Supervised Machine Learning Enables Geospatial Microbial Provenance.

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

The nature, cause and consequence of COVID-19 panic among social media users in India.

The recent pandemic of COVID-19 has not only shaken the healthcare but also economic structure around the world. In addition to these direct effects, it has also brought in some indirect difficulties owing to the information epidemic (hereafter termed as infodemic) on social media. We aimed to understand the nature of panic social media users in India are experiencing due to the flow of (mis)information. We further extend this investigation to other countries. We performed a cross-sectional study on 1075 social media users from India and 29 other countries. This revealed a significant increase in social media usage and the rise of panic (symbolizing a sense of alarm and/or fear) over time in India. Several of these behaviors are unique to social media users in India possibly because of later outbreak of COVID-19 and a prolonged uninterrupted lockdown. The amount of social media usage might not be causal but has a significant role in generating panic among the people in India. As multiple countries are entering into the second phase of lockdown, this study focused on India might provide a unique perspective of how various factors, including infodemic, affect the mental state of individuals around the globe. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s13278-021-00750-2.