Well, thank you for that. And now we have a slightly different approach to the general question of application of multiomics to observational studies. You've heard four excellent talks so far with a little bit of discussion there. And what we're now going to do is challenge four panelists to within a very short period of time, five minutes each, to actually answer two questions or address two questions. Where do we want to be and what's the aspirational goal that we want to achieve? And what are the barriers and opportunities for achieving those goals? I will point out I'm Jonathan Haynes. I'm the chair of population quantitative health sciences at Case Western Reserve University. I've been studying a number of neurological and ophthalmological diseases for far too long. So with that, just remind the speakers they had the option of having some slides. So some of them will have slides, some may not. I will remind them that I will break in with the one minute point just to keep things on time. The first individual, the first person to take up this challenge is Dr. Miriam Furnage, who is a professor at the Center of Human Genetics and the Lawrence and Johanna Favreau distinguished professor in cardiology at the University of Texas Health Science Center in Houston. Her research interests lie in the molecular genetics of complex diseases with an emphasis on using functional genomic and genetic epidemiology strategies. Her focus is on the interaction between genes and environmental factors, and you've got five minutes to enlighten us on the challenges. Thank you, Jonathan. And thank you for the organizers for letting me give my perspective on multiomics. Let's see if I can get that. Here we go. So the first question we were asked was where we wanted to be. So I think I'm going to reiterate a few themes that we've heard over the past couple of days. So the first thing I will say we want a comprehensive set of omics information at a large scale. And I think that scale is going to be dependent on the question and the study design. Of course, we want a large number of people. We want multiple measures on multiple time during the lifespan, perhaps, and also perhaps different sources from single cells to various tissues. With these data, we hope to gain a comprehensive set of standardized and validated biomarkers of disease. I think Nancy started the workshop yesterday asking why there weren't very many biomarkers, so more biomarkers used in clinical settings. And I perhaps would offer that maybe we don't have as many validated biomarkers as we should. And I want to point some of the work we're doing as part of a Mark V CID consortium where we're trying to validate and standardize biomarkers for vascular contribution of cognitive impairment and dementia. So there is some spin-staking work that go into validating biomarkers that consortium is really doing some work there. With that, we definitely want to get to integrated models for disease risk and prediction for disease diagnosis and for therapeutic response. And so hopefully these integrated model would have an established clinical use in diverse population. And finally, the goal is really to have a routine application and adoption of these models in clinical settings and with integration of clinical tools and EHR. So that would be the aspiration in my view. Some needs and challenges and barrier. So I think each individual omics present its own set of challenges. And of course, multi-omics will inherit all of these and face additional challenges of harmonization and integration of heterogeneous and high-dimensional data. So I think we do a good job of identifying confounders on one sort of variation and biases across single omics. And so I think we have to carry that to multiple layers of data. One of the challenges as well is to have interoperable omic data resources and ontologies, sometimes annotation of molecule across platform within an omic, much less across different layers of omics is sometimes difficult. So having standardization there would be helpful. We've heard a little bit about advanced computational methods that account and for and leverage the complex interrelationship between and within omics. I think those are crucial. For that we need suitable computing infrastructure for data storage and management. And of course, these advanced computational methods such as machine learning, deep learning which require specialized expertise and training and therefore multi-disciplinary team science is definitely the needed here. The next step of a challenge I thought was also the need for accessing this information at large scale. And so I think that requires consortia-wide effort for data harmonization and curation. Not only shared data, but perhaps shared workflow and also computing infrastructure. So I think, for example, in top med, in charge, we've been using computing infrastructure that, for example, the commons, the bio-data catalyst that those are the type of infrastructure that definitely will be needed at large scale. And finally, in that same realm, public portals, I think we want to engage larger communities of researcher and clinicians and stakeholder. And so those are definitely something that we should think of. One minute. So additional needs, the need for tissues, disease-specific data set and the need to translate omics knowledge into clinically effective models for prediction. And that's for all. So we're going to need standard and best practice in developing and reporting these risk models and validated, again, validated model across multiple populations. And ideally, these models would be integrated into clinical tools. In terms of opportunities, I think we all recognize that multi-omics are more than the sum of each omics. And so we need to integrate these multi-omics to enhance our ability to understand causal relationship. And so we have genetics, it's very helpful in this setting, using Mendelian organization and also layering XQTL mapping, for example, layering omics into GWAS to understand causal genes. I think we understand also that the value of multi-omics for disease increases when it's integrated in environmental, social and lifestyle exposure, the exposome over the lifespan. And so I think we have to have an effort to really having this integration. And so I think that's where the longitudinal epidemiological cohorts that are exposure driven in their data collection can be very helpful. And so finally, we have a decade of GWAS studies given us the value of open science and data sharing and consortia-based collaboration. I think there is very good model for those. I think we should continue on this realm. And again, getting back to using more effective and meaningful predictive model in clinical setting, I think we can build on what we were starting to do in polygenic risk score, but perhaps supplement them with various panels of validated biomarker that reflect the lifestyle lifespan or the life stage and the disease stage and exposures, for example. Certainly for these models, we're going to have to take advantage of computational methods, advanced such as machine learning, deep learning, and above all, we need to keep the focus on addressing health disparities and improving on our diversity focus. So I'll stop here and let my colleagues continue. Thank you for that. And to remind folks, we are going to have the four panelists each talk, and then we will have about 25 minutes or so to have questions. So if you have questions that come along, you can put them in the chat and then we'll have the open discussion after that. So it's my pleasure to introduce the next panelist. Dr. Adam Buddenworth is a reader in molecular epidemiology in the Department of Public Health and Primary Care at the University of Cambridge, where his interests are in molecular epidemiology and revolve around the identification of genetic variation linked with coronary disease and related phenotypes in molecular omics such as plasma proteomics and metabolomics.  