Good morning all of you yesterday we were looking into the Von Karman polehausen solution for heat transfer problem just want to make a small correction probably when we looked into the solution for ? so we had the ? value for the stagnation region as 7.052 and the resulting expression for ? so we have to so we had to solve this ODE and the solution as a combination of the homogeneous and the non-homogeneous parts came out 0.6699 by Prandtl number okay so you can look at the non-homogeneous part is actually based basically a constant so that is why we know the particular integral has to be a constant okay if it is a function of x in any form then we have to assume that particular form and you have to calculate the particular integral and coming to this particular equation now this is for the case where we have flow past a circular cylinder you have the heated circular cylinder okay and this is the stagnation value of ? which corresponds to 7.052 so now at x is equal to 0 so this is where you are starting your x okay so you should be careful that the boundary layer thickness is actually not 0 okay because the stagnation flow comes ahead and then it basically bifurcates like this so you have a certain value of boundary layer thickness and the same thing holds true for your thermal boundary layer thickness also so you have to be careful when we stated that at x is equal to 0 we came to the conclusion that the constant is 0 essentially because of the fact as x to attend towards 0 the solution will go to infinity okay because this is negative power so in order to make this value of ? t that is the thermal boundary layer thickness finite at x is equal to 0 this constant has to go to 0 okay so this is why we put this constant as a probably I did not explain it very carefully so you have to be careful that at the stagnation region both the thermal boundary layer thickness and the momentum boundary layer thickness are both nonzero okay and therefore in order to make give a finite value of ? t so your constant has to be 0 that is why the final solution was Q is equal to 0.6699 by and from this we have calculated the expression for Nusselton okay so this is I just want to clarify before we proceed further today we will introduce a smaller approximation to the entire approximate method and this is also called as walls approximation some people also referred this to as Thwaites approximation I am not sure why this is attributed to two people I do not know the historical reason but some books some of the more recent books they refer this to walls approximation in some of the earlier books they refer this to as Thwaites approximation and what essentially happened is this person walls he pointed out that we had defined this function H of K right so a complex function which was function of F1 F2 K and so on so he said and we were thinking that we can construct a lookup table where we will change the values of ? for different values of ? will plot F1 F2 and therefore we can plot H so he said when he did this plotting we found this H of K was actually a linear function of K okay so and he got a curve fit of a straight line which was point 47-6 K so he just simply plotted H of K as a function of K at K equal to 0 this was like point 47 and then it was decreasing like this okay so this was a very good news because now since this is a linear relationship we can simply directly use this to calculate Z okay so we know the expression for the momentum reduced momentum integral equation which was DZ by DX is equal to H of K by U 8 so this was the expression that we derived after we substituted those velocity approximate velocity profiles finally where what is your Z ? 2 square by ? so this is nothing but a parameter which involves the momentum thickness okay so so this has to be solved in order to get new values of Z and then we found from there you iteratively solve for new values of ? and keep doing this as you keep marching from one region to the other region till it separates okay now this was because we did not know a very good functional relationship for H of K so now when walls plotted H of K as a function of K we found this is nothing but you can fit it very nicely with a linear line and therefore a linear approximation was derived now we will substitute this into this expression so this will be 0.47-6 K by U 8 okay so this is called the walls approximation the rest of the thing is it is just how do we now simplify this equation now directly we have DC by DX is equal to this we can integrate it out so one more step we can write this as U 8 DZ by DX is equal to 0.47 anybody remember what K is Z x DU 8 by DX okay this is coming essentially from the pressure gradient parameter okay Z x DU 8 by DX right so therefore this is a function of K is nothing but basically a function of Z okay so it is YZ to put this in terms of Z DU 8 by DX now you see we have an equation directly for Z okay which we can integrate it out we can also rearrange this as U 8 6 ? 22 by ? so I am going to substitute my Z as ? 22 by ? so this can be rearranged in this manner 0.47 U 8 5 okay so now you can expand and check so this will be the same so you have ? is a constant which can be taken out so you have D by DX 1 by ? so we can take ? 22 and U 8 so this will be 6 into U 8 power 5 DU 8 by DX plus I have U 8 power 6 okay to ? 2 into ? 2 by DX is equal to 0.47 U 8 5 okay so I have 6 and now my Z is nothing but ? 2 2 by ? I have the this so I can divide throughout by U 8 5 so this will give me ? 2 2 by ? DU 8 this is 6 D 8 by DX plus U 8 2 ? 2 D ? 2 by DX is equal to 0.47 so this is U 8 so this is 6 Z D 8 by DX and I have U 8 yeah so this DZ by DX is nothing but 2 ? 2 into D ? 2 by DX that is equal to 1 so therefore you can you know all this can be combined into one neat way of clubbing together this particular term and this will be nothing but the expanded version of this okay so now with this we can we have an expression directly in terms of ? 2 which we can integrate it out okay so if you if you integrate it so integrating so we can say ? 22 is equal to 0.47 nu by U 8 power 6 of X okay and so I am going to integrate the right hand side which will be 0 to X U 8 to the power 5 of some Z DZ okay so this is my final expression that I now get so I simply integrate both sides okay and just I write this in terms of ? 2 square so therefore I will call this as number one so your earlier expression for solving for Z in fact that is solving for ? 2 was doing numerically you have to solve this OD numerically now once you introduce this linear slope approximation okay constant slope approximation into this and you directly integrate it out you get a simpler integral okay if your function is known U 8 you substitute here you directly integrate it out and you can get an expression for ? 2 right away so there is no numerical work involved in this okay although this is an approximation you know nevertheless it is a very good approximation in fact I can give one homework where you can check this whether this linear approximation is valid okay you can calculate H for different values of K plot and check for yourself all right so this is the thing and here we have used the fact of course when you integrate it out we have a constant here constant of integration okay so now what basically X is equal to 0 when you are integrating this with respect to X is equal to 0 your U 8 that you are using so this this entire term has to be 0 because at the stagnation point your free stream velocity is 0 so therefore this entire term will be 0 the 0 therefore the constant will be 0 okay so we have used that that at X is equal to 0 your U 8 power 6 ? 2 square by ? equal to 0 okay so it is not that your boundary layer thickness is 0 but your U 8 is 0 which leads to this particular conclusion that the constant is 0 therefore this is the final expression okay now we will apply this again for the cylinder problem earlier we had I had given you the algorithm to solve for the cylinder case in a more rigorous way solving the ODE numerically okay so first we will look at the cylinder problem near the stagnation region so if you substitute the profile of I for U 8 your U 8 is basically to V 0 V 8 R 0 by sorry X by R not so if you substitute this and you integrate it out you get ? 