People 'hear' flashes due to disinhibited flow of signals around the brain, suggests studyPeople 'hear' flashes due to disinhibited flow of signals around the brain, suggests study

Some people 'hear' silent flashes or movement.

A synaesthesia-like effect in which people 'hear' silent flashes or movement, such as in popular 'noisy GIFs' and memes, could be due to a reduction of inhibition of signals that travel between visual and auditory areas of the brain, according to a new study led by researchers at City, University of London.

The study is the first to provide insight into the brain mechanisms underpinning such auditory sensations also known as a 'visually-evoked auditory response' (aka vEAR or 'visual ear').

Whilst one theory is that areas of the brain responsible for visual and auditory processing normally compete, this research suggests that they may actually cooperate in people who report visual ear.

It was also found that musicians taking part in the study were significantly more likely to report experiencing visual ear than non-musician participants. This could be because musical training may promote joint attention to both the sound of music and the sight of the coordinated movements of the conductor or other musicians.

Dr Elliot Freeman, Principal Investigator on the study and a Senior Lecturer in Psychology at the University said: "We already knew that some people hear what they see. Car indicator lights, flashing neon shop signs, and people's movements as they walk may all trigger an auditory sensation.

"Our latest study reveals normally-occurring individual differences in how our senses of vision and hearing interact.

"We found that people with 'visual ears' can use both senses together to see and also 'hear' silent motion, while for others hearing is inhibited when watching such visual sequences."

Some neuroscientists believe visual-ear may be a type of synaesthesia, with other examples including music, letters or numbers that can evoke perceptions of colour. However, visual ear appears to be the most prevalent, with as many as 20% of people reporting some experiences of it compared to 4.4 per cent for other types.

The condition has received more attention due to the recent, viral popularity of the 'skipping pylon GIF', and other 'noisy GIFs' depicting silent motion, which in some people evoke very vivid visual ear sensations.

To shed light on what may be going on in the brain when people view such content, the researchers applied a weak alternating current to participants' scalps, using a technique called transcranial Alternating Current Stimulation (tACS), to explore how the visual and auditory parts of the brain interact in those who experience visual ear and those who don't.

The first experiment of the study included 36 healthy participants, including 16 classical musicians from the London Royal College of Music. All were shown auditory and visual 'Morse code' sequences, while electrical simulation (tACS) was applied to either the back of the head (visual areas of the brain) or the sides (auditory areas) using 'alpha-frequency' tACS stimulation. Participants were then classified as visual or non-visual ear depending on whether they reported 'hearing' the silent flashes.

The researchers found that in non-visual ear participants, alpha-frequency stimulation to auditory areas significantly reduced auditory performance but improved visual performance, while the opposite pattern was found for the same frequency of stimulation to visual areas (poorer vision, better audition).

This reciprocal pattern suggests a competitive interaction between visual and auditory brain areas with each normally inhibiting the performance of the other.

However these interactions were strikingly absent in visual ear participants, suggesting that their auditory and visual areas were not competing but cooperating with each other.

A second experiment was conducted to see whether even people without conscious awareness of 'visual ear' sometimes use their auditory brain areas to make purely visual judgements. It found that this might indeed be the case for some, where stimulation to auditory areas of the brain affected accuracy of visual judgements almost as much as stimulating visual areas.

Taken together, the results of these experiments support a popular theory that some kinds of synaesthesia may depend on a disinhibition of pre-existing neural cross-connections between sensory brain areas that are normally inactive. When these connections are disinhibited this may result in conscious awareness of visual ear and other synaesthetic phenomena.

Dr Freeman said: "We were also interested to find that, on average, participants with visual ear performed better on both visual and auditory tasks than those without. Perhaps their audio-visual cooperation benefits performance because more of the brain is engaged in processing visual stimuli.

"Such cooperation might also benefit musical performance, explaining why so many of the musicians we tested reported experiencing visual ear."

Artificial intelligence can predict premature death, study finds

Stethoscope on a computer

Computers which are capable of teaching themselves to predict premature death could greatly improve preventative healthcare in the future, suggests a new study by experts at the University of Nottingham.

The team of healthcare data scientists and doctors have developed and tested a system of computer-based 'machine learning' algorithms to predict the risk of early death due to chronic disease in a large middle-aged population.

They found this AI system was very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts. The study is published by PLOS ONE in a special collections edition of "Machine Learning in Health and Biomedicine."

