Okay, hopefully everyone's back. I want to get started on time because this is the longest and most complicated lecture. I'm sure there'll be many questions. I may skip the questions so that we can all get lunch on time so you're not all starving. But there'll be plenty of time for questions like at the start of the lunch or at the end of the lunch break. So we will get to everyone's questions about everything, but this is a long lecture. Okay, so now we're going to do biological analysis, which is all the awesome stuff. We're going to answer our biological question. So for here we need to keep in mind what is your question and what data do you need in order to answer it? So here's some example questions, right? You could ask something like, what are the progenitor states that exist between mad and pox stem cells that need to differentiate blood cell type? Right, so you need to get some bone marrow stuff. You only have the normal condition, so you only need a bunch of samples for that. Whereas if you have something like the effect of Huntington gene variants on urinal identity and function, right, you're going to have a case control. So you've got to have controls. You've got to have cases. And you want some sort of sampling method where you're going to capture neurons. So you don't want to do single cell RNA seek. Single cell RNA seek won't capture neurons because they're all tangled up. You're going to want to do single nucleus RNA seek. Right, and do things like how many cell types are there in some particular region of a tissue. You can look at communication between different cell types. Or you could look at like what transcription factor regulatory networks are there in our single cell RNA seek. We're going to cover communication at cell cell communication or transcription transcription factor regulatory networks, mainly because there's no good way of knowing if any of those tools work. There's lots of tools to do it. They will give you an answer. I cannot tell you what that answer is true or not. So we're not going to cut this, but you feel free to explore this. Okay, and go back to our classroom workflow. Right. So we did that yesterday. We just did feature selection and dimensionally affection. So now we're going to do how we define k-nearest neighbor networks and do clustering. We're also not going to cover trajectory inference. There's lots of tools to do that. There's over 42 of them at the last benchmarking paper. But out of those 42, PCA range number three or number five, depending on what metric you used. And it was the most consistent performing across different types of data. So if you had a circle trajectory, if you had a fork trajectory, if you had a straight line trajectory, PCA would work pretty well. Whereas each of these methods would could outperform it on one of those cases. So generally, I recommend just use PCA for trajectory analysis, but there are other options available. But we're just not going to cover those because you have limited time and they're not that great. And we're going to do different. So for clustering, we use graph clustering, which is not actually invented for single cell RNA seek. It was invented for social networks. So this was invented to analyze Facebook data basically. But we saw when we single cell RNA seek came around, it's like, oh, we've got tons of stuff, tons of data now. We need a clustering method that's good for big data sets. What's available? Oh, that thing. It works great for huge data sets. Let's use that. So for that, we need a graph. So a graph has made of vertexes, which are things. So in our case, they're cells. And edges are relationships between them doesn't really have an interpretation and single cell RNA seek is basically just a measure of how similar those cells are. So if they're fairly similar, we have an edge. If they're not fairly similar, we don't have an edge. So how do we create turn our single cell RNA seek into a graph. So we use K nearest neighbor networks. So I've been already talking about this a little bit. So here we just take each cell and we connect it to its K nearest neighbors. In case if K was three, each of these dots is our cell. Right. So we take the cell connected to its three nearest neighbors. Take another cell connected to his three nearest neighbors and keep doing that for all of the cells and we get something that looks like this. So hopefully you can immediately see we've got some problems here. Right. So this guy in yellow and this guy in yellow they're outliers. They shouldn't be connected to anything, any of the other cells, but they are because by definition, K is three, they will be connected to three other cells. You can also see the see the small cluster of only three cells, because it has they each have to have three neighbors, one of those neighbors is going to be outside of that cluster. So it's going to get stuck to a second cluster using K K nearest neighbors. The solution to this was that was to not use K nearest neighbors, which is the related method called shared nearest neighbors. So here we take that K nearest neighbor network. And for each cell, we ask, how many of your nearest neighbors for you and your neighbor, how many of your neighbors are shared. For this cell, right for in this neighbor. Both of these two are neighbors of both the cell and the cell. So it gets a weight of two. Same for this one, right this one that one, it has two shared neighbors. So it gets a way of two. Oh, the cell, the yellow cell there and the cell. They have two nearest neighbors, so these two other red cells. So this line here will have a weight of two. Yes, over here. So that's the yellow guy down here and one of these red ones, we have a weight of two. It's got two shared neighbors. So this yellow guy at the far end, he goes to this neighbor. These are neighbors. None of those are shared yellow. So that edge disappears. And so they all appear in the sky. No shared neighbors. So that edge will get removed. Yeah. So each, each cell here was connected to its three nearest neighbors, but other cells could connect to it as well. Just how you get more. So each cell here, so this cell, for instance, it will be connected to its three neighbors. So that one, that one, and that one. But for this cell, it's also it's nearest neighbor is that one. So this one connects to this one, but this one doesn't connect that one. In terms of its nearest neighbors, but because we don't care about the direction, they end up connected. Yeah, so we define it, we define the three nearest neighbors for each cell, but they don't have to be mutual. So a cell can be this cell is the nearest neighbor of this cell. In terms of this cell's perspective, but for this cell, that cell is not it's three nearest neighbors from its perspective. So you can end up more. So I've got this graph, we need to define clusters in this graph. So the method we use is called vain or light in which is a variation of the vain. And it's based on the modularity score. And this is the modularity score q. All right, it's a big, big scary equation. I'll try and simplify it. So first sigma here, first you want CI and CJ, that is one, if these two cells are in the same cluster, or it's zero if they are not in the same cost. Right. So we're going to sum up the score across every pair of cells that exist in the same cluster. So that it could exist in other cost clusters get a score of zero. We then calculate. So a here is the number of edges, or the weight of edges between a pair of cells in the same cluster. And we're going to subtract from that the expected number of edges between those two cells, if we had completely randomized our network. So to M here, that's so M is the number of edges. So to M is the number of ends to that edge. So the number of cells that would be connected from all of our edges. So this is the number of edges involving one of those these cells. So Ki and KJ. So Ki divided by two M is basically the preferred percentage of all the edges that cell I is involved in KJ over two M is the percentage of edges that cell J is involved in. So this is now the expected number of edges between cell I and cell J. Hopefully guys on this side, we're following that as well, covering that term. And we just have a normalization factor start. So this is defining clusters as a continuous set of points that have a high density of edges or higher density of edges within that group, then we expect to be there by chance. So we had a network like this, and these were our clusters in green and purple, we get a modularity score of 0.4. So this is bigger than zero. So that's good. And these are good clusters. Or as if we clustered ourselves like this, this would get a modularity score of negative 0.1. So this is less than expected by chance. So these are bad questions. Okay, so how does Louvain? So Louvain is just going to optimize a set of clusters to get the highest modularity score possible. And it does this in a fast way so that it can apply to large networks. So basically, it's going to start off being greedy, and it's just going to take steps iteratively, making the best choice it can at each step. It's going to be fairly fast, because I'll get to the right answer pretty fast, but it's also going to be so fast, so it's going to randomly pick the order of steps it's going to take. All right, so how does it, what does it actually do? So you randomly choose a node in your graph, so a cell, and you start with every cell in a different cluster. You then ask for that cell that you randomly picked. If I merged you with the cluster that one of your neighbors is in, does my modularity score go up? And the answer is yes, we merge that cell with that cluster. And we just keep doing that over and over and over again until our clusters don't change. So here I've picked this one random cell. I asked which cell of its neighbors would increase my modularity score, and it didn't change. No, my animations are working. So that would turn green. That one would turn purple. That one would turn blue. That one would turn purple. Oh, that one would turn green. That one would turn purple. This one would then turn green. And then that one would turn green. And then we'd be done. And we'd have our nice clusters again. However, you'll notice that when I first presented this equation, there were no parameters to it. But if you've used SUREP before, you'll know it has a parameter called the resolution. So I slightly lit wide. And how Louvain actually works is it has this other parameter in there called gamma. You can see there in red. And that is what the resolution parameter is. So when you run SUREP and you set your resolution parameter, that is what you are doing. So if you make that really high, or above one, you need a lot, you need more edges than you expect by chance to get a good cluster. If you set it lower, you're allowing clusters with fewer edges than you expect by chance to still be a good cluster. Okay, so high means many small clusters that are super dense with edges. Low is large dispersed clusters, but less edges, less dense. So you can also have the parameter k for how you designed your network. Right, so you can, if you have high k, you have lots of neighbors and you'll get large clusters. If you have a low k, you'll have few at few neighbors and you'll get small clusters. It's not intuitive, you can change it in SUREP. It's just extra steps to do that. Okay, so I have a question. We've been talking a few times in the course about rare cell types. So rare cell types are hard to find.  Where do they get lost? So basically you can rely on their default, which is 20 k, and then you can play around with it. There's no real, no one's bothered to try and figure out what the right k is. I think the SUREP people did some sort of benchmarking when they designed it, but it was like a footnote in a supplementary file figure 1,000 or something. So, but mostly it's like 20 seems to work. So people use 20 based on data sets we were analyzing five to seven years ago. So if you want to change it, if you're using a big data set that we weren't using back then and increase it. The question is where you might use the rare cell types. I was thinking in the feature selection stage. Yes. And then kind of logic was you shouldn't like how these variable genes and, you know, rare cell types, like they have like sort of like marketing or whatever. They would have very small variants that they're only trying to use themselves. Yeah. Yeah. So how variable genes feature selection. I mean, this small rare cell types there, they would have high variants, but in terms of total variants in the data set, it would be small because they're only expressed in a few. Yep. Yep. Yeah, you're K if you have a high K, those rare cell types are going to get merged into another cluster. So those are the two main ones. You can also lose them at the dementia reduction stage. If you pick too few dimensions, because they will tend to be at the higher number of dimensions.  So if you pick a really low resolution, you will lose your rare cell types. So when you've run now, let's suppose we've run our clustering algorithm, we've got a set of clusters. Do we believe our clusters are real cell types. So cluster, you can run the vain clustering on absolutely anything.  And it's almost impossible to get it to give you only one cluster and say that there's nothing, nothing there. Theoretically, it's possible. So they get to sale, say in their paper. We can see our algorithm can tell you that there are no clusters in your data set. If you just one cluster in practice, it is almost impossible to get it to do that.  