It's going to be something simple. Let's find out together. I got this at an estate sale. It was on the tire. It's a good thing it's just a plastic tire. It supposedly wasn't run much. The store I got was he used it a couple times. Hey, something went wrong with the carburetor and he had the carburetor rebuilt was when he ran the gas out of the carburetor and out of the tank. So I don't know, but when I pull the dish mold gasoline, it's very thin. Okay, yeah, there's nothing in the tank. It's plastic tank, so that's good. So there's nothing in the tank. Nice and clean, but it's locked up. Okay, so I'm going to set you down here. And the first thing we want to try to do with this thing. Yes, my garage is nasty again, but you know, got to work on stuff to make some money. So what we're going to do, let's get you down here. Okay, so I'm going to raise the shoot up here. Try to get something down in there and see if I can turn this backwards. Inside here, I'll show you what I'm talking about here in just a minute and see if maybe there's something jammed down in there causing that or what. Now apparently when these are new, you can get a bag for them. This one does not have a bag. All right, so let me get something to hold this shoot up here. Okay, I just put a bungee cord on that thing to hold it up right now. Now if you look down in there, so what I'm going to try to do is I'm going to try to get something in there and try to push it down backwards and see what happens. So let me grab a pry bar. Couldn't find my pry bars. So just, but I do have a long screwdriver. Does not want to turn, okay? So not seeing anything.   So stick with me and I'll turn you back on in a second. Okay, we're back. There's three more bolts on the other side. There's two on the side, one on the bottom and there's three on this side, one on the top, two on the side.  We should have all the bolts out. I see some issues that we may have to address. I think this thing was used more than just a couple of times. There's some crack, cracks there and I've seen one down on the bottom so we may have to address them. Probably end up doing this putting bigger washers on there so it covers the crack so it doesn't crack more, okay? So let's go ahead and try to pull this off of here. This is what's inside. This is moving back and forth. I see something right in there. You guys see that? I wonder if that's what's causing it. Maybe there's a stick. Let's get that stick out of there and see if that's what's going on with it. I bet you that's the whole issue. I'm gonna get something and I'm gonna go out there and dig that out without having to take the other side off and I'll be right back with you guys, hang tight. Still kinda stuck in there but if I, there's a little bit of it right down in there. If I pull the cord it may get chopped up there. Let's go pull the cord. Got a little bit down in there. There's still right in there. See it stuck down in there? So it's still got a little bit in there. Let me get a pair of gloves on and I'm gonna get that stick out of there. And then we may put some gas in it and fire it up. So let me get to that. You guys know what's coming next, right? Now, for safety reasons, I'm gonna throw this back on. All it is is just the opposite of how it come off. Three bolts on each side, three nuts on each side. And then we're gonna put some gas in it because it looks nice and clean. We're gonna try to start this thing up. Let me get that put back together and we'll get it outside. All right, slight change of plans. First thing we're gonna do is we're gonna check it for spark, okay? That's the first thing we're gonna do. Second thing we're gonna do is go ahead and change the oil right now because I smell fuel in it. I don't like that idea. Hopefully the carburetor will be fine. There's a little dirt in the fuel tank but I'm just gonna blow that out with some compressed air. But monkey went after some oil for me. So we're gonna go ahead and check it for spark, all right? So let me grab my spark tester. You guys have seen me use this before. It doesn't check for spark for the plug. For the spark plug, it just checks to see if it's got spark to the plug and that'll tell me I have to pull the flywheel. This does not have points. If I have to check the coil, the more expensive stuff. So let's see. Now this thing will light up. If it's got spark, you'll see it light up, okay? Let's try this. Let's turn it on. Turn the switch on to see if it's got spark. Now at the end, when it turns over, when you hear that clunk, clunk, clunk, there's nothing wrong with it. It's a, there's a, they're kind of like a blade that kicks the stuff out and that's what you hear. They're just, they just flap around. And when it's running, centrifugal force holds them out. So don't be alarmed about that. You see a spark? A little closer. I'm seeing it spark. She's got spark. Let's change, let's go ahead and get the oil draining. So that's draining. I'll put the chute back onto the back, the hopper, they call it, on back onto the back. By the time we do that, she should be back and we'll put some oil in it. Okay. I just cut this out of the bottom of a water container. I've had people ask me before where you can get rid of oil at. Get rid of them before you can take them to Walmart. AutoZone will take it. It's free, advanced auto. Most, most AutoZone's not all of them. About 95% of them will take it. And you're allowed up to like 20 quarts per visit, is what they say.   They take batteries as well. But also batteries, if you have old car batteries or anything like that, tractor batteries, you can take them to the scrap yard. They'll give you money for it.  So, we're gonna let this drain out. While this is draining, I'm gonna go ahead and put the chute back on, okay? All right, just in time, monkey just pulled back. I usually like to let him, when I change oil to run him and let it get hot, you know, up to operating temperature and let it drain out, but this will be okay for now, because I didn't wanna run it like that. So, hopefully it runs, period. We don't know yet, do we, Monk? Okay, I've got some oil here. I think, I know a five horse takes 20 ounces, generally. Not sure what this is gonna take, but I'm gonna put 20 in it and I'm gonna put 20 ounces in it and we'll check it. Might be fine. Just put some cheap dollar general store oil in it and check it. Also, guys, if, I don't know if I've ever mentioned this before, but you're checking the oil on anything. Oh, that's pretty much right on the full mark. The dark spot up here is from where it was sent in, but you can see the wet spots right on the full mark. If, for some reason, one side of your dipstick is, where's my rat? Yes, that just wiped it off, so that's how I knew there was a little bit of gas in it at one time, I could smell it, but if one side of your dipstick's lower than the other, always go by the low side, okay? And also, if you put, if you're overflowing oil, that's just as bad, if not worse, for your engine as being a quart low. So keep that in mind as well. Check it one more time just to be sure. Monkey went and got us some gas. We're gonna take this thing outside, right on the full mark. See where it's shiny. So, okay, guys, so 20 ounces for this take. So I did blow the fuel tank out with compressed air. That's the good, it should be okay. So let me go get my jacket on. We're gonna take this outside and see if we can get it to start. Okay, we're looking at the carburetor. What we're gonna look for is I'm gonna put some gas in it and we're gonna look for leaks around here, around here, you know, and see if it's flooding out or anything. Also, I did take the air cleaner out. The air cleaner's fine, it looks like new. Let's go ahead and pour some gas in here, and see what happens. All right, so far I'm not seeing anything. Let's pump the primer bulb. I hate these kind of primer bulbs because you can't see what's going on. You can't see if it's getting fuel. Usually you can feel it or hear it. It just seems to me like it's not doing anything, but let's give it a couple pulls and see what happens. Right there, see the fuel running out of it? It sounds like it seems to me like it's got a stuck float. So, let me see what's going on with that thing. Okay guys, so I tapped on this, on this bowl and what happened, it quit leaking. So I don't, if somebody rebuilt this for this guy, they screwed it.  So let's just go ahead and, as I can tell you right now that carburetor, this bowl has not been off this carburetor. I can tell you that right now, that gasket, but see that carburetor's not been rebuilt. I'm going to get some carburetor cleaner and I'm going to spray some carb cleaner in that. And then we'll go from there and see what happens. We'll try to start it. Okay guys, I pulled the bowl off. Well, you saw that. And I checked the placement of where the depth was on the float. I sprayed it out with some carburetor cleaner. Now, let's see if it'll run without flooding out. I already pulled the air cleaner off because I didn't want it filling up and getting that filter all wet. Let's see if we can get it to run. Now, since it was flooding out, I figured it's probably got plenty of fuel. I'm not going to hit the primer bowl. I might help if I turn the switch on. I'm going to fit it with it for a minute. We'll spray some more carburetor cleaner in there. Let's see what happens after that. I'll be right back with, I think we've got it straightened out. Yeah, and I sold the blowers. Remember, the blowers that left is yours. Yeah, so you're doing pretty good. Well, I'm glad that it's not a major issue. That you were kind of concerned, but you had a good ink link that it probably wasn't. So it just goes to show that a lot of people buy these things and then they break down, they don't know what to do because they don't know how to fix them or they take them to places and they get ripped off. And that's the bad part. Well, carburetor don't seem to be leaking gas. It was leaking gas. Was it? Yeah, it took it apart and checked it and everything. The float was stuck. Is that what you were smelling then? You think? Well, yeah, it flooded out. I got something in the oil, but we're good now. We changed the oil, so. Yeah, that's a good thing. Yeah, very pleased. I kind of like it. I do too. Yeah, the only thing that's missing is the bag, but I do have that bag in there for the blower. Yeah, but, no, I mean that's... Just to break it, you know, until you can get one. No, why am I gonna buy a bag?  You can't get no money out of them.  Let somebody else buy the bag. I'm not keeping it, if that's what you're getting at. Oh, I guess if you don't, I have the bag. You're not supposed to have it sitting right here, but... Well, like if we used it. What do you mean? This thing's going to sell tomorrow. Oh, yeah, for 70 bucks, we got a $450. Well, then you got taxed close to $500 item here. But I was going to... I was gonna put 200 on it and take like 175. I'd sell it, of course, we need the money, so... We need the money, I didn't buy it to keep, huh? No, I know, but it's kind of cool.  Yeah, look at all the mulch I have. Oh, you'd have all kinds of mulch. Yeah, mm-hmm.  Mm-hmm. I mean, that thing, man, it shreds it up now.  Oh, always. So what do you think? I like it, mm-hmm. Very good, did a good job. Yep, thank you. Yep. All right, I'm done for today. Okay, good. All right, well, I could work on a weedier. No, you need to rest. All right, okay, well, with that being said then, thanks for watching. Monkey says she's out for now. Don't forget to check out Monkey's channel. She's getting some good videos up, awesome. Yeah. So we're gonna say, Shea Bear, the mental legend, I'm gone for now, and don't forget to what? Subscribe. Comment, rate, subscribe, and what else? Click the... Button. Bell. Oh, the bell button. Icon. Whatever. Icon. To get notified. You do it. Don't forget to click the bell icon and get notified of more awesome videos like this. Offer void, we're prohibited by law. Really? Shea Bear, the mental legend, gone, bye. Whatever, bye.


Good afternoon everyone and welcome to the next edition of the BioExcel webinar series. My name is Rosen Apostolouf and I will be today's host. The webinar series today are brought to you by BioExcel, which is the leading center of excellence for computational biomolecular research in Europe. In our series, as you might be familiar with them, we feature notable scientists and their work in the domain of computational biomolecular research, developers of popular software applications, new release tools, which we believe might be of interest for you. We also have series of educational webinars for those who are new to the field. Last but not least, we also present major achievements and results of work done in our center and we hope that the series will be of interest to you and will bring value to your research work. In particular, if you'd like us to invite specific speakers or feature given topics, please don't hesitate and contact us. You can visit our website, bioexcel.eu for more information and contact. With that, I would like to present you Gerrit Trebelu, who is our speaker today. He did his undergraduate and PhD at Oxford University and UCL, respectively. Then he spent many years working with Professor Michel Parinello at ETH Zurich and University of Lugano before getting a lectureship at Queen's University of Belfast, where he is currently working. He's doing a lot of research on machine learning algorithms for analysis of molecular dynamics trajectories and he's focused recently on simulations of crystals. He's also one of the four core developers of the plume plugin for molecular dynamics simulations, which is the main topic of our presentation today. With that, I would like to welcome Gerrit and I will now let him continue with the presentation. Okay, so I'm going to assume that everyone can hear me. Welcome to this presentation and thank you for being here today. What I'm going to talk and thank you, Ross, for that nice introduction. What I'm going to talk a bit about today is the software plugin plume of which I'm one of the developers. Because I always forget, I'm going to put the publications on the first slide of the talk so that I don't forget this time. The very first publication about plume is this one here. That was written 10 years ago, so this year we're celebrating 10 years of plume and we had a conference earlier in the year to celebrate that in particular. We rewrote plume in 2014 and we wrote this paper about plume 2 at that time. Just this year, we started this plume to consortium, which was an effort to try and have openness, transparency, and reproducibility in molecular simulations. I'm going to talk a little bit about our efforts in that direction towards the end of the talk. In terms of the outline for my talk, what I'm going to talk about first is really what plume is and what it's for. I'm not going to have a lot of time to go into a lot of the details because we've only got an hour. I'm going to show you some example applications and just show you the range of different ways that it's used. Then I'm going to talk about the plume consortium and what we're working on now. I'm going to finish by talking about what our plans are for this particular piece of software in the future and the direction in which we're trying to move. In terms of our community, we sometimes talk in terms of this division between users of software and developers of software. I've always felt a little bit uncomfortable with that division because even though I write and work on a piece of software, I don't feel like I'm a full-time software developer. I feel I'm very much also involved and primarily driven by the applications. I don't really want to get into the details of making sure that my code is load balanced. I mean, these things are important undoubtedly and it's great that we've got people that want to do that, but that's not my primary focus. Being one of these people who are between this, what we realized a few years ago was that we needed something that would allow us to operate between these two worlds, this world of software users and software developers. I have there a picture of the other developers, Carlo, Max and Giovanni, who are involved in the development of this plumed package. The reason that we developed plumed, the origin of it, was that we were all working in the same group, the group of Michele Paranello and we were working on the development of this method, meta-dynamics and various other related methods. What we found was that every few years we had to write these pieces of software into another code because we would need some other application or because some other code was not being maintained. It got to a certain point where sort of Gromax appeared and we set down to implement meta-dynamics in this again for the umpteenth time. Thankfully, Carlos had the genius idea of saying, what if we do one piece of software that works with all these codes rather than every few years having to rewrite this stuff again because we need some other functionality that's not in the particular one of the codes that we have us already implemented in. So that's the kind of genesis of plumed. What it actually does is you have your MD code there, which does what an MD