2 square is equal to 0.235 into ? into R not divided by V 8 X power 6 okay so this is basically 0.47 divided by 2 and you have V 8 X power 6 okay that is U 8 power 6 actually everything is power 6 but inside the integral also when you substitute for U 8 to the power 5 the extra terms cancel out so you have Z power 5 DZ is what is left out inside okay so you are basically substituting for U 8 here U 8 power 6 here U 8 power 5 so you have 2 U in 2 V 8 by R not which is the same okay so then you will be left with the factor that this is V 8 into V 8 divided by R not here you will have X power 6 here you will have C power 5 so this you can integrate out directly so this will be point so if you integrate this this will be nothing but X power 6 by 6 okay so 0.235 divided by 6 will be 0.0391 and so X power 6 X power 6 cancels so you have ? R not by V 8 okay so therefore there you go you directly have gotten expression for the momentum thickness without solving the ODE rigorously you know near the stagnation point directly you find it okay and you can also calculate the value of ? for doing that we need to estimate the boundary layer thickness which is estimated so how do you estimate the boundary layer thickness ?2 there is an expression because ? is a function of ? ? okay what is the original expression how did we define ? ? ? U by D U 8 by DX right so we defined this parameter ? like this okay so ?2 D U 8 by DX by ? is that right okay so therefore for this particular case we can substitute D U 8 by DX which is nothing but a constant to V 8 by R not and that comes out to be ? by 2 into ? R not by V 8 okay so now we know ? ? as a function of ? okay we have an expression for ? 2 so we can calculate the ratio of ? 2 by ? the whole square so you let me know what this expression will be so if you take the ratio of these two we are not by V 8 will be cancelled out okay this will be 0.0391 into 2 by ? which is 0.0782 what we are trying to do is we are trying to solve for ? okay so ? is a function of ? and we have we have an expression for now ? 2 by ? the whole square which is now a function of ? we also have derived ? 2 by ? square we have expressed this as another function of ? do you remember that originally that is 1 by 63 square 37 by 5 minus ? by 15 or you can say that this is equal to square root of this and then you can you can write like this ? square by 144 okay all right so now we have an expression algebraic equation in terms of ? since ? 2 by ? is originally this in terms of ? okay now we have another expression for this particular problem which is like this so we can equate both and you can find out ? if you do that in fact this has to be also solved iteratively so ? will come out as 7.25 okay now you can compare this with the ? that we got earlier that was 7.05 right so it is slightly off but nevertheless it is very close okay so I think this is now much easier to solve than the earlier case what we will do is now go ahead and complete the heat transfer solution so for the heat transfer problem whatever we have derived we have to assume the cubic polynomial and then we have substituted into the energy integral and we have finally calculated the expression for ? which is the ratio of ? T by ? as point this is a this is ? Q this is point 6699 by right so the same expression is valid because as far as the energy integral is concerned all you are doing is you are substituting the velocity profile approximate velocity profile approximate temperature profile and finally you have expression in terms of ? which you are getting the solution for okay now when you are getting the solution you have substituted a value of ? there okay the original expression was something like this so your expression was ? D by DX X ? square of ? 12 plus is equal to 90 up till here everything is the same okay so this is coming from substituting into the energy integral right for the velocity profile and the temperature profile and simplifying a little bit where we are neglecting all the higher order terms of ? and only ? square comes finally so now from here we should use the particular value of ? which we got from the walls approximation okay so that is basically 7.25 instead of 7.05 which we substituted earlier if you do that earlier you got your ? as point 75875 by PR to the power 1 by 3 now with this you will be getting ? as 0.864 by PR power 1 by 3 so only the constant will change a little bit because now your ? is slightly different if you substitute it your solution for ? will be just slightly differing by constant it will be 0.864 by PR power 1 by 3 so that is it so once your ? is determined you can calculate your heat transfer coefficient and your Nusselt number okay so even we know that the heat transfer coefficient is 3 by 2 a by ? ? okay so the expression for ? is the same for both the walls approximation and the original upon Carmen pull house and solution so only the value of ? is differing slightly if you substitute this you will be getting an expression for H which is like point 645 K by R0 into Prandtl number or one third RE power half okay now if you define your Nusselt number as H into D0 by K okay so this will be what 1.29 Prandtl number power one third RE power half okay where your RE is nothing but V infinity D0 by ? so compare this with the expression that we obtained yesterday you remember that expression when we used ? 7.05 what was the expression that we got 1.1 no that that is the exact solution 1.291 correct okay so this is 1.29 so finally in terms of Nusselt number there is any hardly any difference okay whether you use the walls approximation or not so so that is it I see this is to just give a good idea that walls approximation is pretty good especially in terms of the heat transfer calculation you get a very good agreement with your pole house and solution as well as your exact solution is what 1.145 and that has Prandtl number 0.4 dependent so there is some difference between the exact similarity solution and approximate solution but using quite approximate walls approximation is much more judicious you know you get faster solutions than solving the OD now the same problem where you are looking at circular cylinder can be done by using walls approximation right okay so the same way you take the profile the complete profile u 8 of x is equal to 2 v 8 sin x by r 0 where your x is equal to r 0 ? right you take your full profile which is coming from the potential flow flow for describing the free stream velocity for circular cylinder and that can be substituted into this okay expression number 1 right here so that u 8 can be substituted now you can integrate it now you have to be careful when you integrate it you are integrating sin 5 x by r 0 there okay so in the stagnation region you have made the approximation that for small values of x by r 0 sin x by r 0 is equal to x by r 0 but if you are looking at integrating for the entire flow from the stagnation point still the separation point you have to use this profile as it is okay and when you put this profile you have sin 5 term here sin power 6 term so if you are finding it difficult to integrate manual you can use some numerical technique such as you know trapezoidal rule for integration simple trapezoidal rule or even simple rectangular rule will work so you have to start from some point right here so at this point you know the value of ? 2 that that is what you have determined okay you also know the value of ? and everything so you can start from that point 0 to some point next okay so you can keep on integrating that way okay till any x that you want till you go to separation okay for each point that you go so now you calculate the new value of ? 2 square and therefore you can calculate the new value of ? like this because you know the ratio of ? 2 by ? correct for each value of x you know the new value of ? 