Publications by authors named "Hongbin Dong"

Background: Light chain cardiac amyloidosis (AL-CA) is associated with a high incidence of mortality. Big endothelin-1 (ET-1), the precursor of endothelial-vasoconstrictive ET-1, is closely related to the concentration of bioactive ET-1. Association between big ET-1 and prognosis of AL-CA has not yet been documented.

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The production of bone-like structural scaffolds through bone tissue engineering technology is a promising method for bone regeneration to repair bone defects. Deer antler, an easily harvested and abundantly sourced initial bone tissue structure, resembles the composition and structure of human cancellous bone and can serve as a new material for allogeneic bone transplantation. This study involved the preparation and characterization of antler powder/chitosan/β-glycerophosphate sodium/polyvinyl alcohol (AP/CS/β-GP/PVA) porous hydrogel scaffolds to verify their material properties and osteogenic mechanisms.

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Periodontal disease is the most common type of oral disease. Periodontal bone defect is the clinical outcome of advanced periodontal disease, which seriously affects the quality of life of patients. Promoting periodontal tissue regeneration and repairing periodontal bone defects is the ultimate treatment goal for periodontal disease, but the means and methods are very limited.

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Aims: Premature ventricular contractions (PVC) and non-sustained ventricular tachycardia (NSVT) are commonly observed in light chain cardiac amyloidosis (AL-CA), but their association with prognosis is still unclear. We aimed to evaluate the prognostic value of PVCs and NSVT in patients with moderate-to-advanced AL-CA.

Methods And Results: We retrospectively included patients with AL-CA at modified 2004 Mayo stages II-IIIb between February 2014 and December 2020.

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Drought is a major threat to alfalfa (Medicago sativa L.) production. The discovery of important alfalfa genes regulating drought response will facilitate breeding for drought-resistant alfalfa cultivars.

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Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains.

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Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mechanisms assign a weight for a variable at the same time or the features of each previous time step to capture spatio-temporal correlations.

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Geo-sensory time series, such as the air quality and water distribution, are collected from numerous sensors at different geospatial locations in the same time interval. Each sensor monitors multiple parameters and generates multivariate time series. These time series change over time and vary geographically; hence, geo-sensory time series contain multi-scale spatial-temporal correlations, namely inter-sensor spatial-temporal correlations and intra-sensor spatial-temporal correlations.

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Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability.

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Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN.

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The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory.

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Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult.

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Background/purpose: Dental unit water lines (DUWLs) may be contaminated by aerobic bacteria in clinical settings and comprehensive disinfecting methods should be considered without delay. Herein, this study aims to investigate the timeliness and dynamic bacteriostatic effects of different forms of nanometer silver (NMS) disinfectant on bio-film in DUWLs.

Materials And Methods: Bacterial DUWLs samples were respectively treated with different NMS forms, including liquid phase and solid phase at the concentrations of 0.

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Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect.

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The pathogenesis of alcoholic liver disease (ALD) is not well established. However, oxidative stress and associated decreases in levels of glutathione (GSH) are known to play a central role in ALD. The present study examines the effect of GSH deficiency on alcohol-induced liver steatosis in Gclm knockout (KO) mice that constitutively have ≈15% normal hepatic levels of GSH.

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As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests.

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Fanconi anemia (FA) is a recessively inherited disease manifesting developmental abnormalities, bone marrow failure, and increased risk of malignancies. Whereas FA has been studied for nearly 90 years, only in the last 20 years have increasing numbers of genes been implicated in the pathogenesis associated with this genetic disease. To date, 19 genes have been identified that encode Fanconi anemia complementation group proteins, all of which are named or aliased, using the root symbol "FANC.

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Aldehyde dehydrogenase 1B1 (ALDH1B1) is a mitochondrial enzyme sharing 65% and 72% sequence identity with ALDH1A1 and ALDH2 proteins, respectively. Compared to the latter two ALDH isozymes, little is known about the physiological functions of ALDH1B1. Studies in humans indicate that ALDH1B1 may be associated with alcohol sensitivity and stem cells.

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Alcohol abuse leads to tissue damage including a variety of cancers; however, the molecular mechanisms by which this damage occurs remain to be fully understood. The primary enzymes involved in ethanol metabolism include alcohol dehydrogenase (ADH), cytochrome P450 isoform 2E1, (CYP2E1), catalase (CAT), and aldehyde dehydrogenases (ALDH). Genetic polymorphisms in human genes encoding these enzymes are associated with increased risks of alcohol-related tissue damage, as well as differences in alcohol consumption and dependence.

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Four dioxin-inducible enzymes--NAD(P)H: quinone oxidoreductase-1 (NQO1) and three cytochromes P450 (CYP1A1, CYP1A2 & CYP1B1)--are implicated in both detoxication and metabolic activation of various endobiotics and xenobiotics. NQO1 is generally regarded as a cytosolic enzyme; whereas CYP1 proteins are located primarily in endoplasmic reticulum (ER), CYP1A1 and CYP1A2 proteins are also targeted to mitochondria. This lab has generated Cyp1a1(mc/mc) and Cyp1a1(mtt/mtt) knock-in mouse lines in which CYP1A1 protein is targeted exclusively to ER (microsomes) and mitochondria, respectively.

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Ethanol consumption has effects on the central nervous system (CNS), manifesting as motor incoordination, sleep induction (hypnosis), anxiety, amnesia, and the reinforcement or aversion of alcohol consumption. Acetaldehyde (the direct metabolite of ethanol oxidation) contributes to many aspects of the behavioral effects of ethanol. Given acetaldehyde cannot pass through the blood brain barrier, its concentration in the CNS is primarily determined by local production from ethanol.

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Previously this laboratory has identified the mouse Slc39a8 gene encoding the ZIP8 transporter, important in cadmium uptake. ZIP8 functions endogenously as a electroneutral Zn(2+)/(HCO(3)(-))(2) symporter, moving both ions into the cell. The overall physiological importance of ZIP8 remains unclear.

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In the past, CYP1A1 protein was known to be located in the endoplasmic reticulum (ER; microsomes). More recently, CYP1A1 was shown also to be targeted to the inner mitochondrial membrane; mitochondrial import is dependent on NH(2)-terminal processing that exposes a cryptic targeting signal. It is interesting that microsomal and mitochondrial CYP1A1 enzymes exhibit different substrate specificities, electron donors, and inducer properties.

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