Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations

High uptake of vaccinations is essential in fighting infectious diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Social media play a crucial role in propagating misinformation about vaccination, including through conspiracy theories and can negatively impact trust in vaccination. Users typically engage with multiple social media platforms; however, little is known about the role and content of cross-platform use in spreading vaccination-related information. This study examined the content and dynamics of YouTube videos shared in vaccine-related tweets posted to COVID-19 conversations before the COVID-19 vaccine rollout. We screened approximately 144 million tweets posted to COVID-19 conversations and identified 930,539 unique tweets in English that discussed vaccinations posted between 1 February and 23 June 2020. We then identified links to 2,097 unique YouTube videos that were tweeted. Analysis of the video transcripts using Latent Dirichlet Allocation topic modeling and independent coders indicate the dominance of conspiracy theories. Following the World Health Organization's declaration of the COVID-19 outbreak as a public health emergency of international concern, anti-vaccination frames rapidly transitioned from claiming that vaccines cause autism to pandemic conspiracy theories, often featuring Bill Gates. Content analysis of the 20 most tweeted videos revealed that the majority (n = 15) opposed vaccination and included conspiracy theories. Their spread on Twitter was consistent with spamming and coordinated efforts. These findings show the role of cross-platform sharing of YouTube videos over Twitter as a strategy to propagate primarily anti-vaccination messages. Future policies and interventions should consider how to counteract misinformation spread via such cross-platform activities.

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

BACKGROUND

The onset of the COVID-19 pandemic and the consequent "infodemic" increased concerns about Twitter's role in advancing antivaccination messages, even before a vaccine became available to the public. New computational methods allow for analysis of cross-platform use by tracking links to websites shared over Twitter, which, in turn, can uncover some of the content and dynamics of information sources and agenda-setting processes. Such understanding can advance theory and efforts to reduce misinformation.

OBJECTIVE

Informed by agenda-setting theory, this study aimed to identify the content and temporal patterns of websites shared in vaccine-related tweets posted to COVID-19 conversations on Twitter between February and June 2020.

METHODS

We used triangulation of data analysis methods. Data mining consisted of the screening of around 5 million tweets posted to COVID-19 conversations to identify tweets that related to vaccination and including links to websites shared within these tweets. We further analyzed the content the 20 most-shared external websites using a mixed methods approach.

RESULTS

Of 841,896 vaccination-related tweets identified, 185,994 (22.1%) contained links to specific websites. A wide range of websites were shared, with the 20 most-tweeted websites constituting 14.5% (27,060/185,994) of the shared websites and typically being shared for only 2 to 3 days. Traditional media constituted the majority of these 20 websites, along with other social media and governmental sources. We identified markers of inauthentic propagation for some of these links.

CONCLUSIONS

The topic of vaccination was prevalent in tweets about COVID-19 early in the pandemic. Sharing websites was a common communication strategy, and its "bursty" pattern and inauthentic propagation strategies pose challenges for health promotion efforts. Future studies should consider cross-platform use in dissemination of health information and in counteracting misinformation.

Molecular Evolutionary Genetics Analysis (MEGA) for macOS

The Molecular Evolutionary Genetics Analysis (MEGA) software enables comparative analysis of molecular sequences in phylogenetics and evolutionary medicine. Here, we introduce the macOS version of the MEGA software. This new version eliminates the need for virtualization and emulation programs previously required to use MEGA on Apple computers. MEGA for macOS utilizes memory and computing resources efficiently for conducting evolutionary analyses on macOS. It has a native Cocoa graphical user interface that is programmed to provide a consistent user experience across macOS, Windows, and Linux. MEGA for macOS is available from www.megasoftware.net free of charge.

