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Weiye Loh

Shakespeare? He's in my DNA | plus.maths.org - 0 views

plus.maths.org/dna

Data DNA Science Mathematics

shared by Weiye Loh on 31 Jan 13 - No Cached
  • Weiye Loh on 31 Jan 13
     
    "not only can scientist read DNA sequences from biological samples, they can also "write" them. In the lab they can produce strands of DNA corresponding to particular strings of nucleotides, denoted by the letters A, G, T and C. So if you encode your information in terms of these four letters, you could theoretically store it in DNA. "We already know that DNA is a robust way to store information because we can extract it from bones of woolly mammoths, which date back tens of thousands of years, and make sense of it," explains Nick Goldman of the EMBL-European Bioinformatics Institute (EMBL-EBI). "It's also incredibly small, dense and does not need any power for storage, so shipping and keeping it is easy.""
Weiye Loh

Skepticblog » A Creationist Challenge - 0 views

skepticblog.org/...a-creationist-challenge

Creationism Evolution Logic

shared by Weiye Loh on 01 Mar 11 - No Cached
  • The commenter starts with some ad hominems, asserting that my post is biased and emotional. They provide no evidence or argument to support this assertion. And of course they don’t even attempt to counter any of the arguments I laid out. They then follow up with an argument from authority – he can link to a PhD creationist – so there.
  • The article that the commenter links to is by Henry M. Morris, founder for the Institute for Creation Research (ICR) – a young-earth creationist organization. Morris was (he died in 2006 following a stroke) a PhD – in civil engineering. This point is irrelevant to his actual arguments. I bring it up only to put the commenter’s argument from authority into perspective. No disrespect to engineers – but they are not biologists. They have no expertise relevant to the question of evolution – no more than my MD. So let’s stick to the arguments themselves.
  • The article by Morris is an overview of so-called Creation Science, of which Morris was a major architect. The arguments he presents are all old creationist canards, long deconstructed by scientists. In fact I address many of them in my original refutation. Creationists generally are not very original – they recycle old arguments endlessly, regardless of how many times they have been destroyed.
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  • Morris also makes heavy use of the “taking a quote out of context” strategy favored by creationists. His quotes are often from secondary sources and are incomplete.
  • A more scholarly (i.e. intellectually honest) approach would be to cite actual evidence to support a point. If you are going to cite an authority, then make sure the quote is relevant, in context, and complete.
  • And even better, cite a number of sources to show that the opinion is representative. Rather we get single, partial, and often outdated quotes without context.
  • (nature is not, it turns out, cleanly divided into “kinds”, which have no operational definition). He also repeats this canard: Such variation is often called microevolution, and these minor horizontal (or downward) changes occur fairly often, but such changes are not true “vertical” evolution. This is the microevolution/macroevolution false dichotomy. It is only “often called” this by creationists – not by actual evolutionary scientists. There is no theoretical or empirical division between macro and micro evolution. There is just evolution, which can result in the full spectrum of change from minor tweaks to major changes.
  • Morris wonders why there are no “dats” – dog-cat transitional species. He misses the hierarchical nature of evolution. As evolution proceeds, and creatures develop a greater and greater evolutionary history behind them, they increasingly are committed to their body plan. This results in a nestled hierarchy of groups – which is reflected in taxonomy (the naming scheme of living things).
  • once our distant ancestors developed the basic body plan of chordates, they were committed to that body plan. Subsequent evolution resulted in variations on that plan, each of which then developed further variations, etc. But evolution cannot go backward, undo evolutionary changes and then proceed down a different path. Once an evolutionary line has developed into a dog, evolution can produce variations on the dog, but it cannot go backwards and produce a cat.
  • Stephen J. Gould described this distinction as the difference between disparity and diversity. Disparity (the degree of morphological difference) actually decreases over evolutionary time, as lineages go extinct and the surviving lineages are committed to fewer and fewer basic body plans. Meanwhile, diversity (the number of variations on a body plan) within groups tends to increase over time.
  • the kind of evolutionary changes that were happening in the past, when species were relatively undifferentiated (compared to contemporary species) is indeed not happening today. Modern multi-cellular life has 600 million years of evolutionary history constraining their future evolution – which was not true of species at the base of the evolutionary tree. But modern species are indeed still evolving.
  • Here is a list of research documenting observed instances of speciation. The list is from 1995, and there are more recent examples to add to the list. Here are some more. And here is a good list with references of more recent cases.
  • Next Morris tries to convince the reader that there is no evidence for evolution in the past, focusing on the fossil record. He repeats the false claim (again, which I already dealt with) that there are no transitional fossils: Even those who believe in rapid evolution recognize that a considerable number of generations would be required for one distinct “kind” to evolve into another more complex kind. There ought, therefore, to be a considerable number of true transitional structures preserved in the fossils — after all, there are billions of non-transitional structures there! But (with the exception of a few very doubtful creatures such as the controversial feathered dinosaurs and the alleged walking whales), they are not there.
  • I deal with this question at length here, pointing out that there are numerous transitional fossils for the evolution of terrestrial vertebrates, mammals, whales, birds, turtles, and yes – humans from ape ancestors. There are many more examples, these are just some of my favorites.
  • Much of what follows (as you can see it takes far more space to correct the lies and distortions of Morris than it did to create them) is classic denialism – misinterpreting the state of the science, and confusing lack of information about the details of evolution with lack of confidence in the fact of evolution. Here are some examples – he quotes Niles Eldridge: “It is a simple ineluctable truth that virtually all members of a biota remain basically stable, with minor fluctuations, throughout their durations. . . .“ So how do evolutionists arrive at their evolutionary trees from fossils of organisms which didn’t change during their durations? Beware the “….” – that means that meaningful parts of the quote are being omitted. I happen to have the book (The Pattern of Evolution) from which Morris mined that particular quote. Here’s the rest of it: (Remember, by “biota” we mean the commonly preserved plants and animals of a particular geological interval, which occupy regions often as large as Roger Tory Peterson’s “eastern” region of North American birds.) And when these systems change – when the older species disappear, and new ones take their place – the change happens relatively abruptly and in lockstep fashion.”
  • Eldridge was one of the authors (with Gould) of punctuated equilibrium theory. This states that, if you look at the fossil record, what we see are species emerging, persisting with little change for a while, and then disappearing from the fossil record. They theorize that most species most of the time are at equilibrium with their environment, and so do not change much. But these periods of equilibrium are punctuated by disequilibrium – periods of change when species will have to migrate, evolve, or go extinct.
  • This does not mean that speciation does not take place. And if you look at the fossil record we see a pattern of descendant species emerging from ancestor species over time – in a nice evolutionary pattern. Morris gives a complete misrepresentation of Eldridge’s point – once again we see intellectual dishonesty in his methods of an astounding degree.
  • Regarding the atheism = religion comment, it reminds me of a great analogy that I first heard on twitter from Evil Eye. (paraphrase) “those that say atheism is a religion, is like saying ‘not collecting stamps’ is a hobby too.”
  • Morris next tackles the genetic evidence, writing: More often is the argument used that similar DNA structures in two different organisms proves common evolutionary ancestry. Neither argument is valid. There is no reason whatever why the Creator could not or would not use the same type of genetic code based on DNA for all His created life forms. This is evidence for intelligent design and creation, not evolution.
  • Here is an excellent summary of the multiple lines of molecular evidence for evolution. Basically, if we look at the sequence of DNA, the variations in trinucleotide codes for amino acids, and amino acids for proteins, and transposons within DNA we see a pattern that can only be explained by evolution (or a mischievous god who chose, for some reason, to make life look exactly as if it had evolved – a non-falsifiable notion).
  • The genetic code is essentially comprised of four letters (ACGT for DNA), and every triplet of three letters equates to a specific amino acid. There are 64 (4^3) possible three letter combinations, and 20 amino acids. A few combinations are used for housekeeping, like a code to indicate where a gene stops, but the rest code for amino acids. There are more combinations than amino acids, so most amino acids are coded for by multiple combinations. This means that a mutation that results in a one-letter change might alter from one code for a particular amino acid to another code for the same amino acid. This is called a silent mutation because it does not result in any change in the resulting protein.
  • It also means that there are very many possible codes for any individual protein. The question is – which codes out of the gazillions of possible codes do we find for each type of protein in different species. If each “kind” were created separately there would not need to be any relationship. Each kind could have it’s own variation, or they could all be identical if they were essentially copied (plus any mutations accruing since creation, which would be minimal). But if life evolved then we would expect that the exact sequence of DNA code would be similar in related species, but progressively different (through silent mutations) over evolutionary time.
  • This is precisely what we find – in every protein we have examined. This pattern is necessary if evolution were true. It cannot be explained by random chance (the probability is absurdly tiny – essentially zero). And it makes no sense from a creationist perspective. This same pattern (a branching hierarchy) emerges when we look at amino acid substitutions in proteins and other aspects of the genetic code.
  • Morris goes for the second law of thermodynamics again – in the exact way that I already addressed. He responds to scientists correctly pointing out that the Earth is an open system, by writing: This naive response to the entropy law is typical of evolutionary dissimulation. While it is true that local order can increase in an open system if certain conditions are met, the fact is that evolution does not meet those conditions. Simply saying that the earth is open to the energy from the sun says nothing about how that raw solar heat is converted into increased complexity in any system, open or closed. The fact is that the best known and most fundamental equation of thermodynamics says that the influx of heat into an open system will increase the entropy of that system, not decrease it. All known cases of decreased entropy (or increased organization) in open systems involve a guiding program of some sort and one or more energy conversion mechanisms.
  • Energy has to be transformed into a usable form in order to do the work necessary to decrease entropy. That’s right. That work is done by life. Plants take solar energy (again – I’m not sure what “raw solar heat” means) and convert it into food. That food fuels the processes of life, which include development and reproduction. Evolution emerges from those processes- therefore the conditions that Morris speaks of are met.
  • But Morris next makes a very confused argument: Evolution has neither of these. Mutations are not “organizing” mechanisms, but disorganizing (in accord with the second law). They are commonly harmful, sometimes neutral, but never beneficial (at least as far as observed mutations are concerned). Natural selection cannot generate order, but can only “sieve out” the disorganizing mutations presented to it, thereby conserving the existing order, but never generating new order.
  • The notion that evolution (as if it’s a thing) needs to use energy is hopelessly confused. Evolution is a process that emerges from the system of life – and life certainly can use solar energy to decrease its entropy, and by extension the entropy of the biosphere. Morris slips into what is often presented as an information argument.  (Yet again – already dealt with. The pattern here is that we are seeing a shuffling around of the same tired creationists arguments.) It is first not true that most mutations are harmful. Many are silent, and many of those that are not silent are not harmful. They may be neutral, they may be a mixed blessing, and their relative benefit vs harm is likely to be situational. They may be fatal. And they also may be simply beneficial.
  • Morris finishes with a long rambling argument that evolution is religion. Evolution is promoted by its practitioners as more than mere science. Evolution is promulgated as an ideology, a secular religion — a full-fledged alternative to Christianity, with meaning and morality . . . . Evolution is a religion. This was true of evolution in the beginning, and it is true of evolution still today. Morris ties evolution to atheism, which, he argues, makes it a religion. This assumes, of course, that atheism is a religion. That depends on how you define atheism and how you define religion – but it is mostly wrong. Atheism is a lack of belief in one particular supernatural claim – that does not qualify it as a religion.
  • But mutations are not “disorganizing” – that does not even make sense. It seems to be based on a purely creationist notion that species are in some privileged perfect state, and any mutation can only take them farther from that perfection. For those who actually understand biology, life is a kluge of compromises and variation. Mutations are mostly lateral moves from one chaotic state to another. They are not directional. But they do provide raw material, variation, for natural selection. Natural selection cannot generate variation, but it can select among that variation to provide differential survival. This is an old game played by creationists – mutations are not selective, and natural selection is not creative (does not increase variation). These are true but irrelevant, because mutations increase variation and information, and selection is a creative force that results in the differential survival of better adapted variation.
  • Weiye Loh on 01 Mar 11
     
    One of my earlier posts on SkepticBlog was Ten Major Flaws in Evolution: A Refutation, published two years ago. Occasionally a creationist shows up to snipe at the post, like this one:i read this and found it funny. It supposedly gives a scientific refutation, but it is full of more bias than fox news, and a lot of emotion as well.here's a scientific case by an actual scientists, you know, one with a ph. D, and he uses statements by some of your favorite evolutionary scientists to insist evolution doesn't exist.i challenge you to write a refutation on this one.http://www.icr.org/home/resources/resources_tracts_scientificcaseagainstevolution/Challenge accepted.
Weiye Loh

Bad Health Habits Blamed on Genetics - Newsweek - 0 views

www.newsweek.com/...avior-blamed-on-wonky-dna.html

Habits Genetic Ethics

shared by Weiye Loh on 30 Jun 10 - Cached
  • A new study shows just how alluring “My DNA did it!” is to some people.
  • here are serious scientific concerns about the reliability and value of many of the genes linked to disease. And now we have another reason why the hype is worrisome: people who engage in the riskiest-for-health behaviors, and who therefore most need to change, are more likely to blame their genes for their diseases, finds a new study published online in the journal Annals of Behavioral Medicine.
  • Worse, the more behavioral risk factors people have—smoking and eating a high-fat diet and not exercising, for instance—the less likely they are to be interested in information about living healthier.
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  • The unhealthier people’s habits were, the more they latched on to genetic explanations for diseases
  • Weiye Loh on 30 Jun 10
     
