It's on cancer. How does cancer rise? You may have heard that the prevailing theories that it's caused by a number of mutations, so what, mutation theory, but we have studying it run into more and more difficulties, was it, and come up with essentially a new old theory that is actually chaotic, very much like the evolution of species, exactly like the evolution of species almost, except that it doesn't make it all the way to autonomy like we and the other species that Darwin or God or somebody in between has created. And we don't know how. This is not so clear. With cancer we do know how. We can actually test it. It's a chromosomal evolution process. So this is what I'm going to talk about today. Now briefly, for those of you who are, all of you who are in this, the cancers contain individual clonal karyotypes, much like normal phyllo-genetic species. They're not a normal karyotype with mutation, as in mutation theory such as they have their own karyotypes with their own chromosomal numbers, everything. The karyotypes of cancer differ from normal cells, diploid cells, in cancer specific, abnormal, as you would call relative to the normal cell. Or aneuploid is the technical term, meaning it's a creek, whatever derivative term, not the right ploidy, not the right folding, but that number of chromosomes and structures. And here are two examples. On the left is the pinnacle of evolution. That's the human male with 23 chromosomes. Here diploid chromosomes. And this is by so-called M-fish. You color code these chromosomes. It makes it much easier to do side of genetic analysis. You spread the chromosomes on an object, on a microscope slide, and then hybridizing with color-specific probes. And they all assume these artificial colors. So this is chromosome one, two, three, all the way to 22. And here's X and Y, one each. So that's a normal human male. 46 chromosomes. And here is one particular cancer cell, a karyotype of a colon cancer cell line. It's very stable, in fact, not quite as stable as normal karyotypes are. But you can see the numbers are drastically changed. And here's a gallery of so-called creeps, almost, so-called marker chromosomes that are hybrids of these chromosomes. You can tell from the colors. This is a hybrid of 6 and 1. This is a hybrid of 13. Some 13 marker. This is when the two, with two colors, you could say 17, 22, and 17. The triple hybrids markers, that is the hallmark of cancer cells. When that happens in us normally, then things are very bad.  Here are two more examples. That is a breast cancer, not a good one, as you can see, lots of marker chromosomes, abnormal cells, breast cancer line. And as I said, everybody's cancer is individual. They all have their own karyotypes. Again, like species. And here's a karyotype of a muscle cancer that is simpler than the two others that are highly, more highly developed. So in contrast to the karyotypes of normal species, you can already see from these patterns, the karyotypes and phenotypes of cancers are flexible. I don't like to use the word unstable, because unstable means they would fall apart. But they oscillate in a narrow range, like radiation wave in physics. And they are flexible in these limits. And that's able to evolve ever more like phenotypes spontaneously. That is the daunting property of cancers. It starts bad, but it typically gets worse, because it's so flexible the evolution occurs very fast compared to Darwinian evolution. You have to wait for every step 10 million years. This explains the daunting ability of cancers to go from bad to worse, as Peyton Rouse used to call it, which is also termed now cancer progression. You start off with a manageable cancer that gets more and more drug-assistant, metastatic, and unpredictable. As a result of this inherent inflexibility, the karyotypes of cancers are typically also heterogeneous, and so are the phenotypes. But again, within limits. Now, this is a different way of looking at the instability. It's sort of a thing that is in Excel, a student of mine found, figured it out. You can line up three-dimensionally the metaphases or the chromosomes of 20 cells in the x-axis, and they call it the karyoglyph, the x-axis here. Down there are the chromosome numbers of a given cell. So here's chromosome one, two, three, four, five, and six.  We have two. Here's the y-axis is the chromosome copy number. There are two, because it's diploid. And in men, there's one only. It's haploid, if you like, monosomic of the x- and y-chromosomes. There are two of those here. All others are double. So these are images or essentially different cells. They're 20 usually lined up, and next to each other. And you can see three dimensionally that they're completely parallel, meaning that the karyotypes are identical and very stable.  It isn't no longer the smooth parallel lines of a normal cell. They go up and down. They have a new karyotype. You see the copy number here of the maturity is three, then it goes down to one. Here it's up to four. Here it's even five. And you can see the lines are no longer totally parallel, but they oscillate around this pattern. And this pattern is actually remarkably stable. It's a stability within instability. That is characteristic of the cancer. Even if you passage this cancer 60 generations in culture or put it through an animal and isolate it again, it will look more or less the same, unless you put on some strong selective pressure like carc selection. So then it changes the karyotype, which the cancer cell can do. Normal cell never does. When you have chemotherapy, it's the cancer that becomes resistant and you never do. So as a result of this inflexibility, the karyotypes of cancer and the resulting tumors are heterogeneous and it is therefore still debated and has been debated for nearly 100 years now ever since for very first suggested chromosomal relations like causes of cancer. And subsequently, the majority of geneticists favored the view that it's mutations. It is still debated because they're flexible, but rather specific karyotypes, as I just showed to you, or whether stable and specific mutations are causes of cancer, hidden that we don't see or the so-called oncogenes now, that would, as a consequence of their presence, cause cancer and also destabilize the karyotype. So the destabilized karyotype is recognized even by the mutation people, although you can hardly find it in modern textbooks anymore. You don't see a karyotype anymore. Everything is expressed in genes or in gene arrays. The karyotype is considered something for little old ladies or people of the last generation are looking for microscopes and work on a low budget. But for the real advanced cytogenetic cancer researcher, you have a gene array for $1,000 apiece and show a diagram with thousands of little spots to interpret for the surgeon whether the right breast or the left breast or lymph node should be taken out or not. That's what they really say at the end. I don't know how they see it, but they do see a lot, I guess. So this is still the question that has not been settled in 100 years of debate. Is it chromosomes or is it mutations that are causing cancer? And we are solving it right now. Next 15 minutes, that's what we have. No more. So the mutation theory holds that a set of three to six mutations of specific genes that are termed oncogenes as a result of these mutations, namely, meaning that they cause cancer, transform normal cells to cancer cells independent of carrier-typic alterations. They recognize grudgingly, yes, that a lot of carry-type changes, but they say this is a consequence or an accidental thing because the cell is transformed. Popular proponents of the mutation theory, all of my former friends and even current friends have some of them like Bishop and Varmas and Weinberg and Vogelstein, so all the leading cancer researchers still subscribe to the mutation theory. So, but there is no direct functional proof for the mutation theory. Despite 30 years of research, and despite gene technology, there's marbles, you can put any gene in any cell you want, if you want. And they have done that with these oncogenes. They put them into normal cells, but as you can see, the results are at best ambiguous, if not, certainly no direct proof. What happened this when you take a convenient or whatever they call it, a consensus number of oncogenes that is said to be or sought to be sufficient for cancer, one of them published in Nature recently by Weinberg and by other Bishop and so on have done the same thing. They take three or four, put them into normal human cells, and then they say, see, when you wait long enough, after several months, there is a tumor cell coming up as a clone. What they don't tell you is they put millions of cells into this culture that all have the same oncogenes and are not transformed, out of which over months comes occasionally a clone of transformed cells. So, what that means is when you get for in for transfecting as that is called millions of cells with oncogenes that are supposedly causing the cancer, you get only one in ten to the five to transform, that means these genes are not sufficient to do the job, something else is required. And that is more or less openly acknowledged. So they are not sufficient for tumor genicity. And the answer of the mainstream is we need another gene yet that we haven't found yet. But they have never found a complete set yet, although the Nobel Prize was given for this idea already in 89. So have to catch up soon to justify the Nobel Prize or the next one, whatever it is. So the oncogenes that induce transformation is another result of these experiments. In one of ten to the five cells are not necessary to maintain the transformed phenotype. They do their job transiently because tumors and leukemias induced by oncogenes persist.  