So are we starting on time, Aaron? We've done an amazing job. Everyone has done an amazing job of being on time. Yes, I completely agree. We would absolutely love to start on time, Ajay. So I think as long as we have our panelists here, they could turn on their cameras. Aaron, I want to back up and prepare a few slides. Great. I'll turn it over to you, Ajay. For those of you who don't know Ajay, he is a terrific colleague, a program director at NHGRI in the Division of Genomic Medicine. He oversees a number of large trans NIH programs, including links and has significant involvement in the HubMap program as well. Thanks, Ajay. Over to you. Thank you. Yeah, I was worried that I would have to introduce myself, which is always a bit weird. But we are going to switch now from individual long detailed presentations or various keynotey vision statements to very brief presentations along with followed up by sort of a panel discussion. So please be ready with all of your questions. So essentially, we have three panelists, Dr. Sarah Teichman, Dr. Lana Grammere, and Dr. Neil Hanchard. They have been asked to speak for five minutes each and to address three basic questions. Where do we want to be? What is the aspiration goal and what are the barriers and opportunities? So as I said, each one of them will get five minutes and at the end of four, I'll just briefly pop in and give a one minute warning. So first up is Sarah Teichman. Dr. Teichman is the head of cellular genetics of the Welcome Sanger Institute. Her research focuses on four complementary study areas, transcription regulation and gene expression, similar to genomics, immunology and protein complexes. Dr. Teichman is co-founder and principal leader of the Human Cell Atlas International Consortium. We changed to create comprehensive reference maps for all human cells to further understand health and disease. Dr. Teichman, do you have slides? Well, I have prepared a few, actually. Yeah, just after listening to the talks earlier. Great. Thank you, Adjai. Thanks for inviting me. And it's a real pleasure to be here. And of course, I'm part of the Hubmap Consortium, which Adjai co-organizes via the NIH Commons Fund. And so I'm somewhat familiar with NIH, kind of certain types of modes of investment. And so it's a real pleasure to speak about sort of my feeling about future opportunities. And I just want to put up a few bullet points and then I'll elaborate on why I think that they're valuable. So single-cell multi-omics and electronic health records in a longitudinal manner, I think, will give insights into disease progressions at a very high-dimensional, detailed cellular molecular level. And I'll explain why I think that based on recent COVID research. And then further, you know, we've heard about resources that are genetic sized. You could buy a bank. It's the one that I'm most familiar with. And then there's sort of counterparts here in the U.S., so to speak. And, you know, combining these modalities with genetic-sized cohorts that are genotyped or whole genome sequenced, I think would then allow you to layer on top your interrogation of the tissue or fluid that you're studying by single-cell multi-omics with the clinical metadata and the genotype data. And, you know, we've heard things along these lines earlier in the earlier talks. And this is really just a more specific kind of suggestion and then looking at disease versus control cohorts, which I'll give an example of in COVID. So it will be remiss of me not to say that as a healthy reference in terms of the cells and molecules in our body, I'm obviously partial to the human cell outlasts that Ajay's mentioned. You know, there are some members of the human cell outlasts community here, and I invite all of you to join as well if you're interested in the healthy reference map of the human body. And the technologies that we're using to map the tissues are single-cell genomics, spatial transcriptomics, spatial methods coupled and stitched together by computational methods. There are, I mean, Tully is right that it's sort of, human cells is not there as it were. It's not ready. There is significant amount of data that's fully open access in the data coordination platform and there's a lot of additional data under managed access. And you can see examples of numbers for major tissues and organs here. The kidney, half a million skin over a million airways, many millions of cells that are in suspension data, gastrointestinal tract, human developmental stages and tissues, of course, many millions because they're so varied, liver, half a million, so on. So these are just a few examples. And computational methods for integrating them into and placing them into space are rapidly developing using things like cell to location from over a baracter where the suspension data is mapped onto the spatial transcriptomic data here for reconstructing tissue architecture at single cell resolution and full transcriptomic breadth. And I'll skip on to why I think this can be valuable in disease and what I've learned through COVID-19. And that is studying blood, which is one of these tissues in the body that's very accessible and like urine or saliva or other things that have been mentioned or potentially also biopsies. We got together with three centers in the UK and didn't have much longitudinal data but were capturing COVID patients of different disease severity. The asymptomatic mild moderates were critical and basically this allowed us to define cell types and gene expression signatures in a lot of detail and resolution and define different cell and molecular signatures of the different cohorts. Now these are not longitude and as I said, they're kind of snapshot captured. This wasn't an eight year project like Mike's. It was a few month project and what I realized was that in the UK many of these are collected under so-called bio resource ethics where you can go in and you can apply and you can go into the electronic health records. And so this cohort is 140 patients in total. Sorry. This cohort was 140 patients in total. They're all SNCC-chipped. They all have electronic health records and basically we were only scratching the surface in this Nature Medicine paper that we published a few months ago. And it's such an exciting time. I think there's such rich data for coupling different modalities at the genetics level, at the multi-omics level. This is site-seq. So you've got protein. You've got RNA. You've got BDJ sequencing. You've got electronic health records. There's huge potential here. So I'll leave you with this slide again about what I think is the future of sort of medical functional genomics essentially where you're not just looking at a healthy reference state in the biology but you're also wanting to go into disease. Thank you. Now we'll stop there. Thanks, Dr. Packman. Dr. Garmier. So Dr. Garmier is Associate Professor of the Department of... in the Department of Computational Medicine and Bioinformatics at the University of Michigan. She leads a multidisciplinary team engaged in computational and experimental human genomics works on translational bioinformatics, research interests of single cell sequencing and bioinformatics, and integration of omics and clinical data and high-tropic methods to study non-colonial RNA. Dr. Garmier. Hi, everybody. Thank you for sticking around. It's a great pleasure to be in this workshop and learn from the colleagues about the most recent technologies, assays and ideas, as well as helping planning for the next phase together for HGRII's machine plans. As I was introduced that my group is really a computational group. We focus on translational bioinformatics. In fact, when I started my research group from University of Hawaii, about eight, nine years ago, we started with multi-omics integration as the overarching goal. And now after eight, nine years, our overarching goal is still multi-omics data integration, but we updated it somewhat to include more than multi-omics or also other modality of the data as well in our research. So in the lab, just for those who are not familiar with me, we do multi-modal patient survival prediction supported by one R01. We also do a lot of integration work at a single cell resolution developed by Informatic Tools. We also think about forwardly how we can use the genomic models to make them actionable and develop a drug reposition pipelines and methods computationally. And lastly, I also have a personal and a professional interest to pregnancy adversities. I have another R01 founded to study precomptial multi-omics based precomptial biomarker study. So as you can see, you know, really our research is very centralized around the multi-omics model data integration. So from the data science perspective, I can tell you that currently there are a lot of methods out there that claim to do data integration, but if you examine very carefully, you realize that most of these methods are not supervised by phenotypes. Rather, they start from bottom up by integrating different kind of genomic data and then trying to identify the patterns within the coherent patterns with the different genomic data. For example, this A is a kernel based method and B at the bottom is the neural network based method and C here is correlation based method and D here is the mathematical framework called a matrix factorization method. All those methods, they have one thing in common that is unsupervised starting from the heuristic of the genomics data. So it comes to me that there's something lacking, which is why can't we use a phenotype as part of the rules to help decide what the outcome of the prediction could be. So due to that consideration, our contribution to the community is to develop several versions of deep learning based method. We call it a deep probe. It is a multi-omics data integration framework supervised by survival. We use survival as a phenotype. So without going into the great amount of detail, this method draws an assembly approach where you take different genomics data as an input, you do a bunch of preprocessing. And for each genomic data type where you use auto encoders, you reduce the dimensions of the input features. And then you link those transformed hidden layer features with survival features to define them as new survival features, informative survival features. And after that, we do clustering approach, identify optimal number of clusters in your cohort, and then you build a classification algorithm. And then because it's a supervised method, it can be applied to predict many other cohorts across various types of genomics data types. So this is a framework of this methodology. We tested initially on liver cancer. So we built the model using TCGA and we applied it on top of a variety of other population cohorts of very heterogeneous populations from various ethnicities across different omics measurements, either in RNA-seq or microRNA or DMs, et cetera. So here the result shows that this model is really robust across all the populations, all the cohorts, all the genomic of genomic measurements. More than that, we also expanded this framework to predict the 32 types of TCG cancers to stratify how many patients survival subgroups are there, can we identify? And turns out most of the cancers can be optimized into two survival risk groups. So we are hoping that this work is going to be useful for the clinicians in order to provide a first-line reference for them to predict what their patient survival status could be. This work is supposed to be accepted by genome medicine soon. So that's one example of what we have done from the computational aspect. Overall, from my perspective, I think as data science is working in this field over many years, here are some good points I would like to address in terms of challenges in data integration. One of the challenges, there is very complex confounders. People may actually not even be aware of or take them into account. We talked about earlier why is the cell type and tissue type heterogeneity. To do that, you need to use deconvolution methods. However, during the process of deconvolution, we find that those methods are not reliable. The data sets are not really great. It's a whole another box of worms that we opened up recently. I'll show you a slide at the very end about this issue. There are other confounders that need to be addressed in the study. For example, some phenotypes, AGS, ethnicity and treatment etc. Especially for TCGA, we worked a tremendous amount. The treatment options are really limited. It really limits how powerful we could use this kind of public repository to do new discoveries and treatment suggestions, prediction, etc. In my perspective, there have been a lot of methods to be the best in their own view. However, there really isn't enough unbiased benchmark studies that are very active. I think this field of benchmarking really deserves a lot more attention. One issue is the next slide I will tell you. Also, like many other people already mentioned about the next challenge do you integrate different types of omics data with other data modalities for example, image data and electronic health record data? And further, how would you integrate the different scales of data from the single cell resolution to bulk resolution to the population resolution? So these are all challenges and at the same time presenting themselves as opportunities for us all. So here's the issue that we have right now. Do you have time? No, actually. So do you want a few seconds to summarize? Yes, I just have a few seconds to illustrate the problem is really long trivial.  And so because the availability of the two types of different approaches to try to decombulate the data, why is to use Sarah's data published from 2018 Nature paper on placenta and we use that reference and decombulate the major cell types in placenta. This is the distribution of different cell types using RAC data. However, if you use another reference that claim to be cell-specific DM oscillation markers, you get a very different picture of the cell proportions. And which one is true? Which one is false? I don't have a confirmative answer for that, but I do think there's some limitation in terms of, you know, bulk level cell type-specific DM oscillation markers. We tended to think that single cell RAC data-based references is better. We also have some, you know, private data from placenta in our own lab that matches pretty well with Sarah's paper's cell type proportions. With that, I just end my discussion about the challenges that we're faced. Okay, thank you. So the next presenter is Dr. Neal Hanchard. Dr. Hanchard is a clinical investigator within the medical genomics and metabolic genetics branch at NNGRI Division of Interneural Research. His research interests lie in the use of genomic and genetic tools to understand complex pediatric diseases and traits in diverse populations. Dr. Hanchard aims to understand the molecular mechanisms that lead to disease and to catalog related genes biological pathways and mechanisms. Dr. Hanchard, thank you for starting to share already. Thanks, Ajay. So thank you for the opportunity to sort of share this, you know, realizing that you're the last speaker at the end and the big question is where do we want to be? That's kind of a fraught kind of question to ask. I thought that the talks have been so good and stimulating today that there's actually some good data that can come from this. So I'm coming at this from a sort of physician-scientist-clinician translational research type of viewpoint. And so I started looking from this review I read many years ago where the sort of omics are represented as two-dimensional entities that sort of give rise to a three-dimensional body at the end. And I thought it was really useful and of course it could be updated nowadays with additional omics and sort of environment and diet and EHR and all the other things that we've heard about today. And in the ideal world again you'd have that available for all clinical states, right? So whether that's health or disease you've got this sort of multi-planar viewpoint of each one of these. And in the ideal world you would have this available for differing disease states, different ages, and different ancestries. And so from where we are now obviously there's a lot of, there's the issue of how that would work as a reference if you have to harmonize the data and their governance issues. But I thought about it also in terms of going forward from where we are now, in terms of the three areas that we've kind of discussed a bit more today. So like from a data integration standpoint the real problem and I'm not a statistician but even I can see that there are unique challenges to integrating this kind of high-dimensional large data sets over multi-planes, right? And so this becomes an issue about how you can use hypothesis, how you can use this kind of data for generating a hypothesis for identifying a biomarker or patterns of biomarkers that can be utilized to understand sort of health and disease states. But on the other hand, there are potentially sort of lower-hanging fruit that relate to sort of going from those kind of relationships that we know very well right now, those genotype, phenotype, or transcriptomic phenotype type of relationships and using this kind of data in order to trace the causal pathways and sort of understand more about the disease mechanism that we intervene with. From a technology standpoint again, so I'm a physician scientist, I'm often collecting cohorts of children in far-flung parts of the world and so one of the real limitations to this is sort of being able to assay all of these from a single sort of aliquot of sample. So obviously in the single cell world this is moving towards where we're getting to, we're able to assay all of these layers of omics from a given sort of sample. But you could even think about it as one step above as you sort of collect these cohorts. It's really helpful if you can collect one sample from an individual that will cover everything rather than one sample for RNA, one sample for DNA, one sample for metabolomics. And when you're dealing with kids for instance where you have a limitation in the amount of sample that you can take then this becomes particularly to the four. But I think all of these things relate to this idea about study design and what we've heard today really talking about the utility of having longitudinal study designs to be able to understand temporal, intra-individual or intra-disease variability and being able to utilize those insights to understand trajectories and build new biomarkers and understand even thinking of time points as sort of pre-intervention and post-intervention sort of understanding the utility of it. I think the problem here of course is one of cost, right? So do you have enough can you collect all of these that cost a certain amount of money to recruit one individual and do all of these studies on one individual? You sort of multiply that by doing multiple time points and you multiply multiple time points by a cohort of individual that gets expensive. But I do think that some of the approaches in technology will sort of heavily inform this like if we can get down to single sampling or again with data integration where you might be able to identify those particular modalities that will be most useful for your particular study question so that you're again be able to use imputation where it needs to be utilized in order to sort of make the whole effort a little bit more tractable. And that's really all I had and I'm certainly open for discussion at this point. Thank you, Dr. So you made up for time. Great, so we are now open for a set of discussions and questions to the panelists. I see that there are a few questions in the chat. Does anybody, okay there's Rachel. Rachel your hand is raised. Go for it.  Sorry, I was wondering what the panelists thought about given what we know about the limitations of many of these omics technologies and I work in the field of metabolomics so I'm particularly thinking of that and we really don't have anywhere near the full coverage of the metabolome there's a huge amount of missing information there's a lot that we just don't know about the metabolome and I think the same can be said for many of the other omics. So how can we integrate individual omics first before we try and integrate them or is it even possible to do that I think with the metabolome we're a long way away from being able to capture the whole metabolome but how far can we go with integration before we fully understand each individual omic. If I may Rachel hi this is Laina I actually work on metabolomics space quite extensively so I think I can share with you my thoughts metabolomics data actually you know it's most close to the phenotype data so depending on the purpose of your study if it's for biomarker I would say you know metabolomic data are the greatest measurements for biomarker candidates in terms of data integration if you look at the correlation yes there might be some issues integrating the metabolomic data with other data types but I think you know you could get around with that if your purpose is for diagnosis prediction you could definitely use platform for example deep learning or some other just even like renormalize all of the input features by skinny them to be uniform and stack them together and try to do prediction you can definitely do that however in terms of understanding the mechanism biological mechanism that's a different story you know for metabolomics because you really don't have to get a lot of measurements you're missing out a lot of things you only see the tip of the iceberg but the hope is that you know by looking at other genomics like transcriptomics they can give you some trace mark of you know where things are happening right the enzymes that converts metabolites one to another if there's consistency then it shows some sort of trend you could do some heuristic thing that way yeah I agree that you know you do have the potential where you are now that with integrating depending upon what your question is right so if you're sort of interested in patterns of biomarkers then you probably have a resolution where if you combine that with the other omics you'll get what you want but if your if your goal is really to understand the sort of deep detail of a particular metabolic pathway in a particular disease then you might be a little bit more stuck where you need to spend more energy sort of understanding what this is exactly like so you know where you are now is highly useful for me for instance but maybe somewhat less useful for somebody who's sort of targeting a specific drug pathway for instance I think those are great answers don't think I need to add anything thank you so there is a there's a question in the chat about about how to create benchmarks and I think it was also there in one of our slides and I think Joe Ecker expanded a bit to include both experimental and computational methods and benchmarking these what are people's thoughts on benchmarks I mean it's a sort of task that no one wants to take up I somewhat disagree you know actually having been on a lot of experimental and computational benchmarking papers over the decades they end up being extremely highly cited and the Salahalas community as the standards and technology working group has done a few of them and the community is incredibly grateful for that kind of work so it's I think the challenge is it's a lot of work and effort to implement other people's methods either computationally or experimentally and be tough to do that but it is very worthwhile and it's one of the challenges is having a gold standard then our reference data set and so on but definitely I think that type of work is really valuable Judy has a question in the chat also in fact this is a really area that I'm starting to pay attention to because I have this problem myself as a computation biologist you know we run different methods computational decomposition methods to get the different, vastly different results for example there are different cancer types and so it came to this question okay are those reference data are good enough those data were measured under certain context micro-environment does it really apply to other scenarios other RNA-seq data I don't know I