Saturday, September 24, 2016

ScienceBlogs Channel : Physical Science

ScienceBlogs Channel : Physical Science


Is cold fusion feasible? Or is it a fraud? (Synopsis) [Starts With A Bang]

Posted: 23 Sep 2016 07:01 AM PDT

"Between cold fusion and respectable science there is virtually no communication at all. …because the Cold-Fusioners see themselves as a community under siege, there is little internal criticism. Experiments and theories tend to be accepted at face value, for fear of providing even more fuel for external critics, if anyone outside the group was bothering to listen. In these circumstances, crackpots flourish, making matters worse for those who believe that there is serious science going on here." –David Goodstein

The dream of free, unlimited, clean energy depends only on our ability to find a reaction that's safe, efficient, with abundant reactants, that produces more energy than is required to activate the reaction. Our Sun is a prime example of this, as all it requires is hydrogen — the most abundant element in the Universe — and it produces, through nuclear fusion, an incredible amount of energy each and every second.

A fusion device based on magnetically confined plasma. Image credit: PPPL management, Princeton University, the Department of Energy, from the FIRE project at http://fire.pppl.gov/.

A fusion device based on magnetically confined plasma. Image credit: PPPL management, Princeton University, the Department of Energy, from the FIRE project at http://fire.pppl.gov/.

But an even bigger dream would be to harness this type of fusion reaction here on Earth. While inertial confinement and magnetic confinement fusion, the two most common "hot fusion" scenarios on Earth, have yet to reach the fabled break-even point, there are claims that cold fusion, or Low-Energy Nuclear Reactions (LENR), has done exactly that. Should those claims be taken seriously?

Andrea Rossi and Sterling Allan, during a 2011 demonstration of the E-Cat. Image credit: Sterling D. Allan with Hank Mills of Pure Energy Systems News. Via http://pesn.com/2011/10/28/9501940_1_MW_E-Cat_Test_Successful/.

Andrea Rossi and Sterling Allan, during a 2011 demonstration of the E-Cat. Image credit: Sterling D. Allan with Hank Mills of Pure Energy Systems News. Via http://pesn.com/2011/10/28/9501940_1_MW_E-Cat_Test_Successful/.

Perhaps. And if we want to do it right, here's exactly the kind of scientific robustness we'd require in order to do so!

ScienceBlogs Channel : Medicine & Health

ScienceBlogs Channel : Medicine & Health


Report: New York City’s paid sick leave law had virtually no negative impact [The Pump Handle]

Posted: 23 Sep 2016 09:43 AM PDT

Despite all the concern about shuttered businesses, fired employees and lost profits, a new report has found that New York City's paid sick leave law was pretty much a "non-event" for most employers.

Released this month, "No Big Deal: The Impact of New York City's Paid Sick Law on Employers" reported that in the years following the 2014 implementation of the paid sick leave law, the great majority of businesses surveyed said the law had no effect on overall costs. The report, authored by researchers at the Center for Economic and Policy Research and the Murphy Institute at the City University of New York, is based on telephone surveys of more than 350 random New York City businesses with five or more employees from October 2015 to March 2016. Researchers also conducted on-site interviews with 30 businesses.

New York City's Earned Sick Time Act covered private-sector companies and nonprofits with five or more workers, giving workers the opportunity to accrue one hour of paid sick leave for every 30 hours worked. Employees at businesses with four or fewer workers are entitled to unpaid sick leave. Report authors Eileen Appelbaum and Ruth Milkman write:

There is no evidence that the earned sick days law has been a "job killer." On the contrary…job growth continued in New York City in the years following implementation. Moreover, a year and a half after the law went into effect, the vast majority (86 percent) of the employers we surveyed expressed support for the new law. Another example of an employer who had worried about the potential negative effects of the law and then changed his mind is Tony Juliano, former local chamber of commerce official and general manager of XES Lounge. The new law "hasn't had the kind of impact that I worried about. Not even close," he declared. Although he had worried about potential job losses before the law was implemented, "I don't know anybody that has actually had to cut people because of this policy. I also thought there might be abuse. But in our case there was absolutely no abuse."