Once the tumor is established in a couple of weeks, it is no longer dependent on the oncogene. The oncogene, in other words, is not maintaining the transformed phenotype and it's not sufficient to initiate it. And all cells that have been studied for karyotypes, and that's only very few who have done that, you can imagine one of who did, who have found to be a duplicate. And that is not mentioned in nature when that was one of the leading papers when Weinberg came out. I even sent him the karyotypes. Isn't that interesting information at least? They said, no, no, we don't need that. It's enough that we have in there six mutations, that they have 70 chromosomes in these cells plus the six mutations that is equivalent of another roughly 20,000 genes. It's not relevant to them because they don't have any known mutations in them. So they're not taking this very serious. So what is now the role of mutations or oncogenes in cancer? The conclusions from these three kinds of experiments are oncogenes are not sufficient and are not necessary to maintain the cancer cells. So what is their role in transformation?  Here's the theory that we think how it works and what it does and the effect of cancer is generated in general. In efforts to explain this, we have recently proposed a two-step karyotypic cancer theory. That's once you have seen that, then it's almost, I'm almost over. The first step in this process of carcinogenesis is that carcinogens and also the oncogenes which function like carcinogens destabilize the karyotype by inducing random aneuploidy. Some of them might also induce mutations, but that's secondary. The critical essential point for cancer is that they destabilize the karyotype and that you see in every cancer cell and that explains at the same stroke about half of all known carcinogens that are not mutagens like asbestos and polystyrene and all these things. So once you have an aneuploid cell, the aneuploidy destabilizes the karyotype automatically, autocatalytically by unbalancing teams of proteins that segregate and synthesize and repair chromosomes. Imagine you had the most balanced and physically complex apparatus, a spindle apparatus in the cell, segregating 46 chromosomes symmetrically every eight hours if it has to be. And that works 99.9% accurate. If there's a slightest imbalance, if there's a little bit more of tupelein instead of clopylein or what have you, or myosin or something, then this thing is out of balance and it's like you have a long leg and a short leg. You start limping and you become aeroplane and the aneuploid karyotype constantly changes itself because of these many imbalances, physical imbalances in the spindle apparatus, which is a very balanced sensitive machine, or even in the enzyme teams, the repair enzymes, for example, that fix the DNA. If you ever done NIC translation, there's a DNAs, polymerase, and a ligase in a mix. If you change that balance a little bit, it's either hydrolyzing the DNA or it stops making it. If they are in the right balance, you get optimal yield and that balance is maintained by the balance of genes in the chromosomes that have been balanced for three billion years of life. Now, if you change that, if you change thousands of genes by aneuploidy, thousands of things are out of balance. In most cases, in fact, the aneuploid cell dies. It has to find an equilibrium that it can maintain to survive. So the aneuploidy stabilizes the karyotype autocatalytically and thus keeps changing the karyotype and thus initiates and maintains karyotypic evolutions. They're constantly recouped and changed because of the inherent instability. Most of these newly evolving karyotypes are again random aneuploidies that are functionally inferior to normal cells or even lethal and they're gone. Occasionally, however, like playing roulette and playing darbin, a real cancer-causing karyotype evolves. It's a long way from a real autonomous species that says goodbye mom and climbing up on the tree or flying like a bird. That's not it yet, but it's good enough to be an autonomous parasite. It can compete with normal cells from which it just arose. These cancer-causing karyotypes are then stabilized by the inherent instability against the inherent instability of aneuploidy by selection for transforming function. So they keep, as you could see on these karyocrafts that I showed earlier, they keep fluctuating or oscillating around clonal value, but as soon as they go too far outside it, they will not replicate and not be maintained as cancer cells and they will be lost. And that's a typical phenotype in cancerous, in all of them, the more aneuploid they are, the more necrosis you get. These are dead cells that have actually karyotypically suicided. So this karyotype theory then postulates that clonal, yet flexible karyotypes are the genomes of cancers, not mutations, but genomes mean, I mean now, that part of the chromosome or the genetic material that makes it a cancer cell. It's the karyotype as a whole, just as the karyotype as a whole makes us either a monkey or a dog or a cat or a mosquito or a human. It's not a mutation. You can mutate us as long as you want, you will never get a monkey out of it.  You have to rearrange the assembly lines and that's the equivalent of the chromosomes and that's whatever Darwin or God or whoever was at work. Inspiration has done. Here's a graphic of the whole stem. Here's the normal karyotype with diploid stable cells. In step one, aneuploid arises induced either by a casinogen, which I would call an aneuploidogen from a cancer point of view or sometimes spontaneously by an accident. And then you have randomly aneuploid karyotypes which are stable and they keep changing themselves on their two stable end points only. This one is the most stable, that's where most of them end up. They end up in a coffin because sooner or later a chromosome is missing that is essential for the viability of the cell and it dies. And occasionally, as I said, like in roulette, that arrow is thin for that reason, you come up with a cancer cell and that would be a cancer specific karyotype now. A true model indeed for evolution. And once this happens, the cancer karyotypes continues to evolve here. It was originated in an aneuploid pool of karyotypes. Here's the cancer cell and now it has a certain degree of flexibility depending on the karyotype and moves on over many generations. And occasionally, again, it makes an additional evolutionary step when it's challenged by chemotherapy, then a drug-assistant combination comes up that differs from the parental karyotype or a drug-assistant or metastatic one comes up that again differs from the parental karyotype. Although the basic karyotype is maintained, it is adjusted very much like we go from a monkey to a human or a foreword or backwards whatever the direction is. So here's this one experiment that I was going to mention and then a toolie shut up to test the cancer theory, a karyotypic theory. We have asked whether activated oncogenes that have been tested for so long now do indeed induce new tumorogenic cells with individual cancer specific karyotimes much like new species. Moreover, we asked whether the different tumorogenic cell lines arising from human cells consfected with the same oncogenes. I said in these experiments you consfect millions of cells to get a few transformed clones. So it's a very low efficiency. We would predict if it's an evolution, a spontaneous evolution of a karyotype that each of these clonal new cancers that comes out of a homogeneous pool of human cells all consfected with the same oncogenes would each have their own karyotype. Like all progeny of whatever the original mammal was has a karyotype that is in part still conserved in all their derivatives. And again, like individual species evolving from the same ancestors having individual karyotypes with or phenotypes. So the relevance of this for testing the mutation theory is that if individual tumorogenic lines from the same parental cells with the same oncogenes have individual karyotypes and phenotypes, it would follow that the oncogenes have played only an indirect role in transformation as postulated by our theory. It's the karyotype that makes it the cancer cell when the oncogene was essentially like a casinogen. It's like the radiation 30 years before you get the cancer from a Hiroshima or Nagasaki bomb. By contrast, the mutation theory would predict that tumorogenic cell lines show up from these transfected cells with normal karyotypes. They don't spell it out, but they don't say it should change the karyotype. And would have the same phenotypes because the oncogenes they add are encoding these phenotypes. So indeed, we found what the karyotype theory predicts, and that will be shown on my last slide now. We found that different tumorogenic cell lines rising from human cells transfected with the same set of oncogenes have individual clonal karyotypes and phenotypes. That's essentially prognostic cancer experiment here. So here you see two cells or two cell lines that's just under the microscope. You have to look in the animal, you could probably get more different phenotypes, but you can see these cells are phenotypically, morphologically in a pitviddish, quite easy to distinguish. Yet they're generated by exactly the same oncogenes in human epicellular cells, normal epicellular cells. So these phenotypes cannot be so different from the same oncogene, must be something else. And here you can see what it is. It's these two karyotypes that belong to the two cells we see, they're quite different. And they came from this. These were the cells that were transfected. The oncogenes were added and generated aneuploidy, which you could see, a random aneuploidy, didn't show it here. And then you isolate clonal colonies in agar or in animals or in petri dishes. And these are two of these clonal colonies, a very different pattern. And here's a similar example of two different products, again generated with the same set of oncogenes. So that brings me to my last slide. In some of our experiments, confirm the theory that the genomes of cancer cells are flexible clonal karyotypes other than specific mutations. And I thank you again for your attention. Ready for questions. Thank you. Thank you. Thank you. Thank you. Thank you.


Creating interactive shiny apps for meta-analysis with Meta UI. There is no straight path from the data that you collected for your meta-analysis to the results. It's rather a labyrinth through many different choices. Should you exclude certain outliers, how to deal with different effect sizes, and which type of model should you choose? These choices affect the results in critical ways that meta-analysis have been often criticized for choosing one path over another. In practice, researchers usually have one planned or even pre-registered path that they take, and then they had a few more analysis such as robustness checks or some analysis for exploratory purposes. But still, according to other researchers or reviewers, there is often a path missing. So, how can we do justice to the large number of equally justified but different paths? What we have observed so far are three approaches. First, in what we call the shotgun approach, all types of analysis that make more or less sense are shot at the data. This typically yields a very large table in the manuscript, as you can see on the right. Second, some researchers have run a multiverse analysis. That is, they first conduct many different meta-analysis, and then they run a meta-meta-analysis on the results. The third approach, which is what we have been focusing on, is what we call the open box. Thereby, you provide others with your dataset and tools to run all sorts of different analysis. What if we exclude effect sizes plus minus two standard deviations? What if we only take studies that feature a cover story? How will the results change if I filter out non-pre-registered studies? Can you tell me what effect size you get with the selection model? Sure, the open box is a published shiny app that allows everybody to access the dataset via a website and explore and download the original or filtered dataset. The open box approach is simple as anybody can go on the website instead analyzing. It is also flexible. A researcher once asked us why we chose to report a p-curve but not a z-curve. At that time, we had not really been aware of z-curve, which is why it wasn't there yet. So noting that this would provide us with additional information, we simply added a z-curve analysis to our shiny app. So if you can do it in R, which is the case for pretty much everything, I guess, then you can also feature it in a shiny app. The downside, however, is that creating shiny apps is a bit difficult. And to overcome this problem, we created an app package that creates the shiny app for you. Make sure to check out the tutorial at EsmarConf on how to use our package. But in a nutshell, these are the steps that you need to go through to create a website where you and others can interactively analyze a meta-analytical dataset. To let you know how easy this is, I would like to give you a brief look at the code. Again, we're still at an early stage, so more functions may be added soon and things might look a bit different later on. And everything is also discussed in depth in the tutorial. So your dataset goes up here. Then we need to know what the effect size and standard error variables and so on are called in your dataset. And you can determine filters or moderators that you want to apply. Here we have pre-reg, which includes information about whether or not a particular finding was pre-registered. We also have demographics such as the mean age. And the app will later see the range of these variables and let you pick a value along it. In practice, you might have downloaded data from a meta-analysis that is a few years old. And you added a couple of new studies that have been published since. And now you want to know whether including the new studies affects the results or maybe whether the new studies differ from the old studies. So you could just create a variable indicating whether or not a finding is new and put it right here. And the app will analyze whether this moderates the effect size or not. So the second function needs the formatted dataset.  So that you can customize it, for example. So here's an example of the shiny app that includes anchoring and anchoring dataset. And before we get results, we have to filter or apply our filters. And you can see the mean age, which is the smallest mean age in all of the datasets included here is 18.8. And now we may want to select only items where the scale type was open or visual. And so we have applied, we have selected the filters and now we have to click on analyze data. And here you go. We currently have effect size estimates from several different models below here and a small sample description. And now let's continue conducting some moderator analysis. And again, as you can see, the app recognized that this is a metric variable and creates the plot and the table accordingly. But now we might want to know whether the scale type has an effect on the effect sizes. This is a categorical variable. And here you are just significant with everything that you would need to know for reporting this. You can also download the dataset and it will save the filters that you applied while it's still the entire dataset. And then you can update it later on to continue where you left off. So this was just a small excerpt from the analysis that you can run with the MetaUI package. Currently you also get a sample breakdown with respect to the moderator variables, a forced plot of all included effect size, a final plot with Agis test, p-curve, z-curve and information about the effect size distribution. So thank you for joining us for this presentation and we are looking forward to your thoughts on this.


So a few months ago, I made a video that as I uploaded it, I decided to call European Soccer Explain for Americans, which outlined, among other things, how soccer leagues function differently in Europe than sports leagues, including the National Football League, due in the United States. I mentioned multiple times in the video that I know most people in most places call it football, but I switched back and forth between using the word soccer and football, thinking that most people would understand it would be confusing if I said in football things are like this but in football things are like that. As an American, I've always thought of soccer or football as just another installment in the American versus British English saga, like calling a trunk a boot or a flashlight a torch or a game a match or a field a pitch. Now some phrases will get you in trouble, but this is generally a family friendly channel so we won't go too in depth. The point is that they are both correct, just different places use different words for stuff, and nobody really gets upset about them. But some people seem to get really upset about me having even uttered the word soccer. One commenter on the video even went so far as to say that the word soccer isn't actually a word, it was just made up. I assume that he will be sad to discover that this is kind of how words work, they were all just kind of made up. But there were so many comments like this that I had to make this video to figure out why, because as it turns out, I am far from the first person to hear these types of comments. So first things first, just to make it clear where I stand in terms of ranking which sport is the best with regards to the three that we're going to get into most, football is best, rugby is second, American football is third. This is a heretical thing to say in the United States, but if you disagree with that ranking, a wager that you probably played your favorite sport and likely have brain damage as a result, which has resulted in your skewed list. But our issue here is not with which sport is the best. If you are in the United States and someone in general conversation uses the word football, they are referring to this football, American football. I have never heard an American use the terms either American football or gridiron football. I've also never heard the word egg ball exit an American mouth, although I suppose it just did. Many people like to call American football egg ball with a bit of an insulting connotation, which doesn't really make sense to me. Frankly, the word fits a rugby ball better than an American football. At least, I've never seen a pointy egg. The only times that an American means soccer when they say football is one, if you are literally at a football match slash soccer game, or two, well, that's pretty much it. Look, I don't make the rules. This is probably similar to how people in Europe would never say I'm headed off to the association football match, or as they would say, I'm headed off to the association football match. That's how all English people sound in my head. It's just that American football is by far the biggest sport in the United States, at least for a few more years until video games overtake it. Just as an example, the 2018 World Cup made $5.4 billion in revenue. That same year, the NFL made $14 billion. And keep in mind that the World Cup is a global event that happens once every four years. Even our highest league is called Major League Soccer. Although in a linguistic twist, 12 MLS teams have FC, meaning football club, in their name as opposed to just two with SC. So we get it. And this may be the key point of this whole video. If you want to call us for football, that's fine. But people in the US call it soccer simply to avoid confusion. We don't have some dumb, blind allegiance to one particular sound that we can make with our mouths over another particular sound that we can make with our mouths.  So back in the 1860s, England's football association was kind of in charge of stuff. And they took to using the term association football, because at the time there were other types of football, including what they called rugby football, because apparently every sport needed a two name title. The youth of the day, much like the youth of two day, took to using slang terms to shorten everything. One of the most popular ways to do that was to add ER to half of a word. So a school lecture became lecker, breakfast became breakfast, rugby football became rugger, and they chopped off part of association, keeping the SOC part to which they affixed an ER, thus resulting in soccer. I mean, I said I would tell you where it came from, I didn't really say it made sense. It's not clear whether he invented or just popularized the word, but the person who gets most credit for this is a guy called Charles Weirford Brown. That is, unless you live in the US, then we would say he is a guy named Charles Weirford Brown. While at Oxford, he played cricket, soccer, and I assume quidditch. He was fond of appending ER to the end of words. You actually heard of his work before, he was the Breckers guy from earlier. As in, after I have Breckers, I'm going to get my nimbus back from Erie to play a game of quiddes. Stupid as it may be, the term soccer started to spread and appear in various media in the 1890s, although it was sometimes spelled with a CK rather than ACC. English sports writer E.W. Swanson wrote in his book, sort of a cricket person, rugby football in those days I think was never called anything but rugger unless it were just football. Imagine that, they were using the word soccer because another sport was already called football, what a concept. Although to be fair, the term footer also seems to have been used to refer to football. I mean, soccer. For its part, gridiron football is called football in America because it pretty much started out as rugby, which obviously in the late 1800s would have been called rugby football. People talk about the earliest games of American football being between universities in the 1860s and 1870s. These games were nothing like modern day football. Basically it was just soccer, but you could also use your hands to punch the ball forward too, although you were not allowed to pick up and carry the ball. So it had the aggression of rugby, but with a soccer goal as opposed to a rugby in goal zone, which Americans would know as an end zone. Oh, and both teams had 25 players, which sounds more like Italy's Calcio Fiorentino than any other sport that we talked about until now, but that's a sport for another day. If we go back to England though, the term soccer was widely used in Britain for a long time, although it did tend to be employed more in upper class circles, whereas football was used by the rest of us losers. A research paper called It's Football Not Soccer from Stefan Sismanski found that even in the London times, the usage of the word soccer increased after the Second World War and was used over 10% of the time to refer to the sport in the 1960s and 70s. It's far from a majority, but it's not nothing, and certainly more than if people would have been upset about its very usage as they seem to be nowadays. In the U.S., soccer's usage increased a lot around 1914, just prior to the war, although people aren't quite sure why, and later from the 1960s to the 80s during the life of the North American Soccer League, which popularized things in the U.S. with the signings of players like Pele, Carlos Alberto, Johann Cruyff, and George Best. Sismanski points out that, surprisingly, one of Best's biographies, as well as soccer at the top, the autobiography of Sir Matt Busby, both employed the use of soccer in their titles. This was typical for the period, however, where in everyday life, even in Britain, soccer and football were both used until as late as the 80s. However, despite being used for around 20% of the references of the sport in the 80s, since then, the Guardian and the Independent have both seen a large decrease in their usage of the word soccer since, whereas the U.S.-based Time magazine has seen the opposite. It uses the word soccer for the majority of its references to soccer, or footer, football, whatever the heck it is. If we zoom out from just the U.S. and Britain, though, we find some interesting things, and if you're from one of these places, please correct me in the comments if I am wrong, but from what I could find out, in Ireland, football is sometimes a reference to Gaelic football, and the word soccer is still quite commonplace. In South Africa, despite the official governing body being called the South Africa Football Association, the word soccer is used just as much to refer to the sport as football is. Canada is pretty much on par with the U.S., as gridiron football is popular there, too. However, even while they may look similar, there are rules that are different, kind of like a rugby union versus rugby league thing. Thus, the differentiation between Canadian football and American football. In Japan, the word soccer, obviously the translation for the word soccer, is more commonly used than football's translation, footoburo, although, again, the Japan Football Association is the governing body. Papua New Guinea also refers to the sport as soccer, to differentiate it from the also popular rugby and Australian rules football. Speaking of Australia, the land down under, along with New Zealand, have over the past couple decades or so been switching from using soccer to football, first by removing the term football from various rugby leagues, and then in one situation renaming the Australian Soccer Association to Football Federation Australia. If you go to the Wikipedia page for football, it highlights six different forms of football, association, gridiron, Australian rules, rugby union, rugby league, and Gaelic. It makes sense that different places, especially the places where each of these varieties are most popular, would develop their own vernacular realm. So we have found a few answers in this video. The word soccer comes from England, where it was used as much as football for many decades, but now is primarily used in multiple different countries where other forms of football are more popular. What we haven't done is find an answer to my other question, which is why do people get so upset about other people using the word soccer? I'm not sure there is an answer to that. Perhaps there is a fear that if the U.S. gets into football, we'll try to Americanize it and ultimately ruin it, but I don't think that that's going to happen any time soon. As I've said, there are lots of language differences where different words are used to describe the same thing, but this is the only case I've heard of people getting upset over it. I have just two thoughts on this. The first one is for people who don't like that people in other countries use the word soccer and who are also apparently in denial that other kinds of football exist.  It doesn't matter, get over yourself. Besides, we need your help for something. Americans won't stop calling the sport soccer anytime soon. American football in the NFL is just too popular. On the other hand, there are many signs that soccer is increasing in popularity in the U.S. A 2018 Gallup poll showed that 7% of Americans said soccer is their favorite sport to watch. And keep in mind, this is a head of hockey, auto racing, tennis and golf, all of which seem to attract as much if not more media attention in the U.S. According to Football Observatory, average attendance for MLS games from 2013 to 2018 was just a few hundred short of the crowds drawn by League Un and Serie A matches. The U.S. women have obviously been great and the men are, well, we have Christian. So in all, if we can get the United States on board, there is actually a chance that we can drastically increase the fan base of this one game that we all really enjoy watching and being a part of. And I think that would be really cool for everyone. But that's not going to happen if every time some young American who isn't quite sure what she's getting into, because football doesn't get a ton of coverage here, goes online to try to learn more about the sport, and she's met by some smug winner saying it's football or nothing.