So do we believe those clusters are correct. So here's some data. How many clusters do you think are in this data. I'll give you a few moments to think about that. So hopefully everyone's back and settled, and that was not, not too disruptive, because we're going to try and keep going and get this done before lunch. Okay. So how many clusters do we see here. I had heard some fives. Anyone disagree three. Other numbers out there. Five. Well, four, three fours. Okay. So these are my guesses for what what you would come up with. So how many how many thought of the first one here on the left. Yeah, few. How many think the middle one. No one. How many think the one on the right. Most people. The middle one was actually correct. So hopefully you can see from this, figuring out how many clusters are in your data set is really hard. We even as people, we have our time of it explaining how to do this to a computer is even more challenging. So here's some of the ways we can try and figure out if we've got the right number of clusters and the right clusters. And so you can look at our our clusters robust to our costume parameters. So if we change the resolution a little bit, do we get the same clusters. If we change your K a little bit, do we get the same clusters. If yes, they're probably good cost good clusters and they represent some sort of biology. You can look at the marker genes. Do we have a whole whole bunch of significant marker genes for our different clusters. If yes, I probably represent biology. No, they probably don't. We can look at known marker genes. Okay, I have a I know this is a marker of neutrophils. Do I have a cluster that's exclusively expressing that marker. Yes or no. We can use quality statistics statistics like the silhouette index that basically measure how dense is my cluster compared to other clusters. Are they well defined and distinct from each other. And good scores give you good clusters, low scores don't. You can look at consistency across experimental records. So if you have five samples, and you can cluster each one separately and do you get the same clusters. Or you can look at a reference data set do my clusters that I found match clusters in a reference data set for the same tissue. And you can use tools I guess see map for that. If you have some sort of spatial data available either from publicly available data or as part of your experiment because you have lots of money to spatial transcriptomics and single cell. You can look to see whether there's spatial structure for your different clusters. And if there is that's a good validation, or you can do some sort of experimental validation and spend hours and hours in the lab, and tons of money and try and figure out if your clusters are functionally different from each other. Note that they look good on my you map is not a good metric on this list. The reason for this is that your visualization tool will that's packaged with your clustering method will be based on the exact same assumptions as your clustering method. So they will always agree with each other. So your cluster should always look good in your you map, regardless of those clusters are good or not. So I'll cover a couple of statistics here you can use. So one is the silhouette index. So here we've got a blue cluster and a yellow cluster. So IJ is the cluster that cell I is a member of the IJ is the distance from point I to point J. Okay, so silhouette select index takes each cell and says what is the average distance from you to other cells in your cluster. And what is the distance between you and all the cells of the next closest cluster to yours. So what's the difference between those divided by the maximum. So we get a score between negative one and one, or one means our clusters are completely distinct from each other. And negative one means they're completely mixed together. So high scores are good. And we go score for each cell, which we can aggregate at different levels.  And here's the score for each cell in those clusters. And see all the scores are pretty high. So these are well-defined clusters. Here I've changed the clustering resolution and got an extra cluster. And now you can see the silhouette scores for these two clusters. And sort of orange and green down here. And see that the scores have gone down. And we're starting to see some negative scores. Yeah, there is no threshold. They're basically compared between different clusters. So one cluster might have a higher score than another clustering other than being. Yeah, which whichever question gives you the highest score. And you can also use it in other ways. So here we could see these two clusters have lower scores. So maybe when we come to annotation, we want to give them the same cell type and merge them. Or we can change our clustering resolution to merge them. And it's also important to consider that there might be many good resolutions to your clustering. So if we think about some sort of tissue, you might be able to say, I've got a bunch of T cells, I've got some endothelial cells, I've got some stem cells. Right. And that's a good clustering resolution. Those are well-defined cell types. But then you might also say, if I increase the resolution now I've got T helper cells, T regulatory cells, and whatever. And bunched in different types of endothelial cells. And that might also be a good clustering resolution because those are well-defined cell types as well. So there can be multiple good resolutions for any particular data set. So explore. And hand in hand with your clustering goes annotation.  So you can sort of go back and forth. I've got a bunch of clusters. Can I annotate those clusters as different cell types? If I can't figure out the cell type of a cluster, should I change my clustering resolution and have that merged with a different cluster? Because I think they're the same cell type or not. Right. So this is, oh, yeah, I should mention. This was paper we did with Delaware and one of your TAs. And so we get credit for these, this as well. So we recommend this sort of, there's lots of ways to do annotation. This is what we came up as sort of a good efficient pipeline. They start with your unclustered data. You run it through, you take a reference data set, and you do some sort of automatic annotation and annotate what cell types you can see based on that reference. Right. So to do on the side, unannotated, a reference, you get some sort of automatic annotation. Some of the clusters don't get annotated well. You don't know what they are. Maybe they get some sort of mixed annotation of like half T cell, half endothelial cell or something with really low scores. Whereas some of your others get really good annotations. And quite often you might have lots of clusters getting the same annotation. Right. You might get four different clusters that all get annotated as T cells. Yeah. And you do the single cell annotation. Yes. Yes. So you can use single cell to annotate single nuclei, depending on which automatic annotation tool you use. So some of them will work well across data sets, particularly ones based on marker genes, whereas ones based on like correlations between cells won't work as well between single cell and single nucleus. So you can also use multiple different automatic annotation tools to make sure they agree with each other. And you should also make sure whatever reference data set you're using is appropriate for your data set. So if you're looking at liver, don't use a brain reference data set. That's not going to give you good results. And it's particularly important to keep in mind if you use one of the tools that has a built in database of cell types. So like single R has a whole bunch of built in cell types that can annotate. They might not have the cell types for your tissue. So if you want to deliver a tissue, it could annotate hepatocytes and T cells, but by all that cells did not exist in the reference, so it couldn't annotate them. So make sure your cells exist in the right house. So the other one is based on marker genes. So you could use your reference annotation, annotation, find the marker genes for the clusters in your reference, and then look at the expression of those clusters in your new data set. So if you are one of them, it's really important to use the reference data on your data set. It will depend on the reference data set. So some of them are really well annotated to deep cell types. My guess, though, with that if you're looking at one very particular cell type is that you'll the best to be able to get out of automatic annotation is something pretty generic like glutamineurgic neuron. So that's okay cell type but it's not going to be exactly your specific cell type. So you'll have to do a subsequent step of manual annotation. Yep. It depends what resolution you're looking at in the cell type, obviously. Certainly for things like immune cells versus endothelial cells, you can absolutely use mouse data. If you're looking at like specific types of T cells, probably not. So yeah, it depends on the resolution you're looking at. You can combine more than one. Usually it's better to if you want to look at multiple ones to run it on each one separately rather than combine the references and run on the merged, run on multiple different references and see if they agree. Any other questions? Yeah. Yeah, that is another option. So you can take your, your data set. And the reference data set and integrate them together and transfer the labels that way. So just direct directly comparing them. I don't know if it's any better. It just makes a prettier plot for your paper, because you can see, oh, they're on top of each other, other than here's a heat map with the correlation between them. I think that there is that the annotation tools, as you'll see in the last section today, aren't necessarily as great as we want them to be. So integrating them together can take trial and error to find one that works. So that has the same problem where you have to get a good integration, which can take three or four tries to get a good integration with different methods. So once you've got your automatically annotate your cells, you're going to then do manual annotation, both to check that your automatic annotation worked because sometimes it does silly things depending on the method. The methods will always assign a cell type, even if the cells don't look like anything in the reference, it will give it its best guess and that best guess is off to run. They don't want to double check that these are right. Usually using marker genes from the literature or from one of the various databases now. Yeah, you can also take the marker, you can take each of these clusters by marker genes for them and do something like pathway analysis to confirm their identity as well. And this is typically required for annotating subtypes. So if you're looking at different types of T cells, very, very few automatic annotation tools will be able to tell those apart. You'll almost certainly have to do that manually using marker genes because there's very few genes set to find them.  If you want to say in your paper you discovered a new cell type to prove that it's real now. Didn't used to have to prove it was real, but fortunately, there are viewers have smart and deaf and said actually no, can you prove that's real not just some computational thing you found. Right. So when you're using market chains is also good. Important to take into consideration what someone says in a paper is a marker gene for a cell type doesn't necessarily mean it's a good marker gene to use. So if you look in the databases or in papers, you'll find that it game and fcgr 3b are both always listed as marker genes for neutrophils. And you can see here, one of them is a great marker for neutrophils. Other one, not so much. Right. So you want to make sure you have the right you have to consider sensitivity. So some marker genes, you might not be able to capture with your single cell RNA seek, especially something like CD4 is notoriously hard to capture with single cell RNA seek, even though in every paper it's a huge marker for sensitivity cell types. So, sure, we'd love to be able to use CD4 might not actually work. Important to consider how high, how high its expression is, lots of cell type markers are like transcription factors that define a cell lineage. There tend to be pretty low expressed, it's going to be hard to find them. So if you need to consider specificity, see it can on the right. Is it actually expressed only in that cell type, or is it one of many cell types that express that gene. And look at the log full to full log to full change if you can. Is it really differently expressed or is it only slightly more highly expressed in one cell cell type versus the other. You also want to make sure you're using enough markers. So single cell RNA seek is noisy. Probably not good to rely on a single gene to annotate your cell types. Try and find multiple markers that will agree on a particular annotation for yourself. And finally, what if you have multiple samples. So it's essentially two different approaches here. One is you merge your samples first, you do all your joint analysis of clustering and everything together. You get a set of clusters and you annotate them once at the end with your merge data. The other option is you can take each sample, do the full pipeline get your clusters and take those clusters and then compare them across your differences. You can do an analysis separately and compare or joint and one analysis. The obvious advantage of integrating them and then doing joint analysis is you only have to do the pipeline once, you only have to annotate once. You'll have far fewer clusters to annotate. So it saves you a ton of work. The other side is you have to use some kind of integration, usually, and as mentioned integration is a bit tricky, because the methods don't necessarily work that well, depending on the particular data you're working with. So you might have actually many more steps to get a good integration. And some integration tools can actually cause you to lose certain cell types and certain clusters, if you analyze them jointly. So depending on the size of my data set, if I only have a small number of samples, I will do analyze each of them separately first, or at least each biological condition separately first, and then joint merge together my different biological conditions and analyze them a second time. Any other questions about annotation? Yeah. So merging. So sir, merging just puts your data together into a single object. It doesn't actually make them similar in any way. So you always have to merge first.  