physicsworld--2020-11-11--Deep-learning-based algorithm helps radiologists detect cerebral aneurysms - Physics World

Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images .
Cerebral aneurysms are weak spots in blood vessels in the brain , which can balloon out and fill with blood .
If such a bulging aneurysm leaks or ruptures , it can cause serious symptoms and sometimes be fatal .
The risk of rupture depends on the size , shape and location of the aneurysm , making detection and characterization of cerebral aneurysms vital .
CT angiography , which uses X-ray CT to visualize blood vessels following injection @ @ @ @ @ @ @ usually the first-line imaging exam for detecting cerebral aneurysms .
But this can be a challenging task : the complexity of intracranial vessels and the small size of cerebral aneurysms means that some may be missed during an initial assessment .
As such , the researchers propose that deep learning – a type of machine learning that ’ s increasingly used to develop algorithms for image recognition – could enhance radiologists ’ performance and reduce the number of aneurysms that are initially overlooked .
“ In our daily work we are always faced @ @ @ @ @ @ @ have been missed by the human eye , ” says senior author Xi Long from Tongji Medical College ‘ s Union Hospital , in a press statement .
“ Cerebral aneurysms are among those small lesions that may be overlooked on the routine assessment of radiological images.
” Reporting their work in Radiology , Long and colleagues developed a detection algorithm based on a convolutional neural network .
To train and assess their deep-learning algorithm , they used 1068 head CT angiography images with reported cerebral aneurysms , acquired by four different scanners @ @ @ @ @ @ @ CT angiograms , which included 688 aneurysms ranging in size from 1.2 to 45.6 mm , were used to train the algorithm .
The other half , including 649 aneurysms of 1.2 to 30.8 mm in size , formed the validation dataset .
After training the algorithm , the team used it to detect cerebral aneurysms in the validation dataset .
The algorithm demonstrated a maximum sensitivity of 97.5 % for aneurysm detection , with a rate of 13.8 false-positive findings per case .
These false-positives were observed in areas with bony structures @ @ @ @ @ @ @ calcified plaques .
The authors note that most of these could easily be identified by radiologists .
The algorithm ’ s performance gradually improved in sensitivity with aneurysm size , reaching 100 % in aneurysms 10 mm or larger .
The lowest sensitivity was observed in aneurysms located on the anterior and posterior cerebral arteries , most of which were smaller than 3 mm .
The algorithm also found eight new aneurysms , six of which were smaller than 3 mm , that had been overlooked by the human readers in the initial @ @ @ @ @ @ @ an external validation , using an additional 400 CT angiograms .
Of these , 188 contained aneurysms ( 206 , of 1 to 22 mm in size ) and 212 were negative .
The validation was performed by four radiologists with between one and seven years of experience in head CT angiography .
Each radiologist was randomly assigned 200 CT angiograms for interpretation with/without help from the algorithm .
For each image , they recorded the type , number , location and size of any aneurysms , and the diagnosis time .
Two @ @ @ @ @ @ @ samples again , without/with the algorithm .
Assistance from the deep-learning algorithm improved the radiologists ’ performance in detecting the cerebral aneurysms , increasing their overall sensitivity per lesion from 79.09 % to 88.94 % .
The sensitivity per case was 81.63 % and 91.86 % , without and with the algorithm , respectively .
In particular , performance was most improved for the two least experienced radiologists .
The researchers emphasize that the algorithm is intended to act as a supportive tool , providing the radiologist with a second opinion to improve @ @ @ @ @ @ @ the human reader .
“ The deep-learning algorithm needs to be further validated on external imaging data , ” Long tells Physics World .
“ Some improvements , such as decreasing the false positive rate , may be needed before it can be used clinically .
”

Left: CT angiogram showing an aneurysm of 2 mm maximum diameter on the left posterior cerebral artery (arrow). Right: volume-rendered 3D reconstruction image. The aneurysm was missed in the initial report but successfully detected with the deep-learning algorithm. (Courtesy: Radiological Society of North America)
Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images.
Cerebral aneurysms are weak spots in blood vessels in the brain, which can balloon out and fill with blood. If such a bulging aneurysm leaks or ruptures, it can cause serious symptoms and sometimes be fatal. The risk of rupture depends on the size, shape and location of the aneurysm, making detection and characterization of cerebral aneurysms vital.
CT angiography, which uses X-ray CT to visualize blood vessels following injection of contrast into the bloodstream, is usually the first-line imaging exam for detecting cerebral aneurysms. But this can be a challenging task: the complexity of intracranial vessels and the small size of cerebral aneurysms means that some may be missed during an initial assessment.
As such, the researchers propose that deep learning – a type of machine learning that’s increasingly used to develop algorithms for image recognition – could enhance radiologists’ performance and reduce the number of aneurysms that are initially overlooked.
“In our daily work we are always faced with cases in which some important lesions have been missed by the human eye,” says senior author Xi Long from Tongji Medical College‘s Union Hospital, in a press statement. “Cerebral aneurysms are among those small lesions that may be overlooked on the routine assessment of radiological images.”
Reporting their work in Radiology, Long and colleagues developed a detection algorithm based on a convolutional neural network. To train and assess their deep-learning algorithm, they used 1068 head CT angiography images with reported cerebral aneurysms, acquired by four different scanners at two hospitals. Half of these CT angiograms, which included 688 aneurysms ranging in size from 1.2 to 45.6 mm, were used to train the algorithm. The other half, including 649 aneurysms of 1.2 to 30.8 mm in size, formed the validation dataset.
Examples of false-positive aneurysms: (A) bony structures and vessel bifurcation, (B) veins, (C) vessel curvatures and (D) calcified plaques. Blue boxes show aneurysm candidates provided by the algorithm, the red box indicates the aneurysm annotated by radiologists. (Courtesy: RSNA)
After training the algorithm, the team used it to detect cerebral aneurysms in the validation dataset. The algorithm demonstrated a maximum sensitivity of 97.5% for aneurysm detection, with a rate of 13.8 false-positive findings per case. These false-positives were observed in areas with bony structures, vessel bifurcations and curvatures, and calcified plaques. The authors note that most of these could easily be identified by radiologists.
The algorithm’s performance gradually improved in sensitivity with aneurysm size, reaching 100% in aneurysms 10 mm or larger. The lowest sensitivity was observed in aneurysms located on the anterior and posterior cerebral arteries, most of which were smaller than 3 mm. The algorithm also found eight new aneurysms, six of which were smaller than 3 mm, that had been overlooked by the human readers in the initial radiological reports.
The researchers next performed an external validation, using an additional 400 CT angiograms. Of these, 188 contained aneurysms (206, of 1 to 22 mm in size) and 212 were negative. The validation was performed by four radiologists with between one and seven years of experience in head CT angiography.
Each radiologist was randomly assigned 200 CT angiograms for interpretation with/without help from the algorithm. For each image, they recorded the type, number, location and size of any aneurysms, and the diagnosis time. Two weeks later, they interpreted the same samples again, without/with the algorithm.
Assistance from the deep-learning algorithm improved the radiologists’ performance in detecting the cerebral aneurysms, increasing their overall sensitivity per lesion from 79.09% to 88.94%. The sensitivity per case was 81.63% and 91.86%, without and with the algorithm, respectively. In particular, performance was most improved for the two least experienced radiologists.
The researchers emphasize that the algorithm is intended to act as a supportive tool, providing the radiologist with a second opinion to improve their diagnostic accuracy, but not replacing the human reader.
“The deep-learning algorithm needs to be further validated on external imaging data,” Long tells Physics World. “Some improvements, such as decreasing the false positive rate, may be needed before it can be used clinically.”


physicsworld--2020-11-11--Inside the quantum bubble - Physics World

What happens when the irresistible force of quantum entrepreneurship meets the immovable object of a global pandemic ?
For the past few years , the UK ’ s annual Quantum Technology Showcase has taken place at the QE2 conference centre in London , a stone ’ s throw from the Houses of Parliament , with attendees packing the exhibition hall to hear about the latest academic and industrial research into this growing field .
This year , of course , it didn ’ t look like that .
Instead , the presenters appeared by video link from home offices and @ @ @ @ @ @ @ unseen audience members scattered across the UK and beyond .
Under the circumstances , then , it was strange – and strangely comforting – to hear the leaders of four brand-new quantum-tech firms discussing much the same problems as entrepreneurs always do .
For Richard Murray , the chief executive of London-based ORCA Computing , the biggest challenge he faces is finding and hiring people with the right skills and experience .
“ There are lots of people with PhDs , ” he said .
“ Where the UK does need to boost @ @ @ @ @ @ @ sets.
” Optical engineers , he added , are “ hard to come by ” even for a firm like his that has won awards ( and nearly £3m in venture capital funding ) for its optical-fibre-based quantum computing technology .
Another panellist , Max Sich , said that he is most concerned about the relative immaturity of quantum technologies – including the semiconductor platform that his Sheffield-based firm , AegiQ , is developing for quantum cryptography systems .
“ Quantum is still a very risky technology compared to other things people invest @ @ @ @ @ @ @ We sit here in our quantum bubble and we all believe in it .
Not everybody does.
” The standard logic of a start-up is to grow fast and get big , but Sich noted that quantum technologies are mostly too undeveloped to achieve that yet .
“ Seed ” funding and grants from the UK government via the funding agency Innovate UK , he added , help to bridge the gap .
The third panellist , Anthony Laing , was something of an outlier in the group .
Although he co-founded his @ @ @ @ @ @ @ Photonics , in February 2020 , he remains part of the academic world , as a physicist at the University of Bristol .
Yet even for academics like him , he said , “ entrepreneurialism in quantum technologies is in the air ” .
His current goal is to design and fabricate photonic quantum processors that can simulate phenomena relevant to drug design in the pharmaceutical industry .
When asked what he hoped to bring to next year ’ s showcase , he suggested that Duality Quantum Photonics might have a prototype in @ @ @ @ @ @ @ ’ s five-year plan .
The session ’ s final speaker , Ramy Aboushelbaya , is the co-founder and chief exec of a University of Oxford spin-out called Quantum Dice .
As the company ’ s name implies , the main product Aboushelbaya and his colleagues are building is a quantum random number generator ( RNG ) for encryption systems .
Like AegiQ , Quantum Dice received support from Innovate UK , and Aboushelbaya described such grants as “ a validation ” that helped them attract additional private funding .
He and his @ @ @ @ @ @ @ hire more electronic and photonic engineers , with the aim of bringing their first commercial RNG to next year ’ s showcase .
The coronavirus-shaped elephant in the room finally made an appearance at the end of the session , in remarks by Roger McKinlay , the challenge director for quantum technologies at UK Research and Innovation .
“ It kind of hurts to have seen so many friends and not be able to loiter and chat to find out how life is going , not just how business is going , ” @ @ @ @ @ @ @ , there has been “ much progress in 12 months ” towards developing “ a vibrant quantum start-up culture ” in the UK – a point corroborated by session moderator Anke Lohmann , who noted that her consultancy , Anchored In , has identified six quantum start-ups that incorporated in the UK in 2020 .
“ We live in uncertain times , but in the midst of a crisis , we ’ ve seen so many good and healthy signs , ” McKinlay concluded .