2 by ? you also know the original form original relationship you can equate those two and solve for ? so that will give you a solution for ? basically okay so this you keep doing till you hit separation point where your ? becomes equal to – 12 so now this is a little bit more easier you can do this on an excel sheet you do not have to program it so you can simply do numerical integration and you can solve this equation also you know you can find roots roots of this in an excel sheet itself so that will give you again the boundary layer thickness and the momentum thickness and also the separation point so everything can be obtained from the walls approximation okay then this will be nearly as good as doing it by the solution numerical solution to the OD okay so in fact I will give the next assignment which will be assignment three which will include all the integral solution problems I will also give ask you to do this for sector cylinder and see for yourself how you can basically find the separation point and get expression for boundary layer thickness momentum thing okay so same thing can be done for any wedge problem you know the Falkner's can solutions where you had assumed some kind of a wedge flow and a velocity profile of this particular sort now you can apply the walls approximation you can substitute for you infinity here you can integrate it out okay and finally you can calculate the value of ? and from there of course your boundary layer thickness and again you can use that into the heat transfer problem where you substitute the expression for ? and the particular value of ? you calculated you can derive the expression for Nusselt number so you can do this for any value of m okay like the way you did your similarity solution you can do this and you can compare the result with the similarity solution you will find the agreement once again is pretty good okay so any problem with pressure gradient can be handled with the walls approximation okay so probably we will I will stop here but I will probably will have some kind of a small discussion if you have any doubts because I will start I have one more last topic left under integral method which is non-uniform temperature boundary condition so far we have assumed that the entire plate is heated with a uniform temperature or uniform heat flux in most of the cases that is not true okay the profile will be varying so what happens if you have a variable temperature profile okay so so we cannot do with the similarity solution this kind of a problem of course you can use similarity solution if it varies only linearly but if it is any other variation you have to do integral solution so we will see for a flat plate case what is the technique to how to introduce a non-uniform temperature boundary condition how do we solve this okay so that will be the last topic I will take that about a couple of hours next week before your quiz one okay so quiz one will have all the topics till your integral solutions so pretty much any doubts that you have in this I hope you understood the walls approximation because you will be now using this to solve this flow past circular cylinder okay so if you have any questions you please ask me is it is it clear you have understood this is just a dummy variable yeah this is just a dummy variable you can if you if you are having say C x power m this will be C ? power m so that will be just a dummy variable and it indicates that whatever upper limit till where you are interested to integrate so that will be used into the dummy variable okay so earlier x indicated some arbitrary location now when you are integrating it you have to use the dummy variable because you should not confuse this with the limit of integration okay so many a times we use the same thing no but we know that we should use the upper limit but to differentiate it clearly strictly speaking you always have to use a dummy variable okay so you are clear with all of all of this like how to calculate your ? to numerically and all that you have to numerically integrated by represental rule you can start from say the stagnation point okay assume this is a trapezoid between these two so you can if you are integrating to the first point it is just only one step if you are integrating somewhere till somewhere here you have to assume many trapezoids and some them all together so the area under each trapezoid some them all the trapezoids together will you will give you the area under the integral basically so that is that is the approximation that we are so at each location so you calculate the value of ? to and so like this you will have an expression for ? to right here right now we have completely there is no dependence on X here okay so there when you are putting a sign profile there it will have a dependence on particular value of X so for that particular value of X you will have a particular constant okay for ? to okay so and then you now this is so this is the expression for ? to this expression for ? will be there this is again a function of the position because you are now differentiating sign profile okay so there will be a X dependence so for each position the value of ? to and ? will be different and that you take the ratio so you will be getting an expression in terms of ? okay only this constant will keep changing for each location and then you can solve this find roots of this okay get the new value of ? and you know that where the region is you know as and when the ? keeps going negative and increasingly negative you know that you are approaching close to the separation point and and then you keep doing this integral till where at a particular point where ? becomes minus 12 and then you know that you have hit the separation okay so so all this can be done in a excel sheet you know you do not have to write any program for that separating.


For more videos, please subscribe and click that notification bell so you can be notified when I post a new video. Thank you for supporting my channel.  I am back with another video for you guys. So thank you so much to all of you who've decided to follow me on this journey and subscribe to this channel. Really really appreciate each and every one of you. Can't thank you guys enough. With respect to me and my life, it's super super compartmentalized. Like everything kind of went really quickly. I told you guys my timeline. I literally had maybe a month and a half maybe let's stretch it out to two months just to be like round numbers to plan my life to move to another state move my whole family with me get my daughter in school all of that within that two month period to start PA school. So it was really like a whirlwind. It was super super quick. We had to make sure that we were planning what made it easy was me having a plan. And I think that's what also makes my life as being a mom and a wife and doing YouTube and being in PA school all the much easier. I have a plan. I have like a set schedule of what I'm going to do when I'm going to do it. My kids are embedded at a certain time. I wake up at a certain time. I'm in school from a certain time. I'm studying from a certain time. So it's very very compartmentalized very rigid very kind of just planned out. And although that may sound kind of like whack compartmentalized life it's what works for me. And it's only for a year. You know that's what I keep saying to myself. This is only for a year out of my life. I'm only going to be in school eight hours a day studying all the time for a year. And then once I get into rotations it's a little bit more lax although I'll still be studying because I'm going to still need to keep fresh all the things that I learned the previous year for when I'm ready to take my pants. And then also just as I'm on the different rotations making sure that I'm staying fresh and up-to-date on the information that I'm going to be studying and the people that I'm going to be seeing on an everyday basis for let's say for instance women's health I need to study but I'm not going to be studying and as rigorously. It's not like I'm studying for a test. I mean I guess it's kind of like the test of my life because I'm dealing with patients but I'm not going to be studying as I have been studying in the past. And so that's what I keep telling myself. But specifically I plan my day is planned out. I am up