Let's talk more about why research papers are rejected and what are some of the common reasons for rejections in a research report. First of all, a paper is likely to be rejected if there's no review of the literature with the manuscript. For a research paper, the review of the literature should tell the reader what is known about the topic, what is not known about the topic, and how your idea or your research will fill the gap in knowledge. So a research paper must have a brief, succinct review of the literature that gives the reader the information so they understand the gap in the knowledge that your study will help fill. A second common reason for rejection of research papers is if the actual research method and or the statistics used are inappropriate. That's harder to fix because that may take a lot of work for you to go back and do some additional work in order to make that flaw fixable. Another common reason for rejection of research papers is you don't adequately describe how your sample was obtained, what's the inclusion criteria, what was the exclusion criteria, and you must state in the paper that you had institutional review board permission otherwise research cannot be published if there was no IRB approval. Another common mistake made in research papers is in the results sections the author includes a discussion of the findings. Instead, the results section should simply be a synopsis of the data. Maybe there's tables, figures, but it's simply presenting to the reader the facts or the results of that study. In the discussion section is where you discuss those results, you compare your results to the literature, and you then conjecture why you found what you found. But let me tell you another common mistake in the discussion section. In a paper, for example, an author may say I had a wonderfully adequate sample size for my study. I also had research instruments that were valid and reliable. But unfortunately the results of your study did not support your projected hypothesis. So the mistake authors often make then in the discussion section is to pretend they did find the result they wanted. So rather than discussing why they didn't find the correlation, let's say that they predicted. Instead, you'll see authors say, well, if I had had a bigger sample, if I had had better instruments, I would have found what I wanted to find. And then the discussion continues as if the findings occurred when in reality they did not. So if your findings do not support your hypothesis, you must explain why. Conjecture why you didn't find what you hoped to find. You cannot discuss what you wished you had found. Because it's difficult if you say to us at the end, well, in reality, my sample wasn't big enough. In reality, my instruments weren't good. That should not be the reason to explain your findings. In the conclusion section, it's good to explain the limitations of your study. All studies have some aspect of limitations. And this is the place where you want to describe those limitations. And finally, the paper should conclude with what are areas for future research, what topics need to be studied next. So without all of these elements in a research paper, you put yourself at likelihood of being rejected.


My name is Sandhagni, I am the author of Malignant Self-Love, Narcissism Revisited. Can pathological malignant narcissism be acquired? Can it be learned? Can it be provoked by certain well-defined situations? Robert B. Millman is a professor of psychiatry at New York Hospital and Cornell Medical School, and he thinks the answer is yes. He proposes to reverse the accepted chronology, according to him, pathological narcissism can be induced in adulthood by celebrity, wealth, and fame. The victims of what he calls acquired situation of narcissism are billionaire tycoons, movie stars, renowned authors, politicians, and other authority figures. They develop grandiose fantasies, they lose their erstwhile ability to empathize, they react with rage to slides both real and imagined, and in general they act like textbook narcissists. But is the occurrence of acquired situation of narcissism inevitable? Is it universal, or are only certain celebrities prone to it? It is likely that the acquired situation of narcissism is merely an amplification and manifestation of earlier narcissistic conduct, traits, style, and tendencies. Celebrities with acquired situation of narcissism may have already had a narcissistic personality, and have acquired it long before it erupted. Being famous, powerful, or rich, only legitimized and conferred immunity from social sanction on the unbridled expression of a pre-existing narcissistic disorder. Indeed, narcissists tend to gravitate to specific professions and settings which guarantee them access to fame, celebrity, power, and wealth. We are likely to find narcissists in clusters, in certain professions. As Mayburn correctly notes, the celebrity's life is abnormal. The adulation is often justified and plentiful. The feedback is biased and filtered. Criticism is muted and belated. Social control is either lacking or excessive and vitriolic. Such vicissitudinal existence is not conducive to mental health, even in the most balanced person, let alone in a person with narcissistic personality disorder. The confluence of a person's narcissistic predisposition and his pathological life circumstances as a celebrity give rise to acquired situation of narcissism. Acquired situation of narcissism borrows elements from both the classic narcissistic personality disorder, which is ingrained or pervasive, and from transient or reactive narcissism. First proposed by Lisa Rovingston in 1996. Celebrities are therefore unlikely to heal once their fame or wealth or might are gone. Instead, their basic narcissism merely changes form. Continuous unabated, as insidious as ever, but modified by life's ups and downs. In a way, all narcissistic disturbances are acquired. Patients acquire their pathological narcissism from a beautiful overbearing parents, from peers and from role models.  One of these circumstances is celebrity and fame. When the circumstances are beyond a person's control, whether they are positive or negative, they might provoke a narcissistic defense and in the extreme case, narcissistic personality disorder full-fledged, pernicious and destructive.


