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Objective Oxygen-glucose deprivation (OGD) is used to mimic ischemia in vitro to observe whether endoplasmic reticulum (ER) stress is involved in human dental pulp cells (hDPCs) after OGD and to better understand the regulatory mechanism of hDPCs in ischemia. Methods hDPCs were cultured in glucose-free DMEM and hypoxia (volume fraction 2% O2) to establish an hDPCs OGD model in vitro, which mimics hDPCs ischemia in vitro. hDPCs were divided into a control group (normal culture) and an experimental group (OGD 0 h, 2 h, 4 h and 8 h groups). After pretreatment with OGD for 0, 2, 4 and 8 h, hDPC viability was measured by methylthiazol tetrazolium (MTT) assay. qRT-PCR was used to detect the mRNA expression of ER stress markers [splicing x-box binding protein1 (sXBP1), activating transcription Factor 4 (ATF4) and C/EBP homologous protein (chop)]. Western blot was used to detect the protein expression of ER stress markers [phosphorylated RNA-activated protein kinase-like ER-resident kinase (p-perk) and phosphorylated eukaryotic initiation factor-2α (p-eIF2α)]. Results Compared with OGD 0 h group, cell viability of hDPCs decreased when exposed to OGD treatment for 2 h, 4 h and 8 h. Compared with the control group, mRNA expressions of ER stress makers (sXBP1, ATF4 and chop) and the protein expressions of ER stress protein markers (p-perk andp-eIF2α) increased in OGD treatment cells after 4 h were higher in OGD cells. The differences were statistically significant (P

Bismuth-based anodes are promising for lithium-ion batteries owing to their high theoretical capacity, garnering significant attention in recent decades. Despite its intrinsic advantages, this anode material has the shortcoming of excessive volume expansion during the charging and discharging processes, which significantly reduces the cycling stability. In this work, a nitrogen-doped carbon-coated Bi2Se3−xTex composite (Bi2Se3−xTex@NC) with excellent electrochemical properties was synthesized by the solvothermal method followed by heat treatment. Benefiting from its unique structure and Te-doping properties, the nitrogen-doped carbon-coated Bi2Se3−xTex composite maintains a discharge specific capacity of 464.0 mAh g−1 after 60 cycles at a current density of 0.1 A g−1. This research presents an efficient approach for the preparation of nitrogen-doped carbon-coated anode materials and a fresh perspective on battery electrode material design. [ABSTRACT FROM AUTHOR]

Objectives: To explore the effect of exosomal miR‐126 derived from stem cells from the apical papilla (SCAPs) under hypoxia on human umbilical vein endothelial cell (HUVEC) angiogenesis. Methods: miR‐126 mimics plasmids were used to upregulate miR‐126 in SCAPs. Internalization of PKH26‐labeled exosomes was examined by fluorescent microscopy. CCK‐8 assay, Transwell assay, scratch assay, tube formation assay, and Matrigel plug assay were performed to detect the effects of exosomes on the angiogenic ability of HUVECs. The luciferase reporter assay and rescue assay were performed to examine the relationship between miR‐126 and sprouty‐related, EVH1 domain‐containing protein 1 (SPRED1). The involvement of SPRED1 and the extracellular signal‐regulated kinase (ERK) signaling pathway was evaluated by western blotting. Results: miR‐126 expression was upregulated in SCAPs and in SCAP‐derived exosomes under hypoxia. miR‐126 expression was increased in HUVECs when cocultured with SCAP‐derived exosomes. Induced overexpression of miR‐126 in hypoxic SCAPs and secreted exosomes resulted in enhanced angiogenesis both in vitro and in vivo. Western blot analysis revealed that miR‐126‐mediated SPRED1 downregulation induced activation of ERK signaling. Conclusions: Under hypoxic conditions, exosomes derived from SCAPs can promote HUVEC angiogenesis through expression of miR‐126, which subsequently suppresses SPRED1 and activates the ERK signaling pathway. [ABSTRACT FROM AUTHOR]

In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity–relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are highly computationally efficient artificial intelligence methods due to their advantages in having a biologically plausible computational framework. Recent research has shown that SNN trained using backpropagation (SNN-BP) exhibits excellent performance and has shown great potential in tasks such as image classification and security detection. However, the backpropagation method limits the dynamics and biological plausibility of the neural models in SNN, which will limit the recognition and simulation performance of SNN. In order to make neural models more similar to biological neurons, this study proposes a leaky integrate-and-fire (LIF) neuron model with dense intralayer connections, as well as efficient forward and backward processes in BP training. The new model will make the interaction between neurons within the layer more frequent, enhancing the intrinsic information exchange capability of SNN. An effective probabilistic spike-timing dependent plasticity (STDP) method is also proposed to reduce the overweighted connections between neurons, as well as a hybrid training method using BP and probabilistic STDP. The training method combines the advantages of BP and STDP to improve the performance of SNN models. An intralayer-connected SNN with hybrid training (ISNN-HY) is proposed with the combination of these improvements. The proposed model was evaluated on three static image datasets and one neuromorphic dataset. The results showed that the performance of ISNN-HY is superior to that of other SNN-BP models. The proposed method also makes it possible to accurately simulate biological neural systems. [ABSTRACT FROM AUTHOR]