around like 6 30ish you know maybe a little bit earlier maybe a little bit later depending on my days. As I stated before in the summer and fall sessions a lot of my classes started at 8 a.m. so I had to be up a lot earlier than that. My daughter goes to school for 8 a.m. Luckily the bus just comes and picks her up right down the street so that's a benefit. If she has to be at school for 8 a.m. and my husband has to be there with her I have to be at school earlier than that. So I was usually at school at like 7 30 and then she would be off at school at 8. My 3 year old thank God for my husband he's able to work from home so he really takes care of her. He's the one that directs her care throughout the day while I'm at school which is a blessing. That's one another like major thing that any parent that's trying to go back to school any type of school but PA school specifically. It's so dense the material that you're going to be learning it's so hard and rigorous that you're going to be studying a lot. Therefore it's important to have a great support system to take up like pick up the slack where you may be lacking in terms of the parenting aspect of your life because there are there's going to be some times when you know you can't read a book when you're with your child because you have to read your book because you have a test tomorrow that is a really big test you know it's really really beneficial having him there as my support system so that's one major thing that I do. I plan my life out and I also have a really really great support system at home that can take up the slack when I'm kind of like lagging behind as a mom.  I know somebody asked me if I feel mommy's guilt and of course I do because I mean my kids they're seven and three yeah and I don't know necessarily how it's affecting them. I can ask them questions but like really and truly you know I don't know for sure I don't know what the law in the long room what that's going to look like or you know if there's going to be any resentment but my desire and my hope is that there isn't because they're still young and again it's only four year and that's kind of what I keep telling myself because if I dwell on the man like I can't sit up here and play Barbies with my daughter right now or forgot to take her on this mommy daughter date it's like well dang you know like that it would be like super hard to actually continue on with this like the stress and probably I'd get like a little depressed and feel like a failure so it's like why would you why do that instead focus on the positive so although there are some times when I'm like man I feel kind of guilty that I'm not able to do this I look and I'm like okay I only have six months left or I only have um three months left or now I only have one month of school left until I'm off into my rotations then I have a little bit more time for my uh you know to give to them the time that I wasn't able to give to them in the past so that's like three things right there right being very very like planned out regimented having a great support system and although I do have guilt at times not dwelling on that guilt like you cannot do that because it's gonna you're you don't need any extra added stress and if there's somebody that's also like putting guilt on you in terms of like man you're not really being a good parent right now like you're not even spending time with your kid for whatever reason don't have them in your life because you don't really need that stress at that time when you're trying to like pass school to better your life your family's life your kids life you don't need the negativity also what I like something else that I do so my kids I know I'm when I I used to be a nanny you guys right so I used to be a nanny back in the day and um the person that I used to work for her kids used to go to bed at like five o'clock and I was like dang that's mad early like you's putting these kids to bed early you're just trying to get some time to yourself but um but I get it now uh my kids go to bed at 7 30 and people are like how do you get them to go to bed at 7 30 yet like I don't play any games like it's bedtime it's bedtime even if I have to put you back in the bed three or four times you're in the bed and eventually you'll fall asleep that's important because one they're waking up super early in the morning as well so they need to get like their 10 12 hours of sleep they're young they're growing so that's important but also I need to be able to study as well so it's a benefit for me because if they're in bed by 7 30 and they're super like they're down in rem by eight o'clock I'm able to now spend the next four hours or so studying the next two hours three hours studying before I have to go to bed and make sure that I'm well uh rested for the next day so that's also something making sure that you get your kids on a schedule as well is very important I think like your best friend when you're a parent in PA school is coming in with a plan having a plan and just kind of stick into it you know even if there may be days that you kind of like get off of your plan a little like come back to that plan and make sure that you bring everybody back on that plan with you because that's going to be your best friend and making sure that your life runs smoothly and then like my major like big thing like the most important part of what makes this all work is my time on the weekend so with respect to my religion I observed the Sabbath so sunset Friday to sunset Saturday I don't do any studying I know it's freaking crazy I don't do any studying whatsoever but man God is amazing and he is honoring me following what I believe because I'm making it you guys like so far I am doing fine I'm doing absolutely fine I'm passing and I'm going to make it to rotation so I'm doing fine and and you can do that if you have you know for your for me it's my Sabbath so sunset Friday to sunset Saturday I don't study at all it's family time you know we have devotion we go to church only somebody get your sauce all on the line like a tie to five stand up talk to the one and they ask you what are you going to study I don't know what are you going to do I don't know I don't know well guess what stick together you're the ones you're the ones Azzy how was uh how was uh Children's Church you know just kind of spending time together have a little Sabbath movie but it's important because it's not just important for me so that I can rejuvenate and like kind of refresh my my mind and decompress from school from all of the studying that I've been doing it's also important so that I can spend time with my family because I don't get to see them as much as I would like during the week um so the Sabbath is really really important to me and that it helps me a lot I know some people may not necessarily feel like they can take a day off of studying it may not necessarily work for you but for me having that day that time and really literally the weekend because even though I'm studying on Sundays or there are some Sundays where I'm studying like for the majority of the day like I did that a lot in the summer session when I was fresh newly minted into PA school but I got better at it you know I got better at managing my time and knowing how much I needed to study for a certain class how much I didn't need to study for another class and that helps in kind of focusing your time with respect to spending time with your family and your kids and then also spending time in the books so when you get into PA school the one thing that I can tell each and every one of you is first off it's absolutely doable to be a parent and in PA school and even a single parent because I know of some people that are single parents or have been single parents in PA school and they made it and they're PA's currently or they're coming into PA school or they're currently PA students so it's doable you guys so don't let that deter you from applying to PA school or um actually like taking your acceptance you know accepting your acceptance into PA school but take that first month you'll you'll need that first good month to actually just kind of focus yourself make sure that you have a plan make sure that you have kind of a regiment a schedule to stick by that will be helpful and beneficial to you for you to study and then also for your family with respect to family time and I'm saying that month because it does take a month for you to kind of get acclimated in that month you should have all a test in all of your classes by that time so you would know hey I need to study a little bit more for this or a little bit less for that I can devote a little bit more time here and take a little bit more time away here but after that month you're good you're gonna be fine and especially after that first three months of that semester or um if you're on a quarterly system however long that is for that quarter you'll be fine you should be absolutely okay with that and you'll be a PA soon enough and you just would have a few more months of your didactic gear left and then it's off to rotations and you can read kind of adjust how you study and how you spend your time your free time and how you spend time with your kids that was nice give me a huggy give a bow bow bow yeah that's really how I do it honestly it's all about support planning my Sabbath and um just making sure that everybody is on a schedule and it works so hopefully this helps all of you who have been asking me these questions and any of you who's thinking about becoming a PA or applying to PA school or NPA NP school and you're concerned that you're you know a parent you're a mom specifically um and spending that time away from your kids what's that going to do to them and how does that work because it's not easy but it's doable all right so thank you guys so much for asking me for that question and thank you guys so much for watching if you have any other questions related to PA school or being a mom at all go ahead and leave that in the comment section below and if you haven't already done so go ahead and follow me on instagram at adana the PA thanks i will talk to you guys next time bye


Our next speaker is Nolan Nichols. It's a pleasure to introduce him. Nolan and I got to know each other through the INCF data sharing task force. Many moons back. Mars may have been closed due to it again at that point in time. Sure. And Nolan has done an incredible set of work over the years on trying to represent these ontologies in various biomedical spaces. And then he's gone over to the other side. And I have no idea what he does. So today we are going to hear and find out about what he does on the other side. I hear it's something related to metadata. Yes. Yeah. We'll get into metadata. Hopefully I'm in the right session. So hello everybody. And thank you for inviting me to come present today. It's quite exciting to be here. I really see a lot of familiar faces. And I haven't been to this conference in a couple of years. So it's really great to be invited back to share what I'm up to. Satcha alluded to. So I've now moved to the other side and now working in industry in this company called GenTech, which is a member of a broad organization called Roche. And so when I'm talking about metadata, meaningful metadata at scale, this is really about, you know, at the enterprise level. So there's close to 100,000 employees, lots of stuff going on. And it's really a challenge in terms of all of the socio-technical challenges in terms of getting everybody to work together in order to kind of move forward with science and precision medicine in the clinical trial space. And so I thought I'd motivate our discussion today by introducing, we're talking a little bit about one of our trial programs in Alzheimer's disease. And so as many of you know, so Alzheimer's disease is a progressive disease of the brain that causes problems with memory, thinking, and behavior. And it's the most common form of dementia. And it's also a large and unmet growing need, medical need, where there's over 45 million people worldwide that are affected with Alzheimer's, and that's only growing substantially. And so we desperately need some form of medication to treat these patients and not only treat the patients, but also really the caregivers that are spending money out of their own pockets and spending their time to be able to take care of their loved ones. And so in order to kind of address this issue, one piece that we'll talk about is that a little bit of the biology behind the biomarkers and understanding how Alzheimer's progresses is that here we have like a popular framework or model around how AD biomarkers that help to do in vivo staging of the disease. And so this model focus on five of the most well-known biomarkers, which can be divided into either measures of brain amyloid beta deposition and measures of neurodegeneration. And so the major AD biomarkers become abnormal in a temporally ordered banner, where CSF A beta and amyloid pet are dynamic earliest in the disease, followed by CSF tau and FDG pet, followed by structural MRI and then followed by clinical symptoms. And so the biomarkers that are denoted as upstream are neurodegenerative biomarkers, whereas those that are downstream are considered more on the clinical side. And so within this framework, so right now most of the treatments for Alzheimer's disease are focused on treating the symptoms, but the general consensus is that earlier treatment is considered to be key in order to prevent or delay the onset of neurodegeneration. And so when developing some therapeutics for intervening in this disease, one thing to be aware of is that these amyloid plaques are considered to be a key feature in the pathology of AD, and this amyloid precursor protein that's cleaved releases a variety of different isoforms of the amyloid beta peptide. In particular, there's an aggregation of the A beta 42 isoform that leads to these toxic oligomers, as well as downstream amyloid plaques and tau pathology. And so the downstream beta amyloid aggregation and other processes leading to neurodegeneration and neural loss. And so right now our current kind of portfolio of different medications that are being evaluated target both tau, but the ones that are furthest along the pipeline are this Gantronerumab molecule that targets both forms of this A beta molecule, so both the oligomers and also the amyloid plaques. And then also this Cronuzumab drug that is more specific to the oligomer form of the toxic A beta 42. And so I'll be talking about introducing just a little bit of background about our Cronuzumab plan, our program, and in particular the phase two program that I'll be discussing kind of in the context of metadata to some extent. So there are two programs. So there's one that is in a cognition study called ABI. So this is a larger study. And a second one called blaze. And so this is a smaller biomarker study. So the enrollment criteria for these two trials focused on moderate or mild to moderate AD where patients that were enrolled were between 50 and 80 years old. And there were two different arms for both of these trials. So one used a subcutaneous dose of 300 milligrams every two weeks and the other one was a higher dose that was administered intravenously at 15 milligrams per kilogram every month. And so this is about a two and a half fold higher exposure. And the primary readouts were done after 72 weeks. And so the primary endpoint for the ABI cognition study was a change in the ADOS cog as well as the CDR sum of boxes. And for blaze, this was primarily imaging, medical imaging related. So this was a pet readout. And so I'll be focusing just on the ABI study here and this cognition piece of this. And so in the overall population, the primary endpoint here was not met for this trial and so for this readout. But if you look at the low dose versus the high dose, there was a trend that the high dose may have some kind of treatment effect suggesting that a higher dose may be better. And if we go and look in a pre-specified sub-cohort within this trial and look at progressively milder subsets of patients, we can see that the drug might be working, but the patients would need to be treated earlier with a higher dose. So here you can see that early, if you look at patients