Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study

Recently, ChatGPT has drawn great attention from both the research community and the public. We are particularly curious about whether it can serve as a universal sentiment analyzer. To this end, in this work, we provide a preliminary evaluation of ChatGPT on the understanding of opinions, sentiments, and emotions contained in the text. Specifically, we evaluate it in four settings, including standard evaluation, polarity shift evaluation, open-domain evaluation, and sentiment inference evaluation. The above evaluation involves 18 benchmark datasets and 5 representative sentiment analysis tasks, and we compare ChatGPT with fine-tuned BERT and corresponding state-of-the-art (SOTA) models on end-task. Moreover, we also conduct human evaluation and present some qualitative case studies to gain a deep comprehension of its sentiment analysis capabilities.Comment: Technical Repor

Orthogonal Subspace Learning for Language Model Continual Learning

Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their performance degrades in scenarios where multiple tasks are encountered sequentially, also known as catastrophic forgetting. In this paper, we propose orthogonal low-rank adaptation (O-LoRA), a simple and efficient approach for continual learning in language models, effectively mitigating catastrophic forgetting while learning new tasks. Specifically, O-LoRA learns tasks in different (low-rank) vector subspaces that are kept orthogonal to each other in order to minimize interference. Our method induces only marginal additional parameter costs and requires no user data storage for replay. Experimental results on continual learning benchmarks show that our method outperforms state-of-the-art methods. Furthermore, compared to previous approaches, our method excels in preserving the generalization ability of LLMs on unseen tasks.Comment: EMNLP 2023 finding

Dynamical Variations of the Global COVID-19 Pandemic Based on a SEICR Disease Model: A New Approach of Yi Hua Jie Mu

The ongoing coronavirus disease 2019 (COVID-19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID-19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID-19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID-19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID-19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID-19. The role of the climate on the COVID-19 variations should be concluded with more data and more cautions. The non-pharmaceutical interventions still play the key role in controlling and prevention this global pandemic

MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with explicit and implicit aspects and opinions for ABSA research. Meanwhile, we evaluate generative and non-generative baselines on multiple ABSA subtasks under the open domain setting, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed. The datasets are publicly released at \url{https://github.com/NUSTM/MEMD-ABSA}

SLIT2/ROBO1-miR-218-1-RET/PLAG1: a new disease pathway involved in Hirschsprung\u27s disease.

Hirschsprung\u27s disease (HSCR) is a rare congenital disease caused by impaired proliferation and migration of neural crest cells. We investigated changes in expression of microRNAs (miRNAs) and the genes they regulate in tissues of patients with HSCR. Quantitative real-time PCR and immunoblot analyses were used to measure levels of miRNA, mRNAs, and proteins in colon tissues from 69 patients with HSCR and 49 individuals without HSCR (controls). Direct interactions between miRNAs and specific mRNAs were indentified in vitro, while the function role of miR-218-1 was investigated by using miR-218 transgenic mice. An increased level of miR-218-1 correlated with increased levels of SLIT2 and decreased levels of RET and PLAG1 mRNA and protein. The reductions in RET and PLAG1 by miR-218-1 reduced proliferation and migration of SH-SY5Y cells. Overexpression of the secreted form of SLIT2 inhibited cell migration via binding to its receptor ROBO1. Bowel tissues from miR-218-1 transgenic mice had nerve fibre hyperplasia and reduced numbers of gangliocytes, compared with wild-type mice. Altered miR-218-1 regulation of SLIT2, RET and PLAG1 might be involved in the pathogenesis of HSCR

Identification of membrane protein types via deep residual hypergraph neural network

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network

Bibliometric and visual analysis of spinal cord injury-associated macrophages from 2002 to 2023

BackgroundSpinal cord injury (SCI) triggers motor, sensory, and autonomic impairments that adversely damage patients' quality of life. Its pathophysiological processes include inflammation, oxidative stress, and apoptosis, although existing treatment options have little success. Macrophages have a vital function in controlling inflammation in SCI, with their M1-type and M2-type macrophages dominating early inflammatory effects and late brain tissue repair and regeneration, respectively. However, there is a dearth of rigorous bibliometric study in this sector to explore its dynamics and trends. This study intends to examine the current status and trends of macrophage usage in SCI using bibliometric methodologies, which may drive novel therapeutic options.MethodsIn this study, the Web of Science Core Collection (WOSCC) was utilized to collect publications and reviews on macrophages in SCI from 2002 to 2023. Bibliometrics and visualization analyses were performed by VOSviewer, CiteSpace, the R package “bibliometrix”, and online analytic platforms. These analyses covered a variety of aspects, including countries and institutions, authors and co-cited authors, journals and co-cited journals, subject categories, co-cited references, and keyword co-occurrences, in order to provide insights into the research trends and hotspots in this field.Results1,775 papers were included in the study, comprising 1,528 articles and 247 reviews. Our research analysis demonstrates that the number of relevant studies in this sector is expanding, specifically the number of publications in the United States and China has risen dramatically. However, there are fewer collaborations between institutions in different nations, and international cooperation needs to be reinforced. Among them, Popovich PG became the leader in the field, and significant journals include Experimental Neurology, Journal of Neurotrauma, and Journal of Neuroscience. Research hotspots involve macrophage polarization, microglia, astrocytes, signaling, cytokines, inflammation, and neuroprotection.ConclusionsThis analysis gives, for the first time, a comprehensive overview of bibliometric studies on macrophages in SCI over the past 20 years. This study not only gives an extensive picture of the knowledge structure but also indicates trends in the subject. The systematic summarization gives a complete and intuitive understanding of the link between spinal cord damage and macrophages and provides a great reference for future related studies

Reduced Annexin A1 Secretion by ABCA1 Causes Retinal Inflammation and Ganglion Cell Apoptosis in a Murine Glaucoma Model

Variants near the ATP-binding cassette transporter A1 (ABCA1) gene are associated with elevated intraocular pressure and newly discovered risk factors for glaucoma. Previous studies have shown an association between ABCA1 deficiency and retinal inflammation. Using a mouse model of ischemia-reperfusion (IR) induced by acute intraocular pressure elevation, we found that the retinal expression of ABCA1 protein was decreased. An induction of ABCA1 expression by liver X receptor agonist TO901317 reduced retinal ganglion cell (RGC) apoptosis after IR and promoted membrane translocation and secretion of the anti-inflammatory factor annexin A1 (ANXA1). Moreover, ABCA1 and ANXA1 co-localized in cell membranes, and the interaction domain is amino acid 196 to 274 of ANXA1 fragment. TO901317 also reduced microglia migration and activation and decreased the expression of pro-inflammatory cytokines interleukin (IL)-17A and IL-1β, which could be reversed by the ANXA1 receptor blocker Boc2. Overexpression of TANK-binding kinase 1 (TBK1) increased ABCA1 degradation, which was reversed by the proteasome inhibitor carbobenzoxy-L-leucyl-L-leucyl-L-leucinal (MG132). Silencing Tbk1 with siRNA increased ABCA1 expression and promoted ANXA1 membrane translocation. These results indicate a novel IR mechanism, that leads via TBK1 activation to ABCA1 ubiquitination. This degradation decreases ANXA1 secretion, thus facilitating retinal inflammation and RGC apoptosis. Our findings suggest a potential treatment strategy to prevent RGC apoptosis in retinal ischemia and glaucoma