When scientists carry out research on a given topic, they often start by reviewing previous study findings. Conducting systematic literature reviews or meta-analyses can be very challenging and time consuming, as there are often huge amounts of research focusing on different topics, which may not always be relevant to a researcher's work. Researchers at Utrecht University have recently developed a machine learning framework that could significantly speed up this process, by automatically browsing through numerous past studies and compiling high quality literature reviews. This framework, called ASReview, could prove particularly useful for conducting research during the COVID-19 pandemic. "Researchers and experts face a major challenge to stay up-to-date with the latest developments in their field nowadays," Jonathan de Bruin, lead engineer involved in the study, told TechXplore.
Infection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) could cause dramatic response in coronavirus disease 2019 (COVID‐19) patients at multi‐omics level,1-3 thus it is essential to systematically assess the pathogenesis of COVID‐19. In our previous study, we presented the first trans‐omics landscape of 236 COVID‐19 patients with 4 clinical severity groups (including asymptomatic, mild, severe and critically ill cases) and found that the mild and severe COVID‐19 patients shared several similar characteristics.4 However, it is crucial to discriminate mild from severe COVID‐19 patients to prevent the latter from the progression of disease by facilitating early intervention. Herein, we developed an extreme gradient boosting (XGBoost) machine‐learning model to predict the COVID‐19 severities by leveraging multi‐omics data. Briefly, we randomly stratified samples for the training set (80%) and the independent testing set (20%) (Figure 1A, see Methods in the Supporting Information).
Over the course of the next decade humans will integrate more with technology to'upgrade' our lives including brain chips and exoskeletons, a new report claims. Produced by dentsu, a global advertising and digital agency, the report looks at ways the world could change over the next 10 years and the impact on global brands. 'As brands assess the impact of a seismic year and look to chart a new path to recovery, these trends provide them with a roadmap for the next decade,' the firm wrote in the executive summary to the report. One key area of change will be the continued rise of the'synthetic society' as people increasingly incorporate the latest technology into their lives. The study suggests people could even use brain chips to aid memory and exoskeletons to make us faster and stronger. Dentsu predict there will be a number of'key events' over the next decade including the FIFA eWorld Cup becoming the most watched sporting event in the world Over the next decade as automation takes away jobs and technology becomes a larger part of our lives, we will see a'human dividend' appear. Study authors claim this will come in the form of a premium on human skills robots can't do or that can't easily be automated.
There is likely to be high demand for the limited supplies of vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), so how should vaccine distribution be prioritized? Bubar et al. modeled across countries how uncertainty about a vaccine's characteristics affects prioritization strategies for reducing deaths and transmission (see the Perspective by Fitzpatrick and Galvani). In the model, vaccine efficacy and its ability to reduce disease and/or block transmission was accounted for in relation to age-related variations in susceptibility, fatality rates, and immune decline. In almost all circumstances, reducing fatalities required distributing the vaccine to those who are most at risk of death, usually persons over 60 years of age and those with comorbidities. If a vaccine is leaky or poorly efficacious in older adults, then priority could be given to younger age groups. To increase the available doses, further priority should be given to seronegative individuals. Science , this issue p. ; see also p.  Limited initial supply of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine raises the question of how to prioritize available doses. We used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20 to 49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults greater than 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. Although maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts. : /lookup/doi/10.1126/science.abe6959 : /lookup/doi/10.1126/science.abg2334
Since the outbreak of the pandemic, the world has grown increasingly reliant on artificial intelligence (AI) technologies. Thousands of new innovations -- from contact-tracing apps to the drones delivering medical equipment -- sprang up to help us meet the challenges of Covid-19 and life under lockdown. The unprecedented speed with which a vaccine for Covid-19 was discovered can partly be attributed to the use of AI algorithms which rapidly crunched the data from thousands of clinical trials, allowing researchers around the world to compare notes in real time. As Satya Nadella, the chief executive of Microsoft observed, in just two months, the world witnessed a rate of digital transition we'd usually only see in two years. In 2017, PWC published a study showing that adoption of AI technologies could increase global GDP by 14% by 2030. In addition to creating jobs and boosting economies, AI technologies have the potential to drive sustainable development and even out inequalities, democratising access to healthcare and education, mitigating the effects of climate change and making food production and distribution more efficient.
