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These results demonstrate the potential utility of selected biomarkers to distinguish patients with the highest risk for treatment failure and bacteremia-related complications, providing a valuable tool for clinicians in the management of S. aureus bacteremia. Additionally, these biomarkers could identify patients with the greatest potential to benefit from novel therapies in clinical trials.

This paper proposes a parallel digital image encryption algorithm based on a piecewise linear chaotic map (PWLCM) and a four-dimensional hyper-chaotic map (FDHCM). Firstly, two decimals are obtained based on the plain-image and external keys, using a novel parallel quantification method. They are used as the initial value and control parameter for the PWLCM. Then, an encryption matrix and four chaotic sequences are constructed using the PWLCM and FDHCM, which control the permutation and diffusion processes. The proposed algorithm is implemented and tested in parallel based on a Graphics Processing Unit (GPU) device. Numerical analysis and experimental results show that the proposed algorithm achieves a high encryption speed and a good security performance, which provides a potential solution for real-time image encryption applications.

5-Hydroxymethylcytosine (5hmC) is abundant in the brain, suggesting an important role in epigenetic control of neuronal functions. In this paper, we show that 5hmC and 5-methylcytosine (5mC) levels are coordinately distributed in gene promoters of the rhesus macaque prefrontal cortex. Although promoter hydroxymethylation and methylation are overall negatively correlated with expression, a subset of highly expressed genes involved in specific cerebral functions is associated with high levels of 5mC and 5hmC. These relationships were also observed in the mouse cortex. Furthermore, we found that early-life maternal deprivation is associated, in the adult monkey cortex, with DNA hydroxymethylation changes of promoters of genes related to neurological functions and psychological disorders. These results reveal that early social adversity triggers variations in brain DNA hydroxymethylation that could be detected in adulthood.

In terms of Chua's circuit system, compressive sensing (CS) and Haar wavelet, a novel image compression-encryption scheme (CES) is proposed in this paper. Firstly, the plaintext image is decomposed into approximate component and detail components through Haar wavelet. Then the approximate component is diffused by the threshold processing of local binary patterns (LBP) operator-based chaotic sequence which is produced by the combination of Chua's circuit and Logistic map. Next, the Lissajous map is applied to generate the chaos-combined asymptotic deterministic random measurement matrices (CADRMM) which are employed to measure the detail components in different compression ratios. In addition, the combination of mapped approximate and detail components is shuffled by the Logistic map. The experimental results and simulation analysis prove that the proposed cryptosystem is capable of reducing data for transmission and has good security performance under various attacks, especially for the shear and noise attacks.

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications.

In this paper, we propose an efficient and self-adapting colour-image encryption algorithm based on chaos and the interactions among multiple red, green and blue (RGB) layers. Our study uses two chaotic systems and the interactions among the multiple layers to strengthen the cryptosystem for the colour-image encryption, which can achieve better confusion and diffusion performances. In the confusion process, we use the novel Rubik's Cube Scheme (RCS) to scramble the image. The significant advantage of this approach is that it sufficiently destroys the correlation among the different layers of colour image, which is the most important feature of the randomness for the encryption. The theoretical analysis and experimental results show that the proposed algorithm can improve the encoding efficiency, enhances the security of the cipher-text, has a large key space and high key sensitivity, and is also able to resist statistical and exhaustive attacks.

Tourist arrival and tourist demand forecasting are a crucial issue in tourism economy and the community economic development as well. Tourist demand forecasting has attracted much attention from tourism academics as well as industries. In recent year, it attracts increasing attention in the computational literature as advances in machine learning method allow us to construct models that significantly improve the precision of tourism prediction. In this paper, we draw upon both strands of the literature and propose a novel paired neural network model. The tourist arrival data is decomposed by two low-pass filters into long-term trend and short-term seasonal components, which are then modelled by a pair of autoregressive neural network models as a parallel structure. The proposed model is evaluated by the tourist arrival data to United States from twelve source markets. The empirical studies show that our proposed paired neural network model outperforming the selected benchmark model across all error measures and over different horizons.