think it needs to have tissue specificity tissue specificity or cell type specificity so my feeling is that needs some update in terms of the reference data in terms of computational methods themselves there are you know various methods developed dealing with different omics space RNA-seq, DMF solution and now from singles RNA-seq data to bulk RNA-seq data etc so this area of research is very much underappreciated by folks in the computational biology domain because some of the people just don't think it's novel it's like you're trying to be a policeman and you oftentimes get criticized one method performs better than the other method etc so it's hard work to do that like Athara said you have to understand that method good enough in order to make a really fair comparison so I feel like this is falling into the engineering domain you need to have the benchmark data the comprehensive benchmark data you need to collect the methods the method needs to be well documented easy to be downloaded and run and then you need to have the good metrics to evaluate them systematically without bias so all of those are engineering concepts engineering concepts and people don't necessarily appreciate that so you know I'm trying to use this voice to raise awareness in the community hopefully the Founding ATSC can help support research in this area it is actually research okay thanks I think there are a whole bunch of hands up Howard had his hand up for us I believe and people are sending me direct messages about having questions so let's let me do an order Howard, Lindsay and Thiago thanks AJ this is a question for everybody and so we've heard about these various study designs very ambitious very care for collecting multiple data modalities and my question is what happens when you actually have let's say gaps in the data how are the methods current methods in dealing with that imagine that you start it and then some individuals are missing certain data types they're missing some time points and then in this kind of framework are you always limited always by your weakest link because if that were the case right as soon as you have one gap then you can use that example you cannot use that patient then this makes the study design very challenging and also very expensive because everything has to be perfect what are basically ways of dealing with sort of data imputation ways of addressing this kind of an issue maybe this is an area of challenge can you speak about that and the way I was thinking about that was more on the EHR side rather than on the the multiomics on the genomic side but they could equally apply to the genomic side and I think that the it sort of comes out in the wash in terms of imputation if the numbers are large enough then you can kind of smooth over missing values and that's why one of the things that I emphasised was for this type of disease research because of this challenge that you're mentioning Howard but then also because of simple inter-individual variability and so on just numbers are important numbers are going to be important it's different from sort of healthy reference Salah was saying kind of work I agree I also think that we can't allow perfection to be the enemy of good right or good enough in the sense that if you have if you have multiple modalities on multiple individuals and you can get through that numbers issue then you can sort of make a good guess if you have and if the remaining data that you have is rich enough right so if everybody's missing 5 or the 8 then that's kind of problematic but if you've got sort of a missing whole single missing hole in each person then that's something that you can kind of smooth over and be able to understand particularly as the data sets get larger understand what sort of goes with what in a certain sense I think this speaks to the point that Neil you're making about the cost like multiple modalities multiple time points for people if we actually can then deal with data gaps maybe then for certain modalities we collect every other time other things right we have certain we have much we have a lot more degrees of freedom in what we need to do and therefore we can sample more people or more time points Howard as far as I know there are some statistical methods like group factor analysis that you can utilize under when you have large chunks of data not being available for certain subsets of your sample I don't know in practice how well they they perform but they claim to be able to do under these sorts of things yeah I think the hard part is the dimensionality right so you can sort of you can impute genome data but you also have to you know if you're imputing you know transcription data you also have to take into consideration that you have methylation data that can help with that or you have genome data that can help to fill in the gaps there so I think the dimensionality in some senses might actually working your favor if you have enough of each um so we're actually doing exactly what Howard is talking about some of the you know sample that we have there is no RNA-seq data available at all completely missing that is an extreme case you only have the methylation you need to impute all the populations all the all everybody's RNA-seq data what you're going to do you know you just have to deal with it based on what data are available you can try to use as a training data set and try to build a model and then hopefully the model is good enough to be able to capture the variation within the population but really the best way to do it is to design the study ahead of time well enough you prepare that you know there could be full there there could be problem down the road and have extra samples so if once as they fail you can you can get more measurements um it is really um you know that come up with with small amount of missing data you can impute there are computational methods handling that but if it's a whole you know entire population how are you going to impute that's a that's a grand challenge okay next question Lindsay hi thank you so much um I was thinking kind of in the study design space about comments that Nancy and Tully and other people have brought up about how to get around um I guess more correlation between data types Nancy had mentioned you know if you've got a vast database and you can see that this these sets of patients while they don't have genetic data there's a similar cohort of patients with genetic data and you can kind of infer that they may they may have a similar underlying genetic cause and then Tully's comments about you know how we've got a lot of correlation between tissues and that drills down to cell type commonalities between these tissues and then there was another comment about there seems to be more sharingness among molecular phenotypes than there is at the genetic level and this can kind of compensate for lack of I guess genetic diversity in a lot of studies that have been done and you can kind of look at these molecular phenotypes across populations and get at the root cause of disease I'm trying to think this is really just kind of a broader question is are there ways that we can combine these these thoughts and these correlations and the sharing as models and say you know if we collect these kinds of data and this kind of information this is the core set that can kind of transcend some of the limitations of the broader genetic studies that we're having where we're lacking population information and we're lacking you know potentially the genetic information but we have you know RNA or something else it's just kind of a broader thought process of really drilling down to which modalities will give us the biggest bang for our buck in a larger cohort of any given disease and so I just kind of wanted to bring that up I saw a thread throughout some of the talks today about that and I just wanted to bring that up for discussion. Yeah I agree that's kind of what I was alluding to I think if you on the sort of data integration side can understand which things give you the best bang for the buck then you can design your at least for the study that you're interested in if you know it's going to be different if you're looking for biomarkers whether it's looking for disease mechanisms etc but if you can start to understand you know which modalities are the most useful in that then you can design the study up front to say well at least we need to get these things or if you can't get this then you need to get this and those things might help with as I said bringing the cost down to something that's manageable but also it'll help ultimately with bringing the statistics into you know something where you can be more robust. Yeah and I guess the more discrete question is are we there do we have enough information where we can make those determinations for some some diseases or some data sets of interest or is that a gap that still needs to be filled in certain ways and and just suggestions on how to do that. I think it's a gap but I figure I'm not enough of a statistician or computational person to really say whether or not it is or how much of a gap it is but it seems to me that that um we are if we're there it's not like obvious to everybody but if we so it might be that we are there that the data is there and just hasn't been put together in order to do this kind of effort and I'm not sure which one of those is going to end up being true. Okay so I think at England say you probably hit on a hard question that nobody has anything profound to impart at this point. So let's move along I think it's Tiago Tiago you still have a question yeah hi hello so I was wondering if the panelists could comment on the relative contribution of so basically different aspects to data integration namely I see this routinely in a lot of papers that people struggle to explain the functional links between discordant data sets between you know we assume that for example data methylation will lead to gene silencing but that doesn't always the that's not always the case you assume that RNA is going to be translated into proteins that's not always the case so I'm wondering if there is one aspect about data integration which is that we still don't fully understand from a mechanistic perspective what is the relationship between different types of data that's one question or one aspect another aspect is the sort of static component that we've already talked about like so do we need like really tight time courses do we need a lot more longitudinal data to really understand the dynamics the directionality between the you know the relationship between these data sets or you know all those two things for example are actually just a minor aspect and truly the major gap is that we just don't have computational tools that are refined enough to really infer what is meaningful about these data sets so do we need more sophisticated machine learning tools so I was wondering if you could comment on basically different aspects that contribute to the challenge that is data integration so I can comment on that in fact that you know the fact that most of the population measurements are static it's one snapshot that really isn't a problem I think it contributed part of the problem you see a lack of correlation in a different omic space if you assume since our static in steady state then the correlation could be that assumption could be held more or less however we know that that's not always the case right so in fact you know there's a recent work from Fabian's group they applied non-steady state assumptions to single-sterilistic data they improved the binary velocity algorithm better resolved some of the issues or backflows in the RSI data set so I do think that this is the direction to go given that if you can have time series measurements let me ask when genetics is the other thing so time series genetics yeah I agree with both of those I think there's probably a gap on both sides I think there's a gap in terms of how we you know in terms of being able to actually gauge what we can get from the current data sets but I think there's also a gap in that we don't always have enough longitudinal data and the times where we have