Cornus officinalis vinegar reduces body weight and attenuates hepatic steatosis in mouse model of nonalcoholic fatty liver disease

This study aimed to determine the main bioactive components of Cornus officinalis vinegar (COV) and assess the effects of COV on the body weight (BW) and hepatic steatosis in a nonalcoholic fatty liver disease (NAFLD) mouse model. Seven-week-old KM female mice were divided into five treatment groups: (1) Normal control (NC) group, (2) high fat diet (HFD) group, (3) low concentration treatment group (3.5% COV), (4) medium concentration treatment group (5.0% COV), and (5) high concentration treatment group (6.5% COV). Mice in the NC group were fed with a normal chow diet, and those in the other four groups were fed with a HFD known for causing obesity for 10 weeks. Then, mice in the three COV treatment groups were orally administered with COV once a day for 6 weeks. Results showed that the contents of loganin and morroniside in COV reached 16.82 and 51.17 µg/ml, respectively, and COV also contained multiple organic acids. COV significantly reduced BW, abdominal fat weight, liver weight, and the levels of glucose, triglyceride, and low-density lipoprotein cholesterol of serum and increased the levels of high-density lipoprotein cholesterol of serum (p < 0.05). COV also improved the liver function and anti-oxidant activity of liver (p < 0.05). COV treatments increased the interleukin-10 expression and reduced the tumor necrosis factor-α expression in the liver tissue of NAFLD mice (p < 0.05). Histopathological observation revealed that COV suppressed hepatic lipid accumulation and steatosis. The results suggest that COV may contribute to the alleviation of NAFLD and obesity.     

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

     

Deep learning-based symbol detection algorithm in IMDD-OOFDM system

In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing ( IMDD-OOFDM ) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network (ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio (SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.     

Memory-based dynamic event-triggered control for networked interval type-2 fuzzy systems subject to DoS attacks

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.     

Diffusion and perfusion MRI parameters in the evaluation of placenta accreta spectrum disorders in patients with placenta previa

Magnetic Resonance Materials in Physics, Biology and Medicine - To evaluate the placental function by monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in patients with...     

Semi-supervised Learning with Graph Convolutional Networks Based on Hypergraph

Neural Processing Letters - Graph convolutional networks (GCNs), which rely on graph structures to aggregate information of neighbors to output robust node embeddings, have been becoming a popular...