across the whole spectrum with MMSC from 18 to 26 score, we have a 16.8% effect. Whereas that grows up to 23.8 and finally a 35% effect. And so while the primary endpoints were not met, this still suggested that CRENEZMAB may have some kind of an effect and this is consistent with what's being seen in other anti-amoled studies that are being reported from other pharmaceutical companies. And so from this, the decision was made to still continue moving forward with the phase three program and also evaluate what higher doses were safe in a smaller study. And this would be done in a more prodromal to mild population of patients. And so as part of this trial, we did kind of a post-hoc analysis and looked for trends within the data that would give us some degree of evidence that we would be able to move forward in the program. However, as part of the trial, there's also all of this other auxiliary data that's created. So you have all the different patient visits, all the vital signs, other types of biosamples and imaging data that wasn't really included in this analysis in order to be used to move forward just with the trial. And so there's kind of this left hanging question and this isn't just for Alzheimer's, but really for all the studies that are being conducted, whether they're clinical studies or clinical trials and other therapeutic areas, is really like how can we reuse these data in order to find actionable insights that either allows us to identify new targets or biomarkers or just even understanding the core biology of these diseases. And so if we look at precision medicine, traditionally the approach within clinical trials is you have early research and developments that identifies a target in a molecule and this is used then to go through a series of preclinical and clinical trials and if that medication makes it through the trials it will then end up in clinical practice. However, this is a very rich source of information that was traditionally not really reused. And so there's this concept of repurposing the data, referring to this as reverse translation where you take the results of the clinical trial and all the samples and other information and feeding it back into research and development. And so while this may sound like something that has been done frequently within academics in the clinical trials world, these two parts of the organization really have been separated and so this is still kind of a new-ish idea over the past few years around how do we actually operationalize reusing clinical trials data and putting it into the hands of bioinformaticians and data scientists. However, if you take the data from a clinical trial and go and hand that over to the data scientists and bioinformatics folks that are sitting in research that have never seen this type of data before and don't really understand how clinical trials are designed, et cetera, you're going to end up seeing, they're going to have a hard time actually using that data. And so this is a figure from a Forbes article that came out a few years ago that surveyed data scientists and tried to ask them where they spend their time. And so you can see here that they spend 20% of their time collecting data sets and then another 60% just trying to clean them up and make them reusable before they get to the fun part of actually doing their data analysis and training their models. And I'd argue that probably within biomedical sciences it might even be more than 80% that you spend trying to get your data and wrangle it and actually use it. And so we have this kind of question of how do we pay this price only once and push all this data curation and integration up front so that the people that are trained to do analysis can get directly to work. And so that's where we introduce this concept of FAIR. And so as we heard in Michelle's talk, FAIR is really providing the principles around which we can make optimal reuse of data. And so interestingly, the FAIR principles have really made their way also into industry. And so this is something that Rochewide, the FAIR principles have been like a rallying cry that I see in like all of our internal presentations where they discuss needing to make data FAIR and the FAIR principles are really like the war cry around how we can be better stewards of our data to the extent of which there are internal projects where we've hired internal data curators and integrators and created a variety of new positions that are focused just on making data FAIR. And so just briefly to go over where some of these challenges arise and kind of where the FAIR principles are actually being applied. So as you guys probably already know, so clinical trials generally have a multi-site. So we have all these sites that generally collect a variety of different types of data that go to different vendors that provide certain types of analyses or run assays that then get fed back over to us through these different vendor services. And these are fed into either like our sample biobanks where we keep track of the actual physical samples or the actual assay results or imaging analysis results that then go into in more of like a CSV or like tabular form into a clinical data warehouse. And so when these data come in, they're in a very raw form and they are not ready for analysis. And so the idea of being able to make these analysis ready means that we need to harmonize them and bring them under certain metadata standards. And so one of the first stages after the data come in is that we need to apply standards that can then be used to derive different measures downstream that are used for the actual deliverables for regulatory approval, et cetera. And so the standards that are used are called CDISC. And so as of December of 2016, the FDA requires that any regulatory filing must follow the CDISC standards. And so this is very much the kind of stick approach where it's like you shall follow the standards. And so this has helped and forced adoption of metadata standards within the pharmaceutical industry. And so the idea then is that for clinical scientists that are working with the data that are part of a trial, that the data is more or less fair and follows these standards. But you might ask kind of what is CDISC and what is this framework. CDISC stands for the Clinical Data Interchanges Standards Consortium. So it's been around for I think since the mid-2000s or so. And it really is a framework that covers a broad spectrum of different standards here. So everything from planning a trial, so the protocol and study design, through the actual data collection that's sent in through different vendors, as well as data tabulations. So these are kind of the core workhorse of what we actually end up using. And so this is the SDTM standard, as well as data analysis standards where the data is basically organized in a way that's specific for different deliverables and figures that are part of the clinical trial milestones. And so just to look a little bit closer at the SDTM standards, so SDTM is broken down into a variety of different implementation guidelines. So for medical devices, tabulations, or people that are actually not participating in the trial are the people surrounding them. So these can be family members or physicians, et cetera. And then there are more domain-specific standards that describe how to collect data around questionnaires or pharmacogenomics information and therapeutic extensions that go into areas like Alzheimer's or asthma, et cetera. In addition, there are also a set of controlled terminologies that are allowed to be used. And so looking at the core SDTM implementation guide, we have a variety of different classes, and each of those classes have what are called the different domains that model specific types of data sets. And so these classes go from special purpose, which will be something like demographics, to these three different classes of general observations, which go into interventions or specific events or findings that have their own specific descriptions around what's important to be captured, as well as other kind of auxiliary pieces like findings, trial design, and also