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The Hindu
05-07-2025
- Health
- The Hindu
High-quality diet may have led to bigger human brain: CSIR-CCMB-led international study reveals genetic link between diet and brain size
Our ancestors' shift to higher-quality diets, especially with use of fire and consumption of meat and fruits, had likely triggered genetic changes and may have paved the way for the dramatic expansion of the human brain, deduce researchers at the CSIR-Centre for Cellular and Molecular Biology (CCMB) here in Hyderabad. A ground-breaking international study published in the latest issue of Philosophical Transactions of the Royal Society B, involving scientists from the United States and China, has uncovered the genetic mechanisms linking diet quality and brain size in primates, shedding new light on how some species, especially humans, evolved to have such large and complex brains. It reveals that the quality of food primates eat influences not just their energy levels but also the way their brains evolve at the genetic level. The research team, including CCMB's Laboratory for Conservation of Endangered Species (LaCONES) Chief Scientist Govindhaswamy Umapathy (senior author), Vinay Teja Potharlanka (first author) and others, including Shao Y, Wu D, Banda N and DeCasien A., had analysed over 8,000 genes from 50 whole genomes across primate species to understand how brain size co-evolved with diet and to identify the specific genes that may have helped make it happen. Energy intensive organs Brain is one of the most energy-hungry organs in the body. In humans, the brain consumes nearly 20% of the total energy, despite accounting for just 2% of body weight. For many years, scientists have wondered how such an organ could have evolved and why some species have bigger brains than others. A key idea is that better-quality diets those rich in fruits, seeds, and animal protein provide more energy, allowing for the development of larger brains. But until now, no one had clearly shown how this plays out at the genetic level. Digging Into the Data Researchers combined genetic, ecological, and anatomical data in a novel way by measuring the size of brains and bodies in different primates and used published information on diet composition to create a Diet Quality Index (DQI). They then looked at how quickly brain-related genes evolved in each species using the 'dN/dS' ratio method, which tracks evolutionary changes in gene sequences. Using sophisticated statistical tools that account for evolutionary relationships between species, the team identified hundreds of genes whose evolution was closely linked to diet and brain size. Genes That Build Brains—and Process Energy Many genes linked to larger brains were involved in neurogenesis (brain cell development) and are also known to be involved in human brain disorders such as autism and microcephaly. However, several key genes were also involved in lipid and carbohydrate metabolism — the body's way of processing fats and sugars. These include genes like 'ELOVL6', which helps process fatty acids, and 'EEF1A2', which is involved in brain development and linked to neurological conditions. 'This supports the idea that better diets provided the fuel for building bigger brains. Energy metabolism genes and brain development genes are working hand-in-hand. This link is deeply embedded in the genome,' said Dr. Umapathy. Two-Way Evolution The study's best-fitting model showed that diet quality influences brain size in two ways — directly by providing energy and nutrients, and indirectly by affecting which genes were under evolutionary pressure. 'It's a feedback loop. Better brains help find better food, and better food helps build better brains. Evolution worked through genes to reinforce this loop,' said Vinay Teja Potharlanka. Interestingly, some of the same genes that aided brain expansion may have also increased the risk for certain neurodevelopmental conditions, highlighting a potential evolutionary trade-off. Implications for Human Evolution Although the study did not include modern humans, the findings offer powerful insights into our evolutionary past. The team hopes future research will explore how other lifestyle and social factors like group size, parenting style, or movement patterns may have influenced brain evolution in primates. The full dataset and analysis code have been made publicly available, encouraging further exploration into the complex relationship between ecology, genetics, and cognition.
Economic Times
04-07-2025
- Business
- Economic Times
RBI looks to ensure uniformity in credit bureau data quality
The Reserve Bank of India is reviewing credit bureau operations. This aims to improve data consistency and transparency for customers. A working group is addressing concerns from financial institutions. Suggestions include uniform data standards and a common grievance portal. The RBI also wants quicker complaint resolution and more frequent credit data updates. A unique borrower identifier is also under consideration. Tired of too many ads? Remove Ads Tired of too many ads? Remove Ads ( Originally published on Jul 04, 2025 ) New Delhi: The banking regulator is examining suggestions to further streamline credit bureau processes and reduce information asymmetry with lenders, a move aimed at addressing data inconsistency and transparency for customers, said people familiar with the development."A technical working group has been formed by the RBI (Reserve Bank of India) to address concerns raised by financial institutions related to credit information companies (CICs)," said an executive, who did not wish to be identified.A senior public sector bank executive said the four CICs - TransUnion Credit Information Bureau (India) Limited (TransUnion CIBIL), Equifax, Experian and CRIF High Mark - have given some suggestions to the RBI to ensure data uniformity and also to set up a common grievance redressal portal."There is a need for some more common standards among CICs so that there is a uniformity relating to data quality index (DQI), and also for looking into the records of inactive or written-off customers," the executive said, adding that a single-window platform for data submission can also be emailed to the RBI did not elicit a response till press RBI has extended the alternative grievance redressal mechanism under the Reserve Bank-Integrated Ombudsman Scheme, 2021 to cover grievances against this week, RBI deputy governor M Rajeshwar Rao said a key challenge is identity standardisation and that CICs rely on credit institutions to provide accurate and validated IDs. "There is a need to move towards a unique borrower identifier , which is secure, verifiable and consistent across the system," he said, addressing the TransUnion CIBIL's Credit Conference in RBI has been taking up with CICs the issue of quick disposal of complaints raised against them. In 2023 it had mandated that complainants are entitled to compensation of ₹100 per day if their complaint is unresolved within 30 days from the initial RBI deputy governor, in his speech, also said that CICs must aspire to more frequent updates of credit data, as against the current fortnightly interval.