A virtual display Panellists at a session on start-ups at the UK Quantum Technologies Showcase 2020. (Courtesy: Margaret Harris)
What happens when the irresistible force of quantum entrepreneurship meets the immovable object of a global pandemic?
For the past few years, the UK’s annual Quantum Technology Showcase has taken place at the QE2 conference centre in London, a stone’s throw from the Houses of Parliament, with attendees packing the exhibition hall to hear about the latest academic and industrial research into this growing field. This year, of course, it didn’t look like that. Instead, the presenters appeared by video link from home offices and deserted labs, sharing their experiences with unseen audience members scattered across the UK and beyond.
Under the circumstances, then, it was strange – and strangely comforting – to hear the leaders of four brand-new quantum-tech firms discussing much the same problems as entrepreneurs always do. For Richard Murray, the chief executive of London-based ORCA Computing, the biggest challenge he faces is finding and hiring people with the right skills and experience. “There are lots of people with PhDs,” he said. “Where the UK does need to boost its competitiveness is the more industrial-experience skill sets.” Optical engineers, he added, are “hard to come by” even for a firm like his that has won awards (and nearly £3m in venture capital funding) for its optical-fibre-based quantum computing technology.
Another panellist, Max Sich, said that he is most concerned about the relative immaturity of quantum technologies – including the semiconductor platform that his Sheffield-based firm, AegiQ, is developing for quantum cryptography systems. “Quantum is still a very risky technology compared to other things people invest in,” he observed. “We sit here in our quantum bubble and we all believe in it. Not everybody does.” The standard logic of a start-up is to grow fast and get big, but Sich noted that quantum technologies are mostly too undeveloped to achieve that yet. “Seed” funding and grants from the UK government via the funding agency Innovate UK, he added, help to bridge the gap.
The third panellist, Anthony Laing, was something of an outlier in the group. Although he co-founded his photonic quantum computing firm, Duality Quantum Photonics, in February 2020, he remains part of the academic world, as a physicist at the University of Bristol. Yet even for academics like him, he said, “entrepreneurialism in quantum technologies is in the air”. His current goal is to design and fabricate photonic quantum processors that can simulate phenomena relevant to drug design in the pharmaceutical industry. When asked what he hoped to bring to next year’s showcase, he suggested that Duality Quantum Photonics might have a prototype in 2021 – one year into the company’s five-year plan.
The session’s final speaker, Ramy Aboushelbaya, is the co-founder and chief exec of a University of Oxford spin-out called Quantum Dice. As the company’s name implies, the main product Aboushelbaya and his colleagues are building is a quantum random number generator (RNG) for encryption systems. Like AegiQ, Quantum Dice received support from Innovate UK, and Aboushelbaya described such grants as “a validation” that helped them attract additional private funding. He and his team of five are now hoping to hire more electronic and photonic engineers, with the aim of bringing their first commercial RNG to next year’s showcase.
The coronavirus-shaped elephant in the room finally made an appearance at the end of the session, in remarks by Roger McKinlay, the challenge director for quantum technologies at UK Research and Innovation. “It kind of hurts to have seen so many friends and not be able to loiter and chat to find out how life is going, not just how business is going,” McKinlay lamented. Nevertheless, he added, there has been “much progress in 12 months” towards developing “a vibrant quantum start-up culture” in the UK – a point corroborated by session moderator Anke Lohmann, who noted that her consultancy, Anchored In, has identified six quantum start-ups that incorporated in the UK in 2020. “We live in uncertain times, but in the midst of a crisis, we’ve seen so many good and healthy signs,” McKinlay concluded.


physicsworld--2020-11-11--The hunt for another Earth: a love story - Physics World

It ’ s not often I cry when reading a book about physics .
Massachusetts Institute of Technology ( MIT ) planetary scientist Sara Seager ’ s memoir is a love story , where love comes in multiple forms : romantic love , parental love , friendship , plus love of nature , of the universe and of maths .
Those last two are hardly a surprise , coming from a celebrated scientist , but her enthusiasm for her chosen career is especially warm and welcoming .
Though it isn ’ t her excitement about stargazing that provoked my tears @ @ @ @ @ @ @ : a Memoir opens with Seager recounting a particularly bad day six months after the death of her husband .
She was 40 years old , with two young sons , and describes her grief that day as “ ugly ” .
She compares the dark and empty feelings she was going through to the rogue planet PSO J318.5-22 – an exoplanet that isn ’ t part of a star system , and so is perpetually shrouded in darkness , with a surface covered in molten-iron rain .
It ’ s a powerful @ @ @ @ @ @ @ brilliantly evoked by Seager .
This narrative pattern of looking at her personal life and her work in parallel to elucidate both is an effective tool that Seager uses repeatedly .
Some readers may think this book contains a little too much of the personal , slightly outweighing the physics , but I strongly suspect this is deliberate .
Physics is studied by people , and their personal lives have an impact on their work lives .
It is valuable to have a renowned scientist acknowledge those links .
Between her unconventional childhood @ @ @ @ @ @ @ knows that she sees the world differently from a lot of people .
This has at times made it difficult for her to connect with people , but it has helped her to imagine ways to find and study exoplanets that others didn ’ t think of , or rejected .
Choosing to study exoplanets in the mid-1990s as a graduate student was an unusual and potentially foolhardy move , as it was still an emerging topic scorned by many .
Seager trusted her own gut instinct and that of her academic adviser @ @ @ @ @ @ @ set to work on writing computer code to study Hot Jupiters – so-called because these planets are about the same size as Jupiter , but orbit their host stars much more closely .
When she defended her PhD thesis in 1999 the 100-seat auditorium at Harvard was packed – exoplanets were still a fringe topic but a fascinating one that was gathering pace .
One of the many ways in which this memoir is unusually honest is that it gets into the nitty gritty of how physics research progresses .
Seager talks about @ @ @ @ @ @ @ and collaborators , as well as the different work environments at different institutions .
Early in her career , she and some of her close colleagues were sniped to publication or positions in working groups by older , better known scientists ( invariably male ) and she expresses her anger as well as the lessons she learned .
But for the most part she is generous about her colleagues .
According to Seager , astrophysicists need large egos to think themselves capable of understanding the universe , but they also need to accept @ @ @ @ @ @ @ the seed that another future scientist follows to make an important breakthrough .
It sounds idealistic , but those are traits that she attributes to her own mentors – particularly John Bahcall , who gave her a job at the Institute for Advanced Study in Princeton early in her career – and tries to cultivate in herself .
She was brave and/or lucky to choose a specialism as fast-moving as exoplanets , but she knows that much of what she hopes to see and learn about exoplanets won ’ t happen in her @ @ @ @ @ @ @ , like many scientists , works on several overlapping projects at a time .
Some of them fail or stop getting funding ; some move to other institutions or change their focus ; some she gets to see to completion .
She compares her choice of projects to an investment portfolio : there are the safe bets that can be a little dull ; the medium-risk that are more interesting ; and the “ big swings ” that are high in risk and reward .
Just as a successful career can rest on @ @ @ @ @ @ @ can be trusted , Seager discovers that the same is true outside of work .
Until her husband became seriously ill , he was the only person she considered to be her friend .
The people she subsequently found through necessity and accident , to help her with practicalities , often became friends in time .
As someone who had always worked long hours of intense concentration , Seager found that grief and sole parenting made her re-evaluate her work–life balance .
Her research was still of great importance – indeed , having @ @ @ @ @ @ @ one of the ways in which she coped with loss – but it could no longer consume all her time and energy .
Work travel had to be limited and often combined with family holidays .
For the first time in her career , she missed out on being part of a project she had helped to design because she missed a deadline .
She had to let that project go and move on , just as she was eventually able to move on from grieving and open herself up to new possibilities @ @ @ @ @ @ @ Lights in the Universe Seager remains enthusiastic and eager to learn more about exoplanets .
They are no longer fringe , and her dream of finding a truly Earth-like planet seems entirely possible .
But that dream was based in a wish to find extraterrestrial life , which she long ago calculated might be very different from life on Earth , and therefore might not require a planet that is anything like Earth .
How do you narrow down what to look for if you don ’ t know what you ’ re @ @ @ @ @ @ @ thorny problem that Seager is well placed to solve .
Or perhaps she will plant the seed for someone else to solve it many years from now .
Maybe she already has .

