Utility of Autophagy in Treating Alzheimer’s Disease

Alzheimer’s Disease (AD) is the most common cause of dementia in developed countries. The global prevalence is estimated to be as high as 24 million and is expected to continue growing. Despite more than 100 thousand papers on AD encompassing several decades of research, our understanding of the underlying pathogenesis remains limited, consequently contributing to the stagnancy in developing effective therapeutic treatment options. Enormous data in the literature provides opportunities to theoretically evaluate the most likely effective approach for this disease. By digging into the relationship between autophagy and risk factors of AD, we find that autophagy is directly or indirectly involved in most of these individual factors. For example, natural activation of autophagy has been shown to directly improve all diabetes-related factors that are associated with AD. Other examples include but are not limited to factors related to chronic inflammation, brain damage, infection, mental health, mitochondrial dysfunction, and brain nutrient deficiency. Here, we present our findings and the basis for the hypothesis that naturally generated autophagy is likely the most powerful tool currently existing in fighting AD

Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence

Self-supervised pre-training paradigms have been extensively explored in the field of skeleton-based action recognition. In particular, methods based on masked prediction have pushed the performance of pre-training to a new height. However, these methods take low-level features, such as raw joint coordinates or temporal motion, as prediction targets for the masked regions, which is suboptimal. In this paper, we show that using high-level contextualized features as prediction targets can achieve superior performance. Specifically, we propose Skeleton2vec, a simple and efficient self-supervised 3D action representation learning framework, which utilizes a transformer-based teacher encoder taking unmasked training samples as input to create latent contextualized representations as prediction targets. Benefiting from the self-attention mechanism, the latent representations generated by the teacher encoder can incorporate the global context of the entire training samples, leading to a richer training task. Additionally, considering the high temporal correlations in skeleton sequences, we propose a motion-aware tube masking strategy which divides the skeleton sequence into several tubes and performs persistent masking within each tube based on motion priors, thus forcing the model to build long-range spatio-temporal connections and focus on action-semantic richer regions. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets demonstrate that our proposed Skeleton2vec outperforms previous methods and achieves state-of-the-art results.Comment: Submitted to CVPR 202

An edge-directed interpolation method for fetal spine MR images

Abstract Background Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution. High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still challenging. Method In this paper, we propose an edge-directed interpolation (EDI) method and apply it on a group of fetal spine MR images to evaluate its feasibility and performance. This method takes edge messages from Canny edge detector to guide further pixel modification. First, low-resolution (LR) images of fetal spine are interpolated into high-resolution (HR) images with targeted factor by bi-linear method. Then edge information from LR and HR images is put into a twofold strategy to sharpen or soften edge structures. Finally a HR image with well-preserved edge structures is generated. The HR images obtained from proposed method are validated and compared with that from other four EDI methods. Performances are evaluated from six metrics, and subjective analysis of visual quality is based on regions of interest (ROI). Results All these five EDI methods are able to generate HR images with enriched details. From quantitative analysis of six metrics, the proposed method outperforms the other four from signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM) and mutual information (MI) with seconds-level time consumptions (TC). Visual analysis of ROI shows that the proposed method maintains better consistency in edge structures with the original images. Conclusions The proposed method classifies edge orientations into four categories and well preserves structures. It generates convincing HR images with fine details and is suitable in real-time situations. Iterative curvature-based interpolation (ICBI) method may result in crisper edges, while the other three methods are sensitive to noise and artifacts

Improving the Performance of PCA-Based Chiller Sensor Fault Detection by Sensitivity Analysis for the Training Data Set

An improved approach of fault detection for chiller sensors is presented based on the sensitivity analysis for the original data set used to train the Principal Component Analysis (PCA) model. Sensor faults are inevitable due to the aging, environment, location and so on. Meanwhile, because of the wide range of operational conditions, the fault of a certain sensor is very difficult to be directly detected by its own historical data. PCA is a multivariate data-based statistical analysis method and it is very useful for the sensor fault detection in HVAC&R. The undetectable zone of a certain sensor by Q-statistic is derived from the definition of Q-statistic which is usually employed as a boundary to detect the sensor fault situation. Due to the similar style between Q-statistic and Hawkins’ TH2, the undetectable zone by Hawkins’ TH2 is also obtained. Undetectable zone is a predictive index to indicate the detectability of different sensors by different statistics. Since undetectable zone is the character of the original training data set, it can indicate the quality for the selected training data. One field data set is employed to validate the presented approach. Results show that the undetectable zone of a certain sensor by Q-statistic is quite different from that by Hawkins’ TH2. Therefore, the undetectable zone can be used to improving the performance of PCA-based chiller sensor fault detection by choosing different fault detection statistics with less undetectable zone for different sensor

Attention distraction with gradient sharpening for multi-task adversarial attack

The advancement of deep learning has resulted in significant improvements on various visual tasks. However, deep neural networks (DNNs) have been found to be vulnerable to well-designed adversarial examples, which can easily deceive DNNs by adding visually imperceptible perturbations to original clean data. Prior research on adversarial attack methods mainly focused on single-task settings, i.e., generating adversarial examples to fool networks with a specific task. However, real-world artificial intelligence systems often require solving multiple tasks simultaneously. In such multi-task situations, the single-task adversarial attacks will have poor attack performance on the unrelated tasks. To address this issue, the generation of multi-task adversarial examples should leverage the generalization knowledge among multiple tasks and reduce the impact of task-specific information during the generation process. In this study, we propose a multi-task adversarial attack method to generate adversarial examples from a multi-task learning network by applying attention distraction with gradient sharpening. Specifically, we first attack the attention heat maps, which contain more generalization information than feature representations, by distracting the attention on the attack regions. Additionally, we use gradient-based adversarial example-generating schemes and propose to sharpen the gradients so that the gradients with multi-task information rather than only task-specific information can make a greater impact. Experimental results on the NYUD-V2 and PASCAL datasets demonstrate that the proposed method can improve the generalization ability of adversarial examples among multiple tasks and achieve better attack performance