code normally does. Essentially it calculates forces and it integrates the integrations of motions. Beside that you stick plumed, we call it a plug-in. Other people who are not as nice as us call it a virus. During initialization, there's some initialization done, but the important thing is that at every single time step just after the calculation of the forces, the MD code passes across the positions to plumed. So plumed can do various forms of analysis, calculate various different things. And plumed then has the possibility to pass back forces and by passing back forces obviously affect the trajectory and basically how the equations of motions are integrated or what is integrated. So to illustrate the sorts of things that plumed can do, this is an example plumed input file. So each line in this example is one what we call plumed action. So we have actions that do various different things. Some of them calculate things like sensors of masses, distances, various collective variables, and then we have other actions that calculate a stimulation bias. What you can see in this input file is each of the actions has an associated label. So if you look at the very first line in that file, you'll see that it reads c1, colon, comm, atoms equals 1 to 10. So this is calculating a center of mass and it's telling us that later on in the input file, if you see the symbol c1, that means use the center of mass that is calculated by that first comm command and the same holds everywhere in the input, everywhere else in the input file. So if we take that input file and turn it into a kind of graph, this is how basically data is passed through the code. So we start off with the MD engine, this passes the atomic positions into the plumed. As you can see, those atomic positions are passed into these two comm commands and they calculate these two centers of mass positions, c1 and c2. Those centers of masses are then passed to some of these collective variables. These collective variables, this is angle, torsion, and distance can also take the atomic positions from the MD code. We can then do functions of collective variables and we can do simulation biases. In the end, all of that data is passed to the print command that is within that input and that basically outputs whatever it is the user wants to output to a file. This is not the end of the story though. We have a loop where you go forward through this list of actions and you calculate these biases that depend on functions of collective variables or collective variables and virtual atoms. But because we're applying forces onto the atoms, we also need to calculate the derivatives of all these quantities so that we can with the chain rule basically apply the forces backwards through the code. So this is the sort of second step is what happens going backwards is we take our biases, we calculate the negative derivative potential which is obviously the force. We calculate that with respect to the collective variables that were input and then we basically propagate whatever the output of the action is, the derivative with respect to the input and by doing this process going all the way back, we eventually get back to the forces that we're going to apply on the atoms within the molecular dynamics code. There's one last thing to point out with this, which is that if you look and think about what this input is doing and you look in particular at the quantity t1, the torsion, the only thing that needs that torsion is the print command and that print command is only run every 100 steps. So it's computationally wasteful to calculate that torsion on every time step. So what we have is what are called pilot actions. The particular pilot actions in this case are the upper wall and the metadynamics bias. These need to be calculated on every step and the print command which needs to be calculated every 100 steps and basically these pilot actions control when the other commands are run. So for instance that torsion action which has only done 100 steps is only calculated every 100 steps. It's not calculated on every step and you have this basically avoidance of computational expense. For a torsion there's not much computational expense for something more expensive like a coordination number. This can give you a considerable speed up. Okay, so that gives you a rough outline of what pruned can do. So essentially it takes in the atomic positions, calculates functions of those atomic positions and calculates the derivatives of those functions so that you can propagate that bias potentials. What I wanted to do in the second part of this talk was talk a little bit about some more complicated examples and talk really explain really why you would need this structure of this why we need this extra code because the examples that I've given that far thus far they're not actually that complicated to implement and you might argue that it just makes more sense to have these in the underlying MD engines. So my first example is really is about studying inflexible interfaces and this is some work that I did recently I think two years ago now. And this really started off as a collaboration with this Mario del Popolo person who's based in Mendoza in Argentina and his former postdoc who then became my postdoc Joaquin Klug. And the problem that they were interested in involved basically the binding of nanoparticles to biological membranes. Okay, I've got an example picture showing the that binding process up the top of the slide which you can hopefully see. So we had a grant whereby we had this transferability and mobility between Argentina and Belfast. We definitely got the good end of that deal. And they came with this particular problem with regards to doing these calculations with the membrane. So what they were doing was they were using they were basically calculating the position of the center of mass of the membrane and using the distance between the center of mass of the membrane and the center of mass of the nanoparticle as a collective variable in their calculations. And what they found was that the membrane was doing all sorts of weird things like this. So the membrane would bend during the simulation. The center of mass would be in the center of the cell and it would not be indicative at all of the distance between that center of mass and the position of the nanoparticle would not be indicative at all of the distance between the nanoparticle and the surface of the membrane. So they were interested in can we basically come up with a better collective variable that is able to deal with the fact that this is some flexible object and that is more able to accurately say whether you're inside or outside of the membrane. So if you look at that particular picture there for instance the distance between the center of mass of the membrane and the nanoparticle would be negative which would you would assume would mean that the nanoparticle is inside the membrane but as you can see the nanoparticle is actually sat on the surface of the membrane. So our idea to solve this problem was to say let's suppose that your nanoparticle is here and it's and you look down basically in the direction perpendicular to the surface of the membrane at the density of atoms along that thing. So for each atom that you find along that particular direction you add a Gaussian kernel and you build a model of the density. Okay so you see now that that density is located on the z axis about where those three atoms of the membrane are. If we then move we do that same thing or we do that for a different x and y coordinate so now you see that the membrane is slightly higher and this density field moves to a higher point and we can do that again over here and you see again that density field moves to a different place. So we're just using some Gaussian kernels along the direction perpendicular to the surface of the membrane and the particular x and y coordinates and each atom is just represented by a Gaussian kernel. Okay so that's a kind of schematic representation this is what you get out when you do that analysis you get a a graph that looks something like this so here you have the on the the x axis of this graph the z coordinate and on the y axis you have the density of membrane atoms and the idea was well we take a particular value of the density let's say 0.5 and we say where does this density drop to that particular value so essentially we find the root of this curve we look for the p of the value of p of z where this is equal to some particular value gamma that we're interested in okay so you see I'm just reading off from the y axis where the density goes to that particular value. Now the