Physics World. Members of the Institute of Physics can enjoy the full issue Taken from the November 2020 issue of. Members of the Institute of Physics can enjoy the full issue via the Physics World app
Planet hunter A copy of this NASA poster illustrating rogue planet PSO J318.5-22 hangs on Sara Seager’s office wall, reflecting her role in studying it and its importance to her personally. (Courtesy: NASA/JPL-CalTech)
It’s not often I cry when reading a book about physics. Massachusetts Institute of Technology (MIT) planetary scientist Sara Seager’s memoir is a love story, where love comes in multiple forms: romantic love, parental love, friendship, plus love of nature, of the universe and of maths. Those last two are hardly a surprise, coming from a celebrated scientist, but her enthusiasm for her chosen career is especially warm and welcoming. Though it isn’t her excitement about stargazing that provoked my tears.
The Smallest Lights in the Universe: a Memoir opens with Seager recounting a particularly bad day six months after the death of her husband. She was 40 years old, with two young sons, and describes her grief that day as “ugly”. She compares the dark and empty feelings she was going through to the rogue planet PSO J318.5-22 – an exoplanet that isn’t part of a star system, and so is perpetually shrouded in darkness, with a surface covered in molten-iron rain. It’s a powerful description; difficult to visualize and yet brilliantly evoked by Seager.
This narrative pattern of looking at her personal life and her work in parallel to elucidate both is an effective tool that Seager uses repeatedly. Some readers may think this book contains a little too much of the personal, slightly outweighing the physics, but I strongly suspect this is deliberate. Physics is studied by people, and their personal lives have an impact on their work lives. It is valuable to have a renowned scientist acknowledge those links. Between her unconventional childhood and late-in-life diagnosis of autism, Seager knows that she sees the world differently from a lot of people. This has at times made it difficult for her to connect with people, but it has helped her to imagine ways to find and study exoplanets that others didn’t think of, or rejected.
Choosing to study exoplanets in the mid-1990s as a graduate student was an unusual and potentially foolhardy move
Choosing to study exoplanets in the mid-1990s as a graduate student was an unusual and potentially foolhardy move, as it was still an emerging topic scorned by many. Seager trusted her own gut instinct and that of her academic adviser Dimitar Sasselov, ignored the naysayers and set to work on writing computer code to study Hot Jupiters – so-called because these planets are about the same size as Jupiter, but orbit their host stars much more closely. When she defended her PhD thesis in 1999 the 100-seat auditorium at Harvard was packed – exoplanets were still a fringe topic but a fascinating one that was gathering pace.
One of the many ways in which this memoir is unusually honest is that it gets into the nitty gritty of how physics research progresses. Seager talks about the importance of finding the right mentors and collaborators, as well as the different work environments at different institutions. Early in her career, she and some of her close colleagues were sniped to publication or positions in working groups by older, better known scientists (invariably male) and she expresses her anger as well as the lessons she learned. But for the most part she is generous about her colleagues.
According to Seager, astrophysicists need large egos to think themselves capable of understanding the universe, but they also need to accept that their role might be to plant the seed that another future scientist follows to make an important breakthrough. It sounds idealistic, but those are traits that she attributes to her own mentors – particularly John Bahcall, who gave her a job at the Institute for Advanced Study in Princeton early in her career – and tries to cultivate in herself. She was brave and/or lucky to choose a specialism as fast-moving as exoplanets, but she knows that much of what she hopes to see and learn about exoplanets won’t happen in her lifetime.
Perhaps that is why Seager, like many scientists, works on several overlapping projects at a time. Some of them fail or stop getting funding; some move to other institutions or change their focus; some she gets to see to completion. She compares her choice of projects to an investment portfolio: there are the safe bets that can be a little dull; the medium-risk that are more interesting; and the “big swings” that are high in risk and reward.
Problem-solver Having meaty problems to tackle at work was one of the ways in which Sara Seager coped with loss. (Courtesy: John D and Catherine T MacArthur Foundation)
Just as a successful career can rest on finding colleagues with the right skills who can be trusted, Seager discovers that the same is true outside of work. Until her husband became seriously ill, he was the only person she considered to be her friend. The people she subsequently found through necessity and accident, to help her with practicalities, often became friends in time.
As someone who had always worked long hours of intense concentration, Seager found that grief and sole parenting made her re-evaluate her work–life balance. Her research was still of great importance – indeed, having meaty problems to tackle at work was one of the ways in which she coped with loss – but it could no longer consume all her time and energy. Work travel had to be limited and often combined with family holidays. For the first time in her career, she missed out on being part of a project she had helped to design because she missed a deadline. She had to let that project go and move on, just as she was eventually able to move on from grieving and open herself up to new possibilities.
At the end of The Smallest Lights in the Universe Seager remains enthusiastic and eager to learn more about exoplanets. They are no longer fringe, and her dream of finding a truly Earth-like planet seems entirely possible. But that dream was based in a wish to find extraterrestrial life, which she long ago calculated might be very different from life on Earth, and therefore might not require a planet that is anything like Earth. How do you narrow down what to look for if you don’t know what you’re looking for? It’s a thorny problem that Seager is well placed to solve. Or perhaps she will plant the seed for someone else to solve it many years from now. Maybe she already has.
2020 Penguin Random House 320pp $28/£16.99hb


physicsworld--2020-11-11--Electrically tuneable network learns fast - Physics World

A team of researchers at the University of Twente in the Netherlands has employed deep learning – a form of artificial intelligence , or AI – to optimize the structure of nanoelectronics devices for the first time .
According to team leader Wilfred Van der Wiel , the same method could also be used to tune quantum dot systems for quantum computing and should be generally applicable to other large-scale physical systems for which many control parameters are likewise unavailable .
Earlier in 2020 Van der Wiel and colleagues created an electrically tuneable network of boron dopants in silicon .
@ @ @ @ @ @ @ electrodes : seven that act as voltage inputs and one that serves as a current output .
The signal of the output comes from electrons “ hopping ” from one boron atom to another in a way that somewhat resembles the way neurons in our brain “ fire ” when they perform a task .
So , while the network is not ordered , it has an output signal , and it is possible to “ steer ” this signal in the desired direction by changing the voltages on the control electrodes .
@ @ @ @ @ @ @ , Van der Wiel explains , and although successful it proved cumbersome in practice .
“ While not as slow as Darwinian evolution , it is still quite time-consuming to have the network do what you would like it to do , ” he tells Physics World .
For this latest work , the Twente team turned instead to deep neural networks ( DNNs ) .
These networks have become increasingly common tools in scientific research , where they are typically used to model complex physical phenomena after an initial period of “ @ @ @ @ @ @ @ data .
To generate a DNN model of their nanoelectronic device , Van der Wiel and colleagues began by measuring the device ’ s output signal for many distinct input voltage configurations .
They then used these data and standard deep-learning techniques to train their DNN model to “ understand ” how the real device behaves .
The resulting model predicts the output current of the device , given the input voltage configuration .
Van de Wiel explains that this approach is very similar to the way that standard AI tasks are solved @ @ @ @ @ @ @ learning , we have to find the parameters of the model itself , ” he says .
“ These are the weight factors between neurons and the threshold values of the neurons .
This is what we learn in the first phase : we find the parameters of the DNN model itself , so that it mimics the physical device.
” In the second phase of their work , the researchers keep the parameters of the DNN model constant so that they can learn the optimum control parameters .
“ With this model @ @ @ @ @ @ @ functionality , ” Van der Wiel says .
“ For this , we choose some of the inputs as control parameters and others as data inputs .
We search for the control values on the DNN model again using deep learning .
Mathematically this boils down to the same thing : again , we have to optimize parameters to find the desired output.
” Once they have used the DNN to find the best values for the control parameters , the researchers can apply these values as voltages to the corresponding terminals in @ @ @ @ @ @ @ DNN is a model of the physical device , its output current should then behave in the desired way .
The group found that this new approach works about 100 times faster than the artificial evolution they used before .
“ With this new method we can optimize nanoelectronics devices with many terminals and even optimize systems in which many complex nanoelectronics circuits are coupled , ” Van der Wiel explains .
“ Such systems are expected to increase in complexity in the coming years in , for example , novel information processing @ @ @ @ @ @ @ ” Van der Wiel and colleagues also found that their approach worked for tasks of increasing complexity .
After successfully demonstrating Boolean gates on their nanoelectronics device , they went on to show that the device can perform binary classification and a “ feature map ” task in which 2 × 2 patches of pixels are mapped to a current value ( a subtask of a higher-level image classification task ) .
These last two tasks would have been very challenging to complete without this new method , the researchers say .
The @ @ @ @ @ @ @ , which is published in Nature Nanotechnology , could be used for neuromorphic computing in the future .
As a next step , the team plans to build more energy-efficient large-scale systems of interconnected dopant network devices for state-of-the-art AI performance .