Hi everyone. Yeah, I got the post lens slot and the challenge is accepted. I have a Spider-Man slide coming up. So, those who are sleeping will still get up and it is a 15 minute talk. Who am I? I am Raghuntha Thalur as he just said. I work for Redat. I have been working with Gluster. It is a software defined storage for about 4 years now and for the past few months I have been working on containerizing Gluster and also making Gluster the de facto storage system for containers. And the talk today is about this. Basically how not to do this. When I heard the term persistent storage for the first time I was curious and a little confused. Isn't storage always persistent? Why do we need to call it as persistent storage? So in container world in the container platform that is required and the reason is if your ports are your containers then your nodes from Kubernetes or any other container platform are different ships. When you port a container from one ship to another you have to ensure that the storage is also ported otherwise you risk putting it down into the water. So we will tell you how not to do this and what is our solution for it. Why is this an interesting problem to solve? So a survey which was conducted recently we asked admins what is the most challenging aspect of setting up a container platform and 25% of them said that it is persistent storage. We do not know how to get this right in container platform. Another survey said that it is the complexity of setting up persistent storage which is most compared to even the scale or the cost problems of persistent storage. The same survey we also asked okay so it is a problem and so what are you trying to do? How are you trying to solve this problem of persistent storage? They said okay we considered everything but I think we will go with the existing sand or the nasa appliances we have at least 33% said we will try to use some software defense storage but a very low number only 18% replied saying that we will try to have something which is hyper convinced with my container platform something which is embedded into my content platform and this is a very low number in my opinion a good system is a system which is complete by itself. It should be coherent and if you are trying to glue in two different systems to get a solution it is probably not the right way to do it and that is what this solution will try to solve. For those of you okay so persistent storage as you looked at the slides why is it different? Why is storage that much of a problem when it comes to container platform because storage is not a commodity like CPU cycles and memory are when Kubernetes it is already solved you will get the same number of CPU cycles and memory when the pods move from one container one node to another that is not the case with storage and because it is not yet a integral part of container platform you also have the problem that it requires some extra management effort sometimes even a extra storage expertise what happens in this case is that you have two admins one admin who is maintaining the Kubernetes cluster or the container platform cluster and the other admin who is trying to do only the storage part there and as we saw in the previous slide already most of the times storage is kept external to the platform this is again a problem so before I go into the solution I will have to recap through how you do persistent storage in Kubernetes it's a very simple thing when you want persistent storage for your application you claim for it it's called persistent volume claim you write a spec which is I need this much of space with this access mode and it should come from this storage class this is a request that you send to Kubernetes it will satisfy the request based on the inputs that you gave once the request is satisfied it's called that the PV is now bound once the PV is bound you use it in your application part this is a sample application part spec for engine x all we need to do here is mention what is the name of the PV claim that you want to be bound with this and where you want it to be mounted and that's it so this is how persistent volume works in Kubernetes it's similar for other container platforms too once you know how to use it let us go into the solution let's take example of a typical Kubernetes cluster that you have with three nodes I have one master three node setup now depending on whether it's a private setup or a public cloud setup you will surely have some local disks on the nodes or you might have some block instances on each of the nodes so these green shapes circles are the local disks or the EBS instances once you have this you run a cluster pod on each one of them you create a cluster cluster out of it and now the cluster cluster will aggregate all the storage from all these nodes and will make it look like a single storage system we also deploy a hackety pod here which is the intelligent cluster volume manager that can be run on any of these nodes and it is now made aware of the cluster cluster lastly we have a cluster provisioner it comes with Kubernetes it will be running on the master so whenever a PV claim comes in it is given to this provisioner the provisioner knows how to talk to hackety pod and satisfy a PV claim this is all you need to get things working to use it say you deploy engine export somewhere here the engine export will now talk to a cluster volume plug-in that knows how to talk to the cluster cluster and the data is available everywhere if your engine export moves from node 3 to node 1 it will get the same data there too so this is the solution yeah I know it looks a lot many pods with so many connections and we have solved that too so let's recap the components before I show you how to set up so from Kubernetes side we have a cluster provisioner which knows how to satisfy a PV claim we have a mount plugin which knows how to provide that storage to a application pod cluster cluster is the storage component it is the manager for all your storage which can integrate all the disk hackety is that orange pod that we saw that's intelligent enough to translate a PV claim coming with a storage requirement into a cluster volume and lastly we have gk deploy this is the tool which will simplify the process of setting this up you don't need to run so many pods separately all you need to do is run this tool with a proper input and it will do all the setup for those of you who are not aware of cluster cluster is a storage system it is scalable but this is a better definition in my opinion so what we do is we aggregate local file systems from where from multiple machines we aggregate them we call that cluster of machine as cluster storage pool and we let you aggregate in multiple configurations if you have redundancy requirements please satisfy that if you have resource constraints you don't want to waste too much of storage space then you can use erasure coding so the configurations are many and you can use the task per your requirement there are some added features like snapshot you can take snapshot of a volume you can also use encryption on the wire and we have bitrot and protransistor and it's not only our cluster native protocol which you can use to access it the well-known other protocols like NFS and SMBR supported you also support Swift okay so now let us move to how to setup as I told you it's a single command to run you just give a input saying how your cluster is made up of the container platform this setup is similar to what I showed in the diagram there single master three node setup there are no pods running as of now and this is the input file the input file is very simple you just need to tell me which are your Kubernetes nodes what are the IPs and the hostname and what devices on those nodes are free to be used by cluster so in this sample file I have three nodes each of them having three disk devices we also have a concept of zone this is similar to the failure domain of Amazon so if you have VMs running in each zone and this zone then you can specify that here and that will be taken as an input when we create a volume we will try to make sure that the replica starts spread over different zones so that you have better availability you can reuse the same concept for private clouds by mapping your zones to the power racks that you have okay so that's the input file you give that input file to this tool and the option minus d is used to specify whether you want us to deploy cluster pods or not if you have already deployed cluster to some other means you can skip this part you will have to open the right number of ports it varies from each setup so that you will have to do manually before you run this tool okay so we now started deploying cluster pods we labeled the right number of nodes which meant which are meant to be running cluster is here deployed as a demon set because it is using this it cannot migrate from node to node we have to make sure that the pods always start on the same node now we told akt what the devices are akt is deployed and it's now running so you can see three cluster parts up and running you can see one akt board up and running the video was here played at two x speed but it's only 48 seconds so the whole deployment happened in less than two minutes and this is all you need to do to have a hyperconvert storage up and running in your Kubernetes cluster that's it so there is one small task that I admin has to do before he can ask the app developers to start using it which is common to any other storage system in Kubernetes you have to define a storage class so the cluster cluster is up and ready akt is up and ready you have to now tell Kubernetes this is my Kubernetes cluster this is my cluster cluster and you have to connect them as we saw the connector is called provisioner that's done using a storage class we create a new storage class here we name it as clusterfs we use the provisioner clusterfs we name it as myse and we say okay the provisioner needs to talk to akt at this url the url here is the only important thing that you need to make note of when you run the previous tool the tool would have given you a url you have to use this create a storage class that's it so here the admin's work ends completely now he's free to relax he or she is free to relax no more work from the admin you have to just let your app developers know what's the name of the storage class that you have created from an app developer perspective if he or she knows the concept that I explained about PV claim and PV from Kubernetes they will be able to use it they need to know nothing about cluster here so this is my PV claim I name it as my pvc and I refer back to the storage class that the admin has created by mentioning it here I want to use it from myse I want to GB of space and the access mode will be redirect menu do you when a PV claim is satisfied it is said to be bound so now we have it bound here to show you that it really used luster in the back end what we can do is we can use Hector CLI to list the volumes so the volume had come up this is a typical application part that you have here all you need to do is mention the PV claim that you use to create your volume and you say where it should be mounted let us create the application part and see if it really uses luster in the back end so the application part my app is running now and it has no data in index.html because we gave it a empty volume from cluster what we do is we write hello world into it and see if it shows up when we do a curl there it is this still does not prove that it's coming from cluster so let us do all the investigation what's required so what we do is we now look into the mounts of this application part we see that fuse.cluster first mount is here so the cluster of this volume is found inside the application part we can also verify from the Gluster pod here we go into the Gluster pod and look into one of the brick directories a brick is a unit which makes up volumes so we go into one of the bricks and see what it has in index.html it has hello world so this is how it works I have not told you how we did this but this is sufficient for admin or a DevOps guy to set up a Gluster cluster hyperconverse with Kubernetes in less than a few hours with all the investigation you need to do and it should be easy for any application developer to start using it we have many more features coming up we have file support already this is what I showed you as a file support we have block and object store coming in soon in the next release we have more volume management features up we have also reduced memory footprint because obviously if you are running storage