One dimensional terpyridine-based metal organic framework for stable supercapacitor

In summary, a novel structure of MOF based on 1,4–di ([2,2':6',2''terpyridin] -4'-yl)benzene and 1,4-naphthalenedicarboxylic acid has been constructed through hydrothermal reaction. The Ni-MOF displays one dimensional zigzag chain, which connect each other by hydrogen bonding to form three dimensional supramolecule with large channels. The conjugated systems of the terpyridin and benzene ligands enhance the chain rigidity, accelerate the electron transport. The massive channels provides electrolyte rapid transfer. By the structural feature aforementioned, the Ni-MOF demonstrates stable electrochemical performance as suprocapacitor

Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition

The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR. Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. However, existing methods fail to effectively utilize bidirectional context information during the inference stage. Furthermore, current bidirectional training methods are primarily designed for string decoders and cannot adequately generalize to tree decoders, which offer superior generalization capabilities and structural analysis capacity. In order to overcome these limitations, we propose the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure. Our method extends the bidirectional training strategy to the tree decoder, allowing for more effective training by leveraging bidirectional information. Additionally, we analyze the impact of the visual and linguistic perception of the HMER model separately and introduce the Shared Language Modeling (SLM) mechanism. Through the SLM, we enhance the model's robustness and generalization when dealing with visual ambiguity, particularly in scenarios with abundant training data. Our approach has been validated through extensive experiments, demonstrating its ability to achieve new state-of-the-art results on the CROHME 2014, 2016, and 2019 datasets, as well as the HME100K dataset. The code used in our experiments will be publicly available

Study on the Influencing Factors of Health Information Sharing Behavior of the Elderly under the Background of Normalization of Pandemic Situation

This study aims to solve the problem of unwise judgment, decisions, and correspondingly dangerous behaviors caused by error health information to the elderly. Based on the MOA model and self-determination theory, this paper constructs a health information sharing model for the elderly and analyzes it with Amos\u27s structural equation model. The study finds that media richness, health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic positively influence health information sharing behavior. In contrast, perceived risks have a significant negative impact on health information sharing behavior. At the same time, media richness positively affects health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic but has no significant impact on perceived risks. Health literacy positively affects perceived benefits but does not significantly affect the perceived risks and negative emotions of the coronavirus epidemic. This study aims to assist government and online social platforms in taking relevant measures under the background of normalization of the pandemic situation, controlling the spread of error health information among the elderly, and guiding the elderly to share health information better

Synthetic θ‐Defensin Antibacterial Peptide as a Highly Efficient Nonviral Vector for Redox‐Responsive miRNA Delivery

Synthetic cationic vectors have shown great promise for nonviral gene delivery. However, their cytotoxicity and low efficiency impose great restrictions on clinic applications. To push through this limitation, humanized peptides or proteins with cationic biocompatibility as well as biodegradation would be an excellent candidate. Herein, for the first time, we describe how an arginine‐rich humanized antimicrobial cyclopeptide, θ‐defensin, can be used as a synthetic cationic vector to load and deliver miRNA into bone mesenchymal stem cells with high efficiency and ultralow cytotoxicity, surpassing the efficiency of the commercial polyethylenimine (25 kD) and Lipofectamine 3000. To note, θ‐defensin can redox‐responsively release the loaded miRNA through a structural change: in extracellular oxidative environment, θ‐defensin has large β‐sheet structures stabilized by three disulfide linkages, and this special structure enables highly efficient delivery of miRNA by passing through cell membranes; in intracellular environment, redox‐responsive disulfide linkages are broken and the tight β‐sheet structures are destroyed, so that the miRNA can be released. Our results suggest that synthetic θ‐defensin peptides are a new class of nonviral gene vectors and this study may also provide a promising strategy to design smart‐responsive gene vectors with high efficiency and minimal toxicity.This study describes how an arginine‐rich humanized antimicrobial cyclopeptide, θ‐defensin, can be used as a synthetic cationic vector to load and deliver miRNA into bone mesenchymal stem cells with high efficiency and low cytotoxicity, surpassing the efficiency of the commercial polyethylenimine (25 kD) and Lipofectamine 3000.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/1/adbi201700001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/2/adbi201700001_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/3/adbi201700001-sup-0001-S1.pd

Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views

Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high deformation of hand and heavy occlusions between hand and object. In this paper, we propose a neural rendering and pose estimation system for hand-object interaction from sparse views, which can also enable 3D hand-object interaction editing. We share the inspiration from recent scene understanding work that shows a scene specific model built beforehand can significantly improve and unblock vision tasks especially when inputs are sparse, and extend it to the dynamic hand-object interaction scenario and propose to solve the problem in two stages. We first learn the shape and appearance prior knowledge of hands and objects separately with the neural representation at the offline stage. During the online stage, we design a rendering-based joint model fitting framework to understand the dynamic hand-object interaction with the pre-built hand and object models as well as interaction priors, which thereby overcomes penetration and separation issues between hand and object and also enables novel view synthesis. In order to get stable contact during the hand-object interaction process in a sequence, we propose a stable contact loss to make the contact region to be consistent. Experiments demonstrate that our method outperforms the state-of-the-art methods. Code and dataset are available in project webpage https://iscas3dv.github.io/HO-NeRF

Diffeomorphic Image Registration with Neural Velocity Field

Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to learn the transformation directly, achieve fast inference, but face challenges in accuracy due to the difficulties in capturing the small local deformations and generalization ability. Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations. A multilayer perceptron (MLP) with sinusoidal activation function is used to represent the continuous velocity field and assigns a velocity vector to every point in space, providing the flexibility of modeling complex deformations as well as the convenience of optimization. Moreover, we propose a cascaded image registration framework (Cas-DNVF) by combining the benefits of both optimization and learning based methods, where a fully convolutional neural network (FCN) is trained to predict the initial deformation, followed by DNVF for further refinement. Experiments on two large-scale 3D MR brain scan datasets demonstrate that our proposed methods significantly outperform the state-of-the-art registration methods.Comment: WACV 202

Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR begins with explicit deformations of template meshes to obtain coarsely reconstructed cortical surfaces, based on which the oriented point clouds are estimated for the subsequent differentiable poisson surface reconstruction. By doing so, our method unifies explicit (oriented point clouds) and implicit (indicator function) cortical surface reconstruction. Compared to explicit representation-based methods, our hybrid approach is more friendly to capture detailed structures, and when compared with implicit representation-based methods, our method can be topology aware because of end-to-end training with a mesh-based deformation module. In order to address topology defects, we propose a new topology correction pipeline that relies on optimization-based diffeomorphic surface registration. Experimental results on three brain datasets show that our approach surpasses existing implicit and explicit cortical surface reconstruction methods in numeric metrics in terms of accuracy, regularity, and consistency