New piece in the 'French paradox' diet and health puzzle: Cheese metabolism

Figuring out why the French have low cardiovascular disease rates despite a diet high in saturated fats has spurred research and many theories to account for this phenomenon known as the "French paradox." Most explanations focus on wine and lifestyle, but a key role could belong to another French staple: cheese. The evidence, say scientists in ACS' Journal of Agricultural and Food Chemistry, is in cheese metabolism.



Hanne Bertram and colleagues note that recent research on some dairy products' positive effects on health has cast doubt on the once-firm rule that saturated fats are bad for our hearts. For example, one study found that cheese reduced "bad" cholesterol when compared to butter with the same fat content, suggesting that high cheese consumption could help explain the French paradox. To further investigate this possible explanation, Bertram's team looked into how cheese gets digested.


The researchers compared urine and fecal samples from 15 healthy men whose diets either contained cheese or milk, or who ate a control diet with butter but no other dairy products. They found that those who consumed cheese had higher fecal levels of butyrate, a compound produced by gut bacteria. Elevated butyrate levels were linked to a reduction in cholesterol. Their results, they say, suggest a role for gut microbes and further shore up the connection between cheese and the French paradox.


The authors acknowledge funding from the Danish Council for Strategic Research, Arla Foods and the Danish Dairy Research Foundation.




Story Source:


The above story is based on materials provided by American Chemical Society . Note: Materials may be edited for content and length.



Turning to freshwater sources to fight drug-resistant tuberculosis, other infections

The discovery of antibiotics produced by soil fungi and bacteria gave the world life-saving medicine. But new antimicrobials from this resource have become scarce as the threat of drug resistance grows. Now, scientists have started mining lakes and rivers for potential pathogen-fighters, and they've found one from Lake Michigan that is effective against drug-resistant tuberculosis. Their report on the new compound appears in the journal ACS Infectious Diseases.



Brian T. Murphy and colleagues point out that the emergence of drug-resistant strains of Mycobacterium tuberculosis is a serious, global health threat. In 2013, these bacteria caused 210,000 deaths globally and 480,000 infections, of which more than half were in China, India and Russia, according to the World Health Organization.


The development of new antibiotics to fight them has not yet yielded an optimal solution. Despite a few recent successes, scientists are having a hard time finding new candidates from soil-dwelling microbes. Murphy's team wanted to see whether bacteria that live in freshwater -- a habitat that has been largely unexplored for this purpose -- could be a new source of antibiotics.


The researchers screened an extensive collection of freshwater bacteria metabolites and identified a new compound that stops the growth of M. tuberculosis. In lab tests, the compound worked at least as well as current treatments for tuberculosis, and it inhibited drug-resistant strains.




Story Source:


The above story is based on materials provided by American Chemical Society . Note: Materials may be edited for content and length.



Farewell and Follow Me


Beyond Apollo is dead – long live Beyond Apollo.


As some of you may be aware, I’ve been writing online about plans for space missions and programs that didn’t happen for quite a long time – it’ll be 20 years next year, as a matter of fact. That writing has taken place under different titles. From 1996 to 2006, I wrote a webpage called Romance to Reality. That spun off my NASA-published history HUMANS TO MARS. Then came my blog Beyond Apollo, which moved to WIRED in 2012.


Now, I’ve started a new blog called DSFP’s Spaceflight History Blog. I did this so that I could experiment with crowdfunding and expand my scope to take in a broader range of space history topics.


If you have enjoyed reading Beyond Apollo here on WIRED, please watch for my book by the same title, which should hit bookstore shelves in 2016. In the Beyond Apollo blog – which will remain archived here on the WIRED site – I’ve mostly focused on one study document for each post. In BEYOND APOLLO the book, I am writing chapters that link multiple studies, some of which took place decades apart. Some of the posts that I planned to write on this blog have already morphed into parts of chapters in the book.


Thanks to my fellow WIRED Science Bloggers and to all the people at WIRED who’ve put up with me this past three years. It’s been fun and educational being a part of the team.


Thanks most of all to everyone who has read and enjoyed Beyond Apollo. I hope to see you around.



Now Anyone Can Tap the AI Behind Amazon’s Recommendations


Amazon helped show the world how machines can learn. As far back as the late ’90s, the company’s online retail site would track every book, CD, and movie you purchased. As time went on, it would develop a pretty good sense of what you liked, serving up product recommendations its code predicted would catch your eye.


It wasn’t rocket science. It was an algorithm. But it worked. And in the years since, the field of so-called machine learning has evolved in enormous ways, with the likes of Google, Facebook, and Microsoft training enormous networks of machines to identify faces in photos, recognize the spoken word, and instantly translate conversations from one language to another.


Now, as these tech giants advance the state of the art, there’s a movement afoot to bring machine learning to the business world at large. Many companies are offering online services that anyone can use to build their own recommendation engine, fraud detection system, or some other app powered by machine learning. A startup called MetaMind is democratizing machine learning in this way, as are big names such as Microsoft, Google, and IBM.


On Thursday, Amazon unveiled a similar machine learning service, pitching it as a way for any business to use the AI tech the company has spent years developing inside its own operation. Known as the Amazon Machine Learning Service, it’s designed for software developers “with no experience in machine learning,” AWS head Andy Jassy said on stage at a mini-conference in San Francisco.


The new tool is part of the company’s ever-growing suite of cloud computing services, known as Amazon Web Services, or AWS. Like Google, Microsoft, and IBM, AWS offers all sorts of tools that provide instant access to computing power over the internet. Basically, these are tools that let you build online applications without setting up your own infrastructure. Now, as with its options for servers and storage, you can use Amazon’s machine learning rather than building your own.


Leave the Learning to Us


About two years ago, according to Matt Wood, who helps oversees data science at AWS, Amazon built a machine learning service solely for use inside the company. Basically, this was an online service that let any Amazon engineer built an application that involved machine learning. Among other things, he says, the service now drives a camera-based system that can identify and track products inside the company’s fulfillment centers.


The new service is basically a version of what Amazon was using internally now made available to engineers and businesses outside of Amazon. Judging from Wood’s description, the service is not as sophisticated as what Amazon (or Google or Facebook) use inside their own data centers. But it’s meant to provide the average business with expertise they may not have. It’s focused, he says, on “real-world problems for developers.”


Umair Sadiq, a software engineer at tech giant nVidia who dabbles in machine learning, likes the idea of the service. But he points out that if the tools offered by the service are too general, they may not be all that useful. “There isn’t that much rocket science in machine learning in general,” he says.


Deep Learning


According to Wood, the service is best used to build things like recommendation engines or fraud detection. For example, if you’re running a retail app, Wood says, you can use the service to detect fraudulent orders.


In this light, the service sounds similar to Microsoft’s Azure Machine Learning Service. But Joseph Sirosh, the Microsoft vice president who oversees Azure Machine Learning and once worked at Amazon, says Microsoft’s service also offers the kind of “deep learning” algorithms that are driving things like Skype’s real-time translation tool or Facebook’s face recognition system.


Deep learning—a technology that essentially provides a more complex breed of machine learning via massive networks of computers—is the next frontier. This is also the sort of thing offered by MetaMind, with a special emphasis on natural language processing—i.e. the ability of machines to understand natural language. “That,” says Sadiq, “I do like.”



LinkedIn’s CEO on How Its $1.5B Buy Will Make You Smarter

LinkedIn CEO Jeff Weiner speaks during a panel discussion at the Bloomberg Year Ahead: 2015 conference in Washington, D.C., Nov. 13, 2014. LinkedIn CEO Jeff Weiner speaks during a panel discussion at the Bloomberg Year Ahead: 2015 conference in Washington, D.C., Nov. 13, 2014. Andrew Harrer/Bloomberg/Getty Images



Online job-training site Lynda has built a library of more than 6,300 courses that teach business and technology skills from better navigating Excel to using design software. And now that library belongs to LinkedIn. The $1.5 billion acquisition, which is the largest in LinkedIn’s history, is part of CEO Jeff Weiner’s master plan to make LinkedIn not just a resume repository, but a place for professionals to manage their careers and increasingly, learn new skills—especially in the world’s fastest-growing economies.


In the last year, LinkedIn has more than doubled its Chinese user base, but still only 9 million of its 347 million members are in China. Weiner believes Lynda could be critical to helping it expand there, and in other areas of the emerging world. “Think about what this coursework could mean for people graduating from school and trying to figure out their career paths in these developing economies,” he told WIRED in an interview today, just after announcing the Lynda acquisition. “We think it could be gamechanging.”


Lynda’s customers include individuals, businesses, government and universities, who purchase subscriptions to access its courses. The company has mastered a low-cost production process for high quality content, which it produces in its Santa Barbara offices as well as in a production studio in Austria. So far, Lynda offers courses in Spanish, German, French and Japanese in addition to English, and Weiner says LinkedIn plans to expand. “If we translate the current English coursework on leadership into Chinese, for example, we’re going to add value,” says Weiner. “Then you start thinking about local presence in these markets. What are the skills that are most in demand in the Chinese workforce?”


More Than A Résumé


To make LinkedIn more than a place to look for jobs and post a résumé, Weiner has been doubling down on LinkedIn’s content strategy over the past few years. In October 2012, the company launched its blogging program with 500 LinkedIn influencers; today more than 230 million of its members have the ability to post. In 2013, LinkedIn bought the newsreader Pulse. And it currently hosts 18 million presentations and videos on SlideShare, which it bought in 2012.


Weiner hopes Lynda will amplify these efforts. The company will remain in Santa Barbara where husband-and-wife team Lynda Weinman and Bruce Heavin will continue to operate it independently, but already Weiner envisions many ways to incorporate Lynda into LinkedIn’s existing products. “You can envision a world where we can identify experts who would make a great addition to the Lynda library,” Weiner said. “We can use our publishing platform as a mechanism to identify our best talent.”


Moreover, Weiner anticipates people may be incentivized to bolster their LinkedIn profiles by adding Lynda credentials. “It’s not just about completing these courses and sharing that you have these skills. It’s also about the coursework itself,” says Weiner. “If you’re a domain expert and you have a Lynda video, what better way to represent your expertise than to show that Lynda video on your LinkedIn profile?”


And of course, all of that interaction will keep LinkedIn’s users returning to the site more often, updating their profile more regularly, and spending more time on it. Thus it becomes a virtuous cycle. LinkedIn will be able to better target potential Lynda customers. When someone updates a profile with a promotion, for example, LinkedIn can send them a congratulatory note, pointing out the promotion may require a new set of skills and suggesting Lynda videos to help develop those skills. LinkedIn can also tag and classify the posts and presentations users watch, so if you’re interested in a particular post, says Weiner, “we can show you the Lynda material that would make the most sense for you,” says Weiner.


That’ll be somewhat useful to knowledge workers plugging away in Colorado, say, or California, where they likely have many options for how to learn new skills. But as Weiner suggests, the real opportunity could be in China and other markets where training is harder to come by. If Weiner has his way, Lynda could help make you smarter. But it could make a lot of other people smarter, too.



NEWS FLASH: AI Startups Are Reinventing Media


No technology trend today comes with more hype than artificial intelligence. Yet AI researchers often joke that most people don’t quite see how it’s changing the world—because as soon as something like Siri or Nest weaves itself into our lives, people forget it’s “artificial.”


That sort of quiet takeover of AI has already begun in an industry we humans tend to hold pretty sacred—news. Last year, Associated Press announced that the majority of its earnings reports will eventually be written with AI-enabled software. While the company argues it will free up reporters to do more analytical work, the question it raises is unsettling: Should we just leave it to the machines to interpret what they see in piles of Big Data and write up the results? Shouldn’t they at least have some human editors?


Like it or not, AI tech is “entering the era of the narrative,” explains Kris Hammond, chief scientist at Narrative Science, one of several “natural language generation” startups emerging as vendors in this nascent field. “These are narratives generated by systems that understand data, that give us information to support the decisions we need to make about tomorrow.”


New apps like Banjo claim to be tapping a new pulse on the world by mining social media, search trends, geo-location data, and other digital signals to produce new forms of breaking news instead of waiting for humans to figure out something is important and start sharing on Facebook or Twitter. Other media ventures are tapping into AI to build stories when events are based around simple facts and human interpretation isn’t required. Two years ago, the Los Angeles Times became the first major outlet to report on an earthquake—almost instantaneously—with a bot. Today, companies like Automated Insights and Narrative Science are powering the production of millions of auto-generated “articles,” such as personalized recaps for fantasy sports fans. A similar metrics-based formula can be used to recap a customer’s stock portfolio performance. Here’s a snippet of auto-prose from one of Narrative Science’s investment reports:



“The energy sector was the main contributor to relative performance, led by stock selection in energy equipment and services companies. In terms of individual contributors, a position in energy equipment and services company Oceaneering International was the largest contributor to returns.”



That said, the reason many believe (and AI companies promise) that a writer’s job is still safe is because these stories are purely factual, basically converting raw data into language. Human writers could eventually focus on more complex writing for analysis, opinion, or humor—the layers of news that attract readers. A robot probably can’t offer a good explanation for why Tom Brady seemed distracted in the third quarter. But this is where AI researchers hold up a finger and say, “Yet!”


Over the last five years, processing power and huge corpuses of teaching data have given computers the ability to detect emotions and moods. Soon, perhaps, they will be able to recognize a sideline scuffle or a player’s shift in attitude. Combine that with sensors gathering crowd reactions, the movement and changes in velocity for players and passes, historical statistics that provide context for the game and a player’s performance—and now AI is starting to encroach on analysis as well.


As that definition of “reliable data” continues to expand, so too will AI applications. Here are a few AI-oriented ventures making inroads with content and media:


· Arria — Natural language generation platform that parses complex data sets, in any vertical, from finance to meteorology and writes expert-level reports formerly assigned to human analysts.


· Banjo — Ingests all types of digital signals from Twitter, Facebook and around the Web, to offer up the latest big stories faster than humans can discover them.


· Knotch — AI-based social discovery app that monitors social behavior to capture audience feeling, emotion, and mood.


· Narrative Science — Processes data, from weather to stock prices, to write news stories and insights.


These examples just scratch the surface of what businesses and researchers think AI can do for media. If they come through, these changes will raise complicated questions. What becomes of the media jobs AI eliminates? Who’s on the hook when machine intelligence gets the facts wrong or commits libel? These questions will likely require answers even more intelligent than the technology.



Microbes help produce serotonin in gut

Although serotonin is well known as a brain neurotransmitter, it is estimated that 90 percent of the body's serotonin is made in the digestive tract. In fact, altered levels of this peripheral serotonin have been linked to diseases such as irritable bowel syndrome, cardiovascular disease, and osteoporosis. New research at Caltech, published in the April 9 issue of the journal Cell, shows that certain bacteria in the gut are important for the production of peripheral serotonin.



"More and more studies are showing that mice or other model organisms with changes in their gut microbes exhibit altered behaviors," explains Elaine Hsiao, research assistant professor of biology and biological engineering and senior author of the study. "We are interested in how microbes communicate with the nervous system. To start, we explored the idea that normal gut microbes could influence levels of neurotransmitters in their hosts."


Peripheral serotonin is produced in the digestive tract by enterochromaffin (EC) cells and also by particular types of immune cells and neurons. Hsiao and her colleagues first wanted to know if gut microbes have any effect on serotonin production in the gut and, if so, in which types of cells. They began by measuring peripheral serotonin levels in mice with normal populations of gut bacteria and also in germ-free mice that lack these resident microbes.


The researchers found that the EC cells from germ-free mice produced approximately 60 percent less serotonin than did their peers with conventional bacterial colonies. When these germ-free mice were recolonized with normal gut microbes, the serotonin levels went back up--showing that the deficit in serotonin can be reversed.


"EC cells are rich sources of serotonin in the gut. What we saw in this experiment is that they appear to depend on microbes to make serotonin--or at least a large portion of it," says Jessica Yano, first author on the paper and a research technician working with Hsiao.


The researchers next wanted to find out whether specific species of bacteria, out of the diverse pool of microbes that inhabit the gut, are interacting with EC cells to make serotonin.


After testing several different single species and groups of known gut microbes, Yano, Hsiao, and colleagues observed that one condition--the presence of a group of approximately 20 species of spore-forming bacteria--elevated serotonin levels in germ-free mice. The mice treated with this group also showed an increase in gastrointestinal motility compared to their germ-free counterparts, and changes in the activation of blood platelets, which are known to use serotonin to promote clotting.


Wanting to home in on mechanisms that could be involved in this interesting collaboration between microbe and host, the researchers began looking for molecules that might be key. They identified several particular metabolites--products of the microbes' metabolism--that were regulated by spore-forming bacteria and that elevated serotonin from EC cells in culture. Furthermore, increasing these metabolites in germ-free mice increased their serotonin levels.


Previous work in the field indicated that some bacteria can make serotonin all by themselves. However, this new study suggests that much of the body's serotonin relies on particular bacteria that interact with the host to produce serotonin, says Yano. "Our work demonstrates that microbes normally present in the gut stimulate host intestinal cells to produce serotonin," she explains.


"While the connections between the microbiome and the immune and metabolic systems are well appreciated, research into the role gut microbes play in shaping the nervous system is an exciting frontier in the biological sciences," says Sarkis K. Mazmanian, Luis B. and Nelly Soux Professor of Microbiology and a coauthor on the study. "This work elegantly extends previous seminal research from Caltech in this emerging field."


Additional coauthor Rustem Ismagilov, the Ethel Wilson Bowles and Robert Bowles Professor of Chemistry and Chemical Engineering, adds, "This work illustrates both the richness of chemical interactions between the hosts and their microbial communities, and Dr. Hsiao's scientific breadth and acumen in leading this work."


Serotonin is important for many aspects of human health, but Hsiao cautions that much more research is needed before any of these findings can be translated to the clinic.


"We identified a group of bacteria that, aside from increasing serotonin, likely has other effects yet to be explored," she says. "Also, there are conditions where an excess of peripheral serotonin appears to be detrimental."


Although this study was limited to serotonin in the gut, Hsiao and her team are now investigating how this mechanism might also be important for the developing brain. "Serotonin is an important neurotransmitter and hormone that is involved in a variety of biological processes. The finding that gut microbes modulate serotonin levels raises the interesting prospect of using them to drive changes in biology," says Hsiao.




Story Source:


The above story is based on materials provided by California Institute of Technology . The original article was written by Jessica Stoller-Conrad. Note: Materials may be edited for content and length.