ports on the same nodes where you are running application parts you want as much money free for the application parts so we have done a lot of work here to reduce the memory footprint depending on how many bricks you are going to run or how many volumes you have created you can get up to 5x benefit there were suggestions for auto discovery of nodes because many of you might be using the auto scaling feature from AWS so there are plans of using the node labels to automatically take in the nodes for Gluster cluster and we are working with Kubernetes for the API for snapshot once that is ready we will also have it in okay that's all I had from the talk any questions when a new node is added to Kubernetes if application port runs there it can always use cluster automatically we don't need to do anything admin does not need to do anything if you want storage from that new node to be used in the cluster cluster as of today it's one single command that you need to run a kt cli add node and you give the IP of the node and it's inherited with auto discovery it will be made automatic you can pre-define what label we should be looking at and based on that we will automatically take in the node whenever it's joined demon said because cluster ports were running on all the three nodes here so the cluster part which was started on node one even when node one is rebooted should not move over to node two and node three yes the demon set need to be modified now to so the demon set uses labels to identify which nodes the demon set should be running on so all we need to do is when node four is added we have to label it appropriately and Kubernetes does the rest of the work yes it will we won't label it automatically you have to tell us to label it or you have to label it yourself yes once you do that it will automatically start the port there yeah hi so i had a question i have two questions right so one of them is regarding the performance impact of snapshots in relation to Gluster FS because i have i'm coming from a background of ZFS in which snapshots are like instant now in this case since it is spread across the network what is the exact yeah so Gluster uses LVM snapshot in the backend to take a snapshot the barrier is created for a very small amount of time and we take a snapshot in time it has no performance impact after the snapshot is taken when the snapshot is taken it there will be a my point was like for example if i'm running running it in a production environment yes and i have access to data yes i do not want any at any moment in time that my data is not accessible by my application yes so what in terms of snapshot what happens at that moment will i still have access to my data all the time you will have okay the second question was regarding partition tolerance in case of ZFS sorry in Gluster FS yes suppose one of my nodes goes out goes out crashed ebs volume corrupted whatever yes what does what is protected what what protects it you can always configure it in various ways but the demo i did today with the default setting what we do is we create in replica three mode and with the quorum of more than 50 percent so what that means is if i three node cluster if one of the node goes down you can still do read and write using the other two nodes and guys just take this outside yeah everyone our next


BAG PIPES AT SEA by Clinton Scholar Red for LibriVox.org by Andrea Fiori Above the shouting of the gale, the whipping sheet, the dashing spray, I heard with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed, the sea to him the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked too the eternal stars shine through the rack. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem This recording is in the public domain. BAG PIPES AT SEA by Clinton Scholar Red for LibriVox.org by David Lawrence Of the shouting of the gale, the whipping sheets, the dashing spray, I heard, with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. That were the winds that flailed and flayed the sea to him, the night obscure. In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked too the eternal stars shine through the rack. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Ted C., by Clinton Schollard, read for LibriVox.org by Harry Ink, above the shouting of the Gale, the whipping sheet, the dashing spray. I heard, with notes of joy and wail, along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a skull, and war went down the darkling air. And then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In dreams he strayed some bracken glade, some heathery war. And if he saw the slanting spars, and if he watched the shifting track, he marked too the eternal stars shine through the rack. And so amid the deep-seed din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Bike Pipes at Sea, by Clinton Schollard, read for LibriVox.org by Jess and Mills. Above the shouting of the girl, the whipping sheet, the dashing spray, I heard with notes of joy and wail a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked to the eternal stars shine through the rack. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Bike Pipes at Sea, by Clinton Schollard, read for LibriVox.org by Lucy Burgoyne. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard, with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a scurl, and war went down the darkling air. Then came a sudden subtle swirl, and love was there. What were the winds that flailed and flayed, the sea to him, the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked two, the eternal stars, shine through the wreck. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Red Pipes at Sea, by Clinton Scolard, read for LibriVox.org, by Lucy Perry. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard, with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim-scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked two, the eternal stars, shine through the wreck. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Bagpipes at Sea by Clinton Scolad, read for LibriVox.org, by Neil Harrison. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard, with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk around his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dimsinn pipes a skull, and wore went down a darkling air, and then came a sudden, subtle swell, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In the dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked to the eternal stars shine through the rack. And so amid the deep sea dim, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Bagpipes at Sea by Clinton Scolad, read for LibriVox.org, by Margaret Brichon. Of the shouting of the gale, the whipping sheet, the dashing spray, I heard with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dimsinn pipes a skull, and wore went down a darkling air, then came a sudden, subtle swell, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked to the eternal stars shine through the rack. And so amid the deep sea dim, and so amid the wastes of foam, afar his heart was happy in his Highland home. End of poem. This recording is in the public domain. Bagpipes at Sea by Clinton Scolad, read for LibriVox.org, by Mike Vendetti, MikeVendetti.com. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard with notes of joy and wail a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dimsinn pipes a skull, and wore went down the darkling air, then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed, the sea to him, the night of skewer? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked to the eternal stars shine through the rack. And so amid the deep sea dim, and so amid the wastes of foam, afar his heart was happy in, his island home. In a poem this recording is in the public domain. Bagpipes at Sea, by Clinton Schollert, RedfordLibervox.org by Pat Jambatista. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard with notes of joy and wail a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed, the sea to him, the night of skewer? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked, too, the eternal stars shine through the wreck. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his island home. End of poem, this recording is in the public domain. Bagpipes at Sea, by Clinton Schollert, RedfordLibervox.org by Raven Notation. Up the shouting of the gale, the whipping sheet, the dashing spray, I heard, with notes of joy and wail, a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange ancient god of song and storm. He gave his dim scene pipes a scurl, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed, to see to him the night obscure? In dreams he strayed some bracken glade, some heathery moor. And if he saw the slanting spars, and if he watched the shifting track, he marked, too, the eternal stars shine through the wreck. And so amid the deep sea din, and so amid the wastes of foam, afar his heart was happy in his highland home. End of poem. This recording is in the public domain. BEG PIPES AT SEA BY CLINTON SCOLARD. RED FOR LIBERVOX.ORG BY TOMMY HERSANT. CARL'S BAD, CALIFORNIA. WINTER 2009. Above the shouting of the gale, the whipping sheet, the dashing spray, I heard with notes of joy and wail a piper play. Along the dipping deck he trod, the dusk about his shadowy form, he seemed like some strange, ancient god of song and storm. He gave his dim-seen pipes a scurril, and war went down the darkling air. Then came a sudden, subtle swirl, and love was there. What were the winds that flailed and flayed the sea to him, the night obscure? In dreams he strayed some brackened glade, some heathery moor, and if he saw the slanting spars, and if he watched the shifting track he marked, too, the eternal stars shine through the rack. And so amid the deep-sea din, and so amid the wastes of foam, afar his heart was happy in his Highland home.


All right, our next speaker is Massimo Pagliucci. We are set, yes, we are set. Massimo Pagliucci, he's gonna be doing a demarcation. What exactly is the difference between science and pseudoscience? He is the chair for the Department of Philosophy at City University, but he is an evolutionary biologist as well as a philosopher, so both sides of that coin. His haiku, we have two speakers named M-A-S-S-I-M-O. Salve signore. Please welcome Massimo Pagliucci. Thank you, there will be no singing or dancing on my part during this talk. So it's great to be back here at TAM.  It's part of a new book that is about to come out for University of Chicago Press. Several philosophers, scientists and even skeptics have contributed. My friend Michael Shermer, for instance, has a chapter in that book. You all saw this morning Michael's presentation about science and morality, I hope so, right? And I have one or two caveats about that presentation, maybe three or four, five or six, okay. But we won't get there. If you're interested, actually there is video of a nice discussion we had recently about it. Today it's a demarcation problem, which seems like the kind of thing that we all ought to know about.  I mean, do you think you know what it is? So the first thing is, what is the problem? What exactly we're talking about? We're not talking about something as simple as what this slide is showing, which is, in this case, the difference between astrology and astronomy. It's, that one is pretty easy enough. It doesn't require that much of a cogitation. But a lot of the times it gets more complicated. There are areas at the borderlands between science and pseudoscience, there is bad science, there is quasi-okay science, and then not only that, but things change over time. Something starts out as sounding pretty much pseudoscientific and then it may turn into mainstream science or vice versa. Something starts out as a promising scientific enterprise and then it goes the other way. That's what makes the whole thing, of course, more fun, but also more frustrating. Now why doesn't matter? Why do we care? Precisely, I think there's three reasons why we should care. One, of course, is the sheer intellectual endeavor. I mean, presumably, you guys are here in part because you're intellectually curious, and so this is an interesting problem. It's an intellectual puzzle. The more practical reason is because there's a lot of money that goes into science. And so it does help the public in general and even scientists and philosophers in particular being able to figure out, well, is this actually this kind of thing worth the money or not? For instance, as you know, there is money that the National Institute of Health is putting into research on so-called alternative medicine. So is it worth the money? How much money is it worth? That sort of stuff. And then finally, because pseudoscience hurts or kills, and I don't have to convince this audience about that, that particular picture there shows a woman with a tattoo that says no AZT, so no anti-retrovital drugs. She's also pregnant, you might notice, and so that means that because she doesn't believe that HIV causes AIDS, her baby's probably gonna get AIDS. So these things matter intellectually. They matter in terms of sort of practical terms. They also matter in terms of financial terms, but also in terms of how they affect people's lives. Now, if you've heard of any philosopher, probably it's this guy, Sir Cal Popper, who is responsible for actually beginning discussion on the demarcation problem back in the early part of the 20th century, and also for inventing, for coming up with the first recent solution, recent memory to the problem, and that has to do with falsification, with the concept of falsification. Now, you've probably heard of this before because it's almost no skeptic or scientist that never heard of falsification, although they don't necessarily know exactly what that means. So one of the things that Popper pointed out is of course that the difference between science and science is that science makes progress not by confirmation of theories, but by falsifying theories, by making sure that we reject theories if they're wrong. The idea was that it's too easy to confirm stuff because, well, today we know about the confirmation bias, of course, from cognitive science, but even Popper was pretty well aware that even scientists can easily go out and collect data, information that is actually in favor of their particular special hypothesis, it's much more difficult, however, to find out something that resists falsification. So the idea is the difference was if it's falsifiable, it's scientific, if it's not falsifiable, it's anything goes. And Popper's examples of those two categories were on the one hand Marxist theories of history and psychoanalytic theories, Freudian and other versions. Those were unfalsifiable because you can always find confirming evidence of those things. Anytime you talk to a Marxist historian or to a psychoanalyst, they will say that whatever, regardless of what you tell them, they will say that, yeah, of course that fits, and they come up with a story for why that fits their theory. So every single instance of empirical evidence turns out to be in favor of the hypothesis. Well, that's not gonna work, said Popper. Real science works differently and his best example was the attempt at falsifying Einstein's theory of relativity which happened in 1919. There was a famous eclipses, total eclipse of the sun and Einstein's theory was making a very, very specific prediction about what would happen during that eclipse, particularly the bending of the light of the stars emerging from behind the disk of the sun. And that prediction was so specific that as Popper put it, the theory was sticking its neck out and it could have been shocked if the observations were not in fact in favor of the theory. It turns out that the observation was spectacular in favor of the theory. Einstein became a celebrity overnight and ever since, in fact, the theory of relativity, the general theory of relativity has survived a number of other attempts at falsification. That's why people are so confident that it is on the right track. It's not that it's the true theory, it's not that it is, we're not confident of it because it makes, it has been confirmed a lot of time but because it has, people have tried to falsify it a lot of times and it has survived. Not so Marxist theories of history or psychoanalysis. Now the problem is that this doesn't really work very well. It doesn't work in the history of science, I'll show you an example in a minute. And it doesn't work from a point of view, sort of general point of view of philosophy of science because it is in fact very difficult to honestly falsify a theory for two reasons. These two reasons are often conflated into one it's called the Duhin-Quine thesis but in fact there are two different reasons. One is due to Pierre Duham who was a physicist, who was interested in philosophy of science and he pointed out that scientists can always rescue hypotheses by assuming if the hypothesis fails to predict the data, to square with the data. If they assume that there is something else that is wrong there may be something that is not working with the equipment that has been used to make the observations or it may be that the data analysis were not correct, we just heard examples of this kind of justification, ad hoc justification. But sometimes these ad hoc justification are correct. Sometimes the equipment really doesn't work. Sometimes the data analysis really are not the best you can do. So Duhin pointed out that straightforward falsification as Popper thought does not actually work. And in fact it's worse than that. W.V.O. Quine who was one of the most influential philosophers of the middle part of the 20th century pointed out that you can make the same argument more generally, that is, the entire, what he called the entire web of knowledge, everything we know about the world, including all the assumptions that go into doing scientific research, including logic itself. All of those can be tweaked in theory in order to adjust the match between the theory and the observations, which means you have an infinite degree of number of freedom, you can do whatever you want to and eventually you square things with the observations. If that's the case, if the Duhin-Quine thesis are correct, then you cannot falsify the scientific hypothesis or any hypothesis. And then what? That makes it much more difficult to separate science from pseudoscience, good science from vet science and so on and so forth. Let me give you one example of what I'm talking about. So this is from the history of science and it's in the concerns of Newtonian mechanics. At some point astronomers noticed that there was a problem with the orbit of the planet Uranus, which is the one on the left of the screen, that it wasn't following the predictions that were being derived by using Newtonian mechanics. That is, you calculate the position on the planet and you point the telescope and the planet was slightly off.  Check the calculations, the calculations were correct, so something had to give. Now, a naive falsificationist, a naive properium, would say, well, too bad for the Newtonian theory, mechanical theory. It's clearly wrong. It's giving me approximate predictions but the predictions are actually wrong, so let's throw it away. Of course astronomers were not even thinking about throwing away Newtonian mechanics. What they did instead was to come up with an adult hypothesis. That's well in a minute. Maybe there is another planet out there which we don't know about that is causing these anomalies and it is because of that planet that there is a mismatch between the observations and the data. That sounds like another hypothesis.  Of course, this is science, not pseudoscience, but what happened is they calculated the position of the new planet. They pointed the telescopes in the right direction and sure enough, the planet was there naturally. In fact, the mathematician that actually calculated this whole thing was so sure that the result was correct that he was offered the possibility of being the first one to look into the telescope and he said, no, no, I know it's there. Talk about being confident, right? Now, this is a very nice story which simply shows that scientists don't actually use falsification in actual historical situations and so that enough is a problem for the popper's account of the location, but it gets worse because a few years later, astronomers found themselves in a similar situation as far as the planet Mercury is concerned. Mercury also was showing these anomalies and you were deploying in the calculations based on Newtonian mechanics and the calculations were giving you the wrong position as far as Mercury is concerned. Slightly wrong, but wrong nonetheless and repeatedly so. It was clearly a systematic error somewhere. So astronomers said, well, we know what's gonna happen now. There is another new planet as yet undiscovered. It is these planets that is causing the anomalies so we'll just have to calculate the orbital parameters, point the telescope and discover the new planet. Now, the new planet had to be closer to the Sun than Mercury because otherwise we would have already discovered it and they were so confident that they would find the planet that they actually named it ahead of the discovery, Vulcan, because as we know, astronomers are a bunch of trekkies. There's a problem, however, that they started looking for Vulcan and they looked for Vulcan and they couldn't find it and they redid the calculation and repointed it that as government Vulcan was nowhere to be found. In fact, as it turns out, in this case, it was Newtonian mechanics that was the problem. Turns out that Mercury is close enough to the Sun that relativistic effects, which are not accounted for by Newtonian mechanics, you need the general theory of relativity, are sufficient to cause those anomalies and therefore you need to bring in general relativity. You really throw out, in this case, Newtonian mechanics. So we have a case, an historical case, where exactly the same situation happened. In both cases, apparently, the theory was falsified by the data, but in one case, it turns out, astronomers rightly so kept the theory and modified it and that led to a spectacular discovery. In another case, on the other hand, they actually throw away the theory. So what's the difference between the two cases? There is no formal difference between the two cases. It's just that people were trying to do their best and at some point they figured, well, you know, we tried our best, we've done enough modifications of the basic idea of Newtonian mechanics, it's not working, so at this point we really do throw it away. So the theory was in fact falsified, but it took a hell of a lot more effort than Popper would have imagined. Okay, bring us back a little closer to modern time. So this was the situation up until the 50s or early 60s. And then in 1987, an influential philosopher of science, Larry London, said, well, you know what? I think this all debate on demarcation is wrong and it's not leaning anywhere.   Nonetheless, we're gonna take a look at the three meta-philosophical assumptions because I think we're gonna learn something from it. So the first meta-philosophical assumption is, you know, what are the conditions of adequacy that should be proposed for demarcation criteria? So what is it that makes it something science and something not science? What kind of conditions do we have? Is the criterion under consideration often necessary and sufficient conditions? Philosophers, like effect scientists as well, like this idea of necessary and sufficient conditions. If I can give you a short list of things that are both necessary and sufficient to separate one thing from another, then we got a good definition. And then he was asking, Lowland was asking, what actions or judgments are implied by the claim that something is, in fact, scientific or unscientific? So what are we gonna, once we've done the separation between science and pseudoscience or sudden science and bad science, then what, what are we gonna do about it? So here's the three answers to the three meta-philosophical questions. First of all, what are the conditions of adequacy? So Lowland said that the conditions of adequacy is that philosophers should agree, in fact, he said they should better agree on whatever separation between science and pseudoscience scientists come up with. Because they are the experts, they are doing the science, so we better agree with whatever they come up with. I don't think so. I mean, generally speaking, yes. I think it's indicative of a problem. If a philosopher points out to one solution, the scientist says, no, that's completely wrong, and there's no way to reconcile the two. Then we have a problem. Either one of the two is gonna be wrong. But I think that a better way of going about it is that whatever philosophers are gonna come up with to tell us how to separate science from pseudoscience, generally speaking, agrees with what the scientists say, but there may be exceptions, and those exceptions may actually be the interesting stuff. So the answer should look something like this. This is a clustered diagram that puts together things that are similar. So it clearly should be the case that both philosophers and scientists agree that, say, fundamental physics or evolutionary biology are, in fact, sciences. If they disagree on that, we got a problem. They also better agree on things like, well, astrologists definitely have pseudoscience. So those are the easy case, or intelligent design creationism is pseudoscience. Those are the easy cases, but there may be some interest in disagreement, and in fact, that may be what the interesting stuff is, in the middle. What kind of science is, for instance, evolutionary psychology, or the search for extraterrestrial intelligence? Is there a possibility that we can turn part of psychology into an actual legitimate science? Those kind of cases are much more interesting because there may be a legitimate disagreement there. So the criteria of adequacy, I think, is there should be a broad agreement between practicing scientists and philosophers of science, but up to a point. Second, what about necessary and sufficient criteria? So Lannan said that, ideally, a demarcation criterion would specify a set of individually necessary and jointly sufficient conditions for deciding whether something belongs to science or not. This is a nice, clear-cut kind of definition that comes out of basic logic and basic applications in math and simple science. So, for instance, if I give you definition of water, I say, well, water is H2O, and the story. If your chemical has a composition H2O, that is both necessary and sufficient for being water. You don't need anything else. Unfortunately, a lot of interesting concepts are much more complicated than water, or especially when it comes to the differences between science and pseudoscience. And in fact, philosophers have known this for a while. This guy here is Ludwig Wittgenstein, who, in the middle part of the 20th century, actually thought about the nature of complex concepts such as science or pseudoscience, although he rarely used that particular example. And he figured out that actually there's something intrinsic about these concepts that make it difficult to come up with simple, very clear-cut definitions. His example was the example of games. Now, try it at home, if you like, as an exerciser, tonight over beer, even better. Try to come up with a simple, clear definition of what constitutes a game. We're in a casino, so that seems appropriate. It's much more difficult than you think. Because if you start doing things like, well, games are things that have rules. Yes, but a lot of things have rules that they're not games. They're done, they're competitive. Well, actually, there are some games that are not competitive, and there are some activities that are competitive and are not games. Well, they're done for fun. Well, sex is done for fun, but it's not a game, and so on and so forth. So as it turns out, the more you think about it, the more it's actually difficult to come up with a definition of even something apparently simple as the concept of game, let alone the concept of science or pseudoscience. So what in fact it looks like is that game is a family of concepts, and there are some similarities, and there are some dissimilarities, and it's a little bit more vague, and it's a little bit more, you have to use something closer to sort of fuzzy logic to actually come up with decent definitions of what a game is. And by fuzzy logic, I don't mean woo-hoo logic. I mean technically fuzzy logic. There is a type of logic that actually deals with these kinds of situations. So applied to science as to the science, this is what the bitkensteinian games will look like. So what you have there, for instance, are a couple of axes over which you can represent approximately the, let's say, theoretical sophistication of a particular enterprise and the empirical support for that enterprise. And you can see that at one corner, at the lower left corner, you have the easy cases. Astrology, intelligent design creationism, that sort of stuff. Those have very little, if any, theoretical sophistication, very little, if any, empirical support. At the opposite extreme, you have the easy cases clearly inside science. So you have fundamental physics, you have evolution and biology, chemistry, those kinds of things, where you have a high degree of theoretical sophistication, a high degree of empirical support. But the interesting stuff is in between. Where do we put, let's say, the social sciences? Well, a lot of the social sciences, for instance, have a lot of empirical support. There's a lot of empirical data, and they're repeatable, they really look like sciences, but there's very little conceptual theoretical analysis. There's no overarching theory yet of psychology, for instance, or sociology, even less. Or some other social sciences are suffer of the opposite problem. They have a lot of theoretical structure, like economics, but that theoretical structure doesn't seem to much particularly well with the empirical world. Just look at the 2008 financial crisis, as an example. So those are the, that's the picture you get. It's a fuzzy, gradual picture where some things are clearly science, some things are clearly non-science, and some stuff is in between. You notice also the position of string theory. I don't know what it is, let's show it again. If we can, it's on the lower right corner. Lots of theoretical sophistication, zero data. Yeah, I can't control the focus, I'm sorry. Anyway, lots of theoretical sophistication, exactly zero data. Well, that's simplifying, but anyway. Now, so what I'm suggesting is that we need to move away from sort of standard approaches to using classical logic, like H2O is in fat water, to something more fuzzy logic. Now, if you moved over fuzzy logic to analyze concepts, you lose in precision, and you gain in significance. Now, here's the difference between precision and significance. The guy on the left says, and you have to read exactly what he says. That's 500 kilograms that is coming down at a certain speed and where certain acceleration. And the other guy says, get the hell out of there. The second guy is much less precise, but his advice is much more significant. So that's two. Third criterion was, well, what kind of judgments are implied? If you read that quote from Lallon, it says that, well, philosophers should, in fact, not be afraid of essentially getting into value judgments. That is, philosophers should be prescriptive, not just descriptive. But at the same time, he says, yes, but the problem is that when you start putting things into two piles and you call one science and the other one pseudoscience, now you're making the kind of value judgment that actually does have practical consequences for people. So you need to be careful.  It seems to me that Lallon and people like that actually are in this kind of situation. I don't know if you can read the cartoon, but this guy is working in a very sophisticated theoretical proof and then part of that proof are these two little bits that one says, one cake, the other one says, eat it too. And you can't have both. That's what Lallon is trying to do. He's saying, well, yes, it's okay to make value judgments. On the other hand, be careful because you're making value judgments. I don't have a problem making value judgments if they are based on reason and evidence. And I think that that is precisely what scientists and philosophers and skeptics ought to do if they have good reasons to do it. So to label something pseudoscience actually does do good value. It has a good thing. It is a good thing for society as long as you can back up that judgment with reason and information and data. And you know what I'm talking about. The situation is in fact pretty bad. These are some of the standard statistics that the National Science Foundation keeps churning out about how many Americans believes in ghosts and telepathy and clairvoyance and blah, blah, blah. And those numbers are abysmal. They're really bad. So we clearly do have a problem with a lot of people believing a lot of nonsense. I don't have to convince you guys of that. The situation is real. So value judgments are definitely called for here as long as they're backed up by reason. Now let me give you one example that you're probably familiar with. This happened a few years ago. Where in fact all these demarcation stuff became legally relevant in an important landmark case about intelligent design creationism. That is Judge Jones in Pennsylvania 2005. This was the famous case where for the first time intelligence at Dover, Pennsylvania where intelligent design came on trial. And some of the witnesses on both sides were scientists, though if you wanna call people that are engaged with the discoveries in scientists I think you're pushing the definition of a scientist. But nonetheless they were. And there were philosophers also that were called to help Judge Jones make up his mind about this thing. And if you read the final deliberation that Judge Jones delivered at the end of the trial it's very interesting. It's not that long, it's about 50 pages. And it is a very interesting study both in the legality of separation church and state and in the philosophy of science. Because in fact it was helped, the judge was helped by among other people two philosophers Barbara Forrest and Robert Pennock. And if you read the actual details of what Judge Jones said you will see that he says that I.T. on the point number one intelligent design violates the centuries old rules that discriminate between science and pseudoscience by or religion by invoking supernatural. That's the first point. The second point that he's making is that there are there's an argument of irreducible complexity and which is central to I.T. It's based on the same kind of illogical contrived dualism that characterizes standard creationism. And then finally the number three is well scientists have looked into these claims that actually found them wanting. The third claim is empirical. The judge looked at the scientific literature and said look actually as it turns out scientists have looked into these things the claims that you guys are making and they found them wanting. The first two on the other hand are inherently philosophical in nature. They are straight from the demarcation problem of debate. They're straight from the philosophy of science. They are about the nature of science. They are about what counts as science and what doesn't count as science. And there are legitimate disagreements among philosophers as well as about scientists. Jerry Coyne is my colleague in evolutionary biologists is here at TAM and he will be giving a talk tomorrow. And Ian and I disagree for instance on some important aspects of what counts as science and what doesn't count as science. The point that I wanna make however is that these debates are actually very relevant not just as intellectual debate, not just as meta philosophy, whatever that is. They're important because they actually have consequences for people. As a result of George Jones' interpretation of the current philosophy of science there is no intelligent design creation is being taught in Pennsylvania or anywhere else in the country because that landmark decision, 2005, that's exactly, still stands. In fact that decision has given such a fatal blow maybe not fatal but such a bad blow to intelligent design proponents that there hasn't been any legal challenge since. Every other challenge has been mounted on behalf of intelligent designers immediately the decision by Judge Jones has been brought into the discussion that was the end of the story. I don't foresee any time soon intelligent design coming back all the way up to federal courts let alone to the Supreme Court because of that decision. So that's an instance where thinking carefully about demarcation and taking seriously both what the scientists themselves are saying and what the philosophers are saying actually does make a difference for people and for our children who are now being taught better science than they were before. So this is pretty much what I wanted to say and I'm just on time. If you are more curious there is this book that is coming out it's not out there yet so you go and find it at the table it's coming out at the end of July it's available for pre-order on Amazon of course. It's by Chicago University Press it's a collection of essays by 30 something 35 philosopher, scientist and skeptics including Michael as I said and it's a series of analysis of the philosophy the sociology and the history of demarcation with implications for actual skeptic practice in practice day to day. Thanks very much for your attention. Masimo Pagucci.