    My Alleles Made Me Do It: The Folly of Blaming Bad Behavior on Wonky DNA
Weiye Loh

Arsenic bacteria - a post-mortem, a review, and some navel-gazing | Not Exactly Rocket ... - 0 views

blogs.discovermagazine.com/...a-review-and-some-navel-gazing

Journalism Ethics Science Biology

shared by Weiye Loh on 11 Dec 10 - No Cached
  • t was the big news that wasn’t. Hyperbolic claims about the possible discovery of alien life, or a second branch of life on Earth, turned out to be nothing more than bacteria that can thrive on arsenic, using it in place of phosphorus in their DNA and other molecules. But after the initial layers of hype were peeled away, even this extraordinar
  • This is a chronological roundup of the criticism against the science in the paper itself, ending with some personal reflections on my own handling of the story (skip to Friday, December 10th for that bit).
  • Thursday, December 2nd: Felisa Wolfe-Simon published a paper in Science, claiming to have found bacteria in California’s Mono Lake that can grow using arsenic instead of phosphorus. Given that phosphorus is meant to be one of six irreplaceable elements, this would have been a big deal, not least because the bacteria apparently used arsenic to build the backbones of their DNA molecules.
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  • In my post, I mentioned some caveats. Wolfe-Simon isolated the arsenic-loving strain, known as GFAJ-1, by growing Mono Lake bacteria in ever-increasing concentrations of arsenic while diluting out the phosphorus. It is possible that the bacteria’s arsenic molecules were an adaptation to the harsh environments within the experiment, rather than Mono Lake itself. More importantly, there were still detectable levels of phosphorus left in the cells at the end of the experiment, although Wolfe-Simon claimed that the bacteria shouldn’t have been able to grow on such small amounts.
  • signs emerged that NASA weren’t going to engage with the criticisms. Dwayne Brown, their senior public affairs officer, highlighted the fact that the paper was published in one of the “most prestigious scientific journals” and deemed it inappropriate to debate the science using the same media and bloggers who they relied on for press coverage of the science. Wolfe-Simon herself tweeted that “discussion about scientific details MUST be within a scientific venue so that we can come back to the public with a unified understanding.”
  • Jonathan Eisen says that “they carried out science by press release and press conference” and “are now hypocritical if they say that the only response should be in the scientific literature.” David Dobbs calls the attitude “a return to pre-Enlightenment thinking”, and rightly noted that “Rosie Redfield is a peer, and her blog is peer review”.
  • Chris Rowan agreed, saying that what happens after publication is what he considers to be “real peer review”. Rowan said, “The pre-publication stuff is just a quality filter, a check that the paper is not obviously wrong – and an imperfect filter at that. The real test is what happens in the months and years after publication.”Grant Jacobs and others post similar thoughts, while Nature and the Columbia Journalism Review both cover the fracas.
  • Jack Gilbert at the University of Chicago said that impatient though he is, peer-reviewed journals are the proper forum for criticism. Others were not so kind. At the Guardian, Martin Robbins says that “at almost every stage of this story the actors involved were collapsing under the weight of their own slavish obedience to a fundamentally broken… well… ’system’” And Ivan Oransky noted that NASA failed to follow its own code of conduct when announcing the study.
  • Dr Isis said, “If question remains about the voracity of these authors findings, then the only thing that is going to answer that doubt is data.  Data cannot be generated by blog discussion… Talking about digging a ditch never got it dug.”
  • it is astonishing how quickly these events unfolded and the sheer number of bloggers and media outlets that became involved in the criticism. This is indeed a brave new world, and one in which we are all the infamous Third Reviewer.
  • I tried to quell the hype around the study as best I could. I had the paper and I think that what I wrote was a fair representation of it. But, of course, that’s not necessarily enough. I’ve argued before that journalists should not be merely messengers – we should make the best possible efforts to cut through what’s being said in an attempt to uncover what’s actually true. Arguably, that didn’t happen although to clarify, I am not saying that the paper is rubbish or untrue. Despite the criticisms, I want to see the authors respond in a thorough way or to see another lab attempt replicate the experiments before jumping to conclusions.
  • the sheer amount of negative comment indicates that I could have been more critical of the paper in my piece. Others have been supportive in suggesting that this was more egg on the face of the peer reviewers and indeed, several practicing scientists took the findings on face value, speculating about everything from the implications for chemotherapy to whether the bacteria have special viruses. The counter-argument, which I have no good retort to, is that peer review is no guarantee of quality, and that writers should be able to see through the fog of whatever topic they write about.
  • my response was that we should expect people to make reasonable efforts to uncover truth and be skeptical, while appreciating that people can and will make mistakes.
  • it comes down to this: did I do enough? I was certainly cautious. I said that “there is room for doubt” and I brought up the fact that the arsenic-loving bacteria still contain measurable levels of phosphorus. But I didn’t run the paper past other sources for comment, which I typically do it for stories that contain extraordinary claims. There was certainly plenty of time to do so here and while there were various reasons that I didn’t, the bottom line is that I could have done more. That doesn’t always help, of course, but it was an important missed step. A lesson for next time.
  • I do believe that it you’re going to try to hold your profession to a higher standard, you have to be honest and open when you’ve made mistakes yourself. I also think that if you cover a story that turns out to be a bit dodgy, you have a certain responsibility in covering the follow-up
  • A basic problem with is the embargo. Specifically that journalists get early access, while peers – other specialists in the field – do not. It means that the journalist, like yourself, can rely only on the original authors, with no way of getting other views on the findings. And it means that peers can’t write about the paper when the journalists (who, inevitably, do a positive-only coverage due to the lack of other viewpoints) do, but will be able to voice only after they’ve been able to digest the paper and formulate a response.
  • No, that’s not true. The embargo doens’t preclude journalists from sending papers out to other authors for review and comment. I do this a lot and I have been critical about new papers as a result, but that’s the step that I missed for this story.
Weiye Loh

