Scientists discover why some babies learn to walk later than others
Scientists discover why some babies learn to walk later than others
The age at which babies learn to walk is strongly influenced by their genes, researchers have found (Getty/iStock)
A child's first steps are a momentous occasion, but the age at which this milestone is reached could be determined by genetics, a new study suggests.
Scientists analysed the genetic information of more than 70,000 babies and identified 11 genetic markers that influence when youngsters take their first steps.
In findings published in the journal Nature Human Behaviour, the team, from the universities of Surrey and Essex, suggested that genetics accounts for about a quarter of the differences in when children begin to walk.
Professor Angelica Ronald, senior researcher on the study from the University of Surrey, said that 'most babies take their first step sometime between ages eight months and 24 months, so it is a wide window in which this exciting milestone happens.
ADVERTISEMENT
'It is a big moment for both parents and baby; it symbolises a new phase in a child's life.
'We hope these new genetic findings can advance fundamental understanding about the causes of walking and be used to better support children with motor disorders and learning disabilities.
'While parents should still see their GP if they are concerned, a slightly later start is not always a sign of problems.
'There is a lot of variety in when children take their first step on their own.'
Scientists analysed the genetic information of more than 70,000 babies (Getty/iStock)
Dr Anna Gui, who worked on the study, added that, until now, 'we didn't understand what causes the wide differences between children in when they take their first step'.
'Parents might often worry that walking early or late is a bad sign or that they have done something wrong.
'We see that genetics play a considerable role in influencing the timing of this milestone.'
ADVERTISEMENT
The study found that those genes influencing when children take their first steps are partly the same genetic factors that influence brain development.
There is also a relationship between the later onset of walking and genes that are involved in higher educational attainment.
Walking later, but within the typical range, was further linked genetically with less chance of developing ADHD, the research suggested.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Fast Company
2 hours ago
- Fast Company
Enough hype about AI in health—let's talk about results
There's no shortage of big promises about AI in healthcare. The U.S. government announced a $500 billion Stargate initiative to fund AI infrastructure, while the UK government announced £82.6 million in research funding for three projects, two of which are using AI to tackle cancer and Alzheimer's disease. But ask any patient waiting for a diagnosis, or a clinician searching for certainty, and the real question cuts through all the noise: When will all the innovation deliver real impact where it matters most? We've entered an era where healthcare data is measured in exabytes—genomes, images, clinical notes, labs, and signals from every continent. At SOPHiA GENETICS, we've just achieved the milestone of analyzing 2 million patient profiles. It's a number that was unimaginable a decade ago. Yet the value of data isn't in volume. It's what you do with it. Insight matters only if it changes an outcome, shortens a diagnostic odyssey, or opens up a new chance for a patient. It's time to move from conversation to action. Too often, insights get trapped in institutional silos or left in endless pilot projects, just out of reach of the people who need them. 3 ways AI is already transforming medicine Today, technology can connect, for example, a patient in São Paulo with expertise in Seoul, uncovering patterns invisible to the human eye. Here are several ways AI is helping now. 1. Improving diagnostic accuracy: AI algorithms, particularly those based on deep learning, have demonstrated remarkable accuracy in diagnosing diseases from medical images and test results. These systems are trained on vast datasets, allowing them to recognize patterns and anomalies that might be missed by the human eye. For example, in dermatology, AI systems trained on images of skin lesions have shown the ability to detect skin cancers, such as melanoma, with high levels of precision. 2. Enhancing cancer prevention: AI techniques can be used to screen individuals for genomic markers and develop personalized cancer risk prediction scores. This proactive approach can help screen younger patients for genomic predispositions, empowering them to make informed prevention decisions and proactively monitor their health. 3. Tailoring treatments to genomic profiles: One of the most significant applications of AI is in the field of genomics. AI can analyze vast genomic datasets to identify mutations and variations that might influence an individual's response to certain treatments. For example, our SOPHiA DDM product can identify specific genomic markers that are susceptible to targeted cancer therapies, increasing the efficacy of the treatment and minimizing the risk of adverse reactions, for a more effective and safer treatment plan for patients. To scale up these AI applications there are many regulatory and compliance barriers to overcome. This requires investment in data security, creating clear guidelines, data security measures, and ensuring clinicians are fully trained. Our goal should be to create a regulatory environment that fosters innovation while safeguarding patient data and promoting public trust. We must democratize this powerful data to enable more physicians, practices, and hospitals to incorporate AI into daily clinical use so that a greater number of patients can access data-driven medicine, not just a select few. My message for governments investing in AI is clear: Balance investing in future AI tools with validating existing solutions that have already been proven to improve patient outcomes. Build the bridges that turn breakthroughs into benefits, so that data-driven medicine becomes a reality for every patient.
