Mutation detection dual correlation filter with an object-awareness module for real-time target tracking.

Discriminative correlation filter (DCF) based methods have recently been widely used for visual tracking tasks. The adaptive spatiotemporal-regulation based tracker (AutoTrack) can only partially solve some limitations of the DCF framework including filter degradation and the boundary effect, but its application scenarios need to be broadened, and performance improvements are also required. To further surmount these difficulties, this paper provides an object-awareness-module based mutation detection dual correlation filter (MDDCF-OAM). The main innovation points of this work are: (1) an object-mask based context enhancer is proposed to formulate a more robust appearance model; (2) a dual filter training-learning structure is adopted to allow the dual filters to restrict each other and suppress the filter degradation effect; (3) a Gaussian label map is updated with the refined joint response map to detect and attenuate the response mutation effects. Exhaustive experiments have been conducted to test the efficiency of the suggested MDDCF-OAM on four benchmarks, namely, OTB2015, UAV123, TC128, and VOT2019. The results indicate that: (1) the introduced MDDCF-OAM surpasses nine state-of-the-art trackers; (2) the MDDCF-OAM has a real-time speed of 32 frames per second, which is sufficient for target tracking tasks in numerous scenarios, especially unmanned aerial vehicles and camera tracking.

Akkermansia muciniphila Colonization Alleviating High Fructose and Restraint Stress-Induced Jejunal Mucosal Barrier Disruption.

Akkermansia muciniphila (A. muciniphila) is a mucin-degrading bacterium that resides in the mucus layer, but its potential in intestinal inflammatory diseases has sparked controversy. It is well known that both the consumption of fructose-containing beverages and psychological stress increase the risk of intestinal disease. Our results revealed that a high-fructose diet aggravated the damage to the jejunal mucosal barrier caused by restraint stress, reduced tight junction protein expression and the intestinal digestion and absorption capacity, disrupted the ability of Paneth cells to secrete antimicrobial peptides, and promoted the expression of inflammatory cytokines. A. muciniphila colonization enhanced the defense function of the mucosal barrier by enhancing the function of the NLRP6, promoting autophagy, maintaining the normal secretion of antimicrobial peptides in Paneth cells, promoting the expression of tight junction proteins, negatively regulating the NF-kB signaling pathway and inhibiting the expression of inflammatory cytokines. Our work indicates that A. muciniphila ameliorates the disruption of the intestinal mucosal barrier under high fructose and restraint stress. These results provided a rationale for the development of probiotic colonization for the prevention or treatment of intestinal diseases.

Super-resolution reconstruction of biometric features recognition based on manifold learning and deep residual network.

BACKGROUND AND OBJECTIVE: In daily life, face information has the characteristics of uniqueness and universality. However, in a real-world scene, the image information of the face acquired by the acquisition device often contains noises such as blurring and sharpening. As such, super-resolution reconstruction of face features recognition based on manifold learning is proposed in this paper. METHODS: We reconstruct low-resolution facial expression images, introduce a simplified residual block network and manifold learning, and propose joint supervision through a new hybrid loss function, which not only retains the color and characteristics of the image, but also retains the high-frequency information. The ResNet50 network uses the weight feature of information entropy to optimize the information of the pooling layer, and the esNet50 network uses the improved PSO algorithm to optimize the initial weight of the error back-propagation phase. RESULTS: In the case of inputting extremely low resolution (6 × 6) facial expression images, the accuracy rate is increased by 9.091%. The accuracy of the high-resolution facial expressions after reconstruction with a size of 12×12 is 96.970%. The accuracy rate for happy expressions is 100%, the accuracy rate for anger, disgust, sadness, and surprise recognition is 97%, the accuracy rate for contempt is 94%, and the accuracy rate for fear is 88%. CONCLUSIONS: The experimental results verify the feasibility and superiority of the system, and effectively improve the accuracy of low-resolution facial expressions.