SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution

Food safety remains a crucial concern in both public health and societal stability. In the age of information technology, social media has emerged as a pivotal channel for shaping public opinion and disseminating information, exerting a substantial influence on how the public perceives incidents related to food safety. This study specifically focuses on the "Rat-Headed Duck Neck" incident as a case study, conducting a comprehensive analysis of extensive social media data to investigate how online public discourse molds perceptions of such events. To accomplish this research, data were initially gathered using a custom web crawler technology. These data encompassed various aspects, including user interactions, emotional expressions, and the evolution of topics. Subsequently, the study employed an innovative approach by combining BERT-TextCNN and BERTopic models for a thorough analysis of sentiment and thematic aspects of the textual data. This analysis provided insights into the intricate emotions and primary concerns of the public regarding incidents related to food safety. Furthermore, the research harnessed Gephi, a network analysis tool, to scrutinize the dissemination of information within the network and to monitor dynamic shifts in public opinion. The findings from this study not only shed light on the role of online public sentiment in the propagation of food safety events but also provide fresh perspectives for policymakers and business leaders when responding to similar crises, taking into account the subtleties of online public sentiment. These innovative methodologies and findings significantly enhance our comprehension of public responses to food safety incidents within the realm of social media.

Analysis of public opinion on food safety in Greater China with big data and machine learning

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. To systematically collect and analyze public opinion on food safety in Greater China, we developed IFoodCloud, which automatically collects data from more than 3,100 public sources. Meanwhile, we constructed sentiment classification models using multiple lexicon-based and machine learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1 score achieved 0.9737, demonstrating its great predictive ability and robustness. Using IFoodCloud, we analyzed public sentiment on food safety in Greater China and the changing trend of public opinion at the early stage of the 2019 Coronavirus Disease pandemic, demonstrating the potential of big data and machine learning for promoting risk communication and decision-making.

Updated HIV-1 Consensus Sequences Change but Stay Within Similar Distance From Worldwide Samples

HIV consensus sequences are used in various bioinformatic, evolutionary, and vaccine related research. Since the previous HIV-1 subtype and CRF consensus sequences were constructed in 2002, the number of publicly available HIV-1 sequences have grown exponentially, especially from non-EU and US countries. Here, we reconstruct 90 new HIV-1 subtype and CRF consensus sequences from 3,470 high-quality, representative, full genome sequences in the LANL HIV database. While subtypes and CRFs are unevenly spread across the world, in total 89 countries were represented. For consensus sequences that were based on at least 20 genomes, we found that on average 2.3% (range 0.8–10%) of the consensus genome site states changed from 2002 to 2021, of which about half were nucleotide state differences and the rest insertions and deletions. Interestingly, the 2021 consensus sequences were shorter than in 2002, and compared to 4,674 HIV-1 worldwide genome sequences, the 2021 consensuses were somewhat closer to the worldwide genome sequences, i.e., showing on average fewer nucleotide state differences. Some subtypes/CRFs have had limited geographical spread, and thus sampling of subtypes/CRFs is uneven, at least in part, due to the epidemiological dynamics. Thus, taken as a whole, the 2021 consensus sequences likely are good representations of the typical subtype/CRF genome nucleotide states. The new consensus sequences are available at the LANL HIV database.