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Electronic gaming can precipitate life-threatening cardiac arrhythmias in susceptible children whose predisposition may have been previously unrecognized, according to a new report in Heart Rhythm, the official journal of the Heart Rhythm Society, the Cardiac Electrophysiology Society, and the Pediatric & Congenital Electrophysiology Society, published by Elsevier. The investigators documented an uncommon, but distinct pattern among children who lose consciousness while playing electronic (video) games.
“Video games may represent a serious risk to some children with arrhythmic conditions; they might be lethal in patients with predisposing, but often previously unrecognized arrhythmic conditions,” explained lead investigator Claire M. Lawley, MBBS, PhD, The Heart Centre for Children, Sydney Children’s Hospitals Network, Sydney, Australia. “Children who suddenly lose consciousness while electronic gaming should be assessed by a heart specialist as this could be the first sign of a serious heart problem.”
The investigators performed a systematic review of literature and initiated a multisite international outreach effort to identify cases of children with sudden loss of consciousness while playing video games. Across the 22 cases they found, multiplayer war gaming was the most frequent trigger. Some children died following a cardiac arrest. Subsequent diagnoses of several heart rhythm conditions put the children at continuing risk. Catecholaminergic polymorphic ventricular tachycardia (CPVT) and congenital long QT syndrome (LQTS) types 1 and 2 were the most common underlying causes.
There was a high incidence of potentially relevant genetic variants (63%) among the patients, which has significant implications for their families. In some cases, the investigation of a child who lost consciousness during video gaming led to many family members being diagnosed with an important familial heart rhythm problem. “Families and healthcare teams should think about safety precautions around electronic gaming in children who have a condition where dangerous fast heart rhythms are a risk,” noted Dr. Lawley.
The investigators attributed adrenergic stimulation related to the emotionally charged electronic gaming environment as the pathophysiological basis for this phenomenon. Electronic gaming is not always the “safe alternative” to competitive sports it is often considered. At the time of the cardiac incidents, many of the patients were in excited states, having just won or lost games, or were engaging in conflict with companions.
“We already know that some children have heart conditions that can put them at risk when playing competitive sports, but we were shocked to discover that some patients were having life-threatening blackouts during video gaming,” added co-investigator Christian Turner, MBBS, The Heart Centre for Children, Sydney Children’s Hospitals Network, Sydney, Australia. “Video gaming was something I previously thought would be an alternative ‘safe activity.’ This is a really important discovery. We need to ensure everyone knows how important it is to get checked out when someone has had a blacking out episode in these circumstances.”
The study notes that while this phenomenon is not a common occurrence, it is becoming more prevalent. “Having looked after children with heart rhythm problems for more than 25 years, I was staggered to see how widespread this emerging presentation is, and to find that a number of children had even died from it. All of the collaborators are keen to publicize this phenomenon so our colleagues across the globe can recognize it and protect these children and their families,” noted co-investigator of the study, Jonathan Skinner, MBChB, MD, also from Sydney.
As an accompanying editorial Daniel Sohinki, MD, MSc, Department of Cardiology, Augusta University, Augusta, GA, USA, and coauthors pointed out that, “exertion should be understood to encompass activities outside of traditional competitive athletics. Appropriate counseling regarding the risks of intense video gameplay should be targeted in children with a pro-arrhythmic cardiac diagnosis, and in any child with a history of exertional syncope of undetermined etiology. Further, any future screening programs aimed at identifying athletes at risk for malignant arrhythmias should encompass athletes being considered for participation in eSports.”
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Working alongside robots may contribute to job burnout and workplace incivility, but self-affirmation techniques could help alleviate fears about being replaced by these machines, according to research published by the American Psychological Association.
Researchers found that workers in the U.S. and parts of Asia feel job insecurity from robots, even in industries where robots aren’t being used, and those fears may not be justified, said lead researcher Kai Chi Yam, PhD, an associate professor of management at the National University of Singapore.
“Some economists theorize that robots are more likely to take over blue-collar jobs faster than white-collar jobs,” Yam said. “However, it doesn’t look like robots are taking over that many jobs yet, at least not in the United States, so a lot of these fears are rather subjective.”
Researchers conducted experiments and analyzed data from participants in the U.S., Singapore, India and Taiwan. The study was published online in the Journal of Applied Psychology.
