So let me introduce someone who needs no introduction, Chris Austin, Director of NCADS, the Godfather of COMP. It's all yours. Thank you. Well, it's great to be here and it was great to be asked by Colin to do this. You know, I've been at NIH for almost 16 years now, but I look back on COMP as arguably the most important thing that I started since being here. And it was one of the – it's a singular pleasure for me, not only scientifically, but community-wise. I don't think you may realize how special the mouse genitus community is. And I know you have your fractiousness, and that came out in the original Banbury meeting. If you can imagine, you know, Alan Bradley and Brian Simbrovich and all these folks in the same room at the same time, you know. But still, in the end, it comes together, as it did in that meeting, and it allows you to do things which are quite remarkable, and I hope you value that. It's not always the case in the human world, unfortunately, the human world, whether it's – we're talking about human genetics or other kinds of human interaction. And I would just urge you when you – as you're thinking about the next stage to be as bold as we were back then in 2003, and I just went back and looked at the – at this paper again and looked at what we said in the abstract, and it's interesting to look at what Steve Slides are, because the community has done much of this. But it's hard to – it's easy to forget how unorganized and inefficient this whole process was back 15 years ago. And in this idea that this last sentence is time to harness the new technologies and efficiencies of production to mount a high throughput international effort to produce and phenotype knockouts for all mouse genes and place these resources into the public domain. At the time, that was audacious to the point of being foolhardy in some's view, but you've shown it not to be foolhardy and have shown over and over again that if you get a lot of smart people in the room that are committed to a really ambitious, important vision, these things can happen, and I look forward to being involved in whatever way I can and encouraging the work that you do in the next phase, and I'm excited about the directions you're going. So, what I decided I would do today, and Colin asked me to do, was just to give you some reflections on some things that are going on in the human world and the human genetics world. I'm not going to go into any detail because you've got fantastic people who are going to talk to you about individual programs, but I'll just tell you sort of where I think in some ways we are. So, this is how I often describe the problem that we're in. It's the problem that brought me to NIH back in 2002, but it's gotten arguably even more extreme since then, is that we live in an almost painfully bittersweet time where we know more about ourselves and health and disease than we ever have perhaps exemplified by the genome project and embryonic or induced pluripotent stem cells, that's what you're seeing on the bottom left, or ability to make mouse models of human genetic disease in remarkably rapid fashion and phenotype them, but if you ask how are people doing on the right side, this is a report from the Institute of Medicine now called the National Academy of Medicine. A few years ago, called Shorter Lives, Poor Health, sort of goes into depressing detail about the health of the American people or lack thereof, and the bottom right is a child with progeria, early aging rare disease, and so we are left with this really interesting question to the degree that what's on the right side is related to and built on what's on the left side. Why has the left side so outstripped what's on the right side? Those of you who are clinicians, or you think about the last time you were at a doctor, for the most part, unless you're a cancer doctor or a cancer patient, your care has really not changed very much in the last 30 years. My own clinical specialty is in neurology, and if you think about neurological diseases, the care of neurological and psychiatric diseases has really not changed very much in the last 30, 40, 50 years for the most part, despite these amazing advances in fundamental science, and so the question is how do we do this better? And believe it or not, I think what you're doing is absolutely key to this, so what do I mean by this? The reason I believe that this has remained such a problem is that science has never been brought to this process of translation. It's a remarkably empirical process which has many, many steps, increasing degrees of freedom and the likelihood of a many-step process with an increase in degrees of freedom getting successfully to beginning of the end if you have a trial-and-error approach, that aggregate success rate approach is zero, and that's exactly what you see, but the answer is not to buy more lottery tickets. The answer to this is to understand what the underlying science is that allows us to go from what we see in these more reductionist models, whether they're a genome sequence or a cell on the bottom left or a mouse to what's on the right side as a human, and I must say that for the most part, though we're making some headway, from a patient's point of view, we got a long way to go. I did a calculation a few years ago just to give you an audacious goal or a big problem to think about. I did a calculation a few years ago with the current rate, how many years will it be before every human disease, most of which are rare numerically, have a treatment, and that answer is 2,000 years at the current rate, 2,000 years before every human disease is treatable. If one looks at the cost function, probably the only thing increasing more rapidly than college tuition is drug prices, and no one will be able to afford these drugs at the current rate either, and that fundamentally comes from this relentless empiricism as well. The only way we're going to make this process more effective and efficient is if we change this from a system of essentially pulling the lottery to engineering that has rules that make this predictable, and that's really the challenge, I think, for the next decade or more. So the question is, how can company IPDC contribute this? Now this is a slide that Dave's shown, Francis has shown, I'm sure you've seen, and I showed this at all of my talks, to illustrate the fact that translation is not new, people have been trying to do this for thousands of years, but the opportunity is new, and the need is new, so when I graduated from medical school and I was a postdoc, this was in the mid-80s, actually before this graph starts, so these are the number of human conditions in OMIM that have a molecular basis. Now there are not all diseases, so the Duffy blood group antigens are in OMIM as well, but just take these as a proxy for human diseases. Studying for the boards back in the mid-80s was really easy because there were only about 10 diseases, human diseases, the molecular basis of which was known. Now it's well over 6,000, I don't know how people study for these things, it makes it really easy for David to come up with questions for the boards, you know, he's just like Professor Snape there, you know, he's asking about how to avoid any spots disease this year. Yeah, right, exactly, requires a lot of rubber bands, you know. But from a patient's point of view, you know, this is where the action is, and still, of all of those, the cumulative number of indications that have any treatment, FDA approved treatment is about 500, of rare diseases, I'll get to this, but the number is actually still a bit squishy, but it is known that about 300 have any treatment and that leaves about 95% or more that have no treatment, and some of these you've heard of, a lot of you haven't, honey disease, Alzheimer's or ALS, many other diseases that fit into this category, and some would say, well, gosh, you know, we all know that it takes 15, 20 years to have a therapy after a gene is discovered, sometimes much longer than that, I often remind people, and you will be glad to know that in every talk I give, I ask people what was the first disease, the molecular basis of which was identified. I got to tell you, I would say 98% of the time nobody has any idea, which is just terminally depressing. The answer is sickle cell in case you're, I hope you all knew that, but the point being that this is 1947 and there's still not a drug directed to that molecular pathogenesis, so just because you have a gene doesn't mean you're gonna have a drug anytime soon, but it certainly helps, but the question is, how we doing? Are we getting better? Are we getting worse? Well, during this, so you say, well, gosh, maybe what we need to do is just wait 15, 20 years and there will be a right shifted increase in the number of therapies that come from these discoveries, right? So that's a perfectly reasonable model to have. Unfortunately, you would be wrong, and the reason is that during this period of time, this has happened, so this is Moore's Law, for God's sake, if you don't know what Moore's Law is, don't tell anybody, because that's even more embarrassing than not knowing about sickle cell. After this, you can go Google it and Google, Moore's Law will allow you to Google it, but this is the increasing number of transistors that you can fit on a microprocessor that's revolutionized what we do in computing in our daily lives, but during the same period of time, a therapeutic development has done this. This is E Room's Law. This is Moore's Law spelled backwards. This is a real paper. If you haven't read it, please do. It is really depressing, so make sure you have some Prozac on or Scotch on board before you read this, but what's remarkable about this is this is showing dramatically, monotonically negative productivity growth in developing therapeutics since 1950, such that the number of new drugs that have been produced per billion dollars spent by the entire biomedical research community has gone down by 50% every nine years since 1950. I just think about gone down by 50% every nine years. I think of what's happened since 1950, invention of computers, recombinant DNA, knockout mice, CRISPR, gene therapy, a huge number of cloning, and none of this has had any effect on this decline in productivity. This is depressing in a number of ways. First is that if you draw out this line, if you extrapolate from this line, it goes essentially to zero in 2070. Now, it can't go to zero because it's a log plot, but it becomes asymptotic in 2070, which means that unless something changes, which has been the case since 1950, there will be no more drugs after 2070, or in other words, the cost will be infinity. The other is that NCAS was formed in part six years ago to bend this curve. We have to bend this curve in ways that invention of computers and recombinant DNA and all that have not. We need to take a fundamentally different approach to this. If I've given you two numbers so far, right? No drugs after 2070 if we keep going where we're going in 2000 years, even at the best rate before every disease is treatable. So if anything tells you that this requires a think different kind of approach, the same way as COMP and IMPC has been doing for the last 15 years, this is it. But this is in human genetics now. Okay, so into this Maustrum came NCAS and NCAS in many ways is like, well, you know, look at the landscape do for lots of other things what you all have done for mouse genetics. And so some of the things that we're doing, I got to go through all of them, of course, but we spend a lot of time worrying about the rare disease problem. And Dave is going to talk about this. He will give you a remarkable talk about this. But there is and but but is a connection, of course, between rare diseases and mouse genetics. But it's amazing how tenuous that is. And I believe and I know Dave believes that this, this is a really opportunity, great opportunity for us. The first number you all can really help with. So we all throw around this number about 7000 diseases, maybe it's 6000 diseases, that's the number in orphan that the number of guard, which is the genetic and rare disease database, but and approximately the number in OMIM. But that number is really squishy. When you when you start to push it, it's really squishy. And so one of things that that we're really interested in doing is to is to do.  That was what was done back in the old genome era. If you're trying to figure out how big the genome was that you were trying to analyze, we have got to know that for rare disease. We got to know what the universe of rare diseases is. And how do we define that? And it's it's for those of you who are or clinicians, this is a big lump or splitter problem, if you know what I mean by that. But but I think the mouse genetic community can really help us with this, because to the degree that human and mouse genetics are and physiology are conserved, then essential genes in one with the exception that Colin was talking about the human, you know, homozygous null people walking around.  And that's why we're here. I don't know. But but but but the question, the question is, how many genes in a species, which we can prospectively mutate, give us a phenotype? And it would stand to reason that that number ought to be about the same in mouse and in humans with the exception, perhaps of neuropsychiatric diseases, or other, if there's other, if you believe in the concept of human specific diseases. But currently, we think the number is about 7000. They're thought to be about 80% Mendelian, although that, as you know, that is not quite as crisp a definition as we all thought it was in high school. About 50, 60% have their onset in in children. So they're develop, either developmental, or they're, or they have their onset in in in childhood, that is before age 18. And something that Dave will talk about, which is truly frightening from the perspective of developing treatments, is that that if you look at this from a standpoint of syndrome ology, that these people identifying new syndromes through something like through things like the undiagnosed disease program or novel genotype phenotype correlations is about 250 new ones generated every year. And give you a sense of this, the number of rare diseases, which have a new therapy, I developed approved by the FDA for the first time, every year is around three, or maybe four. So we're, so we're moving three or four from the untreatable to the treatable category every year. And, and, and Dave is discovering 250 new ones every year. So either we got to speed up drug development for rare diseases, or we got to we got to defund Dave Valley. Now, I, you know, so I'm agnostic as to which which which is the better option. But but in Washington, believe it or not, that passes for logic, what I just said, which is truly frightening. Okay, so something that I think this community really needs to inculcate and something that we are really trying to change in the rare disease community is the word rare. The word rare is a real problem.  But, but if you look at the over the cumulative prevalence, 7000 diseases times relatively low prevalence, official definition is 200,000 or below, but most are in the few thousand range. The population president prevalence is thought to be about 8%. Which is about the same prevalence of type two diabetes. And, and these patients account for a disproportionate, very disproportionate amount of healthcare spending, not even including the, the diagnostic odyssey. But we do have problems in the, in the human community. I know it will shock you that people don't even all agree from country to country on what a rare disease is, how they, how they define it. So we got that problem. But the important thing is that it is clear that less than 5%, 5% of them have any regulatory treatment. And I talked about the number of years. So, so Brendan Lee may talk about this later. I don't know, he's, he's one of the, one of the PI's, a PI of one of these networks. This is something that most aprecy is very involved in as well. This is one of our approaches to the rare disease problem. And the idea about this is to, to change the way we approach rare diseases from a, each disease is independent of each other. And these are 7,000 independent syndromes, which had nothing to do with each other biologically, which is of course absurd. But that's, that's the way it would take the extreme phenotype. That's the way this has been addressed to say, well, gosh, these are all related to each other.  They're all one color. So there's no pattern that we can see. But we got to approach this as a unitary problem and look for, and look for commonalities very prospectively. And so these, this network is funded based on some kind of commonality that the PI will identify. It could be all diseases of a biochemical pathway or an organelle or a cell type or an organ type or some sort of phenotype that the humans have. But, but they group them, the three to 40 diseases, studying them together in some sort of a common biological feature. And, and, and this is a, a natural place for you all to interact with. And I think Brendan, I hope Brendan will talk something about this when, when he talks later. The other thing, which I just want to emphasize here, which is really important for you all, which you've learned from the, from the work that you've done so far is, is, is this. That is that, that NCATs is to rare diseases as NICHD is to pediatrics. That is that everybody thinks of NICHD as the place that does all the pediatrics. And it does a plurality of the, of the pediatric work that NIH does, that it does more than any other institutes as far as I'm aware. I don't know, is that true, Melissa? I think, yeah. But but, but most institutes do work which is relevant to child health and human development. And, and rare disease is the same way, of course. And so the already CRN is run as a big consortium of, of, of over 10 ICs that just happens to be coordinated by us. The other thing we have, which I hope you'll take advantage of, is something called the rare disease, the trans NIH rare disease working group, which has, I don't know what this is, 8 plus 9, 17 ICs on it. This, this is, these are your people. You know, you should think about these people as the, these people think about problems from a human genetics and phenotype point of view in many of the same ways that, that you think about mouse genetics and phenotypes. And, and so I hope as you go forward, you will use this group and the, but we're the coordinator, as you can see with the little, the little hashtag there. And, and Pariser, who's our head of office of rare diseases, is our point person on this. But, but I hope you'll use this group as a contact. The asterisks are, are members of something called Erdirk. So, so what is, what is Erdirk? I think, I think there's some, I must have some response element, which, which, which makes me particularly attracted to large international consortia. Maybe it's because my mother kept telling me that many hands make light work and that kind of thing. It actually happens to be true, as you've, as you've demonstrated. So, about three years ago, I took on the chair of this group, which is just what it sounds like. It's a group of 60 organizations all over the world coordinating their work on rare diseases to try to make headway faster. It is in no way as cohesive and organized as, as COMP and IMPC are. But, but we're, we're getting there. So it's about 40 funders, all the biggest government funders and a number of nonprofit funders like Telethon, about 15 companies, or 20 companies, mainly small, but also some large companies, about 15 patient organizations. And this is something that, again, you should really think about taking advantage of. Most, most mouse phenotypes don't have mouse patient organizations. And that's, this is an advantage of the human world is that you, you do have parents or patients that you can talk to. And of course, a number of scientific programs as well. And, and, and I want to, I want to just read you the goals, because these will sound as absurd as the COMP goal was 15 years ago. And that's on purpose. So the first one, well, so first of all, I need to tell you that the current standard state of the art, if you can call it that, is the average child of a rare disease, a child with a rare disease, and maybe mainly their children, but person with a rare disease, will undergo an so-called diagnostic odyssey, wander from doctor to doctor to doctor for five to eight years before they have a diagnosis and end up with a medical chart about Ye Big. And, and, and all during this time, their quality of life and quantity of life is going down and their, their costs, their spending is going up, right? So, and, and these, so we asked ourselves, well, gosh, if all of these really have been defined in the medical literature before, why does it take eight years for this to happen? This is not a science problem. It's an operational problem. So we set this goal at the head of ourself for the next, in the next 10 years, that all patients come, all people with a rare suspected rare disease coming to medical attention will get a diagnosis within a year. And that's, that's about a log faster than it currently happens. And, and there's all kinds of issues, organizational issues, among other things associated with this, getting people to work together, etc. Very much the kind of things that you dealt with already.  That number's around 30%, 40%, depending on who you ask. So there's still many patients who were undiagnosed. But, but and so what we're doing now is to put these in proactively into a globally coordinated diagnostic pipeline. So that within a year, within 10 years, we'll go from, you know, eight to 10 years for diagnosis to one. I actually think that we could do this a lot faster than that. But, but I couldn't get anybody to, I couldn't get them to go, go for a goal faster than a year. The second is 1000 new therapies for rare diseases, which is going to require a lot deeper understanding of pathophysiology of most of these diseases. And then a third one, which is important, but less relevant for this group probably is, it was important for us to articulate that just because we make diagnoses available or make diagnoses and have therapies that are developed. If the patients don't get them, they're not going to help them. And it's not, we can't assume that anybody is going to benefit from having a diagnosis or treatment unless we measure it. And so how do you measure that? And that's, that's really goal three.  This is my co-chair Hugh Dawkins who's in Australia looking at the first seven years of URDIC and now the next 10 years as well. And this has a number of people that you'll recognize, people like Kim Boycott and Gareth Baynham and Hans Lockmueller and Petra Kaufman and a number of other folks that you'll you'll recognize. The task forces, I'll just tell you about to give you a sense of the kind of thing we're doing. A lot of these you recognize, matchmaker exchange, the automatable access and discovery, being able to link records, model consent clauses, these are all coordinated with GA4GH of this, the solving the unsolved is what I mentioned before.  You know, do you look at proteomics? Do you look at metabolism? What do you do to try to figure out what's the wrong, what's, what's, what's going on with these, with these patients? So the, the human genotype phenotype problem is rather silly title, but I use it to illustrate two things what you're going on. One is, in case you missed it last week, the All of Us program released their or announced their awards to three genome centers. There are three of the usual suspects, their University of Washington Broad and Baylor to do sequencing for the All of Us program. The All of Us program, of course, is the hope for a million cohort of Americans who are getting themselves at least lightly phenotyped and some level of genome analysis yet to be determined, but hopefully some sort of sequencing. And, and so that is going to provide a another resource that, that this group needs to be aware of. The other is really interesting discussion going on in the human world, which is very, very much like the discussion that we had at Banbury 16 years ago, 15 years ago, and I'm sure you've had here which is, is it practical? And if so, how would you do it to phenotype humans with a variety of genetic or maybe non genetic abnormalities? And Gareth Fitzgerald at Penn is one of the people leading this, but you'll recognize some of these people, Rory Collins, David Botstein, Rob Califf. And, and so I frequently say to this group, have you talked to the mouse people because they've been doing systematic genotyping or phenotyping for 15 years. And, and, and the user, the reaction is, why would I talk to a mouse person? What can they tell me? So, you know, I feel you feel my pain and vice versa, I'm sure. But, but the point is that, that you notice this, this, this little quote here, deep phenotyping comes close to a human G knockout study. And, and so these folks want to do what you have already done. This is an enormous need in, in human disease for a very simple reason. Unlike mice, humans go to a specific specialist to phenotype and organ, right? So mice don't do that.  It's just the nature of our health care system, for the most part. Kids are a little different because kids have generalists, right? They have pediatricians, but, but older, older adults do not. And so it's, it's a, and the other problems you run into is that insurance will only pay for minimum phenotyping, tier one phenotyping, right? And unless, for the most part, unless it's absolutely required. So it's, so there are all kinds of dynamics here, which are, which are a little different. But I'm not sure how different they are. I mean, if you think about, you know, tier one, tier two, tier three, however you talk about phenotyping in mice now, that it's a cost issue, just like it is in humans. So, so all the things that you've learned, I think would be tremendously valuable for human, the human rare disease and human phenotyping world to move forward. The Semat XL gene editing program, you're going to hear about from from Mary Perry, who's the common fund person, who's, who's in charge of this, PJ Brooks, who some of you probably know, who's in the Office of Rural Diseases is the program coordinator. I'm the IC director who's responsible for this. And the idea is the goal, as you can imagine, is to get more treatments out there based on gene editing. But, but what are the needs that are being addressed? So this comes from a workshop, you know, NIH always says workshops about this thing. And what the workshop, what the workshop identified, was development of error free editing machinery, including the non cutting base editors, if you're not up on David Lou's work, recently please do. It's really rather remarkable, looking at transitions and transversions, without cutting standardized assays for looking at genetic off target effects. And this again, things that you know a lot about. A bugaboo, which you may have done working, I'm not sure, is this problem that's gone back all the way back to antisense. That is, if you have a nucleic acid construct, how do you get it to the tissue of the cell of interest? How do you target it there? And that continues to be a problem. This one relevant, oh this says human and animal models, that should say non human primate, large animal and small animal models for preclinical testing. There's a bunch of RFAs that are out to do this as well. And then interestingly, ways to track these modified cells long term. FDA is very concerned that these genetically modified cells, which, unless they undergo a turnover, are going to live in the organism, live in the human forever. And they might go places in the body where you don't want them and do things that you don't want them. So how do you follow them? How do you mark them? How do you image them over a very long period of time?  These I took out the RFA numbers because they're not open anymore. They're all closed. But these are all programs that have funded grants that are just coming out now to address these. And the ones that you want to interact with, it's certainly are, of course, the small animal models, grantees, maybe some of those in this room, I'm not sure. And then the unattended biological effects and the genome engineering toolkit, highly relevant to what you do. Something which has been an issue over and over and over again, and has come up again, internally at NIH, I just want to mention to you briefly because it does influence the view of what you all do, rightly or wrongly. Root reproducibility issues in research with animals and animal models. The very fact that there is a National Academy Medicine report on this ought to give you pause. And I realize this group is about as OCD when it comes to doing obsessive compulsive disorder, when it comes to doing good science as I know of, but not everybody is. And so one of the things that I think you could get appropriately a lot of benefit from is to demonstrate how to do this right and provide some guidance to the field about how to do this right. And if you haven't read this, this is a paper that I was on, but lots of people from NIH were on, and about transparent reporting to optimize the predictive value of preclinical research. What preclinical in this case means is animal models, that's what it means. And all kinds of issues of blinding and power calculations and all kinds of things. But it's worth looking at this because it's an issue which has come up again. We're going to be talking about this and I've been pulled into this at the annual NIH IC Director's Retreat happening next month. So this is certainly on the minds of many people here. I'm just going to run through this. I'm not going to show you this. What I want to just finish with is another thing I'd like you to think about. So one of the many reasons that therapeutic development fails in this region for this downward slope is that even when a drug gets into people, into a so-called phase one trial, it still has an 80 to 90 percent likelihood of never being approved. That is, and it fails in humans. Things that look safe in animals are not safe in people. Things, drugs that looked efficacious in animals are not efficacious in humans. And that's the case, 80 to 90 percent of the time. So a big problem. Lots of reasons for this, but a big problem that we spend a lot of time working on. And among the approaches that we're taking, and I realize this may be a little, well, maybe, I don't know whether it's whether it's out of scope for you all or not, but I just wanted to have it in your head that what we and many others are doing is to try to ask, well, how would we identify potential drug better using more physiological systems short of a mouse? Because normally what we do is we do a screen down here in some very high throughput setting. And then we generally go right to a mouse. And the lack of any kind of intermediary testing system, whether it's a spheroid or an organoid or printed tissues or organ on a chip, has really created a problem. Eventually, perhaps, we'll be able to go right from an organ on a chip to a human. We're not there yet. But the thing I'd like to suggest is that you might think about doing this in mice. And I'll try to make clear why I think you should think about doing that. But this is the way to think about it. As you go from left to right, you go from a very high throughput compatibility in a 1536 well-plated down to a couple of compounds a day. These human on a chip models have the same throughput as a mouse model currently. And their cost is about the same as a mouse currently. That may come down over time. But that's the current rate. And so one of the things we're doing is to bioprint tissues. This is possible given 3D printing technology and IPS and primary cell technology. We're starting out with laminar tissues, retina, blood vessel, and skin. But a lot of interest in lots of other organs. The idea being that you would then use these to screen for potential compounds, which might be drugs. The Tissue Chip for Drug Screening program is even a more extreme phenotype where the idea here was, could we create a system, a microfluidic system, which represents the structural and functional elements of all human tissues as a way to test for safety and efficacy of novel therapeutics, using the convergence to use a key word these days, convergence of microfluidic stem cell technology, 3D printing, cell sensor technology, optogenetics, those kinds of things. And we started out with 10 tissues. These had to be in a modular, sort of modular platform that you could mix and match these tissues. And they had to be alive with that antibiotics for at least a month. And when we started this seven years ago, again, it was a pipe dream. I got to tell you, this has gone just like this project, has much faster than I ever thought it would for all the same reasons. It's a diverse group of people with different expertise all coming together working to common goals. And that's why it worked. But if you think about what these kinds of so-called microphysiological systems can do, they do all the things that a 2D system can't do. And as somebody who spent a lot of his career doing 2D systems, it gives me pause. But reconstitution of the microarchitecture of the organ, tissue-tissue interfaces, flow control, oxygenation, mechanical cues like stretch in the lung, spacial temporal gradients. Just to give you an example, it is very clear that most cells do not go terminal differentiation until you put them next to cells that they normally are next to in vivo. And if relevant, you stretch them, say, in a gut model or in a lung model. If you don't stretch them in and out the way, hopefully, all of you are doing now, breathing, the cells do not differentiate.  The person is dead. If they're not breathing, they're dead. Why do you care if the cells are differentiated? And so we see dramatic differences in these kinds of models versus 2D models. So if you're interested in two points about this, one, this was done as a massive collaboration, still is, between ourselves and a number of other NIH institutes, a large number of pharmaceutical companies, DARPA and the FDA. If you go to our website, this individual here whose name is Chip, sorry about that, we couldn't resist calling him Chip or her Chip because it's really both, you can click on any of Chip's organs here and see the state of the art of these individual Chips. But the other thing we've done to bring it back to rare diseases is to realize that these microbiological systems might be very good for modeling rare diseases or even common diseases. So we now have, in the next phase of the program, 12 grants of funding, a modeling of all these disorders. And if you look in red, is either the organ or the organ system or the disease that's being modeled. And so tremendous progress just since 2012 when we started this. OK, so some ideas. And I've thrown a lot of these at you already. And you know, per Collins instructions, I've tried to be provocative or set ambitious goals or audacious goals. Certainly, what you've already what you've already set on doing but something which would be amazingly useful is to have knockout mice for all disease, all genes which are designated as rare disease genes. Many of these have even the same gene has tremendous mutation or allelic heterogeneity in the phenotypes that they produce. Salamone is my favorite. Three different mutations in the same gene give three completely different phenotypes. We don't understand at all why that happens. As I'm sure you'll hear about later, knockout models from the Undiagnosed Disease Program, allelic series for understanding more common diseases like all of us in top med mouse models for gene editing effects both good and bad. We're already starting to do that, as I mentioned. Something that you could think about is mouse versus human 3D tissues. The reason is when we go to FDA and we say we think this is a better way of modeling disease, they say, well, the gold standard is the mouse. Show me it's as good as the mouse. And we say, well, the whole reason we're doing this is that animal models are not necessarily predictive. But they say, well, but the gold standard is the animal model. And that's the way regulators think. So give me a tissue chip in a mouse, including a knockout mouse of the disease that you're trying to treat or an allele that represents a rare disease. And then I can compare in vivo to in vitro within a species instead of going in vivo in vitro among species, because you're changing two things at once and you can't figure if there's a discordance why. This is something I've tried to get the tissue chip consortium interested in, but I get the same answer. Why would we do mice?  The whole reason is that we're trying to get out of doing animals, even when the regulators say, no, no, no, the kinds of mechanistic studies that you could do in a mouse you can't do in a human. I've been singularly unable despite pounding the podium to get the human 3D or tissue chip people to work on mice. And I just think they just don't understand the field very well, but you do. Providing guidance, as I mentioned, on the ongoing rigor and root disability of animal model research discussions. I think you could play a big role in that. And providing guidance also on these human deep phenotyping discussions. We are really good at humans in genotyping humans now. I mean, you can do it, well, you know this. How long does it take to do an exome or a whole genome sequence? But the phenotype side of the genotype phenotype has really lagged. And the methods to do it have really lagged. And I think you have a lot to teach on that as well. So I hope I've given you some ideas to think about. I, ironically, have to go and do, in an hour, I have to go and do a teleconference on Ergirk. But then I'm going to be back in the afternoon. And I look forward to the discussion in the afternoon. So it'd be good to be back. Thank you.


for a group of friends. So I'll say like trying to lead atheists is like hurting cats. Everybody wants to take off in their own direction but we only agree on one question. All right so thank you so much. So unbeknownst to the people who are watching this you and I are pretty good friends. We are. We've known each other for a few long while and it's actually been a really interesting pattern going from an atheist or a person who realized he didn't have a good reason to believe in a god in Georgia to realizing I don't have a community that I can like come to and I'm moving to a new city so I'm literally really by myself. I just came out to my parents as like an atheist. They were like fine but we don't really uh it was literally the year I came back from Sweden so like the year just before I graduated so like this is like a whole year term like graduating everything changing new city no one I can talk to and I remember at my house I don't know how I found it I really don't but it was like I saw you it was either the YouTube page or it was the meetup group but there was like I wasn't a member yet so I couldn't see inside of it. You didn't do a search? I did a search on meetup for like atheists to see like maybe there are some. That's the reason I named them like Morbathias because I was a member of the Rational Speech Tennessee and they wouldn't use the A-word so they weren't getting people that were searching for the A-word. Right because I knew I was an atheist but I didn't know what were atheist groups or if there even was one and I saw literally just a picture of you and like I think four is in front of a book and I was like well it's only two guys it may not even be a group anymore so I was like let me just let me just copy and paste in the email and I'll be like are you guys even still a group and you're like yeah sure come down this time this place and I came back and I think my world's completely changing after that and I can't tell you how amazing it was. So glad to hear that. And the group that you saw was like 25-30 people wasn't it? Yeah it was huge yeah good food great conversations and a diverse group too that made me feel immediately comfortable. Yeah one thing I like to tell people is come on down with guaranteeing good conversation because I mean I was single for a long time and sitting at the house you didn't get any conversation. Right. I mean we tend to talk about religious stuff but then again we all agree on that. Yeah. So we tend to talk about other things as well. Right. And we're generally pretty sharp individuals. We tend to go off on pretty much any conversation and we pretty much guarantee it. And something I think I've really appreciated more than anything else was that all the atheists were only in agreement that they didn't believe in a god but they had at different levels. Right. They had different political views. They had you know different outlooks on family laws, politicals anything everything was different and it was just nice to have a really diverse group of friends. So I would say like trying to lead atheists is like hurting cats. Everybody wants to take their own direction but we only agree on one question. Okay. But we also have pretty much agreement on science and supernatural stuff. Sure. Let me flip this over sorry about that. Sure. But we do agree on the god question and that's what makes us atheists. Okay. It really was disheartening to hear about the group that you found in Kentucky that they're authoritarian that they have a harsh stance right on a particular belief that if you don't match that particular belief or have an even more open mind about it then they don't want you. Right. That really hurt. What I'm hoping though is that through the effort of like what I'm doing with this project I can hopefully start like my own subgroups that are a little bit more open and willing to like question even their own positions. But I wouldn't have gone that courage if I had not have met you and I probably would have gone this far if I had to met you either. I just want to say really thank you. Thank you very much. I appreciate that. Cool. So we got three minutes.  I'm just amazing. My atheists. You got a lot of cameras. They're amazing. So let's pull something fun. Normally I bring up a topic or normally the person brings up whatever they want to believe. But I like these. I exist as a soul. Okay. So you don't think that souls exist as aggregate and you're really confident on that. Pretty confident. Pretty confident. How confident are you? Would you say you want to use a scale? You don't have to use a scale if you want it. Well I'd say 90 percent. 90 percent. Okay. Why are you so confident? I'd like to keep an open mind. If I get evidence, you know I would believe it. I just have not seen any evidence for any kind of afterlife. Humans, Germans, dogs, cats, you know whatever. Why would I believe something on insufficient evidence? There's no evidence. Would you say if you had evidence you would increase your confidence that a soul does? So it's really... If it was credible evidence. There's all kinds of evidence. Yeah I was about to say. There's a whole scale of evidence. Yeah I can show you some Christian science journals that tell you multiple authors of. As a Christian what his evidence he's gonna plop down the bible and so that's it. Well you say well yeah that's evidence but it's incredible. It's just a book of 2,000 year old stories based on what people, very superstitious people would tell each other a long time ago. Is that credible? What makes something credible? Well scientific evidence. Testable, repeatable. Being able to pass your peer review. That's important. That would make a good difference. But where in I mean Christians and Muslims and a lot of different religions even Buddhism where they're talking about reincarnation have a belief in a soul and that's been going on for 2,000, 3,000 years even in ancient Egyptians. They believed that the barrow soul was going to go to the stars through a passage that they put in the pyramid. Sure. And even the people of the ancient Egyptians believed in soul. Overall that time there's not been any credible evidence put forward for the existence of a soul. And once you do that doesn't that just knock all these religions that are based on the existence of a soul window? Can I throw something at you? Is it possible for something to factually exist but have no credible evidence for it yet? Can you give me the question again? Is it possible for something to actually do exist in reality but not have any credible evidence for its existence yet? Could that not be the case for souls? Could. Okay. Then can I throw something at you? That's worth a 10%. Is really the belief not so much that you don't believe that a soul exists because it could exist but you know for a fact that there's no credible evidence for souls. You're conflating knowledge with belief. Oh. I don't have any knowledge of souls. Okay. I don't believe in any souls. It's the same as God. So your function of... I can't say that I have knowledge that God doesn't exist or exists. I have no knowledge so I'm an agnostic when it comes to the question of God. And I'm pretty much in the same boat with the belief in souls. I don't have... I'm an agnostic where it comes to souls exist or not but I don't believe in it. I just... I don't believe it. I will rephrase my question to make it more clear. Sure. I'm not asking about knowledge, not asking about belief. Just about confidence. And as far as confidence goes you're at least more confident that there's no good evidence or credible evidence that a soul exists than you are that souls don't exist. At this point you were saying earlier that if evidence were presented or if even if souls existed but have no evidence that we know about currently, if that evidence came to common knowledge or was able to be demonstrated in a scientific lab or repeatable or peer reviewed then I would change my mind and I would believe in them. Do I think that that evidence is out there? No I don't but it can be. Okay. You know it can be. Yeah. Once it comes to life it is a good time to start believing. That's right. The time to believe is when the evidence presents itself and to say I don't know it's good but that's talking knowledge. I normally say and I so my way of doing SE is if someone says something that meets a standard of evidence for me I'm fine with saying I agree and if you say I don't know because I don't have enough evidence that is very mundane but reasonable claim compared to someone who says I believe this but I don't have enough evidence. Now you've triggered my standard of evidence now I'm going to ask more questions but I think at this point I'm fine with that. And remember the knowledge, belief is different and a lot of people can play with those. Yeah and not only that but I noticed that when I bring up questions about confidence it tends to be dragged into a knowledge or belief arena where I'm not talking about either one of those. I think it's its own separate scale. That's kind of like why I like to make the scale more explicit but yeah I think you can say it's a combination of two or maybe where they overlap. Maybe? Yeah that's interesting I never thought about that. Oh there's something else. Thank you Larry. Thank you for everything Larry. I appreciate it. Would you have an issue if I put this on the internet? May I get your consent? I would not have an issue.


When asked why they have not ventured into any business, many individuals tend to mention finance as a major constraint. They believe that one needs to have plenty of money to start a business. However, most people fail to understand that this is not always so. It is possible to make huge profits from a little investment as long as it is a business opportunity with potentials. Are you confused about what business to start? Do you have less than $1,000 in your account? Then watch this video till the end as I will expose you to 19 businesses that you can start with less than $1,000. 1. Photography. Cameras and photography equipment are pretty much expensive. Nonetheless, you can get started in photography with just one camera and one or two lenses. The beautiful thing about photography is that you do not need a studio or the most expensive equipment to kick start. And if you are exceptional in your job, in a few months you will be able to acquire everything you want. You can get a DSLR camera for around $500 and that is the most significant expense for you. The rest adjusts small accessories and may be a website. Depending on the kind of photography you might want to venture into. 2. Professional Organizer This sounds like a colossal title, right? Well, a professional organizer is a person who can help you organize any of the physical and digital spaces in your life. A closet, a kitchen, or an office. He or she turns scattered spaces to arranged spaces by aiding in decision making that involves organizing, facilitating removal of unwanted belongings, setting up tidy systems, etc. The basic necessity for starting up a professional organizing business is excellent organizational skills. Other tools needed include a notepad, a camera, your smartphone can suffice, a tape measure, rubber gloves, face mask, tape and a kit with screwdrivers and power. You may also need to promote your business using flyers, social media, etc. But in all, you will need plenty of money to start. 3. Translator This business is appropriate for multilingual individuals. There are multiple ways to start off a translating business. You may want to take up projects people post online, approach companies or startups that do a lot of international business or check local job postings. However, the startup is relatively low. You need not rent an office space because you can work from home. For starters, all you need is a computer system and other transcription equipment, especially if you will be doing translations from audio. 4. Drop shipping In drop shipping you can sell products without actually having that inventory of the products. All that is needed is to sell the products on behalf of the supplier. You advertise the goods and other people other through you. You then send the order to the supplier who then handles the shipping of the products directly to the client. You can start up drop shipping with no capital. However, to be successful, you have a lot of marketing to do. Hence, you may consider getting a website and also running paid ads on social media platforms. 5. Jewelry making This is one of the cheapest business to set up. You can channel your love for craft and fashion into making jewelry which you can sell either online or physically. You only need to purchase equipment like wires or lines, beads, needle and other cheap supplies. Then you can set up your online store or join established platforms for selling homemade goods like Etsy where you snap and showcase your products. You can as well supply to other jewelry shops or clothes shops or even sell at events. 6. Coffee cart Starting up a full coffee shop is quite expensive but it is not the only way you can start a coffee related business. You can set up a coffee cart which you can set up in events, office buildings, malls and even in busy areas. A coffee cart can be placed practically anywhere because it can be moved easily. You will have to purchase the actual coffee cart and the coffee maker and cups but the prices of those are relatively low. 7. Handyman As a handyman you will be required to go to clients' houses to fix various home related issues. Projects may be pipe cracks, kitchen sink blockages, roof leakages or even electrical fixings and other such problems. With some amount of dollars, usually less than a thousand, you can invest in some vital tools. Also, you must make provision for the transportation so you can get jobs done in time. 8. Home-Based Alteration Service Offering services where you can make alterations to fabric and garments can help bring in lots of profits for you. If you have excellent sewing skills, this is a great business to invest in. You can work from your house for either individuals or companies that require alteration businesses. To start, you can purchase a hand sewing machine for as low as $200 and other small equipment you may need. 9. Makeup Artist What if you can turn your love for makeup into a business? Well, if you love makeup but aren't an expert, consider investing in a class or certificate program. You could start upright from your home or you could be a traveling makeup artist where you provide services at events or on location. 10. Personal Trainer If you are an athletically inclined individual, you can start a business as a personal trainer from your home gene. You can either start small with some equipment like weights, treadmills and benches which will cost less than $600 or purchase fairly used equipment. It is also necessary for you to look out for training programs which you can get involved in for you to be a certified personal trainer. As a personal trainer you will be able to make an average of $18 per hour. 11. Wedding Planner Being a wedding planner requires more contacts than finance to start up. If you love paying attention to detail and don't mind the stress of dealing with last minute changes, a wedding planning business could be a great fit. Wedding planners can earn an average of $19.89 per hour. 12. Catering Caterers are individuals who prepare meals for large groups of people. If you are a great cook and can handle the preparation of various meals, you can start your own catering business. You can start with small meetings and events while you get a cast. Buy more sophisticated utensils and logistical items and hire other people. To kick off, all you need is to purchase a few things like pots, coolers and so on and then start cooking from your kitchen. 13. Hair Stylist You can offer on location hair services so you need not to acquire your own space. As long as you are a skilled hairstylist, getting recommended won't be an issue and in no time you will be able to open your own hair salon. But for starters, all you need to get started here are a few hair products, styling tools, scissors, capes, combs and similar items. 14. Clothing Designer If you are good with stitches and want to make your unique products, you can invest in some materials and sewing equipment to create your clothing line. Costs for start-up equipment can be as small as $500 but you can start designing from your house. You can then sell your clothes online or in local boutiques. 15. Personal Shopper Many people are usually occupied with a lot of things, living little or no time for them to go shopping. If you are looking to make a lot of money and you enjoy shopping, you can start a business by assisting people and doing their shopping.  16. Children's Party Planner According to a report by the International Association of Amusement Parks and Attractions, parents are willing to spend a lot on a birthday party for their kids. To start a business in this line you would need to be a great party planner who also enjoys being around children. Just a few dollars, you can buy stuff like gains and costumes which are needed to organize children's parties. 17. Cupcake Baker If you are a kind of person that enjoys baking, then you can turn your hubby into a means of making money. You can start by baking from your kitchen and sending out samples as a means of marketing. All you need is to purchase the necessary baking equipment which costs less than $1000. You can bake, decorate and sell cakes for birthdays, anniversaries and weddings. 18. Direct Sales Representative Many companies like Merike are willing to pay you to pass out their information to those around you. To become a sales rep you'll need a starter kit. With about $100 you will get everything you require to start. You can choose to sell the products online or you can also host a sales party where you can display products to markets. 19. Menu Planning Menu planning is advisable for someone who is well grounded in nutrition. Menu planning involves drawing out a meal schedule for people who can't make the right decisions on what to eat. You don't need to prepare any meal so it doesn't require any additional resource. Making it a low cost business to start up. Not all businesses require a ton of capital to get started. You can even start some of them with as little as $100. Start now with a small cash you have. Through focus and consistency I bet you will build the empire you desire.