Time of India
04-07-2025
- Business
- Time of India
RBI looks to ensure uniformity in credit bureau data quality
New Delhi: The banking regulator is examining suggestions to further streamline credit bureau processes and reduce information asymmetry with lenders, a move aimed at addressing data inconsistency and transparency for customers, said people familiar with the development. "A technical working group has been formed by the RBI (Reserve Bank of India) to address concerns raised by financial institutions related to credit information companies (CICs)," said an executive, who did not wish to be identified. A senior public sector bank executive said the four CICs - TransUnion Credit Information Bureau (India) Limited (TransUnion CIBIL), Equifax, Experian and CRIF High Mark - have given some suggestions to the RBI to ensure data uniformity and also to set up a common grievance redressal portal. "There is a need for some more common standards among CICs so that there is a uniformity relating to data quality index (DQI), and also for looking into the records of inactive or written-off customers," the executive said, adding that a single-window platform for data submission can also be explored. Queries emailed to the RBI did not elicit a response till press time. The RBI has extended the alternative grievance redressal mechanism under the Reserve Bank-Integrated Ombudsman Scheme, 2021 to cover grievances against CICs. Earlier this week, RBI deputy governor M Rajeshwar Rao said a key challenge is identity standardisation and that CICs rely on credit institutions to provide accurate and validated IDs. "There is a need to move towards a unique borrower identifier , which is secure, verifiable and consistent across the system," he said, addressing the TransUnion CIBIL's Credit Conference in Mumbai. The RBI has been taking up with CICs the issue of quick disposal of complaints raised against them. In 2023 it had mandated that complainants are entitled to compensation of ₹100 per day if their complaint is unresolved within 30 days from the initial filing. The RBI deputy governor, in his speech, also said that CICs must aspire to more frequent updates of credit data, as against the current fortnightly interval. Economic Times WhatsApp channel )
WIRED
27-04-2025
- Science
- WIRED
A New Quantum Algorithm Speeds Up Solving a Huge Class of Problems
Apr 27, 2025 7:00 AM It's been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do so for some critical optimization tasks. Illustration: Daniel Garcia for Quanta Magazine The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a peak to climb—and then they must develop a strategy to solve it. Classical and quantum researchers compete using different strategies, with a healthy rivalry between the two. Quantum researchers report a fast way to solve a problem—often by scaling a peak that no one thought worth climbing—then classical teams race to see if they can find a better way. This contest almost always ends as a virtual tie: When researchers think they've devised a quantum algorithm that works faster or better than anything else, classical researchers usually come up with one that equals it. Just last week, a purported quantum speedup, published in the journal Science, was met with immediate skepticism from two separate groups who showed how to perform similar calculations on classical machines. But in a paper posted on the scientific preprint site last year, researchers described what looks like a quantum speedup that is both convincing and useful. The researchers described a new quantum algorithm that works faster than all known classical ones at finding good solutions to a wide class of optimization problems (which look for the best possible solution among an enormous number of choices). So far, no classical algorithm has dethroned the new algorithm, known as decoded quantum interferometry (DQI). It's 'a breakthrough in quantum algorithms,' said Gil Kalai, a mathematician at Reichman University and a prominent skeptic of quantum computing. Reports of quantum algorithms get researchers excited, partly because they can illuminate new ideas about difficult problems, and partly because, for all the buzz around quantum machines, it's not clear which problems will actually benefit from them. A quantum algorithm that outperforms all known classical ones on optimization tasks would represent a major step forward in harnessing the potential of quantum computers. 'I'm enthusiastic about it,' said Ronald de Wolf, a theoretical computer scientist at CWI, the national research institute for mathematics and computer science in the Netherlands, who was not involved with the new algorithm. But at the same time, he cautioned that it's still quite possible researchers will eventually find a classical algorithm that does just as well. And due to the lack of quantum hardware, it'll still be a while before they can test the new algorithm empirically. The algorithm might inspire new work on the classical side, according to Ewin Tang, a computer scientist at the University of California, Berkeley, who came to prominence as a teenager by creating classical algorithms that match quantum ones. The new claims 'are interesting enough that I would tell classical-algorithms people, 'Hey, you should look at this paper and work on this problem,'' she said. The Best Way Forward? When classical and quantum algorithms compete, they often do so on the battlefield of optimization, a field focused on finding the best options for solving a thorny problem. Researchers typically focus on problems in which the number of possible solutions explodes as the problem gets bigger. What's the best way for a delivery truck to visit 10 cities in three days? How should you pack the parcels in the back? Classical methods of solving these problems, which often involve churning through possible solutions in clever ways, quickly become untenable. The specific