Therapeutic potential of natural products against Alzheimer's disease via autophagic removal of Aß.

The pathological features of Alzheimer's disease (AD), a progressive neurodegenerative disorder, include the deposition of extracellular amyloid beta (Aß) plaques and intracellular tau neurofibrillary tangles. A decline in cognitive ability is related to the accumulation of Aß in patients with AD. Autophagy, which is a primary intracellular mechanism for degrading aggregated proteins and damaged organelles, plays a crucial role in AD. In this review, we summarize the most recent research progress regarding the process of autophagy and the effect of autophagy on Aß. We further discuss some typical monomers of natural products that contribute to the clearance of Aß by autophagy, which can alleviate AD. This provides a new perspective for the application of autophagy modulation in natural product therapy for AD.

Andrographolide derivative Andro-III modulates neuroinflammation and attenuates neuropathological changes of Alzheimer's disease via GSK-3ß/NF-κB/CREB pathway.

Andrographolide has anti-inflammatory and neuroprotective effects, making it a potential therapeutic option for Alzheimer's disease (AD). Our research group optimized its structure in a previous study to minimize the risk of renal toxicity, which would beneficial for future clinical research. This study aims to examine the impact of Andro-III on enhancing cognitive learning ability in 3xTg-AD mice, as well as the mechanisms involved. Andro-III improved spatial learning ability, prevented the loss of Nysted's vesicles, reduced the accumulation of ß-amyloid (Aß) and tau proteins, and suppressed microglial activation. Further research found that the expression of nuclear factor kappa-B RelA (NF-κB p65) expression and glycogen synthase kinase-3ß (GSK-3ß) activity were inhibited, while CREB was upregulated in brain tissue treated with Andro-III. Moreover, Andro-III downregulated the expression of IBA1 and inflammatory factors in microglial cells of mice induced by Aß. The regulation of the GSK-3ß/NF-κB/CREB pathway was similar to that observed in 3xTg-AD mice. Therefore, Andro-III modulates neuroinflammation and attenuates neuropathological changes of AD via the GSK-3ß/NF-κB/CREB pathway.

Exploring Fine-Grained Sparsity in Convolutional Neural Networks for Efficient Inference.

Neural networks contain considerable redundant computation, which drags down the inference efficiency and hinders the deployment on resource-limited devices. In this paper, we study the sparsity in convolutional neural networks and propose a generic sparse mask mechanism to improve the inference efficiency of networks. Specifically, sparse masks are learned in both data and channel dimensions to dynamically localize and skip redundant computation at a fine-grained level. Based on our sparse mask mechanism, we develop SMPointSeg, SMSR, and SMStereo for point cloud semantic segmentation, single image super-resolution, and stereo matching tasks, respectively. It is demonstrated that our sparse masks are well compatible to different model components and network architectures to accurately localize redundant computation, with computational cost being significantly reduced for practical speedup. Extensive experiments show that our SMPointSeg, SMSR, and SMStereo achieve state-of-the-art performance on benchmark datasets in terms of both accuracy and efficiency.

Light Field Image Super-Resolution Using Deformable Convolution.

Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging to incorporate angular information due to disparities among LF images. In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Specifically, we design an angular deformable alignment module (ADAM) for feature-level alignment. Based on ADAM, we further propose a collect-and-distribute approach to perform bidirectional alignment between the center-view feature and each side-view feature. Using our approach, angular information can be well incorporated and encoded into features of each view, which benefits the SR reconstruction of all LF images. Moreover, we develop a baseline-adjustable LF dataset to evaluate SR performance under different disparity variations. Experiments on both public and our self-developed datasets have demonstrated the superiority of our method. Our LF-DFnet can generate high-resolution images with more faithful details and achieve state-of-the-art reconstruction accuracy. Besides, our LF-DFnet is more robust to disparity variations, which has not been well addressed in literature.