Researchers found that nearly 85 percent of survey respondents said the new law had no effect on their overall business costs, with less than 2 percent actually reporting a decline in costs. Among the 14 percent that did say the sick leave law impacted their bottom line, 9 percent reported a cost increase of less than 3 percent, and only 3 percent reported an increase of 3 percent or more. One reason for the limited cost impact was that most employers were able to cover the duties of sick workers. Another reason is that workers used paid sick leave in a fairly limited way — in fact, the survey found that only about three-quarters of workers represented had taken any paid sick leave, while a quarter had taken no paid sick leave in the previous year. Among workers who did use paid sick leave, the average was about four days in an entire year.

Regarding the concern that workers would abuse sick leave — a particularly offensive argument against such ordinances — the New York City survey found that 98 percent of employers reported no such abuse. Less than 1 percent — specifically, 0.3 percent — reported more than three cases of workers abusing sick leave.

On to other potential business impacts, more than 91 percent of employers surveyed reported no reductions in hiring, 97 percent said they didn't reduce worker hours, and about 94 percent did not raise prices. Less than 3 percent said they reduced operating hours and less than 1 percent reduced the "quality" of their services. More than 94 percent said the law had no effect on productivity — in fact, 2 percent said productivity increased.

In regard to awareness about the new sick leave rights, 18 percent of businesses with five or more workers indicated that they had not "read, heard or seen any information" about the law. That, the report authors wrote, could explain why 13 percent of surveyed businesses reported not providing paid sick days.

"Overall, the new law, which extended paid sick days to a million-and-a-half workers in the city who did not have access to them before, was a 'non-event' for most employers," the report concluded.

Click here to download a full copy of the paid sick leave report.

Kim Krisberg is a freelance public health writer living in Austin, Texas, and has been writing about public health for nearly 15 years.

Friday, September 23, 2016

ScienceBlogs Channel : Medicine & Health

ScienceBlogs Channel : Medicine & Health


Microsoft vows to “solve cancer” in a decade. Hubris ensues. [Respectful Insolence]

Posted: 23 Sep 2016 12:00 AM PDT

If there's one thing that irritates me more than government agencies making bold proclamations about making progress in cancer but not providing sufficient funding to have even a shot of realizing such ambitions (I'm talking to you, Cancer Moonshot), it's people in other disciplines that are not cancer biology making bold proclamations about how they're going to "solve" cancer or coming up with new "theories" to explain cancer. That's not to say that cancer research can't benefit from new perspectives from different sciences and disciplines can bring or new ways of thinking about the problem of cancer. I might seem arrogant, but, whether I am arrogant or not, I'm not that arrogant. What irritates me so much is that these scientists who are not cancer biologists inevitably come across as arrogantly overconfident, not to mention as condescending. The attitude seems to be: How come you cancer biologists never thought of this before? How come you never saw this before? Of course, in some cases, cancer biologists did think of this before and did see this before, but ended up rejecting it because it didn't fit with the evidence.

Perhaps the best example of this occurred a few years ago when two astrophysicists, Paul Davies and Charley Lineweaver, decided to jump into the cancer research business with a concept they called atavism as a cause for cancer. Basically, the idea was that cancer is an evolutionary "throwback" to the dawn of intracellular life. Of course, having admittedly "no prior knowledge of cancer," Lineweaver and Davies had stumbled upon a very old idea without realizing how old it was. Indeed, they seemed to think they were the first to have thought of it. As I pointed out at the time, there can be advantages to brining in scientists from different disciplines, but one consequence of doing so is that they often don't know which hypotheses that have been considered before and rejected based on the evidence and therefore frequently act as though they were the first to have thought of a new hypothesis. As blogger Darren Saunders put it at the time, Lineweaver and Davies remind one of a doctor who reinvented calculus.