A schematic of the experiment. (Courtesy: Wilfred Van der Wiel)
A team of researchers at the University of Twente in the Netherlands has employed deep learning – a form of artificial intelligence, or AI – to optimize the structure of nanoelectronics devices for the first time. According to team leader Wilfred Van der Wiel, the same method could also be used to tune quantum dot systems for quantum computing and should be generally applicable to other large-scale physical systems for which many control parameters are likewise unavailable.
Earlier in 2020 Van der Wiel and colleagues created an electrically tuneable network of boron dopants in silicon. This device contained eight terminals, or electrodes: seven that act as voltage inputs and one that serves as a current output. The signal of the output comes from electrons “hopping” from one boron atom to another in a way that somewhat resembles the way neurons in our brain “fire” when they perform a task. So, while the network is not ordered, it has an output signal, and it is possible to “steer” this signal in the desired direction by changing the voltages on the control electrodes.
This process is also called artificial evolution, Van der Wiel explains, and although successful it proved cumbersome in practice. “While not as slow as Darwinian evolution, it is still quite time-consuming to have the network do what you would like it to do,” he tells Physics World.
Making use of deep-learning neural networks
For this latest work, the Twente team turned instead to deep neural networks (DNNs). These networks have become increasingly common tools in scientific research, where they are typically used to model complex physical phenomena after an initial period of “training” on examples drawn from experimental data.
To generate a DNN model of their nanoelectronic device, Van der Wiel and colleagues began by measuring the device’s output signal for many distinct input voltage configurations. They then used these data and standard deep-learning techniques to train their DNN model to “understand” how the real device behaves. The resulting model predicts the output current of the device, given the input voltage configuration. Van de Wiel explains that this approach is very similar to the way that standard AI tasks are solved using DNN. “In standard deep learning, we have to find the parameters of the model itself,” he says. “These are the weight factors between neurons and the threshold values of the neurons. This is what we learn in the first phase: we find the parameters of the DNN model itself, so that it mimics the physical device.”
100 times faster than artificial evolution technique
In the second phase of their work, the researchers keep the parameters of the DNN model constant so that they can learn the optimum control parameters. “With this model, we can search for a desired functionality,” Van der Wiel says. “For this, we choose some of the inputs as control parameters and others as data inputs. We search for the control values on the DNN model again using deep learning. Mathematically this boils down to the same thing: again, we have to optimize parameters to find the desired output.”
In the lab. (Courtesy: University of Twente)
Once they have used the DNN to find the best values for the control parameters, the researchers can apply these values as voltages to the corresponding terminals in the real nanoelectronic device. Since the DNN is a model of the physical device, its output current should then behave in the desired way.
The group found that this new approach works about 100 times faster than the artificial evolution they used before. “With this new method we can optimize nanoelectronics devices with many terminals and even optimize systems in which many complex nanoelectronics circuits are coupled,” Van der Wiel explains. “Such systems are expected to increase in complexity in the coming years in, for example, novel information processing technologies like quantum computing and neuromorphic computing.”
Scalable and applicable to complex tasks
Van der Wiel and colleagues also found that their approach worked for tasks of increasing complexity. After successfully demonstrating Boolean gates on their nanoelectronics device, they went on to show that the device can perform binary classification and a “feature map” task in which 2 × 2 patches of pixels are mapped to a current value (a subtask of a higher-level image classification task). These last two tasks would have been very challenging to complete without this new method, the researchers say.
The dopant network devices described in this study, which is published in Nature Nanotechnology, could be used for neuromorphic computing in the future. As a next step, the team plans to build more energy-efficient large-scale systems of interconnected dopant network devices for state-of-the-art AI performance.


physicsworld--2020-11-11--Ferroelectricity: 100 years since its discovery - Physics World

Today , ferroelectric materials have some amazing applications .
You will find them in many technologies including telescope optics , @ @ @ @ @ storage devices .
But when ferroelectricity was first observed 100 years ago by the physicist @ @ @ @ @ was greeted with little fanfare among the physics community .
Watch this video to discover @ @ @ @ @ obscurity to a key part of several transformative technologies .
For a deep dive into @ @ @ @ @ a look at this feature from the November 2020 issue of Physics World , written @ @ @ @ @ the University of Texas at San Antonio and Avadh Saxena at Los Alamos National Laboratory @

Today, ferroelectric materials have some amazing applications. You will find them in many technologies including telescope optics, medical ultrasound devices and energy storage devices. But when ferroelectricity was first observed 100 years ago by the physicist Joseph Valasek, the discovery was greeted with little fanfare among the physics community.
Watch this video to discover how ferroelectricity transformed from academic obscurity to a key part of several transformative technologies. For a deep dive into the history of ferroelectricity take a look at this feature from the November 2020 issue of Physics World, written by Amar S Bhalla at the University of Texas at San Antonio and Avadh Saxena at Los Alamos National Laboratory.


physicsworld--2020-11-12--g-wave superconductor comes into view - Physics World

Superconducting materials are traditionally classed into two types : s-wave and d-wave .
A third type , p-wave , has long been predicted .
Now , however , researchers in the US , Germany and Japan say they may have discovered a fourth , unexpected type of superconductor : g-wave .
The result , obtained thanks to high-precision resonant ultrasound spectroscopy measurements on strontium ruthenate , could shed fresh light on the Cooper pairing mechanisms in so-called unconventional superconductors .
In conventional superconductors , electrons join up to form Cooper pairs that then move through a material without any resistance @ @ @ @ @ @ @ to be cooled to ultralow temperatures ( or placed under extreme pressures ) before their electrons start behaving in this way , if the process could be made to happen at higher temperatures , it would in principle allow for super-efficient power grids and circuit boards that don ’ t produce waste heat .
The Cooper pairing mechanism stems from interactions between electrons and phonons ( vibrations of the material ’ s crystal lattice ) and results in a “ superconducting order parameter ” that is said to have s-wave symmetry .
In @ @ @ @ @ @ @ like lead , tin and mercury , the Cooper pairs comprise one electron with spin up and one electron with spin down .
As these electrons move head-on towards each other , their net angular momentum is zero .
Unconventional superconductors , in contrast , exhibit d-wave superconductivity .
Here , the electrons in the Cooper pairs have a positive angular momentum in one direction and a negative one in a second direction .
Hence , these materials have a net angular momentum of 2 .
A third type of superconductor , known @ @ @ @ @ @ @ between these s and d “ singlet ” states .
p-type superconductors have one quanta of angular momentum and their electrons pair with parallel rather than antiparallel spins .
Such “ spin-triplet ” materials are of interest because they could be used to create Majorana fermions – exotic particles that are their own antiparticles .
For 25 years , the main candidate for p-wave superconductivity has been strontium ruthenate ( Sr RuO ) .
Several recent experiments have , however , cast doubt on this hypothesis .
To investigate further , researchers led @ @ @ @ @ @ @ sound waves through a Sr RuO crystal as they cooled it through its superconducting temperature of 1.4 K. By measuring the response of the crystal ’ s elastic constants to the sound waves , they were able to determine how the speed of sound changes in response to shifts in temperature .
While this high-resolution resonant ultrasound spectroscopy technique has been used before , it had never previously been tried at such low temperatures – meaning that the researchers had to build a completely new instrument .
“ This is by far the @ @ @ @ @ @ @ at these low temperatures , ” Ramshaw says .
The data these experiments produced indicate that Sr RuO is a “ two-component ” superconductor , which means that the way the electrons pair up can not be described by a single number .
Instead , the description must also include a value representing the direction in which electrons pair up .
This behaviour is not consistent with p-wave superconductivity , and indeed previous studies using nuclear magnetic resonance ( NMR ) spectroscopy had likewise suggested that Sr RuO is not a p-wave superconductor @ @ @ @ @ @ @ up these findings , but they also went a step further , showing that Sr RuO is in fact something else entirely : a g-wave superconductor .
This means that it has a completely different type of angular momentum from either s– or d-wave materials .
“ Resonant ultrasound really lets you go in and even if you can ’ t identify all the microscopic details , you can make broad statements about which ones are ruled out , ” Ramshaw explains .
“ So then the only things that the experiments are @ @ @ @ @ @ @ weird things that nobody has ever seen before .
One of which is g-wave , which means angular momentum 4 .
“ No one has ever even thought that there would be a g-wave superconductor.
” As a next step , the researchers say they plan to continue their search for p-wave superconductivity in other candidate materials .
However , they will also continue studying Sr RuO .
“ This material is extremely well studied in a lot of different contexts , not just for its superconductivity , ” Ramshaw says .
“ @ @ @ @ @ @ @ is , why it ’ s a metal , how it behaves when you change temperature , how it behaves when you change the magnetic field .
So you should be able to construct a theory of why it becomes a superconductor better here than just about anywhere else.
” The team , which includes researchers from the Max Planck Institute for Chemical Physics of Solids in Germany , the National High Magnetic Field Laboratory at Florida State University and the National Institute for Materials Science in Tsukuba , Japan , report their @ @ @ @ @

