The coronavirus behind the global COVID-19 pandemic evolved from nature and not a lab, according to a new genetic study.
Researchers analyzed the genome sequence of the novel SARS-CoV-2 coronavirus that emerged in the city of Wuhan, China, last year and found no evidence that a lab or some other type of engineering made the virus.
“We determined that SARS-CoV-2 originated through natural processes by comparing the genetic sequences and protein structures of other coronaviruses to those of new virus that causes COVID-19,” says Robert F. Garry, professor of microbiology and immunology at Tulane University School of Medicine and senior author of the paper in Nature Medicine.
“It is very close to a bat virus. The adaptations that the virus has made to affect humans are actually very different than what you would expect if you were designing it using computational models in biological engineering.
Coronaviruses are a large family of viruses that can cause illnesses ranging widely in severity. The first known severe illness a coronavirus caused emerged with the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak in China. A second outbreak of severe illness began in 2012 in Saudi Arabia with the Middle East Respiratory Syndrome (MERS).
Last year, Chinese authorities alerted the World Health Organization of an outbreak of a novel strain of coronavirus causing severe illness, subsequently named SARS-CoV-2. As of March 17, 2020, over 179,000 cases of COVID-19 have been documented, although many more mild cases have likely gone undiagnosed. The virus has killed over 7,400 people.
Shortly after the outbreak began, Chinese scientists sequenced the genome of the novel coronavirus and made the data available to researchers worldwide. The resulting data show that the epidemic has expanded because of human-to-human transmission after an initial introduction into the human population.
The ‘backbone’ of the new coronavirus
Researchers used this sequencing data to explore the origins and evolution of SARS-CoV-2 by focusing in on several telltale features of the virus.
They analyzed the genetic template for spike proteins, armatures on the outside of the virus that it uses to grab and penetrate the outer walls of human and animal cells.
More specifically, they focused on two important features of the spike protein: the receptor-binding domain (RBD), a kind of grappling hook that grips onto host cells, and the cleavage site, a molecular can opener that allows the virus to crack open and enter host cells.
The scientists found that the RBD portion of the SARS-CoV-2 spike proteins evolved to effectively target a molecular feature on the outside of human cells called ACE2, a receptor involved in regulating blood pressure. The SARS-CoV-2 spike protein was so effective at binding the human cells that the scientists concluded it was the result of natural selection and not the product of genetic engineering.
Data on SARS-CoV-2’s backbone—its overall molecular structure—support this evidence for natural evolution, researchers say.
If someone wanted to engineer a new coronavirus as a pathogen, they would have constructed it from the backbone of a virus known to cause illness. But the scientists found that the SARS-CoV-2 backbone differed substantially from those of already known coronaviruses and mostly resembled related viruses found in bats and pangolins.
“These two features of the virus, the mutations in the RBD portion of the spike protein and its distinct backbone, rules out genetic engineering as a potential origin for SARS-CoV-2” says coauthor Kristian Andersen, an associate professor of immunology and microbiology at Scripps Research.
The first of two scenarios
Based on their genomic sequencing analysis, Garry and his colleagues conclude that the most likely origins for SARS-CoV-2 followed one of two possible scenarios.
In one scenario, the virus evolved to its current pathogenic state through natural selection in a non-human host and then jumped to humans—how previous coronavirus outbreaks have emerged, with humans contracting the virus after direct exposure to civets (SARS) and camels (MERS).
The researchers proposed bats as the most likely reservoir for SARS-CoV-2 as it is very similar to a bat coronavirus. There are no documented cases of direct bat-human transmission, however, suggesting that an intermediate host likely occurred between bats and humans.
In this scenario, both of the distinctive features of SARS-CoV-2’s spike protein—the RBD portion that binds to cells and the cleavage site that opens the virus up—would have evolved to their current state prior to entering humans. In this case, the current epidemic would probably have emerged rapidly as soon as humans became infected, as the virus would have already evolved the features that make it pathogenic and able to spread between people.
And the second
In the second proposed scenario, a non-pathogenic version of the virus jumped from an animal host into humans and then evolved to its current pathogenic state within the human population. For instance, some coronaviruses from pangolins, anteater-like mammals found in Asia and Africa, have an RBD structure similar to that of SARS-CoV-2.
