Hello everyone and I think I'm going to get us started. I'm Julie Segre and it's a pleasure really to see you all and welcome you today to the first of the NHGRI seminars for this fiscal calendar year and it will be really a pleasure to start to see people again and appreciate everyone who came today to hear this remarkable seminar from our colleague, Sean Kimbrough. Sean is the director of the Julius Chambers Biomedical and Biotechnology Research Institute at North Carolina Central University. They were fortunate to have formed an alliance and recruited him. As you see the title of his talk today is the Biology of Health Disparities Ancestry Driven Drug Discovery. Dr. Kimbrough has all of the laudatory recognition. He received his bachelor's at Wash U in St. Louis in 1987. I hope he didn't develop too much of an allegiance to that baseball team when he was there. We have members who have that affinity in the audience. He went on to get his PhD at Indiana University in Bloomington and did a postdoc at Harvard Medical School and then a postdoc at NIEHS. His research focuses on the molecular characterization of hormonal cancers, prostate and breast cancers and these are cancers that disproportionately affect racial and ethnic groups, which is also a subject of his research. Dr. Kimbrough is very important in the field and has been recognized. He's funded with an transformative R01 to research these geographical ancestry driven variants of immunity and their role in the biology of cancer and health disparities and also as part of the investigator development core at the NCCU, which he will hopefully also give us a little bit more information about as one of the really research focused HBCUs and their accomplishments and implementing the cultivation of researchers focusing on important issues of minority health that's been funded for many years and is viewed as a model. Today, Sean's going to give us a talk that's really going to come up with new solutions to old problems. So with that, welcome to NHGRI and thank you for joining us. All right. Thank you very much. This has been a packed day. I've enjoyed meeting a lot of the students and being some of the colleagues. I want to thank the National Human Genome Research Institute for having me come and speak. This is definitely an honor and a privilege. I also want to thank Dr. Ariel Williams. She is an alum of North Carolina Central University who's here, graduated from my laboratory. So I want to thank her very much and Dr. Berge's and Dr. Surge. I appreciate your introduction and your well comments. And as once again, thank the students. I had a good lunch. We had a good time talking today for lunch, so I appreciate that. So what I'm going to do is get warmed up here. And I'm hoping to cover a couple of things. As the title implies, the biology of health disparities and ancestrally-driven drug discovery. So I'm going to put this talk up to two pieces. First, I'm going to leave you with two things. One, I want you to think about health disparities and the biology and how we get to that, to the biology of the health disparities. Also, I want you to consider ancestry and how we can use that to investigate and identify novel drugs. So what I always do, first, I'm going to share, okay, there we go. So I want to start with this. This is my slide of what is it called my advertisement. But I'm from North Carolina Central University. I'm going to come to this talk with a certain bias. This is a historically black college and university in North Carolina. It's a public university. It is located in the research triangle park. So when you think about research triangle park, do not just think of the three schools that you always hear in football and basketball. There is also North Carolina Central University, and we do do research at that institution. We also have two institutes, as we mentioned, Judith's Chamber of Biomedical Biotechnology Research Institute. I was a pleasure of leading that for a couple of years. And there's also a biomanufacturing research institute and technology enterprise, which looks at actually drug development. And at this university, we have one of the largest depository of chemical drug being donated by Biogen. Over 500,000 compounds are in some freezers. So we have the capacity. So once you think about that, you'll keep that in mind. I also want you to understand my whole perspective, my whole talk is going to come from the perspective of a American descendant of enslaved Africans. That's my perspective. And that's why I bring you the table. Okay. And so from that perspective, I have a certain view and an idea about what health disparities is. And I have a personal commitment to that topic. So ladies and gentlemen, this is my laboratory. Everyone up here identifies as African American, every single one. They check the box. However, you see that they are quite different. Okay. But they all would be Black or African American. And that's the question here. That's the power of ancestry versus the box. And with that in mind, you have to ask, how do we get to this place? How do we get to this level of diversity? And how do we use that diversity to explain some phenomena that we see in health outcomes? So everyone's seen this picture of some version of it. And this is what we're all taught, of course, right? We realize that humans left out of Africa and they migrated out and walked across the land bridge and ended up in the America. So that was the natural trail of evolution. And if you notice the Otis population being in Africa and the new guys are the Europeans and the Americans. All right. Well, we know that that changes 400 years worth of slavery. And now you have to consider the other migration. Okay. You have to consider that forced migration. That is what's not taught in school. And what is the biological impact of that? Okay. What does that really look like? When you move people from one location designed for Central Africa or West Africa, and then those people are transported to Charleston, Virginia. I mean, sorry, Charleston, South Carolina, or to Savannah, Georgia. Okay. That's not where they were three months ago or three weeks ago or four weeks ago. And yet you now move the whole group of people in a short period of time. And we're talking about up to 7 million people. Okay. That's a lot of folks. That's not normal and not doing that at the time. So therefore you don't have the opportunity of having selective pressures, that evolutionary impact of selective pressure that would have occurred naturally. So with that in mind, you know how I said natural selection, but let's talk about that unnatural selection. This is actually found this in the literature. And this is actually a Englishman licking a slave's African's forehead to confirm his age of all things. And whether or not he was salty. Okay. This was one of the theories that was posed long time ago in terms of the African gene theory. There was an African, the slavery hypertension theory. And the idea being that there was a certain selection that went on before they got on the boat. And then there was a selection that occurred. Those that survived the trip. Imagine the mental state of an individual that's packed on that boat. And I'm sure many of you have gone to the to the museum downtown DC. And you've seen that actual how they packed those human beings in there. Imagine that. So imagine this is who you've selected for that selection. Okay. We may not like that because it doesn't fit the natural program that we've all been taught. Because you got 30% of those folks didn't make it. That means you had to have a certain genotype probably to survive that. You definitely had a certain mental capacity to survive that type of treatment. And then to land here in the United States. And now I'm not going to talk about Brazil and the Caribbean. Okay. Because my point of reference is right here in the United States. Okay. So let's think about this. So we're talking about American descendants of enslaved Africans. And so that's different than someone who has immigrated here recently. It might be one or two generations in. Okay. That's a different population. Okay. So we talked about unnatural selection before and after the trip. We talked about that change in environment that occurred when he moved someone from Central Africa into North America. What impact did you have on that individual? That person was not designed for Virginia or Georgia. They were designed for Ghana, Nigeria, Senegal. Okay. That's a different climate. They were optimized. I like to use the word when I go out in the community. I used to word optimized. Okay. You have a good example is Porsche and a Jeep. A Porsche was designed to run around a track. Fast. Right. Not designed to run around a sand and dunes or anything like that or in the forest, but that Jeep is designed to be used for rocky terrain and so forth. Genetically similar. Right. They both have engines, combustible engines, but they're optimized differently. Okay. I see some heads shaking. So someone's making sense. Okay. So at the end of the day, you have the people that were designed for a particular environment relocated them. Well, that's back up.  Right. But then you got a population that also mentally made it. So now you have that population ending up in the Americas. And then you have something that's unique to only America, to only the Americas and slave trade, and that's forced ad mixing. There is probably no other group of folks on the planet that have been ad mixed with two divergent genomes than African Americans. Think about that. Seven million Africans all in the short period of time, not thousands of years that other cultures have had or the peoples have had, but 400 years worth of people, seven million people or plus. And then since they were property, you had ad mixing of European ad mixture at a genotype mixed with an African genotype to divergent folks optimized for different regions of the continent or the planet coming together in Virginia and North Carolina, Georgia. Okay. Then you do something else. Then life changes on you. So here I am, got a nice tan, right. Don't need a bunch of sunscreen really. At least I wasn't designed that way. Right. And I'm now designed to be outside. And so if I'm designed for that environment, now I'm inside with industrialization. Okay. And then I got structural racism on top of that, which creates that chronic stress and that sedentary lifestyle. I now have a group of folks that are no longer designed for the current environment that they're in. That closes the problem. So if we think about these parameters together, now you can appreciate where some of these disparities might emanate from. Okay. And of course, that's going to change our behaviors and ultimately lead to difference in health outcomes. So we talked about this in the publication in reference to cancer and the argument that allowed the genetic variants that were coming out related to and some of the transcriptome work that was coming out was looking at and made observations that the innate immune system and immunity was one of the biggest differences between ethnic groups. Okay. So we proposed that in an article in cancer immunological research in 2019. That same idea, the idea being that you're optimized with pathogens, that same thing goes with sickle cell, APOL1. These are proteins that were modified for the purpose of protecting against malaria and other pathogens. And because of that selective pressure, you end up with a certain immune profile, genetic profile, and then that migration to the United States, you just lost your advantage. There was no need for that genetic profile here. And therefore, we now have a disparity, a health outcome that is reflective of the relocation, the ad mixing in the selection of a group of people here in the Americas. This is further demonstrated. I just pulled out this couple of papers talking about this positive natural selection that occurred. And that's the selection that occurred through that nice traveling through the land bridges, not the one that occurred necessarily on the boats from West Africa to the Americas. And this idea that there's three areas that you see these three biological pathways that are apparently impacted by positive natural selection, metabolism, immune function, and skin pigmentation. Okay. So if you're seeing those three things and look at some of the disparities that we have right now, you can appreciate that metabolism and immune function can account for a lot of those issues that we see now. Okay. If you really think about it. So this is where ancestry becomes relevant. We did some work. We started off this work with a colleague of mine at University of Louisville. Dr. Lucrease Kidd helped me understand, I'm a promoter basher by training. So help me understand the value of looking at variants and SNPs in various populations. We published a couple of pieces of work with a magical gene list that I created. And with that, we looked at variants that were more, the little frequencies were higher in African Americans than Europeans. So a lot of the GWAS work wasn't working when you would go back and apply those hits or potential hits with African Americans. And so I came up with a list and said, oh, the problem is, probably look at those genes that are more common in black folks. I didn't know the complexity of GWAS at the time. And so we had a running list that I created was 100 plus variants. And we found a couple of those hits were prevalent in breast cancer risk in African American women. Most of these women were triple negative. And one that interests us was IRAC4. We got funded for that later on in life. We ended up migrating from that and brought in the list. And we ended up with a couple more papers. Here are some of those papers related to toe-like receptor signaling, which is a review. We also looked at, oh, the genetic variants related to prostate cancer risk in men, Jamaican men, that previously had sexually transmitted infections and did some other work with case study of African descent. And we said African descent because we actually had two populations. We had one population of an African American and one population of Jamaican. And we found out that the SNPs were didn't have the same impact between the two populations. So we had to separate them out. So that's why you hear me talking more from the perspective of African Americans, those here in the United States. There's a lot of admixture in the Caribbean you are aware of. And that also varies per island. And each island has a certain ancestry or admixture. So that's the premise leading up to this idea of, and that's the premise I used to a lot of my work. So now the question is, what would it look like if you actually designed drugs or approached drug discovery from a different perspective? So I was recently awarded on myself and Kevin Williams and Dr. Navarro at North Carolina Central University with an R01 looking at putting ancestry into the mix. So when you start asking that question, what is drug discovery? What does it look like? You have to ask the question about race and genetic ancestry. So we know this is pulled up recently. This is the participation, percent participation in clinical trials by subpopulations of new molecular entities. And these are drugs in 2020. You notice 8% black, six Asian, 11% Hispanic. Half of those studies were done in the United States. You notice that 75% are Europeans. If you break that down a little bit, and I hope this comes out. Oops. Let's just go back. If you look at the drugs here for breast cancer, the last two columns, and you see there's only 7% and 9% have African American women. And yet the mortality is higher in black women than in Europeans. Even though Europeans have a slightly higher increase in incidence, they have a better outcome in the clinic. So here's a case where race, everyone realizes that it's nice to have everyone included in these clinical trials, but now you don't have them represented. So now you have to ask the question, wow, how did we get there? And I also asked some folks, I said, well, if you look at the world populations of major regions, and they did this, and I got this from the demographics, and they got the information from the United Nations, 1950, 2015, you notice how the orange and red are growing. You also realize that if you look at North America in green and in Europe, okay, you realize that, wow, most of the planet is brown or other. Okay. So, but they're not in the studies. Okay, they're not in the studies. So how do you address that? Now, there was one drug that was put out before Bidel, the big Bidel story. Everyone talks about Bidel. And this is one drug that's been approved for self-identified black patients. Okay. Yet when I go out in the community, those that are on this drug, it's only a handful. The communities that go out are out in Halifax County in North Carolina, predominantly African American populations, rural populations, a lot of diversity out there. They were rated, they were rated very low on the health scale of all the counties, 100 counties. They're like they were 99 for a long time. Okay. And I realized that some people didn't even know about the drug. And yet some of them had hypertension, some of them had heart issues, but none of them had even never heard the name. So here's a case where race was the basis for the drug, not ancestors. Well, we wanted to address that a little differently. So we want to apply the ancestry piece. We thought that would be interesting because I was asking the guys at Brighton Institute, what cell lines do you guys use? What do you, do you think about those variants when you design these drugs? I asked a couple of the pharmaceutical companies when I would go to the conferences and some of their medical liaisons said, you know, no one's ever asked that. I never thought about that. And because you think about the fact that many of the drugs are designed against the wild type version of a protein that wild type is probably has a high allele frequency in the Europeans and not necessarily the other groups, the other ancestors. So here's a case of showing you those 1000 plus cancer cell lines. And here in the green and the red, this is the discussion of this is showing you the ancestry, the number of cells in that population of cancer.  There are 51 cell lines in the breast cancer cell line. And most of them are red, which is European. Here's African in the bottom, and then East Asian. And then you have South Asian and then Native American. So most of the drugs, most of the cell lines are actually European cell lines. Some people say, oh, who cares? I've had that discussion as well. We've had one case, as I mentioned earlier, there's no ancestry in cell lines. I said, okay. And then we had discussion about why does it make a difference? Well, we published a paper in conjunction with Rick Tiddles. We published a nice paper talking about that one question asking about the ancestry in the cell lines. This is 2019. Remember, when we started talking about ancestral markers way before 2019, but no one applied it to the cell lines. And so at that time, there was a cell line that was running around, and everyone was using it. And that cell line is in red here, E006AA. This was a cell line that was said to be African American. And people, you would go to meetings and they would have this cell line, and they would be on their posters and be in their talks. And that cell line was so drastic, it was so aggressive. And it was, oh, my goodness, it was horrible cell line, horrible.  Right? Coming to find out, it's European. Dr. Tiddles got a whole bunch of nasty emails following this paper. Even though we were supposed to be co-last author, only his email appeared on the document. So that was a good move. And then we had a couple other cell lines that were looked at. And that's 468, which turned out to probably be a Hispanic because of the high Native American. Okay. And so they were done by two scientists in Rick's lab. So here's an example of misrepresentation for years, for years. And NIH funding also was dumped into these projects that had E006. And it was discussed for years that everyone kind of knew it. People had already done the work, and yet it was still allowed to persist. Okay. Until now, all the papers related to this cell line have been redacted, taken out, and even ATCC realized as they went back and looked at it again, oh, wow, it's Reno. So it wasn't even the right cell line, cell type. So this comes to what my grant is about. I did something very simple. I actually went through and decided that I'm going to look at the genome aggregation database. And I'm going to pull out every variant in that database that is greater than 25%. The little frequency is greater than 25% African versus European. Okay. Therefore, I'm looking at variants that are going to be shifted, of course, to European to African American or African. And then I'm going to look at those and then select out of those a list of immune-related genes and then select out of those those that might be drug-able. Okay. And then we're approaching that to some functional assays. We do CRISPR. And then we're going to do, look at it in four different cell lines. Two of the cell lines are African American and two cell lines of European. Okay.  We're also going to do some interesting media conditions.  This perfect media that's been precise. So a couple of things we decided to look at, and we've included in our analysis, is high sugar, diabetic, and also high lipid. To ask the question, does the drug or does it have a different impact in these two different environments? Okay. We also have one related to stress. Depends on norepinephrine, epinephrine cortisol. Right now we're kind of working out conditions for norepinephrine. And we're finding out that it's already been reported that norepinephrine has an impact on IC50s. I'm going to show you some first round of data that we have looking at that. And we know that that was done originally, believe it or not, mostly in 231 cells, which are a European cell line. Okay. We then will do, of course, the phenotypic characterization with seahorse and incusite. We also are taking those clones, those genes that we identified, and we're doing gateway cloning. By the way, we're also knocking in the variant if we haven't gotten that far yet. We're trying to work out some basic conditions for the CRISPR. But we have plans to knock in some of those variants that we find to be relevant. Those genes are relevant. We go gateway cloning. If you guys are not familiar, the new schoolers might not know this. Old school gateway technology is a homologous recombination. You can do it all in one, like in 30 minutes, 40 minutes. It's really neat. And then you can do a bunch at once. So we can do up to 30 clones. No, I'm sorry, 16 clones at one time into five different vectors, each of those clones. Really nice. We've gotten that worked out. And we're even working out getting that to work on some of our robots over at Bright. We're also working on assay development. That's the expertise of Bright. And then, of course, we do some in-cylical modeling and screening in-house, ultimately leading to a chemical library screen. To give you some idea, we've all gone in and looked at and verified the ancestry of the cell lines. The cell lines in yellow are reflective of the cell lines we're using in our studies, 1806, African American 80%, 468 is 80%. In 231s, the famous 231s, everyone uses the triple negative. And we also have gone down to 1143s as well. We try to find another TNA, triple negative A and B are the two different subtypes. This is the only caveat in our work, is that 231s happen to be TNA, triple negative B. So I want to get back to that stress piece. We have a young lady working in the lab now, Alayna Berrell is working on looking at the impact of norepinephrine, epinephrine, and some of the inhibitors and induces of the beta-engineetic receptors. And we know that chronic stress is related to that structural racism. The idea being that even though my phenotype might lend me to more stress than my colleagues, that chronic stress would mean elevated levels possibly of norepinephrine. What's been interesting that some of the publication that we found actually was that it wasn't that the African-American samples had high levels of norepinephrine, they actually had a higher level of the receptors, not necessarily high levels of the hormone, which was kind of interesting. I've only found one paper to look at that. But our idea is that why not ask that question, does norepinephrine have an impact? So preliminary work, these are the IC50s, and you see with no epinephrine, norepinephrine added, and you see in some of the cases 231, so the African-Americans are the red, and you see a change due to 10 micromolar, which seems like a lot, of norepinephrine. But apparently, we found a couple of papers that that is the local concentration on norepinephrine in some of the ovarian cancers that they found in vivo. So I said, well, toss it on there and we'll see, and that's what most of the publications are using. We also find some change just as low as 0.003. You see there's a slight difference, and you get more of a response in the European cell lines that you do, the African-American cell lines. A change in the IC50, which be change in the efficacy, right? As I mentioned to you before, we are looking at using gateway technology. We're looking at not only the variants, these are two variants here, and then wild type. We put them into, in this case, two vectors P-17 and P-31 is a eukaryotic expression vector that can be induced with doxycycline, and another one can be induced with IPTG. We can now use that to produce a histag protein and create an assay for that. Another one is to look at that stably and cell line that has the receptor or doesn't have the receptor and ask the question, what is the impact on the behavior of the cell? What's neat about that, and why I use IL-4, Dr. Ariel Williams actually discovered for her thesis, one of the papers she published was looking at the levels of circulating cytokines in African-American women who had a BMI over 30 and had elevated A1C. This was a case match study. You see the numbers are very low. We did a case match. They were all over 30 BMI, and we found that just because the sugar was high, their blood sugar was higher, we actually could find a higher level of some cytokines that were not the traditional cytokines. I don't like to chase TNF alpha into looking 1, into looking 6. I'm kind of bored to death with those. Same guy, everyone recycles them. There's a lot more out there. We have attached ourselves to IL-3, 4, and 7. It's interesting enough, IL-3 has a variant that is found in the actual IL-3, into looking itself that is common among African-Americans. It's hypothesized that has an effect on binding to the receptor. IL-4, it's not IL-4 itself that has the variants, IL-4 receptor, and that's what we've latched on to IL-4 receptor. I'll show you some of the things that it's involved in. IL-7 also has a variant that actually increases the amount of soluble IL-7 receptor, which would act as a protective mechanism. Just to show you the importance of targeting IL-4, there's a couple of papers out talking about the role of IL-4 in cancer therapy, IL-4 in breast cancer, so it became an interest of us even more. We also have another parallel study looking at liver and lipogenesis. If you look at knockout mice with IL-4, they respond to high fat diet differently than a wild type mouse. There's mouse mouse that confer the relevance of IL-4. There's breast cancer observations related to IL-4 and IL-13. That pathway looks different between two different cell lines. If you look at lymphoid cells, you find that IL-4 has a gamma chain and IL-4 alpha receptor is signals to that pathway, but we're not looking at lymphoid cell cells. We're actually looking at epithelial cells. We're looking at this path and almost all the variants that are found in our list that have a higher frequency in African-Americans are actually found in the kinase domain. A linear map gets you a description of that and you find that those variants are listed here. I call it 275 and the 5015 happen to be the ones that we really like because they actually change. One is to a proline. Another one is a charge change and they're located once again in the kinase domain. They have shown to be relevant in various other studies. One related to hypercholesterolemia. Other one is related to glioma and another one related to asthma. Mind you, some of these variants are highly prominent in the African-American population. The 5015 is a serine to a proline. In that case study, that's related to skin issues as well as drug resistance to parasites. IL-4 becomes important. That's one of the first ones that we started with. We of course characterized the cell lines. We even looked at B1, B2, and B3 related to norepinephrine receptors. Looked at their status. Remember that these two are the African-American cell lines and these two are the European. It's interesting that some of the genotypes don't always line up like you would expect. These are the frequencies that are found in the populations, not necessarily in the cell lines. In some cases, they are more prominent in Asian Americans, European Americans, and African Americans. We have another protein that we're looking at. These are IL-4s here. Some of those variants are not found at all. However, you see here 70%, 38%, 32%, very high percentage allele frequencies. Here's a protein of mine. This is a passionate, long-standing project of mine. Stick one. I just left it on the list because I like seeing it every once in a while. That's related to an oncogene that we played with as an esterine receptor beta responsive gene. Lastly, I want to share with you that we've done some of the work in terms of structure. This is in collaboration with Cambridge in England and the two Johns working with us, just playing around with the structure to show you the impact of some of those variants that we talked about. The 5015 is that serine to proline and the purple is reflective of that change. Here is a serine to an aniline. We have a change here and then you have the overlay. This lets you know that we're starting to look at this. This is new. We're working on the assays for IL-4R. No one's done, at least we can't find a small molecule for IL-4R. We plan on drugging and we are working on introducing these variants into these cell lines, these breast cancer cell lines. We have some other behavior that's coming up. We've looked at the impact of norepinephrine on wound healing. We also have some data showing the impact of, believe it or not, high glucose and doing wound healing in high glucose media plus or minus IL-4R, some very interesting data coming out. We only did an N of 1R. We're going to wait and present that next time. You can see how that impacts. Our approach has really been, our goal is to characterize 50 immune-related genes and associated variants in an ancestral context. What I mean by that, sometimes it's hard to take a variant that you found in a population and just plug it into another population. It's context. That's what I want you to think about. I want to leave you with that idea of context that if you find a gene that is relevant in my situation or a risk gene and you want to ask it if that variant is functional or has an impact, if you found it in me, you might want to look at me. It might not be the same if I put it into Eric and say, here you go. I'm going to put that variant into Eric. It might not work. It's out of context. What we're referring to there is the possibility of gene-gene interactions. We're talking about cluster gene. If I'm optimized to handle vitamin D differently because of my skin color, because I'm designed to be outside, then the context there needs to be considered.  Well, that's not in context. If you found it in African-American, maybe you should put it in, at least try to put it in an ancestral context of the cell line as well. Get something to think about. We're also going to characterize those in different media conditions because in most cases, the drugs have a comorbidity. Let's ask the question, how does that drug act or does it act at all or does that gene or that variant have a different impact in a different media condition that might be reflective of a high lipid diet or a high sugar diabetic or in high stress? Also, we're going to develop some assays for some genes and their variants for drug discovery. That would be novel. Most drug companies do not do that and we're going to hopefully find some novel small molecules that target these ancestral SNPs as I call them in these appropriate ancestral cell lines. Okay, so keep in mind, I hope you leave here thinking about geographical ancestry as contextual and that drug design we can see is probably biased on the cell lines that they use, which are mostly European and not inclusive and maybe take a twist and think about that a little differently in the future and include that idea that maybe I need to look at that in another context. I will always show this picture. This is my lab out in Tillery, North Carolina. This is 40 acres in a mule. You've heard that before. This is one of the sites that actually set that up and was experimentally trying how would that look if we gave 40 acres of mule to freed slaves and so this is the site in Tillery. We do a lot of our collections and a lot of our exchange of data and communications with that community. They've been very polite and very nice and tolerant of us coming out. It's about trust. It's about experience and it's nice to have a lab that comes out and they love the students to come out and engage and so that's what we do. So we do not only at the bench, we take it all the way out into the community and try to deliver that information and these are the collaborators, NC Central and my lab, the lab techs, Kanisha Webb, Portia Andrews, both graduates of North Carolina Central, Ezekiel, Abraham, Jordan, Danya, Ariel, as you know, left. She's here now. Elena and Susan have been instrumental in really helping me write and get my stuff together. As I said, the PIs on the R01 is Hernan Navarro and Kevin Williams in their labs and associated other relationships with Fox Chase, University of the West Indies, Bright, AC3, the African Caribbean Consortium and of course, Cambridge. So