Computational Analysis of Non-CpG DNA Methylation in the Mammalian Nervous System

Noncanonical forms of DNA methylation, especially non-CpG DNA methylation, play essential roles in the neuronal epigenome, and have only recently begun to be characterized. While most DNA methylation within mammals is found in a CG context and maintained by DNMT1, neurons contain uniquely high levels of non-CpG methylation, such that the total amounts of methylation in non-CpG contexts equals or surpasses the total amounts of methylation in CG contexts. Non-CpG methylation, unlike CpG methylation, cannot be maintained by DNMT1, and must be established by the de novo methyltransferase DNMT3A.One unique characteristic of non-CpG methylation compared to canonical CpG methylation is the extremely wide range of biological signal it exhibits across large regions of the genome. This may enable MeCP2, a critical methyl-binding protein whose disruption causes multiple neurodevelopmental diseases, to repress regulatory elements across entire domains of the genome. How these patterns of varied methylation are established throughout the genome and what factors direct methylation to one location versus another, however, are unknown. In addition, the mechanisms by which these methyl binding proteins function and the transcriptomic effects when non-CpG methylation is lost are not well understood. As such, my dissertation work centers around the patterning, function, and consequences of this unique neural regulatory mark.Firstly, by applying multiscale analysis of bisulfite-sequencing and high-throughput chromatin conformation capture data in the cerebral cortex of mice we find that megabase-scale regions of high non-CG methylation can correspond with topologically-associating domains of chromatin folding, identifying a new mechanism influencing mCA deposition across the neuronal genome. We find that MeCP2 represses enhancers found in these domains when they are enriched for non-CG and CG methylation, with the strongest repression occurring for enhancers located within MeCP2-repressed genes. These alterations in enhancer activity provide a mechanism for how MeCP2 disruption in neurodevelopmental disorders can lead to widespread changes in gene expression. In light of our findings that enhancer-based repression by MeCP2 and mCA is disrupted in models of MeCP2 disorders, we investigated whether this pathway is affected in a new model of NDD caused by DNMT3A disruption.We find that multiple transcriptomic and epigenomic changes are shared between a knockout of MeCP2, and a heterozygous knockout of DNMT3A, the enzyme that establishes neuronal non-CpG methylation. Together these findings demonstrate a previously unrecognized role for non-CpG DNA methylation in the regulation of enhancer activity in neurons, and a role for enhancer dysregulation, stemming from disruption of non-CpG DNA methylation, in multiple disorders. This highlights non-CpG methylation as a possible convergence point between multiple neurodevelopmental disorders

Research Progress on the Modification Methods of Clay Minerals

Clay minerals are widely distributed in nature, and their applications have been rapidly developed in the last decade or so due to their unique physical and chemical properties. Since the most researched is the modification of clay minerals, this paper introduces the types of clay, basic structural characteristics and common modification methods. The methods of modified clay include high-temperature excitation and acid-base excitation methods to stimulate the activity of clay minerals, as well as interlayer ion exchange modification methods, clay surface grafting techniques such as sol-gel method, surface hydroxyl grafting modification and other methods, and also introduces the intercalation methods, including solution intercalation method, In situ polymerization intercalation method, etc. The applications and developments of clay minerals are summarized, from traditional industrial applications to environmental protection and high-tech nanomaterials, mainly in the automotive industry, environment-friendly materials and catalysts

M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the text in the dataset, which will have a negative impact on model learning. Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data. To achieve this goal, we borrow the idea of Curriculum Learning and propose a Multi-grained Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by adjusting the order of training data. Extensive experimental results show that our framework consistently outperforms state-of-the-art work on three sub-tasks of MABSA.Comment: Accepted by EMNLP 202

Effect of distance on photoluminescence quenching and proximity-induced spin-orbit coupling in graphene-WSe2 heterostructures

Spin-orbit coupling (SOC) in graphene can be greatly enhanced by proximity coupling it to transition metal dichalcogenides (TMDs) such as WSe2. We find that the strength of the acquired SOC in graphene depends on the stacking order of the heterostructures when using hexagonal boron nitride (h-BN) as the capping layer, i.e., SiO2/graphene/WSe2/h-BN exhibiting stronger SOC than SiO2/WSe2/graphene/h-BN. We utilize photoluminescence (PL) as an indicator to characterize the interaction between graphene and monolayer WSe2 grown by chemical vapor deposition. We observe much stronger PL quenching in the SiO2/graphene/WSe2/h-BN stack than in the SiO2/WSe2/graphene/h-BN stack, and correspondingly a much larger weak antilocalization (WAL) effect or stronger induced SOC in the former than in the latter. We attribute these two effects to the interlayer distance between graphene and WSe2, which depends on whether graphene is in immediate contact with h-BN. Our observations and hypothesis are further supported by first-principles calculations which reveal a clear difference in the interlayer distance between graphene and WSe2 in these two stacks

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batch-wise per-sample gradient computation method to estimate the pairwise TracIn to represent the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.Comment: 8 page

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible.Comment: Accepted by AAAI2