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Previous studies have shown that perennial Chan training leads to improvements in brain functioning. However, few studies have investigated the effects of short-term Huatou Chan training. The current study explored the effects of a three-day Huatou Chan training on physical and emotional health, as well as brain state. Seventy healthy subjects were recruited and divided into two groups: the Huatou Chan group and the Control group. The Huatou Chan group received a 3-day Huatou Chan training, while the Control group waited for three days. Both groups completed a 6 min Brain State Index recording, the SCL-90, the brief profile of mood state, the meaning in life questionnaire, and the Index of Well-being, prior to and after the training or waiting period. Results showed that short-term Huatou Chan training had significant benefits on some aspects such as physical and emotional health (obsessive-compulsive, depression, hostility, and psychoticism), negative emotions (tension-anxiety, depression-dejection, anger-hostility, fatigue-inertia, and confusion-bewilderment), well-being, and attitude towards life. In addition, short-term Chan training can significantly improve brain state, as shown by the index of depression, anxiety, alerting and intelligence. This is the first study to provide direct evidence for the benefits of short-term intensive Huatou Chan training on physical and mental health. [ABSTRACT FROM AUTHOR]

Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from high-resolution images. However, when applied to low-resolution images, their performance drops rapidly. This paper proposes a unified learning framework (namely SR-FER) by cascading the image super-resolution (SR) task and FER task to alleviate the low-resolution challenge. It effectively feeds back expression-related information from the FER network to the SR network, and returns the quality-enhanced expression images via a SR network. Specifically, a multi-stage attention-aware consistency loss module is introduced to help the SR network achieve discriminative feature restoration guided by attention information. Furthermore, a prediction consistency loss module is also developed to encourage the SR network to restore discriminative features by reducing the difference in prediction information between the restored and original normal-resolution images. Therefore, more accurate results are obtained by performing FER on the restored images. We conduct extensive experiments to demonstrate that the proposed low-resolution FER solution can help SR methods restore features favorable for FER while maintaining acceptable FER performance in various resolution degradation scenarios. The proposed method effectively improves the FER challenge under resolution degradation conditions, which is of good reference value for real-world applications. • Design a joint learning framework to alleviate the low-resolution FER challenge. • Design novel attention-aware consistency and prediction consistency losses. • Extensive experiments demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer's excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks. [ABSTRACT FROM AUTHOR]

This study constructs and investigates the non-fragile asynchronous event-triggered control for uncertain switched neural networks with communication delays and Bernoulli distribution. The system involves both the randomly occurring uncertainties of all parameters (ROUAPs) in the switched system model and randomly gain perturbations of the controller. By introducing several stochastic variables, a new model structure obeying Bernoulli distribution is formulated to describe the uncertainties of system and the controller. In addition, an improved non-fragile asynchronous event-triggered control scheme is proposed and a modified Lyapunov–Krasovskii function (LKF) is chosen via flexible terminal method (FTM) to deal with such uncertain switched systems with delays. By combining newly bounding inequalities and convex combination method, new results are presented for the uncertain delayed switched error systems in terms of linear matrix inequality (LMI). Furthermore, the non-fragile asynchronous event-triggered controller is designed with less communication burden. Finally, a numerical example is provided to verify the merits of the proposed results. [ABSTRACT FROM AUTHOR]

This study constructs and investigates the non-fragile asynchronous H ∞ control for uncertain stochastic memory systems with Bernoulli distribution. The system not only contains the randomly occurring uncertainties of all parameters (ROUAPs) and stochastic disturbances in the system model, but also includes randomly gain perturbations in the controller. By introducing the stochastic variables, a new model structure is built obeying Bernoulli distribution to describe the system and the controller. Moreover, a modified Lyapunov–Krasovskii function (LKF) is constructed, combining Itô’s differential formula and free-weighting matrix method, less conservative results are presented in terms of the linear matrix inequality (LMI). Furthermore, an observer-based non-fragile asynchronous H ∞ controller is designed without any limits on the system parameters. Finally, three numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed results. [ABSTRACT FROM AUTHOR]

ApoC2 is an important activator for lipoprotein lipase-mediated hydrolysis of triglyceride-rich plasma lipoproteins. ApoC2-deficient patients display severe hypertriglyceridemia (sHTG) and recurrent acute pancreatitis. However, due to embryonic lethality in ApoC2 deleted mouse extensive understanding of ApoC2 function is limited in mammalian species. We sought to generate an animal model with ApoC2 deficiency in a rodent with some human-like features and then study the precise effects of ApoC2 on lipid and glucose homeostasis. Using CRISPR/Cas9, we deleted Apoc2 gene from golden Syrian hamster and the homozygous (−/−) pups can be born in matured term but exhibited neonatal lethality. By continuous iv administration of normal hamster serum the ApoC2−/− pups could survive till weaning and displayed severe HTG in adulthood on chow diet. A single iv injection of AAV-hApoC2 at birth can also rescue the neonatal death of ApoC2−/− pups. Adult ApoC2−/−hamsters exhibited a unique phenotype of sHTG with hypoglycemia, hypoinsulinemia and spontaneous atherosclerosis. The sHTG in ApoC2−/− adult hamsters could not be corrected by various lipid-lowering medications, but partially ameliorated by medium chain triglyceride diet and completely corrected by AAV-hApoC2. Our study provides a novel ApoC2-deleted mammalian model with severe hypertriglyceridemia that was fully characterized and highlights a potential therapeutic approach for the treatment of ApoC2 deficient patients. • The first small rodent human-like hamster model with ApoC2 deficiency is created by CRISPR/Cas9 in the present study. • ApoC2-deficient hamsters show neonatal death, which can be rescued by an administration of normal hamster serum or AAV-hApoC2. • Rescued ApoC2-deficient hamsters from normal serum show severe hypertriglyceridemia and spontaneous atherosclerosis. • AAV-hApoC2 completely abolishes severe hypertriglyceridemia and reverses atherosclerotic development in ApoC2-deficient hamsters. [ABSTRACT FROM AUTHOR]

This study addresses a new approach to generalized dissipativity analysis for fuzzy systems with coupling memory sampled-data (CMSD) control. Based on the information of the time derivative of membership functions (MFs), an improved time-dependent Lyapunov–Krasovskii functional (LKF) is proposed to commendably capture the available information related to the real sampling pattern. Together with a switching idea and newly inequalities, further results are established to ensure that the considered system is generalized dissipative. In addition, the CMSD controller is designed such that the generalized dissipative can be guaranteed. Finally, the effectiveness of the presented criteria is verified by the truck-trailer system and Rossler's system. [ABSTRACT FROM AUTHOR]