I want to thank you very much. Amazing seminar. You can maybe it's as easy as we just and we do have a few minutes for some questions and Dr. Williams is going to take questions from online and we'll just start off with one in the room with Eric Green. Oh, great. And there's another microphone over there for anybody in the audience who wants to come to the microphones. Sean, thank you so much. Really insightful talk. Maybe think about a lot of things. One of the things I'd like to hear a little bit more from you about is your view on this, you know, horrifically difficult situation with the cell lines, right? Because it's not even, I mean, this is a pervasive problem with cell lines in general and a lot of different features of cell lines. But these are major conclusions that bring in with bringing it into the fold societal, you know, considerations and it's just scientifically wrong. Because just which just seems so embarrassing because the level of sophistication of the studies being performed on those cell lines is an order of magnitude more complicated than just knowing the answer that you actually have the right ancestry associated with it. I mean, when you saw that data have you given any thought of, you know, how we could try to systematically correct this or going forward, what should be sort of the norm? Well, I don't describe the possibility of acknowledging ancestry in your as a requirement and that they want to prove the origin of the cell lines all the time, right? They require that especially for some of the some of the NIH grants that actually require that that you actually confirm that it is the cell line that you're using. But that doesn't mean that's not a confirmation of ancestry. It's a confirmation of when it's a prostate cancer cell or a renal cell, but not an ancestor. Or it could have been wrong from the very beginning. All you're doing is confirming its cell line 125, which was originally what it was claimed to be and if it was wrong then about something about ancestry. And some of them don't have ancestry attached to them. There are some public databases out there you can screen from. I can always send an email that are out there that they have done some ancestry on some of the known cell lines that are out here. The ones that I showed you are mostly from the European base. I looked at start looking at the NC 60 panel. Most of those are European and looks like and there's only a handful of African-American on there. I think it's going to be a quest also to get more cell lines created that are ethnic and diverse. I think we've been working on PDX models. Now let's get the diverse PDX model, but you're talking about PDX models a lot of times in terms of putting them into a mouse. Now we know that's not in context, right? But at the end of the day that's what we're concentrating on. Maybe we need some ideas just getting some real cell lines that are diverse. That's why I chose the breast cancer cell lines because of the diversity there that I couldn't find anywhere else. There are very little in the prostate cell lines, only like one or two. And that becomes problematic when you start making decisions on one or two cell lines that look like me. Can I ask one follow-up though? I mean even if you say, all right, tomorrow your lab's going to create a cell line and my lab's going to create a cell line, we want to make sure the ancestry is properly associated in each case with those cell lines. Is there a sort of a gold standard now on what set of genomic markers to use or what? Is that standardized? Because that could also downstream lead to incorrect assignments that people are making ancestry assignments based on different methods and different markers and etc. There is no, not that I'm aware of. Rick uses his and then I know there's a couple other there's different programs as well. So there's no standard life for that that I'm aware of. Okay. Okay, we have some questions in the chat as well. So I'm going to read some of those off. An anonymous attendee said, how is the slave trade and your examples of forced lifestyle changes and environmental changes any different for any refugee today? They gave examples of the Moorish Kingdom and slavery in the Mediterranean, the Viking community. Isn't this a continual problem regardless of race? If you look over time, where do you choose to start to look and how do you control for such mixtures of the mixtures of the centuries? Okay, so a lot of those are not at the scale that we saw in slavery and the time frame is a lot different. Okay. And the divergent, like I said, the introduction of two very divergent populations, there was what's the possibility that some guy in England was going to hang out a whole 7 million people from England would have come down to Africa to hang out. Right. And so I think we need to approach that and think that I've heard that similar argument before, why this why that's so unique to this population? One, because of the size of that interaction, 7 million people over two continents, there is no other expansion of a people like that. And that meant the time with that level of ad mixing in Brazil is even more complex with indigenous. Okay. So I would argue that why slavery becomes unique is the fact that you just have so much so divergent genomes clashing together in a different environment. We're not talking about North Europe versus Middle Europe or Southern Europe, you're talking about Europe, England, France, and we're talking about Central Africa. Also, there is comments and questions from Anil Whaley, Dr. Kimbrough, very educational and informative informational talk. Thanks for giving proper context and background for precision medicine, drug development strategies that will have profound impact on various organ based cancers, cancers, clinical management. This transformational, translational research is very important if we plan to address and eliminate cancer health disparities. Thank you for sharing your research with us here at NIH, and they follow up with should ancestry information markers be a requirement for ATC, ATCC cell line repositories? I think it should actually, I think it definitely should. They since pulled that cell line from ATCC and all the associated papers also removed related to that cell line development. The next question is how about applying the 25-passage restriction on the use of any cell lines? How do you feel about that? You know, I know that cell lines change over passage. I always tell the students, from my perspective, that's just be very frank and understand that if a cell can grow in a piece of plastic, you know, down where that's not normal. And that's not what we're looking at. That's not about, you know, it's what we do to get the job done to ask some basic questions. So I'm not been out of shape about the passage number. I do know, as long as you know that when it changes, it's time to go back to the pot. But so I haven't really had too much of that problem that they've been around so long. Anyway, by now, I don't know what to tell you, you know, when you guys say someone from the 1950s and 1960s, it's pretty messed up. Dr. Charles Rotimi said, how do you define ancestry in your studies? Also, given that African Americans have multiple sources of ancestry, how do you take this into consideration in your studies? This is important because at a specific location in the genome, African Americans may have European or African background. So what he's talking about is local ancestry. So I didn't show a picture, I had a picture that has local ancestry for chromosome 12, which IO4R is located. And all those people that were on that screen actually have different chromosome painting that looks really interesting, where you could see one person had a whole chromosome that had Asian ancestry. And so that would have a huge impact if you're looking for the aberrant of interest. I define ancestry geographically based off of a series of SNPs. Most of my ancestry was done through Rick Kettles, who's at Morehouse School of Medicine now. And so there's a set of SNPs variants that correlate with a particular region of the planet. And so that's what I define as ancestry, not necessarily ancestry from where that you come from type of ancestry that my folks came from, England or my folks came from. So part of my family, my heritage actually is from England, and you can trace back all of it. You have to use one of those other programs, ancestry.com, whatever. So yeah, so I have a mixture of that when it comes to the ad mixing. I do not use ancestry as race. I think I've seen a lot of that going around, and I've had a lot of debates. They want to use ancestry and say, look, as I was telling the students today, the word concordance, they check the box, and they have Western African ancestry, and they say, oh, they're concordant.  