Examining Temporalities on Stance Detection Towards COVID-19 Vaccination

Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological and random splits of social media data. Our findings demonstrate significant discrepancies in model performance when comparing random and chronological splits across all monolingual and multilingual datasets. Chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration

Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as the lack of semantic understanding and the presence of overlapping topics. In this work, we investigate the untapped potential of large language models (LLMs) as an alternative for uncovering the underlying topics within extensive text corpora. To this end, we introduce a framework that prompts LLMs to generate topics from a given set of documents and establish evaluation protocols to assess the clustering efficacy of LLMs. Our findings indicate that LLMs with appropriate prompts can stand out as a viable alternative, capable of generating relevant topic titles and adhering to human guidelines to refine and merge topics. Through in-depth experiments and evaluation, we summarise the advantages and constraints of employing LLMs in topic extraction.Comment: Accepted at LREC-COLING 202

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.Comment: Accepted at ICWSM 202

A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9% based on average F1 measure

Function of TRP channels in monocytes/macrophages

The transient receptor potential channel (TRP channel) family is a kind of non- specific cation channel widely distributed in various tissues and organs of the human body, including the respiratory system, cardiovascular system, immune system, etc. It has been reported that various TRP channels are expressed in mammalian macrophages. TRP channels may be involved in various signaling pathways in the development of various systemic diseases through changes in intracellular concentrations of cations such as calcium and magnesium. These TRP channels may also intermingle with macrophage activation signals to jointly regulate the occurrence and development of diseases. Here, we summarize recent findings on the expression and function of TRP channels in macrophages and discuss their role as modulators of macrophage activation and function. As research on TRP channels in health and disease progresses, it is anticipated that positive or negative modulators of TRP channels for treating specific diseases may be promising therapeutic options for the prevention and/or treatment of disease

Isolation and Characterization of Chicken Serum Albumin (Hen Egg Alpha-Livetin, Gal d 5)

Chicken serum albumin, i.e., hen egg alpha-livetin, is a recognized food allergen in chicken meat and hen eggs. Currently, there is no immunoassay available for its detection from food matrices. The characterization of chicken serum albumin-specific antibodies and the extraction of the target protein are essential for immunoassay development. One monoclonal antibody (mAb), 3H4, was used in this study due to its selectivity to a linear epitope on avian serum albumin. To study the extraction of chicken serum albumin, phosphate-buffered saline (PBS) with two additives, i.e., sodium dodecyl sulfate (SDS) and dithiothreitol (DTT), was used for its extraction from chicken blood plasma and hen egg yolk. SDS and DTT improved the chicken serum albumin’s recovery and enhanced chicken serum albumin’s immunodetection. In addition, chicken serum albumin retained the best solubility and immunoreactivity after heat treatment in a neutral condition. It experienced degradation and aggregation in acidic and alkaline conditions, respectively. Overall, PBS containing 0.1% SDS and 1 mM DTT (pH 7.2) was a better extraction buffer for chicken serum albumin. However, the complexity of the food matrix and elevated temperature could reduce its solubility and immunoreactivity

VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter

Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance

Fabrication and Microstructure of ZnO/HA Composite with In Situ Formation of Second-Phase ZnO

Nanometer hydroxyapatite (n-HA) powders were synthesized by the chemical precipitation method, and a novel ZnO/HA composite, which consisted of second-phase particles with different sizes and distributions, was successfully fabricated. ZnO/HA composites were prepared by using powder sintering with different Zn contents and a prefabrication pressure of 150 MPa. Microstructure and local chemical composition were analyzed by a scanning electron microscope (SEM) and energy-dispersive spectrometer (EDS), respectively. The phase composition and distribution of the composite were determined with electron back-scattered diffraction (EBSD) and an X-ray diffractometer (XRD), respectively. The experimental results of the XRD showed that the chemical precipitation method was a simple and efficient method to obtain high-purity n-HA powders. When the sintering temperature was lower than 1250 °C, the thermal stability of HA was not affected by the Zn in the sintering process. Due to sintering in an air atmosphere, the oxidation reaction of Zn took place in three stages, and ZnO as the second phase had two different sizes and distributions in the composites. The compressive strength of ZnO/HA composites, of which the highest was up to 332 MPa when the Zn content was 20%, was significantly improved compared with pure HA. The improvement in mechanical properties was mainly due to the distribution of fine ZnO particles among HA grains, which hindered the HA grain boundary migration and refinement of HA grains. As grain refinement increased the area of the grain boundary inside the material, both the grain boundary and second phase hindered crack development in different ways

Indoor microbiome, air pollutants and asthma, rhinitis and eczema in preschool children - A repeated cross-sectional study

Background: Indoor microbiome exposure is associated with asthma, rhinitis and eczema. However, no studies report the interactions between environmental characteristics, indoor microbiome and health effects in a repeated cross-sectional framework. Methods: 1,279 and 1,121 preschool children in an industrial city (Taiyuan) of China were assessed for asthma, rhinitis and eczema symptoms in 2012 and 2019 by self-administered questionnaires, respectively. Bacteria and fungi in classroom vacuum dust were characterized by culture-independent amplicon sequencing. Multi-level logistic/linear regression was performed in two cross-sectional and two combined models to assess the associations. Results: The number of observed species in bacterial and fungal communities in classrooms increased significantly from 2012 to 2019, and the compositions of the microbial communities were drastically changed (p < 0.001). The temporal microbiome variation was significantly larger than the spatial variation within the city (p < 0.001). Annual average outdoor SO2 concentration decreased by 60.7%, whereas NO2 and PM10 concentra-tions increased by 63.3% and 40.0% from 2012 to 2019, which were both associated with indoor microbiome variation (PERMANOVA p < 0.001). The prevalence of asthma (2.0% to 3.3%, p = 0.06) and rhinitis (28.0% to 25.3%, p = 0.13) were not significantly changed, but the prevalence of eczema was increased (3.6% to 7.0%; p < 0.001). Aspergillus subversicolor, Collinsella and Cutibacterium were positively associated with asthma, rhinitis and eczema, respectively (p < 0.01). Prevotella, Lactobacillus iners and Dolosigranulum were protectively (negatively) associated with rhinitis (p < 0.01), consistent with previous studies in the human respiratory tract. NO2 and PM10 concentrations were negatively associated with rhinitis in a bivariate model, but a multivariate mediation analysis revealed that Prevotella fully mediated the health effects. Conclusions: This is the first study to report the interactions between environmental characteristics, indoor microbiome and health in a repeated cross-sectional framework. The mediating effects of indoor microorganisms suggest incorporating biological with chemical exposure for a comprehensive exposure assessment