Gizmodo
8 hours ago
- Gizmodo
These Are the Dogs Most Likely to Get Diarrhea
Researchers in the U.K. analyzed vet records from over two million dogs to uncover the messy truth about canine diarrhea. Speaking from personal experience; fewer things in the world are messier than a dog with the runs. New research out today reveals how often people will take their dog to the vet for a bout of diarrhea, as well as some potential factors that could raise a pup's risk of it, such as their breed. Scientists at the Royal Veterinary College in the U.K. conducted the study, which examined the vet records of several million dogs. They estimated that one in every 12 dogs experiences diarrhea serious enough to prompt a vet visit each year, with certain breeds, including German Shepherds, more likely to experience it. The study also suggests that dogs are often getting overprescribed antibiotics as a result. Much like their human owners, dogs commonly suffer from diarrhea, and the study's researchers wanted to better understand how often it's diagnosed in primary care vet clinics and how it's typically managed. To do so, they analyzed the (anonymous) medical records of more than two million U.K. dogs that visited vets in 2019. They also studied the characteristics of 1,835 randomly selected cases of confirmed diarrhea within this sample. They calculated that 8.18% of dogs are diagnosed with diarrhea by their primary vet in a given year. But this risk was higher for six breeds in particular compared to the average mixed breed dog: the Maltese, Miniature Poodle, Cavapoo, German Shepherd, and Yorkshire Terrier. Conversely, only two breeds—the Jack Russell Terrier and Chihuahua—had a lower risk of diarrhea. Dogs younger than three and older than nine were also more likely to be diagnosed with diarrhea compared to middle-aged dogs. About a third of the cases they studied involved blood in the stool, a sign of more serious illness; nearly half of all dogs studied also experienced vomiting. But most cases in general cleared up without too much trouble, with more than 80% only meriting a single trip to the vet. In other words, as is true with people, doggy diarrhea is usually miserable but short-lasting. 'Experiencing the dreaded doggy poonami is one of those events every dog owner fears. This new study shows that diarrhea events are very common in dogs but that owners should not panic—no matter how bad the deluge, most dogs will recover in a few days with good veterinary treatment,' said lead study researcher Dan O'Neill, an associate professor of companion animal epidemiology at the RVC, in a statement provided to Gizmodo. The findings might also provide some insights into the nature of doggy diarrhea. It's possible that the higher-risk breeds highlighted in the study are more genetically vulnerable to digestive problems, for instance. The researchers also found other factors tied to a higher diarrhea risk, such as scavenging (the tendency for a dog to try eating things off the ground or from other non-intentional sources). On the more worrying side, the findings indicate that antibiotics are getting overused in dogs as well as humans. Veterinary guidelines have long advised against using antibiotics for mild to moderate cases of diarrhea in dogs, the researchers note. These drugs are not only unlikely to speed up a dog's recovery, but can incidentally increase antibiotic resistance. Despite these guidelines, however, nearly 40% of the cases they studied were still treated with antibiotics. 'High antibiotic use within veterinary clinical management against years of recommendation to the contrary raises concerns about unnecessary antibiotic therapy for this condition,' the researchers wrote in their paper, published in PLOS ONE. Dogs deserve nothing but the best healthcare, of course. But for most cases of diarrhea, time and less intrusive measures like switching to blander foods (boiled chicken, white rice, etc.) are all you need to help your dog through their gastrointestinal ordeal.
WIRED
10 hours ago
- WIRED
A Deep Learning Alternative Can Help AI Agents Gameplay the Real World
Jun 11, 2025 12:30 PM A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI. Photo-Illustration: WIRED Staff; Getty Images A new machine learning approach that draws inspiration from the way the human brain seems to model and learn about the world has proven capable of mastering a number of simple video games with impressive efficiency. The new system, called Axiom, offers an alternative to the artificial neural networks that are dominant in modern AI. Axiom, developed by a software company called Verse AI, is equipped with prior knowledge about the way objects physically interact with each other in the game world. It then uses an algorithm to model how it expects the game to act in response to input, which is updated based on what it observes—a process dubbed active inference. The approach draws inspiration from the free energy principle, a theory that seeks to explain intelligence using principles drawn from math, physics, and information theory as well as biology. The free energy principle was developed by Karl Friston, a renowned neuroscientist who is chief scientist at 'cognitive computing' company Verses. Friston told me over video from his home in London that the approach may be especially important for building AI agents. 'They have to support the kind of cognition that we see in real brains,' he said. 'That requires a consideration, not just of the ability to learn stuff but actually to learn how you act in the world.' The conventional approach to learning to play games involves training neural networks through what is known as deep reinforcement learning, which involves experimenting and tweaking their parameters in response to either positive or negative feedback. The approach can produce superhuman game-playing algorithms but it requires a great deal of experimentation to work. Axiom masters various simplified versions of popular video games called drive, bounce, hunt, and jump using far fewer examples and less computation power. 'The general goals of the approach and some of its key features track with what I see as the most important problems to focus on to get to AGI,' says François Chollet, an AI researcher who developed ARC 3, a benchmark designed to test the capabilities of modern AI algorithms. Chollet is also exploring novel approaches to machine learning, and is using his benchmark to test models' abilities to learn how to solve unfamiliar problems rather than simply mimic previous examples. 'The work strikes me as very original, which is great,' he says. 'We need more people trying out new ideas away from the beaten path of large language models and reasoning language models.' Modern AI relies on artificial neural networks that are roughly inspired by the wiring of the brain but work in a fundamentally different way. Over the past decade and a bit, deep learning, an approach that uses neural networks, has enabled computers to do all sorts of impressive things including transcribe speech, recognize faces, and generate images. Most recently, of course, deep learning has led to the large language models that power garrulous and increasingly capable chatbots. Axiom, in theory, promises a more efficient approach to building AI from scratch. It might be especially effective for creating agents that need to learn efficiently from experience, says Gabe René, the CEO of Verses. René says one finance company has begun experimenting with the company's technology as a way of modeling the market. 'It is a new architecture for AI agents that can learn in real time and is more accurate, more efficient, and much smaller,' René says. 'They are literally designed like a digital brain.' Somewhat ironically, given that Axiom offers an alternative to modern AI and deep learning, the free energy principle was originally influenced by the work of British Canadian computer scientist Geoffrey Hinton, who was awarded both the Turing award and the Nobel Prize for his pioneering work on deep learning. Hinton was a colleague of Friston's at University College London for years. For more on Friston and the free energy principle, I highly recommend this 2018 WIRED feature article. Friston's work also influenced an exciting new theory of consciousness, described in a book WIRED reviewed in 2021.