optimization problem that DQI tackles is roughly this: You're given a collection of points on a sheet of paper. You need to come up with a mathematical function that passes through these points. Specifically, your function has to be a polynomial—a combination of variables raised to whole-number exponents and multiplied by coefficients. But it can't be too complicated, meaning the powers can't get too high. This gives you a curved line that wiggles up and down as it moves across the page. Your job is to find the wiggly line that touches the most points. Variations of this problem show up in various forms across computer science, especially in error coding and cryptography—fields focused on securely and accurately encoding data as it's transmitted. The DQI researchers recognized, basically, that plotting a better line is akin to shifting a noisy encoded message closer to its accurate meaning. But all that came later. When the researchers behind DQI started working on their algorithm, they didn't even have this problem in mind. A Problem Decoded 'It would have been entirely plausible for a goal-oriented researcher to start by stating the problem and then investigating whether quantum algorithms could solve it faster than classical algorithms,' said Stephen Jordan, a physicist at Google Quantum AI and one of the main architects of DQI. 'Of course, for us, that's not how it happened. We came upon it by a backward and circuitous route.' Stephen Jordan helped come up with a quantum approach to certain problems that works better than any classical approach—so far. Jordan embarked on that route in 2023, when he joined Google and found out he'd be working with Eddie Farhi, a physicist at Google whose work has long focused on quantum algorithms that outperform classical ones. (Farhi was once Jordan's doctoral adviser at the Massachusetts Institute of Technology.) Jordan knew that in 2014, Farhi had made a quantum attack on an optimization problem by thinking of energy, with lower energies corresponding to better solutions. For Farhi, energy connected optimization to quantum physics. But Jordan wanted to do something different. He turned to another concept built into quantum physics—recognizing everything as waves. Using a mathematical tool called a quantum Fourier transform, Jordan found a way to translate all the potential answers to a well-known class of optimization problems into quantum waves. In doing so, he could manipulate the quantum system so that bigger waves (in the form of higher quantum amplitudes) corresponded to better solutions. But there was still a huge challenge that had to be overcome. In a quantum system, asking 'What's the biggest amplitude?' is not as simple as recognizing the biggest wave at the beach. The quantum landscape is incredibly complex, and it was unclear how to identify the quantum amplitudes that would correspond to the best solutions. After many false starts, Jordan made a breakthrough: The process of selecting the best solutions turned out to be similar to the process of weeding out errors in coded messages, which is known as decoding. This is a well-studied area of computer science, full of techniques that Jordan could explore. By translating an optimization problem into a quantum one, and then applying the decoding lens to it, he had stumbled into a new way to develop quantum algorithms. Illustration: Daniel Garcia for Quanta Magazine Together with Noah Shutty, also at Google, Jordan began testing decoding schemes, seeing how they fared against classical algorithms on various optimization problems. They needed both the right approach and a problem where it worked. 'It turns out classical algorithms are hard to beat,' Jordan said. 'After a few months of trying, we still had not notched up any wins for quantum.' But eventually, the pair landed on a decoding algorithm first introduced in the 1960s to find and fix individual errors in an encoded message. Finding that problem was the key. 'When we investigated, we seemed to hit success almost immediately,' Jordan said. Finally, they had found a problem and an approach that, together, looked like a quantum speedup. Of course, that didn't mean it was bulletproof. 'Maybe there is some classical method that can efficiently replicate your entire approach,' Jordan said. 'Such dequantizations are not always obvious.' Gaining Confidence To assuage those fears, they consulted with Mary Wootters, a coding theory expert (and Shutty's former doctoral adviser at Stanford University). She carefully searched for any known classical algorithm that might match their quantum speedup. The advantage held. The team's checks likewise suggest that it will continue to hold. 'They did due diligence,' Tang said. Bolstered by this analysis, they looked more carefully at the optimization problem they were solving. Jordan had worried that it might be too niche, with no wider applications, but Shutty recognized that this decoding problem was a variation of well-known and useful problems in encryption and other fields. Jordan acknowledges that without a large enough quantum machine, DQI will remain a theoretical breakthrough. 'DQI cannot run on present-day quantum computers,' he said. But they're still moving forward. Since the group posted their work last August, they have extended the application of DQI beyond the original problem to a broader class of optimization problems, which includes more cases of these 'best path' problems. So far, Jordan said, he expects that DQI can beat classical algorithms in those problems, too. For the moment, the quantum community remains elated. 'Finding quantum algorithms that show an advantage over classical algorithms is a very exciting endeavor of the last three decades, and the number of definite algorithms that show such an advantage is not large,' Kalai said. 'Therefore, every new algorithm is a reason for celebration.' Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