A coronavirus from a pangolin could possibly have been transmitted to a human, either directly or through an intermediary host such as civets or ferrets. Then the other distinct spike protein characteristic of SARS-CoV-2, the cleavage site, could have evolved within a human host, possibly via limited undetected circulation in the human population prior to the beginning of the epidemic.
The researchers found that the SARS-CoV-2 cleavage site, appears similar to the cleave sites of strains of bird flu that has been shown to transmit easily between people. SARS-CoV-2 could have evolved such a virulent cleavage site in human cells and soon kicked off the current outbreak, as the coronavirus would possibly have become far more capable of spreading between people.
“It is pretty well-adapted to humans. That’s one of the puzzles we’re trying to understand as we examine the virus. It could have been circulating in humans for a while now,” Garry says.
Additional coauthors are from Columbia University, the University of Sydney, and the University of Edinburgh. The National Institutes of Health and the Pew Charitable Trusts funded the work.
Source: How we know the new coronavirus comes from nature
Researchers have created a machine learning framework to precisely locate atom-sized quantum bits in silicon.
It’s a crucial step for building a large-scale silicon quantum computer, the researchers report.
Here, Muhammad Usman and Lloyd Hollenberg of the University of Melbourne explain their research and what it means for the future of quantum computers:
Quantum computers are expected to offer tremendous computational power for complex problems—currently intractable even on supercomputers—in the areas of drug design, data science, astronomy, and materials chemistry among others.
The high technological and strategic stakes mean major technology companies as well as ambitious start-ups and government-funded research centers are all in the race to build the world’s first universal quantum computer.
Qubits and quantum computers
In contrast to today’s classical computers, where information is encoded in bits (0 or 1), quantum computers process information stored in quantum bits (qubits). These are hosted by quantum mechanical objects like electrons, the negatively charged particles of an atom.
Quantum states can also be binary and can be put in one of two possibilities, or effectively both at the same time—known as quantum superposition—offering an exponentially larger computational space with an increasing number of qubits.
This unique data crunching power is further boosted by entanglement, another magical property of quantum mechanics where the state of one qubit is able to dictate the state of another qubit without any physical connection, making them all 1’s for example. Einstein called it a “spooky action at distance.”
Different research groups in the world are pursuing different kinds of qubits, each having its own benefits and limitations. Some qubits offer potential for scalability, while others come with very long coherence times, that is the time for which quantum information can be robustly stored.
Qubits in silicon are highly promising as they offer both. Therefore, these qubits are one of the front-runner candidates for the design and implementation of a large-scale quantum computer architecture.
One way to implement large-scale quantum computer architecture in silicon is by placing individual phosphorus atoms on a two-dimensional grid.
The single and two qubit logical operations are controlled by a grid of nanoelectronic wires, bearing some resemblance to classical logic gates for conventional microelectronic circuits. However, key to this scheme is ultra-precise placement of phosphorus atoms on the silicon grid.
What’s holding things back?
However, even with state-of-the-art fabrication technologies, placing phosphorus atoms at precise locations in silicon lattice is a very challenging task. Small variations, of the order of one atomic lattice site, in their positions are often observed and may have a huge impact on the efficiency of two qubit operations.
The problem arises from the ultra-sensitive dependence of the exchange interaction between the electron qubits on phosphorus atoms in silicon. Exchange interaction is a fundamental quantum mechanical property where two subatomic particles such as electrons can interact in real space when their wave functions overlap and make interference patterns, much like the two traveling waves interfering on water surface.
Exchange interaction between electrons on phosphorus atom qubits can be exploited to implement fast two-qubit gates, but any unknown variation can be detrimental to accuracy of quantum gate. Like logic gates in a conventional computer, the quantum gates are the building blocks of a quantum circuit.
For phosphorus qubits in silicon, even an uncertainty in the location of qubit atom of the order of one atomic lattice site can alter the corresponding exchange interaction by orders of magnitude, leading to errors in two-qubit gate operations.
Such errors, accumulated over the large-scale architecture, may severely impede the efficiency of quantum computer, diminishing any quantum advantage expected due to the quantum mechanical properties of qubits.