And I tell them that that can be offensive, folks, because that's what they did in Louisiana and North Carolina and some other states where they tell you the percentage, you know, the one drop rule. So we don't want to use ancestry to identify race. You want to look within that group and look at how ancestry impacts a particular outcome or a variant. So I'll have one last question from the chat in the interest of time. Dr. Kimbrough, very impressive talk. How is your research being used to improve drug development? So we hope that this would need to first identifying some drugs that might impact other populations. If you assume that a drug is biased in this design, then that's ticked at some of that bias out and maybe includes some other folks in the picture. By doing so, if you look at that, remember that pie chart? Really, I had some people argue with me that I'm designing drugs for black people. And I said, so what? Because at the end of the day, you know, why not design drugs for most of the planet? And most of the planet is black and brown or other. So here's an opportunity to design drugs that impact possibly a broader group of people globally. Thank you very much, Dr. Kimbrough, for this insightful talk. I have two questions for you. First question is basically related to the issue. What is the distribution of your candidate variants in immune genes, for example, in people of African American ancestry versus people of African ancestry from West Africa? Have you looked into that because you gave us the allele frequency compared, you know, aisle four variants, for example, in African American versus Asian versus European, et cetera. But I just wonder whether these variants have been under positive or negative evolutionary selection, for example. So in the African populations on this particular database, they're very close to African American and African are very close or even if you go to a thousand genomes, the numbers are very close. African Americans usually have a slightly lower allele frequency because of the admixture I'm going to assume. So a lot of these are really African ancestral variants in that population I'm looking at for African American. Okay, thank you. And the second question is when you talk about using these cell lines and, you know, using them in the context of these genetic variants, et cetera. Again, I wonder why don't you have in your study, for example, a breast cell line from Western African, you know, people again to compare what is the effect of nurture versus the effect of nurtures. So there are none that were greater than 80 percent of West African ancestry that we could find. That was the highest we had was 80 percent. And so that's the cell line that we were working with. And so I don't disagree with you that, yes, it would be better. You would think to have a cell line that had 90, 95 percent West African. I don't have such a cell line. I'll have to talk to some of my colleagues to see if they have established any. No, I was actually what they meant to say is, can you get some cell lines from West Africa? So, yes, I'll probably have to kind of, a couple of my colleagues actually have great collaborations in Africa and they might actually have some cell lines. Yeah, I think that's a good experience. Thank you. Thank you, Dr. Kimber. Okay. Well, you have provoked a lot of great ideas that I hope we will all take with us into the future and wanted to thank you again for visiting us today at NHGRI and for kicking off our seminar series. So thanks so much. And with that, we'll conclude the seminar. Thank you.


Hi, guys. Welcome to lecture number. I don't know what number this is, but it's a long way of numbers in our general biology course. Whoa. Today is our second day of conversation about evolution, and really everything that we're doing from here on out till the end of the semester, we're basically talking about evolution forever now. The interesting thing is you might be thinking, well, we certainly haven't discussed this yet, but kind of the whole course culminates in a conversation about evolution because evolution happens because of genetic changes in populations. So if you don't understand genetics and heredity and DNA and cells and mitosis and meiosis, which is everything we've talked about in the course so far, then evolution is not going to make sense to you. So it's kind of cool because it will make sense because we've talked about all that stuff. And then after we've got all our mechanisms of evolution, mechanisms today, evidence, tomorrow, then we get to look at what's the outcome? It makes evolution, shm-evolution. Why do we care? And I care because the outcome is so cool. Like all the crazy madness of diversity that we see around us and that we've gotten little glimmering glimpses of throughout the semester, all that stuff. It can be explained by the process of evolution. So it's going to get really exciting here to start looking at all the critters around us. Let's start this conversation by reminding ourselves about, dude, what is evolution? And I've got a website up here because I think this might quite possibly be one of my favorite websites on the internet. This is an incredible compilation of resources around evolution, very current, very interesting. So I left the web address up here that you can go check it out, but it's just got, it's a nice resource. We're going to remind ourselves of the two different mechanisms for evolution and mostly what we're going to focus on today is micro-evolution and primarily natural selection, which is kind of the big player, the all-star, the MVP of evolution land. All right, let's go remind ourselves about macro and micro-evolution.


Dear participants, welcome to the course on supply chain digitization. It is jointly being taught by Professor Priyanka Burma, Professor Sosmetha Narayana, and Professor Devapratadash from IM Mumbai. In today's lecture, we will continue the discussion on green field analysis. And specifically, we will focus on how to do green field analysis using Excel solver, and we will also see how can we do the similar analysis using any logistic software. So, before we proceed, let us quickly summarize what we discussed in last two lectures. First we focused on what is digital twin. Then we also discussed various types of digital twin in supply chain and operations management, product level digital twin, process level digital twin, company level digital twin that is enterprise level. Then we also discussed supply chain network level digital twin and also network of networks level digital twin. Then we discussed the digital twin framework from supply chain point of view. Then we focused on a case study to perform green field analysis. And specifically, we developed an optimization model to determine the optimal location of distribution center, so that transportation cost is minimized. We also discussed the results of the optimization model specifically the Excel solver output. We also have shown the output of the green field analysis done using any logistic software. So, in today's lecture, we will explain the Excel solver like how green field analysis can be done using the optimization method in Excel. In the previous class, we only shown you the output. In today's class, we will do a hands on and discuss how can you do the green field analysis and get the exact same output as we have shown in the last class. We also will perform the green field analysis using any logistic software. In the last class, we showed you the output. So, in today's class, we will show you how you can do the green field analysis using any logistic software, so that you can also reproduce the similar results. So, with this, let us relook at the case study, which we discussed in the last class. The senior management of a pharmaceutical company is planning to set up a distribution center to cater the demand of western region of India. And if you see, we have four markets in western region, Pune, Mumbai, Ahmedabad and Surat. We have their yearly demand. We also have the exact location of this market that is latitude and longitude of each of these four places. Our objective is to find the optimal location of the distribution center. I have four customers, customer one is located in Pune, customer two located is in Mumbai, customer three located is in Ahmedabad and customer four located is in Surat. And we have yearly demand at each of these