SELAMAT: A New Secure and Lightweight Multi-Factor Authentication Scheme for Cross-Platform Industrial IoT Systems

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

Validation of PD-L1 clone 22C3 immunohistochemical stain on two Ventana DISCOVERY autostainer models: detailed protocols, test performance characteristics, and interobserver reliability analyses

Immunohistochemical (IHC) stain for PD-L1 as a biomarker for immunotherapy is recommended in non-small cell lung cancer (NSCLC). Under the FDA, the selection of patients for pembrolizumab requires companion diagnostic testing using the Dako Agilent PD-L1 IHC 22C3 pharmDx kit performed on the Dako Autostainer Link 48 platform. However, because it is not widely available, there is need for cross-platform validation. Existing studies provide incomplete protocol detail. In our study, 73 lung tumors were stained using the FDA-approved test ('gold standard'). The same blocks were stained using two different models of the Ventana DISCOVERY platform (ULTRA, n = 73 and XT, n = 70) using different parameters, and interpreted by three pathologists. The ULTRA group met College of American Pathologists (CAP) validation criteria (concordance 91.8%) while the XT group did not (concordance 67.1%). Using tumor proportion score (TPS) ≥1% and TPS ≥50% as cut-offs, the ULTRA protocol had higher sensitivity (97.8% and 91.7%) than XT (73.3% and 60.9%) and similar specificity (ULTRA 88.9% and 100%, XT 88% and 100%). Discordance between ULTRA and XT was 27%, and in all these cases ULTRA was concordant with gold standard. Interobserver reliability was substantial for ULTRA and almost perfect for XT, providing evidence that staining rather than observer variability accounts for XT's inferior performance. Cross-validation of the clinically used 22C3 anti PD-L1 antibody test with substantial interobserver agreement is possible on the commonly used the Ventana DISCOVERY ULTRA automated instrument, while the validation failed on the XT. Cautious attention to detail must be paid when choosing cross-validation parameters.

A forensic examination of four popular cross-platform file-sharing apps with Wi-Fi P2P

File-sharing apps with Wi-Fi hotspot or Wi-Fi Direct functions become more popular. They can work on multiple platforms and allow users to transfer files in a concealed manner. However, when criminals use these apps in illegal activities, it becomes an important issue for investigators to find digital evidence on multiple platforms. At present, there are few studies on this topic, and most of them are limited to the single platform problem. In this paper, we propose a forensic examination method for four popular cross-platform file-sharing apps with Wi-Fi hotspot and Wi-Fi Direct functions: Zapya, SHAREit, Xender, and Feem. We use 22 static and live forensic tools for 11 platforms to acquire, analyze, and classify the forensic artifacts. In our experiments, we find many useful forensic artifacts and classify them into six categories. The experimental results can support law enforcement investigations of digital evidence and provide information for future studies on other cross-platform file-sharing apps.

SpinStudioJ: A cross-platform NMR data acquisition and processing workbench based on a plug-in architecture

Flexibility and extensibility are important issues in the design of nuclear magnetic resonance (NMR) software, as these determine the ability to integrate a variety of continuously evolving data acquisition and processing methods. Here, SpinStudioJ is introduced. It is an NMR data acquisition and processing workbench with a plug-in-based architecture. The workbench is based on Eclipse Rich Client Platform, which provides a plug-and-play runtime mechanism and rich graphical user interface functionality. New data acquisition methods and processing algorithms can be easily integrated into the SpinStudioJ workbench by defining extension points, without the need to redistribute existing modules. The software is independent of operating systems, as it leverages the cross-platform feature of the Java virtual machine.

ARDebug: An Augmented Reality Tool for Analysing and Debugging Swarm Robotic Systems

Despite growing interest in collective robotics over the past few years, analysing and debugging the behaviour of swarm robotic systems remains a challenge due to the lack of appropriate tools. We present a solution to this problem—ARDebug: an open-source, cross-platform, and modular tool that allows the user to visualise the internal state of a robot swarm using graphical augmented reality techniques. In this paper we describe the key features of the software, the hardware required to support it, its implementation, and usage examples. ARDebug is specifically designed with adoption by other institutions in mind, and aims to provide an extensible tool that other researchers can easily integrate with their own experimental infrastructure.

