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Purpose: Doramectin (DRM) is a kind of avermectin drugs, and it has been shown that DRM has anti-cancer effects. However, the molecular mechanism of DRM in programmed cell death (PCD) aspects is still unclear. The objective of this study was to confirm whether DRM induced PCD in glioma cells. Methods: In this experiment, the MTT assay and Ki-67 assay were used to detect in vitro cell viability and in vivo tumor proliferation. Then, the effect of DRM on PCD was analyzed by transcriptome comparison. Next, Endogenous apoptosis was detected by transmission electron microscopy (TEM), the DNA gel electrophoresis, JC-1 assay, western blotting and qRT-PCR. Meanwhile, necroptosis was detected by TEM, Hoechst 33342, FITC and PI staining assay, western blotting. Results: We found DRM induced apoptosis through Bcl-2/Bax/Caspase-3 pathway. And, DRM induced ROS overproduction, then ROS caused necroptosis through RIPK1/RIPK3/MLKL pathway, Mitochondria acted as a bridge between the two pathways. Conclusion: Our research provided new insight with the function of anti-cancer of DRM. These results demonstrated DRM may be used as potential therapeutic agents inducing apoptosis and necroptosis for cancer therapy. [ABSTRACT FROM AUTHOR]

The Monarch Butterfly Optimization (MBO) algorithm has been proved to be an efficient meta-heuristic to directly address continuous optimization problems. In the MBO algorithm, the migration operator cooperates with the butterfly adjusting operator to generate the entire offspring population. Since the individual iterations of the MBO algorithm are not self-learning, the cooperative intelligence mechanism is a random process. In this study, an improved MBO algorithm with a knowledge-driven learning mechanism (KDLMBO) is presented to enable the algorithm to evolve effectively with a self-learning capacity. The neighborhood information extracted from the candidate solutions is treated as the prior knowledge of the KDLMBO algorithm. The learning mechanism consists of the learning migration operator and the learning butterfly adjusting operator. Then, the self-learning collective intelligence is realized by the two cooperative operators in the iterative process of the algorithm. The experimental results demonstrate and validate the efficiency and significance of the proposed KDLMBO algorithm. [ABSTRACT FROM AUTHOR]

Mammography technique is commonly used for diagnosing breast cancer, but mammography images usually show low contrast which cause difficulties to clinical diagnosis. Therefore, improving the visual quality of mammography images is an important issue. This is a challenging problem, because every mammography image consists of rich textures, including bright areas, dark areas, and textural details. Inspired by bio-inspired neural network, this paper proposes a Bidirectional Spiking Cortical Model (Bi-SCM) from the perspective of neural information fusion to enhance the contrast of bright areas and dark areas adequately, as well as textural details. This goal is achieved by utilizing the Bi-SCM to first enhance a mammography image and its inverse separately. The enhanced results are fused by a new fusion algorithm based on non-subsampled contourlet transform (NSCT) to ensure that both of the contrast of bright areas and dark areas are adequately improved. The textual details are then enhanced by an unsharp masking method which consists of cubic filter and log-ratio operation. Sufficient experiments on mammography images are conducted to evaluate the proposed approach. Experimental results show that the proposed method outperforms state-of-the-art methods on both enhancing contrast and details. Besides, over-enhancement and noise sensitivity are also significantly suppressed. [ABSTRACT FROM AUTHOR]

3D human pose estimation has many important applications in human-computer interaction and human action recognition. Simultaneously achieving real-time speed, varying human number, and high accuracy from a single RGB image is a challenging problem. To this end, this paper proposes a multi-task and multi-level neural network structure with physical constraint. The unique network structure estimates 3D human poses from single RGB image in an end-to-end way and achieves both high accuracy and high speed. Experimental results shows that the proposed system achieves 21 fps on RTX 2080 GPU with only 33 mm accuracy loss compared with conventional works. The mechanism of the network is also analyzed through network visualization. This work shows the possibility of estimating 3D human pose from a single RGB monocular camera with real-time speed. [ABSTRACT FROM AUTHOR]

