A University of Glasgow study developed machine learning models that could potentially identify animal viruses capable of infecting humans and classify how much of a risk they pose to humans by analyzing the genomes of viruses. The model outperformed models based on phylogenetic relatedness of specific viruses to other viruses known to infect humans according to the study, published in the peer-reviewed PLOS Biology. The model could have predicted SARS-CoV-2 as a high-risk coronavirus strain, said researchers. Analyses of the model show that there could be generalizable features of viral genomes that may make viruses preadapt to infect humans. These features are independent of the virus taxonomic relationships.

Maria Bartiromo investigates the future of the artificial intelligence industry and its impact on business. Artificial intelligence (AI) could be key in helping scientists identify the next animal virus that is capable of infecting humans, according to researchers. In a Tuesday study published in the journal PLoS Biology, the Glasgow-based team said it had devised a genomic model that could "retrospectively or prospectively predict the probability that viruses will be able to infect humans." The group developed machine learning models to single out candidate zoonotic viruses using signatures of host range encoded in viral genomes. With a dataset of 861 viral species with known zoonotic status, the researchers collected a single representative genome sequence from the hundreds of RNA and DNA virus species, spanning 36 viral families.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study published in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artificial intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study publishing in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artifical intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic--caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study publishing in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artifical intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Video game players have been urged to play at'reasonable times' to avoid putting extra strain on internet networks during the coronavirus outbreak. Social distancing measures to curb the spread of the virus has led to large numbers of people working from home or self-isolating, increasing daytime internet traffic. But gamers have been asked to limit time online during working hours to ensure those in self-isolation trying to get work done aren't affected by slow speeds. The issue could get worse in the UK as schools around the country have been forced to close due to the rapid spread of COVID-19, giving young gamers more time to kill. UK-based video games expert Rik Henderson said people turning to games during isolation was inevitable, as a means of entertainment and social interaction, but he urged players to be aware of going online during working hours. 'While video streaming services, such as Netflix and YouTube, are committed to reducing their digital footprint during the coronavirus crisis, gaming is perhaps the biggest threat to internet bandwidth in the next few months,' he said.

Police in Spain have turned to drones to encourage people to stay indoors and practice social distancing during the country's now surging COVID-19 outbreak. The drones have been equipped with speakers that officers can use to broadcast live messages from their squad cars. The drones are part of neighborhood sweeps police have been implementing to enforce a country-wide lockdown that began on Saturday. The drones have been used in Madrid to help clear parks and other public spaces where many in the country had continued to gather in spite of growing health concerns, according to a report in Popular Mechanics. Under the country's lockdown, which was implemented the same day Prime Minister Pedro Sánchez's wife Begoña Gómez tested positive for COVID-19, people are banned from leaving home for any reason other than to buy essential supplies and medicine or to go to work. As with many other countries around the world, Spain has required schools and all non-essential businesses to close, including museums, sporting events, and restaurants, which are restricted to delivery and takeout orders.

Tinder has posted a warning for its users telling them that coronavirus safety is'more important' than dating and urging them to wash their hands frequently. The dating app also encourages its singletons to carry hand sanitiser, avoid touching their face and'maintain social distance' when out in public. The warning says: 'Tinder is a great place to meet new people. While we want you to continue to have fun, protecting yourself from the coronavirus is more important'. It appears as a pop up while users are flipping between potential matches to warn of the dangers of the potentially deadly virus now called COVID-19. The pop-up warning also includes a link to the latest advice and information from the World Health Organisation (WHO) website.