How We Know by Freeman Dyson | The New York Review of Books - 0 views

www.nybooks.com/...how-we-know

Information Language Data History Science

shared by Weiye Loh on 27 Feb 11 - No Cached
  • Another example illustrating the central dogma is the French optical telegraph.
  • The telegraph was an optical communication system with stations consisting of large movable pointers mounted on the tops of sixty-foot towers. Each station was manned by an operator who could read a message transmitted by a neighboring station and transmit the same message to the next station in the transmission line.
  • The distance between neighbors was about seven miles. Along the transmission lines, optical messages in France could travel faster than drum messages in Africa. When Napoleon took charge of the French Republic in 1799, he ordered the completion of the optical telegraph system to link all the major cities of France from Calais and Paris to Toulon and onward to Milan. The telegraph became, as Claude Chappe had intended, an important instrument of national power. Napoleon made sure that it was not available to private users.
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  • Unlike the drum language, which was based on spoken language, the optical telegraph was based on written French. Chappe invented an elaborate coding system to translate written messages into optical signals. Chappe had the opposite problem from the drummers. The drummers had a fast transmission system with ambiguous messages. They needed to slow down the transmission to make the messages unambiguous. Chappe had a painfully slow transmission system with redundant messages. The French language, like most alphabetic languages, is highly redundant, using many more letters than are needed to convey the meaning of a message. Chappe’s coding system allowed messages to be transmitted faster. Many common phrases and proper names were encoded by only two optical symbols, with a substantial gain in speed of transmission. The composer and the reader of the message had code books listing the message codes for eight thousand phrases and names. For Napoleon it was an advantage to have a code that was effectively cryptographic, keeping the content of the messages secret from citizens along the route.
  • After these two historical examples of rapid communication in Africa and France, the rest of Gleick’s book is about the modern development of information technolog
  • The modern history is dominated by two Americans, Samuel Morse and Claude Shannon. Samuel Morse was the inventor of Morse Code. He was also one of the pioneers who built a telegraph system using electricity conducted through wires instead of optical pointers deployed on towers. Morse launched his electric telegraph in 1838 and perfected the code in 1844. His code used short and long pulses of electric current to represent letters of the alphabet.
  • Morse was ideologically at the opposite pole from Chappe. He was not interested in secrecy or in creating an instrument of government power. The Morse system was designed to be a profit-making enterprise, fast and cheap and available to everybody. At the beginning the price of a message was a quarter of a cent per letter. The most important users of the system were newspaper correspondents spreading news of local events to readers all over the world. Morse Code was simple enough that anyone could learn it. The system provided no secrecy to the users. If users wanted secrecy, they could invent their own secret codes and encipher their messages themselves. The price of a message in cipher was higher than the price of a message in plain text, because the telegraph operators could transcribe plain text faster. It was much easier to correct errors in plain text than in cipher.
  • Claude Shannon was the founding father of information theory. For a hundred years after the electric telegraph, other communication systems such as the telephone, radio, and television were invented and developed by engineers without any need for higher mathematics. Then Shannon supplied the theory to understand all of these systems together, defining information as an abstract quantity inherent in a telephone message or a television picture. Shannon brought higher mathematics into the game.
  • When Shannon was a boy growing up on a farm in Michigan, he built a homemade telegraph system using Morse Code. Messages were transmitted to friends on neighboring farms, using the barbed wire of their fences to conduct electric signals. When World War II began, Shannon became one of the pioneers of scientific cryptography, working on the high-level cryptographic telephone system that allowed Roosevelt and Churchill to talk to each other over a secure channel. Shannon’s friend Alan Turing was also working as a cryptographer at the same time, in the famous British Enigma project that successfully deciphered German military codes. The two pioneers met frequently when Turing visited New York in 1943, but they belonged to separate secret worlds and could not exchange ideas about cryptography.
  • In 1945 Shannon wrote a paper, “A Mathematical Theory of Cryptography,” which was stamped SECRET and never saw the light of day. He published in 1948 an expurgated version of the 1945 paper with the title “A Mathematical Theory of Communication.” The 1948 version appeared in the Bell System Technical Journal, the house journal of the Bell Telephone Laboratories, and became an instant classic. It is the founding document for the modern science of information. After Shannon, the technology of information raced ahead, with electronic computers, digital cameras, the Internet, and the World Wide Web.
  • According to Gleick, the impact of information on human affairs came in three installments: first the history, the thousands of years during which people created and exchanged information without the concept of measuring it; second the theory, first formulated by Shannon; third the flood, in which we now live
  • The event that made the flood plainly visible occurred in 1965, when Gordon Moore stated Moore’s Law. Moore was an electrical engineer, founder of the Intel Corporation, a company that manufactured components for computers and other electronic gadgets. His law said that the price of electronic components would decrease and their numbers would increase by a factor of two every eighteen months. This implied that the price would decrease and the numbers would increase by a factor of a hundred every decade. Moore’s prediction of continued growth has turned out to be astonishingly accurate during the forty-five years since he announced it. In these four and a half decades, the price has decreased and the numbers have increased by a factor of a billion, nine powers of ten. Nine powers of ten are enough to turn a trickle into a flood.
  • Gordon Moore was in the hardware business, making hardware components for electronic machines, and he stated his law as a law of growth for hardware. But the law applies also to the information that the hardware is designed to embody. The purpose of the hardware is to store and process information. The storage of information is called memory, and the processing of information is called computing. The consequence of Moore’s Law for information is that the price of memory and computing decreases and the available amount of memory and computing increases by a factor of a hundred every decade. The flood of hardware becomes a flood of information.
  • In 1949, one year after Shannon published the rules of information theory, he drew up a table of the various stores of memory that then existed. The biggest memory in his table was the US Library of Congress, which he estimated to contain one hundred trillion bits of information. That was at the time a fair guess at the sum total of recorded human knowledge. Today a memory disc drive storing that amount of information weighs a few pounds and can be bought for about a thousand dollars. Information, otherwise known as data, pours into memories of that size or larger, in government and business offices and scientific laboratories all over the world. Gleick quotes the computer scientist Jaron Lanier describing the effect of the flood: “It’s as if you kneel to plant the seed of a tree and it grows so fast that it swallows your whole town before you can even rise to your feet.”
  • On December 8, 2010, Gleick published on the The New York Review’s blog an illuminating essay, “The Information Palace.” It was written too late to be included in his book. It describes the historical changes of meaning of the word “information,” as recorded in the latest quarterly online revision of the Oxford English Dictionary. The word first appears in 1386 a parliamentary report with the meaning “denunciation.” The history ends with the modern usage, “information fatigue,” defined as “apathy, indifference or mental exhaustion arising from exposure to too much information.”
  • The consequences of the information flood are not all bad. One of the creative enterprises made possible by the flood is Wikipedia, started ten years ago by Jimmy Wales. Among my friends and acquaintances, everybody distrusts Wikipedia and everybody uses it. Distrust and productive use are not incompatible. Wikipedia is the ultimate open source repository of information. Everyone is free to read it and everyone is free to write it. It contains articles in 262 languages written by several million authors. The information that it contains is totally unreliable and surprisingly accurate. It is often unreliable because many of the authors are ignorant or careless. It is often accurate because the articles are edited and corrected by readers who are better informed than the authors
  • Jimmy Wales hoped when he started Wikipedia that the combination of enthusiastic volunteer writers with open source information technology would cause a revolution in human access to knowledge. The rate of growth of Wikipedia exceeded his wildest dreams. Within ten years it has become the biggest storehouse of information on the planet and the noisiest battleground of conflicting opinions. It illustrates Shannon’s law of reliable communication. Shannon’s law says that accurate transmission of information is possible in a communication system with a high level of noise. Even in the noisiest system, errors can be reliably corrected and accurate information transmitted, provided that the transmission is sufficiently redundant. That is, in a nutshell, how Wikipedia works.
  • The information flood has also brought enormous benefits to science. The public has a distorted view of science, because children are taught in school that science is a collection of firmly established truths. In fact, science is not a collection of truths. It is a continuing exploration of mysteries. Wherever we go exploring in the world around us, we find mysteries. Our planet is covered by continents and oceans whose origin we cannot explain. Our atmosphere is constantly stirred by poorly understood disturbances that we call weather and climate. The visible matter in the universe is outweighed by a much larger quantity of dark invisible matter that we do not understand at all. The origin of life is a total mystery, and so is the existence of human consciousness. We have no clear idea how the electrical discharges occurring in nerve cells in our brains are connected with our feelings and desires and actions.
  • Even physics, the most exact and most firmly established branch of science, is still full of mysteries. We do not know how much of Shannon’s theory of information will remain valid when quantum devices replace classical electric circuits as the carriers of information. Quantum devices may be made of single atoms or microscopic magnetic circuits. All that we know for sure is that they can theoretically do certain jobs that are beyond the reach of classical devices. Quantum computing is still an unexplored mystery on the frontier of information theory. Science is the sum total of a great multitude of mysteries. It is an unending argument between a great multitude of voices. It resembles Wikipedia much more than it resembles the Encyclopaedia Britannica.
  • The rapid growth of the flood of information in the last ten years made Wikipedia possible, and the same flood made twenty-first-century science possible. Twenty-first-century science is dominated by huge stores of information that we call databases. The information flood has made it easy and cheap to build databases. One example of a twenty-first-century database is the collection of genome sequences of living creatures belonging to various species from microbes to humans. Each genome contains the complete genetic information that shaped the creature to which it belongs. The genome data-base is rapidly growing and is available for scientists all over the world to explore. Its origin can be traced to the year 1939, when Shannon wrote his Ph.D. thesis with the title “An Algebra for Theoretical Genetics.
  • Shannon was then a graduate student in the mathematics department at MIT. He was only dimly aware of the possible physical embodiment of genetic information. The true physical embodiment of the genome is the double helix structure of DNA molecules, discovered by Francis Crick and James Watson fourteen years later. In 1939 Shannon understood that the basis of genetics must be information, and that the information must be coded in some abstract algebra independent of its physical embodiment. Without any knowledge of the double helix, he could not hope to guess the detailed structure of the genetic code. He could only imagine that in some distant future the genetic information would be decoded and collected in a giant database that would define the total diversity of living creatures. It took only sixty years for his dream to come true.
  • In the twentieth century, genomes of humans and other species were laboriously decoded and translated into sequences of letters in computer memories. The decoding and translation became cheaper and faster as time went on, the price decreasing and the speed increasing according to Moore’s Law. The first human genome took fifteen years to decode and cost about a billion dollars. Now a human genome can be decoded in a few weeks and costs a few thousand dollars. Around the year 2000, a turning point was reached, when it became cheaper to produce genetic information than to understand it. Now we can pass a piece of human DNA through a machine and rapidly read out the genetic information, but we cannot read out the meaning of the information. We shall not fully understand the information until we understand in detail the processes of embryonic development that the DNA orchestrated to make us what we are.
  • The explosive growth of information in our human society is a part of the slower growth of ordered structures in the evolution of life as a whole. Life has for billions of years been evolving with organisms and ecosystems embodying increasing amounts of information. The evolution of life is a part of the evolution of the universe, which also evolves with increasing amounts of information embodied in ordered structures, galaxies and stars and planetary systems. In the living and in the nonliving world, we see a growth of order, starting from the featureless and uniform gas of the early universe and producing the magnificent diversity of weird objects that we see in the sky and in the rain forest. Everywhere around us, wherever we look, we see evidence of increasing order and increasing information. The technology arising from Shannon’s discoveries is only a local acceleration of the natural growth of information.
  • . Lord Kelvin, one of the leading physicists of that time, promoted the heat death dogma, predicting that the flow of heat from warmer to cooler objects will result in a decrease of temperature differences everywhere, until all temperatures ultimately become equal. Life needs temperature differences, to avoid being stifled by its waste heat. So life will disappear
  • Thanks to the discoveries of astronomers in the twentieth century, we now know that the heat death is a myth. The heat death can never happen, and there is no paradox. The best popular account of the disappearance of the paradox is a chapter, “How Order Was Born of Chaos,” in the book Creation of the Universe, by Fang Lizhi and his wife Li Shuxian.2 Fang Lizhi is doubly famous as a leading Chinese astronomer and a leading political dissident. He is now pursuing his double career at the University of Arizona.
  • The belief in a heat death was based on an idea that I call the cooking rule. The cooking rule says that a piece of steak gets warmer when we put it on a hot grill. More generally, the rule says that any object gets warmer when it gains energy, and gets cooler when it loses energy. Humans have been cooking steaks for thousands of years, and nobody ever saw a steak get colder while cooking on a fire. The cooking rule is true for objects small enough for us to handle. If the cooking rule is always true, then Lord Kelvin’s argument for the heat death is correct.
  • the cooking rule is not true for objects of astronomical size, for which gravitation is the dominant form of energy. The sun is a familiar example. As the sun loses energy by radiation, it becomes hotter and not cooler. Since the sun is made of compressible gas squeezed by its own gravitation, loss of energy causes it to become smaller and denser, and the compression causes it to become hotter. For almost all astronomical objects, gravitation dominates, and they have the same unexpected behavior. Gravitation reverses the usual relation between energy and temperature. In the domain of astronomy, when heat flows from hotter to cooler objects, the hot objects get hotter and the cool objects get cooler. As a result, temperature differences in the astronomical universe tend to increase rather than decrease as time goes on. There is no final state of uniform temperature, and there is no heat death. Gravitation gives us a universe hospitable to life. Information and order can continue to grow for billions of years in the future, as they have evidently grown in the past.
  • The vision of the future as an infinite playground, with an unending sequence of mysteries to be understood by an unending sequence of players exploring an unending supply of information, is a glorious vision for scientists. Scientists find the vision attractive, since it gives them a purpose for their existence and an unending supply of jobs. The vision is less attractive to artists and writers and ordinary people. Ordinary people are more interested in friends and family than in science. Ordinary people may not welcome a future spent swimming in an unending flood of information.
  • A darker view of the information-dominated universe was described in a famous story, “The Library of Babel,” by Jorge Luis Borges in 1941.3 Borges imagined his library, with an infinite array of books and shelves and mirrors, as a metaphor for the universe.
  • Gleick’s book has an epilogue entitled “The Return of Meaning,” expressing the concerns of people who feel alienated from the prevailing scientific culture. The enormous success of information theory came from Shannon’s decision to separate information from meaning. His central dogma, “Meaning is irrelevant,” declared that information could be handled with greater freedom if it was treated as a mathematical abstraction independent of meaning. The consequence of this freedom is the flood of information in which we are drowning. The immense size of modern databases gives us a feeling of meaninglessness. Information in such quantities reminds us of Borges’s library extending infinitely in all directions. It is our task as humans to bring meaning back into this wasteland. As finite creatures who think and feel, we can create islands of meaning in the sea of information. Gleick ends his book with Borges’s image of the human condition:We walk the corridors, searching the shelves and rearranging them, looking for lines of meaning amid leagues of cacophony and incoherence, reading the history of the past and of the future, collecting our thoughts and collecting the thoughts of others, and every so often glimpsing mirrors, in which we may recognize creatures of the information.
Weiye Loh

Information about information | plus.maths.org - 0 views

plus.maths.org/...information-about-information

Information Data

shared by Weiye Loh on 16 Nov 13 - No Cached
  • since what we actually experience depends on us observing the world (via our measuring devices), reality is shaped by answers to yes/no questions. For example, is the electron here or is it not? Is its spin pointing up or pointing down? Answers to questions are information — the yes and no in English language correspond to the 0 and 1 in computer language. Thus, information is fundamental to physical reality. As the famous physicist John Archibald Wheeler put it, the "It" we observe around us comes from the "Bit" that encodes information: "It from bit". Is this really true?
  • Weiye Loh on 16 Nov 13
     
    "what exactly is information? We tend to think of it as human made, but since we're all a result of our DNA sequence, perhaps we should think of humans as being made of information."
Weiye Loh

Cancer resembles life 1 billion years ago, say astrobiologists - microbiology, genomics... - 0 views

www.lifescientist.com.au/..._years_ago_say_astrobiologists

Cancer Evolution Life

shared by Weiye Loh on 17 Feb 11 - No Cached
  • astrobiologists, working with oncologists in the US, have suggested that cancer resembles ancient forms of life that flourished between 600 million and 1 billion years ago.
  • Read more about what this discovery means for cancer research.
  • The genes that controlled the behaviour of these early multicellular organisms still reside within our own cells, managed by more recent genes that keep them in check.It's when these newer controlling genes fail that the older mechanisms take over, and the cell reverts to its earlier behaviours and grows out of control.
  • ...11 more annotations...
  • The new theory, published in the journal Physical Biology, has been put forward by two leading figures in the world of cosmology and astrobiology: Paul Davies, director of the Beyond Center for Fundamental Concepts in Science, Arizona State University; and Charles Lineweaver, from the Australian National University.
  • According to Lineweaver, this suggests that cancer is an atavism, or an evolutionary throwback.
  • In the paper, they suggest that a close look at cancer shows similarities with early forms of multicellular life.
  • “Unlike bacteria and viruses, cancer has not developed the capacity to evolve into new forms. In fact, cancer is better understood as the reversion of cells to the way they behaved a little over one billion years ago, when humans were nothing more than loose-knit colonies of only partially differentiated cells. “We think that the tumours that develop in cancer patients today take the same form as these simple cellular structures did more than a billion years ago,” he said.
  • One piece of evidence to support this theory is that cancers appear in virtually all metazoans, with the notable exception of the bizarre naked mole rat."This quasi-ubiquity suggests that the mechanisms of cancer are deep-rooted in evolutionary history, a conjecture that receives support from both paleontology and genetics," they write.
  • the genes that controlled this early multi-cellular form of life are like a computer operating system's 'safe mode', and when there are failures or mutations in the more recent genes that manage the way cells specialise and interact to form the complex life of today, then the earlier level of programming takes over.
  • Their notion is in contrast to a prevailing theory that cancer cells are 'rogue' cells that evolve rapidly within the body, overcoming the normal slew of cellular defences.
  • However, Davies and Lineweaver point out that cancer cells are highly cooperative with each other, if competing with the host's cells. This suggests a pre-existing complexity that is reminiscent of early multicellular life.
  • cancers' manifold survival mechanisms are predictable, and unlikely to emerge spontaneously through evolution within each individual in such a consistent way.
  • The good news is that this means combating cancer is not necessarily as complex as if the cancers were rogue cells evolving new and novel defence mechanisms within the body.Instead, because cancers fall back on the same evolved mechanisms that were used by early life, we can expect them to remain predictable, thus if they're susceptible to treatment, it's unlikely they'll evolve new ways to get around it.
  • If the atavism hypothesis is correct, there are new reasons for optimism," they write.
  • Weiye Loh on 17 Feb 11
     
    Feature: Inside DNA vaccines bioMD makes a bid for Andrew Forest's Allied Medical and Coridon Alexion acquires technology for MoCD therapy More > Most Popular Media Releases Cancer resembles life 1 billion years ago, say astrobiologists Feature: The challenge of a herpes simplex vaccine Feature: Proteomics power of pawpaw bioMD makes a bid for Andrew Forest's Allied Medical and Coridon Immune system boosting hormone might lead to HIV cure Biotechnology Directory Company Profile Check out this company's profile and more in the Biotechnology Directory! Biotechnology Directory Find company by name Find company by category Latest Jobs Senior Software Developer / Java Analyst Programm App Support Developer - Java / J2ee Solutions Consultant - VIC Technical Writer Product Manager (Fisheye/Crucible)   BUYING GUIDES Portable Multimedia Players Digital Cameras Digital Video Cameras LATEST PRODUCTS HTC Wildfire S Android phone (preview) Panasonic LUMIX DMC-GH2 digital camera HTC Desire S Android phone (preview) Qld ICT minister Robert Schwarten retires Movie piracy costs Aus economy $1.37 billion in 12 months: AFACT Wireless smartphones essential to e-health: CSIRO Aussie outsourcing CRM budgets to soar in 2011: Ovum Federal government to evaluate education revolution targets Business continuity planning - more than just disaster recovery Proving the value of IT - Part one 5 open source security projects to watch In-memory computing Information security in 2011 EFA shoots down 'unproductive' AFACT movie piracy study In Pictures: IBM hosts Galactic dinner Emerson Network Power launches new infrastructure solutions Consumers not smart enough for smartphones? Google one-ups Apple online media subscription service M2M offerings expand as more machines go online India cancels satellite spectrum deal after controversy Lenovo profit rises in Q3 on strong PC sales in China Taiwan firm to supply touch sensors to Samsung HP regains top position in India's PC market Copyright 20
Paul Melissa

Warning over 'surveillance state' - 9 views

http://news.bbc.co.uk/2/hi/uk_news/politics/7872425.stm The article effectively speaks of how CCTV cameras and DNA database are threats to privacy. Though many states have reasoned them for being...