Thanks very much, Jonathan. And thanks to the organizers for the invitation to join this wonderful meeting. It's attracted me on a Friday night here in the rainy UK. So thank you for listening. So why am I here? Well, my group spent much of the past 10 years working on a study called Interval, which is a cohort of 50,000 largely healthy people from the UK over which we've really tried to elucidate the path from DNA to disease. So anchored in genomic information and with linkages to e-health records on disease status. We've tried a variety of different platforms that you see here in blood and plasma based measurements, particularly around the proteomics and the metabolomics, really trying to understand pathway from DNA to disease. More recently in the last couple of years, we've kind of nationalized this effort by pulling together a 13 now population cohorts that again have genomic data, health record data and some form of multi-omics data with the idea of scaling and understanding reproducibility of cross cohorts that's been mentioned several times. Because these are population based, we can look at largely at things like prediction of incident disease outcomes, but also trying to understand etiology and causal pathways to disease rather than, for example, stratification of patients. So inspired by Eric Green's bold predictions for genomics, I thought the way to present my vision of what we might see in the next decade or so is the bold prediction for multi-omics and cohorts. And I won't go through all of this in detail, but just to pick up a few of the points, diversity of course been mentioned, I think there's a role here for both patient and population cohorts. I've had several people talk about the idea of serial measurements rather than what we mostly have in populations, which is cross-sectional measurements. I think multi-omic layers will be important. We've seen the more layers you have, the more information one can glean. And we're largely really only touching the tip of the iceberg with blood and plasma. So how do we get into, for example, different cell types, tissues, et cetera. As has just been mentioned, I think making the data widely accessible is a key point here and be used for the field. Of course, we need to make sure it's safely stored and managed. And of course, we're going to, as this data continues to grow, we're really going to require novel ways of thinking about this so we can really understand the complexity here. Let me show you a few examples of why I think this is important. My group doesn't focus so much on aging, but Benoit Lalier and Tony Wiskare at Stanford are interested in, because we made a data from the interval study open to people, they applied and accessed our data. And we're interested in looking at aging and the proteome and identifying peaks of aging and proteins that go up and down. One example I see here on the right is MMP12, which is a plasma protein level seen to go up in the peaks that they identified at 60 and 78. So it seems to increase as we age. And others had looked at this and shown that MMP12 levels seem to be positively associated with both primary and recurrent cardiovascular events. So the higher your levels, the higher your risk of heart attack or similar. But when we use genetic data and anchored that and genomic information, use the interval study, we identified that people who carry alleles that give them higher plasma MMP12 levels actually seem to have lower risk. So we think this is actually protective biomarker. It's released in response to myocardial damage. And therefore that gives you quite a different slant on the relationship between biomarker and disease. I think there is a causal effect, but it's potentially in the opposite direction to what you would see with just a longitudinal study. So we think longitudinal studies are important to capture that information before disease onset, but also anchoring in genomics for causality is useful too. Multiple platforms, I think maybe with genomics and then perhaps transcriptomics and some of the earlier stage technologies, we're more standardized, more advanced, I think with proteomics and metabolomics. One thing we've learned is that findings are often comparable across proteins. This is looking at PQTLs for two different plasma protein platforms we used. There is a good correlation, but not all the case. And you see kind of the horizontal and the vertical here, there are some exceptions. For example, this cis PQTL for a protein GDF15, which has a clear and striking signal in one platform, but a P of 0.5 in the other platform, completely different signals. I think we're only just starting to really understand the differences. And so using multiple platforms is going to be important. Finally, to some barriers, clearly cost of this genome sequence has come down dramatically, but not yet for proteins and metabolites in the same way. So I think coordinated efforts, these are scalable, making sure these are scalable and driving the cost down. Much of our omics were funded by industry, so I think partnering there is going to be important. I've mentioned already going beyond blood, we're just getting this snapshot with peripheral blood. Often the action is somewhere else, and we don't know how what's going on in blood relates to the tissue we're interested in. So that's clearly going to be important. Everybody argues we need more of these things. One of the challenges is where to prioritize. Is it more samples, more time points, more layers? And finally, how do we ensure we get that diversity and diversity at scale and diversity that's not a token mistake or sufficiently powered to tell us? So I'll leave you just with my bold prediction. Thank you. Thank you. That's very, very informative. Our next panelist to take up the challenge is Dr. Greg Gibson. He's the patent distinguished professor in the School of Biological Sciences and Director of the Center for Integrative Genomics and a member of the Pettit Institute for Bioengineering and Biosciences at Georgia Institute of Technology. His research interests lie in the use of transcriptomics for personalized medicine, predictive health, and predictive health.  Thank you very much, Jonathan. And thank you to the organizers. It's so wonderful to be here. It's been a great workshop. I want to make four points. I think whether or not there are opportunities or challenges depends on your perspective. I think they're a bit of both. So the first one is that I think we should have much more focus on therapeutic outcomes. I mean, predictive health is terrific, but from a patient perspective, using omics to understand how they're going to respond and how the disease is going to progress is important. So here's just an example. If you take on the y-axis, a sort of a classical histological measure, a Crohn's activity index, on the x-axis is a transcriptomic signature of disease progression and Crohn's disease. Either of them alone are reasonably good, but taken together. They give you much better prediction. In this case, in the bottom left quadrant here, we see almost nobody who's going to progress to the disease, such as the blue dots. So these different types of measures are somewhat orthogonal, but working them together, they can give you much better evidence about outcomes. The other thing about this is that therapeutic outcomes are usually 20 or 30 or 40, 50% of the population, which is going to give you much better precision or positive predictive value than if you're only looking at something like disease incidents, whether it's only 2% or 3% or 5%. So focusing resources on reducing things like the number needed to treat is a clear opportunity. The second point I wanted to make is that omic measures are a lot closer to disease and genotypes themselves. So if you take a typical EQTL, for example, where you have maybe a protective allele from GWAS and a susceptible genotype combination from GWAS, if you're doing genetic risk scores, you're based on just the genotypes. But if you think that actually what the genotype is doing is regulating gene expression, then it turns out that most people with the risk genotype have perfectly normal gene expression. So an orthogonal thing to do is to say, well, let's look at the risk based on transcriptomes. And if you do that, it actually turns out that you can get much, much better predictors. And on the right is an example, which we've got under review of predicting ulcerative colitis from rectal gene expression, where we see really, really good discrimination of collective cases, almost 50% prediction by transcriptomics, which you'd never, ever get with just genotypes alone, although we can actually generate a genotypic score from the EQTL that regulate those genes as well. The third thing running through quickly that I wanted to sort of say is we need to remember that all risk assessments are very much context dependent. So here are examples from the UK Biobank where we've just split the Biobank into two halves based on healthy versus unhealthy environment, whether it's obesity, hard CAD, type 2 diabetes, IBD. In every single case, the environment has a dramatic impact on your risk assessments. And I think this will be no different for audiomic assessments than it is for genetic risk scores. So in a typical example, you're seeing, for example, that the, so this is a prevalence risk plot where your risk, your prevalence goes up as your percentile of the risk score goes up. But in these, in the different environments, typically somebody with, in the 25th percentile and a bad environment has the same risk as somebody in the 75th percentile in the good environment. And those sorts of environments can be actual physical environments, they can be socioeconomic status, and of course, they can be ancestry based differences. So we've really got to be cognizant of the impact of not just ancestry, but all sorts of inputs we've heard many, many times this week, over the last two days on disparities in health. When we do our assessments, we just, we just need to take into account that those assessments have to be informed by our environmental background. And then the final point I wanted to make is that I, I think that actually single cell genomics as, as a great deal to us. So this is some data gathered with Judy Cho and Zubrik Agathasan and others in the inflammatory bowel disease consortium. And we can do our single cell profiling, in this case, of the epithelium of the ileum of patients with Crohn's disease. And when we actually go in and we look at the genes which are both GYs associated with disease, so they're probably causal and they're differentially expressed between patients and healthy controls. We actually see by doing a single cell transcriptomics, which cell types are involved. And so they're not all cell types. So for example, IBD is not a B cell disease, but we do see involvement of dendritic cells and inflammatory immunoregistry T cells. And we actually also see some engagement of epithelial cells. That's great. We, we may be able to get there as Tuli was arguing yesterday from deconvolution, but what you probably won't be able to get there is, is looking at patient specific responses. And I don't have it on this slide, but it turns out that if we go in, we can actually look at individual patients and see that either the cell type or genes within particular cell types are perturbed in very much patient specific manners. And I think the only single cell profiling is about the only way that we're going to get to that level of resolution, which you can then turn, take that a step further and say, well, this is the source of your pathologies. And this is a sort of therapeutic regime that you need to be able to get, whether it's directing at a goblet cells or an immune cells or whatever. And Mike Snide had a great example of that and diabetes yesterday from his own profiling. So if I was to sort of make some broad suggestions, it would be, first of all, that I think we, as many have said, we need more support for consortium based multiomic data acquisition, preferably longitudinally and preferably multi tissue. I really think that the integration of multiomics must be done in the context of the patient's environment, which includes socioeconomic, it includes diet includes microbiome, and includes ethnicity and ancestry. And I think that we need to pay much more attention to precision over accuracy. So forecasting patient outcomes. So accuracy is sensitive and specificity precision is positive predictive value. And I'd love to see in the next few years, we're using multiomics to improve the predictive value of our genetic evaluations. All right, thank you very much. And the last but not least panelist to take up the challenge is Dr. Allison Goat, who is the Professor of Neuroscience and Director of the Ronald Loeb Center for Alzheimer's Disease at the Icon School of Medicine at Mount Sinai in New York. Her research focuses on dementia and addiction with the aim of understanding the molecular basis of disease and ultimately identifying novel targets for therapeutic development. And she, of course, uses multiomics to get make progress in that area. So Allison, take it away. Hopefully you can see the right set of slides. Okay. Yes. So as Jonathan said, my focus is really on neurologic and psychiatric diseases. And so my first point really is around access to the relevant, relevant tissues for this group of diseases. And you can see here, this is looking at SNP heritability across a range of neurologic and psychiatric diseases, taking different cell types from within the brain. And I think sort of pretty stark contrast actually for Alzheimer's disease, you can see it says really in microglial enhances the SNP heritability, whereas across a range of, oh, sorry about that, of psychiatric and behavioral disorders, there's broader evidence of heritability across different cell types. And the reason this I think is really important is microglia represent a very small percentage of total brain cell types. And therefore bulk data from the brain doesn't get you anywhere at all in terms of being able to understand the disease. And so single cell data sets, I would argue, is something that are particularly important in this context. Importantly, we and others have found that there's a lot of overlap between the enhances in microglia and in peripheral myeloid cells. And indeed, when you sort of use peripheral myeloid data, you can actually help to understand a lot of the genetics of Alzheimer's disease, but not all. And I think that that's really the important thing, that maybe there's 50% of the loci from these GWAS studies that we cannot assign genes to the locus. And I think it's very likely that there are the specific differences in this microglial population that we don't pick up in peripheral macrophages or or in monocytes. And so one of our, I think, problems is that we really it's even hard to get to gene discovery because of lack of data for the for the cell types that are most important for understanding the disease. And so if we want to understand molecular mechanisms there, we need to have much better quality data, even from the right tissue and the right cell types, and across diverse populations in order for us to be able to understand this. And the same is going to be true. So we need this information if we want to be able to develop novel therapeutics or diagnostics and biomarkers. And right now, certainly in Alzheimer's disease, we have very few biomarkers. Up until very recently, the ones in cerebral spinal fluid have been the ones that have largely been used. I say there is promise there now in terms of plasma biomarkers. And so certainly anything that we can do with peripheral tissue that will help us will make things a lot easier than if we need brain tissue, because clearly this is not an organ that's accessible to us until people die. And that that's sort of significant problem in trying to disentangle cause from effect in these things. And that's really right now most of our omics data does come from bulk brain for obtained a autopsy. And I think that's a significant limitation. Right. So I think that we need certainly we need more single cell data, but we need a purified cell cell data. And we need a lot more different types of data in order to understand this underlying genetic risk. In terms of opportunities, I think so thinking about longitudinal studies, I mean, also all the sorts of omics data that people have mentioned, I do think, you know, obviously we we will need CSF and brain, but otherwise blood and omics from blood is obviously the most accessible and clearly going to be broadly useful. I think you need longitudinal studies, you need good and deep phenotype data, and genetic data to identify novel biomarkers. The examples I've given here specifically to AD, but I want to illustrate particular things. So the first two examples are longitudinal observational studies in fat families with known causes of disease. And I think we shouldn't forget that there is some value in being able to look at these because we know who's going to get disease, we know when they're going to get disease. And so we can do longitudinal studies in these people and and they're very motivated to participate because they're in high risk families. And so there's a lot of value for those kinds of families. We also have, you know, sort of late onset families as well, where there are large cohorts. And then there are case control data sets, some with longitudinal data, and where we could collect omics data. And then the last example is obviously biobanks, where I think there's also great potential. And the idea of using polygenic risk scores within large biobanks to be able to identify at risk individuals and to be able to collect large scale omics data longitudinally in these kinds of samples. And in addition, I would add in addition to these omics samples, I would argue that that we should be banking IPS lines or at least PBMCs from patients so that we have lines that we can differentiate into any cell type that may be useful for the disease that we're interested in, that we can do omics on those cell types. And we can study function and use them also if we have diverse panels, use them for drug screening as well. So I think there's some great value in having not only omics from fluids, but collecting cells from people as well. So I'll stop there. Thanks. All right, thank you very much. Thank you to all four of the panelists. I would ask that the panelists come on and come on the video so that you have an opportunity to everyone can have the opportunity to see you and ask questions. So we have approximately 20 minutes for questions for our panelists. So anyone who has a question, raise your hand right in the chat or speak up. So, Eric. Thanks. Alison, that was great. I would enthusiastically agree with the first half about the relevant cell types and tissues needing more data. But I guess I worry that we're rather naïve that we don't know what the relevant cell types and tissues are for most late onset chronic diseases. We think we know some of them. Well, first of all, I don't think there's one. And second, we think we know some of them. And I worry sometimes the cell types and tissues that we point to are the cell types and tissues of the symptoms, not so much the causality. I mean, I would agree with you that I think for many diseases we don't know where most of the SNP heritability is because we've been using bulk tissues. And if any of the risk is in cell types that represent minor cell types there, and we're probably missing it all together. But I think it's a really important component of trying to understand the diseases to know the contribution of different cell types, at least from my own perspective. I had a very neuro or neuronal centric view of Alzheimer's disease until about six or seven years ago. And now maybe I'm proselytizing like they're newly converted. But it's sort of, I think it really opened my eyes to how biased we can be about thinking about a disease if we're thinking about it based on symptomatology without really thinking about the causal role of particular cells. So I think it's really an argument for single cell data, right, that with bulk data it's extremely difficult to get beyond the most common cell type in that tissue. Yeah, thank you. Okay, Judy, I think you're next. Okay, I had two questions, one for Greg and one for Allison. For Greg, it was interesting that diet seemed to have the least effect of inflammatory bowel disease. Did you look at Crohn's versus Ulcerative Colitis and kind of how scalable is that? And for Allison, kind of smart watches or devices plus Alzheimer's? Yeah, well, I know who wants to get first? I can if you like. I do think that passive data collection in an Alzheimer population is going to be better than an active one, right, in the sense that, you know, as you, as the disease develops, you're not going to be actively entering data, but definitely wearables where you can collect data I think can be very valuable, right? I mean, I think sort of loss, sort of subtle behavioral changes can be picked up with wearables. And even collecting memory data more frequently has been shown to be much more valuable than collecting memory or more accurate than collecting memory data once a year when someone feels really stressed because they're coming in for their test. Even people who are, you know, in the non-demented but at risk feel very stressed by doing a memory test and anything could make that poor. Whereas if you're collecting data, you know, on a smartphone or something every week or every few months, a couple of months or something, you're going to get a much better picture actually of what the real state of someone's memory is. So, yeah, definitely I think that there's a lot of value in having wearables and for data collection. Or just smart data collection generally, right, rather than just a clinical interview once a year. So, so, Judy, I'll send you the paper, the preprint we put on BioArchive in the next couple of weeks. But yes, there are differences between UC and CD in the environmental impacts on the polygenic risk assessment. You know, nicotine and alcohol consumption are big factors that really discriminate individuals. But it gets to this idea of conditionality. Actually, you know, dried fruit and fresh fruit consumption produce different results. But I don't, I doubt that fruit consumption itself is the important thing. I think their mark is more generically of the type of diet one has. The other component that comes into all of these factors is actual familial disease, presence of disease as well. I think that's probably partly genetic, but I think it's also other aspects of the shared familial environment. But actually, having a shared relative with a disease has a major, major impact on your relationship between prevalence and polygenic risk in ways that we didn't necessarily expect all along. And I was going to actually add to the discussion before your question. It's not just finding the right cell type in my view, but it's finding it in the right circumstance. So a B cell or a T cell, you know, in the gut, it's not the same as a B cell or a T cell in the lung or a B cell on a T cell circulating in the blood. And it's certainly not the same as one at diagnosis as somebody in with established disease. So I think we need to be doing that sort of profiling, not just longitudinally in sense of following patients, but at different time points in different conditions so that we actually get a full spectrum of what the environmental impacts are on the profiles that we generate. Thanks. Okay, Tiago. Thank you very much. So I'd like to bring up a topic that I think is really important. And I haven't seen somebody, you know, tackle this directly. And so this was alluded to yesterday by, you know, Nancy Cox and to the La Palana, where they basically, I think Nancy was more focusing on the importance of multi-omics to identify new biomarkers, and then Tuli was very much talking about the importance of single cell resolution, cell specificity, et cetera. And I think that it might be important to discuss the considerations that need to be taken into account for, you know, for example, what study design and also technologies used based on, you know, in relation to what your goal actually is. Is your goal to understand disease etiology from a really sort of mechanistic perspective, or is it to identify biomarkers? Because I think that it's particularly in a clinical setting, I think it's really important to think what kind of technologies and analyses and computational tools we need in order to address these. So I was wondering if the panel could discuss this a little bit, basically, how can we use multi-omics to identify biomarkers versus disease mechanism? And are these incompatible or not? So maybe I can start. I mean, my observation I'd make, because I don't think they're really separable. So when we did our transcriptional risk score study on IBD a few years ago, a part of it that really struck us was what we call difference between coherent and incoherent associations, which I think was really getting a mechanism. So what this is, is that you'd expect an EQTL, where the risk a little increased expression, you'd expect to see increased expression in cases relative to controls. But very often you didn't see that. In fact, it was the opposite way around. And that made us scratch our heads. But our interpretation of it is that in fact, what this combined data is telling us is that in some cases that elevated expression is actually protecting the individual. And if you have the allele that sends you in the opposite direction, then you can't protect yourself enough. So it's telling us that GWAS associations sometimes are not promoting disease, but they're part of the protective response. So I think in that combined effort of trying to get biomarkers that were predicting disease progression, we were getting insight into the mechanism of what was going on. And so that's part of my response is that I think that I agree with you that different algorithms are going to give different answers for different types of questions, but they're not separable processes. I'd like just to add very quickly, because my point was, for example, I see a lot of studies, multiomic studies that, for example, use whole blood traditionally, so tissues that are easy to access, but then they're studying, let's say, neuropsychiatric diseases. And it's not always clear in multiomic studies that people are using the tissue that is the most relevant for the particular disease they're studying. And sometimes frustratingly, these studies don't find anything, but I would want ideally to hear from you whether should we still be using whole blood? Should we just go full on on single cell analyses? What do you think? Well, I guess Tiago, one response to that is the recent Alzheimer data where plasma levels of phosphotide tau look like they're as sensitive, if not more sensitive than measuring the same protein in cerebral spinal fluid or imaging-related proteins. So I think that that would argue that while you might not intuitively think it's the best place to be looking for a marker of a neurological psychiatric disease, it can actually come up with things that are very powerful. I would agree with that. And I think we've found signals in blood for, you know, particularly when we think about proteins where maybe the single cell and the cell type specific hasn't necessarily come as far, or at least in as large sample sizes that one can pick up signals that you wouldn't expect to see maybe in blood, but it points you towards an etiological pathway in disease that you can then follow in tissue. I think to your first point, Tiago, I completely agree. And I think we've heard during the last two days that it really depends on the type of question you want to ask. And if you're looking at response to treatments, then you're maybe going to take a patient cohort we heard from Mike Schneider about, you know, thinking about this idea about people's veering off their trajectory, moving away from wellness into kind of health conditions. I think if you want to then think about, you know, predicting disease, then you want kind of incident, kind of longitudinal cohort. So I absolutely agree. We're going to, you know, there's no single answer here. It depends on the question you want. And design is crucial to that. Yeah. Okay. We'll move on. Phil, you had a question. Thanks, Jonathan. So let me have a question for Miriam and Alison and for the other powers, but maybe using Alzheimer's and the brain as an example, you know, to what extent do you think that we can leverage the existing studies, you know, to generate this type of multiomic data versus designing the proper study that we may want to do today based on the many things we've learned in the past decade or two. When a lot of the cohort studies were already going on and, you know, we're going to design to do other things. And in particular, I'd say the big divide is the separation between the end organ sort of data that we get from autopsy material, in this case, the brain versus the fluid biomarker, the fluid data that we can get from CSF and blood, which are on different subjects, typically much younger than the individuals from whom we get the brains. Yeah, that's a difficult question, Phil. So I think there may be some advantages in using the cohorts that we have for convenience, certainly having multiple omics on the same person is probably going to be advantageous versus cobbling together omics from participants, a set of participants that may or may not be similar to another set of participants on which you'll have a different set of omics. So I think that's that's a major, that would be a major advantage is having to comprehensive set of omics on a decent amount of participants, all of them. And, and obviously that set of participants would be selected based on the question you're asking, right. So for some question, it may be that longitudinal cohort may be appropriate for some others, maybe that the data, the samples that we have on Alzheimer's disease patients may be more appropriate. But that's a different, that's a very difficult question to address. If we have to do something de novo, I think we better think clearly in our study design and the question you want to ask to make those decision rationally. But for existing data, I think there is also a merit of putting together existing data that's already been collected. But again, I think that's the harmonization is going to be very difficult. But you know, that's a zero cost, it's already been collected. So I think there is some merit as well into looking into trying to harmonize this type of data to ask reasonable questions. Yeah, I don't think I really have much to add. I mean, I agree in both ways that I think that that ideally I think you want the different omics data sets on the same individual and that may necessitate us as you know, collecting either new data on existing cohorts or creating new cohorts. But I mean, I think just from a very practical point of view, some investment does need to be made in trying to harmonize and optimize what we currently have as well because we will learn some things from that. And maybe that will lead us to design better studies for the next generation of studies. But we definitely need these kinds of data on the same individuals. And I do think one of the challenges in Alzheimer's disease and what we have so far is that it's mainly from dead people. Right. And yeah, we just we said earlier about how it's probably got a 20 year course of disease. So I think that that that's a real challenge in this field that we really need some data from existing longitudinal cohorts where we can sample people at different points in the disease. And that's what I tried to illustrate actually at the end of my talk with suggestion of some of these, you know, some of the certainly rare forms of the disease, but predictable in terms of when they're going to onset disease and who's going to get disease and collecting omics in those kinds of families might be extremely valuable. Okay, we're running up on the on the hour. I'm going to give Zhihang the chance to ask her ask the last question. And then I think we're due for a little bit of a break if I'm if I remember correctly. So Zhihang. Thanks so much, Anson. Great discussions. My questions is about how to effectively design multi-omic study in longitudinal studies. In particular, ideally, we want to sample everybody and in the whole cohort think about like a million people and get the multi-omic data. For example, yearly multi-omic data for everybody. But in reality, this is not possible. And so we need to sample a subset of people. Then how can we effectively sample a subset of people in multi-omic study to help with the scientific goal we are interested in as a panelist that discussed. And so the traditional way is in genomic study is to do random sample. But in epidemiological study, we know this is not effective way. And it's not a powerful way to use the limited funding to do. So for example, in epidemiological study, when we'll use like case cohort design. And so suppose you have for each new case, and then for those people at risk, at risk cut, you random sample a subgroup of people. And so this will improve the power because you will sample enough cases as time moves on. And also you have sufficient controls. So by this type of efficient epidemiological design has not been used much in genetic genomic studies. I want to get the south from the panelists how can we encourage incorporating those more efficient epidemiological designs in designing the multi-omic study in longitudinal settings. And that's a key challenge. You hit on there and there's different approaches. One is to take the UK Biobank approach, which is to say everyone's got their own pet disease of interest. So you either measure something in everybody or in a random sample. So you don't favor any particular disease. But of course that has limitations. I think the rationale for doing that is to avoid the piecemeal approach whereby different parts of your cohort have different multi-omics data because everybody's interested in a different disease. And then you end up with this sort of patchwork that you can't knit together. But I think if you want factors to look at, I think PRS as somebody mentioned earlier, I think there's a lot of interest now in identifying either related family members or people with high polydent risk in the age, as people said, for Alzheimer's as well, hitting the age bracket that you want. So I think there are ways to enrich. But I think if you're thinking about a large population cohort or even a patient cohort, how you do that so you don't end up with a piecemeal jigsaw is can be quite challenging. Okay. I think we've unfortunately run out of time. I'm sure it would be more questions and more answers. We could talk about this for a long period of time. But we are due for a short break at this point, I think. So I'm going to turn it back over to Joanne L and Erin. And they can take it away. Thanks, Jonathan. Yes, we are due for a break right now. And everybody come back at 315. We would encourage you to keep connected to Zoom so that we don't have to let everybody back in. So go take a break and come back at 315. Thank you.


Hello there. Today, we are going to learn how to find a video on YouTube. We are also going to learn how to find a video that shows how to open a Facebook account. First, we have to get online. Click on Mozilla Firefox. Or you can use Google Chrome. You can also use Explorer. I'm going to click on Firefox. Now, we need to find a website that has a lot of videos. YouTube is a good website for videos. So, go to the address bar at the top, click inside, and type www.youtube.com. Now look at all of the videos. In the search box at the top in YouTube, type how to open a Facebook account. And the year. This year for me is 2013. There are lots of videos about how to open a Facebook account. Look for when they were made. Right here in the description, it says four months ago. This one says ten months ago. This one says seven months ago. Choose the one that was made a little while ago. I'm going to choose four months ago. And listen. Okay, sometimes the video is not very clear. And if it's not clear, you can look at another one.  You can click on this arrow, the back key. I'm going to try this one, how to make a Facebook account. And listen. Okay, this one looks clear. That's how you find a video on YouTube. So let's review. First, open Mozilla Firefox or whatever. Type YouTube www.youtube into the address bar. Type how to search, how to open a Facebook account with the year. And then click on your video. That's how you do it. Good luck.


Back in action today, I'm going to show you how to add these amazing animated emojis into your After Effects tutorial, and not only that, they're going to be tracked to faces, so they will move with your person, or whatever it is you're tracking, they will move with you. What the heck am I talking about?  Let me show you here, so I'll turn this off. This is the original footage. Boom, there he is. Why am I even doing that? I should just go ahead and show you the original here, okay? This is the original footage, there he is, there's some dude walking down the street and we're walking on the beach, and then I'm just going to go ahead and add that back in, and then let's take a look at what it looks like with the emoji, boing, and you'll notice that the emoji turned and it actually moves with the face. It is tracked to the face very, very well, so let's go ahead and delete all of that stuff. Let's delete everything, actually. Let's just go ahead and chop it out and start from scratch. I'm going to show you how to do this. It's not too tricky, but there's a few steps to it, so let's just get it rolling. Okay, the first step is you've got to grab your footage, so here's the footage. I'm going to drag and drop that into my project panel, like I just did, and then I'm just going to drag and drop it right here on top of New Composition and Presto, we're on our way. The next step is I'm just going to go ahead and stop this around, let's go to like three seconds, just over three seconds, because this is very CPU and resource intensive, so I'm just going to make this a small composition, because I just want to teach you the technique. So I just pulled that in there, and then I'm just going to go to Trim Comp to work area. So now this is only about a three and a half second long clip, so that's manageable. Okay, now making sure you're selected on the video here that you're using, we're going to go up to the top here and we're going to grab this little rectangle tool. We're going to get the ellipse tool, in fact, and we're going to draw a mask around his face. So let's just go ahead and do that. I want to make sure I get all of his face and a little extra doesn't hurt. Okay, something like that, that doesn't look too bad. All right, good enough. Okay, so we've got our mask. Next step, left click on the mask here. We're now going to go over to the tracker. You will see, if you don't see it, it's going to be a window, and there's got to be a check mark beside tracker.  So the first step here is we're going to go ahead and hit the play button. This says track selected masks forward, left click on that, and it will do a mask track. Let's go ahead. There we go. Now, this is very, very quick, and I've got a fast computer, so you'll see here these are the tracking points, and it's being applied almost in real time. That's pretty darn impressive there, Adobe. Nice work. So there we go. Now we've got a track mask on his face, and you'll see here that it sort of floats and discombobulated in space. Not to worry if that bugs you. All you got to do is go over to the mask, and then instead of add, just click it to none. And when you do that, you'll still see the mask, but it'll show you all the stuff in the background too, so you can sort of zoom in and see what's going on. So okay, so we've got a pretty good face track here. So okay, I'm liking it. The next step is we're going to want to set a rest pose. So I'm going to go ahead and just click outside of the composition so I can get rid of that mask stuff so I can just see. And for a rest pose, basically you want a, when he's looking or she's looking at the camera, just basically straight on. If you can get a shot like that, that's the best shot. So without any funny faces or anything like that.  There we go, something like that. That's a good one. We're going to go back to tracker, open it up, and we're going to go ahead and click on the mask again. We clicked out. We're going to click back on it, and then you're going to see here set rest pose. So we're going to set that as our rest pose bingo. Now the next step is to go extract and copy face measurements. You got to make sure you left click on this as well. And then it's going to run some calculations and you'll see them pop up here under your effect controls. So here we go. There's our face measurements and yeah, I won't go into all the details, but it's done it automatically. We are now on our way and we're getting pretty close. The next step is we want to go up to window extensions and we want motion bro version 3.2, whatever it's at when you watch this video. For me, I've got it open right here. So I'm going to open it up and there it is. Now you'll probably see something like this, but what you want to do is you want to make sure you click on this little folder icon and then for this, what I'm working on, I'm working with the free version here. So this is AE starter, AE face tool starter pack. So this is the free version. So this is, you can do this work along with me at least at the time of this. So here we go. So here we go. So what are we looking for? We're looking for the emoji. So let's just take a look here. There it is emoji. Now there's a lot of cool emojis. I don't even know which one I want. I want the crying emoji.  Do I want the devil emoji? I don't even know. I kind of like this one. We're the guys about to chunder.  So we're going to now left click on apply and here we go. It loads the presets. It doesn't do a perfect job though. As you can see here it's done an okay job but it's not positioned correctly. So this is where we got to get in and start doing some fine tune it. All right, you're following along with me. I'm liking it. So I'm going to double click now on this composition and there's going to be a few things. But what we're looking at is we're going to want to look at these settings and let's go ahead and make a few adjustments. The sphere radius is how big the head is. So if you want a massive melon you can change that. We're not going to mess around with that too much but we are going to take a look at a couple things. One, well actually that's not so bad. If I'm looking at this correctly, if I'm seeing it the way I think I'm seeing it, this isn't so bad. So I'm just going to go ahead and flip through here and take a couple of looks at some stuff. One of the things I want to change and you'll see here that under settings, under transform is the anchor point. This one's critical. This one is not in the correct point. You want to resist the urge to double click on this and try and move it to where it's supposed to be. You actually got to move it by anchor point. So I'm going to go ahead and move the anchor point so that it's about right in the middle there, something like that. And then let's see, I'm going to move it up and I kind of want to just to be where I think his head should be. So maybe something like that. Now let's go ahead and close this up and now let's hit spacebar and see what we got. Okay, there he goes. Oh, chunder time. Boom, turns, looks at the camera. Vomits, sort of vomit, a little bit in his mouth. Presto, that's how you attach an emoji using face tracking inside Adobe After Effects. Guys, thanks for watching. Tons more stuff coming up.


What's going on guys and welcome to the channel.  Nani? Hey guys Philly beats you here And if you check out the last video we just did on how to constantly keep resetting raid battles Well, I just discovered with my buddy Zundu How to exploit the whole entire resetting system of raid battles so that you can have Unlimited amounts of the same exact G max Pokemon. So let's just get into this We're gonna have both perspective cameras so you guys can see so I was walking around and I go to the ghost ruin tower And when I get there I see hey, it's another G max butterfree braid. Let's just check it out Let's let's go into it. Let's do it So Zundu joins along and then as we're fighting I'm think and he catches it by the way He catches the butterfree and when he catches the butterfree after that battle I decide to not catch the butterfree and just leave the thing I just leave the raid completely just shut out my game exit out. He catches it successfully his game saves He's got the butterfree right so then I come back in the game again. I'm there I start off the fight again because it saved in the wild area before I was able to get to it But then I decided to save at the spot when I got to it So once we entered in again Oh, I started the raid battle again and Zundu joined me again this time and we beat butterfree and he caught it again Now if you take a look at Zundu's screen You can see that these butterfree's that he got are both exactly the same exact Butterfree from that raid which essentially means something absolutely insane if for some reason You were to find a five IV almost like perfect butterfree, right? You can keep doing it over and over and over again now I didn't catch those two butterfree's but the thing is Zundu can just trade over to me That's specific butterfree in your case your friends or the people you are playing with can just catch unlimited Unlimited amounts of the same exact Pokemon and send it over to you This is actually completely mind-blowing and this means you can farm a G max Charizard Non-stop absolutely non-stop for a whole entire night with the boys and catch as many as Possible and then just share them around with your group of friends and everybody gets a G max Charizard and guess what? It's not even done illegally. It's not done through hacking programs.  It is down in the description below Let's get to the raid exploits and I will see you guys in the next video This is Philly beats you and I'm out


. I would say towards a better process together. Yes. Dynamically. That's right. There's no, there is no end state of truth. Yep. But that value proposition is not characteristic of the current systems. And so it has a unique feature of, it de-risks in a way that's much more effective than the current systems. It requires people to do the cooperative thing, which is like I said, with credit cards or ATMs or swaps or anything with mitochondria. Right? You know, your free existing things have to come together because it's a better deal to be had, right? So that's, and so that, so the convey, are conveying why there's a better deal to be had. We can use the existing problems in the world, climate change, war, water. It's not even a better deal. It's a better negotiating table. Yes. And that's the, that's always the reframe is always from there will be better states to we're going to seek precision today. Yes. Beautiful, beautiful. And that gets back to that now versus past and future thing that I was struggling with before. Right? We're not going to, we're not saying it's better. It's a better way to be. And we don't know where being takes us because that's, we have models of it. You have a model, I have a model, we have a model, let's get together, figure out where we want to be. The reality is the synthesis of those desires I guess as evolving through time. Closing statement from first Steven, then Yvonne, if you want, then back to Scott. Well, yeah, I suppose the takeaway and there was, you had the E, the G, what was the third letter again? There was C is the preference. Okay, let's see. I think that's really useful. I think that's like a nice meeting point there between, and I keep coming back to this, but that ergodicity and non-ergodicity in the sense that, you know, as you get down to the smaller scale, the body, and I think even when you get into this outside of cognition, everything's got to follow that growth, kind of ergodic kind of churn. And then you start to have this ability to bring in these non-ergodics as we get to larger time scales. And, you know, we even see as we get now to the state and how you can borrow money in ways which an individual can't. We've seen this with the EU because they're not constrained by lifespan in the way that mortage is on. So you've got this weird ability to bring in preferences and that about, of non-ergodic sort of ways of just making a choice and creating deliberately steady state, like a fear currency. The whole point is it's steady and non-ergodic. But we actually, and this is where I think a lot of the work with the indigenous cultures is they are, they could say they're trapped in the old, cynical way of understanding the world. But actually that gives you a way to tune into your deeper existential way of knowing about the long-term future because that is where your phenotype knowledge comes from. Not, you know, okay, we have these structures that give us this ability to also do deep temporal depth, but we do lose the ability to integrate free energy in such a new, the dimensions come down and down and down and down to these kind of artifacts. So yeah, this is, anyway, this is what is coming up for me, thanks. Well, I hope, Steven, that you and others can, in the future, you know, listen back to what you've laid out and hopefully see it in a broader perspective what you were hinting at, Yvonne. Any closing thoughts for 2020 indeed? Yeah, sure. I just want to thank you all for brilliant discussion and it's the great discussion for the end of this strange year. I hope to see you all next, next from the beginning, from the January. And thanks for your help in the project from the beginning. Really critical. Thank you. Yeah, so very chill. Scott, closing thought. You guys are, this is beautiful. You guys are beautiful. This is awesome. I'm, I feel like a metamorphosis.  This is really delightful and fascinating and I think it's gonna be really very helpful to a lot of problems that are out there in the world. It feels like there's a lot of solution space that gets opened up with this and in introducing it in digestible chunks. So I always talk to my clients about where are you now? Where do you want to be? That's a tough one. And now I see why it's a tough one because it's in a way the wrong question. Where do we want to be individually and then how do we synthesize? And then what are the ways to get there? And then you triage them. So that's a standard thing I used to use now that's changing because it's a, the exploration of where we want to be is the answer to that really hard second question which is people have difficulty answering. So thank you for inviting me on the journey. This is great stuff. So to close on that, Scott you just, you laid out a helpful way of thinking about it with just where do you want to go? This is common, goal setting. Who are we, where do we want to go? How are we going to get there? But you brought in two pieces, which is the values. The values are what is not explicitly described but they're part of the preference and the policy. And so combining all of these disparate threads together and not having one heat engine for the refrigerator and one power plant for the other one but combining all of these threads together so we can have a discussion from multi-perspective. So that's the intercultural component, multi-perspective but also bringing together these co-extant values that we have for system function, exploration, so many other features. But really my closing thought is for this live stream that tomorrow probably I'm going to record a video called 12.0 that is going to be the clock striking midnight on 2020. And then we will indeed head into a very exciting 2021 and beyond. So both of you, well actually all of you I guess are RSVP'd for the Active Inference Lab. So I'm just really looking forward to those three projects and just like you said, it's got with the digestible bits. The live stream has been our learning process. We've all learned so much along this journey but now we also want to be thinking about could make shorter content, ones that have interactive parts, who knows but there's going to be yeah, great things happening ahead. So thanks everyone who's listening live or in replay. Thanks guys for participating. Fantastic and happy new year everybody. Yeah, happy new year. Cheers. Happy new year. Wow, what a discussion. What a discussion. All right, well, I'm going to terminate the live stream. So thanks everyone and I will talk to you later.