A new study finds that humans aren't the only ones susceptible to COVID-19 infections. Ferrets and cats can also catch the deadly virus. Cats, rabbits and hedgehogs have all been implicated in a new study that aims to predict the animals most likely to launch the next deadly COVID-19 outbreak. With the help of artificial intelligence, biologists were able to design a prediction model that could prioritize potential hosts of virus strains already known to exist, but have not yet reached humans. "We want to know where the next coronavirus might come from," said Dr. Marcus Blagrove, a University of Liverpool virologist who worked on the study, BBC reported. Their findings, published in Nature Communications on Tuesday, describe how artificial intelligence was used to predict previously unsuspected animal hosts of a novel -- and potentially deadly -- coronavirus strain.
Common UK garden animals like hedgehogs, rabbits and even the domestic cat have the potential to harbour new strains of coronavirus, a new study reveals. UK researchers used machine learning to predict associations between 411 strains of coronavirus and 876 potential mammal host species. Their machine learning model integrated characteristics extracted from genomes, such as protein structure, as well as ecological and other traits. The results have'implicated' the common hedgehog (Erinaceus europaeus), the European rabbit (Oryctolagus cuniculus) and the domestic cat (Felis catus) as predicted hosts for new coronaviruses. Amongst the'highest priority' is the lesser Asiatic yellow bat (Scotophilus kuhlii), a known coronavirus host that's common in east Asia but not well studied.
Valentine's Day is just around the corner -- and if you want to maintain the interest of a woman on Tinder, a funny chat-up line is the way to go, scientists have revealed. US researchers tested out various online chat-up lines on 237 young, heterosexual adults -- finding that humour was a better opening gambit than compliments. In fact, they found that men who used funny introductions were seen as more attractive to women, who rated them as more intelligent, kind and trustworthy. Even if some of the lines were a little cheesy, they found that women still responded to them better than bland, unimaginative greetings like'Hi, how are you?' Men, in contrast, were found to overwhelmingly base their evaluations of prospective dates on how attractive they found the woman's profile. The team noted that, thanks to the COVID-19 pandemic closing bars and clubs around the world, singles have surged to apps like Tinder for their dating needs.
It's been over four months since the NHS Covid-19 contact-tracing app launched across the UK, and since then the health services have been short of updates on the performance of the technology, to say the least. Now, some statistics have been revealed to the public, finally shedding light on the scope of the app's contribution to the fight against Covid-19 – and despite the technology's initial shortcomings, the results are encouraging. The Department of Health and Social Care (DHSC) announced that the app has been downloaded 21.63 million times, a steady increase since the technology was released in September. In total, over 1.7 million users across England and Wales have been advised to isolate by the app, after 825,388 positive test results were logged in. Researchers calculated that this has potentially prevented up to 600,000 positive cases.
Artificial intelligence is everywhere, and now a group of developers have created AI software that can tell whether you are likely to die from Covid-19 using health data. University of Copenhagen researchers fed a computer program with health data from 3,944 Danish COVID-19 patients, as well as any underlying conditions. They then trained it to look for patterns in a patients' prior illness to determine the risk factors and potential outcome from Covid-19 and found that BMI, age and being male were the highest risk factors when it came to the likelihood of dying. The results show that AI can, with up to 90 per cent certainty, determine whether an uninfected person will die of the disease if they are unlucky enough to catch it. Results from the new tool could help health officials determine who should be at the front of the line for a limited supply of vaccines, said lead author Mads Nielsen. They say this should be considered when determining who should get the vaccine first.