Privacy Surveillance

started by Paul Melissa on 08 Sep 09 no follow-up yet
Weiye Loh

Kevin Kelly and Steven Johnson on Where Ideas Come From | Magazine - 0 views

m.wired.com/...1

Invention Cultural Industries Innovation

shared by Weiye Loh on 30 Sep 10 - No Cached
  • Say the word “inventor” and most people think of a solitary genius toiling in a basement. But two ambitious new books on the history of innovation—by Steven Johnson and Kevin Kelly, both longtime wired contributors—argue that great discoveries typically spring not from individual minds but from the hive mind. In Where Good Ideas Come From: The Natural History of Innovation, Johnson draws on seven centuries of scientific and technological progress, from Gutenberg to GPS, to show what sorts of environments nurture ingenuity. He finds that great creative milieus, whether MIT or Los Alamos, New York City or the World Wide Web, are like coral reefs—teeming, diverse colonies of creators who interact with and influence one another.
  • Seven centuries are an eyeblink in the scope of Kelly’s book, What Technology Wants, which looks back over some 50,000 years of history and peers nearly that far into the future. His argument is similarly sweeping: Technology, Kelly believes, can be seen as a sort of autonomous life-form, with intrinsic goals toward which it gropes over the course of its long development. Those goals, he says, are much like the tendencies of biological life, which over time diversifies, specializes, and (eventually) becomes more sentient.
  • We share a fascination with the long history of simultaneous invention: cases where several people come up with the same idea at almost exactly the same time. Calculus, the electrical battery, the telephone, the steam engine, the radio—all these groundbreaking innovations were hit upon by multiple inventors working in parallel with no knowledge of one another.
  • ...25 more annotations...
  • It’s amazing that the myth of the lone genius has persisted for so long, since simultaneous invention has always been the norm, not the exception. Anthropologists have shown that the same inventions tended to crop up in prehistory at roughly similar times, in roughly the same order, among cultures on different continents that couldn’t possibly have contacted one another.
  • Also, there’s a related myth—that innovation comes primarily from the profit motive, from the competitive pressures of a market society. If you look at history, innovation doesn’t come just from giving people incentives; it comes from creating environments where their ideas can connect.
  • The musician Brian Eno invented a wonderful word to describe this phenomenon: scenius. We normally think of innovators as independent geniuses, but Eno’s point is that innovation comes from social scenes,from passionate and connected groups of people.
  • It turns out that the lone genius entrepreneur has always been a rarity—there’s far more innovation coming out of open, nonmarket networks than we tend to assume.
  • Really, we should think of ideas as connections,in our brains and among people. Ideas aren’t self-contained things; they’re more like ecologies and networks. They travel in clusters.
  • ideas are networks
  • In part, that’s because ideas that leap too far ahead are almost never implemented—they aren’t even valuable. People can absorb only one advance, one small hop, at a time. Gregor Mendel’s ideas about genetics, for example: He formulated them in 1865, but they were ignored for 35 years because they were too advanced. Nobody could incorporate them. Then, when the collective mind was ready and his idea was only one hop away, three different scientists independently rediscovered his work within roughly a year of one another.
  • Charles Babbage is another great case study. His “analytical engine,” which he started designing in the 1830s, was an incredibly detailed vision of what would become the modern computer, with a CPU, RAM, and so on. But it couldn’t possibly have been built at the time, and his ideas had to be rediscovered a hundred years later.
  • I think there are a lot of ideas today that are ahead of their time. Human cloning, autopilot cars, patent-free law—all are close technically but too many steps ahead culturally. Innovating is about more than just having the idea yourself; you also have to bring everyone else to where your idea is. And that becomes really difficult if you’re too many steps ahead.
  • The scientist Stuart Kauffman calls this the “adjacent possible.” At any given moment in evolution—of life, of natural systems, or of cultural systems—there’s a space of possibility that surrounds any current configuration of things. Change happens when you take that configuration and arrange it in a new way. But there are limits to how much you can change in a single move.
  • Which is why the great inventions are usually those that take the smallest possible step to unleash the most change. That was the difference between Tim Berners-Lee’s successful HTML code and Ted Nelson’s abortive Xanadu project. Both tried to jump into the same general space—a networked hypertext—but Tim’s approach did it with a dumb half-step, while Ted’s earlier, more elegant design required that everyone take five steps all at once.
  • Also, the steps have to be taken in the right order. You can’t invent the Internet and then the digital computer. This is true of life as well. The building blocks of DNA had to be in place before evolution could build more complex things. One of the key ideas I’ve gotten from you, by the way—when I read your book Out of Control in grad school—is this continuity between biological and technological systems.
  • technology is something that can give meaning to our lives, particularly in a secular world.
  • He had this bleak, soul-sucking vision of technology as an autonomous force for evil. You also present technology as a sort of autonomous force—as wanting something, over the long course of its evolution—but it’s a more balanced and ultimately positive vision, which I find much more appealing than the alternative.
  • As I started thinking about the history of technology, there did seem to be a sense in which, during any given period, lots of innovations were in the air, as it were. They came simultaneously. It appeared as if they wanted to happen. I should hasten to add that it’s not a conscious agency; it’s a lower form, something like the way an organism or bacterium can be said to have certain tendencies, certain trends, certain urges. But it’s an agency nevertheless.
  • technology wants increasing diversity—which is what I think also happens in biological systems, as the adjacent possible becomes larger with each innovation. As tech critics, I think we have to keep this in mind, because when you expand the diversity of a system, that leads to an increase in great things and an increase in crap.
  • the idea that the most creative environments allow for repeated failure.
  • And for wastes of time and resources. If you knew nothing about the Internet and were trying to figure it out from the data, you would reasonably conclude that it was designed for the transmission of spam and porn. And yet at the same time, there’s more amazing stuff available to us than ever before, thanks to the Internet.
  • To create something great, you need the means to make a lot of really bad crap. Another example is spectrum. One reason we have this great explosion of innovation in wireless right now is that the US deregulated spectrum. Before that, spectrum was something too precious to be wasted on silliness. But when you deregulate—and say, OK, now waste it—then you get Wi-Fi.
  • If we didn’t have genetic mutations, we wouldn’t have us. You need error to open the door to the adjacent possible.
  • image of the coral reef as a metaphor for where innovation comes from. So what, today, are some of the most reeflike places in the technological realm?
  • Twitter—not to see what people are having for breakfast, of course, but to see what people are talking about, the links to articles and posts that they’re passing along.
  • second example of an information coral reef, and maybe the less predictable one, is the university system. As much as we sometimes roll our eyes at the ivory-tower isolation of universities, they continue to serve as remarkable engines of innovation.
  • Life seems to gravitate toward these complex states where there’s just enough disorder to create new things. There’s a rate of mutation just high enough to let interesting new innovations happen, but not so many mutations that every new generation dies off immediately.
  • , technology is an extension of life. Both life and technology are faces of the same larger system.
  • Weiye Loh on 30 Sep 10
     
    Kevin Kelly and Steven Johnson on Where Ideas Come From By Wired September 27, 2010  |  2:00 pm  |  Wired October 2010
Weiye Loh

Before Assange there was Jayakumar: Context, realpolitik, and the public inte... - 0 views

singapore2025.wordpress.com/...olitik-and-the-public-interest

Double Standard Wikileaks Singapore

shared by Weiye Loh on 04 Dec 10 - No Cached
  • Singapore Ministry of Foreign Affairs spokesman’s remarks in the Wall Street Journal Asia piece, “Leaked cable spooks some U.S. sources” dated 3 Dec 2010. The paragraph in question went like this: “Others laid blame not on working U.S. diplomats, but on Wikileaks. Singapore’s Ministry of Foreign Affairs said it had “deep concerns about the damaging action of Wikileaks.” It added, ‘it is critical to protect the confidentiality of diplomatic and official correspondence.’” (emphasis my own)
  • on 25 Jan 2003, the then Singapore Minister of Foreign Affairs and current Senior Minister without portfolio, Professor S Jayakumar, in an unprecedented move, unilaterally released all diplomatic and official correspondence relating to confidential discussions on water negotiations between Singapore and Malaysia from the year 2000. In a parliamentary speech that would have had Julian Assange smiling from ear to ear, Jayakumar said, “We therefore have no choice but to set the record straight by releasing these documents for people to judge for themselves the truth of the matter.” The parliamentary reason for the unprecedented release of information was the misrepresentations made by Malaysia over the price of water, amongst others.
  • The then Malaysian Prime Minister, Mahathir’s response to Singapore’s pre-Wikileak wikileak was equally quote-worthy, “I don’t feel nice. You write a letter to your girlfriend. And your girlfriend circulates it to all her boyfriends. I don’t think I’ll get involved with that girl.”
  • ...9 more annotations...
  • Mahathir did not leave it at that. He foreshadowed the Wikileak-chastised countries of today saying what William, the Singapore Ministry of Foreign Affairs, the US and Iran today, amongst others, must agree with, “It’s very difficult now for us to write letters at all because we might as well negotiate through the media.”
  • I proceeded to the Ministry of Foreign Affairs homepage to search for the full press release. As I anticipated, there was a caveat. This is the press release in full: In response to media queries on the WikiLeaks release of confidential and secret-graded US diplomatic correspondence, the MFA Spokesman expressed deep concerns about the damaging action of WikiLeaks. It is critical to protect the confidentiality of diplomatic and official correspondence, which is why Singapore has the Officials Secrets Act. In particular, the selective release of documents, especially when taken out of context, will only serve to sow confusion and fail to provide a complete picture of the important issues that were being discussed amongst leaders in the strictest of confidentiality.
  • The sentence in red seems to posit that the selective release of documents can be legitimised if released documents are not taken out of context. If this interpretation is true, then one can account for the political decision to release confidential correspondence covering the Singapore and Malaysia water talks referred to above. In parallel, one can imagine Assange or his supporters arguing that lies of weapons of mass destruction in Iraq and the advent of abject two-faced politics today to be sufficient grounds to justify the actions of Wikileaks. As for the arguments about confidentiality and official correspondence, the events in parliament in 2003 tell us no one should underestimate the ability of nation-states to do an Assange if it befits their purpose – be it directly, as Jayakumar did, or indirectly, through the media or some other medium of influence.
  • Timothy Garton Ash put out the dilemma perfectly when he said, “There is a public interest in understanding how the world works and what is done in our name. There is a public interest in the confidential conduct of foreign policy. The two public interests conflict.”
  • the advent of technology will only further blur the lines between these two public interests, if it has not already. Quite apart from technology, the absence of transparent and accountable institutions may also serve to guarantee the prospect of more of such embarrassing leaks in future.
  • In August 2009, there was considerable interest in Singapore about the circumstances behind the departure of Chip Goodyear, former CEO of the Australian mining giant BHP Billiton, from the national sovereign wealth fund, Temasek Holdings. Before that, all the public knew was – in the name of leadership renewal – Chip Goodyear had been carefully chosen and apparently hand-picked to replace Ho Ching as CEO of Temasek Holdings. In response to Chip’s untimely departure, Finance Minister Tharman Shanmugaratnam was quoted, “People do want to know, there is curiosity, it is a matter of public interest. That is not sufficient reason to disclose information. It is not sufficient that there be curiosity and interest that you want to disclose information.”
  • Overly secretive and furtive politicians operating in a parliamentary democracy are unlikely to inspire confidence among an educated citizenry either, only serving to paradoxically fuel public cynicism and conspiracy theories.
  • I believe that government officials and politicians who perform their jobs honourably have nothing to fear from Wikileaks. I would admit that there is an inherent naivety and idealism in this position. But if the lesson from the Wikileaks episode portends a higher standard of ethical conduct, encourages transparency and accountability – all of which promote good governance, realpolitik notwithstanding – then it is perhaps a lesson all politicians and government officials should pay keen attention to.
  • Post-script: “These disclosures are largely of analysis and high-grade gossip. Insofar as they are sensational, it is in showing the corruption and mendacity of those in power, and the mismatch between what they claim and what they do….If American spies are breaking United Nations rules by seeking the DNA biometrics of the UN director general, he is entitled to hear of it. British voters should know what Afghan leaders thought of British troops. American (and British) taxpayers might question, too, how most of the billions of dollars going in aid to Afghanistan simply exits the country at Kabul airport.” –Simon Jenkins, Guardian
Weiye Loh

takchek (读书 ): How Nature selects manuscripts for publication - 0 views

takchek.blogspot.com/...e-selects-manuscripts-for.html

Academic Journal Academic Research

shared by Weiye Loh on 24 Feb 10 - Cached
  • the explanation's pretty weak on the statistics given that it is a scientific journal. Drug Monkey and writedit have more on commentary about this particular editorial.
  • Good science, bad science, and whether it will lead to publication or not all rests on the decision of the editor. The gatekeeper.
  • do you know that Watson and Crick's landmark 1953 paper on the structure of DNA in the journal was not sent out for peer review at all?The reasons, as stated by Nature's Emeritus Editor John Maddox were:First, the Crick and Watson paper could not have been refereed: its correctness is self-evident. No referee working in the field (Linus Pauling?) could have kept his mouth shut once he saw the structure. Second, it would have been entirely consistent with my predecessor L. J. F. Brimble's way of working that Bragg's commendation should have counted as a referee's approval.
  • ...1 more annotation...
  • The whole business of scientific publishing is murky and sometimes who you know counts more than what you know in order to get your foot into the 'club'. Even Maddox alluded to the existence of such an 'exclusive' club:Brimble, who used to "take luncheon" at the Athenaeum in London most days, preferred to carry a bundle of manuscripts with him in the pocket of his greatcoat and pass them round among his chums "taking coffee" in the drawing-room after lunch. I set up a more systematic way of doing the job when I became editor in April 1966.
  • Weiye Loh on 24 Feb 10
     