The team used health data from just over half a million people aged between 40 and 69 recruited to the UK Biobank between 2006 and 2010 and followed up until 2016.

Leading the work, Assistant Professor of Epidemiology and Data Science, Dr Stephen Weng, said: "Preventative healthcare is a growing priority in the fight against serious diseases so we have been working for a number of years to improve the accuracy of computerised health risk assessment in the general population. Most applications focus on a single disease area but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them.

"We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person's risk of premature death by machine-learning. This uses computers to build new risk prediction models that take into account a wide range of demographic, biometric, clinical and lifestyle factors for each individual assessed, even their dietary consumption of fruit, vegetables and meat per day.

"We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and 'hospital episodes' statistics. We found machine learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."

The AI machine learning models used in the new study are known as 'random forest' and 'deep learning'. These were pitched against the traditionally-used 'Cox regression' prediction model based on age and gender -- found to be the least accurate at predicting mortality -- and also a multivariate Cox model which worked better but tended to over-predict risk.

Professor Joe Kai, one of the clinical academics working on the project, said: "There is currently intense interest in the potential to use 'AI' or 'machine-learning' to better predict health outcomes. In some situations we may find it helps, in others it may not. In this particular case, we have shown that with careful tuning, these algorithms can usefully improve prediction.

"These techniques can be new to many in health research, and difficult to follow. We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care."

This new study builds on previous work by the Nottingham team which showed that four different AI algorithms, 'random forest', 'logistic regression', 'gradient boosting' and 'neural networks', were significantly better at predicting cardiovascular disease than an established algorithm used in current cardiology guidelines. This earlier study is available here.

The Nottingham researchers predict that AI will play a vital part in the development of future tools capable of delivering personalised medicine, tailoring risk management to individual patients. Further research requires verifying and validating these AI algorithms in other population groups and exploring ways to implement these systems into routine healthcare.

Woman with novel gene mutation lives almost pain-free

A woman with a novel gene mutation lives a virtually pain-free life.

A woman in Scotland can feel virtually no pain due to a mutation in a previously-unidentified gene, according to a research paper co-led by UCL.

She also experiences very little anxiety and fear, and may have enhanced wound healing due to the mutation, which the researchers say could help guide new treatments for a range of conditions, they report in the British Journal of Anaesthesia.

"We found this woman has a particular genotype that reduces activity of a gene already considered to be a possible target for pain and anxiety treatments," said one of the study's lead researchers, Dr James Cox (UCL Medicine).

"Now that we are uncovering how this newly-identified gene works, we hope to make further progress on new treatment targets."

At age 65, the woman sought treatment for an issue with her hip, which turned out to involve severe joint degeneration despite her experiencing no pain. At age 66, she underwent surgery on her hand, which is normally very painful, and yet she reported no pain after the surgery. Her pain insensitivity was diagnosed by Dr Devjit Srivastava, Consultant in Anaesthesia and Pain Medicine at an NHS hospital in the north of Scotland and co-lead author of the paper.

The woman tells researchers she has never needed painkillers after surgery such as dental procedures.

She was referred to pain geneticists at UCL and the University of Oxford, who conducted genetic analyses and found two notable mutations. One was a microdeletion in a pseudogene, previously only briefly annotated in medical literature, which the researchers have described for the first time and dubbed FAAH-OUT. She also had a mutation in the neighbouring gene that controls the FAAH enzyme.

Further tests by collaborators at the University of Calgary, Canada, revealed elevated blood levels of neurotransmitters that are normally degraded by FAAH, further evidence for a loss of FAAH function.

The FAAH gene is well-known to pain researchers, as it is involved in endocannabinoid signalling central to pain sensation, mood and memory. The gene now called FAAH-OUT was previously assumed to be a 'junk' gene that was not functional. The researchers found there was more to it than previously believed, as it likely mediates FAAH expression.

Mice that do not have the FAAH gene have reduced pain sensation, accelerated wound healing, enhanced fear-extinction memory and reduced anxiety.

The woman in Scotland experiences similar traits. She notes that in her lifelong history of cuts and burns (sometimes unnoticed until she can smell burning flesh), the injuries tend to heal very quickly. She is an optimist who was given the lowest score on a common anxiety scale, and reports never panicking even in dangerous situations such as a recent traffic incident. She also reports memory lapses throughout life such as forgetting words or keys, which has previously been associated with enhanced endocannabinoid signalling.