A crystal lattice of strontium ruthenate responding to various sound waves as the material cools through its superconducting transition. (Courtesy: Cornell University)
Superconducting materials are traditionally classed into two types: s-wave and d-wave. A third type, p-wave, has long been predicted. Now, however, researchers in the US, Germany and Japan say they may have discovered a fourth, unexpected type of superconductor: g-wave. The result, obtained thanks to high-precision resonant ultrasound spectroscopy measurements on strontium ruthenate, could shed fresh light on the Cooper pairing mechanisms in so-called unconventional superconductors.
In conventional superconductors, electrons join up to form Cooper pairs that then move through a material without any resistance. While all known superconducting materials need to be cooled to ultralow temperatures (or placed under extreme pressures) before their electrons start behaving in this way, if the process could be made to happen at higher temperatures, it would in principle allow for super-efficient power grids and circuit boards that don’t produce waste heat.
Superconducting order parameter
The Cooper pairing mechanism stems from interactions between electrons and phonons (vibrations of the material’s crystal lattice) and results in a “superconducting order parameter” that is said to have s-wave symmetry. In such s-wave superconductors, which include materials like lead, tin and mercury, the Cooper pairs comprise one electron with spin up and one electron with spin down. As these electrons move head-on towards each other, their net angular momentum is zero.
Unconventional superconductors, in contrast, exhibit d-wave superconductivity. Here, the electrons in the Cooper pairs have a positive angular momentum in one direction and a negative one in a second direction, so the total spin angular momentum for each pair is again zero. However, the pairs’ orbital angular momentum is nonzero..
A third type of superconductor, known as p-type, is predicted to exist between these s and d “singlet” states. p-type superconductors have one quanta of angular momentum and their electrons pair with parallel rather than antiparallel spins. Such “spin-triplet” materials are of interest because they could be used to create Majorana fermions – exotic particles that are their own antiparticles.
Measuring the speed of sound waves
For 25 years, the main candidate for p-wave superconductivity has been strontium ruthenate (Sr 2 RuO 4 ). Several recent experiments have, however, cast doubt on this hypothesis. To investigate further, researchers led by Brad Ramshaw of Cornell University sent sound waves through a Sr 2 RuO 4 crystal as they cooled it through its superconducting temperature of 1.4 K. By measuring the response of the crystal’s elastic constants to the sound waves, they were able to determine how the speed of sound changes in response to shifts in temperature.
While this high-resolution resonant ultrasound spectroscopy technique has been used before, it had never previously been tried at such low temperatures – meaning that the researchers had to build a completely new instrument. “This is by far the highest-precision resonant ultrasound spectroscopy data ever taken at these low temperatures,” Ramshaw says.
A “two-component” superconductor
The data these experiments produced indicate that Sr 2 RuO 4 is a “two-component” superconductor, which means that the way the electrons pair up cannot be described by a single number. Instead, the description must also include a value representing the direction in which electrons pair up. This behaviour is not consistent with p-wave superconductivity, and indeed previous studies using nuclear magnetic resonance (NMR) spectroscopy had likewise suggested that Sr 2 RuO 4 is not a p-wave superconductor.
The Cornell researchers have now backed up these findings, but they also went a step further, showing that Sr 2 RuO 4 is in fact something else entirely: a g-wave superconductor. This means that it has a completely different type of angular momentum from either s– or d-wave materials.
“Resonant ultrasound really lets you go in and even if you can’t identify all the microscopic details, you can make broad statements about which ones are ruled out,” Ramshaw explains. “So then the only things that the experiments are consistent with are these very, very weird things that nobody has ever seen before. One of which is g-wave, which means angular momentum 4.
“No-one has ever even thought that there would be a g-wave superconductor.”
Constructing a better theory
As a next step, the researchers say they plan to continue their search for p-wave superconductivity in other candidate materials. However, they will also continue studying Sr 2 RuO 4 . “This material is extremely well studied in a lot of different contexts, not just for its superconductivity,” Ramshaw says. “We understand what kind of metal it is, why it’s a metal, how it behaves when you change temperature, how it behaves when you change the magnetic field. So you should be able to construct a theory of why it becomes a superconductor better here than just about anywhere else.”
The team, which includes researchers from the Max Planck Institute for Chemical Physics of Solids in Germany, the National High Magnetic Field Laboratory at Florida State University and the National Institute for Materials Science in Tsukuba, Japan, report their work in Nature Physics.


physicsworld--2020-11-12--Neutrons give an atomic-scale view of SARS-CoV-2 replication mechanism - Physics World

Neutron scattering experiments have allowed researchers in the US to map out the precise positions of hydrogen atoms in a coronavirus protein for the first time .
The approach , taken by Andrey Kovalevsky and colleagues at Oak Ridge National Laboratory , reveals key clues of how binding occurs between specific enzymes and protein chains involved in the replication of the virus .
The team ’ s discoveries could lead to advanced computational designs of targeted drugs .
Within the SARS-CoV-2 virus ( which causes COVID-19 ) the information required for replication is encoded on two overlapping chains of protein @ @ @ @ @ @ @ the virus must first break these chains down into individual functional proteins .
This requires a specific enzyme called the main protease – which interacts with protein chains at a specific group of atoms called the “ active site ” .
Currently , researchers are aiming to reduce the activity of these enzymes by developing inhibitor drugs that tightly bind to their active sites – preventing further reactions .
Developing these inhibitors requires a detailed knowledge about the locations of hydrogen atoms within the main protease enzyme because these atoms define the nature @ @ @ @ @ @ @ protein chains .
Normally , the locations of atoms in a molecule can be determined using X-ray crystallography .
That technique , however , is not sensitive to hydrogen so the team used neutron scattering instead .
Another benefit of neutron scattering is that unlike X-ray techniques , it does not cause any radiation damage to samples .
“ Half of the atoms in proteins are hydrogen .
Those atoms are key players in enzymatic function and are essential to how drugs bind , ” explains Kovalevsky .
“ If we don ’ @ @ @ @ @ @ @ how the electrical charges are distributed inside the protein , we can ’ t design effective inhibitors for the enzyme.
” Using instruments at ORNL ’ s Spallation Neutron Source , the researchers determined the precise structure of the virus ’ s main protease enzyme , and compared the result to structures obtained through X-ray crystallography .
Whereas the locations of hydrogen atoms could only be inferred from the X-ray data , neutron scattering allowed Kovalevsky and colleagues to determine their positions to subatomic resolution .
This enabled them to identify electrical charges @ @ @ @ @ @ @ hydrogen bonds across the enzyme ’ s active site .
The team is now the first to determine the exact structure of a coronavirus protein .
Their findings represent a crucial advance in our understanding of how the SARS-CoV-2 virus replicates , and will now provide critical information for the computational design of inhibitor drugs that are specifically tailored for targeting the electrostatic environment of the main protease enzyme .
If created , such drugs could soon become a key aspect of global efforts to contain the spread of the virus .
The @ @ @ @ @ @ @ Biological Chemistry .

First structure: a neutron scattering study of the SARS-CoV-2 virus main protease enzyme has revealed unexpected electrical charges in the amino acids cysteine (negative) and histidine (positive), providing key data about the virus's replication. (Courtesy: Jill Hemman/ORNL/DOE)
Neutron scattering experiments have allowed researchers in the US to map out the precise positions of hydrogen atoms in a coronavirus protein for the first time. The approach, taken by Andrey Kovalevsky and colleagues at Oak Ridge National Laboratory, reveals key clues of how binding occurs between specific enzymes and protein chains involved in the replication of the virus. The team’s discoveries could lead to advanced computational designs of targeted drugs.
Within the SARS-CoV-2 virus (which causes COVID-19) the information required for replication is encoded on two overlapping chains of protein molecules. To use this information, the virus must first break these chains down into individual functional proteins. This requires a specific enzyme called the main protease – which interacts with protein chains at a specific group of atoms called the “active site”. Currently, researchers are aiming to reduce the activity of these enzymes by developing inhibitor drugs that tightly bind to their active sites – preventing further reactions.
Developing these inhibitors requires a detailed knowledge about the locations of hydrogen atoms within the main protease enzyme because these atoms define the nature of the hydrogen bonds between enzymes and protein chains. Normally, the locations of atoms in a molecule can be determined using X-ray crystallography. That technique, however, is not sensitive to hydrogen so the team used neutron scattering instead. Another benefit of neutron scattering is that unlike X-ray techniques, it does not cause any radiation damage to samples.
Key players
“Half of the atoms in proteins are hydrogen. Those atoms are key players in enzymatic function and are essential to how drugs bind,” explains Kovalevsky. “If we don’t know where those hydrogens are and how the electrical charges are distributed inside the protein, we can’t design effective inhibitors for the enzyme.”
Using instruments at ORNL’s Spallation Neutron Source, the researchers determined the precise structure of the virus’s main protease enzyme, and compared the result to structures obtained through X-ray crystallography. Whereas the locations of hydrogen atoms could only be inferred from the X-ray data, neutron scattering allowed Kovalevsky and colleagues to determine their positions to subatomic resolution. This enabled them to identify electrical charges, as well as intricate networks of hydrogen bonds across the enzyme’s active site.
The team is now the first to determine the exact structure of a coronavirus protein. Their findings represent a crucial advance in our understanding of how the SARS-CoV-2 virus replicates, and will now provide critical information for the computational design of inhibitor drugs that are specifically tailored for targeting the electrostatic environment of the main protease enzyme. If created, such drugs could soon become a key aspect of global efforts to contain the spread of the virus.
The research is described in The Journal of Biological Chemistry.


physicsworld--2020-11-12--Tumour composition matters in radiopharmaceutical therapy - Physics World

Accounting for the composition and mass of tumours could make dose calculations for radiopharmaceutical cancer therapy significantly more accurate , say researchers in the US .
Edmond Olguin and colleagues , at the University of Florida and Radiopharmaceutical Imaging and Dosimetry , modelled how various radionuclides deposit energy in tumours of differing shape , size and tissue mineralization .
They found that doses calculated for small tumours in bone are especially sensitive to such properties .
The researchers say that their results should increase the effectiveness of radiopharmaceutical therapy , and are ready to be adopted by clinicians immediately .
@ @ @ @ @ @ @ tumours with precisely applied doses of radiation , radiopharmaceutical therapy is used to treat widely disseminated cancers by administering a radionuclide-labelled pharmaceutical to the patient as a whole .
By choosing the right radionuclide , or by conjugating it to a certain drug , clinicians can make the radioactive agent accumulate selectively in tumours .
This maximizes the dose to cancerous tissue while keeping the radiation burden elsewhere in the patient below harmful levels .
The most accurate way to calculate the received dose is to simulate the process using a radiation-transport model @ @ @ @ @ @ @ patient .
But because this technique requires expertise and computational resources not available to most hospitals , it is most commonly used in research settings .
Instead , clinicians tend to use the Medical Internal Radiation Dose ( MIRD ) schema , a simpler method that classifies parts of the body as either radiation sources ( where the radiopharmaceutical accumulates ) or radiation targets ( where there is a medical reason to measure exposure ) .
In tumours that concentrate the radiopharmaceutical , these regions are one and the same .
When a @ @ @ @ @ @ @ , the key to calculating the delivered dose is to determine the absorbed fraction – the proportion of energy emitted within the tumour that is deposited without escaping the region .
This gives the dose per radionuclide decay , or “ S-value ” , which varies according to the type of particle emitted , its energy and the size of the tumour .
Writing in Physics and Medicine and Biology , Olguin and colleagues show that the S-value is also sensitive to the tumour ’ s elemental composition , which until now has @ @ @ @ @ @ @ To investigate the effect of different tumour compositions , the researchers used a supercomputer to run the Monte Carlo N-Particle Transport ( MCNP ) model .
They simulated photon , electron and alpha-particle irradiation of spherical and ellipsoidal tumours of various sizes and axial ratios , and with compositions ranging from 100 % soft tissue to 100 % mineral bone .
Considering a spherical tumour composed of soft tissue , the team found that their calculated absorbed fractions matched previous estimates to within a few percentage points for all radiation types and energies @ @ @ @ @ @ @ cm across , however , the absorbed fraction deposited by electrons was 25 % larger than for soft-tissue tumours .
For photons , the effect was even greater , with fully mineralized tissues absorbing as much as 71 % more radiation than their unmineralized counterparts across all tumour sizes .
Olguin and colleagues also found that ellipsoidal tumours could be approximated as spheres , to a certain degree .
Although electron absorbed fractions for spherical tumours agreed with their ellipsoidal counterparts within 8 % , for photons , these errors were especially large @ @ @ @ @ @ @ ellipticities greater than about 0.98 .
Because dosimetric errors are greatest in small , mineralized tumours , the researchers expect their study to be particularly useful in treating patients suffering from the metastatic spread of the bone cancer osteosarcoma .
To help get their results into the clinic quickly , they compiled a look-up table presenting S-values for a range of tumour sizes and compositions and a comprehensive list of relevant radionuclides .
“ As the S-values are simply the radiation dose per radionuclide decay , multiplying them by the number of decays @ @ @ @ @ @ @ explains Wesley Bolch at the University of Florida , who led the study .
“ This database of S-values allows the complexities of tumour dosimetry to be simplified down to the product of two numbers !
” The team also plan to incorporate the results into a community-developed project called MIRDcalc – a software program that will calculate organ doses in nuclear medicine and X-ray CT in a single package .