    How Nature selects manuscripts for publication Nature actually devoted an editorial (doi:10.1038/463850a) explaining its publication process.
Weiye Loh

Basics - Another Challenge for Ethical Eating - Plants Want to Live, Too - NYTimes.com - 0 views

www.nytimes.com/...22angi.html

Ethics

shared by Weiye Loh on 26 Dec 09 - Cached
  • Food choices are often like that: difficult to articulate yet strongly held. And lately, debates over food choices have flared with particular vehemence.
  • Gary Steiner, a philosopher at Bucknell University, argued on the Op-Ed page of The New York Times that people should strive to be “strict ethical vegans” like himself, avoiding all products derived from animals, including wool and silk. Killing animals for human food and finery is nothing less than “outright murder,”
  • we might consider that plants no more aspire to being stir-fried in a wok than a hog aspires to being peppercorn-studded in my Christmas clay pot.
  • ...11 more annotations...
  • Plants are lively and seek to keep it that way.
  • their keen sensitivity to the environment, the speed with which they react to changes in the environment, and the extraordinary number of tricks that plants will rally to fight off attackers and solicit help from afar
  • When plant biologists speak of their subjects, they use active verbs and vivid images. Plants “forage” for resources like light and soil nutrients and “anticipate” rough spots and opportunities.
  • Plants can’t run away from a threat but they can stand their ground. “They are very good at avoiding getting eaten,”
  • At the smallest nip to its leaves, specialized cells on the plant’s surface release chemicals to irritate the predator or sticky goo to entrap it. Genes in the plant’s DNA are activated to wage systemwide chemical warfare, the plant’s version of an immune response.
  • in less than 20 minutes from the moment the caterpillar had begun feeding on its leaves, the plant had plucked carbon from the air and forged defensive compounds from scratch.
  • Just because we humans can’t hear them doesn’t mean plants don’t howl. Some of the compounds that plants generate in response to insect mastication — their feedback, you might say — are volatile chemicals that serve as cries for help. Such airborne alarm calls have been shown to attract both large predatory insects like dragon flies, which delight in caterpillar meat, and tiny parasitic insects, which can infect a caterpillar and destroy it from within.
  • certain plants can sense when insect eggs have been deposited on their leaves and will act immediately to rid themselves of the incubating menace. They may sprout carpets of tumorlike neoplasms to knock the eggs off, or secrete ovicides to kill them
  • when a female cabbage butterfly lays her eggs on a brussels sprout plant and attaches her treasures to the leaves with tiny dabs of glue, the vigilant vegetable detects the presence of a simple additive in the glue, benzyl cyanide. Cued by the additive, the plant swiftly alters the chemistry of its leaf surface to beckon female parasitic wasps. Spying the anchored bounty, the female wasps in turn inject their eggs inside, the gestating wasps feed on the gestating butterflies, and the plant’s problem is solved.
  • seedlings of the dodder plant, a parasitic weed related to morning glory, can detect volatile chemicals released by potential host plants like the tomato. The young dodder then grows inexorably toward the host, until it can encircle the victim’s stem and begin sucking the life phloem right out of it. The parasite can even distinguish between the scents of healthier and weaker tomato plants and then head for the hale one.
  • It’s a small daily tragedy that we animals must kill to stay alive. Plants are the ethical autotrophs here, the ones that wrest their meals from the sun. Don’t expect them to boast: they’re too busy fighting to survive.
  • Weiye Loh on 26 Dec 09
     
    Sorry, Vegans: Brussels Sprouts Like to Live, Too
  • guanyou chen on 27 Dec 09
     
    Eh bro, even after results are out you're still relentless with the postings. It's the hols man...
Weiye Loh

Odds Are, It's Wrong - Science News - 0 views

www.sciencenews.org/...Odds_Are%2C_Its_Wrong

Statistics Science

shared by Weiye Loh on 20 Mar 10 - Cached
  • science has long been married to mathematics. Generally it has been for the better. Especially since the days of Galileo and Newton, math has nurtured science. Rigorous mathematical methods have secured science’s fidelity to fact and conferred a timeless reliability to its findings.
  • a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
  • science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
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  • Experts in the math of probability and statistics are well aware of these problems and have for decades expressed concern about them in major journals. Over the years, hundreds of published papers have warned that science’s love affair with statistics has spawned countless illegitimate findings. In fact, if you believe what you read in the scientific literature, you shouldn’t believe what you read in the scientific literature.
  • “There are more false claims made in the medical literature than anybody appreciates,” he says. “There’s no question about that.”Nobody contends that all of science is wrong, or that it hasn’t compiled an impressive array of truths about the natural world. Still, any single scientific study alone is quite likely to be incorrect, thanks largely to the fact that the standard statistical system for drawing conclusions is, in essence, illogical. “A lot of scientists don’t understand statistics,” says Goodman. “And they don’t understand statistics because the statistics don’t make sense.”
  • In 2007, for instance, researchers combing the medical literature found numerous studies linking a total of 85 genetic variants in 70 different genes to acute coronary syndrome, a cluster of heart problems. When the researchers compared genetic tests of 811 patients that had the syndrome with a group of 650 (matched for sex and age) that didn’t, only one of the suspect gene variants turned up substantially more often in those with the syndrome — a number to be expected by chance.“Our null results provide no support for the hypothesis that any of the 85 genetic variants tested is a susceptibility factor” for the syndrome, the researchers reported in the Journal of the American Medical Association.How could so many studies be wrong? Because their conclusions relied on “statistical significance,” a concept at the heart of the mathematical analysis of modern scientific experiments.
  • Statistical significance is a phrase that every science graduate student learns, but few comprehend. While its origins stretch back at least to the 19th century, the modern notion was pioneered by the mathematician Ronald A. Fisher in the 1920s. His original interest was agriculture. He sought a test of whether variation in crop yields was due to some specific intervention (say, fertilizer) or merely reflected random factors beyond experimental control.Fisher first assumed that fertilizer caused no difference — the “no effect” or “null” hypothesis. He then calculated a number called the P value, the probability that an observed yield in a fertilized field would occur if fertilizer had no real effect. If P is less than .05 — meaning the chance of a fluke is less than 5 percent — the result should be declared “statistically significant,” Fisher arbitrarily declared, and the no effect hypothesis should be rejected, supposedly confirming that fertilizer works.Fisher’s P value eventually became the ultimate arbiter of credibility for science results of all sorts
  • But in fact, there’s no logical basis for using a P value from a single study to draw any conclusion. If the chance of a fluke is less than 5 percent, two possible conclusions remain: There is a real effect, or the result is an improbable fluke. Fisher’s method offers no way to know which is which. On the other hand, if a study finds no statistically significant effect, that doesn’t prove anything, either. Perhaps the effect doesn’t exist, or maybe the statistical test wasn’t powerful enough to detect a small but real effect.
  • Soon after Fisher established his system of statistical significance, it was attacked by other mathematicians, notably Egon Pearson and Jerzy Neyman. Rather than testing a null hypothesis, they argued, it made more sense to test competing hypotheses against one another. That approach also produces a P value, which is used to gauge the likelihood of a “false positive” — concluding an effect is real when it actually isn’t. What  eventually emerged was a hybrid mix of the mutually inconsistent Fisher and Neyman-Pearson approaches, which has rendered interpretations of standard statistics muddled at best and simply erroneous at worst. As a result, most scientists are confused about the meaning of a P value or how to interpret it. “It’s almost never, ever, ever stated correctly, what it means,” says Goodman.
  • experimental data yielding a P value of .05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct). But many explanations mangle the subtleties in that definition. A recent popular book on issues involving science, for example, states a commonly held misperception about the meaning of statistical significance at the .05 level: “This means that it is 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance.”
  • That interpretation commits an egregious logical error (technical term: “transposed conditional”): confusing the odds of getting a result (if a hypothesis is true) with the odds favoring the hypothesis if you observe that result. A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time. (See Box 2)
    • Weiye Loh
      Weiye Loh on 20 Mar 10
       