Sometimes this happens, sometimes not. If they don't line up, then you'll then you can do a second step as integrating them. And that will do some sort of computational process to make your two samples more similar to each other, either at the gene expression level, or in the lower dimensional space. That's a great question. Do you want them to be the same? No, so maybe you don't want to do integration. Merging won't make them the same. It'll just put them on the same UMAP. So I'll still be able to see them as separate things on the same UMAP. No, integration won't increase the number of populations you find. All that integration does is it takes two samples that look different from each other and make them look more similar to each other. So you don't have all the cells. So just merging them, you'll have all of the cells from all of your samples in one object. Yeah, so I have one for this one. I don't know what it is. Maybe maybe it's also what I like to do. This is because I want to learn harmony on that one because maybe it is a different culture. And so I'm not sure I can do the same. So if you analyze them separately, all you're really doing is adding metadata to your single cell experiment object or your serrat object with the results of your analysis. You can do multiple rounds of analysis. You can analyze your data 20 different ways, if you want, and then look at how the different analyses get the same answer or different answers. So if I am comparing two different samples, which are biologically different, and I want to discover the biologically, I want to find out which process it is for your company and then hear this. Then would you not recommend this type of integration before when you're talking about it? It depends. It depends how you integrate the data set or how you don't integrate the data set. Some integration, it just depends. Like sometimes you can integrate it and you can still see different cell types that are unique to different biological conditions. Sometimes you can't. So we're going to cover different types of integration in the next section. Lastly, we've got these beautiful clusters. Now we want to use differential expression to answer our biological question because everything in biology comes back to differentially expressed genes between A and B. So the first type of differential expression we do is finding marker genes for our clusters. So here we're asking what genes are unique to a particular cluster or what genes are different between cluster one and cluster two. Here we need to think about to understand why tools work the way they do. You have to think about what we care about when we're asking this question. So do we care about the sensitivity of our test? Do we care about finding every single gene that's differentially expressed between our different clusters? Usually that's no because usually we're just trying to use these for annotating our cluster. So we only care about like the top 50 genes. So we don't actually care if we find every single differential expressed gene. What we do care about is the specificity. We want our genes to be very different in our different clusters and only genes that are differentially expressed. We often also don't really care about the significance of this difference, right? We're just taking the top 50 differential, top 50 differential expressed genes that are going to be super significant basically all the time. So we don't super care about our significance. We do care about the effect size. So we want them again to be very differentable change. And often we care about how long it takes to calculate this because we're going to, we may be doing this for many, many clusters in many samples and over many different versions of our clustering to figure out what clustering we like. So we want something to be fairly quick. This means the test that Surat uses and its default parameters are designed for this. If you use the default parameters for finding markers in Surat, do not talk about the P values. Those P values are meaningless. If you use the default parameters, if you want the P value to matter, you will have to change the parameters to make it work to make those P values meaningful. But if all you care about is the top 10 marker genes, it works great and it works best.  So if you imagine we have this distribution of gene expression across all of ourselves. We cluster ourselves based on the gene expression. So if we clustered the genes based on this gene expression, right, we would cut this distribution in half. All the cells on the right would be in one cluster, all the cells on the left would be in the other cluster. And that this gene would be significantly differentially expressed, even though it's a single normal distribution across all of our data. So because by clustering, we've created a differential express gene between our clusters. So you could do something like that. This means that if you just do a standard differential expression test on clusters, your P values will be skewed to be more significant than they really are. And here's sort of what you can see. In the T test for doing differential expression between clusters, you have almost everything is super significant sweet the P value on the X on the X axis here, whereas the uniform expected distribution is way to the left. So they're all going to be biased. If you want to do it properly, you can see this paper and use their test that corrects for this. It's going to take way longer to calculate. So we know it uses it. But if, if this is important to what you what you want to say in your paper, use it. But for just annotating clusters, you don't really need. Yeah, and surat actually pre filters genes before it does marker genes based on log to bold change by default, which will make this even more skewed and more biased. So ignore the P values and just use the rankings from surat. So this is a question of what kind of test do we use. So there's a whole bunch of different tests that people developed. Now pretty much everyone just uses the Wilcoxon rank test which is circled there in red, because all out of all of these tests they most of them worked reasonably well and evenly. Except you can see at the left there's few that didn't work very well at all. And those are ones that surat people developed deliberately for finding marker genes and single cell or any seek data. And this doesn't have the date on it about five years ago, this paper was published saying hey actually that's the worst possible test, even the stupidest most simple test does better than that. And the surat people like oh okay, well, we'll go back to just using the Wilcox test. So now surat uses the Wilcox test. So the Wilcox test is independent of the distribution of data values. So it doesn't assume a negative binomial distribution it doesn't assume a normal distribution it works on anything because it's just it ranks the data and says our rank are my average ranks of group one higher than my average ranks of group two. This is great because it can be applied to batch corrected, scaled, normalized, regressed data anything at all. Any kind of data anywhere in the world you can use a Wilcox test on. But it doesn't account for confounding factors or anything else you have to account for that yourself. It just says group A is bigger than group B for anything, any data. If you want to do case control differential expression. It's much fancier and more complicated. So here we've got a question like what is the effect of a gene mutant on microglia. So we'll have some sort of experimental design where we have here I've just got a bunch of mice got three mice per biological condition. Well tight single knockout and double knockout. So this is sort of the maximally complex experiment. Most people in Canada will be able to afford to do. So this is this is when we actually did with collaborators. So this was $60,000 for this experiment. So I doubt you'll do anything more complex in this. So we get our data. So for each of these mice we collect a whole bunch of cells, several thousand cells, and then we want to do differential expression. So we're considering comparing these different groups. How many biological replicates would be would there be for my wild type versus my APP knockout in this experiment. Let me give you a moment to think about that. And you want to hazard a guess how many biological replicates we've done. We've got. Yes. So the number of cells we sequence doesn't matter at all. What matters is how many mice we did. So we should have three per group for our comparison. We took all of our single cell, or in this case single nucleus RNA seek data, and did differential expression treating each cell as a biological replicate. That would be wrong. We will get everything is significant. When in truth probably very few things will be able to detect because our power is pretty low because we have three biological reference. And the easiest way to do this, there's fancy ways to do it that we're not going to cover the easiest way to do it is to do what's called the pseudo box, where we take our expression in each cell type, and we break aggregated per biological So we take the average. So for our question about microglia will take all the microglia from mouse one and do average expression across all of those microglia. So we have one replicate now for mouse one, we do the same thing for mouse two and mass three. We get the average expression in a particular cell type in a particular biological reference. So let's do this like bulk RNA seek, but three replicates at bulk RNA seek, we can choose any model or tool that we use for bulk RNA seek and use it for a single cell RNA seek data for our case control. And these are all based on the general linear model on GLM. So I'm going to try to explain this, but it's scary looking at general linear model so we're going to stop thinking about this as linear regression. So hopefully everyone has done linear regression. Right, so we had length of ending in jeans versus cognitive decline. Right into linear regression, you get a straight line, why equals MX plus be, and we can fit our slope in our interest. So for these purposes we're going to change our notation a little bit, and we're going to call this be zero for intercept and be one for our slope. So exact same model just linear regression. And we can call be our bees here are called coefficients. And you see the results of your test. That's what a coefficient is. So here X and Y are continuous. So what if X is actually discrete. So pathogenic or not for mutant or wild. So here we've got our discrete groups for X. And but to use linear regression X has to be numbers. So here I've got no or yes for pathogenic. I can just change no to zero. Yes to one. Now our X is numeric, and I can use linear regression. I can just do linear regression. I've got a slope that slope. Right. Now expressly again, that slope is just the difference between not pathogenic and pathogenic. So I can consider this as happily one mutant versus wild type for zero and one. Okay, I have two different mutants right at APP and apoE wild type or knock it so I can imagine this as three dimensions now at APP on one axis, apoE on a different axis and then my expression on the third axis. Now my Y equals MX plus B just gets an extra term for B2 for times Z for my second predictor. Right. So B1 is still APP while mutant versus wild type. So that's that slope in both of my groups. And B2 now is the slope with respect to apoE knockout. Right. So B2 is now the slope going in that direction. And my B0 is still my expression in wild type for both mutants. So just one more step. Now you've done a general linear model. It's not awesome. However, because we're looking at different mutants we might have a third question about what if the effect in the APP and apoE double knockout is different than just adding those two together. So B2 becomes what's known as an interaction term. We write it as B3 is X times Z. So X times Z if X is zero, then X times Z is zero, if Z is zero, X times Z is zero, if X and Z are both one, then that's one. So B3 is only the difference here between the double knockout and the single knockout. And that's the interaction. If you're all prepared, you can write a general linear model for your complex experimental design using your mice as replicates, not your genes or your cells as replicates, and using bulk RNA-seq tools. And these will run on raw counts because we're using a model that will account for the negative binomial distribution because that's another feature of general linear models. We can do other distributions. So this will always be on the raw counts. So that's our model. And we're just going to test whether B1 and B2 or B3 are significantly different from zero for our differential expression test. And it's basically lunchtime, so I'm going to leave this description on the board. And think about how you would design this experiment, the analysis for this experiment before or after lunch, because I'm sure everyone's thinking, let's do a break for lunch now.


and certainly all the distinguished guests here present. I must say that it's a very high honor and a great privilege for me to welcome all of you to the inaugural event of the Ronald Reagan Presidential Foundation. Our purpose this evening, as most of you can imagine, the guest is to begin a process that will lead to the creation of a presidential library and a center for public affairs that will memorialize in my very objective opinion, one of the greatest presidents in American history. I figured you needed that, Mr. President, after last week. Ha, ha, ha, ha, ha, ha, ha. Indeed, it is a fitting tribute to President Reagan that a program be undertaken to create a center for the study of public affairs and a library for the preservation of the records of his administration. The library will be the ninth in the presidential library system. It will preserve all the documents of the president, provide public access to presidential records, and enhance research opportunities for scholars and students through the generosity of private philanthropy. The Ronald Reagan Center for Public Affairs will encourage and facilitate the advanced study of public affairs in the United States. To support this program, the foundation seeks to build and operate the finest research center in all the world. President of the United States. Thank you, thank you very much, please. I have been quite surprised in the last few moments, and every bit of show business instinct in me tells me how to keep my mouth shut and sit down. But Nancy and I want to thank all of you for being here tonight and tell you how grateful we are for your kind efforts on behalf of the foundation. Paul Axholt mentioned the other day how fitting it was that so many old friends there from the beginning should be associated with a project that someday will mark the formal end to eight years of government. Though we pray not an end to the idea and principles for which we stood. Now, like the good chairman that he is, I think Paul also said something about creating a proper mood for fundraising. Entrepreneurial leverage, I think he called it.  You're talking bureaucrates. That's a language peculiar to Washington and there are no interpreters of it. Let me give you an example. Some of you've heard this before, but I'm gonna tell it anyway. One day early in my first term, a native bureaucrat stood in front of my desk and said, action-oriented orchestration of meaningful indigenous decision-making dialogue focusing on multilinked problem complexes can maximize the vital thrust toward non-alienated and viable urban infrastructure. Now that's pure basic bureaucrates. So I took a chance and said, well, why don't we try busing and hope he'd go away? But I look out here tonight and I see so many old friends. I'm gravely tempted to reminisce. I could tell a few stories that I think would interest future historians, but there isn't time for that this evening, except to say the moments that we've spent together are locked away forever in our memory and our hearts. Nancy and I want you to know that we looked forward to this dinner for a special reason. We thought this a particularly good time to extend our thanks, first because of the season and we wanted to share the joy of it with you. And second, through this foundation, you're helping to guide future generations of Americans to a deeper appreciation of our nation's past. Now in that film you just saw, I mentioned how living in the White House can overwhelm you with a sense of the past, so many events, so many presidents. I know all of you share this sentiment, this attachment to history, so I think we're here for a good cause and a noble work. But I can assure you in one century or 10, scholars and students looking through these records will find an anecdote of heart or humor or a detail of warmth and width that will not so much tell the story of one man's presidency as the story of an entire people, a good and generous people, proud of their heritage of freedom, determined that America shall be, as it was said on that tiny ship, the Arabella off the Massachusetts coast some three centuries ago, a light unto the nations, a shining city on a hill. And you know what we've done as friends and fellow citizens, to keep faith with those who came before us, those who won for us the blessings of liberty is at the heart of our purpose here this evening. Have we had some success? Well, I believe we have. We haven't won all the battles, just these last few days we lost a skirmish, but the battle goes on. And it's the nature of this battle and the other battles in these last five years that has changed. We're no longer the embattled few trying to stem the tide of ever increasing government growth, fighting to halt the adoption of new programs and the increase of government interference in the people's lives. No, today the debate has switched. It's over how much government should be reduced, which programs should be eliminated and how best to make government less intrusive in people's lives and less costly. You know, in a play some years ago called Benjamin Franklin in Paris, Franklin sits alone in the final act and wonders what he would find if after 200 years, quote, I too should rise up and stand once more on Pennsylvania land and walk and talk and breathe the free air. For I know in my heart it will be free. I know it, I know it even now. What a dream, 200 years. And I wonder, I wonder how I should find them then. Those Americans to whom the name American will not be new. Will they love liberty, being given at outright in the crib for nothing? And will they know that if you are not free, you are, sir, lost without hope? And will they who reap this harvest of ideas be willing to strive to preserve them as we so willingly strove to plant them? That all men are created equal and that they are endowed by their creator with certain inalienable rights. Well, they have in me makes me think I would have loved to have had a speech like that in a theatrical production. But I think old Ben were he to make such a visit might be a little proud of what he would find. And he might even have a word or two of praise for what the American people have achieved. It is those inalienable rights that were threatened not too long ago. And tonight I believe they're safe. Made so by we the people. My every instinct is to turn to you for answers to our problems. And you have always responded. As long as I'm here, I'll turn to you. In one of the Geneva meetings, I spoke of having read the constitutions of a number of countries, including that of the Soviet Union. And in each, the government enumerated the privileges granted to the citizenry. For the most part, they were very much the same as the privileges that we take for granted here. But I pointed out to the general secretary that there was one difference between those other constitutions and ours, a difference that's often overlooked. And yet a difference so great that it tells the entire story. Those other constitutions are grants to the people by government. Our constitution says we the people grant to government the following powers and government shall have no power or privilege that is not granted in that document by we the people. This administration, which you have so generously helped as one guiding thought, I have said it very often to the people surrounding me in the administration. That guiding thought is when we start talking about government as we instead of they, we've been here too long. For that and for helping through this foundation to record a part of the American saga, Nancy, and I extend to you tonight our heartfelt thanks and wish you the best of the season and a joyous new year. And thank you all more than I can say and God bless you all. Hasn't this been a lovely evening? I'd be remiss indeed if prior to the departure of the president and his lady, if I didn't propose a toast to the president of the United States, the president. Relax. Mm-hmm.


Good afternoon, and thank you all for joining us. My name is Thierry Bracke. I'm Deputy Director General Communications at the ECB, and it is my great pleasure to welcome you to the Seventh ECB Forum on Central Banking. Over the next two days, we'll be discussing the role and policies of central banks in a shifting world. I will go into some further details on the program later, but now, without further ado, let me give the floor to President Lagarde for her introductory speech. Thank you very much, Thierry. And let me begin by welcoming all of you to this year's ECB Forum on Central Banking. Much to our regret, we cannot be together in Cintra this time, but I trust that thanks to the great work that has been done behind the scene with this virtual environment, it will be no less conducive to challenging ideas and productive debates. The purpose of this year's conference is to examine the challenges facing central banking in a shifting world. We will be discussing many of the long-term trends monetary policy has to contend with, including shifting patterns of globalization, climate change, and a lower natural interest rate. Actually, the largest shift central banks are facing today may well turn out to be the pandemic itself. As John Galbreath said, the enemy of the conventional wisdom is not ideas, but the march of events. And the events we are seeing today are momentous. The coronavirus has produced a highly unusual recession and is likely to give rise to a similar unsteady recovery. So today, I would like to talk about how the ECB's monetary policy has responded to this unique environment and how we can best contribute to supporting the economy going forward. The deliberate shutdown of the economy triggered by COVID-19 pandemic has produced a highly unusual recession. Most importantly, it has infiltrated and crippled sectors that are normally less sensitive to the economic cycle. In a regular recession, manufacturing and construction are typically hit harder by the side-click or downturn while services are more resilient. But during the lockdown in the spring, we saw the reverse. Compare our experience in the first half of this year with the first six months following the lemon-lemon crash. After lemon, manufacturing contributed 2.8 percentage points to the recession and services contributed 1.7%. But this year, the loss was 9.7% for services and much less 3.2% for manufacturing. This has three important implications. First, research finds that the recovery from a services-led recession tends to be slower than from a durable goods-led recession as services create less pent-up demand than consumer goods. For example, people are unlikely to take twice as many holidays abroad next year to compensate for their lack of foreign travel this year. Second, as services are more labor-intensive, services-led recessions have an outsized effect on jobs. Five million people in the Eur area lost their jobs in the first half of this year. And of those, almost half, half worked in retail and wholesale trade, accommodation and food services and transportation. Despite these activities representing less than one-fifth of output, half job loss, one-fifth output. In the six months after lemon, the worst affected sector, that is the industry, suffered only 900,000 jobs. So large impact, disproportionate impact. And third, these job losses hurt socioeconomic groups unevenly. In the first half of 2020, the labor force contracted by almost 7% for people with low skills who typically have lower income, while it failed by 5.4% for those with medium skills and rose by 3.3% for those with high skills. This is double the loss of low-skilled jobs we saw in the six months after lemon. In addition to their social impact, job losses for people with lower incomes present a particular threat to the economy because around half of those at the bottom of the income scale face liquidity constraints and therefore consume more of their income. The labor intensity of the worst-hit sectors also heightens the risk of hysteresis and scarring in the labor market. Now, while job retention schemes have played a key role in mitigating these risks, they could not eliminate them entirely. And even though many workers quickly returned to regular employment once the restrictions were lifted, a large number of people who lost their jobs in the spring left the labor force and stopped looking for work with 3.2 million workers classified as, I quote, discouraged. This is so far different from the post-lemon period when the drop in employment was matched by a rise in unemployment, not discouragement. And young people have been particularly affected seeing disproportionate layoffs and delayed entry into the labor market. Research finds that this can have a variety of long-lasting effects, including lower earnings, even 10 to 15 years later and worse, future health conditions. So from the outset, this unusual recession has posed exceptionally high risks. And that is why an exceptional policy response has been required. And what has defined this policy response in Europe in particular is the policy mix. Learning the lessons from the last decade, there has been a renewed consensus that the composition of policies matters for overcoming the crisis. More than ever before, macroeconomic, supervisory and regulatory authorities have dovetailed and made each other's efforts more powerful. So what has this meant for monetary policy? I think there are two main ways in which we have adopted the ECB's policy to the pandemic. We are the design of our tools and the other transmission of our monetary policy. First of all, we have responded to the unique features of the recession by designing a set of tools specifically tailored to the nature of the shock, including recalibrating our targeted, longer-term refinancing operations, teltrose, expanding eligible collateral and launching a new 1.35 trillion pandemic emergency purchase program, our PEP. I will call it our PEP. The PEP in particular has the dual function of stabilizing financial markets and contributing to easing the overall monetary policy stance, thereby helping to offset the downward impact of the pandemic on the projected path of inflation. The stabilization function of the PEP is ensured by its flexibility, which is crucial given the unpredictable course of the pandemic and its uneven impact across economies. In this context, the PEP's flexibility allows us to react in a targeted way and counter fragmentation risks. This was key in reversing the tightening of financing conditions that we saw in the early days of the crisis. In parallel, the stance function of the PEP gives us the scope to counter the pandemic-driven shock to the path of inflation, a path that has also been greatly influenced by the specific characteristics of this recession. Not only has inflation fallen into negative territory, but we have already seen services inflation, which is normally the more stable part of the price index, dropped to historic lows. But the PEP, together with the other measures we have taken this year, has provided crucial support to the inflation path and prevented a much larger disinflationary shock. And its impact has been amplified by interactions with other policies. For instance, the combined effect of the ECB's monetary and supervisory measures is estimated to have saved more than one million jobs. At the same time, the nature of the pandemic also affects the transmission of monetary policy. In normal times, normally, an easing of financing conditions boosts demand by encouraging firms to borrow and invest and encouraging households to bring forward future income and consume more. In turbulent times, monetary policy interventions also eliminate excess risk pricing from the markets. But these are not normal times. When interest rates are already low and private demand is constrained by design, as it is the case today, the transmission from financing conditions to private spending might be attenuated. This is especially true when firms and households face very high levels of uncertainty, leading to higher precautionary saving and deferred investment. In these circumstances, it is crucial that monetary policy ensures favorable financing conditions for the whole economy, private and public sectors alike. And indeed, these are the times when fiscal policy has the greatest impact for at least two reasons. Let me take you through those two reasons. First, fiscal policy can respond in a much more targeted way to the parts of the economy affected by health restrictions. Research shows that while monetary policy can increase overall activity in this environment, it cannot support the specific sectors that would be most welfare enhancing. Fiscal policies, on the other hand, can directly respond where help is most needed. We have seen the efficacy of this, of such targeting in the euro area this year. The ECB Consumer Expectations Survey shows that households with lower income have seen a greater reduction in the hours they work. But they have also received a higher share of government support. As a result, while compensation of employees fell by more than 7% in the second quarter of 2020, household disposable income fell by only 3% because government transfers compensated for that loss of income. Second, fiscal policy can break paradox of thrift dynamics in the private sector when uncertainty is present. Public expenditure in the euro area accounts for around 50% of total spending and can therefore act as a coordination device for the other 50%. Our Consumer Survey demonstrates this. It shows that people who consider government support to be more adequately displayed less precautionary behavior. And in this way, by brightening economic prospects for firms and households, fiscal policy can help reinvigorate monetary transmission through the private sector. But regrettably, the economic recovery from the pandemic emergency could well be bumpy. We are seeing a strong resurgence of the virus, and this has introduced a new dynamic. While the latest news on a vaccine looks encouraging, we could still face recurring cycles of accelerating viral spread and tightening restrictions until widespread immunity is achieved. So the recovery may not be linear, but rather unsteady, stop-start and contingent on the pace of vaccine rollout. In the interim, output in the services sector may struggle to fully recover. Indeed, services were already showing a declining trend before the latest round of restrictions. The services PMI fell from 54.7 in July to 46.9 in October. And while manufacturing has so far remained relatively resilient, particularly in some countries, there is a risk of the recovery in manufacturing also slowing once order backlogs are run down, and industrial output becomes better aligned with demand. So in this situation, the key challenge for policymakers will be to bridge the gap until vaccination is well advanced and the recovery can build on its own momentum. The strength of the rebound in the third quarter suggests that the initial policy response was effective and the capacity of the economy to recover is still in place. But it will require very careful policy management to ensure that this remains the case. Above all, we must ensure that this exceptional downturn remains just that exceptional and does not turn into a more conventional recession that feeds on itself. Even if this second wave of the virus proves to be less intense, than the first, it poses no less danger to the economy. In particular, if the public no longer sees the pandemic as a one-off event, we could see more lasting changes in behavior than during the first wave. Households could become more fearful about the future and as a result, increase their precautionary saving. Firms that have survived up to now by increasing borrowing, drawing on their savings could decide that remaining open no longer makes business sense. This could trigger a firm exit multiplier where the closure of businesses faced with health restrictions cuts demand for complementary businesses in turn causing those firms to reduce the output. And if that were to happen, the recession could percolate through the economy to sectors not directly affected by the pandemic and potentially trigger a feedback loop between the real economy and the financial sector. Banks might start tightening credit standards in the belief that corporate credit worthiness is deteriorating, leading to firms becoming less willing or able to borrow funds, credit growth slowing and banks risk perceptions rising further. The ECB's bank lending server is already signaling a possible tightening in the months to come. We are also seeing indications that small and medium sized firms are expecting their access to finance to deteriorate. So a continued powerful and targeted policy response is vital to protect the economy at least until the health emergency passes. Concerns about zombification or impeding creative destruction are misplaced, especially if a vaccine is now in sight. Let's remember that lockdowns are a non economic shock that affects productive and unproductive firms indiscriminately. Policies that protect viable businesses until activity can return to normal will help all our productive capacity, not harm it. The right policy mix is essential. Fiscal policy has to remain at the core of the stabilization effort. The draft budgetary plans suggest that fiscal support next year will be significant and broadly similar to this year. And the next generation EU package should become operational without delay. Actually, yesterday was a good day for Europe. The European Parliament and the Council agreed on the next multi annual financial framework and new own resources. It is good to see that the majority of the pieces of the budgetary puzzle are beginning to fall in place. We need to make sure that also the other pieces, in particular the legislation for the recovery and resilience facility fall astute and the overall agreement is then successfully endorsed by the Council and the Parliament. In parallel, supervisory authorities are working to ensure that banks can continue to support the recovery by readying them for a potential deterioration in asset quality. And structural policies have to be stepped up so that policy support can accompany the wide ranging changes that the pandemic will bring, such as accelerating spread of digitalisation and a renewed focus on climate issues. So what is the role of monetary policy in this response? It is clear that downside risks to the economy have increased. The impact of the pandemic is now likely to continue to weigh on economic activities well into 2021. Moreover, demand weakness and economic slag are weighing on inflation, which is expected to remain in negative territory for longer than previously thought. This is partially due to temporary factors, but the fall in measures of underlying inflation also appears to be connected to the weakening of activity. And development in the exchange rate in the exchange rate may have a negative impact on the path of inflation. So continued policy support is necessary to achieve our inflation aim. But we should also consider how best to provide that support. The unusual nature of the recession and the unsteadiness of the recovery make assessing the inflation path harder than in normal times. Shifts in consumption baskets caused by supply-side restrictions are creating significant noise in the inflation data. And the stop-start nature of the recovery means the short-term path of inflation is surrounded by considerable uncertainty. In these conditions, it is vital that monetary policy underpins inflation dynamics by supporting demand and preventing second round effects where the negative pandemic shock to inflation feeds into wage and price setting and becomes persistent. To that end, the best contribution monetary policy can make is to ensure favorable financing conditions for the whole economy. Two considerations are important here. First, while fiscal policy is active in supporting the economy, monetary policy has to minimize any crowding out effects that might create negative spillovers for households and firms. Otherwise, increasing fiscal interventions could put upward pressure on market interest rates and crowd out private investors with a detrimental effect on private demand. Second, monetary policy has to continue supporting the banking sector to secure policy transmission and prevent adverse feedback loops from emerging. Firms are still dependent on new flows of credit. And those that have borrowed heavily so far need certainty that refinancing will remain available on attractive terms in order to avoid excessive deleveraging. In other words, when thinking about favorable financing conditions, what matters is not only the level of financing conditions, but the duration of policy support too. All sectors of the economy need to have confidence that financing conditions will remain exceptionally favorable for as long as needed, especially as the economic impact of the pandemic will now extend well into next year. Currently, all conditions are in place for both the public and private sectors to take the necessary measures. The GDP weighted sovereign yield curve is in negative territory up to 10 year maturity and nearly all euro area countries have negative yields up to five years maturity. Bank lending rates are close to the historic lows around 1.5 percent for corporates, 1.4 percent for mortgages. And our forward guidance on our asset purchase programs and interest rates provides clarity on the future path of interest rates. But it is important to ensure that financing conditions remain favorable. And this is why the governing council announced last month that we will recalibrate our instruments as appropriate to respond to the unfolding situation. The council is unanimous in its commitment to ensure that financing conditions remain favorable to support economic activity and counteract the negative impact of the pandemic on the projected inflation path. In the weeks to come, we will have more information on which to base our decision about this recalibration, including more evidence on the success of the new lockdown measures in containing the virus, a new set of macroeconomic projections and more clarity on fiscal plans and the prospects for vaccine rollouts. While all options are on the table, the PEP and TELTROS have proven the effectiveness in the current environment and can be dynamically adjusted to react to how the pandemic evolves. They are therefore likely to remain the main tools for adjusting our monetary policy. Looking beyond our next policy meeting, our ongoing strategy review gives us an opportunity to reflect on the best combination of tools to deliver financing conditions at the appropriate level, how those tools should be implemented and what features our toolkit needs to have to deliver on such a strategy. Let me conclude. The pandemic has produced an unusual recession and will likely generate an unsteady recovery. All policy areas in Europe have responded promptly and decisively. The European policy mix has proven that when different authorities work together within their respective mandates, countries can successfully absorb the pandemic shock. The second wave of COVID-19 presents new challenges and risks, but the blueprint for managing it is the same. The ECB was there for the first wave. The ECB will be there for the second wave. We are and we continue to be totally committed to supporting the people of Europe. In pursuit of our mandate, we will continue to deliver the financing conditions necessary to protect the economy from the impact of the pandemic. This is the precondition for stabilizing aggregate demand and securing the return of inflation to our aim. I thank you all for your attention and I promise that we will meet again and hopefully in Sintra. Thank you.


guys Sam here with late in sports cards we are doing 10 box Tuesday prism noir basketball mixer number 127 random teams here's a look at your random results as always you can find these on the website under the results tab as well as the link in chat so good luck everyone I've got it up in front of me here see what we're gonna do let's do illusions team all first 2020 21 illusions team all so we're looking for some Ant-Man rookies we're looking for some I know I'm hoping I get ready on that Lebron how about a Lebron black shimmer one-of-one for you Frankie I think that might do okay guys here we go start with illusions team all four weeks Mason Jones rookie James Harden Joe Harris gonna roll the dice for this one one two three is gonna be top four five six bottom this will go for the rest of the box so one two three will go RJ Hampton four five six will go Levine that is three it's gonna go top with RJ Hampton season highlights Luke up to the maps we've got Ethan McDaniels and Devin Bissell there you go Spurs Josh Green Robert Woodard and Mike Conley instant impact rookies Desmond Bain there you go Gris nice one there amazing Dan Lillard and for me and a Peyton Prichard rookie for the Celtics we've got Reggie Perry rookie Montrez Harrell got red the Angel Russell there you go T wolves career lineage gonna roll the dice on this one two three left four five six right we'll go for the rest of the break that is one I'm gonna go left with the magic there you go Shaq um we've got that is Malachi Flint Fred van Vleet so that's just an all rafters card that was left yes Danny of Dia Kenyan Martin Jr. Greg who'd you get let's take a look here for you are you in this one Greg or are you in a football got Sadiq Bay on the team all rookie that's Aaron Nesset and PJ Washington season highlights Lou Dort mystique PG Lavert Kuzma and a Maxi on the back there you go Sixers all right let's do next we'll do 2021 mosaic team all next mosaic team all get some of these 2020 products knocked out first so we are looking for two gold waves and eight red waves in this one if everybody's enjoying the stream here as well in the house of pain to Donaldson good I bet I was a homerun there's a Halliburton rookie this guy a gold wave region rondo gonna clips there you go clippers and a couple breaks left for the night guys we've got random divisions for that is elements full case of football Patrick Williams debut hi it's Kevin Durant for the Warriors finals MVP nice one they're golden state now we're looking for some red waves those are two gold waves when warra and there's a condo composo on the red wave the nuggets Isaiah Stewart there's a CJ LB red wave rookie Sadiq Bay NBA debut pistons Miles Bridges red wave scary Terry red wave for the Hornets there's a Lamella debut red wave Jimmy Butler are you talking about the what's in Dodger's game are ya Donaldson hit them off the top of the wall and came back in Shray Jones and man Rashawn Holmes red wave that's nonsense still nonsense Sadiq Bay red wave Kira Lewis Jr. Pelicans nice one their pals Lamar Stevens red wave is Tim Duncan KD red wave Devon Booker nice one there for the Suns there we go IKF a triple for Connor Falefa we've got mosaic step Curry there you go Warriors followed by a red wave Robin Lopez nice way to end that make a taco since in what is your when you make tacos what what do you put on them I want to hear sin victims taco construction let's do a 2021 22 court Kings next let's say well he is he is from the south so like the southwest so I think sin knows tacos being where the state needs from sin knows tacos for sure alright guys here we go some base on top here that's Alfred Shengun Moses moody level 2 we've got a Zach Levine that is 55 out 75 got nice one here we've got a Carl Anthony Towns and the Art Nouveau rookie or not rookie but Jersey believe that should be game-worn yep game-worn on all the vets in this as well followed by a rookie Otto 70 out of 99 Alfred Shengun going to the Rockets nice hit for you there Houston H town have a Luca graffiti greats and work in progress coming up