Working with industrial robots was linked to greater reports of burnout and workplace incivility in an experiment with 118 engineers employed by an Indian auto manufacturing company.
An online experiment with 400 participants found that self-affirmation exercises, where people are encouraged to think positively about themselves and their uniquely human characteristics, may help lessen workplace robot fears. Participants wrote about characteristics or values that were important to them, such as friends and family, a sense of humor or athletics.
“Most people are overestimating the capabilities of robots and underestimating their own capabilities,” Yam said.
Fears about job insecurity from robots are common. The researchers analyzed data about the prevalence of robots in 185 U.S. metropolitan areas along with the overall use of popular job recruiting sites in those areas (LinkedIn, Indeed, etc.). Areas with the most prevalent rates of robots also had the highest rates of job recruiting site searches, even though unemployment rates weren’t higher in those areas. The researchers theorized that people in these areas may have felt more job insecurity because of robots, but that there could be other reasons, such as people seeking new careers or feeling dissatisfied with their current jobs.
Another experiment comprised 343 parents of students at the National University of Singapore who were randomly assigned to three groups. One group read an article about the use of robots in businesses, the second group read a general article about robots, and the third read an unrelated article. Then the participants were surveyed about their job insecurity concerns, with the first group reporting significantly higher levels of job insecurity than the two other groups.
While some people may have legitimate concerns about losing their jobs to robots, some media coverage may be unnecessarily heightening fears among the general public, Yam said.
“Media reports on new technologies like robots and algorithms tend to be apocalyptic in nature, so people may develop an irrational fear about them,” he said.
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Artificial intelligence models that interpret medical images hold the promise to enhance clinicians’ ability to make accurate and timely diagnoses, while also lessening workload by allowing busy physicians to focus on critical cases and delegate rote tasks to AI.
But AI models that lack transparency about how and why a diagnosis is made can be problematic. This opaque reasoning — also known “black box” AI — can diminish clinician trust in the reliability of the AI tool and thus discourage its use. This lack of transparency could also mislead clinicians into over-trusting the tool’s interpretation.
In the realm of medical imaging, one way to create more understandable AI models and to demystify AI decision-making have been saliency assessments — an approach that uses heat maps to pinpoint whether the tool is correctly focusing only on the relevant pieces of a given image or homing in on irrelevant parts of it.
Heat maps work by highlighting areas on an image that influenced the AI model’s interpretation. This could help human physicians see whether the AI model focuses on the same areas as they do or is mistakenly focusing on irrelevant spots on an image.
But a new study, published in Nature Machine Intelligence on Oct. 10, shows that for all their promise, saliency heat maps may not be yet ready for prime time.
The analysis, led by Harvard Medical School investigator Pranav Rajpurkar, Matthew Lungren of Stanford, and Adriel Saporta of New York University, quantified the validity of seven widely used saliency methods to determine how reliably and accurately they could identify pathologies associated with 10 conditions commonly diagnosed on X-ray, such as lung lesions, pleural effusion, edema, or enlarged heart structures. To ascertain performance, the researchers compared the tools’ performance against human expert judgment. More

New research highlights a growing market in AI-powered recruitment tools, used to process high volumes of job applicants, that claim to bypass human bias and remove discrimination from hiring. •These AI tools reduce race and gender to trivial data points, and often rely on personality analysis that is “automated pseudoscience,” according to Cambridge researchers. Academics have also teamed up with computing students to debunk use of AI in recruitment by building a version of the kinds of software increasingly used by HR teams. It demonstrates how random changes in clothing or lighting give radically different personality readings that could prove make-or-break for a generation of job seekers.
Recent years have seen the emergence of AI tools marketed as an answer to lack of diversity in the workforce, from use of chatbots and CV scrapers to line up prospective candidates, through to analysis software for video interviews.
Those behind the technology claim it cancels out human biases against gender and ethnicity during recruitment, instead using algorithms that read vocabulary, speech patterns and even facial micro-expressions to assess huge pools of job applicants for the right personality type and “culture fit.”
However, in a new report published in Philosophy and Technology, researchers from Cambridge’s Centre for Gender Studies argue these claims make some uses of AI in hiring little better than an “automated pseudoscience” reminiscent of physiognomy or phrenology: the discredited beliefs that personality can be deduced from facial features and skull shape.
