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Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization is considering whether the outbreak should be assessed as a “potential public health emergency of international concern”, as was done for the COVID-19 and Ebola outbreaks in the past. During this time, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, a dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 102,452 tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 26th June 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 5 different files based on the timelines of the associated tweets. The following are the details of these dataset files

Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022 to May 21, 2022) Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022 to May 27, 2022) Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022 to June 5, 2022) Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022 to June 11, 2022) Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 33518, Date Range of the associated Tweet IDs: June 12, 2022 to June 26, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

Please cite the following paper when using this dataset:

N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: https://doi.org/10.48550/arXiv.2406.07693)

Abstract

This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

Accurate and rapid characterization of influenza A virus (IAV) hemagglutinin (HA) and neuraminidase (NA) sequences with respect to subtype and clade is at the basis of extended diagnostic services and implicit to molecular epidemiologic studies. ClassyFlu is a new tool and web service for the classification of IAV sequences of the HA and NA gene into subtypes and phylogenetic clades using discriminatively trained profile hidden Markov models (HMMs), one for each subtype or clade. ClassyFlu merely requires as input unaligned, full-length or partial HA or NA DNA sequences. It enables rapid and highly accurate assignment of HA sequences to subtypes H1–H17 but particularly focusses on the finer grained assignment of sequences of highly pathogenic avian influenza viruses of subtype H5N1 according to the cladistics proposed by the H5N1 Evolution Working Group. NA sequences are classified into subtypes N1–N10. ClassyFlu was compared to semiautomatic classification approaches using BLAST and phylogenetics and additionally for H5 sequences to the new “Highly Pathogenic H5N1 Clade Classification Tool” (IRD-CT) proposed by the Influenza Research Database. Our results show that both web tools (ClassyFlu and IRD-CT), although based on different methods, are nearly equivalent in performance and both are more accurate and faster than semiautomatic classification. A retraining of ClassyFlu to altered cladistics as well as an extension of ClassyFlu to other IAV genome segments or fragments thereof is undemanding. This is exemplified by unambiguous assignment to a distinct cluster within subtype H7 of sequences of H7N9 viruses which emerged in China early in 2013 and caused more than 130 human infections. http://bioinf.uni-greifswald.de/ClassyFlu is a free web service. For local execution, the ClassyFlu source code in PERL is freely available.

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Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

Results for different basic classifiers (mean±SD) by using varied numbers of supplementary training data, trained and tested in 10-fold cross-validation on the virus dataset.

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 μM and CC50 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

Bold: genes overlapping with the SARS-CoV list.

In 2008, the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID), the University of Washington, and the Fred Hutchinson Cancer Research Center pioneered a new era in research by developing robotics systems for remote operation of Level 4 laboratories. In 2023, The United States Army Medical Research Institute of Chemical Defense (USAMRICD) opened remotely operated laboratories for monitoring experiments carried out by NASA robots. These non-living lab assistants, immune to viruses and capable of working tirelessly, have revolutionized our research approach. With lab time running 24/7/365, researchers can delve into old cases, outbreaks, and plagues. Studying biology retrospectively allows us to examine previous outbreaks and extract valuable facts. With these robots, we can now look at plague samples that appeared when we had no technology. Gundam robots open safer avenues of understanding in our fight against diseases. https://gundam.solutions Researching biology in retrospect, increases the power of collaboration. Protein sampling and identification of virus protein features and characteristics are now digitized. Modern scientists leverage the capabilities of robots, microscopy, computer databases, and AI to continue investigations started by dead scientists. Robots effectively reinvestigate archived diseases, parasites, and infectious viruses. By examining data and recordings of Pycnogonida (phage virus parasite) and Physalia Physalis (plasmodium parasite) from multiple perspectives, we can amplify our understanding and make significant strides in virus research.

The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.