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Scientists know that certain bits of DNA on the end of chromosomes called “telomeres” shorten each time the cell divides, playing a role in cell aging and eventual cell death

New research has uncovered how stress levels and diet can affect the biological age of an organism as opposed to chronological age, or calendar years.

For years scientists thought that sequencing DNA would uncover the “gene-for” almost everything, unraveling the mysteries of disease and resulting in new drugs and gene-specific treatments. It didn’t exactly pan out that way.

So to uncover the calculus within the genome, it might take some looking beyond the genome. Genes may contribute a few elements to the inner clock, but they interact within a larger scaffolding of cell processes and environmental factors.

Just looking at that giant 'Ogalalla' state and knowing it contains as many people as the 'Atlanta' state, I thump my head thinking of how the demographics, cultural values and natural landscape might be newly described and compared.

But, perhaps the most devastating impact on traditional punditry: politics and campaigning has a relatively small impact on elections. According to Silver’s model, Obama had a strong likelihood of winning several months before the election. Elections favor incumbents and Romney was an uncharismatic opponent, who wasn’t all that well liked even within his own party. Other influential factors, such as the economy, are completely outside the control of campaigns. The economy picked up before the election. Any conservative challenger had an uphill battle.

When it comes to the confidence we have in our beliefs — what can be expressed in terms of probability — we can’t just make up any number we want. There’s only one consistent way to handle those degrees in beliefs.

In the strictest sense, of course, this requires a bit of mathematical knowledge. But Greenberg says there’s still an easy way to use this principle in daily life — and one that can be converted to plain English.

“It’s important to note that the idea here is not to answer the question in a precise way — like saying that it’s 3.2 times more likely — rather, it’s to get a rough sense. Is it a high number, a modest number, or a small number?”

“Much of the time people will automatically try to shoot down evidence, but you can get evidence for things that are not true. Just because you have evidence doesn’t mean you should change your mind. But it does mean that you should change your degree of belief.”

Greenberg also describes Representativeness Heuristic in which people tend to look at how similar things are.

Greenberg also says that we should shy away from phrases like, “I believe,” or “I don’t believe.” “That’s the wrong way to frame it,” he says. “We should think about things in terms of how probable they are. You almost never have anything close to perfect certainty.”

“Let’s say you believe that your nutrition supplement works,” he told us, “Then you get a small amount of evidence against it working, and you completely write that evidence off because you say, ‘well, I still believe it works because it’s just a small amount of evidence.’ But then you get more evidence that it doesn’t work. If you were an ideal reasoner, you’d see that accumulation of evidence, and every time you get that evidence, you should believe less and less that the nutritional supplements are actually working.” Eventually, says Greenberg, you end up tipping things so that you no longer believe. But instead, we end up never changing our mind.

Lastly, he also says that probabilities can depend on the observer — what is a kind of probability relativity. We all have access to different information, so different people should assign different rates of probability to different things based on different sets of evidence.

Lehrer’s neuroscience in Imagine contains some obvious elementary errors—arguably more dangerous than a couple of manufactured Bob Dylan quotes. While Gladwell talks about our amazing powers of cognition in Blink, he doesn’t venture to give a detailed account of how these processes occur in the brain.

Our blogging culture is partly to blame for this. The demand of our 24/7 news cycle, first created by cable television, and now carried on by minute-by-minute updates on the Internet creates constant demand for new information that never quite satisfies the insatiable appetite of the limitless Web.

What a newspaper or magazine would call ‘A model to help cure cancer,’ for instance, could realistically only be “an adaptation of a previous model to simulate cancer tissue in order to determine if it can be used to study cancer cells and eventually help find a cure.”Want to try that for a headline? Exactly.Confirming a hypothesis or a hunch with empirical evidence is the very essence of science, whereas in journalism—like much of the humanities—theories and schools of thought can rest on their own. However, science journalism, like science, needs to be rooted in fact and observation, without which it would lose its basis.

The problem with these examples is not that they are untrue, but the helplessness and futility of the advice. What are you to do to make these “breakthrough” moments happen? Nothing, apparently, except wait for them.In a journalistic equivalent of motivational speeches, these erudite writers hail subconscious processes in the brain that we have almost no control over, stopping just short of saying, “it will happen if you believe.”

“I am certainly not saying that Darwinian ideas are wrong,” he explained. “On the contrary, I am just saying that from the perspective of the physics, you might call Darwinian evolution a special case of a more general phenomenon.”

Eugene Shakhnovich, a professor of chemistry, chemical biology and biophysics at Harvard University, are not convinced. “Jeremy’s ideas are interesting and potentially promising, but at this point are extremely speculative, especially as applied to life phenomena,” Shakhnovich said.

