensis strain NT35, which has strong biocontrol ability, was isolated from the rhizosphere soil of Panax ginseng. The antifungal effects of the NT35 strain against the mycelium and spore growth of Ilyonectria robusta, which causes ginseng rusty root rot, were determined. The inhibitory rate of I. robusta mycelial growth was 94.12% when the concentration of the NT35 strain was 107 CFU·mL−1, and the inhibitory rates of I. robusta sporulation and spore germination reached 100 and 90.31%, respectively, when the concentration of the NT35 strain was 104 and 108 CFU·mL−1, respectively. Strain NT35 had good prevention effects against ginseng rust rot indoors and in the field with the control effect 51.99%, which was similar to that of commercial chemical and biocontrol agents. The labeled strain NT35-Rif160-Stre400 was obtained and colonized ginseng roots, leaves, stems and rhizosphere soil after 90 days. Bacillus velezensis NT35 can induce a significant increase in the expression of five defensive enzyme-encoding genes and ginsenoside biosynthesis-related genes in ginseng. In the rhizosphere soil, the four soil enzymes and the microbial community improved during different periods of ginseng growth in response to the biocontrol strain NT35. The NT35 strain can recruit several beneficial bacteria, such as Luteimonas, Nocardioides, Sphingomonas, and Gemmatimonas, from the rhizosphere soil and reduce the relative abundance of Ilyonectria, Fusarium, Neonectria and Dactylonectria, which cause root rot and rusty root rot in ginseng plants. The disease indices were significantly negatively correlated with the abundances of Sphingomonas and Trichoderma. Additionally, Sphingomonadales, Sphingomonadaceae and actinomycetes were significantly enriched under the NT35 treatment according to LEfSe analysis. These results lay the foundation for the development of a biological agent based on strain NT35

Synchronous post-acceleration of laser-driven protons in helical coil targets by controlling the current dispersion

Post-acceleration of protons in helical coil targets driven by intense, ultrashort laser pulses can enhance ion energy by utilizing the transient current from the targets’ self-discharge. The acceleration length of protons can exceed a few millimeters, and the acceleration gradient is of the order of GeV/m. How to ensure the synchronization between the accelerating electric field and the protons is a crucial problem for efficient post-acceleration. In this paper, we study how the electric field mismatch induced by current dispersion affects the synchronous acceleration of protons. We propose a scheme using a two-stage helical coil to control the current dispersion. With optimized parameters, the energy gain of protons is increased by four times. Proton energy is expected to reach 45 MeV using a hundreds-of-terawatts laser, or more than 100 MeV using a petawatt laser, by controlling the current dispersion

Enhancing 3D object detection through multi-modal fusion for cooperative perception

Fueled by substantial advancements in deep learning, the domain of autonomous driving is swiftly advancing towards more robust and effective intelligent systems. One of the critical challenges in this field is achieving accurate 3D object detection, which is often hindered by data sparsity and occlusion. To address these issues, we propose a method centered around a multi-modal fusion strategy that leverages vehicle-road cooperation to enhance perception capabilities. Our approach integrates label information from roadside perception point clouds to harmonize and enrich the representation of image and LiDAR data. This comprehensive integration significantly improves detection accuracy by providing a fuller understanding of the surrounding environment. Rigorous evaluations of our proposed method on two benchmark datasets, KITTI and Waymo Open, demonstrate its superior performance, with our model achieving 87.52% 3D Average Precision (3D AP) and 93.71% Bird’s Eye View Average Precision (BEV AP) on the KITTI val set. These results highlight the effectiveness of our method in detecting sparse and distant objects, contributing to the development of safer and more efficient autonomous driving solutions

Appropriate water and fertilizer supply can increase yield by promoting growth while ensuring the soil ecological environment in melon production

Water and fertilizer management in sustainable agricultural development needs to balance crop yield, quality, and soil ecological environment. Therefore, we conducted trials with nine treatments over two growing seasons in 2020 and 2021. The treatments included the three irrigation levels W1 (75% Ep), W2 (100% Ep), and W3 (125% Ep) and the three fertilization levels F1 (758.44 kg/hm2), F2 (948.05 kg/hm2), and F3 (1137.66 kg/hm2) with a N/P/K ratio of 2:1:3. The net photosynthetic rate (Pn) gradually decreased with the plant growing period, and was significantly (*) affected by fertilization at the flowering and fruiting stages. The chlorophyll content increased by 43.75% from the vine to flowering and fruiting stages and decreased by 12.50% in the mature stage, reaching a maximum following W1 application. W2F2 was the most effective in promoting total dry mass (TDM) during the vine and mature stages. Irrigation, fertilization and the interaction exerted a highly significant effect on the fruit quality and yield. Soluble solids performed best under W1F1, while free amino acid reached a maximum under W3F2. Moreover, water use efficiency increased with the fertilization amount, and was maximized in W1F3. At the same fertilizer level, soil nitrate N and available P content exhibited an increasing then decreasing trend with the increasing irrigation amount, while soil available K content increased with irrigation at all growth stages. Structural equation models of yield and quality formation were then established based on the co-occurrence analysis. Pn indirectly regulated the melon growth by TDM (0.87) (***), while growth was identified as the most important direct factor affecting the yield and quality of melon. Furthermore, soil residues indirectly affected yield and quality composition through efficiency. This study indicates that the sustainable practices for water and fertilizer management are essential to improve melon yield and quality in arid and semi-arid regions, and contribute to reducing the risk of soil contamination from agricultural production

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function

The value of cardiopulmonary comorbidity in patients with acute large vessel occlusion stroke undergoing endovascular thrombectomy: a retrospective, observational cohort study

Abstract Background Chronic lung and heart diseases are more likely to lead an intensive end point after stroke onset. We aimed to investigate characteristics and outcomes of endovascular thrombectomy (EVT) in patients with acute large vessel occlusion stroke (ALVOS) and identify the role of comorbid chronic cardiopulmonary diseases in ALVOS pathogenesis. Methods In this single-center retrospective study, 191 consecutive patients who underwent EVT due to large vessel occlusion stroke in neurological intensive care unit were included. The chronic cardiopulmonary comorbidities and several conventional stroke risk factors were assessed. The primary efficacy outcome was functional independence (defined as a mRS of 0 to 2) at day 90. The primary safety outcomes were death within 90 days and the occurrence of symptomatic intracranial hemorrhage(sICH). Univariate analysis was applied to evaluate the relationship between factors and clinical outcomes, and logistic regression model were developed to predict the prognosis of ALVOS. Results Endovascular therapy in ALVOS patients with chronic cardiopulmonary diseases, as compared with those without comorbidity, was associated with an unfavorable shift in the NHISS 24 h after EVT [8(4,15.25) versus 12(7.5,18.5), P = 0.005] and the lower percentage of patients who were functionally independent at 90 days, defined as a score on the modified Rankin scale of 0 to 2 (51.6% versus 25.4%, P = 0.000). There was no significant between-group difference in the frequency of mortality (12.1% versus 14.9%, P = 0.580) and symptomatic intracranial hemorrhage (13.7% versus 19.4%, P = 0.302) or of serious adverse events. Moreover, a prediction model showed that existence of cardiopulmonary comorbidities (OR = 0.456, 95%CI 0.209 to 0.992, P = 0.048) was independently associated with functional independence at day 90. Conclusions EVT was safe in ALVOS patients with chronic cardiopulmonary diseases, whereas the unfavorable outcomes were achieved in such patients. Moreover, cardiopulmonary comorbidity had certain clinical predictive value for worse stroke prognosis