【推荐1】Parkinson’s disease is a progressive neurological disorder caused by the deaths of neurons in parts of the brain. Symptoms include loss of motor control, mood disturbance, ruined sleep and altered sense of smell. It is incurable. Early medical intervention can, however, relieve these symptoms and prolong survival. That makes early diagnosis desirable.

Unfortunately, the initial signs of Parkinson’s vary from person to person and there is no specific test at this early stage which can reliably distinguish it from other brain illnesses. It therefore often goes undetected until clear and characteristic manifestations, such as shakes and slowed body movement, appear. But that may soon change. A study published in ACS Omega, by Chen Xing and Liu Jun at Zhejiang University, in China, describes an invention which may be able to detect Parkinson’s before the beginning of trembling. The device in question is an artificially intelligent electronic nose.

The two researchers set out to build this nose in 2019 when they heard reports of Joy Milne, a retired nurse living in Scotland, who could detect people with Parkinson’s from a characteristic smell they emitted—detectable by her, but undetectable by others.

Mrs Milne first noticed this smell when her husband developed the illness. She made the general connection later, when she smelled it at sufferers’ support groups attended by her spouse. She even noted one seemingly healthy individual as having the disease months before other symptoms developed.

Carting Mrs Milne around the world to sniff patients who may have Parkinson’s is, however, not a practical option, so researchers working with her looked for the smell’s source, with a view to detecting it in some other way. They found it in sebum, an oily liquid substance produced by the skin. The sebum of those with Parkinson’s, they discovered, has unusually high concentrations of certain organic compounds. When these are acted on by yeast cells which live naturally on the skin, the result is the mysterious smell.

This   Parkinson’s-specific list can be detected using a laboratory technique called gas chromatography-mass spectrometry (gems). Unfortunately, GCMS machines are still too complex and expensive to be used routinely in clinics. However, Dr Chen and Dr Liu, who are both biomedical engineers, believed they could come up with a cheaper and more portable alternative.

The outcome is a machine not much bigger than a toaster. They found it could correctly identify a Parkinson’s patient as having the disease about 70% of the time and a healthy control as being clear of it about 80%. That is not yet as good as Mrs Milne’s nose, which has a history of correctly identifying the presence of Parkinson’s all the time. But it is a start. If its reliability can be improved, the portability and potential cheapness of the system may eventually prove a blessing for the early diagnosis of the illness.

【推荐3】A research team led by Professors Ki-Hun Jeong and Doheon Lee from the Korea Advanced Institute of Science and Technology (KAIST) Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by mixing light-field camera techniques with artificial intelligence (AI) technology.

Unlike a conventional camera, the light-field camera contains sets of micro-lens (微透镜) in front of the image sensor, which makes the camera small enough to fit into a smart phone while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing and 3D image acquisition, giving rise to many potential applications.

However, the optical (光学的) crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction.

The research team applied a laser in the near-infrared (NIR) range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-30, and 60-degree angles, the light-field camera reduces 54% of the image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and NIR wavelengths between the sets of micro-lens, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times.

Through this technique, the team could overcome the limitations of existing light-field cameras and develop a more advanced NIR-based light-field camera (NIR-LFC) improved for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment.

The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy-a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the inter-dependency of distance information that varies with facial expression in 3D images, the team could identify the information alight-field camera uses to distinguish human expressions.

【推荐1】Alain Aspect, a French physicist, who won a long-expected Nobel Physics Prize on Tuesday (October 4, 2022), contributed a lot in proving the theory of quantum entanglement (量子缠绕), a theory — famously raised by Albert Einstein — that when a particle (粒子) is split into two, the properties of the two new particles remain connected, as if by an invisible piece of string, regardless of how far apart they are. It remained a theory until Aspect and his team proved it in a laboratory experiment for the first time in 1981, involving two photons (光子) at a distance of 12 meters.

Quantum strangeness has fascinated Aspect as a physicist. Awarded along with Austrian physicist Anton Zeilinger and John Clauser from the United States, Aspect emphasised the importance of international scientific cooperation. “He is one of those professors in physics. A whole community today works under his leadership,” said Retailleau, a former president of Paris-Saclay University. “Aspect is also a ‘tireless teacher’ who gives impressive lectures”, she added. His former student Olivier Reymond, who is now working on developing a quantum processor, said, “As well as passing his passion for physics on to his students, Aspect is also a food lover who will tell you all about his recipes for the goose liver.”

Alain Aspect is widely recognized for his experimental test of Bell’s inequalities, proposed by the late John Bell in 1964, and the related tests require measurements to be made on photons moving apart in opposite directions to look for connections between some of their physical properties, for instance, their polarizations. Aspect’s major contribution was to propose a feasible experimental scheme to make these measurements by rapidly changing the orientation of polarizers while photons moved through the device, and even more importantly, to implement it successfully in his experiments.

Sheila Rowan, president of the Institute of Physics which publishes Physics World, congratulated the team on their “well-deserved” recognition. “This is an area of physics with ongoing, profound impact, at a fundamental level to help understand the world around us and being explored for use in highly novel technologies for sensing and communication today,” she added.

Indeed, Aspect’s experiments have attracted enormous attention and triggered a string of theoretical and experimental work on quantum entanglement. As a result, new avenues have been explored in quantum computers, which in principle could outperform traditional computers at some tasks. We are generating more data now than ever before, need to analyse that data in more complex ways, and get results out faster. Quantum computing could help us to find new properties in the production of new materials for everyday use and it could also reduce power consumption within the manufacturing process. With quantum computing, research can be moved from the lab onto the computer where multiple imitations can be run while stretching your companies research and development budget further.