The researchers say that it's possible there are more people with the same mutation, given that this woman was unaware of her condition until her 60s.

"People with rare insensitivity to pain can be valuable to medical research as we learn how their genetic mutations impact how they experience pain, so we would encourage anyone who does not experience pain to come forward," said Dr Cox.

The research team is continuing to work with the woman in Scotland, and are conducting further tests in cell samples, in order to better understand the novel pseudogene.

"We hope that with time, our findings might contribute to clinical research for post-operative pain and anxiety, and potentially chronic pain, PTSD and wound healing, perhaps involving gene therapy techniques," said Dr Cox.

"The implications for these findings are immense," said Dr Srivastava.

"One out of two patients after surgery today still experiences moderate to severe pain, despite all advances in pain killer medications and techniques since the use of ether in 1846 to first 'annul' the pain of surgery. There have already been unsuccessful clinical trials targeting the FAAH protein -- while we hope the FAAH-OUT gene could change things particularly for post-surgical pain, it remains to be seen if any new treatments could be developed based on our findings."

"The findings point towards a novel pain killer discovery that could potentially offer post-surgical pain relief and also accelerate wound healing. We hope this could help the 330 million patients who undergo surgery globally every year," Dr Srivastava said.

"I would be elated if any research into my own genetics could help other people who are suffering," the woman in Scotland commented.

"I had no idea until a few years ago that there was anything that unusual about how little pain I feel -- I just thought it was normal. Learning about it now fascinates me as much as it does anyone else."

Lead funding for the study came from the Medical Research Council and Wellcome.

Fluorine: Toxic and aggressive, but widely used

Crystal structure of alpha-F2, which is stable below 45.6 K. It crystallizes in the monoclinic space group C2/c with F-F- bond lengths of 140.4 pm.

In toothpaste, Teflon, LEDs and medications, it shows its sunny side -- but elemental fluorine is extremely aggressive and highly toxic. Attempts to determine the crystal structure of solid fluorine using X-rays ended with explosions 50 years ago. A research team has now clarified the actual structure of the fluorine using neutrons from the Heinz Maier Leibnitz Research Neutron Source (FRM II).

Fluorine is the most reactive chemical element and highly toxic. It is nonetheless widely deployed. In the first attempt to determine the atomic distances of solid fluorine in 1968, a research team in the United States used X-rays. A difficult task, because fluorine only becomes solid at about minus 220 °C. And already cooling down the aggressive element resulted in explosions.

Nobel laureate Linus Pauling was sceptical about the results of the team and in 1970 proposed an alternative structural model -- without delivering the experimental proof. For 50 years, no other chemist ventured to take on the delicate task.

Using neutrons from the Heinz Maier-Leibnitz Research Neutron Source in Garching, scientists from the University of Marburg, the Technical University of Munich (TUM) and the Aalto University in Finland have now finally elucidated the structure.

Neutrons -- the ideal probes

Neutrons are particularly well suited for localizing fluorine atoms with high precision. Since they can penetrate even thick-walled sample containers, neutrons provided the method of choice for Professor Florian Kraus and his team in Marburg. They were supported in their investigations on the powder diffractometer SPODI at the FRM II by the TUM scientist Dr. Markus Hölzel and his colleagues.

For their investigations, the researchers implemented a special measuring setup to study fluorine at very low temperatures. To this end, they deployed materials that are particularly resistant to fluorine and ensure safe handling.

Application in LEDs, toothpaste and pharmaceuticals

"Extremely precise measurements with neutrons are important to facilitate calculations for a wide variety of applications," says Florian Kraus. "For other elements, high-precision crystal structures have been available for years. The crystal structure of oxygen, for example, has been investigated 35 times and carbon even 108 times."

But fluorine is also an essential part of everyday life. Among other things, fluorides are used as additives to toothpaste. They are used in LED bulbs to turn the cold LED light into a warm white. Fluorine compounds are also added to many pharmaceuticals to increase their effectiveness.

Neutron measurements confirm the suspicions of the Nobel Prize winner

Even though the results of the measurements from the 1960s were not precise, Florian Kraus was nonetheless quite surprised by the great difference: "Using neutron measurements, we were able to resolve the atomic distance 70 percent more accurately," says the chemist. "And the crystal structure shows that Nobel laureate Linus Pauling was spot on with his doubts."