slight problem with this as you will have noticed is that there are actually two z values that satisfy this equation one for the top surface of the membrane and one for the bottom surface of the membrane but this is not really a problem it's and it makes total because it makes total physical sense the membrane does have a top surface and a bottom surface and in practice when you actually do the calculation what you need to do is just pick which one of those two distances to use normally you're going to use the the shorter of those two when you're looking at it so the the the the membrane surface that is closer when you're constructing your free energy surface for the actual process of doing the if you want to use this as a bias what we found the best thing to do was to assume that those are the two those two distances are the roots of the quadratic equation and so to basically use the product of them as the collective variable. So that's a a nice example of this a really quite complicated cv that I don't think you would really want to be implementing in lots of different pieces of software because you know there's quite a bit of code within that that had to be written. On top of that there's a kind of a trick missing here because okay you've got this nice cv that measures the distance between the nanoparticle and the surface of the membrane but there's all sorts of things that we can do if we have information about the height of a fluctuating interface. For example there's all of capillary fluctuation theory that allows us to extract things like the surface stiffness of the membrane so as well as trying to implement this particular methodology whilst we were doing this we also looked at seeing whether we can basically construct the height of the membrane on a sort of contour plot and so we implemented that as well. We basically we calculate the manifold of points that are on that surface and we can build plots like this which shows the height of the membrane the height of the surface of the membrane as a function of x and y okay and in fact we went even further than that because as I as was said in the introduction I'm interested in the process of crystallization and in crystallization we don't have a nice situation where especially if we're looking at crystallization from the melt where you have one atom of one type and another atom of the other type if you have a solid liquid system all of the atoms in the solid are of the same type as the atoms or within the liquid so the first thing you need before you can even get to this question of where does the solid end and where does the liquid start is some mechanism some symmetry function that allows you to tell whether an atom is part of the solid or part of the liquid. Now thankfully lots of those sorts of coordinates exist many of those are implemented in plume you can use things like steinheit order parameters coordination numbers and various other things in this particular instance what I'm using is a cubic harmonic function and this cubic harmonic function if I look at this graph what I've done is I've calculated this the value of the cubic harmonic function for the atoms within a solid that's the blue histogram and I've calculated the cubic harmonic function for the atoms within a liquid that's the red histogram and what you can see is that there's very little overlap between the distribution of this quantity for the atoms that are in a solid and the distribution of this quantity for the atoms that are part of the liquid so I can what I can do is I can take those the values that I get I can transform them by that black dash line which is just the switching function and I can eventually get to a quantity that if I calculate a function that if I calculate that for a solid like atom will it be equal to one and if I calculate that for a liquid like atom is equal to zero so I'm not able to introduce a tag on each of the atoms that tells me whether a particular atom is part of the solid or whether it is part of the liquid and this is what I've done here so this is a box of just lennard-jones atoms just standard good old lennard-jones but all of the blue atoms have this symmetry function that measures the degree of solidness close to one so these are part of the solid and all of the red atoms have this symmetry function close to zero so these are part of the liquid and what you can see is I have a region of the box where you have solid and a region of the box where you have liquid so the nice thing is if I now take those symmetry function values that I get these are the s these are the these symmetry function values are the si in this equation in the numerator of that equation so that's the value of the symmetry function which remember is one if you're in the solid and zero if you're in the liquid the summations here run over all of the atoms in the systems and then I have a Gaussian kernel which is centered at the position of that atom so if you look at this thing what this is doing is saying in this particular position calculate the average degree of solidness at that point so what I've gone from is a discrete model of what a discrete point-based models which says in this part of the box the atom is solid and in this part of the box the atom is liquid to an hour continuous model so I have at every point in the box a value for this solidity or liquidity parameter and this means that I can then insert that into that density model and I can find the position I can find the contours and find the positions of the interface that separate the solid from the liquid and so this is yet another thing that we've implemented in plume and for anyone who's interested this is what the input file for a calculation like that looks like so this first line the one FCC cubic calculates that symmetry function value for all of the atoms this second line multicolour density calculates and constructs that phase field that I talked about so that that function of the Gaussian kernels and then this line here finds the isocontour and then I can print that up to a grid and I can look at that that contour and I can do various forms of analysis that might appear in capillary fluctuation theory so again this is you know a moderately complicated example of something that you can do with this piece of software plume that you might not want to implement implement in a piece of software like Gromance which already has a lot of complexity you can do these sorts of calculations within plume instead so I have a second example of something that we did recently so this one was about using entropy as a collective variable in simulations of the crystallization of pharmaceuticals but this this idea of using entropy as a CV has appeared in other places and people have been using this to look at sort of distributions of bond vectors in biological systems like proteins for instance okay so this again was a collaboration in this case it was a collaboration I met these two gentlemen Jean-Paulo Garbo and Michael Bellucci at a conference and they asked if it would be possible to do what I'm going to talk about using plume and I said I think so and they worked for Bernhard Trout and Giovanni Cicotti was also involved in this work okay so what they were interested in was the basically crystallization from the melt of pharmaceutical compounds and the sort of prototypical example of these pharmaceutical compounds and this process of crystallization from the melt which has been studied extensively in the literature is urea okay and it seems it works a lot of these techniques work very nicely for urea so the problem with looking at molecules rather than and is that rather than say a transition from gas to liquid is that what we're interested in molecules is not really a change of the density of the structure so what I've got in this graph is the radial distribution function for the centers of masses of the urea molecules in liquid urea which is shown in blue and in solid urea which is shown in green here and you can see like the the peaks in those radial distribution functions they're not really shifting it's not the atoms are coming closer together it's not that the molecules are getting closer together when you form the liquid from the solid and we know this this is obvious we know what really happens when you form a solid crystal from a liquid crystal is that you get orientation of order so the the molecules line up in some ways and you see this really clearly if you look at the relative orientations of the co bonds in these urea molecules so the green line again here is the solid and the blue line is the liquid so you see clearly that there is sort of a uniform