Hey everyone, welcome to today's day trading recap. Today is Monday January 25th starting out the week nice plus one thousand one hundred and sixteen dollars on the morning did three different trade three different mighty 90s and All three winners Netflix plus four hundred. This was two different trades in Peton for plus seven thirty and zoom plus one eighty five If we look at the runners, we did two trades went one for one Kind of winner in DK and G for plus two hundred and win minus three ninety nine. When was our only loser of the day? So let's take a look at our mighty 90s to start Netflix was our first one. So let's take a look at Netflix. I had this initial flush down We got long right here Caught this bounce got out of half of our position and never got any continuation to exit the rest and it just kind of rolled Over so we waited waited waited very patient and on this flush right here We got long here again and then caught that bounce back up towards the pivot so nice trade in Netflix for plus four hundred even in Peton Peton was our biggest winner of the day. We had two different trades here. Like I said Our first trade was on this big push up We got short here right at the pivot caught this move down for for a winner And then we turned around not too long later and got long right here and caught this move back up towards the pivot So plus 730 on Peton total and then lastly as far as our mighty 90 trades We took a short position in zoom so when zoom pushed up big move And we got short right here at the pivot and caught this little down move there for plus one eighty five on the runners We had two runners one was in DK and G And so it happened here in DK and G started to get in these well at this time These were big volume bars compared to the The rest of the day little did we know these were coming later? But so when it bounced up back towards the unchanged mark We got short right here and just caught this little move right here now obviously in hindsight This is right when the market just fell apart for about 15 20 minutes and this thing just flushed So we did not catch that part, but of course that's hindsight trading but still a nice winner in in DK and G for plus two hundred and Then lastly when when was our only loser and with when I had this move lower and then this big bounce higher back up above the highs of the day started consolidating and So we got long right in here. We're looking for a little bit of a continuation But again, this is kind of when the rest of the market just fell apart So flushed lower we just ended up cutting our losses and getting out for a loss there So plus one thousand one hundred sixteen on the day good way to start the week if you guys have any questions Let me know


My name is Kaye Felix and I will come you to Kaye Felix Inspire. Always remember if you are coming here for the first time please don't forget to subscribe. Hit the bell icon so that you get notified whenever I post new videos. Please do stay tuned for more details in this video. That's where the billionaires come from so that we use that plan as capital for our local community. Now we serve as a non-villain, we are not a local council but a general part now and we encourage them to do that. But also we want them to use the money that factory being built. That factory here must have local partners, local partners. Now we mind the money. We want a mind to license to go to Kokobi. Then license to get a partnership. When that partnership was told, some share of the traditional leadership, some share of the community, and others learned to. But no one has shown that money knows. They started out, they noticed the ones that were in the station. And where possible they go, go to them, muchamkoum, kumamashmoum, kumamashmoum, kumamashmoum. And they are great. And you are going to see a change in that. But support us in that area. I can assure you that once you came, you have been in office for five years and you remember all the difference. So let's work together. So we never go to work until the first moment. I must offer a lot of things supported here. We knew they were home. But it was equal rights. So I can assure you that was equal rights.  Your people decided for you. That's why you can't use it. So we have no feelings about the chiefs who supported you. Just a democratic one. But we work with everybody. All the chiefs. So when you were in the moment, what was that going to mean?


Where are you at with the receiver room? I love Admiral back. Obviously he changes what we can do. He just has an innate feel for the game, being able to understand when to get open, what the timing is, and he makes plays every time he's out there.  You can't put a value on that.  Just takes one play. And one play just changes your whole mindset. But for him, I feel like the over the shoulder touchdown catch to the head. After that happened, and in the back flip, he's been a different player. He really is.


How do you feel about the concept of tradition? Are you someone who believes the old ways of doing things are something to be cherished? Or is tradition to you something that evolves over time? And what if said tradition seemingly had no place for you? Because in Nicky Caro's hauntingly beautiful drama Whale Rider, one young girl is about to shape the destiny of her family, tribe and identity forever. Just on the 1987 novel written by Witte Himaira, the story follows Pai, played by Keisha Castle-Hughes, a Maori child who lost her twin brother and mother when she was born. Grief stricken and refusing to follow his father's tribal ways, Pai's father emigrates to Germany, leaving his daughter to be raised by her grandparents. Desperate to restore the fortunes of his clan, her grandfather, Koro, played by Rauri Padene, embarks on a rigorous training regime of his village's firstborn boys, teaching them ancient chants, fighting techniques and tribal law, hoping to find a new leader amongst them. However, when Pai wants to help her grandfather by proving she has what it takes, old prejudices and adherents to rigid dogmas of the past blind him from letting her participate. Shot in and around the book's central location of Hoangara in New Zealand, Whale Rider's story came about after the author's daughters asked him why it was that boys always got to be the heroes and girls were always helpless in the films they watched as a family, prompting Himaira to create the protagonist Pai and complete his story in just three weeks. The casting of Keisha Castle-Hughes, who were selected from 10,000 children from several schools in the local area, was made after director Karo stated that in order to find the right Pai, she didn't want a child actor or a girl who could play the part, but someone who was the part. As such, the completely untried Castle-Hughes is simply stunning in the role, bringing a nuanced pain and naive sense of shame from disappointing her grandfather, that is counterbalanced by her fearlessness in resisting his oppressive views on tradition. A remarkable debut performance that was indeed worthy of its Academy Awards nomination for Best Actress, making her the youngest actor to ever be nominated for the award at that time. But it's not just in the performances that makes Whale Rider shine, there is a ghostly ethereal atmosphere created thanks to the astounding score by Lisa Gerard. Her dark, somberest strings combined with Leon Nabi's moody yet beautiful cinematography creates a visual and audio experience that is overwhelming in its uplifting power, leaving you quite mesmerized after viewing and perhaps gifting you a newfound sense of pride for your own humble origins and the boldness to question if it's time for old traditions to make a change.


So next we're going to hear from David Keeter. He's no stranger to INCF. He's part of it's part of the neuroimaging task force now You're leading you're one of the members of the NIDM Special Interest Group, so you're going to tell us a bit about what you guys have What you're up to and what you've done, right? Thank you. Um, so I'm the chair and my co-chair Camille and I are the chairs of the neuroimaging Data model special interest group So just to give you for those of you aren't familiar with NIDM just to give you like the Few sentence spiel on it. It's we use provenance just like we just heard about neuro shapes Basically, we build a series of specifications on top of semantic web technologies and the Prove family of specifications we have a Core of vocabulary and try to sort of push the semantic web Tools or terminologies ontologies in our domain of neuroimaging the idea here is that We would build these models for different stages of the scientific research process make data more broadly discoverable and reusable by having records of Precisely what was done to the data Rich descriptions of the experimental protocols and the study populations all the metadata and stuff that you can't sort of represent in the normal data formats that we use in neuroimaging This is a Sort of a working group that formed out of the INCF data sharing task force in about 2011. There were a few members at the NIDASH Working group who were interested in how metadata and analyses and provenance could be described in neuroimaging We're also sort of graph nerds And we like to work in the graph space and we decided, you know, we'll try Try out these semantic web technologies and see how well they work and how flexible they are And to this day we still meet almost every Monday morning anybody's free and welcome to join we have a Online Google hangout sort of meeting at the times you can see there and If you go to the links will be at the end But if you go to our Google Drive, you'll find our meeting minutes all the way back to 2012 and all kinds of information of what we've done along the way even any grants that have come out or been written About 90 am we try to encourage people to make those available all the code and everything else So try to just be open and transparent and it's just a group of people who are interested in sort of pushing these technologies So we I just put this slide up because I wanted to mention that we have a few posters 80 82 and 84 So kind of after this session will be out there will be some demos. You can see how some of these models look And some of the tools that are available So we had this scientific or special interest group meeting on a few days ago And so if you're interested in seeing the notes for that meeting again, everything's available So I made this little bit Lee bit Lee link 90 I'm sick 2018 so you can go there and you can see the notes and things that were captured during that meeting we had The participants you see there and so one of the discussions it was a three-hour meeting So we didn't really get a whole lot kind of done But we did have some discussions on common data elements. And so this this idea that so people say people are marking up their data or making their data available and One way or the other they use a concept. They're whatever they collected for say handedness They can maybe they collected handedness Edinburgh. Maybe they asked a participant, which you know hand you grab a broom with and Say you want to query across these data sets In a broad