well if not not let's do 2020 21 prism multi-pack or possum cello multi-pack slam the camera sour cream meat and cheese I I completely agree with that actually I'm all in on that thing I respect the hell out of that right there that is a plus taco not bad I'm not the biggest fan in the world of turkey ground turkey on tacos I like ground turkey to substitute in like chili or in like if you make like a pasta like a meat sauce or something I think it's good I for some reason I just like ground beef with my tacos really nice go on a Rockets game Friday nice pretty season brown beef here comes Rory chiming in what's this what's Rory's carne asada all this guy this guy's just getting fancy now I mean I'm down with like steak fajitas I just feel like it's a little too like difficult to do in tacos sometimes we do the hard shell because that it's like you're chewing too much oh freaking cork moss silver green Andre drum and Kevin Durant or Sam get you all right see we got in here auto quarter silver green Tyler hero on the second year there you go eat Zion what's up there big air oh we topping and precious a chew up ain't no stopping Silver Desmond Bain rookie there you go Grizz heck yeah or in W awesome Aaron holiday green that's sick dude Kenyan Martin rookie base next month a heat game nice silver rookie cash or Stanley going to the Pacers green DeAndre Aten Jalen Smith there's an ant man there you go Minnesota John D and Evan Cornia those are good those are those are next level kind of stuff man a little bit of pickled red onion on them too this guy what do you put on nothing cheese James Wiseman silver back to back to back silver rookies there you go golden state men's UK green Malcolm Brogdon sneak Bay rookie Stanley and Lucan art we're gonna say meat and cheese but cheese on a pulled pork taco like with barbecue sauce what do you put cheese on a pulled pork sandwich yep that's sickening that's that's not that's not okay jelly cheese my lord let's go silver ties Jones Patrick Beverly green that's David Robinson on a silver Paris Halbert right Parker Jada McDaniels okay do you do you like pulled pork sandwiches what do you put cheese on them right to see thank you thank you oh my god Alex you're the man get out of here this guy's like oh put some meat and cheese on my pulled pork sandwich that you are completely over come in to you a little ball rookie Elijah Hughes Brandon Clark San Diego carne asada that sounds like it would be the best let's go talk to you and Rory are on like your own on your in your own little world with your your food dates apparently it's population to over there silver maxi cleaver green Russell Westbrook Jimmy Butler Nawara and Malachi Flynn oh yeah absolutely that's a barbecue so far yeah that's all we ate when I was in Houston oh yeah thank you how dare you thank you so there we go there silver Jared Allen green Marcus Saul Alexi Pokhyshevsky there's a Pritchard rookie for the Celtics Van Bley and Kira Lewis Junior thank you Joshua Joshua now on my side as well silver Joe Engels Dorian Spenny Smith green Zeke Najee magic I love Xavier Tillman not on pulled pork man that's weird that's that's weird that's a weird thing to put cheese on I'm sorry I love cheese as much as the next outfit she's on anything to silver Gordon Hayward green Wesley Matthews there's co-anthony instant impact oh no Chris Chris G don't tell me you're a cheese on yeah no Chris boy Chris comes over off of the gaming side onto the sports side causing problems that's my dude right there causing problems that's my dude cheesehead silver Brogdon green Terence Davis there's a Patrick Williams emergent Jake layman there's a Rodney Hood error and Savon Lee rookie that'll do it for the main part of the prism sell out let's go on to our red white Chris Paul Goran Malachi Flynn yeah I know it's been Caleb broke for four straight days over there why do you think we're here true Bradley deal fair enough I'm no Caleb you guys are gonna be disappointed please do sin Jordan Laura bucks I know since send me a picture of barbecue like a couple months back and look so jealous of it Savon Lee red white and blue for the fist ends rookie I don't know honestly thing that is a little weird cheese that was a kind of a stretch yeah Rodney I definitely don't believe in that no I'm all for putting cheese on anything I love cheese dude like I would never eat a burger without cheese on it like there's certain things that absolutely needed tacos need cheese most sandwiches need cheese but whole pork is just not something I'd put put cheese on to be honest to our Parker so on the sports side long before gaming true fair enough Chris fair enough Josh Jackson Evan for me I and you know what I'm not also a fan of I have to say is cheese on fish sandwiches we've got here we go Aaron Gordon Ant-Man on the red white and blue John D pick it up another Ant-Man there you go buddy cheese on an apple see that's not bad because you could do like there's sandwiches that are pretty good it's like Jalen Smith rookie there's certain sandwiches you could do like apple apple a little bit of cheese and like prosciutto on it I say a Stuart cheese on apple pies in Vermont really cheese on apple pies big in Vermont interesting well you guys are big on your cheddar though manual quickly I'm from the Northeast to Chris I know that you Vermont Vermont cheddar is so fire Jake layman same with Wisconsin exactly so that'll do it for Prism multi- cello let's do um see what do we want to do next here to revolution Jake we push it to the end of the night so it could so we could stay on schedule that's okay sin I'm talking like a fried fish sandwich like if I was gonna have like a fried cod sandwich like I would not put cheese on that yeah I will be going later tonight though Jake dandy a group Derek white jayden springer rookie I'm with you like you do have an auto here guys it's gonna be Nikola yokich there you go Denver on the MVP auto the back-to-back MVP Denver is Derek Jay congrats buddy we've got John Wall that is impact to 149 rockets vortex Levine yeah Cole Cole settled in for sure my cold settled in Delano Benton groove rookie rookie Jericho Sims but Colin sucks an astral that's I would assume a rookie revolution well butler's hair this year the dreads dude they're kind of wild for junior vortex is Joel and bead that's fractal and Joe wise camp rookie you all know you do just come clean that means Sean's a big jet air and Gordon fractal shockwave jaw on the group Cameron Thomas on the astral rookie then we got Scotty Barnes rookie there you go you know we all do just come I don't know if you're gonna say we all like the New York Jets John Collins astral and vortex miles bridges going to the let's do I want to say I'm gonna do fast break next I think here 21-22 prism fast break we'll do Chronicles optic noir and finish with prison floral paper cut cardboard cut I should say Paul San JETS jets jets jets absolutely not oh boy now we started a mutiny of jets fans in good god redemption is gonna be Cade Cunningham fast break rookie signatures heck yeah dude the pistons that's what we like to see very easy we got a blue that is Anthony Simons 32 out of 150 got a Dorothee rookie Tim Duncan Josh Christopher prismatic Lamello blue Doug McDermott Dougie Mcbuckets 34 out of 150 Nimie skater and Dwayne Wade got Malcolm Brogdon pink 32 out of 50 Zaya Williams on the rookie there nice Evan Mobley rookie Treyman fearless Ben Wallace got a red Paul George six out of a hundred for the clips Brandon Boston Jr. Corey Kisbert Bruce Brown silver Dwight Howard Jared Allen disco Quentin Grimes rookie Vince Carter suffering just spent 50 years since Chris fireworks ant-man and another ant-man second year bones Highland rookie Carl Malone on a logo frac their breaks probably not down from Mitchell Jason Williams nice white chocolate wings easy Herbert Jones only sent it to you since I know since I don't have Sean there Sean you're gonna get on a discord train Yonis we've got red Roy Hachimora that's 93 out of a hundred want to scion Anderson Nasry oh logo frac their is um is that one of the new ones that's coming out one of the new basketball products I don't know I don't know if we got any Russell Westbrook yeah I'm pretty sure it was direct only maybe how many yellow Kessler Edwards and Josh Giddy to throw it in general chat true which are you on the discord this guy John and the coming is gonna be your variation nice one there Jay Sean takes and Jalen sucks and nice box a fast break there I honestly I don't know Fuji I don't know if we got any we maybe can try to maybe we'll try to get it second hand but I don't know to be honest when you said logo frac there I was like I thought you were trolling for a second because I was like oh you have any low like logo man super frac their rage but no then then it registered I was like oh that's an actual product my bad sorry about that Fuji we've got up next is we'll do Chronicles hobby 21 22 Chronicles hobby you imagine they made you I'm surprised they don't make g league cards to be honest they make the they might make g league night cards who knows like overtime elite does empty there's a giddy rookie stop Chad Greg Brown Jalen sucks rookie got a Zair Williams 16 out of 99 there's a Mowgli rookie we've got Vic Creechy going to the Thunder on the National Pride Auto grats on that one that is 66 out of 149 he just got traded to Atlanta actually Josh Christopher and Kessler Edwards oh it's a complete set I guess that kind of makes sense for a bone Thailand filler there's a Franz Wagner crusade Halliburton update Giannis and Moses Moody Gold 10 out of 10 there you go warriors rats on that you're not on the discord sin I said sim's not a member of late in sports cards anymore though now it's got a Barnes rookie we've got Kate Cunningham nice one there 50 out of 99 on the plates and patches sounds good I got you after this breaks and Josh kitty I have to soon move I have to soon move again and Ant-Man second year Herbert Mitchell there's a Mowgli luminance rookie Chris Duarte 111 out of 149 Cam Thomas auto is Jose Alvarado going to the Pelicans nice one there that is 83 out of 149 Cam Thomas and Alfred Shengyu purple Zire Williams that is six out of 49 there's a Yanis and a Mowgli silver Jalen Green rookie Davion Mitchell overpriced the stressful fair enough they're Chad fair enough fair enough fair enough PG Herbert Jones Jean-Marin there's a Quentin Grimes 86 out of 249 for the Knicks Trayman there's a 75th anniversary James Hardin that's gonna be 58 out of 75 nice one there sixers on the update Suggs rookie and LeBron let's do optic hobby next yes 21 22 Donner's optic hubs let's go Rizzo to run homer baby yanks are so back dude the inks are so back so back oh that was a no doubter yeah try to take take that one back MLB take that one back umps Jalen Suggs odd it was no it was way over James book night silver Damian Lillard that's just conspiracy against the Yankees that's all it is Franz Wagner rookie lime green Anthony Davis 101 out of 149 following yesterday by the time you got a work of tending holy cow dude that's kind of wild actually Chad 75 years LeBron no auto David Johnson 14 out of 99 autographs going to the Raptors then you're gonna roll the dice on this one as well 1 2 3 is gonna be left 4 5 6 right it'll go for the rest of the break that is for gonna go right with the pistons and Isaiah Thomas this is the optic optic yeah oh bam on a bio for the Bradley Beale light it up fresh Christopher orange Eric Gordon for the Rockets 188 out of 199 and jinxed it we got a gold coming up here guys that's gonna be Desmond Bain on the gold to 10 there you go gris 7 out of 10 on a second-year Bain Memphis Orton W Orton oh god they did just jinx it Bubba John and coming up see that's why we can't have nice things the angel Russell Hala Mobley rookie that went which one did this go that's 3 out of 49 optic went sorry which one right that's gonna go right with siacum the Raptors 3 out of 49 