distribution for that angle between the co atoms on adjacent urea molecules if you're in the liquid that distribution is essentially uniform whereas if you're in the solid you really have peaks so the urea molecules are either either have their co's pointing in the same direction or pointing in opposite directions and this is very nice what you can do is you can construct a function that looks like this so you have some function of the angle between the urea molecule between those co bonds this is the theta in that expression and this function is just something that has is a function that has peaks on where you see the solid peaks in the the urea so it's the dashed line the dotted line in that middle figure and then you multiply that by the some switching function and you average that over the first coordination sphere and when you do that you beautifully see that the liquid has a very low value for this average quantity you don't have a lot of molecules that are aligned then next to each other and then the solid in green has a high value for this quantity and you have a nice separation between these two regimes so what Ciampaolo and Michael had done before they involved me is they try to apply this to a different system they try to apply this to benzene and what they and they've done it in two dimensions and what they've seen is stuff like this so here what you've got on the right is you've got a structure in the bottom which is clearly disordered just visually and at the top you've got a structure that is clearly ordered and what I'm showing you here is the distribution of distances and angles between adjacent molecules in these two systems so this is just doing this over all of the atoms and in particular the sort of the colored part is the instantaneous distribution in those two configurations on the right and the black part is the distribution that you see in the crystal and the interesting thing that they'd observed was that even though in this even though in this disordered crystal the distribution is more similar to that of the ordered system than the distribution that you see in this clearly ordered system at the top so that one so this one the top one looks disordered when it if you look at the CV when it's actually ordered whereas the bottom one looks ordered when it's actually disordered so because of this trying to fit these Gaussian because basically what you're doing is essentially counting how often you fall into one of those modes this doesn't really seem to work particularly well for some of these more complicated systems so what they were interested what Michael and Jan Paolo were interested in is can we compare the distribution directly so rather than constructing this surrogate that basically looks at how often we fall into those modes so you know we have these triangular kernels that measure whether or not we're close to the orientations we see in the solid can we instead just compare the distributions directly so obviously this distribution this is the distribution of distances and angles in the liquid that's on the left of the slide and the one on the right of the slide is the distribution in the solid and obviously these are very different so you would hope that you could if you compared those you would have something that when you're close to a solid is small and when you're close to a liquid is big and the way that you can compare two distributions is using this thing called the cool black libala divergence which is calculated using this expression here so here you have an integral over all space in this in for these particular two distributions it would be a two-dimensional integral and you basically in integrate the instantaneous distribution which is the p of x and the q of x is some reference distribution so we went and we implemented this in plume we also have forces for this quantity so you can do a bias on this cool black libala divergence and this is what the input looks like in this particular case we're using a uniform distribution as the reference so you see you calculate your cv's you construct a histogram show basically of how those cv an instantaneous histogram of how those cv's are distributed instantaneously and then you integrate that histogram or you integrate that histogram multiplied by the reference distribution that you've input and again this is a quite complicated piece of programming you know reasonably moderately complicated piece of programming that you might not want to have to redo in all these different codes so that brings me to the the last part of the talk where I'm I'll talk about where we see this this plume thing going so I started at the beginning by talking about this distinction between the software users and the software developers and how as developers as plumes we see ourselves more in the in the space between that so we mainly focused on trying to develop applications but in order to develop the applications we want we often have to do a little bit of programming in order to to to get things up and running and what we've realized from doing this for the last 10 years is that we're not alone in this space there's actually many many people in the community who are in this sort of limbo between users of software and developers of software and so in order to facilitate those people and celebrate this fact that we're all in this this limbo we recently started this plume consortium we have members from all the countries shown on this map here we have about 70 members at the moment and the idea of the plume consortium is that we want to promote the development of this open source plug-in software plume we've been very lucky so far in that people have decided to use it and take it on and we want to encourage other people to contribute to it as well as just us and at the same time we also want to promote scientific reproducibility and good behavior so from early on within the development of this plume we organized a workshop we've organized the number of workshops and user meetings about the methods and the code within plume these have always been very successful and we want to continue doing these things and basically teaching people how to to use these methods because that's an important aspect of what we're doing and we hope that the plume consortium will allow us to to facilitate these objectives so what does this actually mean in terms of the development of plume well the first thing it means is that we no longer just have this plume core developed by these four people Memex Karo and Giovanni we do have that but in addition we have modules that people have contributed and we now have seven of these that have been contributed by well six that have been contributed by other people and one that has been contributed by a subset of the four of us and these sit in sit with in plume and you can you when you download the code you automatically have access to all the pieces of software that these other people have developed and we encourage anyone to to contribute what they've done the only things that we ask the only thing we ask is if you want to contribute a module make sure you have regression tests that test the functionality within it and make sure it's suitably documented and and try to answer if there are questions about your module on the plume mailing list try to answer them so that's the first thing is we're moving away from this model of development that is based on a small core of developers working on this piece of code to a model of development that is really based on people contributing functionality that is explained and curated within this plume repository the second thing that we've done is we started this plume nest so the idea of this was to basically build a repository of the data needed to reproduce the calculations that are in this this has been done in a very sort of plume style so there's many people working on repositories and they have invested a lot of money in the sort of storage aspect we're sort of tagging on top of that we're not building a repository to store the files the information you use something like you could use github or materials cloud or nomad whichever one of these databases out there what plume nest will do is grab that data and basically build a page about the way that you have used plume in your calculations and the idea of this is that users can learn from real life examples we can promote scientifically reproducibility and we can create educational material and this has been quite popular we launched this maybe four months ago when we already have 64 pieces of work within the repository so how does it work let's suppose that you've done some piece of work using plume you come to our website plume nest you click on contribute and it gives you a form to fill in and you can see that you give your project a name it assigns an id to you that where it says url that's where you put in where to download the zip file and i've been told it's very important that i explained that it's a zip file the plume will go looking for the plume input files within you say which which files within that other plume input files and then you provide a little bit of detail about what the calculation you did and ideally a doi for the publication that you generated that this data is part of once we get that information we then build a page that appears on the browse section so if you click browse you get a list of all the input files that people have contributed a table that looks something like this if we pick one of these in particular this is what you find so it gives you details of the calculation where you can download the data from so the zip file that you can get the data from the publication that this was part of and then you have within that the plume input these links that will take you to the plume input files that were used to do the calculation and the nice thing is that we regularly check basically if the calculations that you've submitted to the nest are still valid plume inputs so you know the code is changing as as time progresses but we actually make the effort this this is using the server to basically test that these inputs are still compatible with the way that with the with the current version of plume and you get these badges you know that in this case the first one of those inputs is working with the latest stable version version 2.5 and with the master branch of the code so nice and straightforward so we're trying to expand on this at the moment and make these inputs a little bit more and have more information in the nest so this is something that we're in the early stages of doing so at the moment each of those green words is a link to the appropriate page in the documentation so if you click on group it will take you to the manual page of plume for group so from the plume nest website directly to the plume manual pages that's fine but maybe you lose a little bit of context and you want to look at the whole thing together you just want to find out what a keyword is so what we're trying to do is make it so that these all of the keywords within here are tool tips so if you go and hover over this radius keyword for instance a tool tip comes up that tells you what that what that radius command is doing so that's what we're working on and another aspect of this is in terms of the quantities being calculated and so for example if you if you make it if you click on the restraint one down here it will tell you it'll show you basically in red where that command is being used in the input file and what quantities are are included within that restraint and a little bit about what they are so for example here when you click on that restraint it tells you that the restraint action with the label restraint calculates a quantity called restraint dot bias which is the instantaneous value of the bias potential and restraint dot force two which is the instantaneous value of the squared force due to this bias potential okay so kind of useful again for a user who's maybe new to the code to to basically be able to probe and understand based on inputs rather than having to read the dense old manual because we all know that no one really reads the manuals so this is our attempt to address this kind of crisis in reproducibility that we we have in the field and our sort of feeling is that part of the reason for this crisis is the nature of the way that science is done which is maybe quite unusual if you compare it to other fields so you know if you think about the typical scientific career you you start off as a student and you finish as a member of faculty and as you go through as you go through that process you learn more and more about basically how to run calculations and how to do things and the sorts of things that go wrong but at the same time and in parallel to that process of learning you also find that you have progressively less and less time to do things and in fact that sort of process of going from being a student to being a member of faculty happens in in not a terrific amount of time you're talking 10 15 years normally and so you know we have this very fast turnaround of the people who are doing the the actual calculations and so what we're trying to what the way that to solve this reproducibility crisis I think is to build into the way that we work ways of ensuring that when someone who is new to the field comes to look at those calculations later they can more quickly understand what was done than the purple who did it in the first place and with that I'll finish and invite questions thank you very much thank you gareth could you please change to the slide showing how people can oh yeah yep sorry I can I have the question slide there it is yes thank you mm-hmm thanks again for the great presentation it was really interesting to see the big efforts that people are making not only on the cold side but also tackling things like reproducibility and community building these are some of the aspects that also in Barxell we are very interested that we are encouraging and supporting that so I encourage everybody to use the questions tab to to write your questions there and I will let you speak to Garrett I have to send to Garrett you the regards by Roshan Shrestha who especially thankful for your notes on free energy calculations and he sends his regard okay thank you very much we have a question from Partivan just a moment I will see his audio so I don't see the questions can you see them no yeah well Partivan can you hear us oh yes I can hear you okay great so you can speak hi my question is how can you define the classical and what is the difference between the classical and the enhanced MD and how can you define it to a biologist okay so if if you're doing so when we when we talk about ah that's a very difficult question so it depends how you how you ask the answer the question so in a very naive naive way if we're talking about plume we could we could if we were being a bit more immodest if I'm allowed to be modest for a moment we could say that the classical MD standard MD would be you go and you run your calculation just with whatever MD code you're using okay an enhanced version would you would be you run it with that MD code with pruned insight as I said that's the immodest answer I think a division that people would normally would often use is we have this problem of what's called rare event sampling so there are certain processes that take a very long time and we can only run MD for a very short amount of time and obviously if we run a short MD simulation we don't see those processes occur so I'm talking here about processes like protein folding which take place on time scale of seconds or processes like nucleation of crystals which again takes a very long time for it to occur so there's no way that we're going to see that event if we run a simulation for you know 100 nanoseconds so okay in order to resolve that problem we add additional forces in order to force that event to happen on a much shorter times scale and that's what a lot of people talk about when they talk about enhanced MD and enhanced sampling okay the follow-up question is will this fall into a category of this biased or unbiased simulation yeah that's exactly what I'm talking about so biased versus unbiased simulation but okay but when you enhance it it doesn't necessarily always have to be with a simulation bias there are also methods like transition path sampling which you wouldn't necessarily do with plume there are other codes for that where essentially what you do is run lots of different molecular dynamics trajectories and then you do a kind of metropolis in trajectory space or methods like I'm trying to think of another one or a method like forward flux sampling where you run calculations and periodically stop lots of calculations periodically stop them and then and then see whether some have done things that you're interested in and then you throw away all the ones that are not doing anything interesting roughly speaking okay does that answer your question yes yes sure thank you no problem okay we have a question by june let's see june can we hear each other june hello uh maybe he has an audio yeah okay so I'll read his question does the entropy procedures and inputs have to be included in the nest if not would you expect to see it it has