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Ward Plunet
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60,374 followers
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Mammoth Biosciences launches a CRISPR-powered search engine for disease detection

Most people tend to think of CRISPR as a groundbreaking gene-editing technology that can hunt down and snip away bits of DNA, like the cut and paste function on a keyboard. While many research projects tend to emphasize the potential of that process in replacing target bits of genetic material, for Mammoth Biosciences, the search function is the real game changer. “Control + F is the exciting part,” Mammoth co-founder and CEO Trevor Martin told TechCrunch in an interview. “At core it’s just this amazing search engine that we can use to find things. The way that we search for things is just like Google.” To conduct that search, one must enter a guide RNA and tell a CRISPR protein what to search for, any specific DNA or RNA sequence, from a malaria pathogen to a biomarker for leukemia. Mammoth’s novel technology uses a reporter molecule that exudes color when the sequence is located and snipped, indicating that the genetic search term was present in the sample and making the result of such a search viewable with the naked eye.
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DeepCode cleans your code with the power of AI

Zurich-based DeepCode claims that their system — essentially a tool for analyzing and improving code — is like Grammarly for programmers. The system, which uses a corpus of 250,000 rules, reads your public and private GitHub repositories and tells you how to fix problems, remain compatible and generally improve your programs.
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Exercise and depression

Regular exercise can reduce your risk of depression, no matter what your age or where you live, research suggests. In a new study, an international team of researchers analyzed data from 49 studies that included nearly 267,000 people in North America, Europe and Oceania. The study participants did not have any mental illnesses and were followed for an average of more than seven years. High levels of physical activity were associated with a lower risk of depression in all age groups during the follow-up, the investigators found. However, the new study was not designed to prove that exercise actually caused depression risk to drop. "This is the first global meta-analysis to establish that engaging in physical activity is beneficial for protecting the general population from developing depression," said study author Felipe Barreto Schuch, a professor at Universidade La Salle in Brazil.
Exercise your blues away
Exercise your blues away
medicalxpress.com
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Algorithm take months, not years, to find material for improved energy conversion

A new algorithm uses the chemical elements in a crystal to predict its material properties. The algorithm simplifies computational required for material discovery and speeds up the process by about 10,000 times, compared to existing algorithms. Credit: Second Bay Studios/Harvard SEAS. In even the most fuel-efficient cars, about 60 percent of the total energy of gasoline is lost through heat in the exhaust pipe and radiator. To combat this, researchers are developing new thermoelectic materials that can convert heat into electricity. These semiconducting materials could recirculate electricity back into the vehicle and improve fuel efficiency by up to 5 percent. The challenge is, current thermoelectric materials for waste heat recovery are very expensive and time consuming to develop. One of the state of the art materials, made from a combination of hafnium and zirconium (elements most commonly used in nuclear reactors), took 15 years from its initial discovery to optimized performance. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed an algorithm that can discover and optimize these materials in a matter of months, relying on solving quantum mechanical equations, without any experimental input.
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How Professional Video Gamers Train for a World Championship

Competitive videogaming is a $1.5 billion industry—with resources to match. A team of top female gamers used the tricks and trainers of an NBA franchise to win the biggest competition of their careers....Garrido leads the team’s six-hour training sessions six days a week and assigns each member homework based on performance. She’s better known by her gamer ID: Emuhleet, a mash-up of her first name and “leet,” gamer slang for “elite.” At 27, she’s the oldest member of Team Dignitas and, as a registered nurse, the only one with a fallback profession. Her teammates came of age in an era when professional gaming was a viable career.
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Comparing Google’s TPUv2 against Nvidia’s V100 on ResNet-50

Google recently added the Tensor Processing Unit v2 (TPUv2), a custom-developed microchip to accelerate deep learning, to its cloud offering. The TPUv2 is the second generation of this chip and the first publicly available deep learning accelerator that has the potential of becoming an alternative to Nvidia GPUs. We recently reported our first experience and received a lot of requests for a more detailed comparison to Nvidia V100 GPUs. Providing a balanced and meaningful comparison for deep learning accelerators is not a trivial task. Due to the future importance of this product category and the lack of detailed comparisons we felt the need to create one on our own. This also includes listening to possibly opposing sides. That’s why we established contact early on with Google and Nvidia engineers and let them comment on drafts of this article. To guarantee that we are not treating one side unfairly we additionally invited independent experts to review the article. This makes it to our knowledge the most thorough comparison between TPUv2 and V100 chips to date....As shown above, the current pricing of the Cloud TPU allows to train a model to 75.7% on ImageNet from scratch for $55 in less than 9 hours! Training to convergence at 76.4% costs $73. While the V100s perform similarly fast, the higher price and slower convergence of the implementation results in a considerably higher cost-to-solution.
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Designing great data products - The Drivetrain Approach: A four-step process for building data products

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines to services that predict airline flight times more accurately than the airline itself. But these products are still just making predictions, rather than asking what action they want someone to take as a result of a prediction. Prediction technology can be interesting and mathematically elegant, but we need to take the next step. The technology exists to build data products that can revolutionize entire industries. So, why aren’t we building them? To jump-start this process, we suggest a four-step approach that has already transformed the insurance industry. We call it the Drivetrain Approach, inspired by the emerging field of self-driving vehicles. Engineers start by defining a clear objective: They want a car to drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing. Someone using Google’s self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach. We don’t claim that the Drivetrain Approach is the best or only method; our goal is to start a dialog within the data science and business communities to advance our collective vision.
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Drinking kefir may prompt brain-gut communication to lower blood pressure

Drinking kefir may have a positive effect on blood pressure by promoting communication between the gut and brain. Kefir is a fermented probiotic milk beverage known to help maintain the balance of beneficial bacteria in the digestive system.
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Designer Diary: The Search for AlphaMystica

Spoiler alert: This doesn’t have a happy ending. Digidiced has been hard at work for more than a year trying to produce a Hard version of its AI for Terra Mystica using machine learning. Our results have been a lot less impressive than we were hoping for. This article will describe a little bit about what we’ve tried and why it hasn’t worked for us. If you’ve paid attention to the latest developments in AI, you’ve probably heard of AlphaGo and AlphaZero, developed by Google’s DeepMind. In 2017, AlphaGo defeated Ke Jie, the #1 ranked Go player in the world. AlphaGo was developed by using a massive neural network and feeding it hundreds of thousands of professional games. From those games, it learned to predict what it thought a professional would play. AlphaGo then went on to play millions of games against itself, gradually improving its evaluation function little by little until it became a superhuman monster, better than any human player. The defeat of a human professional was thought to be decades away for a game as complex as Go. But AlphaGo shocked everyone with its quantum leap in playing strength. AlphaGo was able to come up with new strategies, some of which were described as “god-like.”
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Children are as fit as endurance athletes

Researchers discover how young children seem to run around all day without getting tired: their muscles resist fatigue and recover in the same way as elite endurance athletes. The study, which compared energy output and post-exercise recovery rates of young boys, untrained adults and endurance athletes, can be used to develop athletic potential in children and improve our knowledge of how disease risk, such as diabetes, increases as our bodies change from childhood to adulthood. Children not only have fatigue-resistant muscles, but recover very quickly from high-intensity exercise -- even faster than well-trained adult endurance athletes. This is the finding of new research published in open-access journal Frontiers in Physiology, which compared the energy output and post-exercise recovery rates of young boys, untrained adults and endurance athletes. The research could help develop athletic potential in children as well as improve our understanding of how our bodies change from childhood to adulthood -- including how these processes contribute to the risk of diseases such as diabetes.
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