Robinson Earl Desmond Bain that's so brutal dude just got a bigger lead then I'll be okay Miles McBride we don't have Chapman to come in and below it which is good doll baby oh yanks hollow Devin Booker elite dominators hollow Kevin Porter junior your third realtor holy cow dude phone Thailand and we got a black velocity is Anthony Davis 16 out of 39 there you go Frankie nice one there man love that that's such a sick card with the Lakers colors love it be a lot pick dominance Brendan Boston shrey young and last pack of the break here got or not of the break of the box Davey on Mitchell John Stockton look at what's Invictus sent me here always some sort of nonsense we're gonna do noir next to noir is gonna be our next one oh that looks good not bad sin I like it making me hungry sin oh easily another spot for elements came in I'm one set guys I have to for this email then we'll get on to the next one all right let's do noir hobby guys 21-22 noir hobby man this stuff is so fun and then we're gonna finish with prism first off the line we're gonna pull some nasty shims thick box in the war here guys don't totally down to be awesome oh same dude I'm all in on that heartbeat I'd be there got Lamello ball that is 31 out of 99 hornets we've got that is six out of ten or eight out of ten Trey man going to the thunder and back to back trade back to back Trey man's out of ten on the base 8 out of 10 and 9 out of 10 that is that that's weird congrats Joel I mean that's awesome but I have never seen something like that that's actually crazy 8 out of 10 and 9 out of 10 back to back Trey man golds weird but hey not bad we've got Jonathan coming a new wave Jersey that is 17 out of 99 we've got that is Reggie Jackson dish night go into the Clippers 62 out of 75 rats on that there clips we've got dish night go into the Blazers 65 out of 75 is CJ McCollum oh this next one's so sick go into this is going to Cleveland right here Z John L spotlight auto mark price with a calves man 93 out of 99 one of the most underrated players in NBA history during like all those Jordan years he was like the starting point guard in the all-star game every year it's crazy nice hit there we've got rookie auto that is 94 out of 99 Aaron Wiggins going to Thunder again man having a good box here Joe we've got going to the Wizards that is so disgusting that's nasty this next one dude we got sneaker spotlights going to the Wizards Roy Hachimora that is where's the Wizards a GUW dude this one is so sick go into Thomas see in the maves 87 out of 99 Dirk Nowitzki sneaker spotlight boom let's go dude that's so freaking sick that is absolute that is absolutely insane dude this card is crazy the Dirk look at that man the detail on those are crazy that'll go right into a mag Thomas congrats Thomas we are on to prism photo that is 2021 22 prism first off the line baby let's do it good luck here guys there's your photo boom see a black I want to pull a black shimmer dude those are so sick at least a gold shimmer fire good luck here everyone don't look like a shimmer in this one little bronze base Pulsar is gonna be Tony Cuckooch that's one per box Paul Pierce on the hyper fireworks and Nicola Yovitch T-Mac Isaiah Jackson Dirk mind to see that sir dude that thing is so sick on that's such a crazy card right there all George Austin Reeves silver Tyrese Maxi 72nd year scary Terry on the 75th anniversary 58 out of 75 going to the Hornets the Yanis mindset and they run sharp rookie gold shimmer guys gold shimmer let's go dude come on be huge Andre Drummond KD Miles Turner Marvin Bagley we've got Santee Aldema six out of ten on the gold shimmer rookie man that is Memphis with or in W having a huge break with Memphis nasty man that's what we're looking for the rookie Darius Basely blue ice to 125 Kyrie Joshua Primo AI still got another shimmer we're looking for guys full two rookie shimmers in one box why not why the heck not steady Chetty on top silver Dorian Finney Smith there's a Jalen Brown hyper James Harden book night rookie Usman Garuba and Dominique now no gaming tonight Brett Brent's on vacation Caleb just broke the last four straight days how many be all a red wave that is not numbered bam out of bio for the blue for the heat 57 out of 199 widescreen the Jason Williams Joe wise camp and France Franz Wagner the magic nice one there grab magic parents Davis Donald what's up how you doing buddy Rashid Wallace hyper James Wiseman Sondra rookie I usually see Donald over on Twitch to see you over on YouTube Donald Silver Keon Johnson rookie there your clips mindset silver tray young we got auto is Isaiah livers going to the piston sensational signatures rookie auto grats on that Detroit KG Davion Mitchell Hakeem Elijah one red wave Colin Sexton probably a red at Keldon Johnson 53 out of 299 Anthony Davis there's a Kader base rookie Paul Pierce and as I thought yeah they're here Christian Wood that's a red wave Jeremy Grants Jalen Noel hyper son of a Mitchell got them on a bio there's a Miles McBride they look delicious they look really good sin and Jason kid guys missed out trust me I'm starving no overnight I'm real silver Kevin Porter Jr. there's a Xavier Tillman 46 out of 149 that is Isaiah Jackson sorry Williams I'm I was like rising plant played very well in the summer league him and Quinten Grimes played very well the mix all right we got something coming up I think we got a shimmer and an auto in this in this pack Markel folds that's gonna be Brandon Clark 17 out of 30 man grizzly sweeping the shimmers have a good one Donald take care brother have a go man nice one there grids you got a T.J. Warren 44 out of 149 going to the Pistons or not Pistons Pacers and then we've got nice silver rookie auto Jalen Johnson for the Hawks rats on that Atlanta Moses Moody rookie Greg Brown rookie and last pack of the break here guys good luck see a gold true gold put them in the hot box Lamello Silver Alonzo morning that's kind of sick for the Hornets LeBron prismatic John Morant fearless Sharif Cooper and Rudy Gay that'll do it for the break guys let's do a quick recap here pretty solid stuff I would say overall we've got our Jersey CJ McCollum Blazers Reggie Jackson Clippers John and Kaminga for the Warriors Carly Anthony Towns going to the sixth or the Timberwolves Jalen Johnson Hawks Isaiah Livers of Pistons Roy Hachimara wizards that is Aaron Wiggins going to the Thunder Mark Price for the cabs sick one there David Johnson wrappers Jose Alvarado for the Pelicans Vic Crachy going to the Thunder Nicola Jokic nice one there for the Nuggets and Alfred Shengyun for the Rockets we had Goldschimmer Santi Aldema 6 out of 10 going to the Gris so sick Dirk sneaker spotlight dude so nasty 87 out of 99 for Dallas and then a Cade Cunningham fast break rookie autographs going to the Pistons that'll do it for the break guys thanks again for the film next up we've got football


20 Powerful Adjectives in English from espressoenglish.net Are you tired of using the same basic and boring words in English? In today's lesson, you'll expand your vocabulary by learning powerful adjectives. 1. Defining Means extremely noisy. For example, there was a deafening roar as the rocket ship blasted off. 2. Excruciating This means very painful. For example, I have an excruciating headache. I can't even think straight. 3. Destitute Means very poor. For example, he grew up in a destitute family that barely had enough food to eat. 4. Devastating Means very damaging or upsetting. It can refer to physical damage. For example, the town was destroyed by a devastating flood. Remember, a flood is a natural disaster where there is too much water causing damage to buildings and houses. The word devastating can also refer to great emotional impact. For example, they received the devastating news that their son was in a coma. 5. Gorgeous Means very beautiful. For example, wow, those flowers are absolutely gorgeous. After he lost weight, he started dating a gorgeous woman.  6. Swamped Means very busy. For example, I have five major projects to finish by the end of this week. I'm totally swamped. This is an informal way to say that you are overwhelmed with work. You have far too much work, and so you're very busy. 7. Packed Means very crowded. For example, the stores were packed on the day before Christmas. Everyone was doing last-minute shopping. In the picture, you can see an outdoor festival that is packed. There are so many people that there's barely any room to move. 8. Thrilled Means very happy or excited. For example, Joanna's parents were thrilled to hear that she'd won a full scholarship to college. A full scholarship means a program that will pay for your entire college education. 9. Horrific And horrifying. Mean very shocking or scary. For example, 50 people were badly injured in a horrific accident at the factory. The prospect of a nuclear war is horrifying. 10. Mind-blowing Means very amazing. For example, modern medical technology is mind-blowing. They can even operate on a baby before it's born. 11. Miraculous Means very fortunate. For example, this book tells the story of Henry's miraculous escape from a war-torn country. Miraculous is the adjective form of the word miracle, which means something that is so fortunate and so amazing that it seems supernatural. 12. Fearless Means very brave. For example, he fearlessly walked into his boss's office and asked for a big raise. Fearless is the adjective, and fearlessly is the adverb form. 13. Absurd And preposterous. Mean very ridiculous. For example, that book contains some preposterous theories that go against the scientific consensus. The store won't give you a refund even though it was their mistake. That's absurd. 14. Frivolous Means very unimportant or silly. For example, if you want to save money, you have to avoid making frivolous purchases like buying candy at the supermarket checkout. 15. Impeccable Means completely perfect. For example, her academic record is impeccable and she's at the top of her class. 16. Jittery Means very nervous. For example, most people are jittery on a first date. Try to relax and just be yourself. 17. Outstanding Means very remarkable. For example, the results of the educational program were outstanding. More than 1,000 children were able to improve their grades. 18. Prestigious Means very respected. For example, my internship at a prestigious law firm was a great start to my career. 19. Heartfelt Means very sincere. For example, he made a heartfelt speech expressing his gratitude. 20. Vital Means very important. For example, it's vital to pay attention to the road while driving. If you'd like to keep learning, check out my vocabulary builder course. It has two different levels and each one has more than 500 useful English words. When you expand your vocabulary, you can express yourself more fluently in English. Click on the link in the video for more information.


I'm giving the closing talk of this, and I wish I'd been here for more of the two weeks, but I'm going to start with something that somebody said to me, they were part of I think the 35th of these, it's 60 today, she said it changed her life. By giving here for two weeks, in the 35th it changed her career, it changed how she saw the world, she met people that she's still friends with. What could be better than that? Today I'm going to talk about what I've done in my own career. I'm going to talk about the challenges politically that we're going through at the moment, and how we can't accept some of those. And I'm going to choose something which is close to my heart, which is around emerging infections and the challenge the world faces, but really just as an example of how unless we come together as a world and address common challenges, we won't be able to address any of the great challenges of the 21st century. Climate change, emerging infections, inequality around the world, and I think by bringing everyone together in one place for two weeks, that's the sort of thing that changes the world.