Or this:

Earlier this week, I sensed a similar, but related phenomenon when I started seeing headlines like this one in The Independent, Microsoft will 'solve' cancer within the next 10 years by treating it like a computer virus, says company. My first reaction when I read that headline was stunned disbelief that anyone could be so arrogantly ignorant as to make a statement that definitive without apparently knowing much about cancer—or biology for that matter. To be fair, I decided to read the article, because I know that headlines don't always match what was actually said; let's just say they tend to strip nuance from the statement.

Silly me:

Microsoft says it is going to "solve" cancer in the next 10 years.

The company is working at treating the disease like a computer virus, that invades and corrupts the body's cells. Once it is able to do so, it will be able to monitor for them and even potentially reprogramme them to be healthy again, experts working for Microsoft have said.

The company has built a "biological computation" unit that says its ultimate aim is to make cells into living computers. As such, they could be programmed and reprogrammed to treat any diseases, such as cancer.

And:

"The field of biology and the field of computation might seem like chalk and cheese," Chris Bishop, head of Microsoft Research's Cambridge-based lab, told Fast Company. "But the complex processes that happen in cells have some similarity to those that happen in a standard desktop computer."

As such, those complex processes can potentially be understood by a desktop computer, too. And those same computers could be used to understand how cells behave and to treat them.

Yes, there is a resemblance between cancer and computing in much the same way that counting on your fingers resembles a supercomputer. The hubris of this project is unbelievably. Seriously> I thought antivaccinationists demonstrated the arrogance of ignorance, but they've got nothing on Microsoft. (Of course, it is Microsoft.) My reaction was virtually identical to Derek Lowe's, only with more…Insolence. Indeed, he perfectly characterized the attitude of people like Linweaver, Davies, and now Bishop as a "Gosh darn it fellows, do I have to do everything myself?" attitude. Yes, those of us in cancer research and who take care of cancer patients do tend to get a bit…testy…when someone like Bishop waltzes onto the scene and proclaims to breathless headlines that he's going to solve cancer in a decade because he has an insight that you stupid cancer biologists never thought of before: The cell is just a computer, and cancer is like a computer virus. (Hey, you know, viruses cause some cancers; so why not make the analogy to computer viruses?)

Basically, what Microsoft is doing is yet another machine learning approach to cancer. Don't get me wrong. I don't have any objection to computational approaches to biology, cancer, and the treatment of disease. Quite the contrary. What chaps my posterior here isn't necessarily the concept. If you hose off the many layers of hubris and bullshit behind Microsoft's initiative, there might be a germ of a good idea there. In fact, if you strip the bullshit away, you'll see that even Microsoft seems to realize that it's overpromising:

Microsoft says that solution could be with us within the next five or ten years.

Andrew Philips, who leads Microsoft's biological computation group, told The Telegraph that in as little as five years it hopes to be able to develop a system for detecting problems. "It's long term, but … I think it will be technically possible in five to ten years' time to put in a smart molecular system that can detect disease."

Um, I have news for you. There are lots of research groups who've been working on this sort of problem for a long time in clinical medicine and oncology. Indeed, check out this review article, which shows that, while there aren't a huge number of scientific papers being published each year on machine learning tools to predict cancer and cancer recurrence, there are a respectable number, and that number is growing. Such tools are being applied to genomic and proteomic data—and have been for years. This is not a new thing. And notice what Andrew Phillips says: In five-to-ten years maybe he can come up with a smart molecular system to detect disease. Those of you who've read my many posts about overdiagnosis and overtreatment know that detecting cancer at ever earlier stages will not necessarily result in better outcomes or improved survival. It will, however, make overdiagnosis (i.e., the detection of subclinical disease that would never progress to cause a problem within the lifetime of the patient) much more likely, and overdiagnosis always leads to some degree of overtreatment. (See breast cancer and prostate cancer.) We've been down this road before.