Pinpointing qubit atoms
So in 2016, we worked with the Center for Quantum Computation & Communication Technology researchers at the University of New South Wales, to develop a technique that could pinpoint exact locations of phosphorus atoms in silicon.
The technique, reported in Nature Nanotechnology, was the first to use computed scanning tunneling microscope (STM) images of phosphorus atom wave functions to pinpoint their spatial locations in silicon.
The images were calculated using a computational framework which allowed electronic calculations to be performed on millions of atoms utilizing Australia’s national supercomputer facilities at the Pawsey supercomputing center.
These calculations produced maps of electron wave function patterns, where the symmetry, brightness, and size of features was directly related to the position of a phosphorus atom in silicon lattice, around which the electron was bound.
The fact that each donor atom positions led to a distinct map, pinpointing of qubit atom locations, known as spatial metrology, with single lattice site precision was achieved.
The technique worked very well at the individual qubit level. However, the next big challenge was to build a framework that could perform this exact atom spatial pinpointing with high speed and minimal human interaction coping with the requirements of a universal fault tolerant quantum computer.
Machine learning to the rescue
Machine learning is an emerging area of research which is revolutionizing almost every field of research, from medical science to image processing, robotics, and material design.
A carefully trained machine learning algorithm can process very large data sets with enormous efficiency.
One branch of machine learning is known as convolutional neural network (CNN)—an extremely powerful tool for image recognition and classification problems. When a CNN is trained on thousands of sample images, it can precisely recognize unknown images (including noise) and perform classifications.
Recognizing that the principle underpinning the established spatial metrology of qubit atoms is basically recognizing and classifying feature maps of STM images, we decided to train a CNN on the computed STM images. The work is published in the NPJ Computational Materials journal.
The training involved 100,000 STM images and achieved a remarkable learning of above 99% for the CNN. We then tested the trained CNN for 17600 test images including blurring and asymmetry noise typically present in the realistic environments.
The CNN classified the test images with an accuracy of above 98%, confirming that this machine learning-based technique could process qubit measurement data with high-throughput, high precision, and minimal human interaction.
This technique also has the potential to scale up for qubits consisting of more than one phosphorus atoms, where the number of possible image configurations would exponentially increase. However, machine learning-based framework could readily include any number of possible configurations.
In the coming years, as the number of qubits increase and size of quantum devices grow, qubit characterization via manual measurements is likely to be highly challenging and onerous.
This work shows how machine learning techniques such as developed in this work could play a crucial role in this aspect of the realization of a full-scale fault-tolerant universal quantum computer—the ultimate goal of the global research effort.
Source: University of Melbourne
Source: Machine learning pushes quantum computing forward
President Donald Trump on Thursday signed into law a bill that provides $1 billion to help small telecom providers replace equipment made by China’s Huawei and ZTE.
The U.S. government considers the Chinese companies a security risk and has pushed its allies not to use Huawei equipment in next-generation cellular networks, known as 5G. Both companies have denied that China uses their products for spying.
The Federal Communications Commission has already voted to bar U.S. phone companies from using government subsidies for equipment from the two Chinese companies. This bill affects mostly small, rural companies, because the major U.S. network providers don’t use the Chinese equipment.
The White House said that using untrustworthy vendors to build communications infrastructure threatens national security by exposing networks to actors who are potentially influenced by foreign entities.
The legislation creates a reimbursement program that small telecom providers can use when removing and replacing equipment manufactured by entities deemed to pose unacceptable national security risks. The bill is aimed at telecom providers with fewer than 2 million customers.
“The administration will not risk subjecting America’s critical telecommunications infrastructure to companies that are controlled by authoritarian governments or foreign adversaries,” the White House said in a statement.
Donald Morrissey, a Huawei spokesman, said the bill was an “unrealistic attempt to fix what isn’t broken” and will hurt local consumers.
“This legislation remains considerably underfunded, will take longer than anticipated to fulfill and will put at risk some of Huawei’s customers, who operate in the most under-served areas,” Morrissey said.
Source: Trump Signs Bill to Help Telecoms Replace Huawei Equipment
Cyber-threat intelligence company Sixgill this week announced the closing of a $15 million funding round.
Founded in 2014, the Israel-based company leverages automation to help Fortune 500 companies, financial institutions, governments, and law enforcement agencies stay protected from cyber-threats lurking on the dark web.