      Does the problem then, lie not in statistics, but the interpretation of statistics? Is the fallacy of appeal to probability is at work in such interpretation? 
  • Another common error equates statistical significance to “significance” in the ordinary use of the word. Because of the way statistical formulas work, a study with a very large sample can detect “statistical significance” for a small effect that is meaningless in practical terms. A new drug may be statistically better than an old drug, but for every thousand people you treat you might get just one or two additional cures — not clinically significant. Similarly, when studies claim that a chemical causes a “significantly increased risk of cancer,” they often mean that it is just statistically significant, possibly posing only a tiny absolute increase in risk.
  • Statisticians perpetually caution against mistaking statistical significance for practical importance, but scientific papers commit that error often. Ziliak studied journals from various fields — psychology, medicine and economics among others — and reported frequent disregard for the distinction.
  • “I found that eight or nine of every 10 articles published in the leading journals make the fatal substitution” of equating statistical significance to importance, he said in an interview. Ziliak’s data are documented in the 2008 book The Cult of Statistical Significance, coauthored with Deirdre McCloskey of the University of Illinois at Chicago.
  • Multiplicity of mistakesEven when “significance” is properly defined and P values are carefully calculated, statistical inference is plagued by many other problems. Chief among them is the “multiplicity” issue — the testing of many hypotheses simultaneously. When several drugs are tested at once, or a single drug is tested on several groups, chances of getting a statistically significant but false result rise rapidly.
  • Recognizing these problems, some researchers now calculate a “false discovery rate” to warn of flukes disguised as real effects. And genetics researchers have begun using “genome-wide association studies” that attempt to ameliorate the multiplicity issue (SN: 6/21/08, p. 20).
  • Many researchers now also commonly report results with confidence intervals, similar to the margins of error reported in opinion polls. Such intervals, usually given as a range that should include the actual value with 95 percent confidence, do convey a better sense of how precise a finding is. But the 95 percent confidence calculation is based on the same math as the .05 P value and so still shares some of its problems.
  • Statistical problems also afflict the “gold standard” for medical research, the randomized, controlled clinical trials that test drugs for their ability to cure or their power to harm. Such trials assign patients at random to receive either the substance being tested or a placebo, typically a sugar pill; random selection supposedly guarantees that patients’ personal characteristics won’t bias the choice of who gets the actual treatment. But in practice, selection biases may still occur, Vance Berger and Sherri Weinstein noted in 2004 in ControlledClinical Trials. “Some of the benefits ascribed to randomization, for example that it eliminates all selection bias, can better be described as fantasy than reality,” they wrote.
  • Randomization also should ensure that unknown differences among individuals are mixed in roughly the same proportions in the groups being tested. But statistics do not guarantee an equal distribution any more than they prohibit 10 heads in a row when flipping a penny. With thousands of clinical trials in progress, some will not be well randomized. And DNA differs at more than a million spots in the human genetic catalog, so even in a single trial differences may not be evenly mixed. In a sufficiently large trial, unrandomized factors may balance out, if some have positive effects and some are negative. (See Box 3) Still, trial results are reported as averages that may obscure individual differences, masking beneficial or harm­ful effects and possibly leading to approval of drugs that are deadly for some and denial of effective treatment to others.
  • nother concern is the common strategy of combining results from many trials into a single “meta-analysis,” a study of studies. In a single trial with relatively few participants, statistical tests may not detect small but real and possibly important effects. In principle, combining smaller studies to create a larger sample would allow the tests to detect such small effects. But statistical techniques for doing so are valid only if certain criteria are met. For one thing, all the studies conducted on the drug must be included — published and unpublished. And all the studies should have been performed in a similar way, using the same protocols, definitions, types of patients and doses. When combining studies with differences, it is necessary first to show that those differences would not affect the analysis, Goodman notes, but that seldom happens. “That’s not a formal part of most meta-analyses,” he says.
  • Meta-analyses have produced many controversial conclusions. Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity. Similar problems afflicted a 2007 meta-analysis, published in the New England Journal of Medicine, that attributed increased heart attack risk to the diabetes drug Avandia. Raw data from the combined trials showed that only 55 people in 10,000 had heart attacks when using Avandia, compared with 59 people per 10,000 in comparison groups. But after a series of statistical manipulations, Avandia appeared to confer an increased risk.
  • combining small studies in a meta-analysis is not a good substitute for a single trial sufficiently large to test a given question. “Meta-analyses can reduce the role of chance in the interpretation but may introduce bias and confounding,” Hennekens and DeMets write in the Dec. 2 Journal of the American Medical Association. “Such results should be considered more as hypothesis formulating than as hypothesis testing.”
  • Some studies show dramatic effects that don’t require sophisticated statistics to interpret. If the P value is 0.0001 — a hundredth of a percent chance of a fluke — that is strong evidence, Goodman points out. Besides, most well-accepted science is based not on any single study, but on studies that have been confirmed by repetition. Any one result may be likely to be wrong, but confidence rises quickly if that result is independently replicated.“Replication is vital,” says statistician Juliet Shaffer, a lecturer emeritus at the University of California, Berkeley. And in medicine, she says, the need for replication is widely recognized. “But in the social sciences and behavioral sciences, replication is not common,” she noted in San Diego in February at the annual meeting of the American Association for the Advancement of Science. “This is a sad situation.”
  • Most critics of standard statistics advocate the Bayesian approach to statistical reasoning, a methodology that derives from a theorem credited to Bayes, an 18th century English clergyman. His approach uses similar math, but requires the added twist of a “prior probability” — in essence, an informed guess about the expected probability of something in advance of the study. Often this prior probability is more than a mere guess — it could be based, for instance, on previous studies.
  • it basically just reflects the need to include previous knowledge when drawing conclusions from new observations. To infer the odds that a barking dog is hungry, for instance, it is not enough to know how often the dog barks when well-fed. You also need to know how often it eats — in order to calculate the prior probability of being hungry. Bayesian math combines a prior probability with observed data to produce an estimate of the likelihood of the hunger hypothesis. “A scientific hypothesis cannot be properly assessed solely by reference to the observational data,” but only by viewing the data in light of prior belief in the hypothesis, wrote George Diamond and Sanjay Kaul of UCLA’s School of Medicine in 2004 in the Journal of the American College of Cardiology. “Bayes’ theorem is ... a logically consistent, mathematically valid, and intuitive way to draw inferences about the hypothesis.” (See Box 4)
  • In many real-life contexts, Bayesian methods do produce the best answers to important questions. In medical diagnoses, for instance, the likelihood that a test for a disease is correct depends on the prevalence of the disease in the population, a factor that Bayesian math would take into account.
  • But Bayesian methods introduce a confusion into the actual meaning of the mathematical concept of “probability” in the real world. Standard or “frequentist” statistics treat probabilities as objective realities; Bayesians treat probabilities as “degrees of belief” based in part on a personal assessment or subjective decision about what to include in the calculation. That’s a tough placebo to swallow for scientists wedded to the “objective” ideal of standard statistics. “Subjective prior beliefs are anathema to the frequentist, who relies instead on a series of ad hoc algorithms that maintain the facade of scientific objectivity,” Diamond and Kaul wrote.Conflict between frequentists and Bayesians has been ongoing for two centuries. So science’s marriage to mathematics seems to entail some irreconcilable differences. Whether the future holds a fruitful reconciliation or an ugly separation may depend on forging a shared understanding of probability.“What does probability mean in real life?” the statistician David Salsburg asked in his 2001 book The Lady Tasting Tea. “This problem is still unsolved, and ... if it remains un­solved, the whole of the statistical approach to science may come crashing down from the weight of its own inconsistencies.”
  • Weiye Loh on 20 Mar 10
     
    Odds Are, It's Wrong Science fails to face the shortcomings of statistics
Weiye Loh

Rationally Speaking: A new eugenics? - 0 views

rationallyspeaking.blogspot.com/...new-eugenics.html

Eugenics Designer Baby Baby

shared by Weiye Loh on 26 Jan 11 - No Cached
  • an interesting article I read recently, penned by Julian Savulescu for the Practical Ethics blog.
  • Savulescu discusses an ongoing controversy in Germany about genetic testing of human embryos. The Leopoldina, Germany’s equivalent of the National Academy of Sciences, has recommended genetic testing of pre-implant embryos, to screen for serious and incurable defects. The German Chancellor, Angela Merkel, has agreed to allow a parliamentary vote on this issue, but also said that she personally supports a ban on this type of testing. Her fear is that the testing would quickly lead to “designer babies,” i.e. to parents making choices about their unborn offspring based not on knowledge about serious disease, but simply because they happen to prefer a particular height or eye color.
  • He infers from Merkel’s comments (and many similar others) that people tend to think of selecting traits like eye color as eugenics, while acting to avoid incurable disease is not considered eugenics. He argues that this is exactly wrong: eugenics, as he points out, means “well born,” so eugenicists have historically been concerned with eliminating traits that would harm society (Wendell Holmes’ “three generation of imbeciles”), not with simple aesthetic choices. As Savulescu puts it: “[eugenics] is selecting embryos which are better, in this context, have better lives. Being healthy rather than sick is ‘better.’ Having blond hair and blue eyes is not in any plausible sense ‘better,’ even if people mistakenly think so.”
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  • And there is another, related aspect of discussions about eugenics that should be at the forefront of our consideration: what was particularly objectionable about American and Nazi early 20th century eugenics is that the state, not individuals, were to make decisions about who could reproduce and who couldn’t. Savulescu continues: “to grant procreative liberty is the only way to avoid the objectionable form of eugenics that the Nazis practiced.” In other words, it makes all the difference in the world if it is an individual couple who decides to have or not have a baby, or if it is the state that imposes a particular reproductive choice on its citizenry.
  • but then Savulescu expands his argument to a point where I begin to feel somewhat uncomfortable. He says: “[procreative liberty] involves the freedom to choose a child with red hair or blond hair or no hair.”
  • Savulescu has suddenly sneaked into his argument for procreative liberty the assumption that all choices in this area are on the same level. But while it is hard to object to action aimed at avoiding devastating diseases, it is not quite so obvious to me what arguments favor the idea of designer babies. The first intervention can be justified, for instance, on consequentialist grounds because it reduces the pain and suffering of both the child and the parents. The second intervention is analogous to shopping for a new bag, or a new car, which means that it commodifies the act of conceiving a baby, thus degrading its importance. I’m not saying that that in itself is sufficient to make it illegal, but the ethics of it is different, and that difference cannot simply be swept under the broad rug of “procreative liberty.”
  • designing babies is to treat them as objects, not as human beings, and there are a couple of strong philosophical traditions in ethics that go squarely against that (I’m thinking, obviously, of Kant’s categorical imperative, as well as of virtue ethics; not sure what a consequentialist would say about this, probably she would remain neutral on the issue).
  • Commodification of human beings has historically produced all sorts of bad stuff, from slavery to exploitative prostitution, and arguably to war (after all, we are using our soldiers as means to gain access to power, resources, territory, etc.)
  • And of course, there is the issue of access. Across-the-board “procreative liberty” of the type envisioned by Savulescu will cost money because it requires considerable resources.
  • imagine that these parents decide to purchase the ability to produce babies that have the type of characteristics that will make them more successful in society: taller, more handsome, blue eyed, blonde, more symmetrical, whatever. We have just created yet another way for the privileged to augment and pass their privileges to the next generation — in this case literally through their genes, not just as real estate or bank accounts. That would quickly lead to an even further divide between the haves and the have-nots, more inequality, more injustice, possibly, in the long run, even two different species (why not design your babies so that they can’t breed with certain types of undesirables, for instance?). Is that the sort of society that Savulescu is willing to envision in the name of his total procreative liberty? That begins to sounds like the libertarian version of the eugenic ideal, something potentially only slightly less nightmarish than the early 20th century original.
  • Rich people already have better choices when it comes to their babies. Taller and richer men can choose between more attractive and physically fit women and attractive women can choose between more physically fit and rich men. So it is reasonable to conclude that on average rich and attractive people already have more options when it comes to their offspring. Moreover no one is questioning their right to do so and this is based on a respect for a basic instinct which we all have and which is exactly why these people would choose to have a DB. Is it fair for someone to be tall because his daddy was rich and married a supermodel but not because his daddy was rich and had his DNA resequenced? Is it former good because its natural and the latter bad because its not? This isn't at all obvious to me.
  • Not to mention that rich people can provide better health care, education and nutrition to their children and again no one is questioning their right to do so. Wouldn't a couple of inches be pretty negligible compared to getting into a good school? Aren't we applying double standards by objecting to this issue alone? Do we really live in a society that values equal opportunities? People (may) be equal before the law but they are not equal to each other and each one of us is tacitly accepting that fact when we acknowledge the social hierarchy (in other words, every time we interact with someone who is our superior). I am not crazy about this fact but that's just how people are and this has to be taken into account when discussing this.
Weiye Loh

m.guardian.co.uk - 0 views

m.guardian.co.uk/...ips-reprogrammed-stem-cells

Stem Cells DNA Politics

shared by Weiye Loh on 13 Mar 11 - No Cached
  • perhaps the reason stem cells managed to lodge themselves so deep in the public psyche was not just because of their awesome scientific potential, or their ability to turn into the treatments of the future.
  • For years, stem cells dominated all other science stories in newspaper headlines because they framed an ethical conundrum – to get to the most versatile stem cells meant destroying human embryos.
  • Research on stem cells became a political football, leading to delays in funding for scientists, particularly in the US. Not that the work itself was straightforward – the process of extracting stem cells from embryos is difficult and there is a very limited supply of material. Inevitable disappointment followed the years of headlines – where were the promised treatments? Was it all over-hyped?
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  • Key to this is the discovery, in the past few years, of a way to make stem cells that do not require the destruction of embryos. In one move, these induced pluripotent stem (iPS) cells remove the ethical roadblocks faced by embryonic stem cells and, because they are so much easier to make, give scientists an inexhaustible supply of material, bringing them ever closer to those hoped-for treatments.
  • Stem cells are the body's master cells, the raw material from which we are built. Unlike normal body cells, they can reproduce an indefinite number of times and, when prodded in the right way, can turn themselves into any type of cell in the body. The most versatile stem cells are those found in the embryo at just a few days old – this ball of a few dozen embryonic stem (ES) cells eventually goes on to form everything that makes up a person.
Weiye Loh

