Wave interference network with a wave function for traffic sign recognition.

In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets.

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated. METHODS: Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified. CONCLUSIONS: High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Inhibition of protein kinase C activity inhibits osteosarcoma metastasis.

INTRODUCTION: For some cancers bone is the preferred site for metastasis and involves a cascade involving transition of epithelial cells to mesenchymal cells and subsequent intravasation to the blood and lymph vessels, and finally hematogenous dissemination to perivascular niches of the bone marrow sinusoids. It has been shown that protein kinase C can aid metastasis to bone. Hence, pharmacological inhibition of protein kinase C (PKC) activity is thought of as a potential therapeutic option in bone metastatic lesions. The objective of the current study was to investigate how PKCs exert their effect on bone cancer metastasis and to test the efficacy of pharmacological inhibition of PKC on bone metastasis. MATERIAL AND METHODS: The effect of the PKC inhibitor Go6983 on epithelial and mesenchymal cell marker expression in the osteosarcoma cell line DAN was determined by immunoblot and immunofluorescence analysis. The in vivo effect of Go6983 was evaluated with a xenograft model using DAN cells. RESULTS: Treatment with transforming growth factor ß (TGF-ß) led to loss of the epithelial cell marker and gain of mesenchymal cell markers in the osteosarcoma cell line, DAN. This transition occurred concomitantly with PKC activation. TGF-ß-mediated PKC activation resulted in activation of ribosomal protein 6 (S6), but not S6K1. Pharmacological inhibition of PKC activation attenuated these effects. In a xenograft model of experimental metastasis, pharmacological inhibition of PKC activation over a period of 4 weeks reduced both tumor burden and metastasis to lungs. CONCLUSIONS: Our results indicate that PKC potentiates tumor metastasis to the bone by potentiating translation increase and can be putatively inhibited by pharmacological inhibition.

Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains.

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

Point-to-multipoint and ring network communication based on chaotic semiconductor lasers with optical feedback.

In this paper, two configurations of point-to-multipoint (PTM) and ring networks, based on the chaotic semiconductor laser subject to optical feedback, are investigated. A bifurcation diagram and the maximum Lyapunov exponent in the system have been used to distinguish the existence of chaos, and the complex degree of chaotic output is measured through Lempel-Ziv complexity. These results show that feedback strength has a significant effect on the dynamics of the system, namely, an increase in it can induce the system to enter into chaos. In the PTM model, it can be seen that the arbitrary receiver laser (RL) and central transmitter laser (TL) are identically synchronized, and moreover, the synchronization solutions are robust; the message can be encoded by modulating the bias current of the central TL, and at each RL end, the message from TL can be simultaneously recovered by monitoring the power error between RL and TL. As a result, the unidirectional broadcast message transmission, based on PTM, can be well achieved. In the ring network configuration, the coupling between two adjacent lasers through a partially transparent mirror induces the delay and chaotic dynamics. We prove that the dynamics is identically synchronized, and the synchronization against external perturbation also possesses good robustness; the messages introduced on the two arbitrary lasers in this ring network can be simultaneously exchanged.

Numerical investigations of synchronization and communication based on an electro-optic phase chaos system with concealment of time delay.

A modified electro-optic phase chaos system that can conceal time delay (TD) and allows for unidirectional message transmission, is numerically investigated. The configuration includes two cascaded delay loops, and the parallel-coupled microresonators (PCMRs) in one of two loops result in a frequency-dependent group delay. The largest Lyapunov exponent (LLE), Lempel-Ziv complexity (LZC) and permutation entropy (PE) are used to distinguish the chaotic behavior and the degree of complexity in a time series, and the autocorrelation function (ACF) and the delayed mutual information (DMI) are plotted to extract the TD. The corresponding diagrams show that in the electro-optic system phase chaos with high complexity can occur within a certain range of feedback strength. The diagrams also show that, at a fixed feedback strength, the effect of the TD concealment becomes quite good with an increase in the number of PCMRs. The numerical simulation also reveals that the delayed chaotic dynamics can be identically synchronized, and the synchronization solution is robust. Moreover, based on the coherence of Mach-Zehnder interferometers, we convert the phase variations of the transmitter outputs and the receiver into the corresponding intensity variations, so the synchronization error of the two-phase chaotic series can be monitored. At last, we can successfully decipher the message introduced on the transmitting end of a link. In this scheme, the feedback TD has been concealed, which prevents eavesdroppers from listening and makes the proposed chaotic communication system secure.

