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It suggests that there may be a correlation between the electric shock and peripheral nerve injury, and acupuncture manipulation is an essential factor in adverse acupuncture events. Finally, we put forward that the electric shock during acupuncture is a warning sign that the peripheral nerve may be injured, rather than a normal needling sensation. In the future, we hope to have experimental studies on the mechanism of the electric shock or observational studies on the correlation between the electric shock and peripheral nerve injury to verify.In the auditory system, ribbon synapses are vesicle-associated structures located between inner hair cells (IHCs) and spiral ganglion neurons that are implicated in the modulation of trafficking and fusion of synaptic vesicles at the presynaptic terminals. Synapse loss may result in hearing loss and difficulties with understanding speech in a noisy environment. This phenomenon happens without permanent hearing loss; that is, the cochlear synaptopathy is "hidden." Recent studies have reported that synapse loss might be critical in the pathogenesis of hidden hearing loss. A better understanding of the molecular mechanisms of the formation, structure, regeneration, and protection of ribbon synapses will assist in the design of potential therapeutic strategies. In this review, we describe and summarize the following aspects of ribbon synapses (1) functional and structural features, (2) potential mechanisms of damage, (3) therapeutic research on protecting the synapses, and (4) the role of synaptic regeneration in auditory neuropathy and the current options for synapse rehabilitation.Theoretical assessment of the function of the hippocampus has suggested that given certain physiological constraints at both the neuronal and cortical level, the hippocampus is best suited to associate discontiguous items that occur in different temporal or spatial positions. Conceptually, "discontiguous" refers to events that are to be associated with one another but do not temporally or spatially overlap. However, given that humans can actively maintain information "online" by rehearsing it, even when the information is no longer being presented to the sensory system, the right way to experimentally define "discontiguity" is still a question. Does it refer to a "gap" in the presentation of information (temporal discontiguity) or to an "interruption" of the active maintenance of working memory (WM) information (functional discontiguity)? To assess this, participants were imaged by functional magnetic resonance imaging (fMRI) when making judgments on whether two words were semantically related or not. In contrast with recognition memory that can be carried out through perceptual familiarity heuristics, judgments on semantic relatedness can only be accomplished through associative processing. To assess this experimentally, two words are either (1) presented at the same time (Event AB) or (2) one after the other with an unfilled, cross-viewing delay (Event A_B) (the uninterrupted discontiguity) or (3) presented one after the other, between which participants are required to perform a calculation task (Event A#B) (the interrupted discontiguity). Results of event-related fMRI analysis revealed that relative to Event AB, Event A_B was not associated with more hippocampal activity, whereas Event A#B was. selleck chemicals The direct contrast of Event A#B relative to Event A_B also revealed significant hippocampal and parahippocampal activity. This result implied that functional discontiguity (the interruption of online maintenance of the inputted information) could be more apt at engaging the function of the hippocampus.Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model and observational differences is thus vital to reduce uncertainty in the impact of aerosols on the climate. These reported discrepancies arise from the different methods of separating the components of aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model (GCM) output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties.