Cordero, and thank you very much. Thank you. This is OK, the sound? Yes, OK. Thank you. Thank you, Jacopo. Thank you, everybody, for the invitation. It has been very stimulating these couple of days, unfortunately. Tomorrow I have to go back to Vienna when I'm doing my sabbatical. And I'm Otto Cordero from MIT. I am from Ecuador. So I'm going to talk about something that I think last night's conversation made me realize is in many people's minds, which I find satisfying because it's also in mind. And I think it's a really good problem to work on. So I'm going to talk about it from different perspectives. So I'm borrowing here some language from my friend, Cepa Quinn, from the University of Chicago. They're asking the question, what is the right language to describe microbial communities? This is obviously a kind of poetic way to ask the question of what are the right variables? Because we are imposed, or we have been imposed, the variables from sequencing technologies, where you get ASVs, OTUs, whatever. Whatever thing is said, some generic units. And then I think I don't need to convince you that this may not be the right variables, although we're going to look into it in a second. So it turns out ecologists, the traditional ecologists, let's say, have thought about this for a while. And so they have what is called trait-based ecology. And here's just one example, just to give an idea of what this could mean. Somebody mentioned yesterday that when thinking about ants and plants and whatever, people were used to thinking about roles. And so here in this picture, roles could be pollinators, the herbivores, the insects are eating the plants, the organisms in the soil that are recycling or fixing nitrogen, et cetera, et cetera. To some extent, this is a human construct you one can imagine. But in many other cases, at least in the case of plants, I think this is rooted in physiology and people understand the constraints and so on. So they can explain quite a bit from these traits. But the general problem stands in any kind of ecology is what is a trait beyond the things that make sense to our limited understanding of biology. And so in microbial ecology, this is apparently more problematic. But people have tried to address this problem, develop databases of traits, for example. These are noble efforts, in my opinion. But they have inherent problems. And here's an example. The maximal growth rate, whether the organism is dormant or not, cold tolerance, motility, and stuff like that. So I don't know what you guys think. I find this dissatisfying again, because whatever we think is a key, it's supervised learning. So whatever categories that are in our minds that we think are relevant that we know, maybe what we have read. But then this is not necessarily what matters. There is no objective way here to define what really matters. And also, more complicated, is how you determine what matters means. So this is out there. But the question in my mind is how to do this better, because this is based on manual curation, functional annotations, and so on that are problematic. Is there a way to discover these ecological gills? I'm also going to use this terminology of ecological gills. Is there any way to discover these gills in a data-driven way, or in an unsupervised way? OK, that's the challenge I'm going to try to address. Yes? Oh, OK, so gills simply, the roles in the previous case, they look straight. Right, so organisms that have the share a set of traits. And so the roles in the case of a very compelling example would be you have primary producers, you have herbivores, you have the carnivores, and stuff like that. These are roles in an ecosystem. These are roles within the community. And the properties of the community may emerge from interactions between these gills, these functional groups. So just to give you a warning, it's not the right word, this problem is not solved in general. So I'm just going to tell you that I think it's solvable and we're making a little bit of progress in this direction. And I'm also going to tell you the limitations. But to start, I want to introduce you to these beautiful little things that are called the pink berries of Sipah Wizard Marsh. Sipah Wizard Marsh is near Boston in Massachusetts. And these little things, they are about a millimeter approximately in diameter. These are microbial consortia that's self-organized in these little granules. There are other cases in industry and in the environment with microbial consortia organizing in spatially structured aggregates, like spheroids. And this is one of the most charismatic ones. And so before I go on, I'll tell you that this work has been, it's one of these projects that has been going on at a very slow rate in the lab for many years. But I think we're probably going to submit it this year. And this is being led by Axid Goyal. You saw his face previously. And Gary Levental, who's now back in Switzerland in industry. But it's a collaboration with people at Santa Barbara, Lisi Wilbanks, and Boris Schreiman. So what are these pink berries? From a metabolic standpoint, Lisi Wilbanks published a paper some years ago showing more or less a skimware of what they do. So you have, in principle, this is an engine that cycles different chemical species of sulfur, sulfur in different redox states. So the pink things are purple sulfur bacteria. That they are oxotrophs. They oxidize sulfide. So they are also photosynthetic. So instead of using water, like green algae, they use hydrogen sulfide. And then that produces sulfate. It can also produce sulfal granules. I skipped that picture from the slides. But it's a beautiful picture just in the interest of time. But you can see little sulfal granules on the surface of the berries. And the sulfate goes to the sulfate reducer, which is a heterotroph that takes carbon, organic carbon, and uses sulfate as an electron acceptor, producing against sulfide that goes back. So the cycle goes on. This is not so important, but I'm not sure this is necessarily a cycle because there is sulfate, abundance amount of sulfate in where these metacreatures are found. But that's not very important. So there are two main functional roles here. That's what I want to say. Sulfide re-oxidizer, sulfate reducer. Where you find them is a beautiful marsh as Toyosipe, a wizard marsh in the coast of Massachusetts. You can walk in this little, in this beautiful place and the tides are low. It's a tidal place. Sometimes it's completely flooded. And sometimes the tides are low. And then you can look in these little tidal ponds. And you may see already from where you're seeing these little pink dots. These are the pink berries. So you can pick them. And say this is one community, and you pick another one. Say this is another ecological replicate of a community. And that's what attracted me to this years ago when we started looking into these systems. And so what we did is from a methodological standpoint, it's embarrassingly simple. And I sort of regret not doing more now that I know more about it. So we just sequenced the genomes of these things. But when I said regret not doing more, I regret not measuring fluxes, for example, because we could have done much more with that. But I didn't know that at the time in my defense. So because what I was interested at the moment was co-evolution. Because it's a beautiful case where you have this genotype, I should say, of one organism that is metabolically coupled with the other one. And you have the genotype, and you have potentially hundreds and hundreds of these replicates. So you can see if there is any pattern of code diversification. And the other thing that we can do is this structure function type of questions. So about the first one, I can tell you a story. I'm not going to do that in the interest of time. The simple answer is there's an interesting analysis that actually did, but there is no evidence of code diversification. As far as we can tell, these little things assemble and disassemble frequently. So it's not like they are replicating as a unit, in which case you would have a co-inheritance of mutations. So what I'm going to talk about is a structure function question. So we have 182 individual berries that we sequenced with metagenomics. And from that, we assemble 58, these are called max, metagenomically assembled genomes, approximately the consensus genome of the different species in the system. So there are at least 58. They're not just these two things, right? OK, so the structure function question. So this is phylogeny of those 58 things. So it turns out this is one of these cases, like I know. So if you're not a microbiologist or environmental microbiologist, these names don't tell you anything. I have been long enough in the field that I can look at these names and something pops in my mind with a certain function. And sometimes you don't need to know much to do that. So I can tell you, we can also, of course, look at the genomes, and we know where are the genes involved in these pathways. So what you have here that I'm highlighting are the sulfide oxidizers, a specific clade of gamma preto bacteria, well known to be what's called purple sulfur bacteria, as well as some alpha preto bacteria that also have the genetic potential to do that. Then there is the sulfo bacteria, which not surprisingly, as the name indicates, is a sulfate reducer, also a very specialized type of organisms. And there are a few of those in this specific clade there. And then another thing that pops out, I think, is this relatively large clade of bacteroids. Similar, to some extent, to those that are in our guts, they love to degrade complex polysaccharides. And these polysaccharides in the south marshes are very complex and sulfated. So at least, there are some things on the line there that are also predators, bacterial predators. Maybe they're eating other bacteria, but whatever. Now we have 182 replicates, so now you can look at the variation across this system. So this picture is, yes? Is it a biofilm? How do they build this structure? It's like a biofilm, yes. But it's relatively densely packed with cells. They're about a millimeter, all of them. 20% variation around that, I would say, yeah. But in overseas zones, they change in time. In our sampling, they are comparable. When it's cold, they're small. The maximum size could be a couple of millimeters, but those are rare to find. Probably somebody eats them, yeah. You can keep them alive, but you don't see biomass increase. And yeah, you can also kill them very easily. If you put them in the wrong medium, you see accumulation of ammonium and such a stink, and they become dark. But you can keep them alive. And Lissy Wilbans is the expert in that. Therefore synthetic, yeah. Yes, they're in shallow tidal ponds. But from pictures I have seen, and there's only one picture of this. There was no obvious gradient structures. I don't know, maybe you need to do more. There should be something, I think. So let me explain this complicated figure. So I just took, this was previously on the slide. Just took this one, rotated 90 degrees, and chopped the bottom so I can show you the upper part. And then, OK, so that's the phylogeny on the left. Now this confetti thing in the middle. Each of these little bubbles is the fractional abundance of this particular genotype in a given berry. The dark dot is the mean of that fractional abundance. And the colors correspond to different geographic locations, different ponds. But you can ignore that for the purpose of this talk. And then here I'm highlighting the two most abundant things, which are indeed a sulfide oxidizer and a sulfate reducer. And the main thing I want to say with this, there is a huge variation in fractional abundance. That's my main point. There are approximately four orders of magnitude in many cases when you look at the fractional abundance of each individual taxon. OK, so this is not super new, right? So there's a lot of compositional variability when you look at this level of resolution. Now, if you group them according to what we think are functional roles, and then you look, yeah. If you group together all the genes of the 58 genomes that you assembled, and then you look at all the other genes that you found, but you couldn't assemble, which fraction of the functional genes you have? That's a good question. Sorry, I don't remember that number. I don't remember that number. My guess is that it's pretty high, because we get the most abundant organisms, right? But so if you group this taxa in what we think are the functional groups, oxidizers, reducers, and others, then you get something that is obviously, I should emphasize, is much more stable in the statistical sense, that the variation is not for us of magnitude from minus whatever, 10 to the minus 4 to 10 to the minus 1 or something like that. It's all fluctuating, not too much, around 20% for the reducers, around 50% for the oxidizers, and so on. I emphasize this is extremely obvious to anybody that knows elementary statistics because you're just grouping things, right? But everything that you find, there's a lot of highly cited papers in the literature saying that functions are more stable than taxonomy, and it's just statistics, it's just statistics. Because if you take anything random of the same size and you group it, you'll find exactly the same thing. Shaiyu did this, and it's just, there's nothing, there's no signal, right? So it's just statistics. So the question, is this just statistics? Is the question. So this is where, actually, intervened, and then so he took this phylogeny and said, broke it in little pieces, basically all possible subclades, and created all possible groups, say, of size two, all possible groups of size three, and so on. And asked, what is the variability of these groups in the statistical sense again? So this is what I'm describing here. That's the metric used, coefficient of variation, and the sum of the coefficients of variation for a given number of groups. And it turns out that the SOB, so sulfate oxidizer sulfide reducer, by partition, is the most stable statistical one. Which is to say that if we were to, if we don't know any biology, and we just take this thing, and as the computer to give us the most statistically stable grouping, we recover the biologically meaningful set. And I don't think that's totally trivial, right? So this gives me hope that, actually, one can approach this problem in a systematic way. But this is a very simple example. Maybe once we go to more and more functions, then it becomes more complicated. That's where we are at the moment. We don't have a great solution for this yet. So, oh, sorry. And if we do the, so the number of groups equals three, then you find the bacteria, the polysaccharide degraders. And so there is one extension of this idea because here we don't have a readout of function. This is what I said at the beginning. I regret not measuring more about the fluxes or anything. It's just the stability of composition is what we're looking at. But there's other types of data sets where one could say I have a measurement of a function, which let's say this is typically in the context of human health, where you have all these examples of microbiomes from patients and some phenotype, maybe disease, not disease, right? And then the game that people want to play is can I find a predictor of the disease? And then, you know, obviously all the things that would follow. So these are called association studies, like the typical of the GWAS, in the case of genetics, microbiome association studies, when you find a micro-granism that is correlated with a disease or with health. But if you think about this functional redundancy, now you immediately see a problem because there may be nothing, no individual micro-granism that correlates because you're not looking at the right unit if this disease or whatever function it is, is attributed not to a single species, but to a group of species and the statistics breakdown. So I mean, I think it's an important problem, but I'm not so interested in the application of this, but I use it only to introduce the way we approach it as well. So we show you, Sean, a student in my lab who just graduated. We looked into this and he, I actually thought it initially wasn't possible, but he found a solution that now in retrospect is, it makes it very clear that it should be possible to do this in this particular case. When you have some composition of microbes, this is a cartoon, right? So these are the many colors there where the different bars are samples and the colors are different species and the little dark triangles are, say, a function that you measure. Let's say CO2, whatever it is, but this is important, it will come later. And then you want to find the grouping that best explained that function in the statistical sense. It turns out in this example, of course, is the blue and the red. You group together, then you get it. And so what becomes obvious when you do this is that what kind of things you should group, what statistical properties they should have is that the blue and the red should be somewhat positively correlated with the effect that you want to have and ideally they should be as anti-correlated as possible with each other, which is exactly the same idea in the stock market. When you want to have a portfolio of stocks, you don't want your stocks to be correlated because then you are increasing the risk of fluctuations. In order for the portfolio to be stable, you want to spread the risk by having anti-correlated things. Exactly the same idea. And so that's kind of what the algorithm does. This is just explaining the same thing in a bit more formal terms. What you want to maximize is the projection of your group of species on the, because these are vectors because you have many different samples, on the vector of the functions that you measure to the extent that this angle is zero, then your correlation is excellent. And the orthogonal axis is the errors, the residuals of the regression, right? And that you want to cancel out and you only cancel them out when the vectors are pointing in different directions like the blue and the red. It's just what I said. And so Shaiyou came up with an algorithm to do this, which we define and I'm not getting you any details about this, but I can talk more about it. It's just an objective function that is an expression of the R square and a search process that goes through the possible combinations of species and some penalty for group size. We have to regularize. And then turns out that the examples that we have three examples in the paper and in all these cases, the answers are very satisfying in terms of our understanding and expectations of the system. And I'm just gonna show you one, which I think serves the purpose. So we took the TARA oceans data. And the TARA oceans data is just many, so I think 128 stations in the ocean where they were sampled at different depths. And then in the ocean, you have a gradients of many things, especially, for example, nitrate. Dominant nitrogen species in the ocean has a profile with depth. And not only with depth, also with different, but mainly depth. And nitrate, that we know is controlled by microbial activity. That one we know is important. I mean, it's a result of ammonia oxidation, the nitrification and these things. So can we find out of the many species in the ocean which ones are more, when grouped together, allow you to explain statistically the concentrations of nitrate. And this is the group of species. The size of the bubbles indicates how important they are for the statistical regression. And the edges between the bubbles is how important having both together is for the statistical regression. That doesn't matter so much for what I want to say. The two most important ones are these two things, nitro-puluminiciae and the candidatas, scandillua. And if you look at what these things do, the one is, they are both ammonium oxidizers. So they convert ammonium to nitrite. And it turns out one is aerobic and the other one is anaerobic. And so this is exactly what I was telling you about. They perform the same function in terms of ammonium oxidation, but in different environments. When you consider the other variables like oxygen concentration, they will be anti-correlated as they are in this figure. So this is how the thing works, the game works. So there are a lot of different micro environments that we are not taking into account that explain the diversity of species that perform the same function. And you can statistically sort of integrate over that and recover the function that you care about if you have the functional readouts, which in this case is nitrate. Okay. Anyway, there are other examples. This one is maybe relevant, but this is only for those of us. So if you haven't seen this paper from Alberto Sanchez Lab on the emergency simplicity, forget about this slide. If you have seen it, then you know, the functional groups are taxonomic units, families, enterobacteriaceae, and pseudomonas. And you can recover that using this algorithm and you get exactly the same thing. So you don't need to know the phylogeny to know this. Anyway, to me this, yes. Phylogeny. The ammonia oxidizers will be one of these cases. Dressed, completely different. It's by horizontal transfer. I'm not sure what is the history of the ammonia oxidation enzymes, but yeah, this is not monofiletic. Most are archaea, but these guys are distant. Okay, yeah. But in general, I think there is a strong phylogenetic signal, I would say. That's my expectation, yeah. So I'm just saying here, this is an aspirational slide. So what we have now is the microscopic variables that we get from sequencing from the omics. We would like to get to having these mesoscopic variables that we postulate would be much better at explaining the environmental parameters, the functions, the fluxes, and things like that. And I think this is, technically speaking, a solvable problem. And the limitation, I don't think it's computation and I don't think it's math. The limitation is the lack of suitable data sets because there is a whole, I mean, there is a, you cannot count the number of data sets that are quantifying what's on the left of this picture, structure. Thousands and thousands and thousands of data sets from the ocean, from the gut, et cetera, et cetera, from soils, telling you what species are there and what abundance and what genes and so on. But there is almost nothing on the functional side. And I think this is partly a cultural problem, partly a technical problem. It's much more difficult to do than sequencing, et cetera, et cetera, but I think this is what's holding us back. Okay, and this is kind of where we like to work on. And so I'm looking forward to have discussions with people about this because if we have good ideas, we can really put a lot of resources into something that addresses this challenge. Yeah. You're gonna shift to the next topic before you do. Let me just catch you on this. So for this very nice classification you are doing, can you zoom into the next level to see where do you stop? You know, you're, I mean, even when you do this of shuffling and so forth, but you're deciding some kind of a core screen level. And then it is a classification that reproduces your sulfur reducer and oxidizer. Ah, okay. So in principle you can push this forward. Ah, okay. Yeah, yeah, yeah. That's a good question. For the first case, for the sulfur reducer oxidizer. Yes, yes. That's a good question. No, no, no, no, right. That's a great, so I interpret the question as how do you know the, it's like a clustering problem. How do you know the ideal number of clusters? But what's the limit? Yeah, yeah, yeah. I, we have been looked into this, but this is something we can tackle, but it shouldn't be, it should be, you know, what people have thought about in terms of clustering. There has to be some statistical metric. For sure, at some point there should be this agreement and then, but like, at some point it has to be worse than the previous level by some metric. Yeah, yeah, yeah. Yeah, yeah, yeah. I would like you to grade this. If it works, it's great. And I understand the limitations because of the not measuring a lot of functions. I'm just wondering how sensitive is this technique if you're trying to combine data from different sources. So when you work with star ocean, that was kind of single source, but when you are combining data from different, different groups measure the same function, but they measure slightly different techniques and all those negative correlations which are essential for you to bat hedge against risk though to say might be batch artifacts. So that's. Okay, so this to me, if I understand, gets into the problem. Yeah, okay. It's not just measure functions. Now we need to understand a bit more about in what state is this communities. For example, if this community is going over successions then things are gonna be positively correlated because there's a systemic change. So ideally these things should have equilibrated in some way after community assembly and you have variation that comes from the microscopic processes. Let's say the noise as somebody said yesterday, phages, research ordering, you know, the hierarchy of research preference, whatever it is. And then this is the ideal data set. But that can be constrained when you, so okay, it's not just taking samples. I should say, okay, that's a good point, yeah. All right, so yeah, you answered another question which I wanted to ask, but right now I was asking about much more mundane things that if you have one big project like Taro Ocean which measured everything in one kind of standard, then those negative correlations are relatively free of artifacts. But if you have seven groups which measured it using their different standards, when you combine the data and try to find this correlation analysis, you will have the batch effect which will spoil the power. That's true also, yeah. It should be standardized, yeah. Think Martina. Actually I was thinking that over successions, it's actually when you have problems because I thought that over succession you have a lot of anti-correlated abundances because you have species increasing in abundances and species decreasing in abundances because, so. But at different time points. So in one snapshot. Yeah, no. So for example, if I take many replicates of succession at initial time points, everything that is an early successional species will be correlated. And the anti-correlated with the late successional species. Okay, I get it. Thank you. Okay, so can I ask a question? So the existence and the possibility to find these guilds, it seems to me that strongly depends, I mean, let's say a dataset like you should, it strongly depends on how the environment is varying across samples, right? In the sense that what are the axes of the variation of the environments determined, what are the functional groups you can find? It's not something. Yes. Because like, I mean, also this anti-correlation, right? Let's say if the environment is fixed between quotes, like what you can do in experiments, you expect this anti-correlation. But if you're in a natural environment, the anti-correlation between species within the same group. But if you're in a natural environment where these environmental axes is varying, then this correlation becomes positive, right? So, I mean, I think, let's even imagine a case where the experiment is done in the lab under control conditions. But let's imagine it's a complex humidity, but I have control conditions. I'm still also not totally sure about this in the sense of, yes, so depending on if you, how you vary that environment, imagine that I can manipulate, I can put a little bit more of nitrogen, a little bit less oxygen, a little bit of this, whatever, change, put some noise on the dilutions or something like that. All of these will give me some answers, right? But are there, is there a way to do this that is unbiased? And I'm not totally sure of this yet. Let's stop becoming, the data you show, I don't think it's even fluctuation. You know, the nitrogen thing, right? Basically, I look at your data, there's just two clusters. One is when the, what's the x-axis? Oxygen, oxygen, yeah. At oxygen point. Exactly. So it's not bad energy or anything, it's just like, you know, in one regime is doing one thing, the other regime is doing one thing. So if you, in that case, right? If you would perturb the oxygen concentration, then you learn this. No, no, but in that case, that is constant, but then you have fluctuation, I mean, there is something that is determined the fluctuations within, which is what is varying, right? But suppose that these other things that is varying is now constant, and what you vary is whatever determines the fixed level, you should see, I mean, you should see a completely different pictures. It's related to what is varying and what is constant in the environment. Is data such as lumping everything? No, yes. So I can, we can talk more about this. I actually really want to talk about it. I can tell you where we are now is, I think right now, but this thinking is evolving on a daily basis. We're gonna just perturb a set of variables that I think make sense, but knowing that this is still biased because it's whatever we're imposing. But yeah, this is a hard problem. So I have maybe 20 minutes, I think, left, and if I'm not mistaken. So I have a different, I want to tell you about a different approach for this problem, which is not, well, I guess it's partly a way to solve the problem, but maybe from the bottom up. And with that, I also want to tell you about, a little bit of, tell you about the things that we have been doing over the years in a very simple manner. So we have been studying community assembly in marine ecosystems, coastal environments. And the way we conceptualize the ocean, let's say, is as a huge bio-digester that you have primary production on the surface, enormous amount of carbon and nitrogen and complex organic matter that starts to sink in the ocean. And then in the ocean, bacteria are the main recyclers of this form of organic carbon. In soils, fungi are more relevant, but in the ocean, we think it's mainly bacteria. And then the way this happens, so there's a lot of dissolved organic matter where you have oligotrophs that are consuming that, that have very high affinities for the substrates, but you also have these little patches of nutrients that people call marine snow where bacteria sort of congregate. This could be fecal pellets from zooplanktor or it could be dienalgae, a bunch of different things. And then, but these are hotspots of biological activity and this is where the coputrophs, I like, thank you for introducing this terminology earlier on. The coputrophs that love high concentrations of nutrients and have this boom and bust dynamics, that's where they colonize and where they grow. So that's where community assembly happens on well-defined spatial scales. Okay, that's what I said. So here we can ask the question of how is metabolic labor divided? What are the roles of the community, in other words? So I'm not gonna talk too much about this, this is a bit of old news, but the way we started doing these experiments was using a synthetic particle that was in this case a little bead, it's a hydrogel with chitin and has a magnetic core. We put it in seawater and it turns out it's like you can farm little communities and pull them out of seawater and you see these beautiful patterns of colonization on the surfaces. And then, so these are, for example, natural seawater bacteria colonizing and forming little colonies and doing crazy things. Now we are developing techniques to look at this colonization process in real time using microfluidics. So it's something we have to talk. And anyway, so, but then for the purpose of this talk what I want to tell you is that we developed an isolate collection from this system. So a collection of marine bacteria that colonize these particles, right? So the way this works, the pipeline has been, you take this particle, you immerse it in seawater, then you get the colonization, you can sample your particles at different times so you can see the dynamics of the assembly. And then we can culture this bacteria. It turns out that many of the particles, sorry, of the bacteria that we collect on particles are cultural, for reasons I can explain. So then we developed a cultural collection from that and then we have a few hundred isolate genotype and about 200 which have good quality full genome sequences. And then we did the phenotyping, that's what I want to tell you about. And then we can try to understand now how to put them together in a way that makes sense. And so this phenotyping is what's left in my talk. And this was the work of Mati Gralka who's incidentally a physicist who's now leading a group on quantitative ecology or quantitative microbiology. I'm sure I forgot how he calls it in the University of Amsterdam. So about 186 strains are grown on 135 different carbon sources and you see here the different taxa. Of course, this doesn't need to mean anything to you but these are the most abundant groups of copiotrophs in the marine environment. So this is what the data looks like when he does his growth experiments for, this is one organism, the one, the Vibrio that we, you know, these things are organized in 96-well plates. For those of you that know, there are 12 columns, eight rows, one A is the first one. So this is one A. This happens to be a Vibrio. And so you see the growth curves, they look pretty nice I think for different substrates which we, this will come later but here they're classified in terms of sugars, organic acids or amino acids. You can also see in many cases this growth is zero. And so Matti basically fits their growth function from which you can get yields, loss and rates but we are mainly looking at the rates. And then we have this matrix of resource utilization. Okay, so what can we say from this matrix? So here's one thing that in case you care about the relationship between phenotype and genetic distance, there isn't a very strong signal here. It's, and there's no characteristic genetic distance at which things really change. It's, of course it become more phenotypically different as you go to long distances but they slowly go down and the phenotypic similarity of very closely related things is not that similar. That's what I can say about this. And so the other thing that pretty obvious perhaps thing that you can do with type of matrices is the principal component analysis. And this is what that looks like. And it turns out this is interpretable. So here each of these dots is a strain. And so when you project the type of resources, sugars, organic acids or amino acids on this PCA plot, you see that basically you have this coincidence, right? That you have things that are pointed in the direction of sugars, in the direction of TCH cycle intermediates and in the direction of amino acids. And so, and this match is so good that Mati developed a simple index release just based on the growth rates on sugars, KS and the growth rate on the acids. KA, you can have an index, you call it sugar, acid, preference, SAP. And so this first principal component is almost perfectly correlated with that index. So we, in the rest of the talk, I'm just gonna use the index but it's the first principal component. So why would you have this type of specialization on acids and sugars? I'm talking about the first principal component. So I think Terry may disagree with this and I don't know what is the explanation, but I just, I really want to mention because it's very compelling other people's work that I think is, let's say, sounds relevant. So you have sugar metabolism that brings you, that takes you down, well, down in the orientation of this graph, of course, towards the TCA cycle. And then you have a gluconeogenesis that brings you up into the TCA cycle. Okay, that part is fair, this is just textbook stuff. And so, so what I was referring to when I said work of other people is the idea that you may have a frustration if you want to do both things at the same time. So because glycolysis has, and gluconeogenesis have opposite directions in their flux.  And if you want to do both, you may have some type of futile cycle. So this has been looked at in a beautiful, I think, detailed way for E. coli and pseudomonas. What is not clear is whether this is a general explanation, but E. coli and pseudomonas have indeed these preferences for glycolytic metabolism and gluconeogenic metabolism, respectively. Like E. coli does glycolysis preferentially and pseudomonas does gluconegenic preferentially. So, you know, this, I think they could potentially regulate and do this better, but it seems to be somewhat imprinted in their genomes or in their genetic code somehow. So can we actually read that from the genomes? And again, without going into complicated metabolic models which I, well, I mean, if I ever have to go into that, I'll do it, but I'm not super fond of it. See if I can, my question, is there a simple way to make these predictions from the genomes? And as I will tell you, there is. And I think this is kind of interesting. So turns out, and here's a critical slide. So make sure that you're following what I'm saying here. What we are doing here is counting the number of genes that are bringing sugars into glycolysis. So for example, galactose, the sugar goes into glycolysis and propunate, an acid goes into acetyl-CoA or something like that gets into the TCA cycle or goes upwards. So counting the number of genes that are in those reactions that are bringing sugars into glycolysis or in those reactions that are bringing acids into gluconegenesis, okay, just counting genes. So for example, it could be that for galactose, you have the, I'm not sure, I'll show you the exact pathway, but let's say in my scheme, there are three genes, there are more than three genes in reality. It could be that you have many copies of the same gene. They don't need to be identical, but they have the same functional annotation. So you count that up and you count all the other genes in the feeder pathways into central metabolism. When you do that, you see there's a really nice correlation in this particular case of propunate and galactose. When you have the sugar acid index on the horizontal axis, it's a very nice correlation. The more they prefer sugars, experimentally measured, the more genes they have in the pathway that brings galactose into central metabolism. So they have more redundancy in a way or the pathway is longer or something like that and the opposite for propunate for the acid. So in the particular case of galactose, then we can look at that in more detail. So these are all the steps that bring galactose into glycolysis. Where you have, what is expanding in this particular case is the first step of the pathway. That's where you have the correlation with it. That's what's driving the correlation with the SAP. So it turns out that there are six and eight copies of those genes that are doing that step. And when you look at what those copies are, so this is a phylogeny where we have made, we have made a phylogeny of all these genes in the first step, I think it is. The beta to alpha D galactose, second step. You see, these are the different clades in where you find the copies of the gene. The one where I have the arrows, the red arrows, this is one organism, it's a flavobacterium that is a sugar specialist. And you see where the genes are, they are nested in many different clades far distantly related to flavobacteria, in the alpha proteobacteria, in the gamma, et cetera, et cetera, which means that this is horizontal gene transfer. These are not just copies of these genes. These genes are accrued from all over the place and they just have more diversity of things that do approximately the same thing. So the picture is something like this. So there has been an expansion of this part of this pathway. So, okay, so I'm gonna go back to the previous slide where I had the SAP and the galactose and propionate slopes. Sorry, correlations, but I want to first tell you what my opinion on what these things are doing. Why do we have so many genes there? But this is just speculation, okay? So, and this is, and I'm borrowing ideas from what people have observed in other cases. So my speculation about this is that these enzymes are not exactly redundant. They're just optimized for different conditions. So in the case of oxidoreductases in the electron transport chain, it has been shown, for example, that some work better at low oxygen concentrations and some work better at high oxygen concentrations. And therefore you have many copies of the, what seems to be the same thing, but it's really not the same thing. It's just optimized for different conditions. So if I'm an expert, so if you have different environments, you may use the different genes, that's the idea. So I was gonna say if I'm an expert in repairing computers, I probably have a lot of tools to repair computers, right? Not just one type, that kind of thing. Okay, this is speculation, but back to the data. The point here is that we can take the slopes and then put a number into this tendency to lose or gain genes as a function of the sugar-acid preference. And we can put the slopes there, and this works pretty much, there's only one exception, and I'm not sure where it is, but it works for, sorry, there are a few exceptions, but there are a majority of points. If you have an acid pathway, the slope is negative. If you have a sugar pathway, the slope is positive. So galactose and propionate were not special cases. This is generally true for sugars and acid pathways. And so then we can basically aggregate them, and then come up with a simple way to predict this sugar-acid preference that doesn't need any complicated metabolic model. It's just a linear model with two variables. One is how many genes are, how many, the gene counts of the sugar pathways and the gene counts of, sorry, of the acid pathways. Oops, I wrote sugar in both, but this should be sugar and acid, I'm sorry about that. All right, so this simple model, how good is it? So we have to train it on something, right? In our case, I think it's reasonably good. We can predict the sugar-acid preference from the genomes using this simple model. We have a pretty good R-square. And then what I think is more compelling is that Matty went to public data sets, turns out that when people used to be the case, I don't know if they still do it, when they find a new species, they test whether they grow on lactose, on acetate, and a couple of simple things, and they report this data. So there are these tables with like 10,000 species and whether they grow on lactose and so on. And then of course we have the genomes. So we can make the predictions from the genomes using the simple model, and then we can see how well it works based on that data, and there is a signal. That actually depends on your standards. It's pretty good considering that this is data that we have not training at all in the regression. So that's I think compelling in the sense that there are these genomic signatures that are predictive of the function of the system. So I think I'm kind of out of time, right? But how much time do I have? Five minutes, okay, five minutes. If there are no questions, I will transition just to conclude. I will skip some things that I had, but I was thinking that I may need to skip, so I'm gonna press this button. And so just to kind of wrap it around a little bit. What this means for the ecological dynamics. So remember I told you that you can take these particles and sample them at different time points, and you get some picture of the ecological dynamics. So this is all news. It's the first paper I published in my lab, but this is the figure from that paper. And so what you have on the, if you haven't seen this on the rows, it's a different taxa, you know, and the red is the, so it's normalized, the data is normalized per row, and it's a fractional abundance of that tax on different time points. Red means the maximum fractional abundance, and black is when it's undetectable. And so you see a pattern of succession where you have an early arrival, and it stays for a little while, and then other things arrive later, and so on and so forth. But the interesting thing is also that you have these phenotypes attached to it because we have the isolates, we can ask what they do. So if you look on the, this by the way where particles made of chitin, so you ask whether they grow on chitin, or chitin is a sugar, it's a polysaccharide, whether they eat the monomers of chitin or the dimers of chitin, and the answer is pretty much yes for the first part and no for the later arrivers. And so there you have this really drastic shift in a way in the phenotype. And turns out these are glycolytic organisms, primary degraders we call them, or exploiters if they don't produce the enzyme to break down chitin. And the later part are the gluconogenic, predominantly gluconogenic organisms that we call scavengers because they're utilizing metabolic byproducts. So that for reasons that are, well, they're still kind of somewhat unclear, but at that time we had no idea why would this metabolic byproducts be released if the only thing we have there is chitin. And there are different theories for this, but this is kind of the wrong thing to do, I think in terms of, I know that you probably are very tired, so I'm gonna show you something very complicated, but if you want to close your eyes for the rest, it's okay. I think you did really well. Thank you. So, and maybe I just tell you the punchline, right? So what mediated transfer, we don't know, but from the data we have we think is phages, phage predation, prophages that get induced in the early colonizers, release metabolites that the other things can utilize. So maybe I just leave it there and then if you want to see the data I can show you, but I'll stop here and then I take questions. And to the summary, it's just repeating things that I already said. There's a broad pattern of specialization and we can read it from genomes because this has a signature which could be just a correlate of these expansions in the pathways, in the number of genes in the pathways. And then, well, the story, I didn't show you any data, but it's this idea that the transfer of different forms of carbon from the sugar specialist to the acid specialist, at least in the communities that we study, I think it's largely driven by prophages being induced, which, you know, cell slides and all these metabolic byproducts are released for other organisms to take. Okay, so, tons of people to acknowledge, I already did partly during the talk as well as the funding sources and this fantastic collaboration of people that includes Terry, that I'm proud to be a member of. So, thank you. Thanks a lot, we have time for questions, Martina. Thank you, very nice. One thing is, okay, let's say, you see this changing, let's say, before you have a sugar specialist and then acid specialist and it's in a succession. But for example, when in a single resource like glucose, you see both sugar specialist and acid specialist. Do you think that you need phages also in that case or it's enough cross-feeding? I don't know, I think, I don't know. In that case, it may be just enough, well, certainly there's acetate coming out, right? But then there is also the problem in those experiments as well, as Terry mentioned, I think yesterday, you get into stationary phase and then there is death. So this could be phages, it could be natural death, but I'm sure there's tons of things coming out in them from stationary phase. Okay, thank you. Thank you, so I am very fascinated by the last thing you were saying about prophage induction in the degraders. Do you have, I mean, I don't want to steal time if this is gonna take time, we can discuss it in private, but do you have a sense of how prevalent these prophages are in these degraders? Like if you take, I don't know, 100 of these degraders, how many of them will have prophages and how many prophages on average will they have? Well, at least, so we looked at this in the communities. Okay, I also don't want to derail too much because the slides are a bit complicated, that's what's annoying, but so we did commonly say, we looked at the metagenomes of single particles, cornice, and we can detect prophage induction by looking at the ratio of reads that recruit to the prophage versus the rest of the genome. This is not easy to explain, but I think, ah, sorry, I don't have it here, so, but in the communities that we assemble, so on these little particles, there are degraders and there are non-degraders that you can tell from the genomes. And then in those particles, the degraders were more likely, or they were more frequently with prophages than the non-degraders. So in those particles, at least statistically, then they are more likely to, and then when you look at the things that get induced, it's pretty much only on degraders because these are the first colonizers that are also growing fast. So when they're growing, there's something we have no idea what the signals are, but that's when prophages got induced. Thank you for a wonderful talk. I'm interested in the, well, in all parts, but my question is about the second part where you have shown evidence of this extensive horizontal transfer of tools so I have long-time ago collaborators and we have this toolbox model of evolution, but in this toolbox model, we assume that if you already have a tool, you don't need a second one. And you say that it's important to have multiple tools adapted to different conditions. So I'm just wondering how much worse your feet would be if your presence or absence of tools would be binary. So if you have one tool, you already have one, just at least one tool, zero or one. So is it really the tendency to accumulate variants which drive this correlation or just presence, absence is enough? No, okay, I can tell you because I asked Mati many times because I'm also showing this example because I think it's interpretable and I really like it. But he repeated to me many times that this was not the only way. There was also pathway length increasing which I don't understand. That's why I'm not mentioning it. But yeah, let's say 50-50 or something like that. It was not clear, but this definitely happens and this I find it more interpretable. Also somehow pathway length gets larger, I don't get it. The first thing is about this multiplicity of the tool, very interesting. I think let's pick an organism and study it. Yes, okay. And by the way, for the kalatos, you should only count the first two as that because the other is used by everybody because it's to make components of membrane. So that's where the signals, that's great. About the phage part again, what's the rationale that phage will give rise to acid eaters? Because when it lies, you got amino acid, amino acid's fine, but you're not gonna get these acids. Where are you gonna get these acids? No, but acids includes amino acids. Acid eaters includes amino acids and organic acids. But then by your accounts, you're not gonna get any of them. Because amino acid, you're taking generic transport, you're not gonna find signature of amino acid. Cause you directly go into amino acid. What do you mean? I don't understand. Like the way you were doing the propionate, right, these pathway signals. You're not gonna, how are you gonna find signals of amino acid eaters? That's a good question. So that's a good question. But the, yeah, because the, okay, that's a good question. Because the, if I'm not mistaken, this, when we count the number of genes for sugars, we count the number of genes for acids, these are not amino acid pathways. Yeah, yeah, so I'm guessing that you're actually recognizing acid eaters special, right? But still includes, I don't see a logical link. Right, I agree with you. But it still includes the amino acids. The ones that prefer amino acids. Yeah. I guess it's because they're prime to do gluconeogenesis. And amino acids eaters are doing gluconeogenesis. No, right, but yeah. Okay, any other question? Okay, let's thank Kepoto again. Thank you. So,


So I definitely just filmed this entire video without my microphone plugged in So take two. Hey guys, what is up? Welcome to my channel So for today's video, I am going to be reviewing and doing a tutorial showing you some swatches of the new Natasha Denona Coral Palette. So if you are interested in hearing my thoughts on it, then just keep watching A little story about this palette. It actually released while I was out of town I really wasn't paying attention to makeup releases because I didn't want to order anything that was gonna be at my house when I wasn't so I just thought I was gonna pass on this palette altogether and then once upon a time it was a late evening two days ago and Sometimes you're just scrolling through the Sephora section You're like why not her 5 p.m. Palettes are something that I do usually pass on most of the time I try to pick them up if I'm really interested in the color scheme And for some reason this one didn't scream out to me, but now that I'm looking at it in person. I'm like It's really pretty so major facts about this guy. It is available right now on Sephora.com It will be available in stores August 9th, so that's just in a couple days So this is described as an ultimate palette for autumn featuring Natasha's a legendary Saturated pure colored pigments. Do you guys ever read the reviews on the Sephora websites? Some of them are so funny overpriced and underwhelming Alrighty, so this is one of Natasha Denona's full-sized five pan eyeshadow palettes There are 2.5 grams in each pan for some reason online at first This looked like one of her mini ones to me But it's not it's one of the full size I honestly kind of wish this was a mini one because I like the price of that one a lot more even though the values Not as good the price is a lot prettier to my wallet So the packaging of this guy is her typical five pan eyeshadow palette packaging It's just a clear cover which I personally prefer because I love seeing my colors before I grab them Yeah, pretty packaging is all great, but there's just something it's so functional about this and eyeshadows are pretty There's nothing wrong with the clear cover. It also has some holes in the back So if you want to take out the pans you can mix and match with other ones from her other palettes And you could just poke it through the hole and it'll come right out a problem that some Natasha Denona palettes I've had is the eyeshadows falling out this one mine is pretty sturdy none of them are coming out So that's always great because I have had a couple that have fallen out So this one seems quite sturdy. So in this palette you are getting five eyeshadows Two of them are gonna be matte two of them are going to be shimmers and one of them is something more of what I would consider a Duochrome lid topper type shape the whole vibe behind this palette Of course is that it is a coral eyeshadow palette, but it's supposed to be the ultimate palette for fall Honestly this palette just looking at it and all my eyes it really does not scream fall to me at all It seems like it's more of a spring palette, especially if you cover these two I mean you can't get more springy than that if you were to put these two together. Yes, that is very fall But just to advertise it as an autumn palette It's more springy, which I'm okay with because I much prefer a springy palette anyways So first and foremost formulation wise, this is great that's just something I wanted to address right away because for a while Natasha Denona was a little bit Inconsistent in her formulas She would play around with new formulas in her palettes Some of them just were not very good and you know, we're paying a lot. This is $50. You're paying a lot Every eyeshadow in here should be amazing and I have to say in this palette She did it. So this one is a good palette from her. You're not gonna be let down So I'm gonna go over the eyeshadows individually and talk about them So I want to talk about the matte first so she has two mattes first you have this more peachy shade I will say this does apply a bit more pink than it looks in the pan I feel like a lot of times Natasha's formula does do that I don't mind it. I like a good pink eyeshadow, but it's just something that I didn't want to note for you guys It's very pigmented it blended out like a dream. It is a good matte formula Just as all of Natasha's matte formulas are next shade is this terracotta red shade right here again Very pigmented very blendable. It really is not as scary to apply as it looks I did find myself having to build and that's not a bad thing at all I was using a very light hand just because I am wary of some other shades and a lot of times in other brands when there's a shade Like it's extremely powdery a little bit chalky things like that not with this formula It's the perfect texture if you ask me not too much fallout great amount of pigmentation without it being too much I found myself getting a little bit less than I was expecting which I was okay with because it built on perfectly So I really do like this shade. It was surprising to me Okay, so let's talk about the other two shimmer formulas that we have here So this first one is like a peachy color with an orange and yellow shift to it Absolutely gorgeous. I have to say I think this is my favorite shade in the palette. No, I don't think it is I know it is. It's just gorgeous and this all over the lid is gonna be For me, this is like the star of the show I think it's so beautiful and it's a really nice formula her formula is very creamy Super easy to pick up. What's unique about her shimmer formula? Is it just sticks to your skin as soon as you put it on there? I love it so much and then you have this red shade right here the shimmery cranberry I don't know. I'm kind of sick of this shade I think the reason being she had a shade like this in her sunrise palette So I'm kind of over it, but it is really pretty and I cannot fault the quality. I mean look at that It's gorgeous. I just I'm sick of this shade for some reason And then the last one we have is the more interesting formula This is probably my least favorite shade in the palette. It's a very strange color It's like a lavender and peach duo chrome shade and I want you to see there's not much Pigmentation that you're getting so this is more of a lid topper it is going to look really pretty all over the lid alone But I think it's a bit of an odd color in this palette if you ask me I would have preferred something different a different formula as well. This is a good formula It does a good job as a lid topper, but I'm just not feeling it in this palette I'm really not what I would love to see is I Feel like I haven't seen a great Foiled formula like she has in these palettes in a while because the really foiled formulas in here are Absolutely in the same these eyeshadow palettes, by the way have a bit better quality than most of her palettes I mean most of her palettes are still amazing But there's something special about those big palettes and I would have liked to have seen that formula be this color right here I think you could have done some really fun things like a really Metallic foiled peach shade right here would have been Stunning like an out of this world peach shade. Anyways, that shade's good. I'm just being I'm just being me So the look that I created today I wanted to incorporate all five of the colors in here in this look But honestly, this is not the kind of look I would gravitate to at first So I did just want to mention a few color combinations that aren't inspiring me So hopefully this inspires you so the next look I'm going to be doing with this palette It's just going to be these two shades right here. Just a really pretty fresh pinky peach look This is gonna look gorgeous Also, these two shades would go really well together and of course this deep red fall smoky eye right here another color combination would be to play with the crease colors and then Blend these two so maybe put this in the inner half this in the outer half and really blend the two and the Center of the lid that's going to be gorgeous. That was kind of the look I was going for but I wanted to play with this color as well But the way these two were blending together was scrumptious So please try that combo out if you have this palette Let me quickly go into how I did get this look So the first thing that I did was I took the light peachy matte shade And I applied that as my transition color with a big fat blending brush applied beautifully Looks beautiful great transition shade for this palette the next color that I took was the matte cranberry color I know I do my looks like all the same But guess where I put it in my outer corner and kind of blended it in a little bit on the lower lash line as well As you can see it really blended beautiful. It didn't come off too harsh at first Which is nice It just allowed me to build and still look really nice next shade that I took was the foiled cranberry color And I put that right on the outer third of my lid right on top of that matte red shade What's amazing about this color is that it applies great with a brush if you want more control But you know it applies better with a finger if you want more pigmentation then I used my finger I think really can only get by by using a finger with this shade right here And I just applied that right to the center of my eyelid and this adds a fun twist to the look And then I took this shade right here my favorite one I used a brush for more control and then I would kind of alternate between the brush and my finger and applied that to the Inner third of my lid and then I took some time to blend the three together I reapplied the lid topper on top so that you can really see that interesting color shift and That is what led me to this moment right here. So as far as this palette goes for deeper skin tones I really do think this is going to look lovely on you. I think all of these shades are going to work well for you I don't think it's the best suit palette for deeper skin tones I think you are going to have to go into other matte palettes in particular to be able to create more depth and Dimension to the eyes, but for the most part I definitely think you can get all of these colors to work for you But you may need to grab into other places Do I think this palette is worth it? You know what? If you like the color scheme, I do This is a lot of money and with Natasha Denona's past of being a little bit inconsistent It's important to listen and to reviews to know if it really is worth the amount of money It is and I say every shade in here is pretty much a banger really really nice She did a good job with this one. So I'm very happy with this one. So anyways, that is all I have for today's review I hope you guys found it helpful. If you purchased this palette, let me know your experience with it Thank you guys so much for watching. If you have not yet subscribed to my channel I do hope you take the time to do so and I will see you guys in my next video. Bye guys. Have a good one


Everybody forced here we're ripping in 22 23. O. P. G. Hockey hobby Eight box number five random teams. There's a Lincoln chat might be easier to see the results on there rather than the screen They're kind of small like that Let's get started everybody. Thank you very much and good luck Big variation time big rookie time. Come on Nice patches, hopefully nice manufactured relics again guys only 20 bucks a spot 16 to go in the next one It's halfway full. Let's get that filled up so we get the baseball Hope to pull everybody some nice cards tonight the best of my abilities Try to make sure Dylan doesn't stop me. There's a Matt boldie right off the bat premier rookie. I was easy for the Leafs You can put on March, so blue on the Andre Casa next step one release. I don't know honestly Sandberg we got a black marquee rookie Philip Roos for the Hawks Numbered 44 of 100 from the retro marquee rookie Suzuki Ada Hartz is Hubert dough marquee rookie Fujimo Caleb Jones Addered Luke is Raymond Premier Nazim Khadri Khadri six of clubs Brady Kachuk Who'd I give crawls I'm crawl you get the Kings. Hey, there you go. It's pretty good There's a brain Tracy. We've got a Dylan Gunther for the coyotes on the platinum preview rainbow Nice car there coyotes marquee rookie there blue brain Tracy ducks Yeah, not the best first race for him Dylan Gunther to 350 coyotes Arizona doing really well so far marquee rookie rainbow foil Dmitry Orlov, that's Bobby Orlov right there Dmitry Skorlov First star of the month Red Ty Smith devils won a box on the Reds and off Of Kotiuk Kotiuk you got Braden Schneider there a Jennings trophy winner Bob solve I'm so visit so oversaw There you go for the Sabres premiere rookie Braden Tracy for the ducks Spence bunting Premier rookie Dylan Gunther for the yachts Everly Pearson Ada Hartz Hubert dough Terry we've got a blue Batherson Tour premier I see mom we got a pink Mario Ferrero sharks to 75 Macelli rookie Macelli for the yachts Macelli Macelli Celtic Scott Powerhouse last night. They got They look rough at the end They're like they're like not go without our without time Lord They have to have in there to defend the paint Queen of Diamonds ah-ho Let's get that next one filled up you guys Troy Terry on the blue one big variation here I want to pull some puzzle pieces too Matt boldy marquee rookie with a rainbow marquee rookie Jack McBain for the coyotes the 350 Arizona masterclass right now. There's Arizona Brian H is Arizona red on the Matheson Sandberg a Peyton Krebs blue marquee rookie Brayden Schneider retro Zuki Everly March of so Fajimo Jason Roberson five of clubs. Oh and power rookie Yeah, follow cadre, but you know Pink if follow Kings the 75 Eric Holla Tracy Roman you'll see outdoor game variation for the predators Hopkins that so that like the regular base will have no like black on the top the other the variations always have black Just to figure out which variation it is at that point Blue checklist. Okay, interesting. So if you're ready to pull a one-on-one checklist then, right? Bruce pink brain shan blues 75 Taba Brandon Carlo Dan Vladar for the flames Darth of Ladar two of 100 Christensen Kairu That's pretty cool Mike Hold that I pulled that make David three color patch out of the tennis nails. That was pretty fun. That's a huge card Drew Ada spades Connor the checklist spot. We're just I'm sure that the check was just like Random now sort of thing You want the checklist spot Nick? We can give you the checklist spot instead of the The Rangers if you'd like I'll treat. Yeah, Lane will take the Rangers you get the checklist spot There was already a blue checklist pulled so You get one blue start off Linking in Nathan Smith Jake Ottinger. That's pretty good. Actually 12 to go in the next one guys links coming into chatter right now Y'all just keep in mind the more hockey we're able to fill the more hockey rich will keep getting in terms of like Better products. There's leakings to 100. So jackets We're gonna have to fill lower-end hockey in order to fill high-end hockey keep that in mind for anyone That's watching that's hockey thing a boldie tennis clubs. Yeah, it goes like all the other sports guys It hit all price points. Can't just rip the cup. I Wish we could Corsac marquee rookie, I wish that's all I could buy into but Whole channel we would have been doing nothing for the past two years Goes live for four hours a day with like seven cases and then just ends the stream. Oh man Nikita, okay, okay a coach yuck a coach yuck.  This is what happens on ESPN news This is real life. He's doing summer social with a dog She cash savers in Cuzmenco Cuzmenco having a good rookie here in those 27 blue Kaepernan Check list for the low Vasilevsky premiere in pasta My guys, if you think the Cowboys are gonna release Zeke Amen Roberts in there Mike if they get these on Zeke and Pollard are gone Pollard got tagged today. Yeah, Lauren He's okay, right He could still get signed by someone else technically Goulay we got Culper Fetti pink for the Jets to 75 Trophy winners relic Caril Capri saw for the wild Calder trophy relic there Yeah, manufacturer likes born a low premiere rookie sharks Realistically though Mike the Cowboys are literally gonna go up to Zeke and be like look you can either restructure or you're gone And it's gonna be his decision if he wants to stay a cowboy and end up retiring a cowboy or if he just wants money I feel like the market's not really there like I would not want him on the pages. There's no room for There's plenty of teams that you would fit in well Yeah, that's like a part-time player Right goal rust Jeff Skinner Sabres black that much better for third downs to 100 on the Skinner Mark Rossi rookie. I Mean a team like the bills could Yeah, they need more than just cook cook is good everyone else there is Holding them back. I'm never There you go guys. We got a wood card of Markey rookies Owen power Sabres big card It is so good already. He's got Buffalo in this one Gavin J wood card Owen power pasta premiere Seven to go guys. Nice. We also have lots of Bowman inception Teams are on sale and random team styles are after the PYT's guys. We got to get them filled up It's an Everly two left playing cards break a bone Braves blue jays guys both on sale $17 apiece 17 bucks apiece guys Blue jays have Luis Mesa. Who's the who's the braves hit in there? I see my blue rookie jets I think it's Seven left in the next one after that green foil Isaac Ratcliffe to 33 Markey rookie flyers. What a box. This has been y'all random team needs some help big time Let's start buying some random team Yeah, Dylan guaranteed a one-of-one in every box Got red Taylor radish for the Hawks or he'd give you his car Awesome Kofsky rookie Justin hull We just had an Owen power wood card. We're heating up tonight. You guys also pulled two Naomi Osaka autos in the same The same break with a Steph Curry. This is the news. I like to watch right here dogs gentleman through hoops McDavid all-star seven of diamonds Copa tar and remember guys no personals here tonight They're all on drip. Kenzie's live right now Brandon Hagelblub. Oh, yeah, they're Greg Luganis That was out. It was pretty wild make pretty wild Stamco's rainbow foil thatcher Demco Canucks to 350 Thatcher seven of clubs Everly. All right guys halfway through we've hit some nice cards so far Coyotes have done well savers killed it with that one card Yeah Dylan did you go David all-star Lou Van Riems dying Nathan Smith marquee rookie Lane Lane was there for three days I didn't I didn't go Yeah, I'm gonna be there every single day next year taking vacation time I Just gonna stay at my grandma's and walk there, dude. I have to deal with whatever y'all had to deal with Wood cart is much rarer than a case hit. She's Bergeron blue. Oh, that's true Slavkovsky rookie Carrick pink to 75 ducks You got sunburn Donnie is pretty sunburn Yeah, you have to bring it with you and reapply like every two hours when you're in the Sun that long Ekman Larsen red. Yeah, it's tough, dude It's tough bulls. Did you have fun? Yeah, wood cart is pretty damn rare blue Josh Anderson Bobby Brink Cam Talbot premier Adam Fox. Oh to a spades Clayton Keller Christensen blue Roman Yossi Christian Dvorak. I guess your dad went straight from the porch to Bayhills Even though I guess that was Friday night printing plate one of one Mackenzie Blackwood for the Devils There we go Devils one-on-one printing plates, okay, they Chris Crider headshot variation your rangers nice headshot variation of Crider is Zach Hyman Presaw blue coat Cody CC. I would have just I would have just drank the whole time If I went it's all I would have done I've been like, yeah, go golf and then I would just been sitting there chillin I'll call you Chinock. You got a half a slim home black for the Bruins to 100 72 of 100. Yeah, go golf hit the ball Happy going. Oh amazing golf ball whacker guy Nick Suzuki on the five of hearts. You're a check $9 for a spike on a Palmer Blue Cam Fowler Magic game probably worse But honestly sounds pretty good compared to like a magic game Jordan Spence Like Disney prices. It sounds like blue on the Foremonton much on the next one Got black Christian Dvorak Katie ends number 72 of 100 Seven to go guys on the next one We can ride for this Terry Roman Yossi Appleton Appleton like the rum Isamont Edmondson Macelli Markey rookie retro Shane Wright Markey rookie Schneider got a black Victor headman lightning Numbered 44 of 100 There you go bolts Sally blue chase Pearson red wings rookie Salavsky Jack McBain a 350 again for the Yotes rainbow foil Markey rookie Kenton Johnson rookie yuck of training Brinn kit all-star JT comfort blue Kent Johnson Markey rookie retro Corsac Seth Jones blue pass a lefty Manko sit the kid Manko's pretty solid Folder rookie but having a great year nonetheless a Mad boldy wild on the OPG platinum preview card there blue Matthew Chuck for the for the Panthers Laoco Suzuki pink for the Canadian's to 75 got the OPG puzzle piece here bounty number three random at the end to everybody Let's get some love the moment inception everybody Coming up on those right after OPG, but we still have seven left on the next OPG. What's the drop record? Jack Quinn rookie Rip record. What's what how do you break a drip record? Rasmussen How many Rip things you get I guess Is that Cassian black for the coyotes number one of 100 it's like their version of like the heart I guess is like the drip thing Why at Johnston Matt boldy ten of clubs Yeah, J. Row. It is tough out there for the Flyers Carrier my brother just moved to Philadelphia. He's a Bruins fan. He's like, yeah, I'm going to the game in April Bruins at Flyers. I'm like, well, these two probably you're gonna see him win The Flyers are stink man. They're tough right now Keller drew marquee rookie blue for the Ducks Vlasik we got Brady Kuchuk Senators are 350 rainbow foil probably J. Row probably Joel Eriks at Eck Samuelson So guard blue bow and Byram. Yeah, cues. Can you diamonds Cal McCarr? Sanderson marches, so blue marquee rookie Shane right on the retro for the Kraken Rykel Nick Suzuki five of hearts Robert Thomas Marco Rossi Thomas Shabbat in the blue Chris Crieder printing plate again one of one looks like it's Oilers It is Oilers Dylan Holloway 101 printing plate Black printing plate, right? There we go. Well, I see another variation in this in this pack or the last box here. Come on Get another puzzle piece All right, that's number two random to everybody at the end. It's patcher ready Freddy Anderson two puzzle pieces One sec here. We'll see spots coming in six to go on OPG Six to go y'all one to go on that first inception PYT Three 23 22 The vision's inception is still at three. All right guys six to go on the next one I think we have a couple more cases illegal, you know what help us Move it illegal as if you were to buy some spots fun fact because that's off That hits one one of one Starts getting all arrogant chat. It's true. He's big time in us It's gonna use all of his money to buy all the spots and just like throw it away Lou Nazim Khadri, you got the Mariners Invicta to bring here. We got a Adam Pelich Islanders all started 350 Rainbow foil illegal.  It's pretty uh Prescared right now. Prescared right now. That's illegal when Donny comes after him Chris Crieder on the blue Appreciate it drew. Thanks again, man Nick Richie the Yodys 75 traded for his brother. You'll see a premier rainbow foil Nicholas Backstrom Capitals Number 31 what random teams? Fax opera ZC. What do we got here? Markey rookie holographic Shane Wright Yes, that's what I'm talking about right there Shane Wright 3d That's so cool, man. Gotta throw up. Don't keep looking at it. Oh Get nauseous right now, man Congrats to the Kraken That's super rare to haven't seen any of those through three cases so far the wood card or the 3d Pretty tough pulls. Congrats on those cards. Yeah, I forget the exact pack gods, but they are rare Kent Johnson Ryan McNenon, of course, uh, Thomas Shabbat to 100 sends five of 100 to a club's Fariby Manko Forestling McBain McBain Yari line. All right, let's do the Bounty card random and we'll recap numbered cards and other short prints and stuff. I bet Okay, so two puzzle bounty cards random off here two and Three so top spot is gonna get number two in the random second spot. I'll get number three to everybody six right here as a six and Two eight times top two teams each get a piece one two three four five six seven eight capitals Hurricanes capitals get two hurricanes get three. We had outdoor game variation Roman Yossi Preads headshot Chris Krider Rangers Platinum preview of boldy Platinum rainbow preview Dylan Gunther printing plates for the Oilers. That's Dylan Holloway and Mackenzie Blackwood for the Doves of course 3d rookie Shane Wright. It's really cool The 350 Demko Brady Kitchuck Adam Pellick Jack McBain Jack McBain Dylan Gunther do coyotes coyotes Blacks now to 100 Hampus Lindholm Christian Dvorak Brock McGinn Dan Vladaar Elvis Mars Leakins Thomas Shabbat Jeff Skinner Victor Headman Zach Cassian Phillip Bruce Jordan Everly rainbow to 99 Nicholas Backstrom pink to 75 Mario Ferraro Alexia follow Nick Richie Braden Shen Nick Suzuki Cole Perfetti Sam Carrick green to 33 Isaac Ratcliffe Got a Jennings trophy Bob Solve Calder trophy Karyl Caprice off and wood card Oh in power and that'll do it. Thanks again everybody another OPG coming up


Venture Capital VC is a type of private equity, a form of financing that is provided by firms or funds to small, early-stage, emerging firms that are deemed to have high growth potential, or which have demonstrated high growth in terms of number of employees, annual revenue, for both venture capital firms or funds invest in these early-stage companies in exchange for equity, or an ownership stake, in the companies they invest in. Venture capitalists take on the risk of financing risky startup UPS in the hopes that some of the firms they support will become successful. Because startups face high uncertainty, VC investment do have high rates of failure. The startup UPS are usually based on an innovative technology or business model and they are usually from the high technology industries, such as information technology, IT clean technology or biotechnology. The typical venture capital investment occurs after an initial seed funding round. The first round of institutional venture capital to fund growth is called the series around. Venture capitalists provide this financing in the interest of generating a return through an eventual exit event, such as the company selling shares to the public for the first time in an initial public offering IPO or doing the merger and acquisition also known as a trade sale of the company. In addition to angel investing, equity crowdfunding and other seed funding options, venture capital is attractive for new companies with limited operating history that are too small to raise capital in the public markets and have not reached the point where they are able to secure a bank loan or complete a debt offering. In exchange for the high risk that venture capitalists assume by investing in smaller and early stage companies, venture capitalists usually get significant control over company decisions. In addition to a significant portion of the company's ownership and consequently value, start UPS like Uber, Airbnb, Flipkart, Ximein and Divi choosing are highly valued startups where venture capitalists contribute more than financing to these early stage firms. They also often provide strategic advice to the firm's executives on its business model and marketing strategies. Venture capital is also the way in which the private and public sectors can construct an institution that systematically creates business networks for the new firms and industries, so that they can progress and develop. This institution helps identify promising new firms and provide them with finance, technical expertise, mentoring, marketing know-how and business models. Once integrated into the business network, these firms are more likely to succeed as they've become nodes in the search networks for desiring and building products in their domain. However, venture capitalists' decisions are often biased, exhibiting for instance over confidence and illusion of control, much like entrepreneurial decisions in general.


Going on everyone Sam here with late sports cards doing the random for the running back triple of Barry Sanders Frank or an agent Peterson forgot to do it during the video my apologies on that one Cody Cody M Thanks for that good call Gonna roll the dice whatever team is on top after eight times will Get the card so good luck one Two three four five six seven and eight go to the Lions. Congrats on that Lions Very nice. I will upload this one as well with the break appreciate it everybody and let's get this last break filled up


The National Executive Committee, NEC, of the ruling All Progressive Congress, APC has confirmed the appointment of the immediate past governor of Kano State, Dr. Ablahi Ganda J.E. as National Chairman of the party. NEC took the decision at its 12th meeting still in Abuja. It also appointed Senator Bashiru Ajibole as National Secretary. North of office, I, Dr. Ablahi Umar Ganda J.E., do solemnly swear and affirm that I will be faithful and bear the allegiance to the All Progressive Congress APC, that as the National Chairman of the All Progressive Congress APC, I will discharge my duties to the best of my ability. Facefully and in accordance with the constitution of the All Progressive Congress and the law, and always in the interest of the sovereignty, integrity, solidarity, well-being and prosperity of the party as contained in its constitution and manifesto, that I will not allow my personal interest to influence my official conduct or any official decisions that I will do the best of my ability, Prince. I, Senator Surajuddin Ajibole Bashiru, PhD, do solemnly affirm that I will be faithful and bear true allegiance to the All Progressive Congress APC and that I will preserve, protect and defend the constitution of the All Progressive Congress APC and the Federal Republic of Nigeria. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


From the Computer Museum in Mountain View, California, it's theCUBE, covering ACG Silicon Valley Grow Awards, brought to you by ACG Silicon Valley. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the ACG SV 14th Annual Grow Awards, about 300 people. It's their annual event to give out a couple of innovation awards. We're excited to be here. It's our third year, and we're happy to have a board member on. He's Andy Armstrong, a partner at Singer League Walk. Andy, great to see you. Hey, I'm excited to be here. So you said you've been on the board for about a year. Yes. Why are you on the board? What is this organization about? Why are you excited to be part of it? So I actually, this last year has been incredible for me with my affiliation with the group. So the board, it's really, I was able to join so I could co-sponsor and lead the Young Accelerator Program with Jim Chapman. And what we do is we take young startup companies and work with them. We create a panel of experts that is about, you know, five or six experts. And we sit down for two hour increments with these companies and really try to work out some of the issues that they might be beating their head against the wall. And we kind of help them try to jump over those hurdles that they're facing and take them to the next level. And how old are they generally when they're in this process in terms of number of employees or age or how do they, how do you put a bucket in it? Pretty young startup companies. I would say some are pre-revenue. Others are, you know, they might have five to 10 employees and they're, you know, maybe have anywhere from pre-revenue to a million dollars worth of revenue just kind of moving up the ladder, so to speak. Right, and they don't have to be a client of your guys to participate. Absolutely not, no. They just really, we're looking for kind of open-minded executives that are really wanting to take advantage of and tap into some of the, you know, the phenomenal executives that are part of ACG as well as here in Silicon Valley. Right, and then you said your firm is also very involved in Southern California. So there's a number of chapters of ACG. Absolutely, there's chapters of ACG throughout the country. Like I said, the president of ACG LA, for example, is one of my partners down in my firm in LA. We also, my marketing director runs one of the largest ACG conferences in the country which is down in LA every September. So, yeah, we're heavily involved as a firm in ACG. So we're pretty tech-focused up here. Obviously, we're in the Computer History Museum, but I'm curious, in Southern California, what are some of the industries that you guys really help and leverage? You know, we work a lot with, down there, a lot of public companies. We do a lot of public company audits. We also work with what we call our family on an entrepreneurially-owned company. So, we like to say that's the lifeblood of Singer-Liwak in terms of working with companies, again, big or small, mom and pop, but that's really what the focus is down there. Right. So, biggest surprise in working with some of these young entrepreneurs, it's always fun to work with people that are getting started. They're so enthusiastic. What are some of the kind of surprises that you work through some of those sessions with them? You know, maybe not surprises, but just the realization of they're young.  Right. And I think it's fun to get into that environment in the accelerator because it's what I like to call a safe environment for them. It's not like they're coming into pitch for an investment. They're coming in and kind of airing some dirty laundry and just kind of opening up and being honest with us and that's where they get the most out of that program. Right. And I would imagine they get quite a bit also from their peers in that environment as well. Maybe not. Yeah, well, in terms of the peers, on our panel, absolutely. I don't know in terms of peers, in terms of how much they're networking with their competitors, so to speak. Right, right. Yeah, it's phenomenal, the experience that each one of these companies has. Co-opetition's a big part of what drives us up here for sure, so you can be a partner in one area. You can be a competitor in the other and there's so many places and so much opportunity in a lot of the growth areas as well, so there's a lot of room to run. Yeah, absolutely. It's great. Well, Andy, I give you the last word. What are you looking forward to tonight? You know, it's fun just to get into a room full of top executives, very successful people here in the Bay Area and just to get to kind of rub shoulders and meet and talk to them and just appreciate the success that ACG has had in working with these kind of people. Yeah, they bring in good ones for sure. Pat Gelsinger will be keynoting tonight. He's been on theCUBE many times, really doing great things at VMware, so it should be a good evening. It's really exciting. All right, well, he's Andy Armstrong. I'm Jeff Frick. You're watching theCUBE from the 14th annual ACGSV Awards. Thanks for watching.