They say it is a dangerous example of “technosolutionism”: turning to technology to provide quick fixes for deep-rooted discrimination issues that require investment and changes to company culture.
In fact, the researchers have worked with a team of Cambridge computer science undergraduates to debunk these new hiring techniques by building an AI tool modelled on the technology, available at: https://personal-ambiguator-frontend.vercel.app/
The ‘Personality Machine’ demonstrates how arbitrary changes in facial expression, clothing, lighting and background can give radically different personality readings — and so could make the difference between rejection and progression for a generation of job seekers vying for graduate positions. More

Rare diseases are often difficult to diagnose and predicting the best course of treatment can be challenging for clinicians. Investigators from the Mahmood Lab at Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, have developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories. Known as SISH (Self-Supervised Image search for Histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies. A paper introducing the self-teaching algorithm is published in Nature Biomedical Engineering.
“We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training,” said senior author Faisal Mahmood, PhD, in the Brigham’s Department of Pathology. “This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification.”
Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However, the gigapixel size of each individual WSI and the ever-increasing number of images in large repositories, means that search and retrieval of WSIs can be slow and complicated. As a result, scalability remains a pertinent roadblock for efficient use.
To solve this issue, researchers at the Brigham developed SISH, which teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database.
In their study, the researchers tested the speed and ability of SISH to retrieve interpretable disease subtype information for common and rare cancers. The algorithm successfully retrieved images with speed and accuracy from a database of tens of thousands of whole slide images from over 22,000 patient cases, with over 50 different disease types and over a dozen anatomical sites. The speed of retrieval outperformed other methods in many scenarios, including disease subtype retrieval, particularly as the image database size scaled into the thousands of images. Even while the repositories expanded in size, SISH was still able to maintain a constant search speed.
The algorithm, however, has some limitations including a large memory requirement, limited context awareness within large tissue slides and the fact that it is limited to a single imaging modality.
Overall, the algorithm demonstrated the ability to efficiently retrieve images independent of repository size and in diverse datasets. It also demonstrated proficiency in diagnosis of rare disease types and the ability to serve as a search engine to recognize certain regions of images that may be relevant for diagnosis. This work may greatly inform future disease diagnosis, prognosis, and analysis.
“As the sizes of image databases continue to grow, we hope that SISH will be useful in making identification of diseases easier,” said Mahmood. “We believe one important future direction in this area is multimodal case retrieval which involves jointly using pathology, radiology, genomic and electronic medical record data to find similar patient cases.”
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Autonomous optimal navigation of microswimmers is in fact possible, as researchers from the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) showed. In contrast to the targeted navigation of boats, the motion of swimmers at the microscale is strongly disturbed by fluctuations. The researchers now described a navigation strategy for microswimmers that does not need an external interpreter. Their findings may contribute to the understanding of transport mechanisms in the microcosm as well as to applications such as targeted drug delivery.
Whereas the shortest way between two points is a straight connection, it might not be the most efficient path to follow. Complex currents often affect the motion of microswimmers and make it difficult for them to reach their destination. At the same time, making use of these currents to navigate as fast as possible is a certain evolutionary advantage. Whereas such strategies allow biological microswimmers to better access food or escape a predator, microrobots could this way be directed to perform specific tasks.
The optimal path in a given current can readily be determined mathematically, yet fluctuations perturb the motion of microswimmers and deviate them from the optimal route. Thus, they have to readjust their motion in order to account for environmental changes. This typically requires the help of an external interpreter and takes away their autonomy.
“Thanks to evolution, some microorganisms have developed autonomous strategies that enable directed motion towards larger concentration of nutrients or light,” first author of the study Lorenzo Piro explains. Inspired by this idea, the researchers from the Department of Living Matter Physics at the MPI-DS designed strategies that allow microswimmers to navigate optimally in a nearly autonomous way.
Light as a guide for autonomous navigation
When an external interpreter defines the navigation pattern, microswimmers on average follow a well-defined path. Thus, it is a good approach to guide the microswimmer along that path within the current. This can be achieved autonomously via external stimuli, despite the presence of fluctuations. This principle could be applied to swimmers that respond to variation of light, such as certain algae, in which case the optimal path can simply be illuminated. Remarkably, the resulting performances are comparable to externally supervised navigation. “These new strategies can moreover conveniently be applied to more complex scenario such as navigation on curved surfaces or in presence of random currents,” concludes Ramin Golestanian, director at MPI-DS.