relationships between data sets. And so looking at an example of one of these, so the data that comes in to our organization from the different sites maybe organized kind of like this lab test results here at the bottom. So we may have a study number that we can map over directly. We may have some patient identifier where we need to modify the header or the column name, but then also modify the value. So just here it's just removing a dash, but it could also be applying some other type of transformation. And then you can see that these other fields also map over. But in addition to those mappings, we also include additional codes. So here we have basically key value pairs where you have the lab test name, what category it's in, the units that it was collected with, as well as being able to bring in knowledge from outside. So from the World Health Organization or other places that say like what that value is with the low ranges or the high range. And so really this is kind of augmenting the data that you receive with additional information that can be used to automate downstream analyses and speed up our ability to generate deliverables and results. However, across the large organization like this, how are you able to pull all this information together and be able to follow like the same comic guidelines. And so really what this comes down to is data governance and being able to have a metadata repository. So a core location where everybody references for the gold standard of how to represent information. And so internally we have something called the Global Data Standards Repository. And so here we are able to model each of these SDTM domains. So this has all of the different classes that we can access as well as what the applicable or valid values are for each of the fields. And then also breaks down for each of the kind of columns like exactly what the scheme is that you're allowed to use. And furthermore, we create releases of these standards and so that when you have some specific trial they have to use specific versions of the standards. And this whole metadata repository is actually kind of interestingly modeled in a fair way. So it also uses kind of link data or RDF in the background and it's natively like a what's called a triple store that models all of this relative information. So all the terms, IDs and versions also have these URIs or unique identifiers that can be referenced. And so in addition to this, so that's part of the actual clinical trial process. But for discovery-based research we need to be able to take not just these clinical measures and be able to pull those over, but we also want to take the biological samples from these studies and be able to feed those back into research for other exploratory assays. And we want to be able to provide those to the bioinformatics scientists and data structures that they're familiar with and able to use. And so we can pull both the clinical data that's already been harmonized and follows these nice standards into a data structure that we can then provide to bioinformatics scientists in a fair way. And the data structure that many of these folks are used to using, at least within our organization, follow something from a group called, or an organization called Bioconductor. And so the idea is that we can eliminate this 80% of time of cleaning all the data sets up by using these curators and integrators that pull the data sets together into these concise data objects and they can start with their analysis right away. And so we may have a variety of analysis type, or assay types like RNA-C, nanostring, or fluid ion that can be integrated into these multi-assay experiment objects. And so these are data structures that provide all the methods for manipulating and integrating multiple assays for, in this case, a given clinical trial so that you have an efficient way to construct these and subset and as well as a variety of other types of analysis. So like survival analysis and other tools that can operate on top of these objects, as well as building dashboards and interactive apps using tools like Shiny. And so, you know, but doing this is hard. It's not something that is done, can be automated. So it's really something that is highly custom and you have people that have to go in and do manual quality control, make sure all the pipelines for the assays are following the same versions, as well as mapping all the different identifiers, which is extremely challenging. And so what we want to be able to do is provide the consumers of these data so that they can trust them and be able to reuse them and also discover what data is available and what results are being provided. And so along these lines, so we've developed a system internally that we call GREX. And so what is this? So it's a system for recording, tracking, storing, finding and retrieving computational results, including data and these multi-assay experiment objects in a fair manner. And so the key goal is really to apply the fair principles, but applying them to generated exploratory and also one-off types of lymph analyses. So not something that can be incorporated into a standard pipeline. And so there's our logo. So it has a dinosaur because dinosaurs are cool, but also because the idea that we want to be able to leave behind this kind of like archeological record of all the information that went into constructing both data and results that people can then unearth later in use. And so GREX is based on four pillars. And here I have kind of a simple mapping to fair. However, I guess it could go into more detail now that I know about the fair metrics. But we have the kind of core piece is just an archival system where we can store a standard directory structure of the data that's being produced along with metadata, a provenance pillar that is used to basically create relationships in a graph around how the different results of data are connected, a discoverability portal for being able to find data, and also this other component of reproducibility where we can take the code and data and environment that's submitted and be able to reproduce or provide certain guarantees that the object you're consuming was actually constructed by the system and could be reproduced. And that piece, by next point, is that the R in fair is not for reproducibility. And so GREX really provides some extensions and additions to fair where we want to be able to intrinsically link code and results and also be able to include all the versions that went into producing some results so that you'd be able to reconstruct the environment for later reproducibility. And furthermore, the client that we're using is able to do some smart things in terms of introspecting the objects that are being created and be able to generate metadata that really describes exactly what took place during the construction of some dataset. And the reproducibility part is something we're still working on, but that's also something we're developing. And so just to give you an idea of how this information flows through the system, so there's an R client that primarily works on right now with an R markdown. And so you can take an R markdown file, so this is a description of both the code and also the narrative along how either a piece of data or result is being integrated or created, and then submit that into the system where it constructs something we're calling a client bundle where it has all the metadata for the versions of R, all of the code, who's doing the submission, and all this other information of the actual data objects or for RMD it will also include the HTML file so you'll be able to see the report. And so this is handled by a controller that then stores this in the archive and also so the metadata that's constructed uses something called JSON-LD. And so that's loaded into a provenance triple store, so this is represented as a graph. So this will allow us to link across different R sessions for the objects that are being submitted to the system. And then also a publication step where all this information is made available where this discoverability portal is able to query the provenance and also download those results. And we have just a small couple of screenshots of that. And so this is just kind of like one of the early interfaces of what the system looks like. And so this provides you just like some simple facets that you're used to being able to see. It also provides free text search and well free text search and pretty much any search that the solar search engine that supports. And all of the results that are submitted get a unique identifier that's based on a hash. And then also metadata that's actually extracted from these R objects themselves. So if you submit a plot, it can go in and grab the title and axes and other related metadata. In experiments, it can also read out the actual variables that are consistent with this CDISC standard. So then we have the actual IDs of like within the data sets what kind of information can be made available in search and retrieved from the object. And then just for like an individual result it has links to be able to download these. Eventually we'd like to include links to other data sets that may be related to the one that was submitted. And then also the code that the actual exact code that was used to submit the object. So we actually traverse the kind of execution path of all the top level functions and provide a description of what called what. So you actually know exactly what code was executed to submit that object. Cool. So the GRX platform implements some of the fair principles. So we store results in associated metadata with persistent IDs. We make them findable and accessible and we're partially interoperable using this JSON-LD standard. So we map to some public vocabularies such as Schema.org or DCAT for describing the data sets themselves as well as the Provantology. And we also make these data sets reusable through these R packages called History and Tracker. And so these two tools are the kind of smarts behind the client that does all the metadata extraction. And so these are used to really describe exactly what took place during the session. And so that's kind of the summary of where GRX is at. And so I'll add here that so out of the bioinformatics group so both the History and Tracker packages are shared. So those are open source tools. And along with that, there are probably 36 or so other packages out of the group that have made their way onto bioconductors open source tools. And with that, I wanted to segue over to this talking a little bit about, you know, I've talked about how we're processing data making it available for our internal sharing but there are also some external sharing efforts that I wanted to make you aware of. So one is something called the Clinical Study Data Request website. And so this is a collaboration across several different clinical trial sponsors to make the actual patient level data available to external researchers. So this also has a website. And so if you go and browse this site, you can view over 3,800 trials from across these different organizations and submit requests to be able to download patient level data and use them in your research. And so at Roche alone, there's a little bit over 200. And so this number is going to continue to grow and there's a lot of internal kind of effort to go into making these data available to the outside community. That's seen as something that's valuable to contribute back. And along those same lines and to link back to the Alzheimer's story that we started with. So the Alzheimer's Prevention Initiative, so this is something headed by the Banner Institute. So we have a collaboration with them for this Columbia trial. So this is a study of, it's actually closer to 250 patients that are enrolled that have this familial early onset AD. So this is essentially all the people that are in this trial will get Alzheimer's disease at some point in their life. And so we're enrolling people as early as 30 years of age. And so they're taking our, this Kurnooza Mab drug starting early in life and then we're looking at other primary points in terms of cognition and imaging to understand if we can delay the onset of Alzheimer's disease for these folks. And so with that, I just wanted to make one final point around this is that I think that data standards and these notions of harmonization and the open source tools and software and platforms that are being constructed by the community are a great place where people in industry and people that are in academics can collaborate in order to move the field forward. There may be competitive space in the actual development of molecules in terms of actually collaborating on projects like CDISC which Roche is an active contributor to. I think that that is something where we can contribute and move the whole field forward. And so with that, I'll acknowledge the G-Rex team and folks within Bioinformatics as well as our clinical data standards team and then the folks that I'd worked with on AD and the Kurnooza Mab public program. And with that, I can take any questions. So while we take a few questions for Nolan, maybe I can ask the other panelists to come sit on the chairs. Nolan, that was a great talk. In terms of the R tools that you've developed for the clinical, I guess the Bioinformatics people that are using the data that comes out, how does there are workflow change when their analysis is being tracked by the G-Rex system? Like is anything actually different from the code differently? Can you just talk about that a bit? Yeah, sure. So the tracker package, so you do need to do a library tracker call at the beginning and essentially what that means is it'll keep track of all the different evaluations of functions as you're going along. But other than that, they don't have to change any part of their workflow. When submitting an R markdown file, there's actually a separate function called knit and record. So that basically sticks with the whole literate programming. So you basically just feed that in our markdown file and it evaluates that and automatically kind of loads this tracker package and keeps track of everything that's going on. And so you really don't have to modify the workflow of the analyst that's working with the data, which has ended up being like, I mean it's a critical piece of, if you want adoption, don't change somebody's workflow. Another question in the back here? And thanks again for the talk. Two very related questions. One is clearly a lot of moving parts in this whole well engineered system. How do you keep the sheer complexity of it from overwhelming users? And second, you talked a lot about what you're trying to deliver, what evidence or feedback do you have that those mechanisms are useful beyond compliance or beyond sort of tracking things, but their people are able to do things they couldn't do otherwise with that? What are your success stories? Yeah, so on the first part, yeah. I mean it is very complex. And so mainly the way that we've done this is by scoping down to specific use cases. So we'll use like cancer immunotherapy and we'll just focus just on a G-REx that supports the use cases that are around that specific project. And in terms of the complexity, or I mean in terms of like success stories, so I mean even internally this was released fairly recently but we got a lot of good feedback from folks. I mean they're really used to working on like file systems and directory structures and so just like migrating to web applications that actually have the data and providing tools where they can computationally access these objects from a session and do searches for certain studies and see what data is available. That's still like a step forward from where they're kind of currently coming from. And so from what we're hearing back, yes they support it but there are a whole bunch of other issues around data versioning and people will identify oh there's an issue with this data set and we have to go back and redo it. So there are challenges that are unsolved there but I've been learning to from other projects that are similar to this even here at this conference and trying to understand what the best path forward for doing like data versioning and other types of things as well.