England’s theoretical results are generally considered valid. It is his interpretation — that his formula represents the driving force behind a class of phenomena in nature that includes life — that remains unproven. But already, there are ideas about how to test that interpretation in the lab.

“He’s trying something radically different,” said Mara Prentiss, a professor of physics at Harvard who is contemplating such an experiment after learning about England’s work. “As an organizing lens, I think he has a fabulous idea. Right or wrong, it’s going to be very much worth the investigation.”

Hot things cool down, gas diffuses through air, eggs scramble but never spontaneously unscramble; in short, energy tends to disperse or spread out as time progresses.

It increases as a simple matter of probability: There are more ways for energy to be spread out than for it to be concentrated.

cup of coffee and the room it sits in become the same temperature, for example. As long as the cup and the room are left alone, this process is irreversible. The coffee never spontaneously heats up again because the odds are overwhelmingly stacked against so much of the room’s energy randomly concentrating in its atoms.

A plant, for example, absorbs extremely energetic sunlight, uses it to build sugars, and ejects infrared light, a much less concentrated form of energy. The overall entropy of the universe increases during photosynthesis as the sunlight dissipates, even as the plant prevents itself from decaying by maintaining an orderly internal structure.

In Schrödinger’s day, they could solve the equations of thermodynamics only for closed systems in equilibrium.

Jarzynski and Crooks showed that the entropy produced by a thermodynamic process, such as the cooling of a cup of coffee, corresponds to a simple ratio: the probability that the atoms will undergo that process divided by their probability of undergoing the reverse process (that is, spontaneously interacting in such a way that the coffee warms up).

Using Jarzynski and Crooks’ formulation, he derived a generalization of the second law of thermodynamics that holds for systems of particles with certain characteristics: The systems are strongly driven by an external energy source such as an electromagnetic wave, and they can dump heat into a surrounding bath.

Having an overarching principle of life and evolution would give researchers a broader perspective on the emergence of structure and function in living things, many of the researchers said. “Natural selection doesn’t explain certain characteristics,” said Ard Louis, a biophysicist at Oxford University, in an email. These characteristics include a heritable change to gene expression called methylation, increases in complexity in the absence of natural selection, and certain molecular changes Louis has recently studied.

Then came the reversal: from the 1920s to 1980, it shrank back to levels not seen since the mid-19th century.

Does observing just one and a half cycles really show that there is a regular pattern in the dynamics of inequality? No, by itself it doesn’t.

In our book Secular Cycles (2009), Sergey Nefedov and I applied the Phillips approach to England, France and Russia throughout both the medieval and early modern periods, and also to ancient Rome.

And the cycles of inequality were an integral part of the overall motion.

Cycles in the real world are chaotic, because complex systems such as human societies have many parts that are constantly moving and influencing each other.

Understanding (and perhaps even forecasting) such trend-reversals is at the core of the new discipline of cliodynamics, which looks at history through the lens of mathematical modelling.

One of the most important forces affecting the labour supply in the US has been immigration

Another reason why the labour supply in the US went up in the 19th century is, not to put too fine a point on it, sex.

This connection between the oversupply of labour and plummeting living standards for the poor is one of the more robust generalisations in history.

The population of England doubled between 1150 and 1300.

causing the population of London to balloon from 20,000 to 80,000.

fourfold increase in food prices and a halving of real wages.

when a series of horrible epidemics, starting with the Black Death of 1348, carried away more than half of the population, the same dynamic ran in reverse.

Much the same pattern can be seen during the secular cycle of the Roman Principate.

Naturally, the conditions affecting the labour supply were different in the second half of the 20th century in the US. An important new element was globalisation

Falling wages isn’t the only reason why labour oversupply leads to inequality. As the slice of the economic pie going to employees diminishes, the share going to employers goes up.

And so in 13th-century England, as the overall population doubles, we find landowners charging peasants higher rents and paying less in wages: the immiseration of the general populace translates into a Golden Age for the aristocrats.

the number of knights and esquires tripled between 1200 and 1300.

Only the gentry drank wine, and around 1300, England imported 20,000 tuns or casks of it from France per year. By 1460, this declined to only 5,000.

In the US between around 1870 and 1900, there was another Golden Age for the elites, appropriately called the Gilded Age.

And just like in 13th-century England, the total number of the wealthy was shooting up. Between 1825 and 1900, the number of millionaires (in constant 1900 dollars) went from 2.5 per million of the population to 19 per million.