GSHR, a Web-Based Platform Provides Gene Set-Level Analyses of Hormone Responses in Arabidopsis

Phytohormones regulate diverse aspects of plant growth and environmental responses. Recent high-throughput technologies have promoted a more comprehensive profiling of genes regulated by different hormones. However, these omics data generally result in large gene lists that make it challenging to interpret the data and extract insights into biological significance. With the rapid accumulation of theses large-scale experiments, especially the transcriptomic data available in public databases, a means of using this information to explore the transcriptional networks is needed. Different platforms have different architectures and designs, and even similar studies using the same platform may obtain data with large variances because of the highly dynamic and flexible effects of plant hormones; this makes it difficult to make comparisons across different studies and platforms. Here, we present a web server providing gene set-level analyses of Arabidopsis thaliana hormone responses. GSHR collected 333 RNA-seq and 1,205 microarray datasets from the Gene Expression Omnibus, characterizing transcriptomic changes in Arabidopsis in response to phytohormones including abscisic acid, auxin, brassinosteroids, cytokinins, ethylene, gibberellins, jasmonic acid, salicylic acid, and strigolactones. These data were further processed and organized into 1,368 gene sets regulated by different hormones or hormone-related factors. By comparing input gene lists to these gene sets, GSHR helped to identify gene sets from the input gene list regulated by different phytohormones or related factors. Together, GSHR links prior information regarding transcriptomic changes induced by hormones and related factors to newly generated data and facilities cross-study and cross-platform comparisons; this helps facilitate the mining of biologically significant information from large-scale datasets. The GSHR is freely available at http://bioinfo.sibs.ac.cn/GSHR/.

Personalized reminiscence therapy M-health application for patients living with dementia: Innovating using open source code repository

Dementia is known to be an illness which brings forth marked disability amongst the elderly individuals. At times, patients living with dementia do also experience non-cognitive symptoms, and these symptoms include that of hallucinations, delusional beliefs as well as emotional liability, sexualized behaviours and aggression. According to the National Institute of Clinical Excellence (NICE) guidelines, non-pharmacological techniques are typically the first-line option prior to the consideration of adjuvant pharmacological options. Reminiscence and music therapy are thus viable options. Lazar et al. [3] previously performed a systematic review with regards to the utilization of technology to delivery reminiscence based therapy to individuals who are living with dementia and has highlighted that technology does have benefits in the delivery of reminiscence therapy. However, to date, there has been a paucity of M-health innovations in this area. In addition, most of the current innovations are not personalized for each of the person living with Dementia. Prior research has highlighted the utility for open source repository in bioinformatics study. The authors hoped to explain how they managed to tap upon and make use of open source repository in the development of a personalized M-health reminiscence therapy innovation for patients living with dementia. The availability of open source code repository has changed the way healthcare professionals and developers develop smartphone applications today. Conventionally, a long iterative process is needed in the development of native application, mainly because of the need for native programming and coding, especially so if the application needs to have interactive features or features that could be personalized. Such repository enables the rapid and cost effective development of application. Moreover, developers are also able to further innovate, as less time is spend in the iterative process.

MetaboLights: An Open-Access Database Repository for Metabolomics Data

MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools.

Cross-platform Comparison of Two Pancreatic Cancer Phenotypes

Model-based approaches for combining gene expression data from multiple high throughput platforms can be sensitive to technological artifacts when the number of samples in each platform is small. This paper proposes simple tools for quantifying concordance in a small study of pancreatic cancer cells lines with an emphasis on visualizations that uncover intra- and inter-platform variation. Using this approach, we identify several transcripts from the integrative analysis whose over-or under-expression in pancreatic cancer cell lines was validated by qPCR.

Gromita: a fully integrated graphical user interface to gromacs 4

Gromita is a fully integrated and efficient graphical user interface (GUI) to the recently updated molecular dynamics suite Gromacs, version 4. Gromita is a cross-platform, perl/tcl-tk based, interactive front end designed to break the command line barrier and introduce a new user-friendly environment to run molecular dynamics simulations through Gromacs. Our GUI features a novel workflow interface that guides the user through each logical step of the molecular dynamics setup process, making it accessible to both advanced and novice users. This tool provides a seamless interface to the Gromacs package, while providing enhanced functionality by speeding up and simplifying the task of setting up molecular dynamics simulations of biological systems. Gromita can be freely downloaded from http://bio.demokritos.gr/gromita/.