Welcome all to this SIB Virtual Computational Biology Seminar series. Today we have the pleasure to host Sarah Mitri, who is a tenure track assistant professor at the Department of Fundamental Microbiology of the University of Lausanne and a group leader of the SIB, the Swiss Institute of Bioinformatics. So Sarah studied computer science at the American University in Cairo in Egypt and also cognitive science and natural languages at the University of Edinburgh in the UK. So she earned in 2009 a PhD in computer and communication science at the laboratory of intelligent systems of the EPFL, so just next door. And from 2004 to 2010, she, sorry, from 2010 she pursued a career with the first postdoctoral training at the FAS Central for Systems Biology at the Harvard University in the US and as well as the second postdoctoral training from 2010 to 2014 at the Department of Geology at the University of Oxford in the UK. In 2015 she came back to Switzerland and she became a met assistant with a ambitione research grant from the FNS and in 2017 she obtained a tenure track at the Department of Microbiology, Fundamental Microbiology here at the University of Lausanne and where she's leading the evolutionary biology group. So Sarah's group is interested in the understanding of how ecosystems of microbes function and evolve over time and how they are shaped by the interaction taking place between individual microbial cells. The current research goal is to develop the theoretical and experimental tools to study and control the social interactions within microbial ecosystem. The mathematical modeling and the computer simulations approaches are developed in combination with prediction testing in the laboratory. So today Sarah will share with us the work done in her group on the ecology and evolution of small bacteria communities. So thank you again Sarah for accepting the invitation and the floor is yours. Great, thank you very much Diana for the introduction and thank you for the invitation. It's really nice to speak in this seminar series. I quite like the idea that it gets recorded and is shared worldwide so it's quite an honor to participate. Okay, so if you're following science news these days then you know that the microbiome is a big deal that people really think about how the microbes in us and on us are shaping our lives. We know that they're really important for our health. Of course this picture is a little bit exaggerated. We're not just vehicles for the microbes but it does still show that it's important to try and understand how these microbial communities function and how they affect us and of course it's not just humans and health related issues but microbes are also in the soil. They affect agricultural productivity so understanding these communities would also be helpful and of course there are many other environments that we're also interested in. So we're part of these groups that are starting to study microbial communities and we know that of course within these microbial communities we're not looking at the single species but many different species living in the same environment sometimes even up to hundreds or thousands. However when we look at what people have done in microbiology in the past most of it has been studying single species or even single clonal groups. So in the field there needs to be a transition from studying single species towards trying to understand microbial communities and how they function. So we are trying to take part in this and really taking multiple species into the lab and trying to grow them together and understand how they interact with one another. So this is what we study. We focus on bacteria and not other types of microbes. And in terms of the complexity of systems that we could be looking at so the couple of examples that I gave before whether it's the microbiome or the soil communities are more on this end of the spectrum so really with many different species and quite complex in environments that are constantly changing. And instead in our work we prefer to focus on the less complex scale of things so with just a few species. So up to let's say 10 species is sort of what we're interested in also because we can understand all the different mechanisms and what's going on within a community and then the hope is in the future to build up towards understanding the more complex communities. So we study small bacterial communities and in particular we focus on how the different members of the community so whether it's species, strains or different cells how they interact. So when I talk about interactions what we're thinking of is any way in which one cell can affect the growth and the survival of another. So these broadly come in two flavors. Cooperation where this effect is positive on the fitness of another individual or competition where there's a negative effect due to the presence of another species, strain or individual. And these have been studied in quite a lot of detail so there is really a huge field of research behind these two different types of interactions. In terms of cooperation I've put up some examples of what they may be. So one species might for example have a positive effect on another because it's secreting enzymes into the environment and these enzymes might break down complex molecules. So there might be some large proteins that these enzymes can break down and the products of this breakdown can then be available to all the cells that are around and not just the cell that created or that secreted this enzyme. And similarly they can also break down toxins that detoxify the environment for their neighbors. They're also famous for producing polymers so if you've seen these large biofilms that are sort of these sticky surfaces so those are made up of polymers that the bacteria have secreted and these can protect their neighbors from the immune system for example or from antibiotics, et cetera. In terms of competition we know that microbes tend to compete for nutrients so if they land in the same environment typically the environment will have a limited amount of nutrients which they need to compete for and so they might end up doing quite aggressive or exhibiting aggressive phenotypes like producing antibiotics to kill their neighbors or even stabbing them with so-called type 6 secretion systems that punch a hole in their neighbor and then they can eat up their DNA or their cellular contents. So there's a lot of positive and aggressive interactions in microbial communities and we're interested in understanding what do these communities look like so if we take a look at a given community will we find more cooperation? Will we find more competition? And how do these types of interactions affect the community as a whole and the functioning of the ecosystem? So that's the second element of what we're looking at is interactions and then finally we're interested in their evolution. So if we start out with a community that looks like this over time what will happen? If some strains mutate for example and perhaps the interaction between them and another strain will also change and what do we expect? Can we build up our understanding in a way that we can predict how this evolution will take place? Okay so before I give some examples and go into the research that we're doing I'd like to first just tell you a little bit about my own background. So my research, the red line of my research has always been about the evolution of social interactions and I studied computer science as Diana was saying in the introduction and my PhD was building on that so I decided to look at social interactions in groups of robots so essentially how individual robots could affect each other's behavior and each other's fitness and how these interactions would evolve over time. And so this got me really interested in evolutionary biology but I did feel quite restricted with the tool that I had because as a computer scientist who was interested in biological evolution just taking my robots and trying to apply it to different biological questions was quite restrictive. So I felt like I had a hammer and I was looking for the right nail to hit so the right scientific question. So instead I decided that I should move a bit more towards biology to expand my tool set. And so I found a position in the lab of Kevin Foster who was very kind to take me on as somebody who'd never held a pipette and he was happy to teach me how to do microbiology experiments in the lab. But his condition was that I would also bring along the skills that I had from before in building computational simulations and mathematical models and then using all those three to understand something about microbial social evolution. And these are the three tools that I've been taking forward as well in my current group that we started here at the University of Lausanne. So the point I guess that I'm trying to bring across within this talk is what tools are good ones to address biological questions? How do you choose which tool to apply to which question? So my approach was to expand my tool set and have different kinds of approaches that we could apply. But I cannot say that I can already answer this question. I just want you to keep in mind that I'm thinking about this throughout the rest of the talk. So just think about whether are we using the right tools and can one combine them in a useful way. Okay, so in today's talk I present to you three small projects. So first of all, understanding how microbes find their neighbors. So how do how does the environment determine which microbe ends up next to which other one? Secondly, how the identity of these neighbors can affect the fate of each of the of these cell strains or species? So will they end up surviving better because of their neighbors or being harmed? And then finally, I'm going to talk a little bit about what we're doing today in my group. So just to give a bit of an overview of how we're starting to expand on these ideas to think of small bacterial communities. Okay. So let's start with the first part. So this is a little cartoon that I draw to illustrate the types of problems that we're thinking about when we look at microbial interactions. So these little cells are two strains of the same species, each one being an individual bacterial cell. And hypothetically, we can say that this one here is a cooperator. So it secretes some enzyme into its environment that breaks down some complex molecule and provides food for whatever cells are close enough. And the green one doesn't secrete this enzyme. So then what we can hypothesize from this, at least the way I've drawn this, is that this brown cloud is where the enzymes are going. And so the benefit of the presence of the enzyme will mostly go to the cells that are within this cloud of enzyme and cells that are a little bit further away won't benefit from it. So in this particular configuration, if you're a blue cell, you're making some effort to produce these enzymes, you and your other clone mates are the ones that will benefit from it. And so as a group, this blue group of cells should grow faster than the green group of cells. Okay. So in this case, cooperation is a good idea. But what happens if instead we have a different setup where the two cell types are mixed together? In this case, the enzymes are equally distributed amongst all of the cells. And since the green ones are not producing this enzyme, they will tend to grow faster because they don't have to pay the cost, but they can get the benefit from the presence of this enzyme. So from the broken down nutrients, for example. So the point here is that the prediction of what will happen and which strain will end up dominating in a population will differ depending on what the environment looks like and how these different cells are distributed in space. So spatial patterns should affect whether bacteria cooperate and whether cooperation evolves in the long term or not. But first, let's talk about how these spatial patterns form in the first place. So how do we get mixed groups versus segregated groups? So this was one of the questions that I started out with when I started my postdoc in Kevin's lab. And so we use these computer simulations to study spatial patterns in microbes. And these simulations were developed previously to my joining the lab. And the way they work is that you have a solid surface and on the solid surface are cells, in this case, of two different colors. So they're identical. None of them are secreting anything. They're not doing anything special. They just have two different colors. And then you should imagine that from the top here, there's a concentration of nutrients that's diffusing down. And the cells can take up these nutrients. And as they take up the nutrients, they grow in size. And then they can divide and push the cells that are next to them. And then this just keeps going for many iterations. And you get this large population of cells and these nice spatial patterns that we can then analyze. And we can do lots of things with these types of simulations such as changing nutrient concentrations, the diffusion of molecules within this environment, the growth rates of different types, etc. So we can really play around with the different cell types and have them do different things and try and explore the parameter space of what we might get. So one of the first things that I looked at was if we change the nutrient concentration, then we end up with very different spatial patterns. Okay, so in the case of low nutrients, that was the simulation that I just, the video that I just ran for you, you get these towers of cells where each tower is a clonal group. So you only have one cell type in there. Whereas at high nutrient concentration, you get a large mixture of these different cell types together. So the first thing that I did when I started working in the lab was to try and test this hypothesis. So to do this, I used pseudomonas aeruginosa, which is a model system because it's quite a dangerous pathogen that particularly affects people with cystic fibrosis. So that's why it's very well studied and we just took it because it's a well studied bug. And then tagged these two strains with fluorescent markers. So we had one that was fluorescing in green and the other one in blue. Then we grew each one of those separately in liquid. We mixed them together at equal ratios, so 50-50 of each.  So onto a petri dish. And then you can observe as this colony grows over time, you can look at it under the microscope. And basically what you get is a nice colony like this. So the part in the middle where everybody's mixed is where I put the drop. And then as the two strains grow over time, they tend to separate from one another. What we did then to test whether this would give us something similar to what we saw in the computer simulations was to change the amount of nutrient in the agar plate. So we just made a gradient of nutrients. So this is showing what the colonies look like if you do this nutrient gradient. So what you can see obviously is that, so this of course is just the top part of each colony. And obviously the more food you give the colony, the more it grows. But what we were expecting was to see something like in the computer simulations where you would get nicely mixed cells over here and then less mixed cells at the low nutrient concentration. And that's not really what we see. So what we see is that all of the colonies ended up separating into these two colors. So we got sectors in all of our conditions. But then we proceeded to do a bit more of image analysis of our different colonies. So let me just explain this with this diagram over here. So what we wanted to do was to see to what extent these colonies were mixing and how that was changing over time or over distance from the center. So in this picture here you can see in the circle in the middle is where I put the drop. So where it landed initially. And then from there we can go slowly outwards in circles. And at each circle we want to measure something called heterozygosity. So this is a term that comes from physics and it's basically measuring the variance around the circle. So if you go between if you keep switching back between green and blue as you go around along the circle the more you switch the higher your heterozygosity. So the minimum heterozygosity would be blue all the way around. Okay and the maximum is what we see at the middle is 0.5 where they're completely mixed together. So basically what you see in this on this graph here is as you go away from the center the colonies lose their diversity. So they become less mixed as you go away from the center. And you can see that the different nutrient concentrations you get slightly different patterns. So basically what we observed is that the high nutrient concentrations initially there was a flat part. So the diversity or the mixing remained for longer before the diversity was lost and then this curve was less steep afterwards when they did start to lose their diversity. And so we defined a term called the demixing distance for the point at which we decided that the colony had segregated into its different sectors which we basically took to be the maximum slope of these lines. And then we can plot these. So this is when we reached the maximum slope at what distance against the nutrient concentration and we found a nice linear relationship. So these are basically these red lines that you see in these pictures. So each one of these is the distance at which the colony demixed. Okay so the idea of all of this so it seems like a lot of detail to try and understand what's going on. But basically there are a lot of physical models that try to explain how populations change and how they demix over time. So we used these physical models and tried to find out in our case what is the main factor that's affecting this loss of diversity. And after fitting a lot of models we found that the main explanatory variable was colony expansion velocity. So how fast your colony is growing. So it doesn't matter for example diffusion rate or yeah there are different values that might be important but the one that we found correlated the best was this colony expansion velocity. And so just to illustrate this we took all of our colonies in our experiments and we measured how quickly they were expanding and then we plotted this against the rate of diversity loss which is essentially the slope in the graphs that I showed you before and we can show a very nice correlation. So the faster the colony is growing the slower it loses the strains that were there in the center at the beginning. Okay another thing that I told you at the beginning is that we were surprised that these colonies always demixed. So even when we put more nutrients they still made sectors. And you can sort of see this prediction here. So if we were to draw a line through this then you can see that it's converging it's flattening out but it's not reaching zero. So zero would mean that eventually if you add enough nutrients it would stay mixed forever. So we wanted to understand why this was the case so why did they remain mixed? And one of the hypotheses was that they're running out of food. So in a petri dish eventually the ones towards the edge of the colony are going to start to starve. And this is something that's quite difficult to test with an experiment because I guess you could try to pump nutrients into the edge of your petri dish into the agar but that's quite complicated. And so we went back to our computer simulations and used those instead. So we redid the computer simulations to resemble more of a colony so where we put our cells in the middle and then allowed them to expand. And then again we changed their nutrient concentrations and we saw results that were quite similar to the experiments. So again the more nutrients you put the further the demixing occurs from the from the inoculum. But then what's nice here is that we can actually switch off the nutrient gradients. So what you can see here is in gray or black you see the concentration of the nutrients and as you approach the colony you can see sort of this white halo and that's where the nutrients are getting depleted. So there's a gradient of less and less nutrients towards the center of the colony. So we just switched this off. We basically said okay these cells are not going to take up any nutrients they're just going to use them but then magically these nutrients will reappear in the agar below and then you see that they actually remain mixed forever. So it doesn't, the segregation is basically caused by the starvation effect or the fact that they run out of nutrients. So that was our second conclusion from this. So this might seem like a trivial project where we're really looking at a lot of detail into these small differences that are occurring in these colonies but if you think of this as a group of cells that are within a cancerous tumor for example then this might be an important type of effect that one would need to understand. So if you start with lots of different genotypes of tumor cells eventually you will start to lose different cells and this will depend on the gradients of nutrients or gradients of oxygen for example that you have in a different environment. Okay, so just to summarize this first part what we found is that abundant nutrients in an environment can make it less likely that a cell is surrounded by its clone. So you stay in a well-mixed group for longer if you're in a high-nutrient environment. Now let's move on to the second part of the talk. So why does this matter? Why does the identity of your neighbors matter? So this diagram I showed you at the beginning to motivate why we're interested in looking at spatial structure in the first place. So the argument was that if you're in a mixed group that you're less likely to succeed as a cooperator because you're being exploited by other cells that are not producing these same enzymes for example. And so one of the things that they did in Kevin's lab was to use the simulations to test this hypothesis. So basically they rerun the simulations but this time with one cell type that's secreting something useful and then another cell type that doesn't secrete it and then they looked at under what conditions do you get the success of one lineage or the other. So low-nutrient concentrations as we predict since you end up in these clonal groups where you're surrounded by cells that are the same as you, identical to you then you get the success of the cooperator cells that are secreting the enzyme. And on the other hand at the high-nutrient concentration where you ended up in a very well-mixed group what happens is the opposite is that you get more red cells I don't know if it's very visible here but you get more red cells growing than the blue ones that were secreting the enzyme. And recently so the paper just came out a couple of weeks ago was an experimental test to look at this. So we used our colony system to test whether... So the phenotype that we were looking at, this secretion was enzymes to break down antibiotics. So we could show that cells that are resistant to an antibiotic succeed or not depending on whether they're in a well-mixed or a segregated environment. But I'm not going to present this work today just if you're interested in this then I recommend you go and read this paper. Okay, instead I'd like to look at something a little bit different with the computer simulations which is what happens if your genotypes don't start at equal proportions. So here what I showed you is all the results when you start at the beginning with 50-50 of the two cell types. So the blue cooperators and the red non-secretors. But what typically happens in a natural environment is that maybe you have a mutation that occurs where suddenly a cell starts to produce some cooperative enzyme and it's the minority in its population or perhaps you have cells that are landing in an environment and again maybe that cooperator is a minority. So what happens in that case? Can they still succeed? So we tried to do that so we changed the initial proportion of the secretors to the non-secretors. So here you see one to one which is the same as the experiments I showed before. Well the simulations that I showed before and you can see that in all cases you get secretors that win. So just to explain this graph a little bit better so here on the y-axis we have the fitness of the secretor compared to the non-secretor. So any points above that means that we ran a whole simulation and by the end of the simulation there were more secretors than non-secretors. Okay then it would be above the dashed line and anything below means that it's the blue non, sorry the red non-secretor cells that ended up dominating the population. So one effect that we see is that the more cells you have in the population the more competitive it becomes. Actually you see this more here but the point of the graph is that the fewer secretor cells you have the more they start losing. Okay so the problem is with if you're a rare cooperator in an environment then you get out-competed by the non-secretors. So here's just a little diagram to illustrate why this is. So if you're a cooperator so you're secreting this enzyme and you're in an environment on your own then the more you secrete the better and you can outgrow any other strain. But if you're in a mixed group like this and you're the only cell there that's paying a cost and is secreting then at the beginning there's not much benefit to producing this enzyme but you're growing slower than everybody else so you end up being covered by these green cells that are growing a little bit faster than you. So the initial point in a competitive environment is really important in order to be able to break out in your population and that's why we see this effect here at the lower frequency. So when only a quarter of the cells are these ones that are secreting especially in very crowded environments where we have more cells then we find that in most cases they end up losing. So how can we fix this? Why do we think that cooperation might still be able to succeed in a natural environment? So we were thinking if only cells could measure their population size then maybe they could mediate this. So perhaps they could wait until they're large enough group until it's worth making this enzyme and then they could switch it on and in that case maybe they would succeed. And so the principle that I've just put into words is called quorum sensing. So quorum sensing is essentially a way in which cells while they're growing they secrete some signaling molecules and these go into the environment around them and at the same time they can sense these signaling molecules and once there are enough signaling molecules around then they start to do some behavior. And the point is that these signaling molecules are quite cheap to make and so using those you can sort of mediate your behavior and decide when to start producing something that's more costly. So the canonical example for this comes from a really interesting system so this guy here is the Hawaiian bobtail squid and it's really cool because it has an organ inside that picks up bacteria from the environment and then those bacteria they start out as a small clonal group and then they start dividing and once they reach a certain large enough population size they start producing a protein called luciferase which essentially makes the whole squid light up. And this is supposed to be the way the squid can camouflage itself from its predators. And so this is how people discovered quorum sensing was basically in these bacteria that were producing these signaling molecules to know when to light up because if they started to light up already at the beginning of the day before it's dark and there are only a few cells then they would be making all this luciferase for nothing but instead if they waited until the group was large enough then it would make it worth it. So we were thinking why could this not be also useful in the case where you're competing against other strains? So the model that we were thinking of was something like this so if now the red cell here is a quorum sensor then at the beginning it would be secreting nothing still nothing and so while it's not secreting any enzyme then it's not paying any cost and it can grow just as fast as the green cells around it and then once it has become a big enough group then it can start to secrete and break out and overtake the other cells that are around it. So that was the hypothesis and so fortunately we have these computer simulations where this is relatively easy to implement. So this is a bit of a busy slide but I'll try and explain it slowly. So what you see is the same kind of graph as before with your relative fitness of the secreter and here you have different genotypes. So the C is the constitutive producer so that's the same one as before that was losing out when it's at low frequency and N is just a control so it's non-producers so essentially here we would be doing a ratio of one to four of non-producers against non-producers. So anyway it's clonal population and then these Q lineages are the quorum sensors and the difference between them is just that they start producing the enzyme at different amounts of signaling molecules. And again we have our three initial proportions so at proportion one to one so this was the case where anyway the cooperators the constitutive producers were winning so there's no difference and it doesn't really matter if you quorum sensor or not but the interesting case here was when these quorum sensors were rare in the population and we could see that especially at this particular threshold here that this quorum sensing behavior could rescue the population and manage to make use of this enzyme that they were secreting. So just to show you what these simulations look like helps but to visualize this so here this is a simulation where we made a mixture of all three types but the two secretors so the either quorum sensors or the constitutive secretors are in a minority and you can see that the constitutive secretors they never really managed to break out of the group and they get suffocated by the cells around them whereas the quorum sensors that only start to produce later they managed to make large populations and overcome the competition. Okay so the summary then from this part is that if your neighbors are clones then you're more likely to succeed if you make something cooperative so that's what we saw from this toy example here and I showed you with the simulations and then the other point was that if you're in an environment that's highly competitive then quorum sensing can be used as something to overcome competition so this is quite different from what people thought in the field because typically quorum sensing is really put as a canonical cooperative trait but we're saying it's cooperative within the group but it can help you to compete against other groups. Okay so the third part of today's talk I just wanted to briefly introduce how we're starting to look at expanding this research from single species, different strains towards looking at different species together. So at the moment in my lab we're working on quite a different system from the pseudomonas aeruginosa experiments that I showed you before and we've moved to studying four different species of bacteria. So the idea with this research is that we want to see what happens if you start to add in complexity in the lab. So a bit like I was talking at the beginning if we start to bring many species together and try and see how they're interacting with each other. So these four species of bacteria we chose to study because they are able to grow in something called the metalworking fluid. So these liquids are used to lubricate and cool down machines in manufacturing facilities. So essentially it's an industrial oil so similar to petroleum in composition but they're used as emulsion so they're mixed with water which makes them these milky substances and then they're used in the factory and at the end of their lifetime they need to be degraded because you can't just pour them down the drain obviously so they need to be treated. And one way to treat them which was something discovered by some collaborators of ours at Oxford is that you can put in these four species of bacteria and then they seem to be able to degrade the pollutant compounds within this liquid. So that's what you see here so this is a picture from our own work where in the left you see this metalworking fluid with no bacteria and we can quantify the pollution load of this liquid and then if we leave it for a week with these four species of bacteria we see that the pollution load goes down. So for us this was quite a nice system to study because it lies at an intermediate level of complexity so we're starting to get more than two species into the lab but at the same time it's simple enough that we can really work out all the different interactions that are taking place between the species. We can look specifically at the phenotypes of the species so for us it's a first step towards these complex communities that we see in the environment. They're quite manageable in the lab so they all grow overnight and they're relatively easy to work with. They're all aerobic and we can also measure the productivity so in terms of this pollution load so we can try to study what happens if you change certain things in the environment or you add in or take away different species how will that change the efficiency of the system or the functioning of an ecosystem? So what we're trying to do then is to build this into a model system for studying different questions related to bacterial communities and in particular small bacterial communities. So we're not claiming that any of these results would necessarily apply to larger communities but it's a good place to start. So just to illustrate the type of things that we're doing with this system so one of the first questions was to try and measure how the different species interact and this is work done by Philippe Picardi where he started experiments first putting each of the different species alone into this metal working fluid and then starting to combine species. So he did all the pairwise combinations of species groups of three and then finally all groups of four and he grows these in cultures and then measures the population sizes and tries to correlate how the growth patterns change depending on who's there. We're also working to quantify all of this so Björn Vestman is doing a PhD that's more focused on the modeling side of things so he is trying to find the right equations that would capture these types of interactions and put them more into a mathematical model that will allow us to predict what would happen under different conditions and so this is an example of one of the models that he's using which is called the Lotka Volterra model where essentially you can describe the change in population size of one species of bacteria based on its own growth rate and also how it's affected by the presence of others that may be in the same co-culture with it and essentially what he's doing then is to take all the data from the experiments and try to estimate growth rates and interaction strengths that would minimize the difference between the model and the experimental data. So for example, here's some of our data where we see one of the species is Ocrobactrum that's dying on its own so this would have a negative growth rate and then if you grow that same species in the presence of another one Comomonas you see that it starts to grow quite well. So in this case you would end up with a negative growth rate and a positive interaction strength. So it's just to illustrate the kinds of models that we're starting to build and to look at in order to study and predict and also to control our system. So if we have a good model then we can also see if we manipulate the growth of Comomonas for example by changing the environment can that change the interaction between those two bacteria? And then a final thing that I wanted to show you was that we're also looking at spatial structure within our system. So you might think that this is not an interesting point because we're working in liquid culture but if you look at this culture over here you see on the surface of it that the air-liquid interface there's this thin film and this is basically bacterial biofilm so this is where they're secreting these polymers and they make these very thick groups of cells so if we take a little bit of that surface so you can see it here in a little tube, a flask then you can see that it's sort of this sticky substance and we can look at this under the microscope as well and see where the different species are and the reason why we can see the species is because of work done by these three guys which was really a lot of hard work to try and fluorescently label the different species with different colors and setting up microscopy to be able to visualize everything but the idea is from that to see whether we can map the interactions to the spatial structure of the different species and we're also setting up some experiments with the colony model so again if we can grow our different species in monoculture and then mix them together spot them onto an agar plate and if we look at that under the microscope then we start to see some nice pictures so this again here is the center where all of the species fell in the middle of the drop and then we can see how they're separating over time into these sectors like what we were studying before and of course we can start to look at parallels between that and our computer simulations where again we got these nice clonal sectors so that was a quick overview of the type of work that we're doing at the moment in the lab so I'll get back to this question so I've shown you that we've been using a combination of these different tools so whether they're computational simulations mathematical models and trying to combine them with experiments so I don't know if the way we're doing it is correct but at least it seemed intuitive to be able to have these different tools and pick them up depending on the question that we needed to address and how easy it was to use these tools in the different situations so what I think I'm trying to say is that even though I came from a different background it was nice to be able to use these different tools and to learn different tools because it allows you to expand the number of questions that you can address with your research and so I think that this is sort of my message that I think biology students should become more quantitative that at the moment people either learn the computational or mathematical side or they learn more of the biology and I think that doing such computational analyses building models is as important to biologists as it is for physicists for example so I really think that this should be a focus in education and one does see that there is more on the side of genomic analysis so people have picked up that people need skills in bioinformatic analysis but I think that's not the only thing so more general quantitative methods I think would also be very helpful and this would allow people in the future to be able to be a bit more versatile with the tools that they use and at least to be able to more effectively collaborate with people who are more specialized in more of the mathematical or computational side okay so that's all I wanted to show you today but just to finish off I wanted to especially thank people at the DMF at the department where I am here at Emil who've been extremely helpful with us setting up in the lab these are the people at the zoology department who participated in the project that I showed today especially Jonas and Armin are the two first authors on the paper with the quorum sensing and we collaborated with Joao who was one of the first developers of these computer simulations and then I want to especially thank the members of my lab who have been extremely enthusiastic and just a great group to start our research with and finally some funding bodies and thank you for your attention


Everybody forced here ripping 2022 flawless collegiate football hobby one box number 16 left side serial numbers Let's go ahead and do our random Our names and serial numbers. Good luck everybody. Oh cool. John. You got it. Yeah, sorry man We stock must have been off. What's the soul one of the front or something and not adjusted six and two eight times eight times names first one two three four five six seven and eight James a down to James B and eight times in the numbers now one two three four five six seven and eight Four down to 23 James a for Mauricio 15 Ryan D 9 Daniel L 7 Richard L 6 Daniel L 13 19 Ruben Y 2 Haley 22 Daniel L 18 Joe W 21 James a 14 Daniel L 17 25 Patrick E 3 Sean H 8 KC 24 Charlie G 16 Kevin G 1 Matthew C 11 JT 20 Steven B 12 Chris S 5 Gabe with 10 in James B 23 Let's get those posts and we'll get started Here's your link if you want to look him over again there in chat right now Those can always you found a late in sports cards.com also call out who's hit his who's as we go along here. That's really close Pull the bio junior red shimmer to five out of a 2020 boom blaster. Geez. That's crazy. Even if not first woman That's big It ships sealed to you. Good luck everybody Dual patch three of 20 James and Williams three spot Patrick E Couple of two color patches barely in the top one see better in the bottom Campus legends auto Peyton Manning one of ten Kevin G the sheriff's going to you Omaha What guys Peyton Manning here now Omaha scares me He's annoying that David Beckham commercial. I know so annoying. It's the worst Peyton Manning there Rookie auto Felipe Frank six out of 15. That's where 2021 flawless collegiate six spot Richard L Dual patch Drake London 22 of 25 22 spot Haley H Or Drake Jackson, excuse me same team or same collegiate team Drake Jackson not Drake London Drake Jackson That's the best Peyton Manning though Paul for sure. That's the best version of him a Star swatch signatures of Hunter Renfrow 15 of 15 Mauricio with the 15 spots player worn three color patch Hunter Renfrow College legend for sure right there Chris Godwin auto 11 to 25 Matthew see you with the 11 spot Yeah, I like the foil designs this year too Godwin Hotty-totty Jersey Auto Matt Corral 12 out of 25 Steven B with the 12 spots Hotty-totty Sapphire gem Emmett Smith two out of 20 Ruben Y with the two spot Emmett sapphire color match Diamond gem auto Breeze Hall four of 25 James a four spot Breeze Hall the Cyclone Breeze Hall and the last hit of the break Is a Jersey auto Malik Willis with the go flames die-cut James a four spot again sweet Malik Willis. That's awesome the Liberty Flames dude Baker just brought it back for the win. No Seconds left in the game. He's the good. I told you. Oh my god There you go. That's gonna do it. Thanks again, everybody Prism and selects coming up


The top place to teach English in Thailand, Chiang Mai. Thailand's revered northern capital offers a vastly different option, from the hectic pace of Bangkok and the tourist magnet of Phuket. Due to its relatively high elevation and location in the north of the country, Chiang Mai enjoys a considerably cooler climate than many other places in the region. There are also plenty of public and private schools to be found, although salaries are generally lower than some other areas. However, this hasn't stopped its popularity among foreign teachers, as many are prepared to earn a little less in return for all the benefits of living in Thailand's cultural capital. Although Chiang Mai is many miles from the nearest beach, it is surrounded by a backdrop of stunning proportions. In all directions, there is natural beauty to be enjoyed, including jungle-covered mountains and spectacular river valleys. The area around the city provides the perfect playground for enjoying a wide range of adventure sports, as well as some of the best trekking to be found in Southeast Asia. Within the city, you will also find many cultural highlights, such as ancient temples and handicraft markets, as well as cool coffee shops, bars, and cafes. If a mix of nature, adventure, culture, and nightlife is what you are looking for, look no further than Chiang Mai. Thanks so much for watching. Here I, TTT, the leading provider for Tefal and TESOL training courses. If you liked this video, please subscribe by clicking the button down here and click on any of the videos here on the left for more interesting teaching tips for getting certified to teach English abroad and online.


One, two, one, two. There we go. Fantastic. I'm so excited to be here. Thank you so much for having me. This is my first time in Prague, which I say to my great shame, because I grew up in Europe, and I've traveled throughout Europe, but I never managed to visit Prague until today. It's really an amazing city, quite spectacular. Parallel polys is just such an incredible experiment in community here. I was told that everybody who's here bought their ticket with Bitcoin, which the average meet-up in the United States would get three people if we tried that. They'd have to teach the other two hundred people how to get the Bitcoin. I titled my talk, Thoughts on the Future of Money. In fact, I titled my talk for all of the European cities. I'm visiting Thoughts on the Future of Money, which in summary means I haven't yet picked a topic, and I won't actually pick a topic until 24 hours before the talk, and I picked one this morning. So, actually what I'm going to talk about is scaling. A lot of you probably have noticed that there's a very interesting debate in Bitcoin today about how do we scale Bitcoin. That's the topic I want to address, not from a technical perspective, but from a broader perspective, to understand what it means to scale. So, gather around, and we'll talk about a long, long time ago. It was 1989, and the internet was dial-up. And not just the connection of users to the internet, the backbones of the internet, in most cases, were dial-up between universities, between research stations. There were a few permanent connections, high-speed connections, high-speed, 256 kilobits, 512 kilobits, high-speed connections, but the internet was mostly dial-up. Email had not yet really started to take hold, but there was a really special place on the internet called Usenet. Usenet was a discussion system, a system of discussion groups where you could post a message in text. And other people would see it, and then they would respond. But this was not instant messaging. This was slow messaging. Because in order for Usenet to work, all of the messages had to be transmitted by a dial-up system, and propagated from node to node in a system called Store and Forward, Store and Forward. So you'd post a message, and it would take 24 hours, 48 hours to reach everyone, and then they could respond. And it would take 24 hours for you to see their response. Today, we would compare that to trying to communicate with Matt Damon on Mars as part of the Martian movie. And so, at that time, there was a really big conversation among the engineers of the internet, because Usenet was getting very popular, and it was getting very big. Kilobytes and then megabytes of text information that needed to be transmitted. And at first, these dial-up connections would take 30 minutes to an hour, but then they started getting longer and longer, and to get all of the Usenet messages would take one hour, two hours, and then three hours. And so all of the experts predicted, if you draw the point we are here, the point we were six months ago, and you connect them in a line, very soon it will take 26 hours to transmit one-day messages. And then we have a problem, because we only have 24. So what happens then? The internet will collapse. Clearly. It can't scale. It won't possibly scale. At the time, there were two parts to Usenet. There was the regular part of Usenet, which contained very carefully structured groups for academic discussion. And then there was another little part of Usenet called the alt, the alternative groups. And this was optional. You could carry it if you wanted to. You didn't have to carry it. So if you were a Usenet provider and you made these groups available, the really interesting providers offered the alt groups. And of course, all of the interesting stuff was in the alt groups. Some of the early, amazing groups, alt folklore, computing, alt security. And of course, like everything else that's been driving scale on the internet, alt sex. And these alternative groups being optional were the focus of this great debate. Should we carry them? Because at this point, we've started seeing the world's first spam. I remember receiving the first spam. It was a message posted by a Los Angeles lawyer firm that was posted to every Usenet group. And you did not do that. That was not cool. About 1,000 people told them it was not cool. That was the first internet backlash. So the discussion was, do we carry alt groups?  And there's no way it can ever scale. If this becomes popular, people will discuss more. And if they discuss more, we won't have enough capacity to deal with this data. This conversation lasted for more than two years. And there were a few brave service providers that carried the alt groups. And they used massive hard drives. Five megabyte hard drives that were the size of a very big thing. And again, the main idea was, if you take where we are here and where we're going, up there we hit a wall. So the internet couldn't scale. That was the basic beginning of the scaling issue on the internet. And it couldn't scale. And wouldn't scale. Clearly. And many people wrote their PhDs on why it wouldn't scale. But of course, the thing is, networks don't scale. Networks fail to scale. And some networks fail to scale gracefully for decades. And those are the ones that succeed. So we solved the use net problem. The digital connections were upgraded, more systems connected with leased lines, and direct connections, dialogue gradually got replaced by leased lines. People started investing in the infrastructure, and we could comfortably carry use net. So people started using email. And we started the problem all over again, because now email started getting popular. And as email got popular, it started replacing and eclipsing the size of use net. Now we had an even bigger problem, because people wanted to communicate directly. Now a message didn't take 24 hours. It took two hours to cross the internet, which meant that people started having real-time conversations near real-time. So email started exploding, and so the internet couldn't scale. Because if you looked at where email is today and where it was six months ago, and you draw a line, it will melt down. And people wrote more PhD theses about how the internet would die under the load of email and never scale. But then gradually we started optimizing that and solved it.  But we as people, as humanity, we solved the problem, so we scaled it. So the internet failed to scale for use net, and then it succeeded to scale for use net, so that it could fail to scale for email. And then it succeeded in scaling for email.  And these attachments were ten times the size of the text, because people started sending bigger things, like drawings and pictures. And of course, once again, sex. So we could scale for email, but not for email attachments, so everybody was up in an uproar. We were never going to be able to scale for email attachments. The internet will surely melt down. And then we solved it, until some British guy, Sir Tim Berners-Lee, who then was just Tim, invented the web. And now you could put the pictures into frames. This was about 1992. I downloaded and ran the first web browser, NCSA Mosaic. At my university lab, we gathered together three or four friends. We worked for hours to get NCSA Mosaic downloaded, compiled, and installed. And then we launched it, and we visited the web. All of it. I can say a sentence not many people can say, because in 1992 I visited the entire web in an afternoon. Both sites. Because there were two. And I visited both sites. And I looked at that, and I thought, oh my God, this is going to be huge, huge. The internet will never scale. And just imagine what you can do with sex on the web. Of course, this became the scaling application. As we all know, it's been driving internet development since. But we don't talk about that in polite company. So then the internet was failing to scale for the web. And people say, we can never do all of these images in hypertext documents. And it will totally fail to scale. And more PhDs were written, and more discussion. And the internet was still failing to scale. But by now it had been failing to scale for more than a decade. Very gracefully, very successfully.  And now some other people decided, why don't we just replace the entire phone system with the internet? Now that was a crazy idea. So the phone companies then started this massive campaign to inform us of why packet switch networks could never carry voice. And really, the true quality approach to voice was always going to be hierarchical switch networks owned by national monopoly telecom companies. Because the internet couldn't possibly scale to carry the world's phone calls. Those same companies now route all of the phone calls over the internet. So first, they didn't want the internet on their phone networks. Then they allowed the internet on the phone networks. Then they built their phone networks on top of the internet. And then we started sending videos. And then the internet couldn't scale again, because YouTube was going to melt down the internet clearly.  Because there are already 1,000 cat videos.  By the end of this decade, there will be a billion cat videos on the internet, which is exactly what happened. Pardon me. But we scaled. And now we do 3D video and 4K video, and then Netflix came along. And so we see the same mistake, which was that in 1992, when I visited the first website, my thought was, wow, TV is so dead. Because one day we will be able to transmit movies instantaneously.  Because clearly, if we had Netflix in 1992, a single video stream to a single user would melt down the entire internet. And yet, here we are today. And by the way, the internet is failing to scale for Netflix, and all of the other companies that are doing live video, and it will continue to fail to scale incrementally and gracefully. And soon we will be doing Oculus Rift holographic 3D, 4K VR. And then it will really fail to scale. And people will still write PhD thesis on why the internet is about to melt down. And the reason for this is really simple. It is because scale is not a goal to achieve. It is the definition of what can you do with the network today? And the moment you increase the capacity, the very definition of what can you do with the network today changes. Because somebody says, hang on a second. You mean I can now do X, which has ten times more demand than what I did before? Let's do some of that. And then you fail to scale again. So scaling is a moving target. Scale defines the edge of today's capability. And as it moves forward, capability increases. Bitcoin is failing to scale. And if we are really, really lucky, Bitcoin will continue to fail to scale gracefully for 25 years, just like the internet. Because the very same types of companies that then were saying, the internet can never work for all of the email. It can never work to do quality voice calls. It can never work to do quality video. The same kind of corporate arguments are now being said. So why Bitcoin can never do retail payments. It can never do visa scale. It can never do global scale. And if it's actually adopted, it will collapse. And right now, there are a dozen people writing their PhD thesis on why Bitcoin will fail, has failed, is dying, was dead, and has died again. There's a beautiful site called bitcoinobituaries.com, where you can read the pronouncements of the death of Bitcoin since 2009, regularly, like clockwork every three to six months, major newspapers, scientists, et cetera, saying, OK, that's it. Bitcoin is dead. In fact, this has now become an amazing recruitment opportunity. Because all you have to do is wait for people to hear, first, that Bitcoin died. The CEO of Bitcoin was arrested. Bitcoin was shut down by Putin.  Bitcoin is still there. Is the marketing slogan of this community? If we can just keep doing Bitcoin is still there, people are surprised. They are confounded. It doesn't match their expectations. It's not possible that Bitcoin is still there, because very serious people, with very serious titles, working for very rich companies, told them that Bitcoin would not still be there, but Bitcoin is still there. Because we are failing to scale gracefully. When we fail to scale during a stress test or a capacity test, when the network is flooded with transactions, what happens? Some users experience a terrible situation. They do a transaction with 0.1 millibits, like they've always done. And it takes three days to confirm. And during that time, they're freaking out. Especially if they're new users. Because new users assume that the money has left their account. There are no accounts in Bitcoin. And it's en route to the destination account. Again, there are no accounts in Bitcoin. And therefore it's somewhere in limbo in between. But the money is really still in their account. It's just that their wallet says it hasn't been confirmed yet. It's either at the source or at the destination, atomically, with one transaction. There is no intermediate state. It can't be in limbo because Bitcoin doesn't transmit. It settles. So we experience these sudden problems, and some wallets, behave intelligently, and they increase their fees, sometimes by 100%.  In seconds, anywhere around the world will full censorship, resistance, and open innovation. Open access to everyone. It takes seven cents to send that transaction. And clearly this is an indication, together with the people who had three days, to confirm the transaction that Bitcoin surely is dead now. And some of the developers go, ah, I give up. Bitcoin is dead. And the newspapers write, Bitcoin is dead. Transactions are not going through. Transactions are going through. They went through for me. I was running a wallet that was intelligent, doing its transaction calculations. But what happens in the aftermath of this capacity crunch? We get better wallets. And that's really the essence of a dynamic system responding to pressure. Because as we get better wallets, these better wallets calculate fees more correctly. And it's a lot easier to jam the network, if there's a lot of dumb wallets doing 0.1-millibit fees. Because then all you have to do is do 0.11-millibit fees, and you are king of the hill.  And you jam the network with your transactions. But if they are able to do 0.12-millibits, now you have to do 0.13-millibits. And now we are in a race, and before you know it, you are spending 0.5-millibits on a transaction.  If you are trying to jam, the network starts getting really expensive, really fast. Which brings up an interesting question. What is a spam transaction? What is a legitimate transaction? What is an illegitimate transaction? There are two ways to answer this. One is a paternalistic, top-down approach that says, This is what is allowed. This is what is not allowed. And by making a list, we will prevent the network from filling to capacity. But that breaks the fundamental capability of Bitcoin, which is net neutrality. Bitcoin doesn't care who the sender or the receiver is, what the application is, what the value of the transaction is, all it cares about. Did you pay the fee? And if you pay the fee, your transaction is legitimate, by definition. Because you thought it was legitimate enough to attach that fee. The very act of paying the fee legitimizes the transaction. Because if we start making decisions about what is spam and what is not, we are now choosing the future of Bitcoin and constraining it into a set of applications that we can imagine. And the brilliant person who creates the application we can't imagine, that may look like spam to us, doesn't get carried across the network. Because we made a top-down decision to say that transaction is legitimate. And the other way of doing this is to say, how about we use a market to solve this problem? We have a market, we have a currency. Use the market to solve this problem and allow the market to establish the minimum fee that meets the requirements of supply through the miners and their need for propagating blocks fast, and the demand of the users for the applications they care about. And if you pay the fee, your transaction is legitimate. There is no spam transaction. There is no such thing as a legitimate transaction. There are only transactions that did get mined, and transactions that didn't have enough fee to get mined. And so this is how Bitcoin is going to play out. And this is not going to be solved. It's not going to be solved because we will have the scaling discussion every year for decades into the future, hopefully. And every year we will fail to scale for the next application and succeed to scale for the previous ones. And as soon as we do better, people will invent new applications, and we will fail to scale again. The Internet failing to scale gracefully for 25 years. Bitcoin, let's keep failing to scale gracefully, and Bitcoin is not yet dead. Thank you. We'll do some questions? Yes. Very good. So thank you very much. We have questions. If you have questions, please just raise your hand, and I will rush to you with a microphone. So yes, please. And I will give you the first question. It is already, I think, two or three months that might have heard quite loudly about the Bitcoin community. And me personally, I admire him for his work. I was very surprised by his strict position on that the Bitcoin community failed. And this is why the Bitcoin failed. Can you tell us your position on his move? Well, coincidentally, I saw Mike two days ago. I was at a conference in Zurich, and Mike was speaking right after me on the other stage. And so we met, again, we've met a few times. We actually spoke at the same time at the very first conference in San Jose in 2013. His room was full, and my room was empty. I had begged them to give me a speaking slot, and they gave me one very reluctantly, and nobody came. You can see that video. It's online. It's pretty good. I talked about Bitcoin neutrality. Anyway, Mike was in the other room. He was doing his talk. He's been around for a very long time in Bitcoin. And so when I saw Mike, I said, again, hey, thanks for everything you did. Because the thing you have to realize is that even when we disagree, we're on the same side. Even when we disagree violently, we're on the same side. There are plenty of people out there who don't want to see Bitcoin succeed. But the one thing we all have in common is that we love this idea. And here's the interesting thing you might not realize, but Mike loves this idea, too. He just didn't think it was working right, but he still loved it. He wants it to succeed. He just doesn't believe it will. And more importantly, he's made incredible and enormous contributions to Bitcoin. His last contribution was to drop the price by $40, and allow more people to buy Bitcoin at a reduced price. Because of an article in The New York Times. And so people had the opportunity again to buy Bitcoin at $380. That might be a good opportunity for some, we'll see. And Bitcoin failed to die. So thank you, Mike, for everything you do. That's all I have to say. I don't believe in the angry debates, the paranoid conspiracies. I think Mike is wrong. I think my entire presentation was why Bitcoin will continue to scale. In fact, I think the governance problems that seem so acute are not really acute. What they are is the fact that Bitcoin is hard to change. And in many systems, when there is a very strong debate, the debate is resolved... because somebody says, you know what, that's all great, but I'm the boss. So this is what we're going to do. Okay, discussion over. And in Bitcoin, there is no discussion over, because there is no boss. Because no one can impose their position on anybody else. So we can go back and forth, and back and forth, and back and forth forever... arguing about this.   Because Gavin and Jeff, and Tumen wrote Classic, and... Greg and Peter, and all the others went and wrote Segwit. Guess what's going to happen by the end of this year? We're going to implement Segwit. And we're going to do a hard fork to do Meg. And we're going to do Thin Blocks, and RBF. And Child Pays for Parent and Prioritization, and Extra Thin Blocks, and Extreme Thin Blocks, and other optimizations. And Invertible Bloom lookup tables that Gavin started working on almost two years ago. We're going to do all of those things, just to get to the next level of scale.  Thank you. So let's take the next question. Who wants to ask a question? My name is Galio. I'm writing for the Contelegraph, Chief Republic. Hello, Kara. Nice to meet you. And I think I'm a bit peculiar, but sometimes I'm in doubt. Yes, I'm in doubt. And my question is, if you have ever been in doubt about the future of Bitcoin, that's very important for me. Thank you. Yes, I have been in doubt. There was a moment where I really, really got worried. And that was the day I got a call from one of the people I know in Bitcoin. And they say, you know what? We have it on good information, but empty boxes and solvents and they're going to blow up today. And get ready for it because it's going to be ugly. And in those days, there were really just one exchange. And it did all of the ball human if the empty box blows up, we have a big problem. And it did blow up that day. But an hour after I heard it was going to blow up, it blew up. And I did, for a moment, get scared there. I was like, whoa, this is just going to be so bad. And then part of me, this little voice in my head is going, maybe it will go to 10. This other little voice in my head was like, if it goes to 10, you could buy a 10 again. And these were fighting because I wanted to panic. But at the same time, I was like, this is going to work. Here's the thing, there are so many people who will not give up no matter what the price of Bitcoin. They will not give up. I will not sell my Bitcoin. I would rather watch it go to zero than some ever. And I don't really have much Bitcoin. But that's not the point. But I will be one of the many thousands of people who absolutely refuse to give up. And all it takes for Bitcoin to continue to exist is two nodes. Hopefully one of them, at least, or both are mining nodes. And that's it. Maybe the difficulty will go back to where I'm mining on my laptop. I never got to do that. That would be fun. And so sometimes people tell me, Bitcoin will go to zero. I'm like, really? If you go to Rome, you can buy Roman cisterci at a store that hasn't been in circulation for 1700 years. And it's not zero. You can buy Drachmas in Athens that haven't been in circulation for a decade, and it's still not zero. Currencies actually don't go to zero. Because sometimes they just have sentimental value, and touristic value, and historical value. And I can tell you something, there's nothing that has more sentimental and historical value than Bitcoin.  So nowadays I don't really worry anymore. I really don't. Thanks. Ask again. Please. But how do you... We know that there is around 600 of altcoins already. Yes. About 100 of altcoins went to zero already. So how do you... Well, they didn't go to zero. They went to 0.000. 015. Someone still has them. Yeah. But how do you know, or how do you believe that the Bitcoin is not the case with another altcoin? What is the difference between, for example, Bitcoin and, I don't know, Lightcoin, or another cryptocurrency? Why do you preserve Bitcoin? We did it first. We did it best. We've got the absolute best development team in the industry. The most intelligent, amazing scientists, engineers, software engineers, and development teams that are building amazing stuff every day. I can barely keep up. Just when I think, okay, I'm done reading for the day, and somebody drops a segregated witness on me, or some new innovation, and I'm amazed again. So, I mean, here's the other important thing, which people don't realize, is that Bitcoin today is not what Bitcoin was in 2009. It's the same name. It's the same 21 million coin cap. It's more or less the same transaction structure, but a lot of other things have changed quite dramatically. One of the stories of scaling is that sometimes the only thing that scales is the brand. I'm 44 years old. Not a single cell in my body is one that I was born with. They've all been replaced by now. They're all gone. All that remains is the pattern. When I was in college, they said Ethernet can't scale to one megabit, and Ethernet can't scale beyond five megabits, and Ethernet can't scale beyond ten megabits. Ethernet is a networking system, if you're not aware. I installed Ethernet that was a coaxial cable as thick as my thumb, and it could only go 100 meters, and it could only do five meg. Today, all of the local area networks run on Ethernet, but can now do ten gig over fiber. But how much of that is really Ethernet, and how much of that is just the word Ethernet attached to what we made it into? Because it's a different distribution medium, a different architecture. The only things that are really the same are maybe the frame size and the brand. One of the issues that you have to reconcile yourself with is that bitcoin, 15 years from now, may only share with today's bitcoin three or four fundamental properties, a 21 million-coin cap. That's not going away. If that goes away, it's not bitcoin. But the brand name and some basic architecture characteristics, everything else may change. Everything else may be completely revised, and we'll just still call it bitcoin. I don't believe that we need to worry too much, but altcoins, to me, are a very important part of the ecosystem. In fact, the more we have and the more they experiment, the better it is. Something really important happened this year. People fled bitcoin into Ethereum, and that was awesome. Because all of the previous times, when they fled bitcoin, they left the cryptocurrency economy. This time, they simply switched lanes. They didn't abandon cryptocurrencies. They left bitcoin and went into Ethereum to see what the other side is doing. Look at what's going on over there. They're not fighting yet. They're still scaling for now. It's all cozy and nice, and they've got contracts and smart stuff. They also have humans, and human nature always prevails. Pretty soon, you're going to see interesting things like the ones we have in bitcoin. Bitcoin was fine, it was scaling fine in 2012. Nobody was arguing then. The bottom line is that we're now keeping people in the cryptocurrency space. As people went to Ethereum and the price went up, new people came in from the outside, who in order to buy Ethereum, because they didn't believe that bitcoin was a good medium of exchange, first bought bitcoin to use it as a medium of exchange to buy Ethereum. We'll see how it goes, but I'm very optimistic. Hi, I'm Janne from Zarku. My question is, what is your personal selection for bitcoin if you go for the classic? We have started a new feature in Zarku in the past few weeks that allows voting for the type of coin you want to be in the future. I think today, Marek posted his own personal opinion, and we started a big friendly discussion.  I'm wondering what is your personal opinion and what is your personal preference?  They never attack people. What happened? I run Core, three nodes. I run Classic, two nodes. I run Ethereum, one node so far. I ran XT, one node. I will run everything that comes out, that I have an interest in evaluating, understanding, and propagating. To me, this will be resolved one way or another. The simple answer is that, as long as the plan is working, then Core can execute that plan. If Core stops executing effectively, they will lose the confidence vote. The immense centralized monopoly power of Core will melt away in less than 24 hours. People will run an alternative client, and guess what will happen the day after? Core will merge the hard fork and keep working, because they will receive that vote and go with consensus. There are people who believe in big blocks and small blocks. You can have the most well-developed beliefs in Bitcoin up until one week before the fork. Everybody will say, I'm ideologically X, but the winds are blowing classic today, so I'm going with consensus. No matter what opinion you have, in the end, you may think this may not be good for you, or maybe good for you.  The safe bet is always consensus. All of the ideology melts away, because at the very end, what matters only is the personal motivation, the invisible hand, that guides each person to make a choice on their own about their own best interests. That is the genius of Satoshi, and consensus wins every time. I'm not worried, because I'm not a classic or core supporter, or any other kind of supporter. I'm a supporter of good code that works and is currently in consensus. If that changes, I will take the new data and change my opinion accordingly. Okay, thank you. I'll try to keep my answers shorter, so that we can do more questions. Sorry if I've been rambling. Hi. So basically you are saying that you are depending on the decision of the miners, on the Chinese pools. That's what you are saying. It's not our decision. It's the decision of the miners. No. There are five consensus communities in Bitcoin. There's developers, there's miners, there's exchanges, there's merchant processing, and there's wallets, and they go together.  And then you're on the wrong side. You can get the longest chain, and if the economic consensus is on the other side, you're wasting your time. And of course the miners know this, so they're not going to make any move until they believe that the economic side is with them, and they know for sure that this is the way that's going to be the most successful way forward. And they're very conservative in that decision, because they have millions of dollars invested. You've got to realize something else. A lot of the miners today buy electricity on long-term contracts, six months to a year. Which means they've already paid for this. They've already got all of the hardware. And the only option they have now is to run and pay it off. They cannot turn off this hardware. So they will continue, no matter what the difficulty, what the price, or which side of the fork they're on, they are not stopping to mine. It's not just the miners making decisions. Just one additional question. Right now, new wallet is supporting SegWit. And I don't know if it's going to be in the red in BitcoinJ, because Mike, I don't know if he's used to working on that. So I'm not sure if... Well, that's not entirely true. That's not entirely true. One wallet supports SegWit, and that's the Core fork by Peter Woolley that runs SegNet, and that supports SegWit and works, and is being tested now for more than four months, I believe. SegNet, the production-level segregated witness test network, has been working and doing transactions for four months, with a wallet that obviously is producing SegWit transactions, but the standard is not finalized yet. And therefore, without a finalized standard, you can't go ahead as a wallet developer. I can guarantee you all of the developers are writing test code right now, all the serious ones, the ones that still want to be around, are trying to implement prototypes of SegNet and SegWit. I'm writing a prototype SegWit implementation for my own uses and company, and everybody is behind the scenes waiting to see what happens. But the current roadmap is for somewhere between April and June for the deployment of SegWit into production. So, have some patience, it's going to happen, yes. Okay, hi. No? There, yes. I'm asking everyone for proton mail, I just wanted to ask a generic question for Bitcoin. Proton mail is awesome. Thank you, thank you. Do you believe Bitcoin can survive government intervention and or regulation-alike global skip? Like, let's say, the big countries decide. I have never seen global collaboration between countries on regulating anything.  You think they're going to get together and agree on Bitcoin, especially when, strategically, they're all thinking, well, Bitcoin happens, I'm fucked.  So, maybe, if we let it happen, it can cause them to have a currency problem before it causes us to have a currency problem. And then we win. And I'm not joking about that, because I'm pretty sure that would be the kind of calculation that you would see in a lot of countries that are thinking about whether Bitcoin should be regulated. And so they regulate it, so what? What are they going to do? Threaten people with death if they use Bitcoin? They already do that in some countries. And guess what people will do? They'll use Bitcoin, and then when the policeman comes, they'll bribe the policeman in Bitcoin. And up the chain it goes until the head of the state is stuffing a wallet full of Bitcoin. The bottom line is that in countries where the rule of law matters, money is a form of speech, freedom of association, freedom of expression, freedom of speech protects political expression through currency. In the United States, we put that into a United States Supreme Court decision on the Citizens United that said you can't regulate campaign contributions because money is speech. So in countries where the rule of law matters, you pick that fight, head on with Bitcoin, and we're going to take it all the way to the Supreme Court. Do you know how many lawyers per day Mark Andreessen can hire? And the problem is that they may lose. And losing on a decision that says Bitcoin is protected speech is much worse than the status quo, which is kind of gray. So that's not going to happen.  Bitcoin is evil, really. You say it's evil. I'd better look into this, because every time you say something is evil, it's because it's good for me and bad for you, and I don't like you. This is happening in Venezuela right now.  Don't pay attention to our 550% inflation rate. And please leave your money in the currency of choice and go with us to the bottom as hostages of our insane policies. And what are some obviously technically literate, a very tiny percentage of the population in Venezuela are probably now thinking, really, I should look into this. Yes, who's got the microphone? I think over here was the next one. Yes, go ahead. 34,000 years in the future, there are dreams or visions. Is some face change going to happen in the nation's face with something like this? I try not to make predictions in Bitcoin exceeding 30, 40 days into the future, because the best way to be correct is to not make a prediction, because the future is very harsh when it comes to predictions. The worst part is that if you make a really stupid prediction, you will go down in the history books like that guy who said, the world will only ever need one computer, and that guy who said, electricity is a fad for the Paris fair and will disappear as soon as we dismantle the Eiffel Tower. Wrong two counts in the history books. I can't do a prediction out to 30, 40 years. I do know that the nation-state, as a system of organization, is being severely threatened by network-centric organizations on a global scale. Today, the ten largest populations in the world, four of them are nation-states, six of them are internet applications. Facebook is the most populous concentration of human beings on the planet. Instagram, Snapchat, WeChat, etc., follow that. Somewhere down there is China, India, and then in seventh place the United States. So the world has already changed. Whether that will affect nation-states, we'll see. One of the people in Bitcoin who is really interesting and has some interesting ideas, Balaji Srinivasan, who is the CEO of 21 Incons, told me, the future of politics is not left versus right, it's land versus cloud. Most of the world still lives on land, but some of us now live in cloud. Land wants to keep us tied up and tax us based on where we live, but we don't live in any specific place. We travel by air all over the world. This is going to be the dominant battle of the future, the global community of the network versus the land-locked feudalist past. We'll see. Hello, my name is Martin, I have a question for you. Andreas, what's your personal view on Halloween this year, and its impact on accentuating and difficulty? Sorry, what was the... Halloween accentuating? The halving, yes. The great halving of 2016. To explain, for those of you who are not quite familiar with this, every four years the amount of subsidy in each block decreases by 50%. We started with 50 Bitcoin per block, we're now in the era of 25. We are going to enter the 12.5 era, approximately on July 22nd, 2016. One of the interesting things about Bitcoin is that we know what the monetary policy will be in 2140. With the Federal Reserve, we don't know what the monetary policy will be this Friday, or with any of the other central banks. Although I have a premonition that will probably involve more stimulus and printing money. That hasn't worked a hundred times, but a hundred and first, it probably will. What happens in the halving? As I said, miners prepay electricity in many parts of the mining enclosures. That's not universal, but it is one of the characteristics of the mining community, which actually has some really serious implications on their decision-making process, because it's sunk capital. Secondly, we've now achieved a situation in mining, where we've seen from the CPU to the GPU to the FPGA to the ASIC, increases of 100 or 1,000-fold performance increases, until we accelerated straight into Moore's law. That's a wall, because 16 nanometers are done. Now, where do we go? We slow down to 2x increases every 18 months. Everyone can get the same chip, and there is no advantage in pre-ordering. You no longer have to switch chips every three to six months. Therefore, capital, connections to silicon fabrication, centralization of purchasing, no longer matter. This has started happening at the beginning of this year. We will go into the halving with a situation where there will be the haves and the have-nots, those who have 16 nanometers and those who don't. Those who do not have 16 nanometers will find themselves unprofitable very quickly, and the rest will not. We will see. I predict the price will go up and down. Then it will probably go up and down again, because the primary driver of price is still, by a great extent, sentiment. The halving is coming, and I think Bitcoin will go up. Bye, bye, bye, bye. Everybody else sees it. They are like, bye, bye, bye, bye. Everybody, bye. It is great. I am not too sure. I am a bit worried.  Up and down we go. It is going to be a roller coaster. Volatility will probably increase. We are in a period of pretty low volatility. We have been for at least a year now. The volatility of 2011 and 2013 is in our past. We have been relatively stable for a tiny $6 billion global currency. We are going to see increased volatility. And so my suggestion is take a deep breath, relax. Don't try to play the casino unless you are an experienced stock gambler. In which case, good luck to you. Sit back, relax, watch the fireworks and read the news about how Bitcoin is dead or about to die because of the halving. And then wait until right after when Bitcoin is not yet dead. Who has another question? Yes, go. Hello there, so I would like to, like, continue with the Carlos question, the very first one, I would like to touch it a bit. As when he asked you if you were, like, doubt sometimes, I'm becoming, like, really believer. I'm pretty new to Bitcoin, though. And I'm just, when I'm reading the articles about, like, big corporate companies and banks investing, like, a million of dollars into research of, like, blockchain technology and stuff, but what do you think about, like, why should they adopt a currency which, like, a 60, 65% is already out there between the people where, like, early adopters have planted all that. And what makes them to stick with Bitcoin and not to make their own currencies and, like, you know. Nothing. Banks cannot adopt Bitcoin. They cannot adopt Bitcoin. Bitcoin is the poison pill of global finance. Bitcoin is the pill you cannot swallow. Because Bitcoin is global, is borderless, is not controlled, is peer-to-peer, is censorship-proof, and none of the financial world can allow any of that. And therefore, they can't do it. Because if they are banks, they are institutionally trapped in a regulatory system that they built as a castle around them to prevent competition. And in which they are now prisoners in their own castle and can't leave. Because they cannot go outside of regulation. The same thing that prevented competition for 50 years is now their prison. And so they cannot do Bitcoin. Until they have to, because everyone is doing it. Maybe. Or maybe not. And they will build. They say, great, so we heard about this very interesting, open, decentralized, peer-to-peer, borderless, uncontrolled, censorship-persistent currency. We would like to create one just like it, only not open, not borderless, not peer-to-peer, not decentralized, not censorship-proof, and controlled by us. The problem is that we use a blockchain, which is an incredibly inefficient way of settling global transactions, because we want to get the benefit of financial freedom, censorship-proof, global access, open access, empowerment for all. And if you don't want these things, and I can guarantee you the bank stones, then why pay the inefficiency price? For a blockchain that doesn't give you anything, because it's no longer immutable if you're doing signing. It's no longer unforgivable if you're doing signing. It's a giant honey-put for anonymous. It's going to be so much fun when they start breaking into the bank blockchains. Or when they take over the signing keys of a central bank's fancy new blockchain-based digital currency that they launched, and hold them to the most amazing ransom-wherever, we have your country. We will begin issuing or not issuing currency with your keys, or you have to change them in a very disruptive operation. Unless your queen dresses in a hot dog suit and dances in the garden of the palace on YouTube, you have two hours. Blockchains without proof of work are not secure, and so I have this free advice for the banks. Purchase and install Microsoft SQL, data center edition, so it's scalable, make one of the fields a hash pointer to the previous field, and one of the fields the digital signature of the proof of authority. That's not a blockchain, but it's a hell of a lot more efficient and does everything you want to do, which is signing instead of mining and chains of transactions with hashes, right there.  Let them do blockchain. We are building the internet of money. They are building the Microsoft front page of money, the outlook of money, the intranet of money. What is the intranet? It's the place that is not secure, where you can't run any of the cool applications, where all you can do is read stale content that your IT department approved six months ago, about what are the latest HR policies. That's what they're building in a currency. Okay, let's take maybe one more question and then wrap it. Hello, I have one. It's amazing. Thank you. Can patent law slow down Bitcoin? Can patent law slow down Bitcoin? Patent law can slow down Bitcoin companies, but it cannot slow down Bitcoin, because Bitcoin is not a company. Who are you going to sue if Bitcoin core software implements something that someone claims as a patent? The people running it, the people who coded it, the people who coded the first commit of that specific feature, or maybe subsequently patched it, or then rewrote it completely. Who are you going to sue with patent law? You can't sue it. You can sue companies. Patents are going to be a very big problem for Bitcoin startups. Trademarks are already a problem for, as you may have noticed in my little fight online, with a company that trademarks the intranet of money, which they did not invent. These are going to be problems for Bitcoin companies, so we must reinvent what it means to be a company. What it means to be an ad hoc association of human beings operating virtually across the world in a decentralized manner on a blockchain with voting rights and dividend rights in an association. That is theoretically still a company, but it's also something that doesn't look anything like a company. If all of the members are anonymous, you've got a bit of a problem enforcing your patent law. Things are going to get interesting. As always, the law lags by a decade from the application of technology. They are writing laws now to regulate Bitcoin of 2009.  Because Bitcoin moved on. By the time they catch up with this, we are going to be 10 years ahead. We've been playing this game on the internet for 25 years. I think we are winning. Thank you all for coming here today. Thank you.