On the face of it, this is a wonderful testament to merit-based upward mobility. But there are side effects. Don’t forget that most people are stuck with stagnant or falling real wages. Upward mobility for a few hollows out the middle class and causes the social pyramid to become top-heavy.

As the ranks of the wealthy swell, so too do the numbers of wealthy aspirants for the finite supply of political positions.

The civil wars of the first century BC, fuelled by a surplus of politically ambitious aristocrats, ultimately caused the fall of the Republic and the establishment of the Empire.

very intense status rivalry

Archaeology confirms a genuine and dramatic shift towards luxury.

Social Darwinism took off during the original Gilded Age, and Ayn Rand (who argued that altruism is evil) has grown astonishingly popular during what we might call our Second Gilded Age.

Labour markets are especially sensitive to cultural norms about what is fair compensation, so prevailing theories about inequality have practical consequences.

the US political system is much more attuned to the wishes of the rich than to the aspirations of the poor.

Inverse relationship between well-being and inequality in American history. The peaks and valleys of inequality (in purple) represent the ratio of the largest fortunes to the median wealth of households (the Phillips curve). The blue-shaded curve combines four measures of well-being: economic (the fraction of economic growth that is paid to workers as wages), health (life expectancy and the average height of native-born population), and social optimism (the average age of first marriage, with early marriages indicating social optimism and delayed marriages indicating social pessimism).

In some historical periods it worked primarily for the benefit of the wealthy. In others, it pursued policies that benefited the society as a whole. Take the minimum wage, which grew during the Great Compression era and declined (in real terms) after 1980.

The top marginal tax rate was 68 per cent or higher before 1980; by 1988 it declined to 28 per cent.

In one era, government policy systematically favoured the majority, while in another it favoured the narrow interests of the wealthy elites. This inconsistency calls for explanation.

Unequal societies generally turn a corner once they have passed through a long spell of political instability.

We see this shift in the social mood repeatedly throughout history — towards the end of the Roman civil wars (first century BC), following the English Wars of the Roses (1455-85), and after the Fronde (1648-53), the final great outbreak of violence that had been convulsing France since the Wars of Religion began in the late 16th century.

The worst incident in US labour history was the West Virginia Mine War of 1920—21, culminating in the Battle of Blair Mountain.

The US, in short, was in a revolutionary situation, and many among the political and business elites realised it.

The US elites entered into an unwritten compact with the working classes. This implicit contract included the promise that the fruits of economic growth would be distributed more equitably among both workers and owners. In return, the fundamentals of the political-economic system would not be challenged (no revolution).

while making such ‘categorical inequalities’ worse, the compact led to a dramatic reduction in overall economic inequality.

The co-operating group was mainly native-born white Protestants. African-Americans, Jews, Catholics and foreigners were excluded or heavily discriminated against.

When Barry Goldwater campaigned on a pro-business, anti-union and anti-big government platform in the 1964 presidential elections, he couldn’t win any lasting support from the corporate community. The conservatives had to wait another 16 years for their triumph.

It is no coincidence that the life of Communism (from the October Revolution in Russia in 1917 to the fall of the Berlin Wall in 1989) coincides almost perfectly with the Great Compression era.

when Communism collapsed, its significance was seriously misread. It’s true that the Soviet economy could not compete with a system based on free markets plus policies and norms that promoted equity.

all of these trends are part of a complex and interlocking system. I don’t just mean that everything affects everything else; that would be vacuous.

Cliodynamics also explains why historical reversals in such diverse areas as economics and culture happen at roughly similar times. The theory of secular cycles was developed using data from historical societies, but it looks like it can provide answers to questions about our own society.

In other words, we are rapidly approaching a historical cusp, at which the US will be particularly vulnerable to violent upheaval. This prediction is not a ‘prophecy’. I don’t believe that disaster is pre-ordained, no matter what we do. On the contrary, if we understand the causes, we have a chance to prevent it from happening. But the first thing we will have to do is reverse the trend of ever-growing inequality.

In the 1950s, when modern academic research took shape after its successes in the second world war, it was still a rarefied pastime.

Nowadays verification (the replication of other people’s results) does little to advance a researcher’s career. And without verification, dubious findings live on to mislead.

And as more research teams around the world work on a problem, the odds shorten that at least one will fall prey to an honest confusion between the sweet signal of a genuine discovery and a freak of the statistical noise.

The failure to report failures means that researchers waste money and effort exploring blind alleys already investigated by other scientists.

When a prominent medical journal ran research past other experts in the field, it found that most of the reviewers failed to spot mistakes it had deliberately inserted into papers, even after being told they were being tested.