Mr. President, Mrs. Reagan, welcome to the National Gallery of Art. Distinguished guests, ladies and gentlemen, if you would remain standing while the Color Guard presents the national colors. Trustees of the National Gallery, Mr. Chief Justice, members of the Cabinet, members of Congress, distinguished guests, I will not speak as long as I did in the State of the Union address. Nancy and I are pleased to be here to honor Andrew Mellon and to celebrate another milestone in the realization of his vision of a National Gallery of Art. An American gallery created for the enrichment of all the people of the United States and second to none in its commitment to excellence. Tonight we inaugurate some 40,000 square feet of new gallery space. It's a great privilege for me to share in this. President Roosevelt accepted the original gifts of the West Wing, the West Building and Mr. Mellon's collection in 1941 and President Carter accepted the East Building in 1978. It's taken 42 years, but now a Republican has a chance to share in the fund. The man we honor tonight, Andrew Mellon, contributed so much as a captain of finance and industry, as a highly principled public servant, and, which is the reason we remember him this evening, as an individual whose dedication to the arts still enriches the people of the United States. In business he contributed to the great industrial expansion that provided the American people with the bounty of freedom and wealth the world had never known. His personal commitment to the arts added a further dimension of meaning and beauty to the liberty in which he believed so deeply. Mr. Mellon's philanthropy was not, as some would have us believe, a rare exception. It was and is a vital part of the American character. From our earliest days the arts in America have depended on this generosity and, yes, on the appreciation of the cultural underpinnings of our society. Our country has been blessed with great patrons like Andrew Mellon, but also with millions of less wealthy Americans who give what they can in order to elevate the cultural level of their community and of their country. We can all be proud that in 1981, the most recent figures available, Americans contributed the record-breaking $3.35 billion to cultural institutions and organizations. It represents an increase of 13.2% from the year before. Americans, more than any other people, have always understood the relationship between personal freedom and individual responsibility, something which is especially true in the arts. However, we have enjoyed our cultural freedom for so long that sometimes some of us may take it for granted. A group of young Americans were touring Latvia a few years ago. They were given an opportunity to visit with a local artist. This painter, careful with her words because she was speaking through a government interpreter, suggested that the artist fared better under communism because the system demanded quality before an artist's work could be shown, thus preventing an undeveloped artist from ruining his or her reputation. This painter, for example, said she had worked hard and was soon to be permitted for a showing in Moscow, and she pulled out some examples of her work. And as is so often the case with socialist realism, her work lacked a certain personality and feeling. Before the young Americans could leave, however, this artist insisted that they see some of the examples of her earlier work before her skills had matured enough for a showing in Moscow. And she removed from her closet some photographs of her earlier paintings, paintings that were alive with expression reflecting warmth and vitality. She had given those young Americans a message without ever saying a negative word about artistic freedom under totalitarianism. The National Gallery, as was Mr. Mellon's wish, has gone to great lengths to prevent political interference with its decisions. We can all be grateful that God provided this country with leaders who loved culture and also loved liberty, as exemplified by Andrew Mellon. And tonight, I would like to take an opportunity to thank his son, Paul Mellon, for all he's doing for the arts and for what he has done for the National Gallery. I understand that at today's meeting, the trustees accepted the extraordinary gift of paintings, sculpture, and graphic art that we've seen from Paul Mellon. Well, I too am proud to accept this gift on behalf of the people of the United States. Paul, it would be inappropriate to say that you are following in your father's footsteps because you're leaving some mighty big footprints of your own. Of course, one would expect nothing less from an old horse cavalry, man like yourself, and you can take that from an old horse cavalryman himself. Seriously, though, the work that you're doing, like the generous contributions of so many here tonight, is something for which all of you can be rightfully proud. Andrew Mellon's original gifts, his collection, and the funds for the gallery were made in the condition that the gallery would not bear his name, but the name of the nation instead. He knew well that a country is as refined and decent as its people. Our cultural future, as it should be, is not in the hands of a minister or commissar of arts, but instead is dependent on farsighted men and women who are dedicated to cultural betterment of America, people who yearn to share their love of art with their fellow citizens and take it upon themselves to do what is necessary for cultural and artistic advancement. Early in our republic, our country was often referred to as a new Athens. Many basic ideals of democracy can be traced back to that ancient city-state, a city with elections and an open marketplace called an agora, and the city, a gathering center for mankind, where intellectual and artistic creativity reached new heights, left artistic treasures that speak to us through the ages. Today we should all be grateful to citizens like Andrew Mellon, who left as did the founding fathers a legacy like that of Athens, which will speak to mankind for a thousand years. I don't know that it was Athens, but I do know that there was a Greek city-state back in that time that had a custom that has intrigued me very much. When a citizen had a proposal to make for a government program, he made it standing on a chair with a noose around his neck. And if the people liked his proposal, they removed the noose. If they didn't, they kicked the chair. I don't suppose we could institute that. We'll stick with the arts and the other democratic ideals. But I thank you for letting me be a part, and Nancy be a part of your efforts on behalf of the National Gallery of Art. Thank you all, and God bless you. Thank you very much, Mr. President. I'm sure that I speak for all those assembled here in saying that you can count on the continued support of all of us as private citizens for his reminiscences of his...