A new database of S-values for radiopharmaceutical therapy could prove particularly useful when treating metastatic bone cancer, in which cells in the primary tumour (A) invade surrounding tissues (B) and enter secondary sites (C, D). (Courtesy: CC BY 4.0/T M Fan et al Front. Oncol. 10.3389/fonc.2020.00013)
Accounting for the composition and mass of tumours could make dose calculations for radiopharmaceutical cancer therapy significantly more accurate, say researchers in the US. Edmond Olguin and colleagues, at the University of Florida and Radiopharmaceutical Imaging and Dosimetry, modelled how various radionuclides deposit energy in tumours of differing shape, size and tissue mineralization. They found that doses calculated for small tumours in bone are especially sensitive to such properties. The researchers say that their results should increase the effectiveness of radiopharmaceutical therapy, and are ready to be adopted by clinicians immediately.
While most forms of radiotherapy target individual tumours with precisely applied doses of radiation, radiopharmaceutical therapy is used to treat widely disseminated cancers by administering a radionuclide-labelled pharmaceutical to the patient as a whole. By choosing the right radionuclide, or by conjugating it to a certain drug, clinicians can make the radioactive agent accumulate selectively in tumours. This maximizes the dose to cancerous tissue while keeping the radiation burden elsewhere in the patient below harmful levels.
The most accurate way to calculate the received dose is to simulate the process using a radiation-transport model and a personalized digital phantom of the patient. But because this technique requires expertise and computational resources not available to most hospitals, it is most commonly used in research settings.
Instead, clinicians tend to use the Medical Internal Radiation Dose (MIRD) schema, a simpler method that classifies parts of the body as either radiation sources (where the radiopharmaceutical accumulates) or radiation targets (where there is a medical reason to measure exposure). In tumours that concentrate the radiopharmaceutical, these regions are one and the same.
When a single tumour is both source and target, the key to calculating the delivered dose is to determine the absorbed fraction – the proportion of energy emitted within the tumour that is deposited without escaping the region. This gives the dose per radionuclide decay, or “S-value”, which varies according to the type of particle emitted, its energy and the size of the tumour. Writing in Physics and Medicine and Biology, Olguin and colleagues show that the S-value is also sensitive to the tumour’s elemental composition, which until now has typically been modelled as soft tissue.
To investigate the effect of different tumour compositions, the researchers used a supercomputer to run the Monte Carlo N-Particle Transport (MCNP) model. They simulated photon, electron and alpha-particle irradiation of spherical and ellipsoidal tumours of various sizes and axial ratios, and with compositions ranging from 100% soft tissue to 100% mineral bone.
Considering a spherical tumour composed of soft tissue, the team found that their calculated absorbed fractions matched previous estimates to within a few percentage points for all radiation types and energies. For mineralized tumours smaller than 1.5 cm across, however, the absorbed fraction deposited by electrons was 25% larger than for soft-tissue tumours. For photons, the effect was even greater, with fully mineralized tissues absorbing as much as 71% more radiation than their unmineralized counterparts across all tumour sizes.
Olguin and colleagues also found that ellipsoidal tumours could be approximated as spheres, to a certain degree. Although electron absorbed fractions for spherical tumours agreed with their ellipsoidal counterparts within 8%, for photons, these errors were especially large, exceeding 20% for tumours with ellipticities greater than about 0.98.
Because dosimetric errors are greatest in small, mineralized tumours, the researchers expect their study to be particularly useful in treating patients suffering from the metastatic spread of the bone cancer osteosarcoma. To help get their results into the clinic quickly, they compiled a look-up table presenting S-values for a range of tumour sizes and compositions and a comprehensive list of relevant radionuclides.
First author Edmond Olguin (left) and study leader Wesley Bolch from the University of Florida.
“As the S-values are simply the radiation dose per radionuclide decay, multiplying them by the number of decays gives us the tumour dose,” explains Wesley Bolch at the University of Florida, who led the study. “This database of S-values allows the complexities of tumour dosimetry to be simplified down to the product of two numbers!”
The team also plan to incorporate the results into a community-developed project called MIRDcalc – a software program that will calculate organ doses in nuclear medicine and X-ray CT in a single package.


physicsworld--2020-11-12--Microchip produces laser light of different colours - Physics World

Years of research and development work have made compact , high-performance lasers ubiquitous for the near-infrared wavelengths used in telecommunications .
However , lasers with an output in the visible range of the spectrum rely mainly on atomic or solid-state systems .
Such systems often require bulky table-top lasers or a series of different semiconducting materials to function .
A team of researchers at NIST in the US has now addressed this problem by making a microchip-based laser that converts near-infrared laser light into any of a range of visible light colours – including red , orange , yellow and @ @ @ @ @ @ @ optical parametric oscillation ( OPO ) .
The device could allow for a host of applications in areas such as spectroscopy , precision timekeeping and quantum information science .
Nonlinear material converts incident light into two different frequencies The principle behind the third-order OPO technique is to use a nonlinear material – in this case , silicon nitride ( Si N ) – to convert incident light in the near-infrared into two different frequencies .
One of these output frequencies is higher than the frequency of the incident light , in the visible @ @ @ @ @ @ @ and lies deeper in the infrared .
In the nonlinear regime , light with a high enough intensity exits an optical material with a wavelength that does not necessarily match the wavelength of the light that has entered it .
This is because bound electrons in the material re-radiate the light at frequencies that are different from those of the incident light .
This situation is very different to an ordinary ( linear ) optical material , which radiates the same colour ( think of light bouncing off a mirror or refracting through @ @ @ @ @ @ @ , researchers led by Kartik Srinivasan directed a beam of near-infrared laser light into a microresonator .
This is a ring-shaped structure with a radius of 50 microns fabricated on a silicon chip .
The light inside this device circulates roughly 5000 times before dissipating , building enough intensity as it does so to access the nonlinear regime – where it is converted to the two different output frequencies .
The researchers fabricated several of these microresonators , each with slightly different dimensions on a chip .
By carefully choosing these dimensions , @ @ @ @ @ @ @ microresonators would produce different-coloured output light .
This approach allowed a single near-infrared laser operating over a narrow range of wavelengths ( from 780 nm to 790 nm ) to generate visible-light colours ranging from green to red ( 560 nm to 760 nm ) , and infrared wavelengths ranging from 800 nm to 1200 nm .
Any one of these wavelengths can be accessed simply by adjusting the dimensions of the resonators , Srinivasan explains .
While the work is currently still at the proof-of-principle stage , the researchers hope to combine @ @ @ @ @ @ @ laser technology to create new types of on-chip light sources for a variety of applications .
They report their work in Optica .

A series of nanophotonic resonators generates different colours of visible light. Credit: NIST
Years of research and development work have made compact, high-performance lasers ubiquitous for the near-infrared wavelengths used in telecommunications. However, lasers with an output in the visible range of the spectrum rely mainly on atomic or solid-state systems. Such systems often require bulky table-top lasers or a series of different semiconducting materials to function.
A team of researchers at NIST in the US has now addressed this problem by making a microchip-based laser that converts near-infrared laser light into any of a range of visible light colours – including red, orange, yellow and green – using a technique called third-order optical parametric oscillation (OPO). The device could allow for a host of applications in areas such as spectroscopy, precision timekeeping and quantum information science.
Nonlinear material converts incident light into two different frequencies
The principle behind the third-order OPO technique is to use a nonlinear material – in this case, silicon nitride (Si 3 N 4 ) – to convert incident light in the near-infrared into two different frequencies. One of these output frequencies is higher than the frequency of the incident light, in the visible range, while the other is lower and lies deeper in the infrared.
In the nonlinear regime, light with a high enough intensity exits an optical material with a wavelength that does not necessarily match the wavelength of the light that has entered it. This is because bound electrons in the material re-radiate the light at frequencies that are different from those of the incident light. This situation is very different to an ordinary (linear) optical material, which radiates the same colour (think of light bouncing off a mirror or refracting through a lens).
Microresonators produce output light of different colours
In their work, researchers led by Kartik Srinivasan directed a beam of near-infrared laser light into a microresonator. This is a ring-shaped structure with a radius of 50 microns fabricated on a silicon chip. The light inside this device circulates roughly 5000 times before dissipating, building enough intensity as it does so to access the nonlinear regime – where it is converted to the two different output frequencies.
The researchers fabricated several of these microresonators, each with slightly different dimensions on a chip. By carefully choosing these dimensions, they were able to ensure that different microresonators would produce different-coloured output light. This approach allowed a single near-infrared laser operating over a narrow range of wavelengths (from 780 nm to 790 nm) to generate visible-light colours ranging from green to red (560 nm to 760 nm), and infrared wavelengths ranging from 800 nm to 1200 nm. Any one of these wavelengths can be accessed simply by adjusting the dimensions of the resonators, Srinivasan explains.
While the work is currently still at the proof-of-principle stage, the researchers hope to combine their nonlinear optics technique with well-established near-infrared laser technology to create new types of on-chip light sources for a variety of applications. They report their work in Optica.