it been included in the nest that's a good question did I put that in the nest I don't think I did actually and thank you for reminding I I should put that in there yes so I don't think I did it yet no but there are if I remember correctly there is there is information in the support and information of the paper so what we put further okay then we have a question by marcello marcello do you have a microphone okay I'll read his question besides multiple walkers which other protocols deal with a single cv with slow convergence besides multiple walkers yes so so things like meta dynamics would be one umbrella sampling steered md the problem with steered md is you're not going to get a free energy surface out of it unless you use a judzinsk inequality which is very difficult to converge a variationally enhanced sampling would be another I mean that there's a there's a zoo of different methods that you can use for basically enhancing sampling even even something like along a slow cv even even a method like parallel tempering would would work where you're you know you're swapping between replicas at different temperatures and so the high temperature replicas are taking you over the barriers and the lower temperature replicas are sampling inside the basins that you're interested in so yes I'd recommend looking at some of the reviews on on these techniques okay thanks and marcello has one more question is there input in the nest for calculations of six ring members or five ring members occurring I don't know off the top of my head there are examples in the manual I think then there is a question by roshan I don't know if his audio works let's see hi hi hi gareth how are you doing very well thank you how are you thanks a lot for the notes that you provided on my email okay okay my question okay thank you my question is like why for now in this webinar we are only talking about meta dynamics right why I'm sorry yeah sorry can you just on on sure sure sure I mean on this webinar you are only talking about meta dynamics while they're all enhanced sampling simulated annealing and rammed as well so I just want to understand what makes meta dynamics better or good enhanced sampling technique than others or it depends on the system or processes we are just studying right now okay so why am I why do I talk about meta dynamics I talk about meta dynamics because it's what I know yeah that's what I use personally yeah sure they there are other methods out there there's there's a huge number of different methods out there yeah the the nice thing with meta dynamics is a lot of other people use it so it has recognition when you when you when you publish a paper and you say I did this using people know what that means they know how the method works they have some intuition as to what is going on in the paper and there's also been that work on basically looking at how the free energy surface that you get out and how the performance of the method depends on the parameters but sometimes isn't there for some other methods not all of the methods for many methods that is there yes so that's that's one reason but yeah the reason the reason that I speak about meta dynamics is that it's what I know thank you thank you so there was one more question by June which is are there any additional efforts to elaborate plume with the recent Boltzmann generator apparently Frank Noe and other authors mentioned such possibility in particular so the which one the what the the Frank Noe was talking about about some recent Boltzmann generator I don't know exactly what it means but that's the question okay okay there is a more from June there's been a science paper on Boltzmann generator June writes okay and they so there's been a science paper on the Boltzmann generator and he's wondering whether there are plans to extend plummet with that I wouldn't really I mean I wouldn't really the the short answer is no but there may be it may be that Frank Noe and and company have plans to to contribute this to plume that but I'm not not aware of well this is the the the advantage and the disadvantage of this community development to a certain extent is that obviously you don't exactly know what's going on in the future and yeah and people are free to extend it may appear it may not yeah I was actually wondering because we're very interested in like the high performance aspects of MD I guess it depends on the type of plume that you have but to what extent the extensions affect the performance the raw performance the scalability of the MD engines so it very much depends on the application like you say the the big thing that would slow stuff down a lot is if you try and pass all of the atoms into plume that's going to slow it down a lot but for most bio applications where you're looking at a small molecule in a lot of water and you're just interested in passing the atoms in the small molecule this is not a big worry that you know the the plume does not causing you an enormous slowdown but again it's it's one of these things where to a certain extent you I mean and this is another reason for doing all this stuff to do the reproducibility aspects is the you know the there's such a zoo of architectures and machines out there that you you need to spend quite a bit of time working out how to optimize the compilation especially if you've got multiple bits of pieces working together so it would be really useful in the future to sort of share the efforts in that direction as well as the efforts of what the input files are and you know someone's indeed thanks I don't see any other questions there is only a question regarding the recording yes as with all of our webinars soon after maybe later today or tomorrow we will post a recording on our youtube channel and on the main website so for those of you who have joined later halfway through the talk you will have an opportunity to listen through the whole presentation again and as well you can check out some of our other webinars in the youtube channel I would like to tell you about our next webinar could you go to the next slide Garrett thanks so in a couple of weeks time we will have a presentation by Berkes one of the core developers of Gromax who will present a new method for weighted histograms accelerated sampling implemented in Gromax and those of you who are interested in sampling methods this will be a very interesting and useful talk we believe so you're welcome to attend this webinar as well and with that I would like to thank Garrett again and we look forward to new developments in Plumpt okay thanks for the presentation yeah thank you everyone


In this video, I wanted to give a quick shout out to David Goggins' book, Can't Hurt Me, which I just finished reading for the absolute third time already in the last few months since it came out. So I first learned about David Goggins via some maybe podcasts that he did, but maybe some video I don't even remember, but the moment that I saw him, the guy who's an actual hero, a seal, a guy who's considered the toughest man in the world, I was immediately hooked because I knew that there was something missing in me, some sort of a connection with pain, with suffering, some sort of adaptability that I wanted to put in my life. Now since reading his book, his autobiography basically, which is absolutely life-changing, I've started changing my habits completely. So for the last two and a half months, I've been running every morning very, very early in the cold with a tank top, waking up every morning at a quarter to five a.m. and I'm loving it. Since I've started doing it a few months ago, I've been doing it consistently pretty much every single day and the book transformed the way that I look at discipline, that I look at my life, that I look at pain, that I look at mental toughness, the book helped me reach new physical heights, new mental capacities, new mental limits, so if you have that urge, the yearning for something tougher, for something that would make you go the extra mile, go the next level, then I highly recommend that you check out this book by David Goggins or even just watch a podcast with him, maybe with Joe Rogan, for example. He has hours upon hours of really, really good content. So check out David Goggins and his book, Can't Hurt Me.  It will make you tougher, more disciplined, and it give you a completely new outlook for your confidence and for your abilities as a strong, successful person. I give this book five out of five and I'm going to keep reading it maybe for the fourth time this year. So thank you for watching and check out my other videos. If you haven't, I'll be happy to see you in the next video.


