New COVID-19 Human Challenge Study Reveals More Insights into How the Virus Spreads

A new analysis published in The Lancet Microbe shows how the SARS-CoV-2 virus spreads from the nose to the air and surfaces in the immediate surroundings. The findings are the second batch of results to come from the COVID-19 Human Challenge Program, led by Imperial College London and partners, and provide granular insights into how people infected with SARS-CoV-2 spread the virus to their immediate surroundings. In February 2021, 36 healthy, young participants with no previous immunity to the virus were infected with SARS-CoV-2 under controlled clinical conditions in a residential facility at the Royal Free Hospital in London, where they could be carefully monitored. They remained at the facility until they were no longer infectious. The facility enabled researchers to track the course of infection in great detail, with the clinical team taking daily swabs from participants’ noses and mouths, as well as environmental samples of the air and swabs of surfaces in their rooms.

Viral emissions

Out of 36 initial participants, a total of 18 became infected. Analysis showed that two individuals emitted substantially more virus into the air than the other infected participants, but displayed no significantly worse symptoms. The researchers suggest this may represent the small proportion of individuals who have potential to be highly infectious, sometimes described as “superspreading.” Analysis found large amounts of viral RNA in air samples, in exhaled breath, as well as swabs of participants hands and on surrounding surfaces, including frequently touched surfaces such as door handles and TV remote controls, showing how an infected person contaminates their surrounding environment and can spread the virus. Viral emissions correlated strongly with the level of virus detected in people’s noses, more than in their throat, highlighting the nose as a significant route for infected people shedding virus into the air and environment. According to the researchers, the latest analysis further highlights the routes by which the virus is transmitted from person to person—directly into the air, depositing onto nearby surfaces, and transferred from contaminated hands to frequently touched surfaces, such as door handles and remote controls.

Reliable indicators

They also show that positive lateral flow tests and visible symptoms were reliable indicators of when people were infectious and emitting virus into the air and environment. The vast majority of virus was emitted after people noticed their first symptoms, with very little virus released into the environment before that (pre-symptomatically). They found no significant link between the severity of participants’ symptoms and the amount of virus they shed into the environment. According to the researchers, the findings highlight the need to reinforce public health messaging around proper face mask use, hand washing and surface cleaning, as well as how human challenge studies continue to add to our knowledge of COVID-19 infection and transmission. Dr. Anika Singanayagam, NIHR Academic Clinical Lecturer in the Department of Infectious Disease at Imperial College London, and joint first author of the study, said, “Our latest findings add to the existing body of knowledge on COVID-19 transmission. By studying infection in a controlled environment, we can collect unique, detailed measurements of virus emitted that allow us to understand how and when people with COVID-19 are contagious to others. These types of measurements are challenging to collect in real-world studies. “Our data indicate that much of the virus people shed comes from the nose, further highlighting the importance of face masks covering the nose as well as the mouth when they’re used. But it also shows how virus can be transferred from hands to contaminate surfaces, like door handles or remote controls, which become a source of infection.”

 

Dr. Jay, Jie Zhou, research associate in the Department of Infectious Disease at Imperial College London, and joint first author of the study, said, “Understanding when infected people are contagious and how to detect when they are contagious is important—it can help us to use interventions like face masks or social distancing more effectively. The data in our study highlights that being aware of, and acting on, the first minor symptoms that signal an infection, coupled with frequent self-testing with lateral flow tests, can effectively reduce onward spread.” Professor Wendy Barclay, head of the Department of Infectious Disease at Imperial College London, said, “One of the most important things we need to know for controlling the spread of respiratory viruses, such as SARS-CoV-2, is when are people who are actively infected with the virus most likely to infect others? That information can help to tell us how the virus will spread, and how best to use interventions to stop the outbreak. “Human challenge studies enable us to gain granular insights into the infection which we might not otherwise be able to. They play an important role in our understanding of infectious diseases, and should be considered a part of future pandemic preparedness.”

 

Previous findings

The latest findings add to several key clinical insights already gained from the COVID-19 Human Challenge Program, published in February 2022. These include that symptoms start to develop on average two days after contact with the virus, that infection first appears in the throat, infectious virus peaks about five days into infection and, at that stage, is significantly more abundant in the nose than the throat. The first analysis also found that lateral flow tests (LFTs) are a reassuringly reliable indicator of whether infectious virus is present in the nose and throat (i.e., whether they are a likely to be infectious to other people).