Again, don't get me wrong. Maybe Microsoft has a new way of applying machine learning to cancer. Maybe it has new ways of modeling the cellular processes that lead to cancer. If so, its software engineers would do well to talk less and code more, instead of saying something like this:

"The field of biology and the field of computation might seem like chalk and cheese," Chris Bishop, head of Microsoft Research's Cambridge-based lab, told Fast Company. "But the complex processes that happen in cells have some similarity to those that happen in a standard desktop computer."

As such, those complex processes can potentially be understood by a desktop computer, too. And those same computers could be used to understand how cells behave and to treat them.

If that were possible, then those computers wouldn't only be able to understand why cells behave as they do and when they might be about to become cancerous. They'd also be able to trigger a response within a cell, reversing its decision and reprogramming it so that it is healthy again.

Model intracellular processes leading to cancer and look for ways to reverse the process? Well, golly gee! Why didn't cancer researchers think of that? It's only what they've been trying to do for the last 100 years! What is systems biology but doing exactly that, using genomic, proteomic, and metabolomic data? What is "precision medicine" but almost exactly this? After over 15 years of having the tools to analyze the expression of every gene in a cell simultaneously and the computational power to model it, we're only just scratching the surface of systems biology and computational biology, and Microsoft is going to "solve" this problem in five to ten years. Would that it were so easy! As another blogger put it, it "would be great if genetics were just one big Intel Core I7 that one could program in binary assembly language after decoding its instruction set, but I have doubts it’s that simple."

Here's the thing. Cancer biology like all biology, is probabilistic, not deterministic. Computers are deterministic. Their instructions consist of binary strings of 0s and 1s. True, computers can model probabilistic situations, the number of possible outcomes rapidly becomes incredibly large, and in cancer biology the number of potential interactions is astronomical. Worse, we don't understand many of the alterations in cancer cells. As I've pointed out many times before, cancer cells are really messed up, and, worse, cancers themselves, thanks to the power of evolution, are made of a very heterogeneous bunch of cells with a very messed up genome. That's why cancer researches like Derek Lowe (and I) get a bit testy reading this sort of thing:

I have beaten on this theme many times on the blog, so for those who haven't heard me rant on the subject, let me refer you to this post and the links in it. Put shortly – and these sorts of stories tend to put actual oncology researchers in a pretty short mood – the cell/computer analogy is too facile to be useful. And that goes, with chocolate sprinkles on it, for all the subsidiary analogies, such as DNA/source code, disease/bug, etc. One one level, these things do sort of fit, but it's not a level that you can get much use out of. DNA is much, much messier than any usable code ever written, and it's messier on several different levels and in a lot of different ways. These (which include the complications of transcriptional regulation, post-transcriptional modification, epigenetic factors, repair mechanisms and mutation rates, and much, much, more), have no good analogies (especially when taken together) in coding. And these DNA-level concerns are only the beginning! That's where you start working on an actual therapy; that's what we call "Target ID", and it's way, way back in the process of finding a drug. So many complications await you after that – you can easily spend your entire working life on them, and many of us have.

And I haven't even mentioned the role of processes like epigenetics, the immune system, and all the other myriad biological processes that contribute to cancer. Nor have I mentioned that using machine learning on the medical literature, as also proposed by Microsoft, will be limited by the fact that there are a lot of crappy studies in the literature. Then there's the consideration that the analogy itself is suspect. Computers are designed, programmed, and debugged by human beings. Organisms and cancers are the result of millions of years of biological evolution.

I'll leave Microsoft with this analogy, quoting Douglas Adams in The Hitchhikers' Guide to the Galaxy, "Space is big. You just won’t believe how vastly, hugely, mind- bogglingly big it is. I mean, you may think it’s a long way down the road to the chemist’s, but that’s just peanuts to space." Well, cancer is complicated. Microsoft will find out how vastly, hugely, mindbogglingly complicated it is. I mean, you might think it's complicated to trick people into upgrading to Windows 10, but that's peanuts compared to cancer.

An analogy, and a relevant xkcd cartoon:

Yep that about sums it up.

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