One priority should be for all disciplines to follow the example of those that have done most to tighten standards. A start would be getting to grips with statistics, especially in the growing number of fields that sift through untold oodles of data looking for patterns.

Some government funding agencies, including America’s National Institutes of Health, which dish out $30 billion on research each year, are working out how best to encourage replication.

The human world we build is established in mind and heart and spirit.  It will come down to what we hold sacred. Yes those words spirit and sacred must be included however you choose to define it.

Maybe sometimes it’s the questions that are biased, not the answers,” he said, flashing a friendly smile.

That question has been central to Ioannidis’s career. He’s what’s known as a meta-researcher, and he’s become one of the world’s foremost experts on the credibility of medical research.

He charges that as much as 90 percent of the published medical information that doctors rely on is flawed.

“I take all the researchers who visit me here, and almost every single one of them asks the tree the same question,” Ioannidis tells me, as we contemplate the tree the day after the team’s meeting. “‘Will my research grant be approved?’” He chuckles, but Ioannidis (pronounced yo-NEE-dees) tends to laugh not so much in mirth as to soften the sting of his attack. And sure enough, he goes on to suggest that an obsession with winning funding has gone a long way toward weakening the reliability of medical research.

“I assumed that everything we physicians did was basically right, but now I was going to help verify it,” he says. “All we’d have to do was systematically review the evidence, trust what it told us, and then everything would be perfect.” It didn’t turn out that way. In poring over medical journals, he was struck by how many findings of all types were refuted by later findings. Of course, medical-science “never minds” are hardly secret. And they sometimes make headlines, as when in recent years large studies or growing consensuses of researchers concluded that mammograms, colonoscopies, and PSA tests are far less useful cancer-detection tools than we had been told; or when widely prescribed antidepressants such as Prozac, Zoloft, and Paxil were revealed to be no more effective than a placebo for most cases of depression; or when we learned that staying out of the sun entirely can actually increase cancer risks; or when we were told that the advice to drink lots of water during intense exercise was potentially fatal; or when, last April, we were informed that taking fish oil, exercising, and doing puzzles doesn’t really help fend off Alzheimer’s disease, as long claimed. Peer-reviewed studies have come to opposite conclusions on whether using cell phones can cause brain cancer, whether sleeping more than eight hours a night is healthful or dangerous, whether taking aspirin every day is more likely to save your life or cut it short, and whether routine angioplasty works better than pills to unclog heart arteries.

“I realized even our gold-standard research had a lot of problems,” he says.

This array suggested a bigger, underlying dysfunction, and Ioannidis thought he knew what it was. “The studies were biased,” he says. “Sometimes they were overtly biased. Sometimes it was difficult to see the bias, but it was there.” Researchers headed into their studies wanting certain results—and, lo and behold, they were getting them. We think of the scientific process as being objective, rigorous, and even ruthless in separating out what is true from what we merely wish to be true, but in fact it’s easy to manipulate results, even unintentionally or unconsciously. “At every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded,” says Ioannidis. “There is an intellectual conflict of interest that pressures researchers to find whatever it is that is most likely to get them funded.”

He chose to publish one paper, fittingly, in the online journal PLoS Medicine, which is committed to running any methodologically sound article without regard to how “interesting” the results may be. In the paper, Ioannidis laid out a detailed mathematical proof that, assuming modest levels of researcher bias, typically imperfect research techniques, and the well-known tendency to focus on exciting rather than highly plausible theories, researchers will come up with wrong findings most of the time.

The article spelled out his belief that researchers were frequently manipulating data analyses, chasing career-advancing findings rather than good science, and even using the peer-review process—in which journals ask researchers to help decide which studies to publish—to suppress opposing views.

Ioannidis was putting his contentions to the test not against run-of-the-mill research, or even merely well-accepted research, but against the absolute tip of the research pyramid. Of the 49 articles, 45 claimed to have uncovered effective interventions. Thirty-four of these claims had been retested, and 14 of these, or 41 percent, had been convincingly shown to be wrong or significantly exaggerated. If between a third and a half of the most acclaimed research in medicine was proving untrustworthy, the scope and impact of the problem were undeniable. That article was published in the Journal of the American Medical Association.