 

Original research published (June 9, 2023) at The Lancet Microbe:

https://doi.org/10.1016/S2666-5247(23)00101-5 

Read the full article at: medicalxpress.com

Generative AI for Streamlined Content Creation with curioustone

In this article, we will explore the benefits of using generative AI web tools for content creation, discuss AI platforms, and provide tips on how to get started with these innovative tools.What is Generative AI?Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on existing information. It uses machine learning algorithms to analyze patterns in data and generate outputs that closely resemble the original input.

Read the full article at: curioustone.io

curioustone: Making AI Accessible to Everyone

Introducing curioustone:
The Ultimate AI Tools and Solutions for All – test it today

curioustone is a revolutionary suite of AI applications tailored to empower individuals and professionals across industries, including marketing experts, business owners, chefs, and politicians. Our state-of-the-art AI tools are designed to address the unique needs of each user, streamlining tasks and optimizing results. Six key features set curioustone apart and make it an indispensable addition to your professional toolkit. 

curioustone is a suite of AI applications designed to work across industries, including marketing, sales, finance, food, journalism and the social sector. Our state-of-the-art AI tools are made to provide answers and solutions to simple tasks.

Full working demos are available for all applications. Try them today!

DarkBERT: A Language Model for the Dark Side of the Internet

Original article is here

 

Large language models are all the rage these days and new ones are popping up every other day. Most of these linguistic behemoths, including OpenAI’s ChatGPT and Google’s Bard, are trained on text data from all over the internet – websites, articles, books, you name it. This means that their output is a mixed bag of genius. But what if instead of the web, LLMs were trained on the dark web? Researchers have done just that with DarkBERT to some surprising results. Let’s take a look.

 

What is DarkBERT?

A team of South Korean researchers have released a paper detailing how they built an LLM on a large-scale dark web corpus collected by crawling the Tor network. The data included a host of shady sites from various categories including cryptocurrency, pornography, hacking, weaponry, and others. However, due to ethical concerns, the team did not use the data as is. To ensure that the model wasn’t trained on sensitive data so that bad actors aren’t able to extract that information, the researchers polished the pre-training corpus through filtering, before feeding it to DarkBERT.

 

If you are wondering about the rationale behind the name DarkBERT, the LLM is based on the RoBERTa architecture, which is a transformer-based model developed back in 2019 by researchers at Facebook.

 

Meta had described RoBERTa as a “robustly optimized method for pre-training natural language processing (NLP) systems” that improves upon BERT, which was released by Google back in 2018. After Google made the LLM open-source, Meta was able to improve its performance.

 

Cut to the present, the Korean researchers have improved upon the original model even further by feeding it data from the dark web over the course of 15 days, eventually arriving upon DarkBERT. The research paper highlights that a machine with an Intel Xeon Gold 6348 CPU and 4 NVIDIA A100 80GB GPUs was used for the purpose.

Read the full article at: indianexpress.com

Scientists Get Closer to Harnessing Solar Power From Space

Caltech isn’t the only organization that has become interested in solar power stations. The Chinese government is planning a 2028 mission to demonstrate the technology in low Earth orbit. And last November, science ministers in the E.U. greenlit Solaris, a joint project between the European Space Agency (ESA) and aerospace company Airbus to look into the possibility of building gigantic solar power stations in geostationary orbit over Europe. (Whether intentional or not, the linkage to the world of mid-century sci-fi remains, with the project sharing the title of Stanislaw Lem’s classic 1961 novel.)

 

Learn more / En savoir plus / Mehr erfahren:

 

https://www.scoop.it/topic/21st-century-innovative-technologies-and-developments/?&tag=Solar+Energy

 

Read the full article at: time.com

NVIDIA’s Neuralangelo Research Reconstructs 3D Scenes from 2D Information

Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures — generating lifelike virtual replicas of buildings, sculptures and other real-world objects.

 

Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures. Creative professionals can then import these 3D objects into design applications, editing them further for use in art, video game development, robotics and industrial digital twins.

 

Read the full article at: blogs.nvidia.com