Science, Strong Inference -- Proper Scientific Method - 0 views

256.com/...strong_inference.html

Science Statistics Media Representation Methodolatry Falsifiability

shared by Weiye Loh on 20 Mar 11 - No Cached
  • Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.
  • Why should there be such rapid advances in some fields and not in others? I think the usual explanations that we tend to think of - such as the tractability of the subject, or the quality or education of the men drawn into it, or the size of research contracts - are important but inadequate. I have begun to believe that the primary factor in scientific advance is an intellectual one. These rapidly moving fields are fields where a particular method of doing scientific research is systematically used and taught, an accumulative method of inductive inference that is so effective that I think it should be given the name of "strong inference." I believe it is important to examine this method, its use and history and rationale, and to see whether other groups and individuals might learn to adopt it profitably in their own scientific and intellectual work. In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: Devising alternative hypotheses; Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly is possible, exclude one or more of the hypotheses; Carrying out the experiment so as to get a clean result; Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.
  • On any new problem, of course, inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown. Steps 1 and 2 require intellectual inventions, which must be cleverly chosen so that hypothesis, experiment, outcome, and exclusion will be related in a rigorous syllogism; and the question of how to generate such inventions is one which has been extensively discussed elsewhere (2, 3). What the formal schema reminds us to do is to try to make these inventions, to take the next step, to proceed to the next fork, without dawdling or getting tied up in irrelevancies.
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  • It is clear why this makes for rapid and powerful progress. For exploring the unknown, there is no faster method; this is the minimum sequence of steps. Any conclusion that is not an exclusion is insecure and must be rechecked. Any delay in recycling to the next set of hypotheses is only a delay. Strong inference, and the logical tree it generates, are to inductive reasoning what the syllogism is to deductive reasoning in that it offers a regular method for reaching firm inductive conclusions one after the other as rapidly as possible.
  • "But what is so novel about this?" someone will say. This is the method of science and always has been, why give it a special name? The reason is that many of us have almost forgotten it. Science is now an everyday business. Equipment, calculations, lectures become ends in themselves. How many of us write down our alternatives and crucial experiments every day, focusing on the exclusion of a hypothesis? We may write our scientific papers so that it looks as if we had steps 1, 2, and 3 in mind all along. But in between, we do busywork. We become "method- oriented" rather than "problem-oriented." We say we prefer to "feel our way" toward generalizations. We fail to teach our students how to sharpen up their inductive inferences. And we do not realize the added power that the regular and explicit use of alternative hypothesis and sharp exclusion could give us at every step of our research.
  • A distinguished cell biologist rose and said, "No two cells give the same properties. Biology is the science of heterogeneous systems." And he added privately. "You know there are scientists, and there are people in science who are just working with these over-simplified model systems - DNA chains and in vitro systems - who are not doing science at all. We need their auxiliary work: they build apparatus, they make minor studies, but they are not scientists." To which Cy Levinthal replied: "Well, there are two kinds of biologists, those who are looking to see if there is one thing that can be understood and those who keep saying it is very complicated and that nothing can be understood. . . . You must study the simplest system you think has the properties you are interested in."
  • At the 1958 Conference on Biophysics, at Boulder, there was a dramatic confrontation between the two points of view. Leo Szilard said: "The problems of how enzymes are induced, of how proteins are synthesized, of how antibodies are formed, are closer to solution than is generally believed. If you do stupid experiments, and finish one a year, it can take 50 years. But if you stop doing experiments for a little while and think how proteins can possibly be synthesized, there are only about 5 different ways, not 50! And it will take only a few experiments to distinguish these." One of the young men added: "It is essentially the old question: How small and elegant an experiment can you perform?" These comments upset a number of those present. An electron microscopist said. "Gentlemen, this is off the track. This is philosophy of science." Szilard retorted. "I was not quarreling with third-rate scientists: I was quarreling with first-rate scientists."
  • Any criticism or challenge to consider changing our methods strikes of course at all our ego-defenses. But in this case the analytical method offers the possibility of such great increases in effectiveness that it is unfortunate that it cannot be regarded more often as a challenge to learning rather than as challenge to combat. Many of the recent triumphs in molecular biology have in fact been achieved on just such "oversimplified model systems," very much along the analytical lines laid down in the 1958 discussion. They have not fallen to the kind of men who justify themselves by saying "No two cells are alike," regardless of how true that may ultimately be. The triumphs are in fact triumphs of a new way of thinking.
  • the emphasis on strong inference
  • is also partly due to the nature of the fields themselves. Biology, with its vast informational detail and complexity, is a "high-information" field, where years and decades can easily be wasted on the usual type of "low-information" observations or experiments if one does not think carefully in advance about what the most important and conclusive experiments would be. And in high-energy physics, both the "information flux" of particles from the new accelerators and the million-dollar costs of operation have forced a similar analytical approach. It pays to have a top-notch group debate every experiment ahead of time; and the habit spreads throughout the field.
  • Historically, I think, there have been two main contributions to the development of a satisfactory strong-inference method. The first is that of Francis Bacon (13). He wanted a "surer method" of "finding out nature" than either the logic-chopping or all-inclusive theories of the time or the laudable but crude attempts to make inductions "by simple enumeration." He did not merely urge experiments as some suppose, he showed the fruitfulness of interconnecting theory and experiment so that the one checked the other. Of the many inductive procedures he suggested, the most important, I think, was the conditional inductive tree, which proceeded from alternative hypothesis (possible "causes," as he calls them), through crucial experiments ("Instances of the Fingerpost"), to exclusion of some alternatives and adoption of what is left ("establishing axioms"). His Instances of the Fingerpost are explicitly at the forks in the logical tree, the term being borrowed "from the fingerposts which are set up where roads part, to indicate the several directions."
  • ere was a method that could separate off the empty theories! Bacon, said the inductive method could be learned by anybody, just like learning to "draw a straighter line or more perfect circle . . . with the help of a ruler or a pair of compasses." "My way of discovering sciences goes far to level men's wit and leaves but little to individual excellence, because it performs everything by the surest rules and demonstrations." Even occasional mistakes would not be fatal. "Truth will sooner come out from error than from confusion."
  • Nevertheless there is a difficulty with this method. As Bacon emphasizes, it is necessary to make "exclusions." He says, "The induction which is to be available for the discovery and demonstration of sciences and arts, must analyze nature by proper rejections and exclusions, and then, after a sufficient number of negatives come to a conclusion on the affirmative instances." "[To man] it is granted only to proceed at first by negatives, and at last to end in affirmatives after exclusion has been exhausted." Or, as the philosopher Karl Popper says today there is no such thing as proof in science - because some later alternative explanation may be as good or better - so that science advances only by disproofs. There is no point in making hypotheses that are not falsifiable because such hypotheses do not say anything, "it must be possible for all empirical scientific system to be refuted by experience" (14).
  • The difficulty is that disproof is a hard doctrine. If you have a hypothesis and I have another hypothesis, evidently one of them must be eliminated. The scientist seems to have no choice but to be either soft-headed or disputatious. Perhaps this is why so many tend to resist the strong analytical approach and why some great scientists are so disputatious.
  • Fortunately, it seems to me, this difficulty can be removed by the use of a second great intellectual invention, the "method of multiple hypotheses," which is what was needed to round out the Baconian scheme. This is a method that was put forward by T.C. Chamberlin (15), a geologist at Chicago at the turn of the century, who is best known for his contribution to the Chamberlain-Moulton hypothesis of the origin of the solar system.
  • Chamberlin says our trouble is that when we make a single hypothesis, we become attached to it. "The moment one has offered an original explanation for a phenomenon which seems satisfactory, that moment affection for his intellectual child springs into existence, and as the explanation grows into a definite theory his parental affections cluster about his offspring and it grows more and more dear to him. . . . There springs up also unwittingly a pressing of the theory to make it fit the facts and a pressing of the facts to make them fit the theory..." "To avoid this grave danger, the method of multiple working hypotheses is urged. It differs from the simple working hypothesis in that it distributes the effort and divides the affections. . . . Each hypothesis suggests its own criteria, its own method of proof, its own method of developing the truth, and if a group of hypotheses encompass the subject on all sides, the total outcome of means and of methods is full and rich."
  • The conflict and exclusion of alternatives that is necessary to sharp inductive inference has been all too often a conflict between men, each with his single Ruling Theory. But whenever each man begins to have multiple working hypotheses, it becomes purely a conflict between ideas. It becomes much easier then for each of us to aim every day at conclusive disproofs - at strong inference - without either reluctance or combativeness. In fact, when there are multiple hypotheses, which are not anyone's "personal property," and when there are crucial experiments to test them, the daily life in the laboratory takes on an interest and excitement it never had, and the students can hardly wait to get to work to see how the detective story will come out. It seems to me that this is the reason for the development of those distinctive habits of mind and the "complex thought" that Chamberlin described, the reason for the sharpness, the excitement, the zeal, the teamwork - yes, even international teamwork - in molecular biology and high- energy physics today. What else could be so effective?
  • Unfortunately, I think, there are other other areas of science today that are sick by comparison, because they have forgotten the necessity for alternative hypotheses and disproof. Each man has only one branch - or none - on the logical tree, and it twists at random without ever coming to the need for a crucial decision at any point. We can see from the external symptoms that there is something scientifically wrong. The Frozen Method, The Eternal Surveyor, The Never Finished, The Great Man With a Single Hypothcsis, The Little Club of Dependents, The Vendetta, The All-Encompassing Theory Which Can Never Be Falsified.
  • a "theory" of this sort is not a theory at all, because it does not exclude anything. It predicts everything, and therefore does not predict anything. It becomes simply a verbal formula which the graduate student repeats and believes because the professor has said it so often. This is not science, but faith; not theory, but theology. Whether it is hand-waving or number-waving, or equation-waving, a theory is not a theory unless it can be disproved. That is, unless it can be falsified by some possible experimental outcome.
  • the work methods of a number of scientists have been testimony to the power of strong inference. Is success not due in many cases to systematic use of Bacon's "surest rules and demonstrations" as much as to rare and unattainable intellectual power? Faraday's famous diary (16), or Fermi's notebooks (3, 17), show how these men believed in the effectiveness of daily steps in applying formal inductive methods to one problem after another.
  • Surveys, taxonomy, design of equipment, systematic measurements and tables, theoretical computations - all have their proper and honored place, provided they are parts of a chain of precise induction of how nature works. Unfortunately, all too often they become ends in themselves, mere time-serving from the point of view of real scientific advance, a hypertrophied methodology that justifies itself as a lore of respectability.
  • We speak piously of taking measurements and making small studies that will "add another brick to the temple of science." Most such bricks just lie around the brickyard (20). Tables of constraints have their place and value, but the study of one spectrum after another, if not frequently re-evaluated, may become a substitute for thinking, a sad waste of intelligence in a research laboratory, and a mistraining whose crippling effects may last a lifetime.
  • Beware of the man of one method or one instrument, either experimental or theoretical. He tends to become method-oriented rather than problem-oriented. The method-oriented man is shackled; the problem-oriented man is at least reaching freely toward that is most important. Strong inference redirects a man to problem-orientation, but it requires him to be willing repeatedly to put aside his last methods and teach himself new ones.
  • anyone who asks the question about scientific effectiveness will also conclude that much of the mathematizing in physics and chemistry today is irrelevant if not misleading. The great value of mathematical formulation is that when an experiment agrees with a calculation to five decimal places, a great many alternative hypotheses are pretty well excluded (though the Bohr theory and the Schrödinger theory both predict exactly the same Rydberg constant!). But when the fit is only to two decimal places, or one, it may be a trap for the unwary; it may be no better than any rule-of-thumb extrapolation, and some other kind of qualitative exclusion might be more rigorous for testing the assumptions and more important to scientific understanding than the quantitative fit.
  • Today we preach that science is not science unless it is quantitative. We substitute correlations for causal studies, and physical equations for organic reasoning. Measurements and equations are supposed to sharpen thinking, but, in my observation, they more often tend to make the thinking noncausal and fuzzy. They tend to become the object of scientific manipulation instead of auxiliary tests of crucial inferences.
  • Many - perhaps most - of the great issues of science are qualitative, not quantitative, even in physics and chemistry. Equations and measurements are useful when and only when they are related to proof; but proof or disproof comes first and is in fact strongest when it is absolutely convincing without any quantitative measurement.
  • you can catch phenomena in a logical box or in a mathematical box. The logical box is coarse but strong. The mathematical box is fine-grained but flimsy. The mathematical box is a beautiful way of wrapping up a problem, but it will not hold the phenomena unless they have been caught in a logical box to begin with.
  • Of course it is easy - and all too common - for one scientist to call the others unscientific. My point is not that my particular conclusions here are necessarily correct, but that we have long needed some absolute standard of possible scientific effectiveness by which to measure how well we are succeeding in various areas - a standard that many could agree on and one that would be undistorted by the scientific pressures and fashions of the times and the vested interests and busywork that they develop. It is not public evaluation I am interested in so much as a private measure by which to compare one's own scientific performance with what it might be. I believe that strong inference provides this kind of standard of what the maximum possible scientific effectiveness could be - as well as a recipe for reaching it.
  • The strong-inference point of view is so resolutely critical of methods of work and values in science that any attempt to compare specific cases is likely to sound but smug and destructive. Mainly one should try to teach it by example and by exhorting to self-analysis and self-improvement only in general terms
  • one severe but useful private test - a touchstone of strong inference - that removes the necessity for third-person criticism, because it is a test that anyone can learn to carry with him for use as needed. It is our old friend the Baconian "exclusion," but I call it "The Question." Obviously it should be applied as much to one's own thinking as to others'. It consists of asking in your own mind, on hearing any scientific explanation or theory put forward, "But sir, what experiment could disprove your hypothesis?"; or, on hearing a scientific experiment described, "But sir, what hypothesis does your experiment disprove?"
  • It is not true that all science is equal; or that we cannot justly compare the effectiveness of scientists by any method other than a mutual-recommendation system. The man to watch, the man to put your money on, is not the man who wants to make "a survey" or a "more detailed study" but the man with the notebook, the man with the alternative hypotheses and the crucial experiments, the man who knows how to answer your Question of disproof and is already working on it.
  • Weiye Loh on 20 Mar 11
     
    There is so much bad science and bad statistics information in media reports, publications, and shared between conversants that I think it is important to understand about facts and proofs and the associated pitfalls.
Weiye Loh