When a five-year study of 10,000 people finds that those who take more vitamin X are less likely to get cancer Y, you’d think you have pretty good reason to take more vitamin X, and physicians routinely pass these recommendations on to patients. But these studies often sharply conflict with one another. Studies have gone back and forth on the cancer-preventing powers of vitamins A, D, and E; on the heart-health benefits of eating fat and carbs; and even on the question of whether being overweight is more likely to extend or shorten your life. How should we choose among these dueling, high-profile nutritional findings? Ioannidis suggests a simple approach: ignore them all.

the odds are that in any large database of many nutritional and health factors, there will be a few apparent connections that are in fact merely flukes, not real health effects—it’s a bit like combing through long, random strings of letters and claiming there’s an important message in any words that happen to turn up.

nd these problems are aside from ubiquitous measurement errors (for example, people habitually misreport their diets in studies), routine misanalysis (researchers rely on complex software capable of juggling results in ways they don’t always understand), and the less common, but serious, problem of outright fraud (which has been revealed, in confidential surveys, to be much more widespread than scientists like to acknowledge).

And so it goes for all medical studies, he says. Indeed, nutritional studies aren’t the worst. Drug studies have the added corruptive force of financial conflict of interest. The exciting links between genes and various diseases and traits that are relentlessly hyped in the press for heralding miraculous around-the-corner treatments for everything from colon cancer to schizophrenia have in the past proved so vulnerable to error and distortion, Ioannidis has found, that in some cases you’d have done about as well by throwing darts at a chart of the genome.

Though scientists and science journalists are constantly talking up the value of the peer-review process, researchers admit among themselves that biased, erroneous, and even blatantly fraudulent studies easily slip through it.

Of those 45 super-cited studies that Ioannidis focused on, 11 had never been retested. Perhaps worse, Ioannidis found that even when a research error is outed, it typically persists for years or even decades. He looked at three prominent health studies from the 1980s and 1990s that were each later soundly refuted, and discovered that researchers continued to cite the original results as correct more often than as flawed—in one case for at least 12 years after the results were discredited.

Medical research is not especially plagued with wrongness. Other meta-research experts have confirmed that similar issues distort research in all fields of science, from physics to economics (where the highly regarded economists J. Bradford DeLong and Kevin Lang once showed how a remarkably consistent paucity of strong evidence in published economics studies made it unlikely that any of them were right).

Ioannidis initially thought the community might come out fighting. Instead, it seemed relieved, as if it had been guiltily waiting for someone to blow the whistle, and eager to hear more. David Gorski, a surgeon and researcher at Detroit’s Barbara Ann Karmanos Cancer Institute, noted in his prominent medical blog that when he presented Ioannidis’s paper on highly cited research at a professional meeting, “not a single one of my surgical colleagues was the least bit surprised or disturbed by its findings.” Ioannidis offers a theory for the relatively calm reception. “I think that people didn’t feel I was only trying to provoke them, because I showed that it was a community problem, instead of pointing fingers at individual examples of bad research,” he says. In a sense, he gave scientists an opportunity to cluck about the wrongness without having to acknowledge that they themselves succumb to it—it was something everyone else did.

The irony of his having achieved this sort of success by accusing the medical-research community of chasing after success is not lost on him, and he notes that it ought to raise the question of whether he himself might be pumping up his findings.

Tatsioni doesn’t so much fear that someone will carve out the man’s healthy appendix. Rather, she’s concerned that, like many patients, he’ll end up with prescriptions for multiple drugs that will do little to help him, and may well harm him. “Usually what happens is that the doctor will ask for a suite of biochemical tests—liver fat, pancreas function, and so on,” she tells me. “The tests could turn up something, but they’re probably irrelevant. Just having a good talk with the patient and getting a close history is much more likely to tell me what’s wrong.” Of course, the doctors have all been trained to order these tests, she notes, and doing so is a lot quicker than a long bedside chat. They’re also trained to ply the patient with whatever drugs might help whack any errant test numbers back into line.

patients often don’t even like it when they’re taken off their drugs, she explains; they find their prescriptions reassuring.

Already feeling that they’re fighting to keep patients from turning to alternative medical treatments such as homeopathy, or misdiagnosing themselves on the Internet, or simply neglecting medical treatment altogether, many researchers and physicians aren’t eager to provide even more reason to be skeptical of what doctors do—not to mention how public disenchantment with medicine could affect research funding.

being wrong in science is fine, and even necessary

Rand herself became irate when informed by Joan Blumenthal that the tree she thought she saw outside her hospital window was really an IV pole.

“Objectivism is a version of empiricism.” As such it is subject to the standard criticisms of empiricism, in particular the difficulty of explaining necessity, mathematics and logic without the aid of a priori knowledge. Another example is Rand’s belief that man’s mind is tabula rasa, which makes it subject to various objections from evolutionary psychology.

If an Objectivist historian of science and an Objectivist physicist can’t get issues right in their own field whereas Leonard Peikoff (who has expertise in neither) can, what’s the hope for the rest of us?