Background-The haplotypes in the gene vitamin K epoxide reductase complex subunit 1 (VKORC1) have been found to affect warfarin dose response through effects on the formation of reduced-form vitamin K, a cofactor for ␥-carboxylation of vitamin K-dependent proteins, which is involved in the coagulation cascade and has a potential impact on atherosclerosis. We hypothesized that VKORC1-dependent effects on the coagulation cascade and atherosclerosis would contribute to susceptibility for vascular diseases. Methods and Results-To test the hypothesis, we studied the association of polymorphisms of VKORC1 with stroke (1811 patients), coronary heart disease (740 patients), and aortic dissection (253 patients) compared with matched controls (nϭ1811, 740, and 416, respectively). Five common noncoding single-nucleotide polymorphisms of VKORC1 were identified in a natural haplotype block with strong linkage disequilibrium (DЈϾ0.9, r 2 Ͼ0.9), then single-nucleotide polymorphism (SNP) ϩ2255 in the block was selected for the association study. We found that the presence of the C allele of the ϩ2255 locus conferred almost twice the risk of vascular disease (odds ratio [OR] 1.95, 95% confidence interval [CI] .58 to 2.41, PϽ0.001 for stroke; OR 1.72, 95% CI 1.24 to 2.38, PϽ0.01 for coronary heart disease; and OR 1.90, 95% CI 1.04 to 3.48, PϽ0.05 for aortic dissection). We also observed that subjects with the CC and CT genotypes had lower levels of undercarboxylated osteocalcin (a regulator for the bone), probably vascular calcification, and lower levels of protein induced in vitamin K absence or antagonism II (PIVKA-II, a des-␥-carboxy prothrombin) than those with TT genotypes. Conclusions-The haplotype of VKORC1 may serve as a novel genetic marker for the risk of stroke, coronary heart disease, and aortic dissection.

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Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of Long Short-Term Memory (LSTM) in processing time series, in this paper, we propose a novel Encoder-LSTM-Decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long term prediction problems, and suits the networks of different scales with finetuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed dynamic network link prediction methods and obtain the state-of-the-art results.

Recent studies of network science have revealed the sensitive dependence of the network collective behaviors on structures, here we employ this feature of topological sensitivity for the purpose of pattern control. By simple models of networked chaotic oscillators, we are able to argue and demonstrate that, by manipulating just a single link in the network, the synchronous patterns of the system can be effectively adjusted or controlled. In particular, by changing the weight or the connection of a shortcut link in the network, we find that not only various stable synchronous patterns can be generated from the system, but also the synchronous patterns can be successfully switched among different forms. The stability of the synchronous patterns is analyzed by the method of eigenvalue analysis, and the feasibility of the control is verified by numerical simulations. Our study provides a step forward to the control of sophisticated collective behaviors in more complex networks, as well as giving insights to the evolution and function of some realistic complex systems.

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Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.

Synchronization transition in networks of nonlocally coupled chaotic oscillators is investigated. It is found that in reaching the state of global synchronization the networks can stay in various states of partial synchronization. The stability of the partial synchronization states is analyzed by the method of eigenvalue analysis, in which the important roles of the network topological symmetry on synchronization transition are identified. Moreover, for networks possessing multiple topological symmetries, it is found that the synchronization transition can be divided into different stages, with each stage characterized by a unique synchronous pattern of the oscillators. Synchronization transitions in networks of nonsymmetric topology and nonidentical oscillators are also investigated, where the partial synchronization states, although unstable, are found to be still playing important roles in the transitions.