customers location. So, we need to meet this demand. So, where can I locate the distribution center? So, that my total transportation cost is minimized. So, let us say if we locate it here, then I need to transport products from DC to Mumbai, from DC to Pune, from DC to Surat, from DC to Ahmedabad and these are my distance. So, I need to minimize the transportation cost that is minimize the distance multiplied by how many units I am sending to this customers location. So, that I need to minimize. So, with that objective in mind, we developed an optimization model in the last class. And if you see x i and y i are coordinates of market i. So, x i is x coordinate that is latitude of market i, y i is the y coordinate that is longitude of market i. Then we also have demand i that is demand of each of this market. Then x dc represents the location of distribution center which is a decision variable. I do not know the value yet, but it represents what would be the optimal latitude of the distribution center. y dc represents the y coordinate of optimal location of dc. That means what would be the optimal longitude of the distribution center. So, these two value right now I do not know this will be decided based on the optimization model. Then transportation cost per kilometer per unit is represented by dc and z is the total transportation cost. So, we have distance between each market from the optimal location of dc. I need to multiply it with the demand because these many units I need to transport from dc to demand location. So, if I sum it up from 1 to 4, I will get it for 4 markets and then that is value should be multiplied by transportation cost per kilometer per unit. Then I will get total cost of transporting products from dc to customers locations. So, these value I need to minimize. So, I have to minimize z. In the last class we showed you the excel solver output. We also showed you the output of any logistics model. And if you see the output says that my distribution center should be located here. And optimal latitude is 19.09 and optimal longitude is 72.87. So, this is my optimal location of dc with latitude 19.09 and longitude is 72.87. Now, this is now in today's session we will try to reproduce this result using excel solver as well as using any logistics. So, to do that first we need to open the excel file greenfieldanalysis.xlsx then make sure that you have solver add-ins is installed and then follow the steps which I am showing it to you. So, for that I will now go to excel solver. This is the data. So, let us this is the optimal value, but I do not want to show the optimal value to you right away. I want excel solver to give you the optimum value. So, now let us look into this excel file. So, initially this is my initial value. So, latitude of dc, longitude of dc this is my decision variable. I do not know this value yet. So, let us assume that initial value is 20 and initial value is 72 for longitude. Now, if that is the longitude and latitude of dc then what would be my distance. So, I have this data given in the case Pune, Mumbai, Amandabad, Surat these are four markets. They are demand their exact latitude longitude and also cost per kilometer per unit which we have assumed 1 dollar. Now, we have to find out the distance from Pune to this dc, Mumbai to this dc, Amandabad to this dc, Surat to this dc and this is the distance which is coming up 254.54 kilometer from Pune to this dc if latitude is 20 and longitude is 72. Now, how did you get this value? So, for that we need to look into this formula which we have discussed in the last class. So, now I need to first calculate d long that is deviation in longitude value which is nothing but y dc minus y i, y dc is this value. So, longitude of dc, y i is a longitude of ith market that is Pune. So, that is how d long is calculated which is in column h. So, if I click it over here you will see d long equal to radians b 8 minus e 2. So, b 8 is longitude of dc. So, this is the longitude of dc that is y dc and e 2 is longitude of Pune that is market 1 that is y 1 and we have also applied radians because radian is the standard unit of angular measure. So, we will get the value of d long. Similarly, I will also get the value of d long for Mumbai, Amandabad and Surat. The same formula I have applied. Now, we need to calculate d lat. So, d lat is deviation in latitude. So, for that first I need to get the value of x dc that is latitude of the dc minus latitude of ith market. So, if you look into this formula radians b 7 minus d 2, b 7 is latitude of dc minus d 2 is latitude of first market because I equal to 1 here that is Pune and we have again applied radians over here. The radian is the standard unit of angular measure. Then same thing we have calculated for Mumbai, same thing we have calculated for Amandabad and same thing we have calculated for Surat. So, we got d long as well as d lat value for Pune, Mumbai, Amandabad and Surat. Then I need to calculate A. What is A? Sine square d lat by 2 I have d lat value then cos into x i into cos x dc into sine square d long by 2 I also have the value of d long. I also know x i and x dc. So, what is x i? x i is nothing but latitude of ith market and x dc is nothing but latitude of the dc. So, if we use this formula we will see sine g 2 by 2. So, g 2 is nothing but d lat by 2 then square. So, that is sine square d lat by 2 plus cos of radians of d 2 that means cos of radians of d 2 means latitude of Pune. So, that is x 1 then cos of radians of b 7. So, cos of radians of b 7 is latitude of the dc multiplied by sine h 2 by 2 sine d long by 2 then square. So, we will get the value of A same way you can get the value of A for Mumbai, Amandabad and Surat. Then we need to get the value of C what is C? C is nothing but 2 times A tan 2 then square root of 1 minus A comma square root of A. So, in excel we have used this value and if you enter you will get the value of 0.03995. So, I can get the value of C for Pune, Mumbai, Amandabad and Surat. Using the formula now I got C, I got A, I got d long, I got d lat, I need to calculate distance. So, distance between the market and distribution center is r into C. So, what is r here? r is 6 371 which is a constant value I need to multiply it with C which you already derived. So, if you do this we will get the distance from Pune to this dc for which latitude is 20 longitude is 72. The same way I will be able to get the distance of Mumbai from the dc distance of Amandabad from dc distance of Surat from dc. So, now whole sheet has been explained to you. Now, what is the cost? Cost is some product of k 2 to k 5 that is distance I have to multiply this distance with how many units I am sending. So, from Pune from dc to Pune I will be sending 157 680 units and how many kilometer I have to travel 254.54 and the cost per kilometer per unit is 1. So, therefore, I am multiplying 1 into this into 254. So, if we do that I will get it for one market. I need to again sum it up with 1 into this into this 1 into this into this 1 into this into this. So, all of this can be easily done using formula sum product. So, we will get the cost value this. Now, this is not the optimal cost because we have not applied any optimization yet. This is the cost if latitude of dc is 20, longitude of dc is 70, but is this the optimum value that is what we have to find out. So, for that we will go to data and then solver make sure you have solver installed. Once we go here I need to set the objective value. The objective is to minimize b 9 minimize this cost this is b 9 by changing variable b 7 to b 8. So, these are my decision variable which are in yellow color I need to change this. So, optimization model will tell me what is the optimal value this value will be changed and then I am applying non-linear method because in this case if you see the distance formula is non-linear. So, therefore, I need to apply non-linear method. If you press solve you will say solver has converged to the current solution all constants are satisfied. Although there is no constant here, but solver has found an optimal solution and optimal value of latitude is 19.09, optimal value of longitude of dc is 72.87 and if you see this is exactly same as Mumbai location and that is why from Mumbai to this is distance has become 0 also. So, if I locate dc at this location 19.09 comma 72.87 I will get the optimal location of the distribution center and the total cost of transporting products from dc to demand points will be minimized. So, I am sure all of you understood how excel solver works. Now, for a small problem this is very easy because I have only one dc to determine and