gotten longitudinal data we've learned a lot more so it seemed to me that we've probably got a gap on both sides like computation and to be able to make the most of these set of cross sectional simple point things but also to really understand that kind of variation of the temporal variation which is to me almost a completely different ball game the tools may be the same but I think it gives you a different insight to the biology A.J. you're muted thank you we have two more or less than a minute left I'm going to pick a question from the chat this is extending Tess's question and also to Sarah HCA's work what are some of the bottlenecks and solutions in more comprehensive multi-omics studies in a global context both regarding sampling and developing capabilities to do omics assays and data analysis outside the western world yeah I was sorry I started to type a response to that in the chat so I didn't have to delete all of that so I think that there's a challenge to some extent in sort of selling the utility of doing a lot of this work when you have because you also have to sell the idea that we want to sample people multiple times on multiple occasions and you want to be able to take multiple samples from each person at each one of those occasions and sometimes that's a tough sell in terms of what you are going to be able to return on investment for some groups I also think there's a sort of governance issue which is probably largely around education because the same thing happened with the genomic data that was generated is sort of how is this going to be used and will this have some benefit to us and are there going to be loss of information and privacy etc so I do think there are governance issues that have come up as we have started to do this at least in H3Africa we've started to do this but then there's also ownership left to be done to sort of convince people that there's some utility in having these multiple modalities to be done on each individual so I think they're so mountable but I also think that there are large collections that are going ahead that are very unidimensional Okay, so with that I would like to thank Neel, Lana and Sarah from SpeakingThing for an excellent breakout sort of session here discussing some of where we could go and what sort of challenges still remain out there there's also an excellent set of questions and discussions in the chat you can actually save the chat if you want with that I think I'll bring the session to an end and microphone back to Aaron in the journal Judy Ajay, thank you, we at this point will turn it over to our co-chairs Judy and Howard to provide a quick summary of today which is not necessarily an easy thing to do Thank you everyone for a very stimulating day just to briefly sort of refresh, we've heard quite a bit about the vision for NHGRI the current portfolio, the really growing area of investigation to multiomics and then really some of the exciting work that's possible and also then some of the challenges sort of really scaling up this area and some of the kind of the current themes I've heard about really I think including a scale and also the kind of diversity of projects that can be done using these new technologies Judy, what do you want to add to that? Yeah, no I think the charge we got from NHGRI is there's a lot of great work going on that we've heard about and what are the specific recommendations for NHGRI in terms of staying at the forefront it's obvious there's data explosion I actually think Neil Hancher gave a terrific summary of the kind of the triangulation of the three different things, the layers and so forth but I would go back to the keynote talks from Nancy Cox and Mike Snyder, a lot of common themes and Nancy's charge I think is extremely well taken, which is we've done a lot of great research, substantial advances and that's the excitement of doing research but in terms of clinical implementation that's slow and I think that we're all feeling kind of that tension between the advances in research and what you can actually implement the value of passive data collection that you get through the electronic health records or the wearables are amazing, as a community we have to make sure that that doesn't actually worsen health disparities, you can easily see how that might be the case the older I get the more interested I am in aging questions so we had a couple of very nice references to aging and one of the kind of there was a couple of dichotomy from the notes of really comparing the n equals one studies versus population base references rare variants versus common variants and I think there was multiple speakers talked about kind of the what's rate limiting and a lot of it it's interesting we've kind of solved the genetics part of it but then the phenotype part I think is I think we all agree that how do you track disease at the earliest stages such that you're really going from that transition point of health and disease and then we've had a very nice discussion about what are we missing right now and so that's kind of what I got from this any other comments we want to make or those are my major kind of cross cutting themes well thank you Judy I think maybe we can bring today's meeting to a close just to remind everybody that the meeting continues tomorrow and there will be many more discussions and perhaps tomorrow can be a little more directive and think about what specific recommendations we might want to make and I see Erin also has a comment yeah thanks Howard I think before we sign off I did want to just come back to what I mentioned at the beginning about Juneteenth being commemorated as a federal holiday I just want to quickly share my screen and put up a slide if that's okay with everyone so here we go can you see that okay so as I mentioned in the beginning we we're really excited to hear that President Biden is creating this federal holiday to celebrate and commemorate Juneteenth we were expecting to hear a little bit more from the Office of Personnel Management and HHS and NIH leadership throughout the day but we quite haven't received that information yet so I did just want to let everyone know especially our colleagues at our federal employees that we do plan to have the workshop tomorrow but we completely understand if any of you feel like you'd rather use your day tomorrow reflecting and celebrating this holiday I did put up on the screen a campaign with a hashtag move for equity which is being celebrated at the NIH in collaboration with 8 changes for racial equity so if you have a chance you can check that out as well but I did just want to put this out there if we hear anything more we will reach out to you throughout the evening but otherwise we're really looking forward to seeing you tomorrow this has been incredibly productive and insightful day Joannella did you have anything else you wanted to say before we sign off? Thanks Sarah, no I just wanted to say thank you to everybody for joining it's been a very stimulating discussion and I am very much looking forward to tomorrow see you tomorrow at one o'clock thank you very much


So I want you to know, no thought is spiritual, thought is just a thought. But it's a tremendous instrument, it can do things. A thought does not take you anywhere, but a thought can set the course of your life. Suppose I sit here and think of Vishakapatnam. How much ever in detail I imagine Vishakapatnam, still I don't get there, isn't it? It may fire up a desire within me to go to Vishakapatnam. But I'll not get there thinking about it. So thought doesn't take you anywhere, but it sets the direction. Whatever you think of, naturally you tend to go in the direction. So keep having spiritual thoughts. One day, if I keep on thinking about Vishakapatnam, one day I'll buy the ticket to go there, isn't it? So keep thinking, one day you'll want to go there. Desire will come, isn't it? What you're thinking of is what you desire, isn't it so? Keep having spiritual thoughts. But be clear, what is a spiritual thought? I don't know what it is because I've never had a spiritual thought till now. So do not classify your thoughts as spiritual or non-spiritual. Just look at this. Don't even think about spirituality.  That's all.


Hi everybody, Joe for Jaspy's CaseBrakes.com coming out at you with yesterday's brand new release. Still hot. 2022 tops Diamond Icons Baseball, one box pick your team number six. Very big thanks to this group for buying their team straight up, which I appreciate. Joe was the last one to buy the team, gets the LastBotMojo star next to his name, seventy percent of the time. LastBotMojo hits a hundred percent of the time. These are the people who got into the random number block randomizer. We did that in a separate video, but we pulled all those teams right up there and then we turned into a serial number break. And it's a four box case. Remember I marked those one, two, and three. We did box number four yesterday. Let's see which box we do today. So this time we're going to go one, two, three, four, five, six on the dice roll. One, two, three, four, five, six. And it's two. Two. Top box. And then next time we'll probably, we'll just go one, two, three, four, five, six. And the next one box break of Diamond Icons is almost in single digits. Help us get us there. You know, with any luck we may be able to do, we may be able to sell out a box straight up without a filler or we can use a filler to get it across the finish line. So either way it works for me. So if you want to get your team straight up, make sure you do that. Make sure you do that now. A book. Usually those are dual relic books. Dual relic book card. Good luck. Looks like Yankee pinstripes. Both Yankees. Wow. Nice. Derek Jeter on one side and Mariana Vera on the other side. Moe and the captain. Nice low number two. Nine out of 10 for the Yankees.  Nice start. Next up is a Joey Vadovado. 18 out of 25 for the Red Legs. Dennis picking up the Reds straight up. I'm a triple top loader here for Joey Vadovado. We got John Smoltz. Nice. Three color relic and autograph. 25 out of 25. Scary with the Braves. Pedro Martinez. Three out of five. Jersey and autograph. Sean Gola picked up the Boston Red Sox straight up and out of fives and under. SG is a train whistle. The world famous. Love the design on the diamond icons. This little, there's sort of like little cutouts right here and then a matte finish underneath which I really like. Sean. All aboard the Big Hit Express. Woo-woo. All right. Next up is Silver Inc. Darrell Strawberry. One out of 10. That's our first number block hit. So we go by that digit right there. And that is number one. Metz one is for Rob. There you go Rob. Next up is Red Inc. Autograph. 11 out of 25. Nomah. Nomah Garcia-Para. Currently does some work with the Dodgers. Television network out here. Who's doing the color commentary today? Sean Gola with the Red Sox. Red Inc. Red Sox. Silver Inc. Don Mattingling. 23 out of 25. Donnie Baseball. Gary with the Yankees. A few more to go. We have Eddie Murray. 12 out of 25. And that is for the Orioles. Orioles are a number block team. Got a lot of ex-Dodgers in this break too. Wasn't Eddie Murray a Dodger for a little bit? Orioles are a number block team so we go by that digit right there. Baltimore two. That's going to be for Anthony. And the last one looks like a train whistle. No, there's two more but it is a train whistle. One out of five. Miggy. Miguel Cabrera. Future Hall of Famer. Easily first ballot for Miguel Cabrera. And the Tigers are a number block team. That's for Tigers one. Detroit one. Is for Rob. Rob. All aboard the Big Hit Express. And the last hit. Five out of ten for the Angels. Shohei Otani. Showtime. And that's for Rob as well. There you go Rob. He's heating up. He's on fire. Nice one. Bought the Angels straight up. Scared money don't make money. There you go gang. That's a nice break of 2022 tops diamond icons baseball. We've got more one box breaks in the store. Pick your teams before they go into a filler or wait for the filler and get in on a serial number break. Thanks for watching. I'm Joe for jaspyscasebreaks.com and I'll see you next time for the next break. Bye bye.