fashion, you know without knowing what each individual data set collected it'd be kind of hard to query across them So this discussion is about How do we push the terminologies and the ontologies to say, you know Maybe we need to define sort of a summary or a federated common data element that says, you know, simply binary handedness was collected and Maybe it was left or right and then you could use this to query across these data sets broadly Once you download the data sets then you find out that data set one really collected the Edinburgh handedness scale and Data set to ask these individuals what hand they grab the broom with and so forth So kind of what do we need to do to the terminologies that we have available and the ontologies that we have available in our space of neuroimaging to facilitate finding data and Subsetting it and using it essentially So then along those same lines. We had a discussion about a standardized query API. So You know, could we define a high-level query API? that folks who develop databases folks who develop semantic web applications Folks who work on data formats could sort of use to define queries for their data and so the idea is that You know be neat if I could issue a get project or what projects do you have query to a bunch of data resources that were available on the web and Expected that they had implemented this where appropriate and in some cases, you know Data from databases aren't broadly available and all you can get back as counts. So we had a discussion about What kind of queries that would work Among star communities so maybe we have we all implement a query that says just give me counts of what kind of how many subjects You might have in your database So that kind of discussion can we come up with a spec for a standard API and then all the different groups could go off and implement it And this would help, you know application developers and people trying to find data and so forth Worked with the Neuroshapes people Excuse me learning about shackle shapes descriptors and about how writing schema for your Particular data format Could be used by a tool called a pine For developing standardized query apis. So very appropriate. So we had some discussion surrounding that and then a subset of folks went off and worked on a little project We had other projects that were sort of submitted for You know things that people would like to do have done during our special interest group meeting But we didn't have time So one of the projects though that did make some progress is this this this project of being able to search through 90m documents or graphs with data lads search routines and So the Camille and others would Use 90m results documents, which you heard about yesterday And so in 90m results documents are documents that contain results from mass univariate neuroimaging sort of statistics And put them in this sort of RDF graph based models and the the goal was to you know How can we facilitate queries with data lad of these graphs? And so The top box which I guess it's not highlighted in gray but the top box is kind of what RDF looks like in particular serialization format called turtle And you can read that top line that says Neary ID one is a 90m design matrix. That's a URI you can go out and find out what 90m design matrix means And so forth. So then you read it neary ID one is at prov location Design matrix dot CSV and so forth. And so the idea is that you'd Prefer for tools like data lad and probably a lot of other tools a flatter sort of JSON structure That you can query over without using query tools like sparkle and so they did make some progress in that regard using indexing features of Json LD And Trying to sort of flatten out these graph structures. I mean these are graphs So, you know, you can't really flatten it to a tree without picking a node But so but you know get it as flat as we can Such that we can use some generic search tools within data lad to find data within these graphs And that's all I have We have a whole bunch of resources. Hopefully these slides would be made available But essentially if you want to see the sig notes 90m sig 2018 bitly If you want to go to our Google Drive where we have all this information 90m drive also a bitly link and then Get hub under incf-night-ash. There's a whole bunch of our repose That's it


This video will show you how to find Descendant articles or research that's come as a result of prior research within the field I'm back in my psych info database and I'm running a search using my index term Animal assisted therapy stress in college students notice as I scroll down on some of these results We have a couple of links that are below the subject terms here such as result number four evaluating college students interest in pet therapy I see two links here cited references and time cited in this database The cited references refers to the ancestor articles all the references that these authors cited well They were actually writing their research paper, but the time cited in this database is a little bit different I want to go ahead and click on that what this is referring to is the number of times the specific article Evaluating college students interest in pet therapy was actually cited by other articles So it's articles and information research that was done after this particular one So you might see that these are the children of this particular article or the descendants of and by looking at the Descendant articles we can see what research has come as a result of the information presented by these researchers But notice not every single article that we look at in our psych info search results have those links to cited references Or times cited in this database. What do you do then? Here's a chance where we can leverage Google scholar to help us out I'm gonna go back to result number 10 that we saw in our ancestors video Petting away pre-exam stress the effect of therapy dog sessions on students well-being I'm gonna go ahead and click into the article again This time what I want to do is copy the full title of this article and on a new tab I'm gonna open up scholar google.com Google scholar is sort of like Google's version of a database It's attempting to connect you to more reputable information weed out some of that internet chatter such as blogs and Popular press newspapers and things like that and really focus in on research that's coming from academic institutions or scholarly Publications we still have to be careful when we're using Google scholar because not everything is a peer-reviewed article But we can get close Am I Google scholar search? I'm gonna go ahead and type in the title of that article We were just looking at in our psych info search and I'm gonna go ahead and click search When we search in Google scholar for an article title one of the first things that will come up is the publisher's website Let's take a look at this. I can see we're searching on Wiley online Which is a popular publisher of academic journals within the journal stress and health This looks really similar to the database pages. We've got the title at the top We have the author and then we have that abstract that we've been used to seeing here So we're on the right track with scholarly research Notice as well that I can actually see a brief description of where the source is coming from the publishing information And this short abbreviation here stress and is actually the title of the journal and then we've got this abstract here So use those cues to help determine whether you are finding a scholarly research or not The most effective and interesting way that we can use Google scholar is to find Descendant articles and that's what this cited by link is referring to down here So we know that our article petting away pre-exam stress was published in 2018, but since 2018 It's already been cited by 36 more articles, which is a lot Clicking on the cited by link will show us a list of all of those citing resources And we can start to narrow it down and filter which ones might be interested in to find which one I'm gonna click on next I'm using a combination of the titles of the articles to make sure that it's related to my research topic So in the case of this one therapeutic for all observational assessments of therapy canine stress and an on-campus stress reduction program That seems relevant. I like that this article is more recent. It was published in 2019 And it's from the journal of veterinary behavior So I'm on the right track with finding a peer-reviewed research article I can click into the title again read the abstract I can even see additional keywords for this article all things that can help me continue to develop my vocabulary For how I talk about this topic. So this is great We found even more research and even more current research about our topic But how do we get access from articles from Google Scholar? You'll notice that when we clicked into the publishers web page We see this link that says get access, but unfortunately we're blocked by a paywall when we come here Find out if we have access to this particular article We'll follow the same steps that we did when we were looking for ancestor articles from someone's reference list We first want to look up by journal title. So in this case the journal of veterinary behavior I'll go back to my library home page click on the journals tab Click on search journal finder and type in journal of veterinary behavior and go ahead and click search now In this case sadly no results were found which means the library does not have access to this particular journal So what do you do then? You found this awesome journal from Google Scholar and you really want to use it for your research You are not out of luck nor should you ever pay $31 or more for an article Instead you're gonna use our inner library loan service Once again return to the library home and under the green services box You can click on the link that says inner library loan from this page You're asked to select what kind of inner library loan you're looking for and in this case You want an article so click on the article request form this form allows you to enter information about the article You're looking for and then we can go ahead and try to find it for you The first thing that I asked you for is have you searched journal finder? Yes, we did We just checked that and then you can fill in the field Periodical title is the journal you're looking for in this case journal of veterinary behavior And then the rest of the information on this form is exactly found on that publisher's website that we're looking at So we can copy all the names of the authors put them in the author field Copy the name of the title put it in the title field and so on volume date range and our page numbers Complete the form by saying the date that you need it by your name Email and your barcode number click submit and we'll try to track it down It will be emailed to you as a PDF when it comes here So look carefully through these results that you're getting through Google Scholar Pay attention to the sources or journal where they're coming from and the publisher that they're coming from and see if you can locate Descendant articles for your topic