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Due to the complexity and non-stationarity of the voice generation system, the nonlinearity of speech signals cannot be accurately quantified. Recently, the recurrence quantification analysis method has been used for voice disorder detection. In this paper, multiscale recurrence quantification measures (MRQMs) are proposed. The signals are reconstructed in the high-dimensional phase space at the equivalent rectangular bandwidth scale. Recurrence plots (RPs) combining the characteristics of human auditory perception are drawn with an appropriate recurrence threshold. Based on the above, the nonlinear dynamic recurrence features of the speech signal are quantized from the recurrence plot of each frequency channel. Furthermore, this paper explores the recurrence quantification thresholds that are most suitable for pathological voices. Our results show that the proposed MRQMs with support vector machine (SVM), random forest (RF), Bayesian network (BN) and Local Weighted Learning (LWL) achieve an average accuracy of 99.45%, outperforming traditional features and other complex measurements. In addition, MRQMs also have the potential for multi-classification of voice disorder, achieving an accuracy of 89.05%. This study demonstrates that MRQMs can characterize the recurrence characteristic of pathological voices and effectively detect voice disorders.

Loss of E‐cadherin (ECAD) is required in tumor metastasis. Protein degradation of ECAD in response to oxidative stress is found in metastasis of hepatocellular carcinoma (HCC) and is independent of transcriptional repression as usually known. Mechanistically, protein kinase A (PKA) senses oxidative stress by redox modification in its β catalytic subunit (PRKACB) at Cys200 and Cys344. The activation of PKA kinase activity subsequently induces RNF25 phosphorylation at Ser450 to initiate RNF25‐catalyzed degradation of ECAD. Functionally, RNF25 repression induces ECAD protein expression and inhibits HCC metastasis in vitro and in vivo. Altogether, these results indicate that RNF25 is a critical regulator of ECAD protein turnover, and PKA is a necessary redox sensor to enable this process. This study provides some mechanistic insight into how oxidative stress‐induced ECAD degradation promotes tumor metastasis of HCC. [ABSTRACT FROM AUTHOR]

This paper presents a new image synthesis method for image style transfer. For some common methods, the textures and colors in the style image are sometimes applied inappropriately to the content image, which generates artifacts. In order to improve the results, we propose a novel method based on a new strategy that combines both local and global style losses. On the one hand, a style loss function based on a local approach is used to keep the style details. On the other hand, another style loss function based on global measures is used to capture more global structural information. The results on various images show that the proposed method reduces artifacts while faithfully transferring the style image's characteristics and preserving the structure and color of the content image.

In magnetic resonance imaging (MRI), a gradient preemphasis module with more compensation channels and adjustable parameters is often desired for better imaging quality and faster imaging speed. With the conventional design, however, a high performance preemphasis module often requires high resource consumption such that the field programmable gate array (FPGA) may encounter huge resource overhead. In this study, a new gradient preemphasis implementation scheme based on time-division multiplexing was proposed. With the new design, an 11 channels×4 sets of parameters implementation could be achieved with only 1/44 of the resources required by the conventional scheme. To test the performance of the new implementation, eddy current curves and magnetic resonance images before and after preemphasis compensation were measured on a 0.35 T scanner and compared. The results demonstrated that the module could effectively reduce eddy currents and related artifacts on the images.