physicsworld--2020-11-12--Striving for diversity and inclusion in engineering, 100 years of ferroelectrics - Physics World

This episode of the Physics World Weekly podcast features an interview with Carol Marsh , who was recently honoured by @ @ @ @ @ Elizabeth II for her work on diversity and inclusion .
Edinburgh-based Marsh talks about her @ @ @ @ @ electronics engineering at the aerospace and defence company Leonardo and about her efforts to get @ @ @ @ @ engineering .
This week we also celebrate 100 years of ferroelectrics – materials that have @ @ @ @ @ harvesting and night vision – and report from the UK ’ s annual Quantum Technology @ @

This episode of the Physics World Weekly podcast features an interview with Carol Marsh, who was recently honoured by the UK’s Queen Elizabeth II for her work on diversity and inclusion. Edinburgh-based Marsh talks about her role as deputy head of electronics engineering at the aerospace and defence company Leonardo and about her efforts to get more women into science and engineering.
This week we also celebrate 100 years of ferroelectrics – materials that have found myriad applications including energy harvesting and night vision – and report from the UK’s annual Quantum Technology Showcase.


physicsworld--2020-11-13--Deep learning enables rapid detection of stroke-causing blockages - Physics World

Strokes are life-threatening medical emergencies where urgent treatment is essential .
They occur when part of the brain is cut off from its normal blood supply .
The most common type of stroke ( accounting for almost 85 % of all cases ) is an ischemic stroke , which is caused by a clot interrupting the supply of blood to the brain .
Large vessel occlusion ( LVO ) strokes occur when such a blockage is found in one of the major arteries of the brain .
As LVO strokes are more severe , they require immediate diagnosis and opening @ @ @ @ @ @ @ possible .
In clinical practice , the most common method used to detect LVOs is an imaging modality called CT angiography .
This method provides clinicians with a detailed , 3D image of the blood vessels in the patient ’ s brain .
A newer CT technique , multiphase CT angiography , provides more information than its single-phase counterpart through the acquisition of cerebral angiograms in three distinct phases : peak arterial ( phase 1 ) , peak venous ( phase 2 ) and late venous ( phase 3 ) .
The main @ @ @ @ @ @ @ potential to detect any lag in filling of vessels , thus allowing the clinicians to perform a time-resolved assessment .
A group of researchers led by Ryan McTaggart from Brown University has developed a tool with the potential to quickly identify and prioritize LVO patients in an emergency setting .
To achieve this , they built and trained a convolutional neural network capable of classifying the presence of LVOs on CT angiographies .
This is the first study that uses deep learning to identify LVOs in both anterior and posterior arteries using multiphase @ @ @ @ @ @ @ summarized in Radiology .
To train , validate and test their model , the researchers used a dataset of 540 subjects with multiphase CT angiography exams .
Of these , 270 patients had confirmed presence of an LVO , while the other 270 were LVO-negative .
Each CT scan underwent a series of pre-processing steps .
First , the researchers standardized their scans through isotropic resampling ( voxel resolution of 1 mm3 ) , image resizing ( 500 x 500 pixels ) and intensity normalizing ( between 0 and 1 ) .
Then @ @ @ @ @ @ @ to increase the images ’ signal-to-noise ratio .
Finally , to further enhance the blood vessels , they selected 40 most-cranial axial slices from each subject and produced a single 2D image through a technique called maximal intensity projection .
To evaluate the diagnostic performance of the proposed deep-learning model , the group decided to experiment with seven training strategies .
In each strategy , the team used a different subset of the multiphase CT angiography data : each phase alone , or various combinations ( phases 1 and 2 , phases 2 @ @ @ @ @ @ @ , and all three phases together ) .
The group used a dataset of 62 patients ( 31 LVO-positive and 31 LVO-negative ) to test their seven strategies .
The model performed best when using all three phases as the input , where it achieved a sensitivity of 100 % ( 31 out of 31 ) and a specificity of 77 % ( 24 out of 31 ) .
Moreover , combining the peak arterial ( phase 1 ) and late venous ( phase 3 ) , or the peak venous ( phase @ @ @ @ @ @ @ 3 ) , resulted in significantly better models than using just single-phase CT angiography .
The model gave good predictions across patient demographics , multiple institutions and scanners .
Also , it detected both anterior and posterior circulation occlusions .
“ [ Our model ] could function as a tool to prioritize the review of patients with potential LVO by radiologists and clinicians in the emergency setting , ” the researchers conclude .
For future work , the group aims to evaluate the model ’ s clinical utility by testing it in a @ @ @ @

Examples of patients with large vessel occlusions correctly predicted by the deep-learning model. Top row: CT angiography slices; middle row: maximal intensity projection images; bottom row: heatmaps showing the most discriminative regions, which strongly correlate with occlusion location. (Courtesy: RSNA)
Strokes are life-threatening medical emergencies where urgent treatment is essential. They occur when part of the brain is cut off from its normal blood supply. The most common type of stroke (accounting for almost 85% of all cases) is an ischemic stroke, which is caused by a clot interrupting the supply of blood to the brain. Large vessel occlusion (LVO) strokes occur when such a blockage is found in one of the major arteries of the brain. As LVO strokes are more severe, they require immediate diagnosis and opening of the blocked artery as fast as possible.
In clinical practice, the most common method used to detect LVOs is an imaging modality called CT angiography. This method provides clinicians with a detailed, 3D image of the blood vessels in the patient’s brain. A newer CT technique, multiphase CT angiography, provides more information than its single-phase counterpart through the acquisition of cerebral angiograms in three distinct phases: peak arterial (phase 1), peak venous (phase 2) and late venous (phase 3). The main advantage of this method comes from its potential to detect any lag in filling of vessels, thus allowing the clinicians to perform a time-resolved assessment.
A group of researchers led by Ryan McTaggart from Brown University has developed a tool with the potential to quickly identify and prioritize LVO patients in an emergency setting. To achieve this, they built and trained a convolutional neural network capable of classifying the presence of LVOs on CT angiographies. This is the first study that uses deep learning to identify LVOs in both anterior and posterior arteries using multiphase CT angiography images. Their results are summarized in Radiology.
A deep-learning model that can classify LVOs…
To train, validate and test their model, the researchers used a dataset of 540 subjects with multiphase CT angiography exams. Of these, 270 patients had confirmed presence of an LVO, while the other 270 were LVO-negative. Each CT scan underwent a series of pre-processing steps.
First, the researchers standardized their scans through isotropic resampling (voxel resolution of 1 mm3), image resizing (500 x 500 pixels) and intensity normalizing (between 0 and 1). Then, they employed a vessel segmentation algorithm to increase the images’ signal-to-noise ratio. Finally, to further enhance the blood vessels, they selected 40 most-cranial axial slices from each subject and produced a single 2D image through a technique called maximal intensity projection.
To evaluate the diagnostic performance of the proposed deep-learning model, the group decided to experiment with seven training strategies. In each strategy, the team used a different subset of the multiphase CT angiography data: each phase alone, or various combinations (phases 1 and 2, phases 2 and 3, phases 1 and 3, and all three phases together).
… achieves high diagnostic performance
The group used a dataset of 62 patients (31 LVO-positive and 31 LVO-negative) to test their seven strategies. The model performed best when using all three phases as the input, where it achieved a sensitivity of 100% (31 out of 31) and a specificity of 77% (24 out of 31). Moreover, combining the peak arterial (phase 1) and late venous (phase 3), or the peak venous (phase 2) and late venous (phase 3), resulted in significantly better models than using just single-phase CT angiography.
Example of a false-positive prediction that showed no evidence of infarct on the CT angiography data (A: arterial phase; B: venous phase), together with a diffusion-weighted MRI acquisition (C) that found a small ischemic infarct. D: the heatmap shows localization of the blockage in the patient’s brain. (Courtesy: RSNA)
The model gave good predictions across patient demographics, multiple institutions and scanners. Also, it detected both anterior and posterior circulation occlusions. “[Our model] could function as a tool to prioritize the review of patients with potential LVO by radiologists and clinicians in the emergency setting,” the researchers conclude. For future work, the group aims to evaluate the model’s clinical utility by testing it in a real-time emergency setting.


physicsworld--2020-11-13--Japanese Nobel-prize-winning neutrino pioneer Masatoshi Koshiba dies aged 94 - Physics World

