MiR-646 targets PDK1 to recede aerobic glycolysis and cell proliferation in nasopharyngeal carcinoma

Purpose: To investigate the effect and mechanism of miR-646 on aerobic glycolysis and cell proliferation in nasopharyngeal carcinoma. Methods: MiR-646 expression in human nasopharyngeal carcinoma cell lines was determined by quantitative real-time polymerase chain reaction) (qRT-PCR). Cell counting kit-8 (CCK8) was used to evaluate cell viability, and colony formation assay was also performed. The target of miR-646 was determined by luciferase activity assay. The effect of miR-646 on aerobic glycolysis was assessed via glucose uptake, and lactate and ATP production. Western blot analysis was conducted to unravel the underlying mechanism involved in the regulation of miR-646 in nasopharyngeal carcinoma. Results: MiR-646 was downregulated in human nasopharyngeal carcinoma cell lines. MiR-646 mimics decreased cell viability and inhibited cell proliferation, whereas miR-646 inhibitor increased cell viability and promoted cell proliferation. Pyruvate dehydrogenase kinase 1(PDK1) was identified as a target of miR-646, and its expression was negatively regulated by miR-646. MiR-646 probably inhibited aerobic glycolysis via regulation of PDK1, as shown by decreased glucose uptake and decreased lactate and ATP production. The inhibitory effect of miR-646 on nasopharyngeal carcinoma cell proliferation was partly via PDK1 regulation. Conclusion: MiR-646 inhibits aerobic glycolysis in nasopharyngeal carcinoma and promotes cell proliferation via suppression of PDK1, suggesting miR-646 as a potential therapeutic target in nasopharyngeal carcinoma

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.Comment: Accepted by EMNLP 202

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

We investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi

Sequential method for rapid early diagnosis of white spot syndrome virus in crayfish

We developed a practical method to rapidly detect and diagnose latent white spot syndrome virus (WSSV) in infected crayfish that were non-symptomatic for WSSV. This method included a simplified extraction of DNA template, optimized loop-mediated isothermal amplification (LAMP), and final visualization of the product by means of staining with SYBR green I. Using this method, WSSV was detected in crayfish that had been artificially infected in two ways: at 5 h after injection, and 24 h after feeding with tissue from WSSV-infected crayfish (at a stage when such infected crayfish were non-symptomatic), and a thousand times or more dilution can omit fluorescent background when SYBR green I was used. Results indicate that this was a rapid, convenient, and highly sensitive method for the early diagnosis and detection of WSSV. The whole detection procedure took less than one hour to complete.Key words: White spot syndrome virus, loop-mediated isothermal amplification, SYBR green I, Procambarus clarkii, early diagnosis

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

Exchanging-based Multimodal Fusion with Transformer

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE

Study on Wind-Induced Response of Transmission Tower-Line System under Downburst Wind

Downburst is the main source of extreme wind speed in non-typhoon areas, which has caused a large amount of transmission line damage all over the world. In order to reveal the wind-induced vibration response characteristics of a transmission tower-line system under downburst, the nonlinear dynamic analysis of a single tower and tower-line system is carried out, and the amplification effect of tower-line coupling and fluctuating wind on the dynamic response is studied. Then, the effects of three wind field parameters closely related to the average wind profile on the wind-induced response of the tower-line system are studied. The results show that under the action of the downburst, the tower-line coupling weakens the dynamic response to a certain extent, and the dynamic amplification factor of a single tower and tower-line system is 1.1 ~ 1.3; for the self-supporting tower, when the height of the peak wind speed is close to the height of tower, the responses of the structure are more unfavorable. When the vector superposition method is used, the storm moving speed (Vt) has little effect on the wind-induced response of the tower-line system. For large-span structures such as tower-line systems, to ensure the safety of the structural design, the value of the characteristic radius (Rc) should not be too small

Study on Wind-Induced Response of Transmission Tower-Line System under Downburst Wind

Downburst is the main source of extreme wind speed in non-typhoon areas, which has caused a large amount of transmission line damage all over the world. In order to reveal the wind-induced vibration response characteristics of a transmission tower-line system under downburst, the nonlinear dynamic analysis of a single tower and tower-line system is carried out, and the amplification effect of tower-line coupling and fluctuating wind on the dynamic response is studied. Then, the effects of three wind field parameters closely related to the average wind profile on the wind-induced response of the tower-line system are studied. The results show that under the action of the downburst, the tower-line coupling weakens the dynamic response to a certain extent, and the dynamic amplification factor of a single tower and tower-line system is 1.1 ~ 1.3; for the self-supporting tower, when the height of the peak wind speed is close to the height of tower, the responses of the structure are more unfavorable. When the vector superposition method is used, the storm moving speed (Vt) has little effect on the wind-induced response of the tower-line system. For large-span structures such as tower-line systems, to ensure the safety of the structural design, the value of the characteristic radius (Rc) should not be too small