Basin stability in delayed dynamics

Acknowledgements S.L. was supported by the China Scholarship Council (CSC) scholarship (Grant No. 501100004543). W.L. was supported by the National Natural Science Foundation (NNSF) of China (Grants No. 61273014 and No. 11322111).Peer reviewedPublisher PD

UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification

Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Networks (HGNNs), and their well-designed variants, on some commonly used benchmark graphs and hypergraphs, they are outperformed by even a simple Multi-Layer Perceptron. This observation motivates a reexamination of the design paradigm of the current GNNs and HGNNs and poses challenges of extracting graph features effectively. In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features via a normalized projection matrix. The resulting edge/hyperedge features, together with the original node features, are fed into a neural network. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix, of the network's output, which can be further used for tasks such as node classification. The proposed architecture, in contrast to the traditional spectral-based and/or message passing approaches, simultaneously and comprehensively exploits the node features and graph/hypergraph topologies in an efficient and unified manner, covering both heterophilic and homophilic graphs. The designed projection matrix, encoding the graph features, is intuitive and interpretable. Extensive experiments are conducted and demonstrate the superior performance of the proposed framework on twelve representative hypergraph datasets and six real-world graph datasets, compared to the state-of-the-art methods. Our implementation is available online at https://github.com/MinhZou/UniG-Encoder

Partial cross mapping eliminates indirect causal influences

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data

The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding

Grapevine is one of the most economically important crops worldwide. However, the previous versions of the grapevine reference genome consisted of thousands of fragments with missing centromeres and telomeres, which limited the accessibility of the repetitive sequences, the centromeric and telomeric regions, and the inheritance of important agronomic traits in these regions. Here, we assembled a telomere-to-telomere (T2T) gap-free reference genome for the pinot noir cultivar (PN40024) using the PacBio HiFi long reads. The T2T reference genome (PN_T2T) was 69 Mb longer with 9026 more genes identified than the 12X.v2 version (Canaguier et al., 2017). We annotated 67% repetitive sequences, 19 centromeres and 36 telomeres, and incorporated gene annotations of previous versions into the PN_T2T. We detected a total of 377 gene clusters, which showed associations with complex traits, such as aroma and disease resistance. Even though the PN40024 sample had been selfed for nine generations, we still found nine genomic hotspots of heterozygous sites associated with biological processes, such as the oxidation-reduction process and protein phosphorylation. The fully annotated complete reference genome, therefore, provides important resources for grapevine genetics and breeding.This work was supported by the National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas) to Yongfeng Zhou, the National Key Research and Development Program of China(grant2019YFA0906200), the Agricultural Science and Technology Innovation Program (CAAS-ZDRW202101), the Shenzhen Science and Technology Program (grant KQTD2016113010482651), the BMBF funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI). We thank Bianca Frommer, Marie Lahaye, David Navarro-Payá, Marcela K. Tello-Ruiz and Kapeel Chougule for their help in analyzing the RNA-Seq data and in running the gene annotation pipeline. This study is also based upon work from COST Action CA17111 INTEGRAPE and form COST Innovators Grant IG17111 GRAPEDIA, supported by COST (European Cooperation in Science and Technology).ViticultureT2Tgap-fregene clustercentromeretelomerePublishe