Simultaneous trilateral communication based on three mutually coupled chaotic semiconductor lasers with optical feedback.

In this paper, we propose a chaos-based scheme allowing for trilateral communication among three mutually coupled chaotic semiconductor lasers. The coupling through a partially transparent optical mirror between two lasers induces the chaotic dynamics. We numerically solve the delay rate equations of three lasers and demonstrate that the dynamics is completely synchronous. Herein, each laser is not only a transmitter but a receiver; three different messages are encoded by simultaneously modulating bias current of the three lasers. By monitoring the synchronization error between transmitter and receiver, and comparing the error with the message of the local laser, we can decipher the message of the sender. The investigation indicates that these messages introduced on the two ends of each link among three lasers can be simultaneously transmitted and restored, so the system can realize simultaneous trilateral communication. In this scheme, an eavesdropper can monitor the synchronization error, but one has no way to obtain the bits that are being sent, so the trilateral communication is secure.

Safety of tumor necrosis factor-alpha inhibitors for treatment of ankylosing spondylitis: A meta-analysis.

BACKGROUND: Ankylosing spondylitis (AS) is a chronic immune-mediated disease affecting the sacroiliac joints and the spine, manifesting with new bone formation and osteopenia. Five tumor necrosis factor-alpha (TNF-α) inhibitors (infliximab, etanercept, adalimumab, certolizumab, and golimumab) are available for the treatment of AS, however, the results for the safety of TNF-α inhibitors in the treatment of AS are not consistent. METHODS: In this study, we conducted a meta-analysis to determine the safety of TNF-α inhibitors compared with placebo in reducing pain, swelling, and inflammation of AS patients. Eight relevant articles including 2049 patients were included for this meta-analysis study. We observed that the incidence of adverse events (RR  =  1.22, 95% CI: 1.12-1.33; P  =  .501, I  =  0%) and injection-site reaction (RR  =  2.93, 95% CI: 2.02-4.23; P  =  .691, I  =  0%) in AS patients' treatment with TNF-α inhibitors was significantly higher than that with placebo. RESULTS: However, there was no significant difference in the incidence of serious adverse event, infection, serious infection, and discontinuations due to adverse event. TNF-α inhibitors may be a promising treatment for AS, but carries an increased incidence rate of adverse events and injection-site reaction. CONCLUSION: Due to the existence of the unstable factors, further studies need to be done to verify the result of this study.

A Riemannian distance approach for constructing principal curves.

The determination of principal curves relies on the arc-length as a global index to describe the middle of the data distribution. With a non-constant data distribution, however, curves that are constructed by the approach introduced in reference may not reflect the middle of data distribution, as demonstrated in this article. This is particularly so for curve segments that have a large curvature and a high data density. To overcome this problem, the paper revisits the projection of the samples onto the curve by incorporating Riemannian distances. This analysis suggests estimating the density value of each sample relative to its neighbors and utilize this value to compute the projection index for the curve. The use of density values, in turn, allows penalizing distances between samples along with the arc-length. In a similar fashion to conventional principal curves algorithms, for example proposed by Hastie and Stuetzle and Tibshirani, the incorporation of Riemannian distances gives rise to an iterative algorithm that includes a projection and a self-consistent step. Application studies to simulated and experimental data sets shows that the proposed modification has the potential to outperform existing algorithms in areas of high curvature under an non-constant data distribution.