I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection

Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not adequately represent the full range of simile forms. To address this issue, we propose a simile data augmentation method based on \textbf{W}ord replacement And Sentence completion using the GPT-2 language model. Our iterative process called I-WAS, is designed to improve the quality of the augmented sentences. To better evaluate the performance of our method in real-world applications, we have compiled a corpus containing a more diverse set of simile forms for experimentation. Our experimental results demonstrate the effectiveness of our proposed data augmentation method for simile detection.Comment: 15 pages, 1 figur

Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics

Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at \href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and \href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.Comment: 7 pages,3 figures, submit to emnlp 2023 demo trac

Examining the Effect of Pre-training on Time Series Classification

Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image data lack consensus. To delve deeper into the unsupervised pre-training followed by fine-tuning paradigm, we have extended previous research to a new modality: time series. In this study, we conducted a thorough examination of 150 classification datasets derived from the Univariate Time Series (UTS) and Multivariate Time Series (MTS) benchmarks. Our analysis reveals several key conclusions. (i) Pre-training can only help improve the optimization process for models that fit the data poorly, rather than those that fit the data well. (ii) Pre-training does not exhibit the effect of regularization when given sufficient training time. (iii) Pre-training can only speed up convergence if the model has sufficient ability to fit the data. (iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence. (v) While both the pre-training task and the model structure determine the effectiveness of the paradigm on a given dataset, the model structure plays a more significant role

Effects of Escherichia coli outer membrane vesicles on proliferation of breast cancer cells and tumor growth of tumor-bearing mice

Objective·To analyze the effect of Escherichia coli outer membrane vesicle (E.coli-OMV) on the proliferation of 4T1 breast cancer cells in vitro and the inhibition of tumor growth in BALB/c-4T1 tumor-bearing mice in vivo.Methods·OMVs were collected from the culture supernatant of E.coli and characterized. The uptake of E.coli-OMV by 4T1 cells was detected by fluorescent label tracking method. The effect of E.coli-OMV on 4T1 cell proliferation was detected by CCK-8 method. The effect of E.coli-OMV on 4T1 cell cycle was detected by flow cytometry. The BALB/c-4T1 tumor-bearing mouse models were established by subcutaneous inoculation, and the mice were divided into E.coli-OMV group and Control group, with 10 mice in each group. The mice in the E.coli-OMV group were injected with 0.25 mg/kg E.coli-OMV every 2 d, while the mice in the Control group were injected with equal doses of PBS. The changes in body weight, 40 d survival rate, tumor volume and tumor weight of the two groups of tumor-bearing mice were observed. The pathological morphology of the tumor tissues was evaluated by hematoxylin-eosin staining (H-E staining). The expression of proliferating cell nuclear antigen (PCNA) and CyclinD1 in tumor tissues was observed by immunohistochemical staining.Results·E.coli-OMV was spherical membrane vesicle structure with a particle size of (216.00±18.30) nm, which expressed E.coli outer membrane protein A (OmpA) and OmpC. Fluorescence microscopy results showed that 4T1 cells could intake E.coli-OMV. CCK-8 results showed that the inhibitory effect of E.coli-OMV on 4T1 cells was positively correlated with time-dose. Flow cytometry results showed that E.coli-OMV arrested the growth cycle of 4T1 cells in G0/G1 phase. In vivo experiments showed that compared with the Control group, body weight of mice in the E.coli-OMV group decreased slightly after the initial injection (P=0.031), and then recovered, while 40 d survival rate increased (P=0.037). The growth of tumor volume and weight of mice in E.coli-OMV group were lower than those in the Control group (P=0.041, P=0.004). Its tumor volume inhibition rate reached 29.69%, and tumor weight inhibition rate reached 49.81%. The results of H-E staining showed that nuclear splitting images of tumor tissues of mice in the E.coli-OMV group decreased compared to the Control group (P=0.038). The results of immunohistochemical staining showed that the positive expression of PCNA and CyclinD1 in the tumor tissues of mice in the E.coli-OMV group decreased compared to the Control group (P=0.031, P=0.002).Conclusion·Both in vitro and in vivo studies show that E.coli-OMV can significantly inhibit the proliferation of 4T1 cells

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background: Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China. Methods: Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate. Results: A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model. Conclusions: The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults

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Auditory brainstem response thresholds (db SPL) in different experimental conditions.

<p>In the column of animal number, the first number stands for Experiment One, Two, and Three, respectively. The last two numbers stand for the serial number of rats used in the study. The following letters “V”, “S”, “A”, “VA”, “SA” in the parentheses stand for the saline group, salicylate group, ablated group, saline+ablated group and salicylate+ablated group, respectively.</p><p>The threshold of click and tone meet the inclusion criteria.</p><p>Auditory brainstem response thresholds (db SPL) in different experimental conditions.</p