I have 4 markets, but imagine if you have 4000 markets located across the countries then where should I have dc? Can I do it using excel solver? I cannot do it because excel has limitation. So, therefore what should I do? So, is there any method? Is there any software which will help me to do this? Now, if I have 4500 markets located across the countries and I need to open let us say 3 dc, 4 dc, 5 dc. So, then excel solver would be difficult to solve. So, I need to take help of some other software which has these facilities, but excel solver is good for a small scale problem. So, we have done this step already and we got this result using excel solver. Now, let us make this problem little advanced. So, if you see the data we have taken 4 SKUs. So, in the previous case which was very simple we have only one product at each market Pune, Mumbai, Surat, Ahmedabad. Now, I have 4 different SKUs, SK1, SK2, SK3, SK4 and we also have daily demand. So, in place of yearly demand we have daily demand. So, imagine the complexity has increased. So, earlier I had only one product, now I have 4 different SKUs and imagine if these SKUs increases and typically company has multiple SKUs. Let us you have 20 SKUs and 40 100 markets located across the country. So, then how do you find out the optimal location of DC and the demand is also daily. So, it will be difficult to solve in excel because excel has limitation, it cannot solve a optimization model which has more than 200 decision variables. So, therefore, we need to take help some other software. So, any logistics is one such software which helps us to do green field analysis and get the results in seconds. So, if you want to read more you can go to this website anylogistics.com and you will be able to find out the benefits of any logistics specifically if you want to do green field analysis. So, now let us see how we can do green field analysis, how we can develop a supply chain digital twin using any logistics as per as green field analysis is concerned. So, this is the output of the green field analysis which we have done and I am showing it over here. So, before I go and do the hands on this is how the screen of any logistics looks like. So, this is version 3 any logistics version 3 PLE means personal learning addition. So, if you are a student you can use this addition and it has 3 module one is green field analysis, second one is network optimization and third one is simulation. Let us say in our case we are focusing on green field analysis. So, we will go into the module of green field analysis. When we will do network optimization we will see the module of network optimization and in this course we are not doing simulation, but however you can perform simulation using any logistics. So, let us first open the any logistics software and see how we are getting this output. I already have the any logistics installed, but if you do not have this install please go to this website www.anylogistics.com then download any logistics. Personal learning addition you can download enter the relevant data in logistics in the green field analysis module run the analysis. So, if you are more interested to do it on your own then I would request that you go to this website www.anylogistics.com then under that there is an academic tab under that ALX educational toolkit. So, you will get all help over there. They have explained step by step how to do green field analysis they have also given examples data everything is given you just have to change your data set and run the model. So, it is very easy for you to do it I have already done it myself I have changed the data and put an Indian context you can also do depending upon your context depending upon your data you need to change the input value and input parameters. So, now let us go to the any logistics software this is how it will look. So, now I already have the data enter. So, I will show how the data will look like. So, this is network optimization so I need to go to green field analysis. So, this is the green field analysis I will show you how the data look like. So, the customers I have 4 customers Pune Mumbai Amdabad and Surat. These are all customers then I have disease and factories right now I do not have anything you can see here nothing is there then demand I have. So, as you have seen I have demand at 4 different locations Pune Mumbai Amdabad and Surat and each location I have 4 different products. So, if you see at Pune I have SQ 1 SQ 2 SQ 3 SQ 4 I have in Mumbai Surat Amdabad and my demand is periodic demand and if you see the click over here you will see the demand in Pune per day is 130 units. So, you have to enter this value demand at Pune for SQ 2 is 90 units for SQ 3 demand at Pune is 95 units and so on. So, similar way you can enter if you have in place of 4 markets if you have 5 markets you add one more market and enter the demand per day. Then we have their locations of if you see Pune location Mumbai Amdabad and Surat the Pune location has latitude 18.52 comma 73.85 Mumbai location has latitude this longitude 72.87 Amdabad latitude 23.02 longitude 72.57 Surat latitude 21.18 longitude 72.83. So, I have 4 products you can see SQ 1 SQ 2 SQ 3 and the units are in pieces. This way you can enter the values and one thing I want to mention right now I have entered the latitude and longitude manually, but you also have auto field coordinates. Suppose you enter the name correctly let us say I want to enter the name of Akola. If I enter Akola automatically the coordinates will be auto field. So, now once all the data are entered correctly then I will go here and run this GFA experiment if I run this I will get the output and if you see my output is here. So, it is telling me where my DCC. Earlier this GFA DCC was not mentioned, but now after running it is telling it has to be exactly at same location as Mumbai and I can also use this connector then it will also tell me how these locations are connected. I can also use label then it will give me the name. So, I have now name Pune Mumbai Surat Amdabad. So, all this information I will get it on the map. So, if I use any logistic software I can see that this is my advantage and not only able to see the location of the distribution centers. I am also able to see where my customers are located how far it is from the distribution center I can visualize it in a better way. This is for a small problem with 4 customers. If I have more customers then any logistics would be much more useful the visualization will be much more better and it will be needed also. For the decision maker it will be very good to visualize it properly. So, now let us see the results. So, if you see the results new site GFA DC the location is latitude 19.096 longitude 72.87. So, the same value which we got in excel also 19.0972.87. So, the same value has been given by new logistics then I can also see the flows. That means, from DC to various demand points what is the flow. So, DC to Amdabad for SQ 1 I am sending how many 43800 the distance is 437.988 and this is the flow cost. So, same way I can see from distance from distribution center Amdabad for SQ 2 how much units are sending 34675 and then distance in kilometer is 437.988 and flow cost is this. So, you will be also able to see not only the new site and its location you also able to see the flow from DC to this various demand points how many products are flowing. I can also see demand by distance I can also see distance by demand. So, these are all possibilities are there and the visualization will be much more better if I have in logistics software with me I will get a much better visibility I will be also able to see where are they located and how the flow of products are happening from DC to various demand location. So, again I urge all of you to go to this website download this educational toolkit and I am very sure you would be also able to do the same analysis on your own. Only thing you have to do you have to change your data set because they have given the example from European point of view you have to change it based on your requirement based on your company based on your country. So, latitude longitude has to be changed demand data has to be changed then you can also perform the same analysis. So, till now we have seen how infill analysis can be done using excel we also have discussed how it could be carried out using analytics. We have discussed the advantages of analytics in this class. So, now what we will do in the next class we will see how network optimization can be done using analytics in detail. So, thank you so much look forward to seeing you in the next class.