The new funding, Sixgill says, will be invested in expanding its global operations and strengthening core products to support its growing portfolio.
Specifically, the company will use the funding to increase its presence in North America, EMEA and APAC by growing its customer base.
Furthermore, it plans on investing in improving its Automated, Actionable Intelligence (A2I) solution and Dynamic CVE Rating.
The funding round was led by Sonae IM and REV Venture Partners and also saw participation from Our Crowd and from previous investors Elron and Terra Venture Partners.
Sixgill told SecurityWeek that this was its first institutional round, but described it as more of a Series A1 round. The company said it previously raised $1 million in a pre-seed funding round and another $5 million in a seed round in 2016.
“Sixgill uses advanced automation and artificial intelligence technologies to provide accurate, contextual intelligence to customers. The market has made it clear that Sixgill has built a powerful real-time engine for more effective handling of the rapidly expanding threat landscape; this investment will position us for significant growth and expansion in 2020,” said Sharon Wagner, CEO of Sixgill.
Source: Threat Intelligence Company Sixgill Raises $15 Million
A new tool can monitor influenza A virus mutations in real time, researchers report.
The tool could help virologists learn how to stop viruses from replicating, according to the new study.
The gold nanoparticle-based probe measures viral RNA in live influenza A cells. It is the first time in virology that experts have used imaging tools with gold nanoparticles to monitor mutations in influenza, with unparalleled sensitivity.
“Our probe will provide important insight on the cellular features that lead a cell to produce abnormally high numbers of viral offspring and on possible conditions that favor stopping viral replication,” says senior author Laura Fabris, an associate professor in the materials science and engineering department in the School of Engineering at Rutgers University-New Brunswick.
Viral infections are a leading cause of illness and deaths. The new coronavirus, for example, has led to more than 24,000 confirmed cases globally, including more than 3,200 severe ones and nearly 500 deaths as of February 5, according to a World Health Organization report.
Influenza A, a highly contagious virus that arises every year, is concerning due to the unpredictable effectiveness of its vaccine. Influenza A mutates rapidly, growing resistant to drugs and vaccines as it replicates.
The new study highlights a promising new tool for virologists to study the behavior of influenza A, as well as any other RNA viruses, in host cells and to identify the external conditions or cell properties affecting them.
Until now, studying mutations in cells has required destroying them to extract their contents. The new tool enables analysis without killing cells, allowing researchers to get snapshots of viral replication as it occurs.
Next steps include studying multiple segments of viral RNA and monitoring the influenza A virus in animals.
Additional researchers from Rutgers and the University of Illinois at Urbana Champaign contributed to the study, which appears in the Journal of Physical Chemistry.
Source: Rutgers University
The post Tool monitors flu mutations in real time appeared first on Futurity.
Source: Tool monitors flu mutations in real time
Deep Instinct, a cybersecurity company that uses deep learning to predict, identify and prevent attacks, announced on Wednesday that it has raised $43 million in a Series C funding round.
The latest funding round, which brings the total raised by Deep Instinct to $100 million, was led by Millennium New Horizons, with participation from Unbound, LG, and NVIDIA. The company says it will use the money to accelerate sales and marketing, and expand business operations globally.
Deep Instinct provides endpoint, mobile, and virtual desktop infrastructure (VDI) protection solutions, as well as automatic threat analysis. Its solutions leverage a deep learning platform trained to identify previously unknown threats.
Its customers include Fortune 500 companies in the financial services, healthcare, aviation, technology and insurance sectors. Deep Instinct claims to have increased its customer base by 300 percent last year, and says its annual recurring revenue increased by 400 percent in 2019.
Deep Instinct powers HP Sure Sense, a security solution launched by HP last year for its EliteBook and ZBook laptops.
“There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis,” said Ray Cheng, partner at Millennium New Horizons. “What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems is a paradigm change.”
“Traditional cybersecurity is broken,” said Guy Caspi, co-founder and CEO of Deep Instinct. “Current solutions based on ‘assume breach’ are simply insufficient for the highly sophisticated attack landscape we all face. Deep Instinct takes an entirely new approach, preventing attacks before they are executed.”
Source: Deep Instinct Raises $43 Million in Series C Funding Round