All right, so I would like to thank you all to be here for this SID virtual seminar. Today we have the pleasure to host Joshua Payne, who's a system professor of computational biology, so at the Institute of Integrative Biology of the Department of Environmental Systems Science from the ETH Zurich. So Joshua did his undergrad in mathematics in computer science at the Regist University in Denver, Colorado, his master's in operation research at the Rensselaer Polytechnic Institute in Troy, New York, and he obtained his PhD in computer science in 2009 at the University of Vermont. Then from 2009 to 2011 he was a postdoctoral researcher in the computational genetics laboratory at Dartmouth College and then from 2012 to 2015 he was at the Department of Evolutionary Biology and Environmental Studies at the University of Zurich, where he continued as a junior group leader, funded by an SNF ambitione. And then since 2017 he's a SNSF assistant professor at the ETH Zurich and since 2019 he's also a group leader at the SID, the Institute of Bioinformatics. So Joshua is very interested in evolution in vivo and in silica. His current focus is on gene regulation, particularly of the level of transcription, as well as genital phenotype maps and networks. The group is also therefore interested in understanding the design constraints, robustness and evolution of gene regulatory systems, particularly at the level of transcription using both modeling and data-driven approaches. So today Josh will tell us more of empirical genital phenotype maps of transcriptional and post-transcriptional regulation. So thank you again Josh for accepting our invitation and the thought in yours. Thank you for the kind introduction and for the invitation. Thank you everyone who's here in the real world since everyone is watching virtually. Right, so the title of the talk is empirical genotype phenotype maps of transcriptional and post-transcriptional regulation. I'm broadly interested in evolution. I happen to have this emphasis on gene regulation right now, particularly at the level of transcription, although as this title suggests I've also done a bit of work at the level of post-transcription and I'll talk about that a bit today. I think that this sort of interest in gene regulation and the evolution of gene regulation really comes from a fascination with the kinds of evolution or adaptations, innovations that are caused by DNA sequence changes that affect gene regulation. And we have loads of examples of this, right? We have examples of pigmentation patterns that have changed via DNA sequence changes that impact gene regulation including important evolution innovations such as the formation of isopods on butterfly wings which helps butterflies to avoid predators. We also have examples from morphological evolution, right? So we have examples of changes in the helmet structures of tree hoppers, body armor and fish and entire body plans and invertebrates. These are just a small number of examples. There are also many examples in physiology and behavior where we have real adaptations and innovations that have come about by mutations that affect the level, timing or location of gene expression. And all of these examples serve to highlight the evolvability of gene regulation and by that I mean the ability of mutation to bring forward different phenotypes, some of which may be adaptive. But of course not all of these changes in phenotype are adaptive, right? So mutations that affect when, where and to what extent genes are expressed are also commonly implicated in disease. A classical example is polydactylic in humans where you're born with extra digits and this is caused by a mutation in a DNA sequence that affects gene expression. This particular kind of sequence is called an enhancer. And this is clearly not like the worst disease that you can be born with but these kinds of mutations that affect gene regulation are also implicated in truly devastating diseases including many cancers. And so just as it's important for mutations to that affect gene regulation to be able to bring forth phenotypic variability it's also important that gene regulation is robust to such mutations. And for the past several years I've been kind of studying this interplay between robustness and evolvability in gene regulation. And in this talk I'd like to give sort of an overview of a series of studies that I've conducted over this period of time. More generally my approach to studying evolution in particular with the evolution of gene regulation is sort of I sort of have three parts of this study. So three approaches. One is purely theoretical so I work with computational models of gene regulatory systems and I use these models to try to answer questions that we can't currently address using experiments. The second approach of my research program is data driven so I work with publicly available functional genomics data such as data that describe when and where regulatory proteins bind the genome to affect gene expression, actual measurements of gene expression in terms of the abundance of RNA molecules, and also measurements of chemical modifications of DNA and histones that cause or cause by gene regulation. And the third approach from my research program is to collaborate with experimentalists. So I'm a computer scientist by training but I wouldn't want to be part of a lab that would let me in it. But I do work closely with some experimental groups in particular I work with Yolanda Shirley who's here at the University of Lausanne. And this is a really nice opportunity to bring some of the ideas that we have from our theoretical analyses into the lab. It also provides us an opportunity to help them sort of make sense of some of the experimental results that they're generating by complementing those results with the kinds of mathematical and computational techniques that we're familiar with. But for the purposes of today's talk I'm actually just going to focus on a series of studies that come from this data driven side of my research program. And what I'd like to highlight with these studies is how we use some old ideas from evolutionary theory to sort of make sense of how gene regulatory systems are robust and evolvable. And in so doing this allowed us to sort of reanimate some old ideas in evolutionary theory. The first idea has its roots in a paper that John Maynard Smith wrote to Nature in 1970 called this paper Natural Selection in the Concept of a Protein Space. And this letter was actually a response to a letter that was written a year earlier by Frank Salisbury in which Salisbury put forward what he thought was sort of a problem in evolutionary theory. And I think that this purportive problem is summed up well with this couple of sentences here where Salisbury says if life really depends on each gene being as unique as it appears to be then it is too unique to come into being by chance mutations. There will be nothing for natural selection to act on. And he sets up this problem by considering a hypothetical protein. So this hypothetical protein is encoded by a nucleic acid sequence of length from 1,000 and since the DNA alphabet has four letters to it there are therefore four of the 1,000 possible DNA sequences of this length which is roughly 10 to the 600 which exceeds the number of carbon atoms in the interrobable universe by several hundreds of orders of the diagnostic. And Salisbury said well if functional sequences are so rare then even if one of these functional sequences were to come into being by chance natural selection have nothing to work on because if you mutate that sequence it's highly unlikely you're going to get another one of these exceedingly rare function sequences. The Maynard Smith said well you know in this argument there's sort of this implicit assumption that those sequences that are functional are just distributed randomly throughout the space of all possible sequences but sequence space might actually not be organized that way. And he put forward this argument using a really simple and effective word game. This word game works as follows. You take two words from the English language in this case word gene they have to be in the same way. And you try to convert one word into the other by a series of single letter changes such that all of the intermediate words are also members of the English language. Alright so you can change word into war, war into gore, gore into gone and finally gone into gene. And the analogy with evolution I think is straightforward. So if you're talking about proteins the letters represent amino acids, the single letter changes represent amino acid substitutions and this requirement that the intermediate words are also members of the English language is akin to the requirement that the protein is somehow functional or has some particular phenotype. And I think this went a long way to emphasize Maynard Smith's point that once you have a functional sequence you're highly likely to have other functional sequences in the sort of mutational vicinity of that one functional sequence. But I also think this example helps to put you in Salisbury's mindset. If you think about this, the English language has 26 letters so that means there are 26 to the 4 possible 4 letter words. That's 456,976 possible 4 letter words. According to Scrabble there's only 4,175 real English words that are length 4. So less than 1% of the space of all possible English words of length 4 is actually populated by English words. So it's not trivial that you should just be able to draw these paths. I don't think Salisbury was crazy for saying what he said. I think that it was a reasonable thing to think. And the reason that Salisbury was wrong is that language like biological sequence space exhibits a correlation structure. So once you have a sequence that has a particular function, it's likely that it's going to be surrounded mutationally by other sequences that have the same or similar function. English, if you look at for instance the words around word and the words around gene, again according to Scrabble, you see there's these clusters of words. I think what else is surprising about this particular figure is that these are all English words. These are not necessarily colloquial English words, but they're technically correct. So a name is a monetary unit of Samoa for instance. A sword is the collective noun for mallards. You might, I don't know, next cocktail party. Put that to use. Right, so John Maynard Smith sort of postulated that these, what I would call genotype networks. So networks of genotypes, networks of sequences that all have the same phenotype. In Maynard Smith's example that phenotype was, the person was functional, but you could be more general about that. So he postulated these genotype networks would exist and that they would populate the space of possible genotypes and that this would help to facilitate evolution because once you had a sequence that was somehow functional, then in fact natural selection would have something to work with because mutations would be likely to create other functional sequences. But it actually wasn't for another 20 years before this idea was borne out in any kind of in this case it turned out to be a computational model of the biological system, but it took more than two decades to have any kind of real validation of Maynard Smith's idea and that first came about in a computational model of protein folding. So you have amino acid sequences, that's your genotype and then your phenotype is the particular tertiary structure that sequence folds into. And these genotype networks were found to populate the space of possible proteins. And this was then found in a variety of other systems all computational models of biological systems and people started to think about other implications for the existence of these so-called genotype networks. And there are two implications that are relevant for today's talk. The first is mutational robustness. So what I'm showing you here is a genotype network and this box is sort of the space of all possible genotypes. So each vertex in this network is a genotype, so it's a sequence and edges connect vertices, their corresponding sequences differ in single small mutations, such as a point mutation. And I've colored the vertices according to their phenotypes, so these all have some phenotype that's indicated by the color black. So you have all of these different genotypes that are mutationally interconnected with one another, and they all have the same genotype. And this has an implication for mutational robustness because if you were to take one of these genotypes and mutate it, there's a chance that you're going to get another sequence or another genotype that's also on the genotype network which means it also has the same phenotype. So the phenotype would be robust to that particular mutation. So the existence of genotype networks confers robustness to the genotypes, those networks harbor. The second implication for the existence of genotype networks is the vulnerability, or regards to vulnerability, which I mean the ability of mutation to bring forth phenotypic variation, some of which is adaptive. And the reason that the existence of genotype networks have implications for vulnerability is that these genotype networks do not exist in isolation in genotype space. Rather, these genotype spaces are populated by many genotype networks of different phenotypes, and these genotype networks interface and overlap with one another. So as a population spreads out neutrally on one of these genotype networks, subsequent mutations can create new phenotypes. So since these genotype networks spread throughout the space of possible genotypes and they bump into the genotype networks of other phenotypes, mutation can bring forth phenotypic variability. So the existence of genotype networks has implications for robustness and vulnerability, and it also helps us to understand how these two properties can be synergistic. So how can something, how can some system be simultaneously robust and evolvable? And this particular question is one that had been addressed in computational models of biological systems, but never in experimental data. And the reason, getting back to Salisbury argument, is that the space of possible sequences is so vast that if you wanted to characterize this space experimentally, it would simply be impossible for even like small macromolecules. However, we were at this time studying gene regulation, and we had thought well, maybe one way to get around this would be actually to study a sub-component of a larger biological system. That larger biological system is a gene regulatory circuit, and these are important for driving expression patterns that embody crucial biological functions and development, physiology, behavior, and the important sub-component are interactions between regulatory proteins called transcription factors and DNA. So these proteins are sequence-specific DNA binding proteins that bind DNA to regulate gene expression, either by recruiting RNA polymerase or by getting in the way of RNA polymerase. And importantly, the strength of this binding event is directly related to this regulatory effect, to its activating or inhibitory effect. And in studying gene regulation, you are aware of some data sets that exhaustively characterize the binding preferences of these proteins to all possible DNA sequences of a given short length, specifically all sequences of length 8. So you can think of this as a mapping between genotype and phenotype, or the genotype is a DNA sequence, and its phenotype is whether or not it binds a particular regulatory protein. You can also think of it as the actual strength that it binds a regulatory protein. So just to give you a feel for these data, all these data come from a technology called protein binding microarrays. These are chip-based technology and on these chips you have probes, in each probe you have double-stranded DNA sequences that are chosen in such a way that every single DNA sequence of length 8 is represented on this chip at least 16 times. And so you can get an assessment of the affinity with which a regulatory protein binds to every single possible DNA sequence of length 8 by looking at the fluorescent intensity of the spots that contain the DNA sequence relative to the spots that do not contain the DNA sequence. What's important for today's talk is just to know that for every single DNA sequence of length 8 you have a measurement of binding strength, the binding affinity to that sequence of length 8. And you have such data for a large number of transcription factors. The first studies I'll talk about we worked on the order of 100 transcription factors and later we're available, we're working on the order of 1,000 transcription factors. So we can use these data to construct these gene type networks that Manner-Smith had postulated and I'll show you sort of how we do this by example. So let's say you were to pull down protein binding microarray data for a single transcription factor in this case it's a transcription factor called SRY and you were to look at the distribution of the number of binding sites that have a given binding affinity. The distribution that looks just like this. So the mode of this distribution just represents sequences that bind non-specifically. So these transcription factors are just attracted to the DNA backbone. But then you also have this right tail of the distribution that represents sequences that bind the transcription factor specifically. So what you can do is you can just set a threshold on this tail and you can say okay everything above this threshold we consider specifically bound by the transcription factor and everything below is non-specifically bound. And you can then build up a gene type network out of these sequences and you can do so for a large number of transcription factors to sort of populate this space of all possible genotypes. So I'll show you how we do that by example. So let's say that this particular transcription factor just binds three sequences. You'd represent each sequence as a vertex in a network and you would connect vertices by an edge if the corresponding sequences differ by a single small mutation. So in this case we have this one point mutation that makes this top sequence differ from the middle sequence. And here you see another single point mutation that makes this bottom sequence differ from the middle sequence. Now obviously this particular transcription factor binds more than just these three sequences. This is the sub network of this much larger gene type network. This is kind of Maynard-Smith's idea born out in real data. So just to try to point home, each one of these vertices represents a sequence that binds this particular transcription factor, S-R-Y, and edges connect vertices if their corresponding sequences differ by a single small mutation. And we can study the structure of these gene type networks to ask quite a few questions, but for instance we can ask how the structure of these networks relates with mutational robustness. We can also ask how these gene type networks facilitate evolvability, and we can do that by not just looking at a single gene type network for a single transcription factor but populating this space of possible sequences with other gene type networks for other transcription factors, which I'm just showing schematically here, and asking how these gene type networks overlap with each other and how they interface with each other. This gives us a feeling for how mutations in these binding sites could bring forward phenotypic variation. In this case, that variation is new binding phenotypes. We can also look at how a mutation can abrogate binding, which is also very important in regulatory evolution. The loss of binding sites can cause important phenotypic variation that is sometimes adapted. Alright, so to show this more quantitatively, what I'm showing here on the Y axis is our measure of mutational robustness. So I can just explain this briefly how we do this. So the mutational robustness of transcription factors binding sites is the average mutational robustness of the individual binding sites. The mutational robustness of an individual binding site is simply the fraction of all possible mutations to that binding site that create another sequence that also binds the sequence of transcription factor of interest. So it's said more simply it's on average, how often does a mutation to one of a transcription factor binding sites abrogate binding. So when this robustness value is high, then it's very rare that these mutations break binding, and when robustness is low, it's very often with these mutations abrogate binding. Okay, so on the Y axis we have this measure of mutational robustness. On the X axis we have the size of the genotype network, shown as a fraction of genotype space. So this is just how many sequences are in this genotype network, divided by the total number of sequences of life bait. Each data point represents a transcription factor, and in this case we have the close symbols representing data from mouse, and the open triangles representing data from the yeast. And what we see is there's this sort of rhythmic increase in mutational robustness as a function of the size of the genotype network. And now if we do the same thing where we have a vulnerability on the Y axis, and the vulnerability is now defined as the ability of mutation to bring forth new binding phenotypes. So for all of the genotypes in the genotype network we look at all of the one mutant neighbors and ask which transcription factors to those one mutant neighbors bind. And that's our measure of the vulnerability. It goes from zero to one because we're normalized by the number of transcription factors in our dataset, that's the maximum. And again we're showing this as a function of the size of the genotype network. And here we see this increase that's much more abrupt, such that these genotype networks only have to occupy about one percent of genotype space before they're maximally evolvable. So this study helped us to understand how a genotype network sort of mediates this synergistic relationship between robustness and availability, and this was the first time that this particular relationship was demonstrated in experimental data. And getting back to the schematic here, it looks like this genotype space of transcription factor binding sites is organized in such a way that these genotype networks are overlapping with one another and interfacing with one another. So they're all just like this mess, they're all really highly intertwined with one another. In this way they're both robust and evolvable. And we're interested in understanding sort of the generality of this result, especially in the context of gene regulation, and more specifically in the context of regulatory proteins interacting with the nucleic acid sequence ligands. And so we decided to sort of go one step up in the hierarchy of gene regulation and look at RNA-mediated gene regulation, particularly post transcriptional gene regulation that's mediated by RNA binding proteins. So RNA binding proteins bind RNA molecules to regulate their stability, their transport, their decay, among other aspects of RNA biology. And what's important here is that we can actually do a comparative analysis of these genotype-phenotype maps, where in this case the genotype is now an RNA sequence and its phenotype is molecular capacity to bind RNA binding protein. We can do a comparative analysis here because the biophysics of binding in these two levels of gene regulation are highly similar to one another. And what's more, the computational pipeline that's used to go from the fluorescent intensity of these chips to this measurement of binding affinity that we work with, that's literally identical between these two experimental protocols. So we really have sort of a head-to-head comparison that we can make. So what we want to know is whether or not at this level of gene regulation this particular genotype-phenotype map is organized in the same way. So now we're looking at a different set of data, so now we have the closed symbols representing transcription factors, here the data is for human and fly, and the open symbols correspond to RNA binding proteins. And again on the y-axis we're looking at robustness, and on the x-axis again the size of this genotype network. And we see once again this sort of logarithmic scaling as the genotype power gets larger, robustness increases logarithmically. And this was a nice thing to see because this is something that's been predicted in computational models of biological systems where there's some nice math explaining this. And this is the first time that we were able to show using real data that this scaling relationship holds across multiple levels of gene regulation in this case. And now, so the relationships between mutational robustness and the size of the genotype network is really similar for these two classes of regulatory proteins, right? But now we're moving on to vulnerability that the story changes. So remember the closed symbols correspond to transcription factors, the open symbols to RNA binding proteins, and we already saw this really abrupt increase in vulnerability with the size of the genotype network on the previous slide for transcription factors. Now we're also seeing this for RNA binding proteins, except the rate of increase is slightly slower, and more importantly the maximum level of vulnerability is quite lower, right? So this hints that the architecture of these genotype genotype maps really differ between these two levels of gene regulation. And indeed if we look at sort of the average mutational distance between these genotype networks from one another, that's what I'm showing on the y-axis for fly and human broken down into transcription factors and RNA binding proteins that we find is that these transcription factor binding sites, these genotype networks of transcription factor binding sites tend to be much closer together in the space of all possible binding sites than are the genotype networks of RNA binding protein binding sites. What's more, the genotype networks of transcription factor binding sites tend to overlap with one another to a much greater extent and do those for RNA binding protein binding sites. And I think this is particularly surprising because in these data sets I can show you the numbers, but we have lower RNA binding proteins in our data sets than we do transcription factors, and we have fewer binding domains in the RNA binding protein data set than we do in transcription factors. Proteins, regulatory proteins with the same binding domain typically bind similar set sequences. So in fact the data set as it's set up should stack the deck in favor of RNA binding proteins having more overlap, we just observed the opposite. Whereas this really suggests that the architecture of these genotype phenotype maps are just fundamentally different, so if we go back to this sort of schematic it seems that these genotype networks of RNA binding protein binding sites are just kind of farther away from one another in this space of possible binding sites, and in that regard they are less available than our transcription factor binding sites. Alright, so that was a lot of detail. We can kind of come up for air now. So I had said earlier that I wanted to show how the two sort of ideas from evolution that helped us to think about these data and how in turn this helped us to bring new life into these old ideas from evolution theory and now I'd like to present this second idea which is much better known. So this is the metaphor of the adopted landscape which was put forward by Stuart Wright in 1932. It's a metaphor that really pervades the biological sciences and has shaped evolutionary thoughts ever since its inception. This metaphor is, it's not a perfect metaphor like any metaphor but it is in a metaphor it's akin to a physical space where coordinates in physical space corresponds to genotypes in an abstract genotype space and where the elevation of these coordinates corresponds to some quantitative phenotype or to sort of the ultimate phenotype of organismal fitness. Evolution can then be viewed as a hill climbing process in these landscapes where populations tend to move towards adaptive peaks as a consequence of mutation and natural selection. So in the context of transcription factor body size, we think about where phenotype is this binary you know, is this sequence bound or not. If we instead start thinking about the phenotype of these sequences as quantitative, so what is the actual strength in which this particular sequence is bound, we can transform these sort of flat genotype networks that I was showing you before into these adaptive landscapes where we can study the ruggedness of these landscapes. The ruggedness in the adaptive landscape has several important implications for evolutionary processes ranging from the evolution of reproductive isolation to the evolution of sex, how is genetic diversity generated and maintained but what's germane to this particular talk is that the ruggedness in the adaptive landscape has important implications for vulnerability. So for the ability of mutation to bring forth phenotypic variation. And the reason that the ruggedness in the adaptive landscape has implications for vulnerability is that both the shape of the landscape and the populations location within a landscape determine the amount of phenotypic variation that mutation can bring forth. So for instance, a population that resides in this particular region of the adaptive landscape may have no problem just by a mutation in natural selection marching directly up this hill to the global peak in this particular landscape. There's a population that is navigating in this section of the adaptive landscape might get trapped by this local optimum that's separated by this adaptive valley from the global adaptive peak. So in this way, a population's location within a landscape really determines the kind of phenotypic variation that mutation can bring forth. Right. So we analyzed the topographies of adaptive landscapes and transcription factor binding affinities for a large number of transcription factors specifically for 1137 transcription factors from 129 eukaryotic species representing 62 DNA binding domains. I believe that each of which you can think of is like a distinct biophysical mechanism by which the transcription factor interacts with the DNA. And we could characterize the ruggedness of these landscapes in a variety of ways. Some of these ways are really simple like just counting peaks, how many peaks are in the landscape for instance. Other measures pertain to things like epistasis. And then we can compare these measures of landscape ruggedness with kind of a pair of expectations that come from no models. One no model generates very smooth sort of Mount Fuji like landscapes and these kinds of landscapes would pose no obstacle for evolution. It doesn't matter where you are in a landscape. You could always move uphill to the global peak. And we also considered a no model that produces very rugged landscapes and these really hinder the navigability of these landscapes. Right. So here we can look at these data and how they compare to these no models. So we're looking at three distributions here. Each distribution shows you the number of peaks in a landscape. The top panel shows you this distribution for the additive model. The middle panel shows you this distribution for the empirical data. So all 1137 transcription factors. And the bottom panel shows you this data for this shuffled model which generates really rugged landscapes. And what we find is that the empirical landscapes are much closer to this additive model. They tend to be single peak landscapes. They're very different from what you'd expect in this highly rugged model. But there's also variation in the number of peaks. Some landscapes do have more than one peak. The next thing that we could look at is the number of binding sites in a peak. So when you think of a peak you might be tempted to think of just a single sequence being in that peak but our data don't always agree with that expectation. So here again we're looking at distributions for the additive model, the empirical data, and the shuffled model. And what we find here is that again this distribution for the empirical data much more closely resembles that of the additive model that it does of the shuffled model. So these peaks in these empirical landscapes tend to contain multiple sequences. And importantly there's variation in the number of sequences per peak and we're going to come back to that in a moment. We also studied the mutational accessibility of these peaks in each landscape and I explained what I mean by that by example. So here I'm showing a genotype network with transcription factor binding sites. I've just chosen this one because it's small. What we'll do is we'll zoom in on this peak here. So this is the peak sequence. And when we have the zoomed in view the number in each vertex represents the mutational distance to that peak sequence. And for each mutational distance what we'll do is we'll look at all possible mutational paths to the global peak. So for instance here I'm showing one mutational path that goes from a sequence that's two mutations from the global peak to the global peak. And we'll just ask what fraction of all of these paths are just increasing in binding affinity. So this particular path, so here in the y-axis we have binding affinity and the x-axis we have the distance to the peak. We see that this binding affinity is just increasing along this path so we'd say that's an accessible mutational path. Similarly this other path from the same starting sequence is also mutational accessible. We're just increasing in binding affinity. And then in contrast if we start with this sequence and take this mutational path we have to go through this valley in binding affinity. So we'd say that is not an accessible mutational path. So for any given mutational distance we'd say mutational accessibility is simply the fraction of paths that are accessible. So the number of black lines divided by the total number of blacks. Okay, so I can show you these data. First just let me orient you to the structure of these genotype networks. So first you're just looking at a distribution of the mutational distance to the global peak. So the average is between three and five mutations to the global peak. And now if we look at the mutational accessibility of these peaks as a function of the distance to the peak, we see first of all what we expect. The farther you are from the global peak the less likely you are to be able to get there by mutational accessible paths. So we have this decrease. And I think what's actually quite surprising about this trend is that even near as far away as possible there's still around 20% of all possible paths that are mutational accessible. So in this regard these landscapes are highly navigable right? They're smooth, they're mutational accessible. And if we compare these findings to what we expect from our 2-0 models we see that in this case we're really intermediate between the 2-0 models. You can't really say that it's this internal mutational accessibility of these peaks are more like the additive or the rugged null model. Alright. So as I said before there was variation in the number of sequences in the global peak. And when we were doing these analyses and actually all the analyses that I talked about today we were always acutely aware of the fact that we were working with data that were generated by an in vitro assay. So we wanted to know to what extent do our findings say anything about the evolution of binocytes in vivo. And in each of these studies we really looked into that and presented one analysis from this study. So since we saw that there is variation in the number of sequences per peak we hypothesized that those transcription factors that had global peak sequences that were very narrow visited less diversity in the binding sites then would transcription factors that have global peaks that are very broad. So if you're a transcription factor that has a global peak that looks more like the Matterhorn than it does the Millaisone, we would expect you to have fewer sequences I'm sorry less diversity in these peaks yet. So yeah fewer sequences. Okay. So what we did is we looked at 19 Saccharomyces cerevisiae strains and we calculated diversity in these strains for 23 yeast transcription factors and we then related that measure of diversity with the size of the global peak. And we found this very striking correlation and this is a correlation that holds up even after we control for things like the overall specificity of the transcription factor as measured by the information content of its position weight matrix. So this was one of several analyses that made us feel that these landscapes that were constructing from the vitro data actually tell us something about how binding sites evolve in vivo. And this is surprising right because there's just tons of other factors involved in gene regulation in vivo that are completely abstracted away in these vitro acids. Okay. So that is the research part of the talk and just to sort of zoom out I think that these studies have sort of helped us to better understand how gene regulation is simultaneously robust and capable of bringing forth phenotypic variations such as changing pigmentation patterns and body plans that really embody a lot of diversity of life around us. I know since this is a talk for a bioinformatics audience I would also just like to highlight a tour that we developed that allows you to take whatever data is that you're working with that you think these kinds of analyses might help you better understand and to use those data to perform all of the analyses that I talked about today in a really just easy to use format. This web server is called the genometh server so it's a web server that's also underlined on package that I'll talk about briefly but basically all you need is these two columns of an input file so you have to have some genotype and then you have to have some hierarchical phenotype assigned to each genotype. So in our case here I'm showing you the genotypes are DNA sequences and the phenotype are the regular proteins that bind those sequences and then you can also have a quantitative phenotype which I'm calling score here so in our case this is binding affinity and then you have some measurement of noise. These two columns are totally optional though. So to give another example if you're interested in relationship between RNA sequences and RNA structures, the genotypes could be RNA sequences the categorical phenotype could be like which particular structure that sequence folds into. The score could be the folding energy and the noise could be some measurement of your confidence in that folding energy. The point is simply to show you this is super simple input form and all you need is to put your data in this form, you pipe it into this server and it will literally perform every analysis that I described today in this talk. Not literally, with the exception of the binding affinity one. It doesn't do anything with the data. So it's I think a pretty convenient tool. It also has these nice interactive visualizations that you can play around with so you can look at your genotype networks, you can move your genotypes around where you get a feel for your data and if you're not the type that likes to work with a web server, the software engineer that worked on this, he developed a really nice pipeline package that implements all of these analysis that I described today. I don't expect you to be able to read this code. Where I put this up here just to show you there's only 13 lines of executable code here as the ones that are not created out and those 13 lines are sufficient to reproduce all the analysis that I talked about today. So it's really like it's a nice little tool. So I do not do this work alone. This has been all very collaborative. A lot of the work was done with a Ph.D. student member of this group named Jose Avalora-Driguez. The TUNETS tool and this RNA binding protein research was done in collaboration with Jav Khalid and then finally all of this was done in collaboration with Professor Andreas Barber. These are my funding sources over the course of the period we did this work. And finally if you're interested in reading more, this is really the body of work that I just went over. That's it. Thanks.


Hi everybody, Joe for jasmiescasebreaks.com, doing our last filler. Last filler is what we'll usually say last one or last filler right there in the title of a prism basketball, last filler for the immaculate basketball that is. Last 10 spots being given away for break 29. But we got to do this break first. So let's do the break first and then we'll give away the spots at the end. Big thanks to this group. Did I grab the correct list? Yeah, big thanks to this group for making it happen. Appreciate it. So before the teams right there, let's roll it and randomize names and teams four and a three, seven times, one, two, three, four, five, six, and seven and final time. After seven, we've got those names right there. This is remember, as always, this is just for the break. The immaculate video will be in a separate video, by the way, four and a three, seven times for the teams, one, two, three, four, five, six, and seven and final time. All right. No trade windows. Here's the first half of the list. There's the second half of the list. All right. So one of these are going to be for break 29. The other one is going to be for break 30. Break 30 is on the site right now. If you ask me, casebreak.com and the two fillers or the first of the two fillers is on the site as well. There's little Virginia's, Contavia's, Colo Pope, an emergent insert, Lamello ball. And that is for Charlotte. Where's my list right here? And that'll go to Alex. All right. Bonus pack. Another rookie, red wine and blue rookie, Cassius Stanley, for the Pacers. That'll be for Allen, I believe. All right. There we go. Now let's see who's going to win those immaculate spots. Let's flip back to the list. Let's gather everybody's names, new dice, new list, and it'll be the top 10 after five. Good luck. One out of four. Five times. One, two, three, four, and five. So from 11 on down, no go. Thanks, Thomas. Thanks, everybody. Appreciate you giving it a shot. Sad times, though. Really. Top 10, though. Happy times. Happy times for Jack, Allen, Nick, Nick, Nick, Nick, Victor, Allen, Brian, and Nick Smith. There you go. Nick did have a lot of spots. So congrats to you. Congrats to everybody in the top 10. Thanks everyone else for trying. We've got another box and a couple fillers if you wanted to feel like trying again, just ask these casebreaks.com. We'll see you next time for the next one. Bye-bye.


If you never fail, it just means you're not trying hard enough, right? Yeah. Leaders are people that push the edge a little bit. So by definition, you're going to stumble and fall. And if you've never failed, it just feels to me like you're just not pushing that hard.


All right, so now that you've got everything set up in Zotero, now it's time to start throwing things in there. So this is a page from Articles Plus, and I just did a quick generic search here, and then let's just say that this article is the one you want. So we click on it going the distance online course performance and motivation of distance learning students. Now because you've downloaded the connector at the in the first tutorial, you're going to click on that. Now it's telling me it's saving it to the training folder. There's the article, and there's the full text.  So here is the article title. Here is the citation information.  So if I clicked on that there, the PDF would be pretty easy. Now sometimes it does mess things up, but you can always go in and edit the citation. So for example, APA likes this to be in sentence instead of title case. You could just right-click and change it, and now it's there. If they got the authors wrong, if there's a spell, a typing error, or some sort of error here, you could just go in and edit it, and then every time you cite it later, it will be good to go.


What is going on guys? Today I am back with another Destiny 2 video and today we have a mass dead orbit loot package engram opening. But before we go into the video guys, do you want to win some loot of your own? I've got emblem calls, I've got sparrow calls, I've got gift cards, I've got more. To win some simply drop a like on the video and leave a comment down below. Okay so I have 600 tokens to spend with Mr. Arajaul the dead orbit vendor. I got these within about an hour and 10 minutes doing that also legendary token farm grind which if you don't know about I will link within the video description. And 600 is all you want because you can only open more than 30 packages but after the first heap package you will only get shaders. So unless you want tons and tons of shaders there's no point in grinding past 30 packages. You might as well switch to another character and grind another faction on another character. But let's get into this anyway.  Obviously future walk up first and new monarchy, then dead orbit. Well let's go. Let's go. I'll be happy with whatever I get to be honest. Plenty of shaders, I know the shaders are okay. I got the dive promise two times in a row there. I want my full armor for my titan. And we've got I think they're the boots. I'm guessing they are. Oh not for a minute, let's have a quick look at these because I'm not even sure. I'm not sure what these are. I don't think they are not. But what was that? What was the pulse? Nice. We'll keep, we'll look at these in a bit. We'll open these packages first people. Let's go. Okay so that anti extinction, that is the actual armor's name. Okay that looks pretty cool. That looks pretty cool. And I got the chest piece. Nice. And I got the gravity sling shots at. There is the shotgun. Awesome. Got the chest piece again. The dead orbit's fate in the shader. And I'm guessing I just got the chest piece again. No I never know I never mind that. Okay so I got a negative space sword. I got the dive promise for the third time. Really game. I got the class item which is awesome. I got two class items there actually with another shader. The tarantula and the gravity slingshot again. A lot of these are gonna go on pulse master but that doesn't matter. A lot of these will be spent on legendary shards either way The third promise again. There isn't much of a selection with these factions. Not really. Not in my opinion. I don't think there is. The three graves again. I got the scapher luck which is a great auto rifle by the way people if you don't know. Wow come on. Come on stop missing that. Now what do I actually need? Oh dear there's about an eighth. Dive promise I've had. Let me just put on my armor. So we know what I need. I need the gauntlets and the legs because I'm not that chest piece. I need the gauntlets and the legs. Pretty cool though. Pretty cool. Pretty cool when the way will look like a vain future warcraft shader on it. Because that's how I'm gonna look this thing. Chest piece again. Pulse again. Damn. Dive promise again. Legs. Thank you. Now all I need is the gauntlets. Giving them gauntlets. Giving them gauntlets. Let's get rid of them. Sorry if I do get them. I can put them on. Giving them gauntlets. Giving them gauntlets. Chest piece again. We don't want that. Giving that gauntlets. Giving them gauntlets. No. Chest piece again. Oh no. Shotty again. Giving the gauntlets. Please. Oh my. Use gift. Incredible weapon. I ain't gonna get it am I? I am not gonna get them am I? I love power. Oh we ain't got many packages left people. We haven't got many packages left people. No. We've got two packages left people. We've got one package left people. Last package.  Awesome. Awesome. Awesome. Wow.  Jesus. But I also got this which I've never had before which I'm quite happy with. So we're gonna lot that it looks incredible by the way if you haven't seen it. I'm guessing you have. Okay look at this. I mean why is that? Craziness. Come on. Should we have a look at what some shaders look like on these armor first? And then we'll check out the weapons I got. So shaders. Where's my future walkout ones? Right here. Let's check this out. Walkout rain. No. Let's have a look at the other one. Uh whatever. This will walkout camo even. Not camo. See that looks alright. Black, blue and white. That looks pretty good. That looks pretty good. There's so many good shaders I got. And it's selling an arctic pill. Well Tess ever versus selling an arctic pill this week. So I'm spamming that dust on these for sure. Oh look how good that looks. The armor itself don't look that great. Standard. But with certain shades on it can make it look incredible. It's serious again. Nice. Nice. But I'm quite happy I got all that. To be honest let me just luck all this gear.  I don't, to be honest I don't even look most of the time what I'm deleting. Let's quickly run to my, my uh postmaster. Because I'm mad. A load of things go there. Which is fine. But there's a lot of shaders going to be made today. I know of that. There's a lot of shaders going to be made today. For sure. This time was too big. I could have had one room with all the vendors in it. Other than any, way better. Way better. I find myself spending most of my time running around the time getting to vendors. Don't know anything else in this game. Okay so. Oh we got a postmaster full. We got loads of dive premises. We got quarter serves. We've got chess pieces. We've got pool shrivels. We've got snipers. Shutties. More chess pieces. Autos. Man. But I've got all these in my inventory anyway. So I don't have to go through them. Let's quickly have a look then. Okay so the dive premise I've never had before. So I'm going to unlock that one. And I'm going to delete the rest. I'm going to unlock that as well. I know I'll have them. Don't need any of these. Delete that. I mean keep that. Delete the rest because I've got all this already. And I've got the shutties as well which we are going to keep. But let's have a look at the dive promise hang cannon. Now this was obtainable from chess from K6 treasure chests. I mean loads of people like these. But it don't even look that great. It just looks like a six year to me. Now so this is the reason. This is the sole reason that I chose dead or last is because of how poor the weapons looked. They just look poor. I mean I chose future walkout first because I'm future walkout through and through from testing the year one people. But they all sort of weapons look great. The armor isn't that great. The shades aren't that great. But the weapons with future walkout look incredible. New monarchy armor is just absolutely B.E.A. beautiful. This is not too bad. I'm not sure about that wooden effect though. I think it just doesn't suit anything cute on this gun. Yeah it don't does it. But we're going to try out some of these. Definitely going to try out some of these new weapons I've obtained. The sidearm, the 11th hour. It's a voiced weapon. Just something. They all look bad and broken and just poor. See that don't look too bad. But I'm not sure what this gold thing is on the end. It looks like something but I'm not going to say it in my video. Okay and we also got this shotty. Did we get something else? There's no sniper to obtain or anything like that. See that looks pretty cool. Reminds me of the sparse 12th. I mean modern warfare 2 days. See that don't look too bad. See that don't look too bad. But it looks better with the future walkout gear. I mean the new monarchy armor on the gear armor. Shader you know what I mean. I like the gold on that. Looks quite good. Looks quite good. But besides that people that was basically all I got. So it's the same old same old rubbish. Which will be used for them also non-important legendary shots. Which we've got just nothing to spend them on. But yes guys this was another faction package opening. If you enjoyed the video leaving a like really does help me out. If you're new around here and enjoy daily destiny videos be sure to subscribe. And people hopefully I will see you on the next one.


Quickly Brown and it's waiting for Brown to go to the basket and get to Alan's younger. Brown, some of the offenses we will see out of that kid. Sounds like an old guy here, not doing a very good job. Tatum says I'll shoot at that and very early. How's it again? Tries to spin, pushes off, passes the hole for the corner. Celtics Highlights are presented by Metro by T-Mobile.


I'm here with Dr. Jim Davenport and we are about to talk about the search for extraterrestrial intelligence or SETI. I think this is one of the first times on Strange New Worlds that we've actually discussed this topic as our main topic for the episodes. I'm really excited to be speaking to you about this. I find that shocking because it seems like the topical overlap between SETI and Star Trek would be so ripe. I'm excited to be the inaugural voyage of this topic. Astrobiology is this still very fledgling fields but it's you know face the issues of the origin of life and the habitability for extremophile organisms these microscopic bacteria and archaea that live in hot springs and so forth and that is considered serious science and the detection of biosignatures of these more simple life forms on other worlds is also considered serious science but all of a sudden once you ask the question is there intelligent life on another world there's this laugh factor associated with it. Do you think science fiction actually plays a role in generating this laugh factor that within the scientific community not taking the search for extraterrestrial intelligence seriously? Yeah and I think that's why this is a really interesting venue to talk about this. Two real scientists, we'll characterize ourselves as real scientists. Two real scientists talking about this idea on a podcast specifically about Star Trek is like the perfect like neutral zone territory to talk about this. Yeah the the giggle factor it's a few things it's the giggle factor related to sci-fi that sci-fi is celebrated we all love most astronomers love some kind of science fiction but we are always quick to point out that we're real scientists we're not just like speculating about whatever Arthur C. Clarke wrote about or whatever. The other issue is related to that is the sort of cranks the the pseudoscientists and the UFOologists these communities have also co-opted a lot of what would be SETI research. The saying that I heard recently about astronomy outreaches if you don't if you don't do outreach about your science the pseudoscientists will somebody will find out we'll see a tweet about it or an article you wrote and we'll misconstrue it into something that is absurd or obscene and it will end up in the weird fringes of society. SETI has been an area that has been co-opted a lot by people wearing tinfoil hats and that has caused the sort of amplification of the giggle factor. My take is that very few scientists very few astronomers would look at SETI and say like oh that is without merit wholesale. You know part of this paper is just saying this is an opportunity that I think has gone under-noticed it would be it would be unfair to say that it's totally original there were there have been a couple papers in the last 20 years that have pointed out these kinds of ideas but it's I hope a call to action for people like me people like you who have astrobiology interests to come together we work in the same department and we sort of exist in different camps. The Astrobiology Camp which thinks a lot about what life would look like and I work in sort of the software and big survey and big data group which thinks about what is it that our data can provide and what kind of products can we make out of our data and I dream of bringing those communities people together I want to bring the astrobiologists who think a lot about biology and chemistry to the table with people who are producing catalogs of supernova and exoplanets and bring them together and see what what kind of signals can


not good fellas not good freaking buried or can't get her out quick a deep slash i'm gonna walk back it not good not good at all i did not make it back in all right guys uh probably an hour before light it is cold leaving the hotel establishment and i'm gonna head in and unload my sled back into the truck my tow sled and take the snowmobile and the empty out to get the gear and then restart the truck sweet i got one of these gopros to work everything's frozen solid oh man all right so it's like an hour in the daylight i recovered my gear got the snowmobile going it was a little frozen i couldn't get all the slush out of the undercarriage last night ah man my nose is run i'm in the truck heating up my snowmobile helmet so i could see i can't see a damn thing but i got all the gear that was in the tow sled unloaded into my truck from last night everything's frozen solid there's a little bit of bait still alive in the smelt bucket i haven't even looked in the other bucket yet went out empty with the tow sled filled up all my stuff i found it i was on a different island than i thought anyway oh man this trip now i got that tow sled full with the first load again what i'm thinking about doing is pulling the pin going out dry with the snowmobile finding a good spot out of the wind for the week or for a couple days or whatever i could pull off now all my batteries are frozen solid none of the cameras would work so i couldn't show you where i got stuck or what the lake looked like this morning was absolutely gorgeous uh negative 21 degrees so pretty chilly there's no one out there i got the whole end of the lake to myself i'm at a definite critical point where i could just load up this load and go home or i could keep battling and see if i could get back out there on the lake and salvage this trip and make a great trip out of it just consider the trip starting today and a good lesson learned and a really really fortunate night that could have gone poorly in many ways my body is definitely smoked tired from trying to lift that damn thing out for an hour and pulling and lifting and pushing and the walk out you know walking through a lot of like slush and sinking in past my boots and carrying probably 30 pounds of ice on each leg so definitely tired i woke up kind of cramped this morning both arms are cramped up but i'll come out of that once i start drinking some water and get some coffee in me that'd be really nice to get the tent set up today get the coffee going all right i found a spot i could get to and it should be out of the wind over here i'm tucked in real nice in this corner snow is really deep i hit one small slush pocket coming across that should freeze up pretty quick but i went further up lake to a spot that really looked good on the map and there's two three maybe two or three guys already up in that area there's plenty of room for other people but i just couldn't i don't want to encroach on those guys anyway i know there's all sorts of room to fish but part of coming up to moosehead and fishing this great beautiful lake is being alone or having areas to yourself there's plenty of lake for everybody it's a big lake so i left that bay and came down to this area here not too far from the truck i'm within a couple miles from the truck so trips will be short getting the gear out uh dug this out for for the tent it's really deep snow as you could see but this should give me a nice flat base when i bring the the boards over for the setup i'll flatten this out a little bit more i'm gonna unload this load and go get the next load and start to set up and get the old coffee pot going and the heater going all right i got it pretty flat i got the second load in with my floor and i kind of flatten this out a little bit i could scratch it down to bear ice even though i'm not really on the lake i'm on the shore but it's kind of up and down like a shore would be so i want to keep this tent as flat as possible and you get really one shot at this when you're putting down a hard floor like this so i'm gonna use the kind the snow to kind of equal it all out and and hopefully it'll be pretty flat so i'm gonna look at it one more time and scratch it down and then bring the boards in and stump them let's get that heater going start to throw some things out i like to keep my propane tank outside for a couple reasons in case for some reason the valve or the connection ever leaks it's not in the tent dumping a bunch of propane in there catching fire and then the other reason makes more room to keep it outside so i run the hose underneath my tent that's a neat little trick let's see if she'll start all right so these things you you do the pilot usually this one's about oh that was second time this time usually it's third time then you hold the pilot until that blue flame kind of catches and then you cross your fingers let off and flip around see if it lights it did not all right start over there's the third i'm always a little nervous with this thing because it's kind of the lifeblood of the trip not having a wood stove in here this is my source of heat i don't trust the propane stove burner because that's going to put out a lot more co2 with that open flame if this thing doesn't work that's a bad trip sometimes you just get to let her heat up a little bit she's set outside all night go ahead and put that on high for a wow finish unpacking let's see how the bait fared not looking good one swim don't the smell do any better nope all right guys camp is pretty much set up it's a scattered mess right now if you if you want me to be honest they got stuff everywhere i got stuff thrown out i got stuff laid out and still kind of organizing and unpacking the nice thing is the coffee is percolating so i'll have some coffee this morning or i don't even know what time of this 1052 so yeah so i'm set up now and it's uh still morning so i think what i'm going to do is drive some coffee into me i'm going to assess my bait situation it looks like there is still a bunch alive in there but a bunch is dead it's starting to throw out right now and i got a bunch of that cat ice out of there i'm throwing out my battery packs my apple might never throw and yeah that's about it guys i might get something to eat too and then jeez maybe even set some traps put put together a plan once everything kind of gets unpacked and throw it out here and i feel a little bit more comfortable i'm pretty comfortable now that that i should be able to have a good night tonight all right got a little cup of coffee going there and i'm feeling really good about staying you know there was a little voice in my head probably five or six times and was saying just pick up and go home and tail between your legs type thing but man that's just not in me and i really really like the challenge and think about taking on a challenge is when a challenge gets tough you can't just quit and go home so as long as i'm still semi-safe and think i can have a good time i'm staying all right we might as well do what we came up here to do and do a little fishing should be rocky shallow i'm guessing right around here i'm not sure how shallow hopefully we don't hit bottom but i'm gonna set some brookie traps and i got a treat for you for the very first trap we're going super old school like older than your grandpappy's grandpappy old school we're gonna throw down an antique fishing trap that a buddy of mine gave me that's really really cool let me show you there she is right there look at that contraption the only thing new about it is the line in the hook but you just jam that in the snow and balance this and put a loop on it and i don't know we're gonna see don't forget there's like state record brookies in here so we're freaking around with the opportunity to catch state record brookies and i love it see what we got for depth first like nothing for depth let's set it anyway we could always move out later it might be like six inches here for depth that's the idea right there when he pulls on it the flag goes up and then it's just free spooling off to the races after that old wooden hand carved reel pretty neat all right we're set up old antique here's the shack here's the mountains and let's go the cold has not been friendly shattered my ipad this morning at 20 below zero just uh i don't know just kind of maybe tweaked it a little bit and i got a really weird one too this one's maybe not because of the cold but i've got a it's the second time i've seen one of my replacement blades on my augers get a crack coming out of the screw and i don't think i tighten them down too hard actually don't even hardly tighten them down at all this one actually broke i drilled a couple holes and it wasn't drilling good with those new blades i'm like what the heck's going on but that should be out here so good thing i brought another set of blades oh man i'm going through some blades this year wait a second is this a rerun didn't we just do this last episode well we're going to change that out anyway because even though it will drill it doesn't drill very well i can't do anything right yet because it's still frozen i'm going to leave it in front of the heater for a while yeah that thing wasn't even that tight i don't understand maybe i got a weird bend or something here that when it sucks down it cracks right through there it's the same exact spot that last one had a crack the last one didn't break it just had that crack there weird i remember what i said about not going towards the blades right out of that hole you guys have ever had one break day like that let me know this thing's like not even a week old just barely tight the other one looks sharp so we'll run with that a lot of stuff breaking this trip but sometimes that's just the way it goes but i just had a nice visit from an old timer that lives down the way he's 82 years old he came over and told me i am in a phenomenal spot to be out of the wind all week but a really bad spot for fishing he says he's never seen anybody catch one over here and he's out all the time hey there's always the first time and maybe they weren't using big bait i don't know try it out some bigger bait than typical for brook trout i bought a bunch of nice smelt but of course they they all froze last night uh being left outside in that 20 below but the shiners lived a lot of them looked dead but i changed out the water and got the oxygen going again got rid of all the ice and the cat ice and that bucket and they seem to come back to life pretty good there might be a couple dead is all and that bucket is full i've probably got seven dozen shiners in there different sizes it'll be fun this week to try some different sizes for brook trout and see which ones they eat day two is winding down nothing really going on for brookies in there maybe it is a terrible spot like the old timer told me got everything set up made it so i can be comfortable enough no problems nothing's broken a little sore a little beat up pride's pretty much trampled but you know the cool thing is there's always tomorrow and tomorrow i'm gonna go at it pretty hard i think i'm gonna run up the lake a couple miles and and try to whale on some brook trout in a spot i think it's gonna be good looking at the maps and as long as there's not too much wind i'll i'll be able to do that if not then we'll put together another plan that's the cool thing about being it being here and right on the lake is it's really easy just to jump around change plans so thanks for tuning in so far it's not the fish smashing video that i would really like it to be but we'll get you some of those too and hopefully tomorrow's the day


This tick could make you allergic to cheeseburgers. The Lone Star tick can carry a sugar molecule that's also found in cows, pigs, and lots of stuff we eat. When it bites you, it transfers that molecule to you and your body flags it as a threat.  Lone Star ticks can be found mostly in the southern U.S., but also all along the east coast. The best way to avoid this or any tick is to avoid grassy, brushy, or wooded areas, walk in the center of trails, and always use EPA-registered bug spray.


Have you got any of your injured players returning or any fresh injury concerns? It's going to be difficult these times. Sometimes I'm very clearly telling you this will be or not. And this week it's going to be difficult because like I told you after the game we have some cases of, I don't know to say illness but they are not probably feeling 100% but we still have to days. And these things probably tomorrow they can freeze and they are a value for the game. Man Definitely we have some players in in in physical conditions, to perform if the game was today. Luckily we still have two days today and I hope they can recoverinsi, to help us, not fresh injuries, not en crying. But it's true that we have some situations that we are dealing with them Day to Day. How many players are affected by this at the moment? Ah... É difícil para mi para xeir. É difícil para mi para xeir. Sim... Não, porque se te xeir e xeir xeir na sátura, te xeirá xeirá e te xeirá, xeirá xeirá e te xeirá xeirá, e xeirá xeirá e te xeirá 100%. Mas é a síndota que nós temos, xeiramos. Ok. O jogo contra Luton, obviamente o jogo por aqui e o combate fue uno dos bocos em history de Premier League, o que refleteis naquele jogo? Cante-me xeir xeir na chave e é unha condicción que é unha. Eu acho que nós já nos brincamos do tipo dox, no mesmo a primer jogo que brincamos, que non terminamos, e podíamos ver jogos con múteis, a lota de gols, cobrá-los, porque xa sometimes they go to mark man to man, xa go pretty similar, e despoises, transiciones... Eu acho que é xa differenti, because of their pitch, they have the advantage that they play at home this time and I'm sure it's going to be difficult because it has been difficult for every team that has gone there and they are also coming from, I think, a very difficult schedule, the last two months they faced almost all the top teams in the competition so I'm sure they will be looking forward to our game and trying to beat us just we have to be ready. A head of the home game born with printed Tom Lockyer's photograph on the front of the programme there was a lot of awareness created for heart related issues. Do you think it's strengthened the bond between these two clubs? Definitivamente, because when you go in a situation like it happened here with Tom you leave football on one side and you talk about other things much more important and I think there will always be a connection in these difficult moments when you are together I think also the history of the last years of both clubs that they played each other in different divisions and luckily for both now we are facing the Premier League and I wish them all the best, all the best but the problem is we are facing them and we also need the points, we need the points and we want to win the game and it's important also for us. Do you think it helps you this Saturday that they are coming into this game off the back of two really difficult away matches against Tottenham and Arsenal, especially if your squad is struggling with illness? I think they've not lost two games, I would say last two months they've been facing very very difficult opposition, very difficult opposition and I think they've been doing good things but it's very difficult to win these top teams, it's difficult for them and it's very difficult for us, for everyone. And I'm sure they will be looking ok, we are playing at home against Bournemouth, it's a very good chance for us to put us in a better spot but we also are thinking the same way. We are coming from two hard fought wins and we want to finish the weakest strong and put us in a good position because we also know that the other games we have until the end of the season are in theory really difficult. I know you obviously can't mention the players who are doubtful, can I just ask what have you told them, is it a case that they've gone home to rest and what's the cut off point that you're going to have to decide about whether they're good? Most of the times the players take their own decisions, we cannot decide because they are feeling what they are feeling, they recover very well, next day they are fine, for me the key is if they have temperature or not, if you have temperature probably you will be two, three days with these symptoms and it's difficult to play football with temperature so it's a matter of different situations but I hope we still have two days I hope we don't miss at least more players. Just a final one for me, you've gone past, Bournemouth have gone past the points tally from last season, how much are you delighted by the progress and especially in this kind of after the difficult start, how much are you excited about your squad and your team at this moment in time? Obviamente nós estamos en un buen lugar, estamos en un buen lugar, creo que nós hemos improved a lot since the beginning of the season obviously and we are more solid I think we started the season considering too many goals and once we've balanced this better I think the results have been better also now because I think we've always been a threat offensively we have good players there and we normally have chances but once we've improved our defensive numbers I think the results have been better but we still have more games and we can improve and we want to improve the points we have right now knowing that it's not going to be easy