A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively

CRISPR/Cas9-based editing of a sensitive transcriptional regulatory element to achieve cell type-specific knockdown of the NEMO scaffold protein

The use of alternative promoters for the cell type-specific expression of a given mRNA/protein is a common cell strategy. NEMO is a scaffold protein required for canonical NF-κB signaling. Transcription of the NEMO gene is primarily controlled by two promoters: one (promoter B) drives NEMO transcription in most cell types and the second (promoter A) is largely responsible for NEMO transcription in liver cells. Herein, we have used a CRISPR/Cas9-based approach to disrupt a core sequence element of promoter B, and this genetic editing essentially eliminates expression of NEMO mRNA and protein in 293T human kidney cells. By cell subcloning, we have isolated targeted 293T cell lines that express no detectable NEMO protein, have defined genomic alterations at promoter B, and do not support canonical NF-κB signaling in response to treatment with tumor necrosis factor (TNF). Nevertheless, non-canonical NF-κB signaling is intact in these NEMO-deficient cells. Expression of ectopic NEMO in the edited cells restores downstream NF-κB signaling in response to TNF. Targeting of the promoter B element does not substantially reduce NEMO expression (from promoter A) in the human SNU-423 liver cancer cell line. We have also used homology directed repair (HDR) to fix the promoter B element in a 293T cell clone. Overall, we have created a strategy for selectively eliminating cell type-specific expression from an alternative promoter and have generated 293T cell lines with a functional knockout of NEMO. The implications of these findings for further studies and for therapeutic approaches to target canonical NF-κB signaling are discussed.GM117350 - National Institutes of Health; CA077474 - National Institutes of HealthPublished versio

Chinese Named Entity Recognition Method for Domain-Specific Text

The Chinese named entity recognition (NER) is a critical task in natural language processing, aiming at identifying and classifying named entities in text. However, the specificity of domain texts and the lack of large-scale labelled datasets have led to the poor performance of NER methods trained on public domain corpora on domain texts. In this paper, a named entity recognition method incorporating sentence semantic information is proposed, mainly by adaptively incorporating sentence semantic information into character semantic information through an attention mechanism and a gating mechanism to enhance entity feature representation while attenuating the noise generated by irrelevant character information. In addition, to address the lack of large-scale labelled samples, we used data self-augmentation methods to expand the training samples. Furthermore, we introduced a Weighted Strategy considering that the low-quality samples generated by the data self-augmentation process can have a negative impact on the model. Experiments on the TCM prescriptions corpus showed that the F1 values of our method outperformed the comparison methods

CRISPR/Cas9-based editing of a sensitive transcriptional regulatory element to achieve cell type-specific knockdown of the NEMO scaffold protein

The use of alternative promoters for the cell type-specific expression of a given mRNA/protein is a common cell strategy. NEMO is a scaffold protein required for canonical NF-κB signaling. Transcription of the NEMO gene is primarily controlled by two promoters: one (promoter B) drives NEMO transcription in most cell types and the second (promoter D) is largely responsible for NEMO transcription in liver cells. Herein, we have used a CRISPR/Cas9-based approach to disrupt a core sequence element of promoter B, and this genetic editing essentially eliminates expression of NEMO mRNA and protein in 293T human kidney cells. By cell subcloning, we have isolated targeted 293T cell lines that express no detectable NEMO protein, have defined genomic alterations at promoter B, and do not support activation of canonical NF-κB signaling in response to treatment with tumor necrosis factor. Nevertheless, noncanonical NF-κB signaling is intact in these NEMO-deficient cells. Expression of ectopic wildtype NEMO, but not certain human NEMO disease mutants, in the edited cells restores downstream NF-κB signaling in response to tumor necrosis factor. Targeting of the promoter B element does not substantially reduce NEMO expression (from promoter D) in the human SNU423 liver cancer cell line. Thus, we have created a strategy for selectively eliminating cell typespecific expression from an alternative promoter and have generated 293T cell lines with a functional knockout of NEMO. The implications of these findings for further studies and for therapeutic approaches to target canonical NF-κB signaling are discussed.Published versio

Occlusion facial expression recognition based on feature fusion residual attention network

Recognizing occluded facial expressions in the wild poses a significant challenge. However, most previous approaches rely solely on either global or local feature-based methods, leading to the loss of relevant expression features. To address these issues, a feature fusion residual attention network (FFRA-Net) is proposed. FFRA-Net consists of a multi-scale module, a local attention module, and a feature fusion module. The multi-scale module divides the intermediate feature map into several sub-feature maps in an equal manner along the channel dimension. Then, a convolution operation is applied to each of these feature maps to obtain diverse global features. The local attention module divides the intermediate feature map into several sub-feature maps along the spatial dimension. Subsequently, a convolution operation is applied to each of these feature maps, resulting in the extraction of local key features through the attention mechanism. The feature fusion module plays a crucial role in integrating global and local expression features while also establishing residual links between inputs and outputs to compensate for the loss of fine-grained features. Last, two occlusion expression datasets (FM_RAF-DB and SG_RAF-DB) were constructed based on the RAF-DB dataset. Extensive experiments demonstrate that the proposed FFRA-Net achieves excellent results on four datasets: FM_RAF-DB, SG_RAF-DB, RAF-DB, and FERPLUS, with accuracies of 77.87%, 79.50%, 88.66%, and 88.97%, respectively. Thus, the approach presented in this paper demonstrates strong applicability in the context of occluded facial expression recognition (FER)

Dark Count of 20-inch PMTs Generated by Natural Radioactivity

The primary objective of the JUNO experiment is to determine the ordering of neutrino masses using a 20-kton liquid-scintillator detector. The 20-inch photomultiplier tube (PMT) plays a crucial role in achieving excellent energy resolution of at least 3% at 1 MeV. Understanding the characteristics and features of the PMT is vital for comprehending the detector's performance, particularly regarding the occurrence of large pulses in PMT dark counts. This research paper aims to further investigate the origin of these large pulses in the 20-inch PMT dark count rate through measurements and simulations. The findings confirm that the main sources of the large pulses are natural radioactivity and muons striking the PMT glass. By analyzing the PMT dark count rate spectrum, it becomes possible to roughly estimate the radioactivity levels in the surrounding environment.Comment: 10 pages, 8 figures, and 5 table

The cosmic ray test of MRPCs for the BESIII ETOF upgrade

In order to improve the particle identification capability of the Beijing Spectrometer III (BESIII),t is proposed to upgrade the current endcap time-of-flight (ETOF) detector with multi-gap resistive plate chamber (MRPC) technology. Aiming at extending ETOF overall time resolution better than 100ps, the whole system including MRPC detectors, new-designed Front End Electronics (FEE), CLOCK module, fast control boards and time to digital modules (TDIG), was built up and operated online 3 months under the cosmic ray. The main purposes of cosmic ray test are checking the detectors' construction quality, testing the joint operation of all instruments and guaranteeing the performance of the system. The results imply MRPC time resolution better than 100$ps$, efficiency is about 98$\%$ and the noise rate of strip is lower than 1$Hz/$($scm^{2}$) at normal threshold range, the details are discussed and analyzed specifically in this paper. The test indicates that the whole ETOF system would work well and satisfy the requirements of upgrade