Navigating COVID-19 linguistic landscapes in Vancouver's North Shore: official signs, grassroots literacy artefacts, monolingualism, and discursive convergence

This article describes the changing linguistic landscape on the North Shore of Vancouver, British Columbia, Canada, during the first three months of the COVID-19 pandemic. I present an account of the visual representation of change along the area's parks and trails, which remained open for socially-distanced exercise during the province's lockdown. Following the principles of visual, walking ethnography, I walked through numerous locations, observing and recording the visual representations of the province's policies and discourses of lockdown and social distancing. Examples of change were most evident in the rapid addition to social space of top-down signs, characterised mainly by multimodality and monolingualism, strategically placed in ways that encouraged local people to abide by social-distancing. However, through this process of observation and exploration, I noticed grassroots semiotic artefacts such as illustrated stones with images and messages that complemented the official signs of the provincial government. As was the case with the official signs and messages, through a process of discursive convergence, these grassroots artefacts performed a role of conveying messages and discourses of social distancing, public pedagogy, and community care.

Muslim preachers' pandemics related discourses within social media: A corpus-based critical discourse analysis

Pandemics have been extensively represented in different discourse genres including journalistic discourse, media discourse, medical discourse, social media discourse, and academic discourse. This study explores the representation of COVID-19, Swine flu, and Monkey pox in the Arab Muslim preachers' discourses on Twitter and Facebook. The Muslim preachers' discourses remain one of the influential discourses that informs the ideology of its believers, as it is largely based on the Islamic authoritative discourses of the Quran and the Hadiths of Prophet Muhammad. The data set of 538 postings was generated through an extended observation of purposively recruited Arab Muslim non-mainstream scholars' postings on Facebook and Twitter from March 2019 to August 2022. The data were analyzed using corpus-based critical discourse analysis. The twofold analytical lens involving CL and CDA revealed that Muslim preachers frequently used ideological semantic patterns in communicating to the Muslim society at large regarding the pandemics. The utilized semantic patterns emerged as embedded in certain ideological frames established in the Islamic authoritative discourses of the Quran and the Hadiths of Prophet Muhammad. In their ideological representation of the pandemics, Muslim preachers framed the entire three pandemics mostly as the wrath of God. Religious scholars' postings cannot be considered an account of teaching and preaching;rather, they merely consume and produce Islamic ideology in a way to manipulate and influence Muslims' knowledge of existing reality by adding new meanings in line with the chosen ideological frames.

Healthy citizens, healthy democracies? A review of the literature

A growing literature over the past 10 years on health and political behavior has established health status as an important source of political inequality. Poor health reduces psychological engagement with politics and discourages political activity. This lowers incentives for governments to respond to the needs of those experiencing ill health and thereby perpetuates health disparities. In this review article, we provide a critical synthesis of the state of knowledge on the links between different aspects of health and political behavior. We also discuss the challenges confronting this research agenda, particularly with respect to measurement, theory, and establishing causality, along with suggestions for advancing the field. With the COVID-19 pandemic casting health disparities into sharp focus, understanding the sources of health biases in the political process, as well as their implications, is an important task that can bring us closer to the ideals of inclusive democracy.

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.

Introduction: Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods: The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs. Results and discussion: The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

BACKGROUND: Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. RESULTS: In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. CONCLUSIONS: By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY: The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Diversity and inclusion in clinical trials: Evolution throughout the development of an mRNA COVID-19 vaccine.

Despite the importance of equitable representation in clinical trials, disparities persist with racial and ethnic minorities remaining largely underrepresented in trial populations. During the coronavirus disease 2019 (COVID-19) pandemic, wherein disease disproportionately affected racial and ethnic minority groups, the necessity for diverse and inclusive representation in clinical trials has been further highlighted. Considering the urgent need for a safe and efficacious vaccine, COVID-19 vaccine clinical trials faced marked challenges in rapidly enrolling participants without forgoing diverse representation. In this perspective, we summarize Moderna's approach toward achieving equitable representation in mRNA-1273 COVID-19 vaccine clinical trials, including the COVID-19 efficacy (COVE) study, a large, randomized, controlled, phase 3 trial of mRNA-1273 safety and efficacy in adults. We describe the dynamics of enrollment diversity throughout the COVE trial and the need for continuous, efficient monitoring and rapid pivoting from initial approaches to address early challenges. Insights gained from our varied and evolved initiatives provide key learnings toward achieving equitable representation in clinical trials, including establishing and listening to a Diversity and Inclusion Advisory Committee, repeatedly engaging with key stakeholders on the necessity for diverse representation, creating and disseminating inclusive materials to all trial participants, establishing methods to raise awareness for interested participants, and enhancing transparency with trial participants to build trust. This work shows that diversity and inclusion in clinical trials can be attained even in the most extreme circumstances and highlights the importance of efforts toward building trust and empowering racial and ethnic minorities with the knowledge to make informed medical treatment decisions.