Open science: a future shaped by shared experience | Education | The Observer - 0 views

www.guardian.co.uk/...ience-shared-research-internet

Open Source Science

shared by Weiye Loh on 25 May 11 - No Cached
  • one day he took one of these – finding a mathematical proof about the properties of multidimensional objects – and put his thoughts on his blog. How would other people go about solving this conundrum? Would somebody else have any useful insights? Would mathematicians, notoriously competitive, be prepared to collaborate? "It was an experiment," he admits. "I thought it would be interesting to try."He called it the Polymath Project and it rapidly took on a life of its own. Within days, readers, including high-ranking academics, had chipped in vital pieces of information or new ideas. In just a few weeks, the number of contributors had reached more than 40 and a result was on the horizon. Since then, the joint effort has led to several papers published in journals under the collective pseudonym DHJ Polymath. It was an astonishing and unexpected result.
  • "If you set out to solve a problem, there's no guarantee you will succeed," says Gowers. "But different people have different aptitudes and they know different tricks… it turned out their combined efforts can be much quicker."
  • There are many interpretations of what open science means, with different motivations across different disciplines. Some are driven by the backlash against corporate-funded science, with its profit-driven research agenda. Others are internet radicals who take the "information wants to be free" slogan literally. Others want to make important discoveries more likely to happen. But for all their differences, the ambition remains roughly the same: to try and revolutionise the way research is performed by unlocking it and making it more public.
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  • Jackson is a young bioscientist who, like many others, has discovered that the technologies used in genetics and molecular biology, once the preserve of only the most well-funded labs, are now cheap enough to allow experimental work to take place in their garages. For many, this means that they can conduct genetic experiments in a new way, adopting the so-called "hacker ethic" – the desire to tinker, deconstruct, rebuild.
  • The rise of this group is entertainingly documented in a new book by science writer Marcus Wohlsen, Biopunk (Current £18.99), which describes the parallels between today's generation of biological innovators and the rise of computer software pioneers of the 1980s and 1990s. Indeed, Bill Gates has said that if he were a teenager today, he would be working on biotechnology, not computer software.
  • open scientists suggest that it doesn't have to be that way. Their arguments are propelled by a number of different factors that are making transparency more viable than ever.The first and most powerful change has been the use of the web to connect people and collect information. The internet, now an indelible part of our lives, allows like-minded individuals to seek one another out and share vast amounts of raw data. Researchers can lay claim to an idea not by publishing first in a journal (a process that can take many months) but by sharing their work online in an instant.And while the rapidly decreasing cost of previously expensive technical procedures has opened up new directions for research, there is also increasing pressure for researchers to cut costs and deliver results. The economic crisis left many budgets in tatters and governments around the world are cutting back on investment in science as they try to balance the books. Open science can, sometimes, make the process faster and cheaper, showing what one advocate, Cameron Neylon, calls "an obligation and responsibility to the public purse".
  • "The litmus test of openness is whether you can have access to the data," says Dr Rufus Pollock, a co-founder of the Open Knowledge Foundation, a group that promotes broader access to information and data. "If you have access to the data, then anyone can get it, use it, reuse it and redistribute it… we've always built on the work of others, stood on the shoulders of giants and learned from those who have gone before."
  • moves are afoot to disrupt the closed world of academic journals and make high-level teaching materials available to the public. The Public Library of Science, based in San Francisco, is working to make journals more freely accessible
  • it's more than just politics at stake – it's also a fundamental right to share knowledge, rather than hide it. The best example of open science in action, he suggests, is the Human Genome Project, which successfully mapped our DNA and then made the data public. In doing so, it outflanked J Craig Venter's proprietary attempt to patent the human genome, opening up the very essence of human life for science, rather than handing our biological information over to corporate interests.
  • the rise of open science does not please everyone. Critics have argued that while it benefits those at either end of the scientific chain – the well-established at the top of the academic tree or the outsiders who have nothing to lose – it hurts those in the middle. Most professional scientists rely on the current system for funding and reputation. Others suggest it is throwing out some of the most important elements of science and making deep, long-term research more difficult.
  • Open science proponents say that they do not want to make the current system a thing of the past, but that it shouldn't be seen as immutable either. In fact, they say, the way most people conceive of science – as a highly specialised academic discipline conducted by white-coated professionals in universities or commercial laboratories – is a very modern construction.It is only over the last century that scientific disciplines became industrialised and compartmentalised.
  • open scientists say they don't want to throw scientists to the wolves: they just want to help answer questions that, in many cases, are seen as insurmountable.
  • "Some people, very straightforwardly, said that they didn't like the idea because it undermined the concept of the romantic, lone genius." Even the most dedicated open scientists understand that appeal. "I do plan to keep going at them," he says of collaborative projects. "But I haven't given up on solitary thinking about problems entirely."
Weiye Loh

Evaluating The Evidence for Cell Phones and WiFi « Critical Thinking « Skepti... - 0 views

www.skepticnorth.com/...dence-for-cell-phones-and-wifi

Mobile Wi-Fi Radio Waves Cancer Uncertainty

shared by Weiye Loh on 05 Jun 11 - No Cached
  • he “weight of evidence” approach to evaluation of causality is often vilified by cell phone and WiFi scare mongers as being an inadequate way to judge the evidence – often because it disagrees with their own sentiments about the science.  If you can’t disqualify the evidence, then you can go after the method of evaluation and disqualify that, right?  Of course, the weight of evidence approach is often portrayed as a dumbshow of putting all the “positive” trials on one side of the scale and all of the “negative” trials on the other and taking the difference in mass as the evidence.  This is how Dr. Phillips characterised it in his paper on electromagnetic fields and DNA damage, as well as his appearance on CBC Radio.  Of course, the procedure is much more like a systematic review, where all of the papers, regardless of their outcomes, are weighed for their quality. (The higher quality studies will have good internal and external validity, proper blinding and randomisation, large enough sample size, proper controls and good statistical analysis; as well as being reproduced by independent investigators.) Then they are tallied and a rational conclusion is offered as to the most likely state of the evidence (of course, it is much more involved than I am stating, but suffice it to say, it does not involve a scale.)   This is standard operating procedure and, in fact, is what we all do when we are evaluating evidence: we decide which studies are good and we pool the evidence before we make a decision.
  • Weiye Loh on 05 Jun 11
     
    n many discussions of the "dangers" of WiFi and cell phones, the precautionary principle is evoked. It is the idea that we have "an obligation, if the level of harm may be high, for action to prevent or minimise such harm even when the absence of scientific certainty makes it difficult to predict the likelihood of harm occurring, or the level of harm should it occur."  It is important to note that the precautionary principle or approach is required when we do not have a scientific consensus or if we have a lack of scientific certainty.  It is used often in European regulation of potential health and environmental hazards.  "Scientific certainty" is an important clause here, because it does not mean 100% certainty. Science can never give that absolute a result and if we required 100% certainty of no risk, we would not walk out our front doors or even get out of bed, lest we have a mishap.
Weiye Loh

Alzheimer's Studies Find New Genetic Links - NYTimes.com - 0 views

www.nytimes.com/...04alzheimer.html

Genetic Alzheimer Data

shared by Weiye Loh on 04 Apr 11 - No Cached
  • The two largest studies of Alzheimer’s disease have led to the discovery of no fewer than five genes that provide intriguing new clues to why the disease strikes and how it progresses.
  • For years, there have been unproven but persistent hints that cholesterol and inflammation are part of the disease process. People with high cholesterol are more likely to get the disease. Strokes and head injuries, which make Alzheimer’s more likely, also cause brain inflammation. Now, some of the newly discovered genes appear to bolster this line of thought, because some are involved with cholesterol and others are linked to inflammation or the transport of molecules inside cells.
  • By themselves, the genes are not nearly as important a factor as APOE, a gene discovered in 1995 that greatly increases risk for the disease: by 400 percent if a person inherits a copy from one parent, by 1,000 percent if from both parents.
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  • In contrast, each of the new genes increases risk by no more than 10 to 15 percent; for that reason, they will not be used to decide if a person is likely to develop Alzheimer’s. APOE, which is involved in metabolizing cholesterol, “is in a class of its own,” said Dr. Rudolph Tanzi, a neurology professor at Harvard Medical School and an author of one of the papers.
  • But researchers say that even a slight increase in risk helps them in understanding the disease and developing new therapies. And like APOE, some of the newly discovered genes appear to be involved with cholesterol.
  • The other paper is by researchers in Britain, France and other European countries with contributions from the United States. They confirmed the genes found by the American researchers and added one more gene.
  • The American study got started about three years ago when Gerard D. Schellenberg, a pathology professor at the University of Pennsylvania, went to the National Institutes of Health with a complaint and a proposal. Individual research groups had been doing their own genome studies but not having much success, because no one center had enough subjects. In an interview, Dr. Schellenberg said that he had told Dr. Richard J. Hodes, director of the National Institute on Aging, the small genomic studies had to stop, and that Dr. Hodes had agreed. These days, Dr. Hodes said, “the old model in which researchers jealously guarded their data is no longer applicable.”
  • So Dr. Schellenberg set out to gather all the data he could on Alzheimer’s patients and on healthy people of the same ages. The idea was to compare one million positions on each person’s genome to determine whether some genes were more common in those who had Alzheimer’s. “I spent a lot of time being nice to people on the phone,” Dr. Schellenberg said. He got what he wanted: nearly every Alzheimer’s center and Alzheimer’s geneticist in the country cooperated. Dr. Schellenberg and his colleagues used the mass of genetic data to do an analysis and find the genes and then, using two different populations, to confirm that the same genes were conferring the risk. That helped assure the investigators that they were not looking at a chance association. It was a huge effort, Dr. Mayeux said. Many medical centers had Alzheimer’s patients’ tissue sitting in freezers. They had to extract the DNA and do genome scans.
  • “One of my jobs was to make sure the Alzheimer’s cases really were cases — that they had used some reasonable criteria” for diagnosis, Dr. Mayeux said. “And I had to be sure that people who were unaffected really were unaffected.”
  • Meanwhile, the European group, led by Dr. Julie Williams of the School of Medicine at Cardiff University, was engaged in a similar effort. Dr. Schellenberg said the two groups compared their results and were reassured that they were largely finding the same genes. “If there were mistakes, we wouldn’t see the same things,” he added. Now the European and American groups are pooling their data to do an enormous study, looking for genes in the combined samples. “We are upping the sample size,” Dr. Schellenberg said. “We are pretty sure more stuff will pop out.”
  • Weiye Loh on 04 Apr 11
     
    Gene Study Yields
Weiye Loh

Evolutionary analysis shows languages obey few ordering rules - 0 views

arstechnica.com/...s-languages-obey-few-rules.ars

Language Chomsky Universal Linguistics

shared by Weiye Loh on 18 Apr 11 - No Cached
  • The authors of the new paper point out just how hard it is to study languages. We're aware of over 7,000 of them, and they vary significantly in complexity. There are a number of large language families that are likely derived from a single root, but a large number of languages don't slot easily into one of the major groups. Against that backdrop, even a set of simple structural decisions—does the noun or verb come first? where does the preposition go?—become dizzyingly complex, with different patterns apparent even within a single language tree.
  • Linguists, however, have been attempting to find order within the chaos. Noam Chomsky helped establish the Generative school of thought, which suggests that there must be some constraints to this madness, some rules that help make a language easier for children to pick up, and hence more likely to persist. Others have approached this issue via a statistical approach (the authors credit those inspired by Joseph Greenberg for this), looking for word-order rules that consistently correlate across language families. This approach has identified a handful of what may be language universals, but our uncertainty about language relationships can make it challenging to know when some of these are correlations are simply derived from a common inheritance.
  • For anyone with a biology background, having traits shared through common inheritance should ring a bell. Evolutionary biologists have long been able to build family trees of related species, called phylogenetic trees. By figuring out what species have the most traits in common and grouping them together, it's possible to identify when certain features have evolved in the past. In recent years, the increase in computing power and DNA sequences to align has led to some very sophisticated phylogenetic software, which can analyze every possible tree and perform a Bayesian statistical analysis to figure out which trees are most likely to represent reality. By treating language features like subject-verb order as a trait, the authors were able to perform this sort of analysis on four different language families: 79 Indo-European languages, 130 Austronesian languages, 66 Bantu languages, and 26 Uto-Aztecan languages. Although we don't have a complete roster of the languages in those families, they include over 2,400 languages that have been evolving for a minimum of 4,000 years.
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  • The results are bad news for universalists: "most observed functional dependencies between traits are lineage-specific rather than universal tendencies," according to the authors. The authors were able to identify 19 strong correlations between word order traits, but none of these appeared in all four families; only one of them appeared in more than two. Fifteen of them only occur in a single family. Specific predictions based on the Greenberg approach to linguistics also failed to hold up under the phylogenetic analysis. "Systematic linkages of traits are likely to be the rare exception rather than the rule," the authors conclude.
  • If universal features can't account for what we observe, what can? Common descent. "Cultural evolution is the primary factor that determines linguistic structure, with the current state of a linguistic system shaping and constraining future states."
  • it still leaves a lot of areas open for linguists to argue about. And the study did not build an exhaustive tree of any of the language families, in part because we probably don't have enough information to classify all of them at this point.
  • Still, it's hard to imagine any further details could overturn the gist of things, given how badly features failed to correlate across language families. And the work might be well received in some communities, since it provides an invitation to ask a fascinating question: given that there aren't obvious word order patterns across languages, how does the human brain do so well at learning the rules that are a peculiarity to any one of them?
  • Weiye Loh on 18 Apr 11
     
    young children can easily learn to master more than one language in an astonishingly short period of time. This has led a number of linguists, most notably Noam Chomsky, to suggest that there might be language universals, common features of all languages that the human brain is attuned to, making learning easier; others have looked for statistical correlations between languages. Now, a team of cognitive scientists has teamed up with an evolutionary biologist to perform a phylogenetic analysis of language families, and the results suggest that when it comes to the way languages order key sentence components, there are no rules.
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