The Japanese physicist Masatoshi Koshiba , who shared 2002 Nobel prize for the detection of cosmic neutrinos , died on 12 November aged 94 .
One of the founders of neutrino astronomy , Koshiba ’ s most famous work involved detecting neutrinos from a distant supernova explosion using a vast detector based in a mine in central Japan .
He shared half the 2002 Nobel prize with Raymond Davis Jr “ for pioneering contributions to astrophysics , in particular for the detection of cosmic neutrinos ” .
The other half went to Riccardo Giacconi for his work on the discovery @ @ @ @ @ @ @ born on 19 September 1926 in Toyohashi , Japan and graduated from the University of Tokyo in 1951 .
He then moved to the US to complete a PhD at the University of Rochester , graduating in 1955 .
After three years at the University of Chicago he moved back to Tokyo , where he remained for the rest of his career .
In the 1980s , Koshiba was instrumental in the construction of a neutrino detector located 1000 metres underground in a lead and zinc mine in Japan .
Called Kamiokande , @ @ @ @ @ @ @ by photomultiplier tubes to detect the flashes of light produced when neutrinos interacted with atomic nuclei in water molecules .
Although vast numbers of neutrinos are produced by the Sun , they are difficult to detect because they interact very weakly with matter .
In 1967 Davis , who was then at the Brookhaven National Laboratory , built the first experiment to detect solar neutrinos .
Consisting of 600 tonnes of dry-cleaning fluid in the Homestake gold mine in South Dakota , it detected less than half the flux of neutrinos predicted by @ @ @ @ @ @ @ The results – known as the “ solar-neutrino problem ” – could be explained only if these models were wrong or if the neutrino had mass .
For 20 years Davis ’ s detector was the world ’ s only solar neutrino detector .
But 20 years later Koshiba and colleagues began taking data with their Kamiokande detector and in 1987 confirmed the lower-than-expected neutrino flux that was reported by Davis by detecting neutrinos from a distant supernova explosion .
For this work , Davis and Koshiba were awarded the 2002 Nobel Prize @ @ @ @ @ @ @ ’ s successor – SuperKamiokande – found convincing evidence for neutrino mass in the form of oscillations between tau and muon neutrinos , which required new physics beyond the Standard Model of particle physics .
This result led to Takaaki Kajita , a former student of Koshiba , sharing the 2015 Nobel Prize for Physics with the Canadian physicist Arthur McDonald for the discovery of neutrino oscillation .
In comments to the Mainichi newspaper , Kajita said that Koshiba had taught him everything about physics .
“ I was very lucky to be @ @ @ @ @ @ @ was very important for me in becoming a scientist , ” he said .
Neutrino physicist Dave Wark from Oxford University in the UK , who has been involved with the Super Kamiokande experiment for the past two decades , says that it was Koshiba ’ s “ force of will ” that got Kamiokande approved in Japan and the Nobel laureate ’ s initiative that led to the creation of the huge photomultiplier tubes that have “ been so important in the advance ” of neutrino physics .
“ The sequence of @ @ @ @ @ @ @ to Hyper Kamiokande has so far earned two Nobel Prizes , vindicating his original vision , ” Wark told Physics World .
Wark adds that Koshiba was “ a great guy to be around , filled with insight and energy and humour ” and that Koshiba has “ created a legacy that will continue to drive the field forward making scientific advances for many years .
”

Lasting legacy: Masatoshi Koshiba was instrumental in the construction of the Kamiokande neutrino experiment in central Japan. (Courtesy: CERN/Patrice Loïez)
The Japanese physicist Masatoshi Koshiba, who shared 2002 Nobel prize for the detection of cosmic neutrinos, died on 12 November aged 94. One of the founders of neutrino astronomy, Koshiba’s most famous work involved detecting neutrinos from a distant supernova explosion using a vast detector based in a mine in central Japan. He shared half the 2002 Nobel prize with Raymond Davis Jr “for pioneering contributions to astrophysics, in particular for the detection of cosmic neutrinos”. The other half went to Riccardo Giacconi for his work on the discovery of cosmic X-ray sources.
Koshiba was born on 19 September 1926 in Toyohashi, Japan and graduated from the University of Tokyo in 1951. He then moved to the US to complete a PhD at the University of Rochester, graduating in 1955. After three years at the University of Chicago he moved back to Tokyo, where he remained for the rest of his career.
In the 1980s, Koshiba was instrumental in the construction of a neutrino detector located 1000 metres underground in a lead and zinc mine in Japan. Called Kamiokande, it was an enormous water tank surrounded by photomultiplier tubes to detect the flashes of light produced when neutrinos interacted with atomic nuclei in water molecules.
Koshiba created a legacy that will continue to drive the field forward making scientific advances for many years Dave Wark
Although vast numbers of neutrinos are produced by the Sun, they are difficult to detect because they interact very weakly with matter. In 1967 Davis, who was then at the Brookhaven National Laboratory, built the first experiment to detect solar neutrinos. Consisting of 600 tonnes of dry-cleaning fluid in the Homestake gold mine in South Dakota, it detected less than half the flux of neutrinos predicted by widely accepted models of the Sun.
The results – known as the “solar-neutrino problem” – could be explained only if these models were wrong or if the neutrino had mass. For 20 years Davis’s detector was the world’s only solar neutrino detector. But 20 years later Koshiba and colleagues began taking data with their Kamiokande detector and in 1987 confirmed the lower-than-expected neutrino flux that was reported by Davis by detecting neutrinos from a distant supernova explosion. For this work, Davis and Koshiba were awarded the 2002 Nobel Prize for Physics.
Creating a legacy
In 1998, Kamiokande’s successor – SuperKamiokande – found convincing evidence for neutrino mass in the form of oscillations between tau and muon neutrinos, which required new physics beyond the Standard Model of particle physics. This result led to Takaaki Kajita, a former student of Koshiba, sharing the 2015 Nobel Prize for Physics with the Canadian physicist Arthur McDonald for the discovery of neutrino oscillation.
In comments to the Mainichi newspaper, Kajita said that Koshiba had taught him everything about physics. “I was very lucky to be able to meet Koshiba, and it was very important for me in becoming a scientist,” he said. Read more Neutrinos: ghosts of matter
Neutrino physicist Dave Wark from Oxford University in the UK, who has been involved with the Super Kamiokande experiment for the past two decades, says that it was Koshiba’s “force of will” that got Kamiokande approved in Japan and the Nobel laureate’s initiative that led to the creation of the huge photomultiplier tubes that have “been so important in the advance” of neutrino physics. “The sequence of Kamiokande leading to Super Kamiokande and now to Hyper Kamiokande has so far earned two Nobel Prizes, vindicating his original vision,” Wark told Physics World.
Wark adds that Koshiba was “a great guy to be around, filled with insight and energy and humour” and that Koshiba has “created a legacy that will continue to drive the field forward making scientific advances for many years”.


physicsworld--2020-11-13--Hundreds of copies of Newton’s Principia found, how to cross stitch a black hole - Physics World

I think few would argue that Isaac Newton ’ s Philosophiae Naturalis Principia Mathematica is the most famous book ever written about physics .
First published in 1687 , the tome outlines Newton ’ s laws of motion and universal gravitation – which underline much of modern physics .
Now , Caltech ’ s Moti Feingold and Andrej Svorenčík of the University of Mannheim have scoured the planet for first-edition copies of Principia and discovered nearly 200 more than were previously listed in a census done in 1953 .
This brings the total of known first editions to 386 , @ @ @ @ @ @ @ to have been printed .
The duo spent more than a decade tracing and studying copies of the book .
By looking at ownership marks and notes scribbled in the margins of some of the books – as well as related letters and other documents – they have concluded that Newton ’ s masterpiece has been more widely read than previously thought .
“ One of the realizations we ’ ve had , ” says Feingold , “ is that the transmission of the book and its ideas was far quicker and more @ @ @ @ @ @ @ will have implications on the future work that we and others will be doing on this subject ” .
Some of the newly-identified copies were found “ behind the Iron Curtain ” in eastern European countries that were not accessible to those doing the 1953 census .
The team even found a stolen copy of the book , which can fetch as much as $ 3m at auction , but unfortunately the owner did not act quickly enough to recover it .
You can read more in Caltech News .
As the nights @ @ @ @ @ @ @ and COVID-19 restrictions become tighter , cross stitching could be the ideal hobby to keep the gloom at bay .
Physics has a lovely article called “ Pixels to stitches : embroidering astronomy images ” , which looks back on five decades of cross-stitching astronomical images .
Author Erika Carlson explains why cross stitching is a perfect medium for capturing the beauty of space .
The article is illustrated with the work of embroiderers including Adi Foord , an astrophysicist at Stanford University , who has reproduced that iconic image of a black @ @ @ @ @ @ @ .

Marginalia: this copy of Principia is owned by Caltech. In the 18th century, it belonged to French mathematician and natural philosopher Jean-Jacques d'Ortous de Mairan, whose signature can be seen in the left margin of the title page. More recently, it was in the collection of Caltech physicist Earnest Watson. (Courtesy: Caltech Archives)
I think few would argue that Isaac Newton’s Philosophiae Naturalis Principia Mathematica is the most famous book ever written about physics. First published in 1687, the tome outlines Newton’s laws of motion and universal gravitation – which underline much of modern physics.
Now, Caltech’s Moti Feingold and Andrej Svorenčík of the University of Mannheim have scoured the planet for first-edition copies of Principia and discovered nearly 200 more than were previously listed in a census done in 1953. This brings the total of known first editions to 386, out of 600–750 copies that are believed to have been printed.
The duo spent more than a decade tracing and studying copies of the book. By looking at ownership marks and notes scribbled in the margins of some of the books – as well as related letters and other documents – they have concluded that Newton’s masterpiece has been more widely read than previously thought.
“One of the realizations we’ve had,” says Feingold, “is that the transmission of the book and its ideas was far quicker and more open than we assumed, and this will have implications on the future work that we and others will be doing on this subject”.
Stolen copy
Some of the newly-identified copies were found “behind the Iron Curtain” in eastern European countries that were not accessible to those doing the 1953 census. The team even found a stolen copy of the book, which can fetch as much as $3m at auction, but unfortunately the owner did not act quickly enough to recover it.
You can read more in Caltech News.
As the nights draw in here in the northern hemisphere and COVID-19 restrictions become tighter, cross stitching could be the ideal hobby to keep the gloom at bay.
Physics has a lovely article called “Pixels to stitches: embroidering astronomy images”, which looks back on five decades of cross-stitching astronomical images. Author Erika Carlson explains why cross stitching is a perfect medium for capturing the beauty of space. The article is illustrated with the work of embroiderers including Adi Foord, an astrophysicist at Stanford University, who has reproduced that iconic image of a black hole captured by the Event Horizon Telescope.