So, this paper, Food and Security Gender and International Migration, is with my dissertation advisor, Maria Floreau, at American University, and for the sake of bureaucracy, I have to say that the views expressed by me today do not reflect the economic research service of the USDA and the United States government at all. Now that we're past that, okay, so the takeaway message of this paper is that I find that food and security is an important determinant of migration behavior for both migration intentions and migration decisions. I find that migration intentions increases monotonically with the severity of food and security. So the more food and security you are, the more likely you are to intend to migrate. I find that the association between food and security and migration intentions is larger than for any other covariate using the model, and this includes household income, social networks, or education, which are typically the biggest influences on migration intentions. And I find that migration decisions also decreases with the severity of food and security. Last, I find that these relationships vary significantly with gender as well, and I attribute these to socially inscribed gender norms and differential access to resources and employment opportunities. And I conclude that not accounting for these relationships may yield biased findings and may yield unreliable policy prescriptions. So to tell you why these results are important, migration is obviously an increasing important strategy in the developing world, but there's a notable lack of research on the relationship between food and security and migration. Further, there seems to be an institutional disconnect between migration and food security development agendas, where each has their own international agency, each has their own international gathering, and their own body of scholarly research, but often disregard each other completely or treat each other tangentially. One of these reasons for this gap in the literature was there was previously lacking a common measure of individual level food and security that could be applied around the world. To address this, FAO instituted the Voices of the Hungry Project and developed the FI scale, the Food and Security Experience Scale, and in 2014 implemented into the Gallup World Poll over 150 countries. Further, while some research has examined the overall trends of migration by gender, a few have modeled explicitly the differences in international determinants or migration determinants between men and women, despite the fact that the global share of international migrants are now increasingly women, where the common assumption in the economics literature is usually that the costs and potential returns to international migration are gender neutral, so the same between men and women, and more and more we're learning this is not true. So the objective of this paper was to examine food and security as a determinant of migration behavior, both migration intentions and migration decisions from potential movements in developing countries. We first developed a theoretical framework to demonstrate this relationship from a gendered perspective, and then we empirically examined the set of hypotheses derived from the theoretical framework. So hypothesis one is that we expected to find food and security to have a positive relationship with migration intentions, but we expected ambiguous relationship between food and security and migration decisions. For example, being food and secure may lead you to want to smooth your consumption by using migration, but you might not be able to afford it since you can't afford food. Hypothesis two is that we expected the magnitude of these relationships to increase with the severity of food and security, and given socially described gender norms and unequal gender relations in developing countries that we expected these relationships to differ by gender. Okay, so the contribution of the paper, we first provide a better understanding of the gendered relationship between international migration and food and security through our theoretical framework. We provide empirical evidence for the first time on this relationship for individuals among 90 developing countries, and we empirically demonstrate that this relationship is gender-specific. So in the paper, I drive each one of these terms, but for time we'll just jump right in. So we model differently the individual's migration intention versus the individual's migration decision. So starting here, the probability of a migration intention from country K to country J is just the probability that their expected utility in their destination is greater than the utility of staying where they are, given that they have enough resources, A, to meet the migration cost threshold to gamma C. And therefore the probability of not migrating of staying is the probability that they cannot afford to migrate, or if they can, that their expected utility is in the destination country is lower than their utility of staying where they are, Sylvester. The gender effect on the migration intentions is ambiguous. So on the one hand, higher psychic costs and fewer resources can constrain women from conserving migration, but on the other hand, higher probability of employment in certain female labor-intensive sectors in destination countries can increase their intentions to migrate. For example, there's recently an increase in the demand for care industry migrants who are typically female. In the second stage, we model the migration decision, but we follow the new economic labor migration literature, and we model the migration decision as a function of the household, so the individual and the household members are maximized as joint utility function for the benefit of the entire family. So this is displayed by N here, and so the first component of N is the individual's expected utility in the destination minus the utility of staying where they are, and the second component here is the household's utility having sent a migrant minus the household's utility of not sending a migrant. WI is the weight for the individual migrant's intention, and WH is the weight of the rest of the household, which sum to one. So the probability of migration taking place, migration decision, is that N is positive, given that the household has enough resources, A, to cover the migration cost threshold. So gender influences the household decision in three ways. So the psychic costs of migration endured by the other household members is also influenced by gender. So if the intending migrant is female, her migration may violate certain gender norms or social expectations, which could increase social costs in the household, which could increase the overall migration cost. The bargaining position of the prospective migrant is gender influenced by gender. If the female members are likely to have lower bargaining power in the household, then their weight, the WI, will go towards zero, and the weight for the household will go towards one. So the closer you are to one, the more bargaining power you have in this migration decision. And the probability of employment in destination countries, and therefore the expected remittances, is also gender. So data for the paper come from the 2014 to 15 waves of the Gallup Role poll, which is an annual survey of adults in over 150 countries. We include only low income and middle income countries and restrict the ages to 1864. Our dependent variable is the one you just saw, actually, for migration intentions. Ideally, if you had the opportunity, would you like to move permanently to another country in the next 12 months? And the migration decision variable is have you done any preparation for this move? For example, have you applied to residency or visa? So we assume implicitly that if the individual has purchased an airline ticket or applied for a visa successfully, that the household has collectively decided to send that migrant. Food and security information comes from the FAO's Food and Security Scale, which is a series of eight questions about the individual's experiences, meaning the basic food needs of the past year. So we use item response theory to assess and combine individual's responses to the survey, and a raw scale is then estimated for each country. And each country scale is then normalized to a global reference scale. And this makes that the severity of food and security equivalent across different countries. So the food security in Ghana, after this procedure, is now the same as the food security in Mexico, for example, which is a pretty big innovation for this data. And then, so how I code the person's food and security, so an individual can be food secure, mildly food and secure, moderately food and secure, severely food and secure, depending on how many items of the survey they affirm. So we model separately migration intentions, and separately we model migration decisions. So starting with migration intentions, we use a hierarchical linear model, which controls for the clustering of standard errors, the individual, the region, and the country level. X here is a demographic characteristics and socioeconomic characteristics. F is a function of the individual's ordinal food security status, W. And X is a vector of country level characteristics, GDP unemployment. For the migration decision, we use a hierarchical binary choice for the sample selection. So in our data, we only observe the migration decision data for those who selected one or said they had a migration intention. So for example, Y here is the migration decision, and M is the migration intention. So we only observe Y if M equals 1. So to control for the sample selection, we have to use the sample selection. So here's some example results that we control, obviously, a lot more covariance to this. So hypothesis one is that we expected a positive relationship. So this table is for migration intentions. Hypothesis one was that we expected to see a positive significant relationship between food insecurity and migration intentions, and we see this as the case. Hypothesis two is we expected migration intentions to increase with the severity of food insecurity. And we see that the more food insecurity you are, the more likely you are to intend to migrate. Hypothesis three is that we expected gender differences in migration intentions, and we see that women intend to migrate 3.8 percentage points less than men. In columns two and three, we decompose a sample by gender, so female individuals in column two and male individuals in column three. We see that for each level of food insecurity, the coefficients are slightly larger for men. And interestingly, given the different care responsibilities for women in developing countries, even the most mildest level of food insecurity acts as a driver of potential migration. And some other determinants, so we find that those living in rural areas intend to migrate less, and those that live in urban areas intend to migrate more compared to those that live in small towns or villages. The more educated intend to migrate more, those who are married intend to migrate less than those who are not. Those who are socially connected in the country of origin intend to migrate less, and those that are more socially connected outside of their country of origin intend to migrate more, which corresponds to the previous literature in the diaspora's migration. So for the migration decisions, this is a sample selection model, so the top panel is the outcome equation, which is migration decisions, and then the bottom panel is the selection equation, which is migration intentions. So for the first hypothesis, we see that food insecurity significantly decreases the probability of making a migration decision. The more food insecurity you are, the less likely you are to decide to migrate. We see the gender plays no significant role in the migration decision, although the gender effect could be captured by other co-bearants in the model. When we look at decomposed sample by gender, we see that the coefficients are slightly larger for women now, and we see that the mild food insecurity category is no longer correlated with migration decisions. So we find that individuals in rural areas intend to migrate more, but this is almost completely being driven by women in developing countries. And in the selection equation, we again find that migration intentions increase monotonically. So to conclude, I find a significant relationship between food insecurity and migration behavior. This implies that this vast literature, the determinants of migration, may regularly be ignoring an important component of migration decision making process. I find that significant gender heterogeneity, so this implies that gender-related migration policies are crucial, and that blanket approaches to development assistance may not be effective. And the results also imply a crucial need for an increased coordination between the international food security and migration policy agendas.


Maine Coon Cat vs Persian Cat. What are the differences between these two cat breeds? A Maine Coon is a big, rugged cat with a smooth, shaggy coat who looks as if they could put in a full day mousing on a farm in all weather conditions. Indeed, they were built for just such work in the harsh Maine climate, and their breed standard reflects their heritage, calling for a medium-sized to large cat with a well-proportioned body that is muscular and broad-chested. A Maine Coon has substantial, medium-length legs and large, round paws, well-toughed with fur, to serve as snowshoes during winter. A heavy coat is shorter on the shoulders, longer on the stomach and bridges, with a rough in front and a long, furry tail waving a greeting. A medium-width head is slightly longer than it is wide and has a squarish muzzle. Large, well-toughed ears are wide at the base, tapering to a point, and large, expressive eyes are green, gold, greenish gold, or copper. White or bi-colored Maine Coons may have blue or odd eyes. The brown-tabby pattern is so common in this breed that many people don't know Maine Coons can come in any other colors or patterns. They might be surprised to learn that Maine Coons are found in solid colors that include black, red, or white, all-tabby colors and patterns, bi-color such as blue and white or red and white, and patterns such as tortoise shell and calico. The Persian has a distinctive appearance, a large, round head, large, round eyes, a short nose, full cheeks, and small ears with rounded tips. The head is supported by a short, thick neck and a deceptively sturdy, muscular body, a type known as Khabi. A Persian's legs are short, thick and strong with large, round, firm paws. The tail is short but proportional to the length of the cat's body. A long, thick, shiny coat with a fine texture completes the Persian's look. It's long all over the body and includes an immense rough around the neck, a deep frill between the front legs, long ear, and toe tufts, and a full brush or tail. Persians can come in different looks. Some, known as peak face Persians, have an extremely flat face. Think twice about the breathing problems such a cat might have before deciding to get one. Doll face Persians are said to have a more old-fashioned appearance, with a face that is not as flat as the show Persian or the peak face Persian. Eye color is related to coat color. For instance, white Persians have deep blue or brilliant copper eyes, other solid colored Persians have brilliant copper eyes, silver and golden Persians have green or blue-green eyes, and so on. Hello. This video is sponsored by Beemix Pets. Are you looking for high-quality cat collars at an affordable cost? Check out BeemixPets.com. Use coupon code KittenLife to get 20% off. The good-natured and affable Maine Coon adapts well to many lifestyles and personalities. They like being with people and have the habit of following them around, but they aren't needy. They're happy to receive attention when you direct it their way, but if you're busy, they're satisfied to just supervise your doings. Close a door on them and they will wait patiently for you to realize the error of your ways and let them in. They're not typically lapping cats, but they do like to be near you. They also retain their skill as a mouser. No rodents will be safe in a home where a Maine Coon resides. Even if you don't have any mice for them to chase, they'll keep their skills sharp by chasing toys and grabbing them with their big paws.  They can climb as well as any cat but usually prefer to stay on ground level. That's where their work is, after all. They're also very smart and will happily learn tricks or play with puzzle toys that challenge their brain. Maine Coons usually enjoy a kittenish level of play well into adulthood. Males, especially, are prone to silly behavior. Females are more dignified, but they aren't above a good game of chase. Not especially vocal, they make any requests in a soft chirp or trill. The dignified and docile Persian is known for being quiet and sweet. She is an ornament to any home where she can enjoy sitting in a lap, surely her rightful place, being petted by those who are discerning enough to recognize her superior qualities, and playing house with kind children who will gently comb her hair, will her around in a baby buggy, then serve her tea at their parties. Persians are affectionate but discriminating. They reserve their attention for family members and those few guests whom they feel they can trust. Loud environments aren't a Persian style. They are sedate cats who prefer a serene home where little changes from day to day. With large, expressive eyes and a voice that has been described as soft, pleasant, and musical, Persians let their simple needs be known, regular meals, a little playtime with a catnip mouse or feather teaser, and lots of love, which they return tenfold. This is one cat who is unlikely to climb up your curtains, jump on your kitchen counters, or perch on top of your refrigerator. She is perfectly happy to rule her domain from the floor or more accessible pieces of furniture. When you are at work or are busy around the house, the Persian is content to adorn a chair, sofa or bed until you are free to admire her and give her the attention she willingly receives but never demands. Despite the length of the main coon's coat, it has a silky texture that doesn't mat easily, if you groom it regularly. It is easily cared for with twice-weekly combing to remove dead hair and distribute skin oils. Useful grooming tools include a stainless steel comb for removing tangles and what's called a grooming rake to pull out dead undercoat, which is what causes tangles when it's not removed. Use it gently, especially in the stomach area and on the tail. Main coons are patient, but they don't like having their hair pulled any more than you do. Check the tail for bits of poop stuck to the fur and clean it off with a baby wipe. Be the main coon as needed, which can range from every few weeks to every few months. If their coat feels greasy or their fur looks stringy, they need a bath. The most important thing to understand about caring for a Persian is the need for daily grooming. That long, beautiful coat doesn't stay clean and tangle free on its own. It must be gently but thoroughly combed and brushed every day, and regular bathing, at least once a month, is a good idea. Excessive tearing can be a problem in this breed. Brush the teeth to prevent periodontal disease. Daily dental hygiene is best, but weekly brushing is better than nothing. Trim the nails every couple of weeks. Wipe the corners of the eyes with a soft, damp cloth to remove any discharge. Use a separate area of the cloth for each eye so you don't run the risk of spreading any infection. Check the ears weekly. If they look dirty, wipe them out with a cotton ball or soft, damp cloth moistened with a 50-50 mixture of cider vinegar and warm water. Avoid using cotton swabs, which can damage the interior of the ear. Keep their litter box spotlessly clean. Cats are very particular about bathroom hygiene, and a clean litter box will help to keep the coat clean as well. It's a good idea to keep Maine Coon and Persian cats as an indoor only cat to protect them from diseases spread by other cats, attacks by dogs or coyotes, and the other dangers that face cats who go outdoors, such as being hit by a car. Cats who go outdoors also run the risk of being stolen by someone who would like to have such a beautiful cat without paying for it. The friendly, laid-back Maine Coon is a perfect choice for families with children and cat-friendly dogs. They love the attention they receive from children who treat them politely and with respect, and they don't mind playing dress-up or going for a ride in a baby buggy. They're happy to live with cat-friendly dogs, too, thanks to their amiable disposition. Introduce pets slowly and in controlled circumstances to ensure that they learn to get along together. Persians aren't the best choice for a house full of boisterous children and dogs, but they have no objection to being the object of a gentle child's attentions or to rubbing along with a friendly dog who doesn't chase them or otherwise cause them anxiety. If you enjoyed this video, kindly press the like button. Also don't forget to subscribe with notifications on, so that you don't miss out on videos like this. Thank you for watching.


Welcome to Toffy TV, it's the player ratings for Newcastle United 1. Everton 2, a fantastic 3-pointer St James's Park, propelling Everton up to 10th in the Premier League, and hopefully now looking up rather than looking over our shoulder way. We pretty much way after the beginning of the month, so it's been a decent month since the, well, since Michael Silver got sacked, it's been a decent month. Let's get it right. A second consecutive win for Carlo Ancelotti, and he started off pretty good at Everton. Yes, let's get into it. Jordan Pickford, I'll give Jordan Pickford a 7, big game for him last season. I acted the goat obviously, lost focus today, made one really good save in the first half, always switched on his kick and was good, never got into any of the difficulties he did last year. And with someone like Andy Carroll knocking about a lot of high balls going into the box, I thought he dealt with it mostly quite well, so yeah, I've given him a 7. Jibril Cyddeby, I've given him a 6. I thought certainly first half they tried to exploit him going forward. Let's be honest, he's not the greatest defender in the world. He just isn't, his strength is attacking. And he doesn't like to defend in his own half. And I thought certainly first half they exploited that, and obviously we made changes in the second half to sort of time negate that. So don't think it was his best game for Everton. But he's still a really good player and I think the manager will start to understand how to use him and how he can be a big asset for the team because I think he is a big asset for the team. And we've already seen that with Seamus Coleman almost playing as a third centre back against Burnley and how that can work going forward. Now he's a really good player going forward, he's just not the greatest defender in the world. So left me with my heart, my mouth towards the end as well because I thought he'd give a penalty away when he just nudged Andy Carroll, which I've seen him giving, let's be honest. He pushed him in the back, I've seen him giving. So I've given him a 6. On the other side, Leighton Baines, I've given him a 7. Very solid, very professional, just good performance, good solid performance.  And I think that's the thing with Leighton Baines. I think for a while the temptation was he can't play and Dean needs to play every week. But now and again you need to be able to have someone to come in and give someone a rest. Under Michael Silver, I think we flogged a very small collection of players where we had alternators but we were never willing to just let them have the odd game every six or seven games or whatever. I don't think you can go on like that, I don't think you can do that. And I think what Carl-Ansholotty has recognised today is with two games in the space of three days we had to make changes and I think people like Leighton Baines coming in was massive whereas last year we didn't do that. We went from barely winning 5-1 and went to Blighton and three days later the next day it was, wasn't it? And with the travelling and everything as well though, two away games. We played I think basically the same team bar, maybe one or two. And it caught up with us and it will catch up with us and it catches up with you every game. We've got another game on Wednesday, we've got another game Sunday and they're big games. So we're going to have to put a lot of effort into it. It was a massive decision to make, well it was the right decision to make those changes today and Leighton Baines coming in and did an excellent job. So I've given him a seven. Michael Keane, I've given him a seven as well. He started off really, really slow, like really, really, he worried me really early on in the game. I mean very early on like in the first minute. And he did lose most of his aerial battles with Michael Keane, with Andy Carroll, I'll say that. But most people will lose their aerial battles with Andy Carroll. It's what you do after it I think, it's how you respond to it. And I thought he defended quite well for most of the game. We know what he can't do, we know that. But today I think he showed what he can do and he did what he can do well. And then that's the most important thing. Last year in this game, in this particular fixture, he just lost his way completely. Today I just thought he kept his head and we had to deal with a lot of long balls in the box and he dealt with a lot of them as well. The ones with Carroll maybe didn't win a lot of them but the ones he had to win he won. So I think he started off fairly shaky but got better. So I've given him a seven. Mason Holgate, I've given him a seven and a half. I think he started off a little bit nervous next to Michael Keane. But I think he grew into the game really, really well. I don't think he looks the same player next to Michael Keane. But I think he's got a lot about him and he had a little bit of a spat with one of their players. I believe he had a bit of a spat with Fabio Delph as well or Fabio Delph at the end. I don't know, that's what I believe. I'm not sure what that was over. But he's got something about him. He's got something about him this kid. He's got an attitude about him which I really like. And it's that kind of attitude if home the right way can take you to the very top. He's a good defender, he's a good footballer as well. He hasn't got a lot of this nonsense hype around them like John Stones did. And I think with players like him, they can be the backbone of Everton's team for years to come. And I hope they are, I really hope they are. It's just about putting the right people around them because they're youthful, their energy and their attitude is exactly what this team needs. And I think Mason Holgate has really come on in the last couple of months massively. So I've given him a seven and a half. Sigarton I've given a seven, quite a steady game, not brilliant. Not sure why he was marking Andy Carroll. He had a football why sitting in the first half. He kept things ticking over. Just playing in the midfield as a good midfielder like we said for a long time. Just doing his job, not doing anything outrageous, not trying to play number 10. Just playing in a role where you're asking him to just... And you know what he does as well? He wins the ball back quite a lot. And I think the fact that I think we've been playing him in the wrong position for so long. I think he should have always been playing deeper and asked to do more. Cos he does win the ball back quite a lot. Not saying he's well beaten or anything. But recently he's done what being asked of him, that's all you can do. And when he's told where to play and he can't go missing, I think he does stay in the game a lot more. So I've given him a seven. Tom Davis I've given a six and a half. Obviously he started to midfield and then he went over to the left. And I thought he did a lot right today. But for me my issue with Tom is, and this will be a lot of people's issue, and I think this has always been a lot of people's issue with Tom. He gives the ball away in silly areas needlessly. And I think he did that a few times today. It's far too many times when you can play a simple ball or run into a certain area of the pitch and he almost tries to take the hard option and then gets caught out. And because he's not a big lad, he is good at winning fouls, don't get me wrong, but you can't rely on that all the time. And if you're losing the ball in the middle of the park cos you're just running with it and then they're taking it off you. Especially when there's times in that second half and in the first half when we needed to just keep the ball. And Tom's got a lane to do that better.  But he's got to start realising that the most important thing to do is keep the ball. And all round I tell you the decent game today. It was just those little moments that annoyed me, just really annoyed me at times, running in blind alleys where he should be realising what he should be doing and not losing the ball. So I'll give him a six and a half. Theo woke up, I gave him a five. He struggled to get into the game and when he did get in the game I don't think his end product was really that good. Obviously went off. I don't think he gave much help. He came back, he'd been injured, he's come back. It was good that he came back into the side cos we needed the body, we needed the legs, but he just didn't play very well so I've given him a five. With Charleston I've given eight. I thought he was excellent today. I thought he was excellent. He just works so hard. He just works so hard, he just wants it doesn't he? He just wants to win every ball and run at people and he'll get fouls and he's clever and he makes opportunities and he's got an assist today. He just wants to work hard for the team. Theo, what more can you ask from the kids? He scores goals, he sets goals up, works hard and he plays support him. He does it for the team. He doesn't really have any ego. He seems to be enjoying himself at this club and that'll take him really, really far. I just hope that now that we've got a manager like Carlo Ancelotti that he'll want to do that with Carlo Ancelotti. And all the commentators keep on telling me he's been linked with United, blah blah blah. Why would you want to go to United? What are they doing that way now? Nothing. So I just love his determination and his hard work and yeah, brilliant. The attitude is absolutely brilliant. And between him and Dominic Alvent Louing I think they've been the bedrock on what we've built this recent run on. I really do think that and they've been absolutely brilliant. Moose Keane, I've given him a six. I've got a starter day. I thought he worked hard and he tried things that didn't come off. I think he should have scored and he had a couple of really good opportunities to score and didn't take them. Obviously he got subbed off. It's going to be difficult for him, but I do think he's going to get a chance with this manager. I think he's going to get a chance to show that the manager is going to fill him with confidence and the manager is going to know how to play him as well. And that's the most important thing. It's that when you look at a Moose Keane now you look at Dominic Alvent Louing and that's the level you need to get at. And I think earlier in the season when we bought him a lot of people thought well, Dominic Alvent Louing will be chasing Moose Keane's. You know, and there's a way of that like that. And I think that's what you need.   That's a long time ago, doesn't it? Do you know what? Sometimes people just need a bit of tough love in their life. I'm told they're just not doing what they need to do. And some people respond differently. And he actually has responded in the right way. He has a sult. He's going out there and he's working out. It's not coming off for him. But he's still doing a job. So fair play. I've only given him a six. But I appreciate his effort. He should have took one of his chances. He should have. And Dominic Havel did not give him a nine. Two strikers goals today. A ball bounce around in the box. And he's put it in. And he's got a tapping in the far post with really good movement. From a brilliant with Jalys and one. And the manager said after the game on Boxing Day he had to improve his movement. So straight away the manager has identified what he needs to get better at. So straight away the manager knows what he needs to coach him on. So straight away you've got a point. And he's moving for the second goal. He's pulled away to the back post. He let the two forwards, let defenders go towards the ball. With Jalys and Ro, the lovely ball to the back post. And he's got a tapping from a yard. And that's what's been missing out of his game. And he's got two today. And he's already equaled or bettered his goal with him from last season. His evidence number nine is where could he be magnificent. He wants it. And no matter what anybody says. No matter what anybody says about Tom and Carmel. He's improved more than anybody in the Everton squad in the last 12 months. Massively improved. And you can throw me all what he's scored. He's getting better. He's getting better. And that's all you can ask. All you can ask. He works hard and he's getting better. And now he's scoring goals. And that's all you can ask from a player. And I'm delighted for him. Delighted for him. Because he's... It comes down to him realising what he needs to do. And I think the penny's dropped. And the manager's already said he's not going to bring in another striker. And that's fine. Listen. I was all on the slatt end frame. Cos I was thinking, well, you know what? That's someone who can help these young players out. But I didn't want them to replace. Places like Tom and Carmel and Jalys. Cos I want those players playing. I've never heard about Mason Hogan before. They're the spine of this team. They're the players. They've got the attitude. They're the young lads who want to get us to European football and winning trophies. And I want that. That's what you want. You've got that good mix. And it was fantastic to see Tom get those two goals. It really was. Really, really was. And I know on Wednesday... I know Wednesday you're playing in the city. And I know he'll have a good game again. Cos he loves playing in the city. And you know we get a goal against city. I remember we played city last time. We just scored that goal. But he got it on the line. I was like, what are you robbing that goal for? That's why he's robbing that goal. That's what you want from year number nine. You strike a robbing goal. So the confidence levels keep on going up and up and up and up and up. And I was delighted with his performance today. Him and Jalys were absolutely fantastic. Absolutely fantastic today. So there you go. He's my man of the match. If I've been dealt with come on, I've given him a seven. Really helped to get his back in the game. Got a grip in that midfield. Manager would probably want him in more often than not. But because of legs and stuff. Yeah, he mean again I've given him a seven. Came in the last 10 minutes. Steady got helped control on the car a little bit more.  And James Coleman gave him a seven as well. Cos he's done the same thing. The subs were brilliant. The subs were all brilliant. They were all manufactured. Not to tactically to do a job. And they all done the job perfectly. And that's what good managers do. Good in-game management. So brilliant. Delighted with three points. Let me know your thoughts in the comments on my play ratings. You agree? Do you disagree? Let me know. Make sure to check out my Instagram active action and blazes videos. Subscribe if you haven't already. Subscribe. It's going to be boss. It's going to be a big year. 2020.  Don't forget about what anyone else is doing. Massive year forever under Carlo Ancelotti. So make sure you subscribe. Make sure you give this video a like. And join us on Patreon for more great videos. Thanks for watching Toffy TV. I'll see you later.


Hello everyone welcome to another video, so just thought I'd make a video to make a few corrections because you know me a lot to get things Right I made a video about this place gippy the goat this Family run cafe. That's been getting harassed by vegan activists. They've had to shut their doors You know the boohoo story of the poor innocent cafe owners and then upon further investigation We found out this guy John Gohmans is actually multi-million dollar dairy investor goat's dairy one of the biggest goat's dairy Producers in Australia, so he owns this big goat milk company called Seabus goats Australia with the company has about 10,000 goats apparently I put some leaked footage in the video And I said this footage was taken by activists and the farmers owned by give you the goat the owner The owners of give you the goat cafe.  So, okay. Yeah, technically The footage wasn't taken from out the back of gippy cafe gippy goat cafe But it was taken at a farm that John Gohmans owns Down the road. Oh big mistake. Wasn't it big mistake. Sorry about that guys Oh, you know, it wasn't out give you the goat cafe the actual, you know footage that we've seen It was down the road still owned by John Gohmans. He owns many goat dairies But also I wanted to make I wanted to give John Gohmans a little bit of credit Because he doesn't just do goats dairy and I was saying in the in the video Why don't you invest in vegan milk? Why don't you invest in vegan milk? So through his company new lack foods new lack foods. He has released the vegan yogurt vegan coconut yogurt Well done. Well done. That's what I want to see more of more of the vegan yogurts Less of the exploitation and harm of the goats the baby boys are bludgeoned to death with a spiked hammer That is true too. I've seen pictures of the hammers. They use quite horrific another correction I want to make from the video is there was a guy who was holding a baby who the guy was actually Threatening to kidnap and kill another activist Joanne left it on the public page. This is not me even snitching on the guy the guy snitching on himself putting his big Post up saying he's gonna kidnap Joanne take her out bush and you know what happens when someone takes someone else out bush They don't come back. I said he was he had a picture of his baby or something And he's gone on missing you little man It wasn't because his baby was was passed away like I had said the baby had actually been removed from his custody Now that kind of makes sense if you're sending death threats like that to female activists I don't know what goes on in your personal life not for me to say but it's probably a reason why babies been taken off Yeah, anyways, not good sending death threats to anyone This is not how we want to play the game the lameness that you saw from the goats in the Goman's goat farms That's from a disease called it's called Kay or something CAE stands for Capri Nathoritis Encephalitis if I even pronounce that properly Contagious viral disease of goats the disease is typically spread from mother to kid through ingestion of colostrum or milk So that's where they got their lameness from Gibby goat cafe. I put three privacy complaints on my video so far three Three privacy complaints. So they're trying to get these videos removed They're they want the videos down. They can't get them removed. Everything I put in the videos is publicly accessible Information yeah the footage from leaked that I'll leave the leaked website down here It wasn't from the actual farm at Gibby the goat But it was just from a neighboring farm that John Goman's owns and he also owns the cafe I mean come on, mate Come on mate. That wasn't a very big mistake, but you can see why these people they want these this video down John Goman's connected to Kerry Stokes. Kerry Stokes has shares in John Goman's 226 million dollar Bob's Australia powdered milk formula company. What they're doing is that they're fueling this public outrage through news media You know, oh poor Gibby goat.        Don't you have any Like common decency what's going on here? So basically the footage is from John John Gohmans farms. He owns many farms neighboring farms surrounding Gibby the goat He they think they might even supply Gibby the goat cafe They probably do supply Gibby the goat cafe these horrible farms But anyway, they're feeding in this infant formula made by the Gibby the goat goats milk to children Really crazy really crazy and they're trying to get the videos removed They're actively trying to get these videos removed so watch them share them They obviously don't want this information getting out there They're abusing animals and they're trying to silence people who are exposing that fact So they're not the victims here like the newspapers try to make out. They're actually the perpetrators in this They pretend to be a family-owned farm.   Thank you very much everyone. I'll see you all in the next video Now let's just look at how meat industry funded Channel 7 Reported on the activists here. I Have a hit the activists rescuing a goat now They didn't just jump and steal a goat for no reason there was definitely just caused to rescue those goats They weren't under good care


Hi everyone, we're so pleased today to be able to let you know that the Mediterranean fruit fly outbreaks that we've been managing in Adelaide's metropolitan area have now all been eradicated. This means that if you were in a red outbreak area, you can move fruit and vegetables from your property. However, keep in mind there is still a single Queensland fruit fly outbreak in Riddleton and the yellow area for that is 15 kilometres wide. What this means is that many red outbreak areas now become yellow, so for many people you can once again pack your fruit for lunches. The yellow area surrounding the Riddleton outbreak spans from Parallelowie to Montecute to Darlington and along the coast from Outer Harbour to South Brighton. You can move your fruit within this yellow area, but you can't take it outside to other parts of South Australia which is still green zones. Please check the fruit fly map to see how this affects you at fruitfly.sa.gov.au Look, we'd just like to say a huge thank you to everyone who's helped support us over the last two years in getting this result. All your support has helped us, including practicing good garden maintenance, checking fruit from maggots, not moving restricted fruit or vegetables around and giving our field teams access to your garden. Thanks once again and have a great Christmas and a happy new year everyone. Thanks so much for making sure we keep Adelaide MedFly free.


Take two. Let's go. Welcome to Code Corner with Katie. Let's kick it. Let's see what's on thinking man's mind today. I see fire doors propped open all the time. Is this a big problem?  That's a great question. Come on in and let's find out. Fire door assemblies play a vital role in the built environment. They range in protection ratings from 20 minutes up to three hours. The varying degrees of protection are based on where they're used in a building. Among other things, fire doors protect our means of egress while allowing building occupants to reach safety in the event of a fire or other similar emergency. We have learned throughout history that smoke is a leading cause of death in a fire. A fire door that is propped or blocked open is very dangerous in that it won't form a barrier to stop the spread of smoke or fire. This is a video of a 20 minute rated apartment door after a fire. You can see the hall is black and covered in soot. When the apartment doors open, it looks brand new and untouched by the fire and smoke. Thank goodness this door was closed and latched at the time of fire like it was supposed to be. I travel a lot for work and stay in a variety of hotels across the country. This happens to be a housekeeping laundry room and the doors propped open by a laundry cart, presumably by an employee. I registered a complaint at the front desk the last time I stayed here. My guest room was directly across from this door and it was right next to the stair. They said that they would take care of it, but their comment was there isn't anything of any real value in that room. I calmly explained that it's not the value of what's in the room.  This video was filmed two months later, the door still being blocked open daily. This time I spoke with the manager of the hotel and I reiterated the importance of the door being closed and latched. I asked why would the housekeeping want to block that door open, especially after hours. They said it's probably because they have to use their key every time and this was faster and easier. I told the manager that she could buy a new lock with a different function that would be able to be left unlocked but at the same time the door could stay closed or there's legal ways to keep a fire door open with an electromechanical closer or a closing holder device that is tied to the fire alarm that can close and latch the door at the time of fire. Owners and building inhabitants just don't realize what they're doing when they block open or disable the latching on a fire door and it's our job to continue to educate the people that inhabit these buildings. For more information and continuing education opportunities, please visit Osa Abloy Academy by clicking in the link in the comments below. Please click like and subscribe to this channel. You can follow me on Twitter at our consultant and or connect with me on LinkedIn for updates. If you have a code question, feel free to email me at Catherine.Flower at OsaAbloy.com. Thanks for joining me in the Code Corner today. My name is Katie Flower and my goal is to help you achieve safe purity in the built environment.


Capitalism, as we know it, is dead. Let us not fool ourselves. There can be no social justice without tax justice. It is time to level the playing field and to think about how we properly deploy taxes in a way that catalyzes sustainable markets. Fair taxes, green taxes, and the depth of capitalism. Hi, I'm Huvann Baam. This is the day two of Davos. Sorry. Let us not fool ourselves. There can be no social justice without tax justice. But I believe that the time has come to go one step further. It is not enough to redistribute income through taxes. We must advance towards predistribution. And it was a different kind of tax that Britain's Prince Charles wanted to speak about when he was on the stage. It is time to level the playing field and to think about how we properly deploy taxes, policies and regulation in a way that catalyzes sustainable markets. Everything I've tried to do and urge over the past 50 years has been done with our children and grandchildren in mind. Because I did not want to be accused by them of doing nothing. If we look at the long-term situation we should fight climate change, we should do more, we should do it faster. Actually Finland is committed to be climate neutral by 2035 and we are committed, our government, to tackle climate change now. Capitalism as we know it is dead. This obsession that we have had with maximising profits for shareholders alone has led to incredible inequality and a planetary emergency. One of the highlights of the day two for me was hearing from the CEO of Google, Sundar Pichai, about the future of tech. It will allow us to understand the world in a deeper way. We can simulate nature better. So that means simulating molecular structures. So maybe we can discover better drugs, understanding climate in a deeper way so that we can predict weather patterns and tackle climate change. We can design better batteries. It's clear from what we are seeing here in Davos the climate change is real and we can't ignore it any longer. It's up to the companies, the countries and the people to come together and act accordingly. And those are just some of the key moments from day two. But check out the forums website for all the live panels and the weekly updates and the big headlines here from Davos.