Possible applications of the study thus range from targeted drug delivery at the microscale to the optimal design of autonomous micromachines.
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Optics, the study of light, is one of the oldest fields in physics and has never ceased to surprise researchers. Although the classical description of light as a wave phenomenon is rarely questioned, the physical origins of some optical effects are. A team of researchers at Tampere University have brought the discussion on one fundamental wave effect, i.e., the debate around the anomalous behaviour of focused light waves, to the quantum domain.
The researchers have been able to show that quantum waves behave significantly differently from their classical counterparts and can be used to increase the precision of distance measurements. Their findings also add to the discussion on physical origin of the anomalous focusing behaviour. The results are now published in the journal of Nature Photonics.
“Interestingly, we started with an idea based on our earlier results and set out to structure quantum light for enhanced measurement precision. However, we then realised that the underlying physics of this application also contributes to the long debate about the origins of the Gouy phase anomaly of focused light fields.,” explains Robert Fickler, group leader of the Experimental Quantum Optics group at Tampere University.
Quantum waves behave differently but point to the same origin
Over the last decades, methods for structuring light fields down on the single photon level have vastly matured and led to a myriad of novel findings. In addition, a better of optics’ foundations has been achieved. However, the physical origin of why light behaves in such an unexpected way when going through a focus, the so-called Gouy phase anomaly, is still often debated. This is despite its widespread use and importance in optical systems. The novelty of the current study is now to put the effect into the quantum domain.
“When developing the theory to describe our experimental results, we realised (after a long debate) that the Gouy phase for quantum light is not only different than the standard one, but its origin can be linked to another quantum effect. This is just like what was speculated in an earlier work,” adds Doctoral researcher Markus Hiekkamäki, leading author of the study.
In the quantum domain, the anomalous behaviour is sped up when compared to classical light. As the Gouy phase behaviour can be used to determine the distance a beam of light has propagated, the speed up of the quantum Gouy phase could allow for an improvement in the precision of measuring distances.
With this new understanding at hand, the researchers are planning to develop novel techniques to enhance their measurement abilities such that it will be possible to measure more complex beams of structured photons. The team expects that this will help them push forward the application of the observed effect, and potentially bring to light more differences between quantum and classical light fields.
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A group of scientists in Nagoya University, Japan, have developed a possible solution to one of the biggest problems of the Internet of Energy, energy efficiency. They did so by creating a controller that has a sleep mode and only procures energy when needed.
Widespread generation of electricity based on renewable energy has become necessary to combat the climate crisis. One solution to realize society’s electrification needs is the Internet of Energy, which would operate like the information Internet, except that it would consist of energy linked by smart power generation, smart power consumption, smart interconnection, and cloud sharing.
When information is sent over the Internet, it is divided into transmittable units called ‘packets’, which are tagged with their destination. The energy Internet is based on a similar concept. Information tags are added to power pulses to create units called ‘power packets’. On the basis of requests from terminals, these are then distributed over networks to where they are needed. However, one problem is that since the packets are sent sporadically, the energy supply is intermittent. Current solutions, such as storage batteries or capacitors, complicate the system and reduce its efficiency.
An alternative solution is what is known as ‘sparse control’, where the terminal’s actuators are active part of the time and are in sleep mode for the rest of the time. In sleep mode, they do not consume fuel or electricity, leading to efficient energy saving and reducing environmental and noise pollution. Although sparse control has been used with a single actuator, it does not necessarily provide good performance when multiple actuators are used. The problem of determining how to do this for multiple actuators is called the ‘maximum turn-off control problem’.
Now, a Nagoya University research group, led by Professor Shun-ichi Azuma and Doctoral student Takumi Iwata of the Graduate School of Engineering, has developed a model control scheme for multiple actuators. The model has an awake mode, during which it procures and controls the necessary power packets for when they are needed, and a sleep mode. The research was published in the International Journal of Robust and Nonlinear Control.
“We can see our research being useful in the motor control of production equipment,” explains Professor Azuma. “This research provides a control system configuration method based on the assumption that the energy supply is intermittent. It has the advantage of eliminating the need for storage batteries and capacitors. It is expected to accelerate the practical application of the power packet type energy Internet.”
This research was supported by Japan Science and Technology Agency Emergent Research Support Program and Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
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