New, more realistic simulator will improve self-driving vehicle safety before road testing

University of Maryland computer scientist Dinesh Manocha, in collaboration with a team of colleagues from Baidu Research and the University of Hong Kong, has developed a photo-realistic simulation system for training and validating self-driving vehicles. The new system provides a richer, more authentic simulation than current systems that use game engines or high-fidelity computer graphics and mathematically rendered traffic patterns.

Their system, called Augmented Autonomous Driving Simulation (AADS), could make self-driving technology easier to evaluate in the lab while also ensuring more reliable safety before expensive road testing begins.

The scientists described their methodology in a research paper published March 27, 2019 in the journal Science Robotics.

"This work represents a new simulation paradigm in which we can test the reliability and safety of automatic driving technology before we deploy it on real cars and test it on the highways or city roads," said Manocha, one of the paper's corresponding authors, and a professor with joint appointments in computer science, electrical and computer engineering, and the University of Maryland Institute for Advanced Computer Studies.

One potential benefit of self-driving cars is that they could be safer than human drivers who are prone to distraction, fatigue and emotional decisions that lead to mistakes. But to ensure safety, autonomous vehicles must evaluate and respond to the driving environment without fail. Given the innumerable situations that a car might encounter on the road, an autonomous driving system requires hundreds of millions of miles worth of test drives under challenging conditions to demonstrate reliability.

While that could take decades to accomplish on the road, preliminary evaluations could be conducted quickly, efficiently and more safely by computer simulations that accurately represent the real world and model the behavior of surrounding objects. Current state-of-the art simulation systems described in scientific literature fall short in portraying photo-realistic environments and presenting real-world traffic flow patterns or driver behaviors.

AADS is a data-driven system that more accurately represents the inputs a self-driving car would receive on the road. Self-driving cars rely on a perception module, which receives and interprets information about the real world, and a navigation module that makes decisions, such as where to steer or whether to break or accelerate, based on the perception module.

In the real world, the perception module of a self-driving car typically receives input from cameras and lidar sensors, which use pulses of light to measure distances of surrounding. In current simulator technology, the perception module receives input from computer-generated imagery and mathematically modeled movement patterns for pedestrians, bicycles, and other cars. It is a relatively crude representation of the real world. It is also expensive and time- consuming to create because computer-generated imagery models must be hand generated.

The AADS system combines photos, videos, and lidar point clouds -- which are like 3D shape renderings -- with real-world trajectory data for pedestrians, bicycles, and other cars. These trajectories can be used to predict the driving behavior and future positions of other vehicles or pedestrians on the road for safer navigation.

"We are rendering and simulating the real world visually, using videos and photos," said Manocha, "but also we're capturing real behavior and patterns of movement. The way humans drive is not easy to capture by mathematical models and laws of physics. So, we extracted data about real trajectories from all the video we had available, and we modeled driving behaviors using social science methodologies. This data-driven approach has given us a much more realistic and beneficial traffic simulator."

The scientists had a long-standing challenge to overcome in using real video imagery and lidar data for their simulation: Every scene must respond to a self-driving car's movements, even though those movements may not have been captured by the original camera or lidar sensor. Whatever angle or viewpoint is not captured by a photo or video has to be rendered or simulated using prediction methods. This is why simulation technology has always relied so heavily on computer-generated graphics and physics-based prediction techniques.

To overcome this challenge, the researchers developed technology that isolates the various components of a real-world street scene and renders them as individual elements that can be resynthesized to create a multitude of photo-realistic driving scenarios.

With AADS, vehicles and pedestrians can be lifted from one environment and placed into another with the proper lighting and movement patterns. Roads can be recreated with different levels of traffic. Multiple viewing angles of every scene provide more realistic perspectives during lane changes and turns. In addition, advanced image processing technology enables smooth transitions and reduces distortion compared with other video simulation techniques. The image processing techniques are also used to extract trajectories, and thereby model driver behaviors.

"Because we're using real-world video and real-world movements, our perception module has more accurate information than previous methods," Manocha said. "And then, because of the realism of the simulator, we can better evaluate navigation strategies of an autonomous driving system."

Manocha said that by publishing this work, the scientists hope some of the corporations developing self-driving vehicles might incorporate the same data-driven approach to improve their own simulators for testing and evaluating autonomous driving systems.