Representing Alzheimer's Disease Progression via Deep Prototype Tree

For decades, a variety of predictive approaches have been proposed and evaluated in terms of their predicting capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment (MCI). Most of them focused on prediction or identification of statistical differences among different clinical groups or phases (e.g., longitudinal studies). The continuous nature of AD development and transition states between successive AD related stages have been overlooked, especially in binary or multi-class classification. Though a few progression models of AD have been studied recently, they mainly designed to determine and compare the order of specific biomarkers. How to effectively predict the individual patient's status within a wide spectrum of AD progression has been understudied. In this work, we developed a novel structure learning method to computationally model the continuum of AD progression as a tree structure. By conducting a novel prototype learning with a deep manner, we are able to capture intrinsic relations among different clinical groups as prototypes and represent them in a continuous process for AD development. We named this method as Deep Prototype Learning and the learned tree structure as Deep Prototype Tree - DPTree. DPTree represents different clinical stages as a trajectory reflecting AD progression and predict clinical status by projecting individuals onto this continuous trajectory. Through this way, DPTree can not only perform efficient prediction for patients at any stages of AD development (77.8% accuracy for five groups), but also provide more information by examining the projecting locations within the entire AD progression process.Comment: Submitted to Information Processing in Medical Imaging (IPMI) 202

Exploring the Influence of Information Entropy Change in Learning Systems

In this work, we explore the influence of entropy change in deep learning systems by adding noise to the inputs/latent features. The applications in this paper focus on deep learning tasks within computer vision, but the proposed theory can be further applied to other fields. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this paper aims to rethink whether the conventional proposition always holds. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of over 95% on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.Comment: Information Entropy, CNN, Transforme

Silk Fibroin/Polyvinyl Pyrrolidone Interpenetrating Polymer Network Hydrogels

Silk fibroin hydrogel is an ideal model as biomaterial matrix due to its excellent biocompatibility and used in the field of medical polymer materials. Nevertheless, native fibroin hydrogels show poor transparency and resilience. To settle these drawbacks, an interpenetrating network (IPN) of hydrogels are synthesized with changing ratios of silk fibroin/N-Vinyl-2-pyrrolidonemixtures that crosslink by H2O2 and horseradish peroxidase. Interpenetrating polymer network structure can shorten the gel time and the pure fibroin solution gel time for more than a week. This is mainly due to conformation from the random coil to the β-sheet structure changes of fibroin. Moreover, the light transmittance of IPN hydrogel can be as high as more than 97% and maintain a level of 90% within a week. The hydrogel, which mainly consists of random coil, the apertures inside can be up to 200 μm. Elastic modulus increases during the process of gelation. The gel has nearly 95% resilience under the compression of 70% eventually, which is much higher than native fibroin gel. The results suggest that the present IPN hydrogels have excellent mechanical properties and excellent transparency.This work was supported by The National Key Research and Development Program of China (Grant No. 2017YFC1103602), National Natural Science Foundation of China (Grant No. 51373114, 51741301), PAPD and Nature Science Foundation of Jiangsu, China (Grant No. BK20171239, BK20151242).info:eu-repo/semantics/publishedVersio

The Roles of H19 in Regulating Inflammation and Aging.

Accumulating evidence suggests that long non-coding RNA H19 correlates with several aging processes. However, the role of H19 in aging remains unclear. Many studies have elucidated a close connection between H19 and inflammatory genes. Chronic systemic inflammation is an established factor associated with various diseases during aging. Thus, H19 might participate in the development of age-related diseases by interplay with inflammation and therefore provide a protective function against age-related diseases. We investigated the inflammatory gene network of H19 to understand its regulatory mechanisms. H19 usually controls gene expression by acting as a microRNA sponge, or through mir-675, or by leading various protein complexes to genes at the chromosome level. The regulatory gene network has been intensively studied, whereas the biogenesis of H19 remains largely unknown. This literature review found that the epithelial-mesenchymal transition (EMT) and an imprinting gene network (IGN) might link H19 with inflammation. Evidence indicates that EMT and IGN are also tightly controlled by environmental stress. We propose that H19 is a stress-induced long non-coding RNA. Because environmental stress is a recognized age-related factor, inflammation and H19 might serve as a therapeutic axis to fight against age-related diseases

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu