大家好，本来捉妖想约CDer们来集思广益帮助今天过节的童鞋~~
可是在Speed第一篇中科学家说了头脑风暴是最糟糕的办法之一，大家还是好好学习，只能靠自己..
Speed 第二篇是讲婴儿对不上眼的病，今天过节的童鞋现在关注则为时过早了，just enjoy it~
Part I：Speaker
Do These Microbes Make Me Look Fat?
Mice that were implanted with the gut bacteria of obese humans gained more weight than mice that got microbes from thin people. Katherine Harmon reports.
[Rephrase 1]
[Dialog, 1:15]
Transcript hided
Our bodies are home to trillions of other organisms that influence our health—and probably our weight.
Researchers found that mice given gut microbes from obese humans became fatter than those that got microbes carried by slim folks. When the husky and lean mice shared microbes with each other, the bigger ones picked up some of the beneficial gut flora—and had improved metabolisms.
But this shift only occurred if the mice were on a high-fiber, low-saturated fat diet. If they were on a junk food diet, no improvement. The findings are in the journal Science. [Vanessa K. Ridaura et al, Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice]
It’s not clear how humans might remodel our microbial communities to change health or weight class. The mice in the study were raised in germ-free environments and had no native microbiomes of their own.
In people, so-called fecal transplants have been reserved for more severe conditions than a bulging belly. And probiotic products, such as yogurt, are minimally effective. But flat or fat, what your belly looks like on the outside might have a lot to do with what's on the inside.
—Katherine Harmon
Source: Scientific American
http://www.scientificamerican.com/podcast/episode.cfm?id=do-these-microbes-make-me-look-fat-13-11-09
Part II：Speed
Myna birds don’t benefit from brainstorming
by Sarah Zielinski 3:00pm, November 8, 2013
[Time 2]
If you put two birds together and gave them a problem, would they be any better at solving it than if they were alone? A study in Animal Behaviour of common mynas finds that not only are they no better at problem solving when in a pair than when on their own, the birds actually get a lot worse when put in a group.
Andrea S. Griffin and her research team from the University of Newcastle in Callaghan, Australia, began by using dog food pellets as bait to capture common mynas (a.k.a. the Indian mynah, Acridotheres tristis) from around Newcastle. Then they gave each of the birds an innovation test, consisting of a box containing a couple of drawers and some Petri dishes. To get to the food hidden in spots in the box, the birds would have to get creative and figure out how to open one of the four containers by doing things like levering up a lid or pushing open a drawer. The scientists then ranked the birds by innovative ability before pairing them up. Half the pairs consisted of a high-innovation and a low-innovation myna, and the other half were pairs of medium-innovation birds. Then the pairs each received an innovation test similar to the one with boxes. Another experiment tested the birds in same-sex groups of five.
On their own, 29 of 34 birds were able to access at least one container. But in pairs, only 15 of the 34 birds did so, and they took a lot longer. Performance dropped for both high- and medium-innovation birds, and it didn’t improve for the low-ranked ones, which had done so poorly the first time around that their results couldn’t get any worse. In groups of five, birds’ results fell even further: No mynas solved any of those tasks.
[302 words]
[Time 3]
The researchers had thought that mynas might be more innovative in a group, they write, in part because “social gatherings group together individuals with differing skills to bring to bear on a novel problem, thus facilitating problem solving.” That brainstorming behavior obviously did not happen. Instead, risk and competition probably were bigger players in this equation. The birds might not have wanted to try something creative if another bird were just going to steal their food. Or, the researchers say, the mynas might somehow avoid a potentially risky behavior when in a group – innovation might, for instance, increase predation risk.
The myna study reminded me of something written by Susan Cain, who is famous for her book Quiet: The Power of Introverts in a World That Can’t Stop Talking. She debunked the value of brainstorming sessions for humans. It may seem that putting together a group of smart people and asking them to get creative would result in more imaginative solutions than they would come up with on their own. But the truth is the opposite, as Cain noted in 2012 in the New York Times:
Brainstorming sessions are one of the worst possible ways to stimulate creativity…. Decades of research show that individuals almost always perform better than groups in both quality and quantity, and group performance gets worse as group size increases…. The reasons brainstorming fails are instructive for other forms of group work, too. People in groups tend to sit back and let others do the work; they instinctively mimic others’ opinions and lose sight of their own; and, often succumb to peer pressure.
It may have been unrealistic for the researchers in this bird study to expect mynas to act any differently than humans.
[288 words]
Source: Science News
https://www.sciencenews.org/blog/wild-things/myna-birds-don%E2%80%99t-benefit-brainstorming
Infants already look people in the eyes, but autism-spectrum disorders already begin to jeopardize this skill between two and six months after birth.
Autism symptoms seen in babies
by Ewen Callaway 06 November 2013
Infants that will later be diagnosed with the disorder begin to avoid eye contact at two months of age.
[Time 4]
Children with autism make less eye contact than others of the same age, an indicator that is used to diagnose the developmental disorder after the age of two years. But a paper published today in Nature1 reports that infants as young as two months can display signs of this condition, the earliest detection of autism symptoms yet.
If the small study can be replicated in a larger population, it might provide a way of diagnosing autism in infants so that therapies can begin early, says Warren Jones, research director at the Marcus Autism Center in Atlanta, Georgia.
Jones and colleague Ami Klin studied 110 infants from birth — 59 of whom had an increased risk of being diagnosed with autism because they had a sibling with the disorder, and 51 of whom were at lower risk. One in every 88 children has an autism spectrum disorder (ASD), according to the most recent survey by the US Centers for Disease Control and Prevention in Atlanta.
At ten regular intervals over the course of two years, the researchers in the new study showed infants video images of their carers and used eye-tracking equipment and software to track where the babies gazed.
“Babies come into the world with a lot of predispositions towards making eye contact,” says Jones. “Young babies look more at the eyes than at any part of the face, and they look more at the face than at any part of the body.”
[243 words]
[Time5]
Diagnoses made
Twelve children from the high-risk group were diagnosed with an ASD — all but two of them boys — and one male from the low-risk group was similarly diagnosed. Between two and six months of age, these children tended to look at eyes less and less over time. However, when the study began, these infants tended to gaze at eyes just as often as children who would not later develop autism.
Jones and Klin were surprised by that distinction, because they had expected that infants who go on to develop autism would make noticeably less eye contact from birth. But Jones says that the team’s finding points to a window for interventions aimed at treating autism: “It does give us hope, because it suggests that, were we able to identify children at this early time point in life, there’s a bit of a foundation we could begin to build on with treatments.”
Simon Baron-Cohen, an autism researcher at the University of Cambridge, UK, says that the new study of the ability to make eye contact is “remarkable in containing so many time points that the investigators can catch the time window when the atypical development of this skill begins to emerge”. The work has the potential to reveal how autism develops and could enable early detection and thus early intervention, Baron-Cohen adds.
Jones says that it is too early to use his team’s findings to diagnose autism. For that to happen, much larger population studies need to establish that a decline in gazing at eyes can accurately predict the development of autism. “That’s our long-term future objective,” he says.
[269 words]
Source: Nature
http://www.nature.com/news/autism-symptoms-seen-in-babies-1.14117
Driverless cars will travel around Milton Keynes in the U.K. by 2017.
Driverless cars coming to U.K. in 2017
By Virginia Harrison @vharrisoncnn November 8, 2013: 9:39 AM ET
For the first time, driverless cars will soon be making their way through the streets of the U.K. Slowly. [Time6]
The electric powered "pods" can carry 2 people, and will operate on designated pathways in the town of Milton Keynes, north of London.
Twenty driver-operated pods will be tested by 2015, before a fleet of 100 fully-automated vehicles are rolled out two years later.
The pods will travel at about 12 miles per hour and use sensors to avoid obstacles.
Similar vehicles have been operating at Heathrow Airport, taking passengers between the terminal and car park, since 2011.
It's the first time driverless cars will be used in a pedestrianized area in the U.K.
The government said Friday it will provide £1.5 million ($2.4 million) to help fund the project, which involves engineering firm Arup and the universities of Oxford and Cambridge.
Related: Musk says Tesla is at work on autopilot feature
Business Secretary Vince Cable said driverless cars have the potential to "cut congestion and pollution and improve road safety."
Carmakers are pouring money into self-driving vehicles that boast safety and efficiency features.
Japanese giant Nissan (NSANF) said automated cars could be rolling off its production lines by 2020. General Motors (GM, Fortune 500), Toyota (TM) and Germany's Audi, part of Volkswagen (VLKAY), are also working on driverless vehicles.
Google (GOOG, Fortune 500) too, wants a slice of the action. The internet giant has been developing the technology and testing its self-drive cars on public roads for years.
[229 words]
Source: CNNMoney
http://money.cnn.com/2013/11/08/technology/innovation/driverless-cars-uk/index.html
Part III： Obstacle
Is the enthusiasm of Big Business for Big Data causing a "brain drain" from science?
Are "Big Data" Sucking Scientific Talent into Big Business?
By John Horgan | November 8, 2013
[Paraphrase 7]
Over the last few years, we’ve heard a lot about how “Big Data”—which as far as I can tell is just data mining in a glossy new wrapper–are going to revolutionize science and help us create a better world. These claims strike me as all too familiar. They remind me of the hype generated in the 1980s by chaos and in the 1990s by complexity (which was just chaos in a glossy new wrapper). Chaos and complexity enthusiasts promised (and are still promising) that ever-more-powerful computers plus jazzy new software and math were going to crack riddles that resisted more traditional scientific methods.
Advances in data-collection, computation and search programs have led to impressive gains in certain realms, notably speech recognition, language-translation and other traditional problems of artificial intelligence. So some of the enthusiasm for Big Data may turn out to be warranted. But in keeping with my crabby, glass-half-empty persona, in this post I’ll suggest that Big Data might be harming science, by luring smart young people away from the pursuit of scientific truth and toward the pursuit of profits.
My attention was drawn to this issue by a postdoc in neuroscience, whose research involves lots of data crunching. He prefers to remain anonymous, so I’ll call him Fred. After reading my recent remarks on the shakiness of the scientific literature, he wrote me to suggest that I look into a trend that could be exacerbating science’s woes.
“I think the big science journalism story of 2014 will be the brain drain from science to industry ‘data science,’” Fred writes. “Up until a few years ago, at least in my field, the best grad students got jobs as professors, and the less successful grad students took jobs in industry. It is now the reverse. It’s a real trend, and it’s a big deal. One reason is that science tends not to reward the graduate students who are best at developing good software, which is exactly what science needs right now…
“Another reason, especially important for me, is the quality of research in academia and in industry. In academia, the journals tend to want the most interesting results and are not so concerned about whether the results are true. In industry data science, [your] boss just wants the truth. That’s a much more inspiring environment to work in. I like writing code and analyzing data. In industry, I can do that for most of the day. In academia, it seems like faculty have to spend most of their time writing grants and responding to emails.”
Fred sent me a link to a blog post, “The Big Data Brain Drain: Why Science is in Trouble,” that expands on his concerns. The blogger, Jake VanderPlas, a postdoc in astrophysics at the University of Washington, claims that Big Data is, or should be, the future of science. He writes that “in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research… From particle physics to genomics to biochemistry to neuroscience to oceanography to atmospheric physics and everywhere in-between, research is increasingly data-driven, and the pace of data collection shows no sign of abating.”
Vanderplas suggests that the growing unreliability of peer-reviewed scientific results, to which I alluded in my last post, may stem in part from the dependence of many research results on poorly written and documented software. The “crisis of irreproducibility” could be ameliorated, VanderPlas contends, by researchers who are adept at data-analysis and can share their methods with others.
The problem, VanderPlas says, is that academia is way behind Big Business in recognizing the value of data-analysis talent. “The skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree. The unfortunate result is that some of the most promising upcoming researchers are finding no place for themselves in the academic community, while the for-profit world of industry stands by with deep pockets and open arms.”
VanderPlas and Fred, who are are apparently software whizzes themselves, perhaps overstate the scientific potential of data crunching just a tad. And Fred’s aforementioned claim that industry “just wants the truth” strikes me as almost comically naïve. [See Fred's clarification below.] For businesses, peddling products trumps truth–which makes the brain drain described by Fred and VanderPlas even more disturbing.
Fred is a case in point. Increasingly despondent about his prospects in brain research, he signed up for training from the Insight Data Science, which trains science Ph.D.s in data-manipulation skills that are desirable to industry (and claims to have a 100 percent job placement record). The investment paid off for Fred, who just got a job at Facebook.
Should “Big Data” be treated as plural or singular? I polled my students, and they said plural, so I went with plural.
Re his comment about industry bosses wanting “truth,” “Fred” just emailed me this clarification: “I think there is a distinction, which I perhaps should have made clearer, between ‘marketing’ and ‘analytics.’ When it comes to marketing a product to consumers, I agree it’s pretty obvious that business incentives are not aligned with truth telling. No one disputes that. But when it comes to the business’s internal ‘analytics’ team, the incentives are very aligned with truth telling. Analytics teams do stuff like: determining how users are interacting with the product, measuring trends in user engagement or sales, analyzing failure points in the product. This is the type of work that most data scientists do.”
A couple of afterthoughts on this topic: First, Lee Vinsel, my Stevens colleague and former friend, points out in a comment below that industry has long lured scientists away from academia with promises of filthy lucre and freedom from the grind of tenure-and-grant-chasing. Yup. Wall Street “quants” are just one manifestation of this age-old phenomenon. So what’s new about the Big Data Brain Drain? Does it differ in degree or kind from previous academia-to-business brain drains? Good questions, Lee. I have no idea, but I bet Big Data can provide the answer! (Unless of course it’s subject to some sort of Godelian limit on self-analysis.)
Second, a fascinating implication of the rise of Big Data is that science may increasingly deliver power—that is, solutions to problems—without understanding. Big Data can, for example, help artificial intelligence researchers build programs that play chess, recognize faces and converse without knowing how human brains accomplish these tasks. The same could be true of problems in biology, physics and other fields. If science doesn’t yield insight, is it really science? (For a smart rebuttal of the notion that Big Data could bring about “the end of theory,” see the smart blog post mentioned below by Sabine Hossenfelder.)
[1155 words]
Source:Science American
http://blogs.scientificamerican.com/cross-check/2013/11/08/are-big-data-sucking-scientific-talent-into-big-business/
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