Isolation, identification and complete genome sequence analysis of a strain of foot-and-mouth disease virus serotype Asia1 from pigs in southwest of China

Abstract Backgroud Foot-and-mouth disease virus (FMDV) serotype Asia1 generally infects cattle and sheep, while its infection of pigs is rarely reported. In 2005-2007, FMD outbreaks caused by Asia1 type occurred in many regions of China, as well as some parts of East Asia countries. During the outbreaks, there was not any report that pigs were found to be clinically infected. Results In this study, a strain of FMDV that isolated from pigs was identified as serotype Asia1, and designated as "Asia1/WHN/CHA/06". To investigate the genomic feature of the strain, complete genome of Asia1/WHN/CHA/06 was sequenced and compared with sequences of other FMDVs by phylogenetic and recombination analysis. The complete genome of Asia1/WHN/CHA/06 was 8161 nucleotides (nt) in length, and was closer to JS/CHA/05 than to all other strains. Potential recombination events associated with Asia1/WHN/CHA/06 were found between JS/CHA/05 and HNK/CHA/05 strains with partial 3B and 3C fragments. Conclusion This is the first report of the isolation and identification of a strain of FMDV type Asia1 from naturally infected pigs. The Asia1/WHN/CHA/06 strain may evolve from the recombination of JS/CHA/05 and HNK/CHA/05 strains.</p

Optimisation of Signal Timing at Intersections with Waiting Areas

Unconventional geometric designs such as continu-ous-flow intersections, U-turns, and contraflow left-turn lanes have been proposed to reduce left-turn conflicts and improve intersection efficiency. Having a waiting area at a signalised intersection is an unconventional de-sign that is used widely in China and Japan to improve traffic capacity. Many studies have shown that waiting areas improve traffic capacity greatly, but few have con-sidered how to improve the benefits of this design from the aspect of signal optimisation. Comparing the start-up process of intersections with and without waiting areas, this work explores how this geometric design influenc-es vehicle transit time, proposes two signal optimisation strategies, and establishes a unified capacity calculation model. Taking capacity maximisation as the optimisation function, a cycle optimisation model is derived for over-saturated intersections. Finally, the relationship among waiting-area storage capacity, cycle time, and traffic ca-pacity is discussed using field survey data. The results of two cases show that optimising the signal scheme helps reduce intersection delays by 10–15%

Partition function zeros of the Q-state Potts model for non-integer Q

The distribution of the zeros of the partition function in the complex temperature plane (Fisher zeros) of the two-dimensional Q-state Potts model is studied for non-integer Q. On $L\times L$ self-dual lattices studied ($L\le8$), no Fisher zero lies on the unit circle $p_0=e^{i\theta}$ in the complex $p=(e^{\beta J}-1)/\sqrt{Q}$ plane for Q<1, while some of the Fisher zeros lie on the unit circle for Q>1 and the number of such zeros increases with increasing Q. The ferromagnetic and antiferromagnetic properties of the Potts model are investigated using the distribution of the Fisher zeros. For the Potts ferromagnet we verify the den Nijs formula for the thermal exponent $y_t$. For the Potts antiferromagnet we also verify the Baxter conjecture for the critical temperature and present new results for the thermal exponents in the range 0<Q<3.Comment: 12 pages, 7 figures, RevTe

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, another kind of approaches based on multi-layer perceptrons (MLPs) are proposed with simple structure, low complexity, and {superior performance}. However, most MLP-based forecasting methods suffer from the point-wise mappings and information bottleneck, which largely hinders the forecasting performance. To overcome this problem, we explore a novel direction of applying MLPs in the frequency domain for time series forecasting. We investigate the learned patterns of frequency-domain MLPs and discover their two inherent characteristic benefiting forecasting, (i) global view: frequency spectrum makes MLPs own a complete view for signals and learn global dependencies more easily, and (ii) energy compaction: frequency-domain MLPs concentrate on smaller key part of frequency components with compact signal energy. Then, we propose FreTS, a simple yet effective architecture built upon Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components. The above stages operated on both inter-series and intra-series scales further contribute to channel-wise and time-wise dependency learning. Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods

FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. However, the uncertain compatibility of the two networks puts an extra burden on handcrafted model designs. Moreover, the separate spatial and temporal modeling naturally violates the unified spatiotemporal inter-dependencies in real world, which largely hinders the forecasting performance. To overcome these problems, we explore an interesting direction of directly applying graph networks and rethink MTS forecasting from a pure graph perspective. We first define a novel data structure, hypervariate graph, which regards each series value (regardless of variates or timestamps) as a graph node, and represents sliding windows as space-time fully-connected graphs. This perspective considers spatiotemporal dynamics unitedly and reformulates classic MTS forecasting into the predictions on hypervariate graphs. Then, we propose a novel architecture Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. FourierGNN accommodates adequate expressiveness and achieves much lower complexity, which can effectively and efficiently accomplish the forecasting. Besides, our theoretical analysis reveals FGO's equivalence to graph convolutions in the time domain, which further verifies the validity of FourierGNN. Extensive experiments on seven datasets have demonstrated our superior performance with higher efficiency and fewer parameters compared with state-of-the-art methods.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0309

Biomechanical evaluation of a new intramedullary nail compared with proximal femoral nail antirotation and InterTAN for the management of femoral intertrochanteric fractures

Purpose: Surgical treatment is the main treatment method for femoral intertrochanteric fractures (FIFs), however, there are lots of implant-related complications after surgery. Our team designed a new intramedullary nail (NIN) to manage such fractures. The purpose of this study was to introduce this new implant and compare it with proximal femoral nail antirotation (PFNA) and InterTAN for treating FIFs.Methods: An AO/OTA 31-A1.3 FIF model was built and three fixation models were created via finite element method, comprising PFNA, InterTAN, and the NIN. Vertical, anteroposterior (A-P) bending, and torsional loads were simulated and applied to the three fixation models. Displacement and stress distribution were monitored. In order to compare PFNA and the NIN deeply, finite element testing was repeated for five times in vertical load case.Results: The finite element analysis (FEA) data indicated that the NIN possessed the most outstanding mechanical properties among the three fixation models. The NIN model had lower maximal stress at implants compared to PFNA and InterTAN models under three load conditions. The trend of maximal stress at bones was similar to that of maximal stress at implants. Besides, the NIN model showed smaller maximal displacement compared with PFNA and InterTAN models under vertical, A-P bending, and torsional load cases. The trend for maximal displacement of fracture surface (MDFS) was almost identical with that of maximal displacement. In addition, there was significant difference between the PFNA and NIN groups in vertical load case (p &lt; 0.05).Conclusion: Compared with PFNA and InterTAN, the NIN displayed the best mechanical properties for managing FIFs, including the lowest von Mises stress at implants and bones, and the smallest maximal displacement and MDFS under vertical, A-P bending, and torsional load cases. Therefore, this study might provide a new choice for patients with FIFs