3D human pose estimation has many important applications in human-computer interaction and human action recognition. Simultaneously achieving real-time speed, varying human number, and high accuracy from a single RGB image is a challenging problem. To this end, this paper proposes a multi-task and multi-level neural network structure with physical constraint. The unique network structure estimates 3D human poses from single RGB image in an end-to-end way and achieves both high accuracy and high speed. Experimental results shows that the proposed system achieves 21 fps on RTX 2080 GPU with only 33 mm accuracy loss compared with conventional works. The mechanism of the network is also analyzed through network visualization. This work shows the possibility of estimating 3D human pose from a single RGB monocular camera with real-time speed. [ABSTRACT FROM AUTHOR]

Compared with the great successes achieved by supervised learning, e.g. convolutional neural network (CNN), unsupervised feature learning is still a highly-challenging task suffering from no training labels. Because of no training labels for reference, blindly reducing the gap between features and image semantics is the most challenging problem. This paper proposes a Self-Taught Encoder-Decoder Network (STED-Net), which consists of a representation sub-network and a classification sub-network, for unsupervised feature learning. On one hand, the representation sub-network maps images to feature representation. On the other hand, using the features generated by representation sub-network, classification sub-network simultaneously maps feature representation to class representation and estimates pseudo labels by clustering feature representation. By minimizing the distance between class representation and the estimated pseudo labels, STED-Net teaches the features to represent class information. Through the self-taught feature representation, the gap between features and image semantics is reduced, and the features are promoted to be more and more "class-aware". The whole learning process of the STED-Net does not refer to any ground-truth class labels. Experimental results on widely-used image classification datasets prove that STED-Net achieves state-of-the-art classification performance compared with existing supervised and unsupervised feature learning models. [ABSTRACT FROM AUTHOR]

In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA. [ABSTRACT FROM AUTHOR]

Establishing local visual correspondence between video frames is an important and challenging problem in many vision based applications. Local keypoint detection and description based pixel-level matching is a typical way for visual correspondence estimation. Unlike traditional local keypoint descriptor based methods, this paper proposes a comprehensive yet low-dimensional local feature descriptor based on superpixels generated by over segmentation. The proposed local feature descriptor extracts shape feature, texture feature, and color feature from superpixels by orientated center-boundary distance (OCBD), gray-level co-occurrence matrix (GLCM), and saturation histogram (SHIST), respectively. The types of features are more comprehensive than existing descriptors which extract only one specific kind of feature. Experimental results on the widely used Middlebury optical flow dataset prove that the proposed superpixel descriptor achieves triple accuracy compared with the state-of-the-art ORB descriptor which has the same dimension of features with the proposed one. In addition, since the dimension of the proposed superpixel descriptor is low, it is convenient for matching and memory-efficient for hardware implementation. [ABSTRACT FROM AUTHOR]

Local structural information is crucially important for human visual system to perceive natural scenes. Recent years, a variety of local image structure description methods have been proposed for the artificial modeling of visual perception. Although existing local image structure descriptors have shown successful performances, one general limitation is their numerical instability caused by ignoring the information of the spatial correlation of local orientation. In this paper, we propose a local image structure descriptor by modeling the anisotropic mechanism in the primary visual cortex. In particular, the pixel-wise anisotropy values of a given image are calculated by pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy. Then the excitatory/inhibitory interactions among visual neurons in the local receptive field are modeled by measuring the similarities between their anisotropies. By mapping visual neurons to image pixels, the correlation between a central pixel and its local neighbors can be represented by a binary pattern which is named as local anisotropic pattern (LAP). Experimental results on texture classification verified that the proposed LAP has satisfactory texture classification accuracy, rotation invariance, and noise robustness; experimental results on no-reference image quality assessment demonstrated that the proposed LAP achieves state-of-the-art performance in objective evaluation of the perceptual quality of natural image, and this reflects that LAP can accurately represent the degradation of local image structure. [ABSTRACT FROM AUTHOR]