and you can do it great. Great. You're good. Well, hi everyone. I organized this symposium, and I carry on that speech, but not mine, so I have name. I am Yongjin, Data Information Starwatch Local Library. They're really running behind, so that's enough about me. Disclaimer, what I'm presenting may look like a research research, but it's not. It's more of an idea at this stage. I'm still finding some pros in my methodology, but it's an interesting idea. The previous speakers have focused on NHANES data. My focus is after you researchers study the data and find interesting and significant and publish. That's my data. So there are thousands of research articles published using NHANES data. So my question is, what stories do they tell? So as you can see, my research question with it is not really scientific. But to analyze publication, citation, you first have to get all the publications. So I use PubMed. So I'm looking at from 1980 to 2019, 40 years. So the way I harvest it is I searched NHANES full title, National Health and Nutrition Examine Survey, in quotation mark, and just to look for Titan abstract. I also searched NHANES in abbreviation and or it with the first search. One of the questions came when Yutaka was presenting, asked about, is there repository that we can look at all the publication? The answer I sent was no. That's why the search was complicated. So after I got more than 14,000 citations, I started cleansing, eliminate first positive. I found that there are more than almost 3,000 studies using Korean National Health and Nutrition Survey. So I deleted that. And there are about 20 South African National Health and Nutrition Examine Survey. And then I also deleted, not written in English and also in terms of publication type, anyone editorial. I also deleted CDC's Mobility and Motility Weekly report because I only care about journal articles. And because I'm looking in 1980s in 2019, I deleted previous to 1980 and this year. This is not complete. So if you want this data, you want to make appointment with our informationist, we probably carry unnecessary, but if you don't know anyone contact us. So this is what I have, 7,056. So I'm looking at this, well. I'm sorry to interrupt.  I think you're in presenter mode possibly. So just. Okay. Is it better to my query? So I have 11,056. So I'm looking at this data. And in a brief glance, as you can see that the number of publications using NHANES is rapidly growing from 2000. So in terms of method of analyzing the publication, I first used topic analysis using MASH. If you never heard of MASH, again, time to make appointment with your librarian. And you go to PubMed. Let's say you find an article. This is published by Hopkins authors near vision impairment and frailty. Evidence of an association. It is published in American Journal of Ophthalmology. So if you scroll down forever, then you will find MASH. MASH stands for Medical Subject Heading. And these are terminologies that are used by National Library of Medicine to describe what the study is about. So you can see that about aged, or aged meaning 65 and older, and then aged, comma, 80 is older than aged. And then you can see that about frailty and of course, about human. When you look at this MASH, there are about 10 to 20 signed to each article. And some of them have at the end, asterisk star sign. It indicates these terms are major, major terms to describe the study. So these are my data. I only collected not all MASH terms because there are too many, and some of them are minor. I only collected major MASH terms. So when I look at them from 1980 to 1989, there are 234 major MASH terms. So if you look, this is a previous graph I showed you. So in 1980 to 1989, there are not many. So I rendered them using tree map. As you know, tree map graph is not very precise, but it can give you some general idea. From left to right, the frequency, patient frequency, the box will get smaller. From top to down, it will also get smaller. If you look at on the right bottom, that's where fewer ones are existing. What I noticed is that in 1980 to 1989, the articles assigned with obesity was actually smaller than articles tagged with lead. Then I also found there's something interesting that I didn't, there's a major topic for cancer and studying enhance. Until next decade, the number of unique major MASH increased to almost 600. So we're talking about this period. The number of publications have grown slightly. So if you look at the tree map, the thing that will jump at you is that there are a lot of terms that are used to describe the new MASH terms you described. That's the first thing. And then obesity became the number one condition assigned to the studies. Unfortunately, that is still here. Going to the next decade, the topics or subject assigned to studies of enhance has grown to 1500. We are looking at this period where the number of publication has grown substantially. If you look at this tree map, again, the one that jumped right at you is that there are so many new topics assigned to the studies that it's not even squared, it's almost dot. And then obesity is still number one topic. And again, lead is still here as one of the major topic studying enhance. Going to the most recent decade, the terms now have grown to almost 2500. So many new topics, new terminology have been tagged to the enhance publication that now it's no longer possible to have the dot. So here it indicates that there are just so many. Maybe there's one topic for one study. Again, obesity is still number one condition. So I am going to pause a little bit here because I don't want to lose any more completely. Looks like you're following, so I'm gonna move on. As for method two, I did, I analyzed title word. For any of you who have never heard of text analysis, this might be a little bit confusing. I'm not expert myself, but bear with me. What I did was I harvested title word. Remember the publication 11,056. I harvest all their title. So this is article written quarter by Stella, Asian-American dietary source of sodium and salt behaviors compared with other racial ethnic group and hence 2011 and 2002. I harvest all this title of all article. So it looked like this without frequency. What do I do with this? First I found that there are over 7,800 unique title words. Unique title word. The word that appeared most in title was adult. Now I'm gonna ask everyone, what condition appeared most in titles? Can someone tell me if anyone answered my question, the number one condition used in title word? Hi, Yuck Ju, someone said hypertension, someone said diabetes and obesity. Whoever said obesity, have been paying attention to me because you remember in my tree map, the obesity was number one, a mesh pegging. When I look at this different word in terms of frequency, what jumped right at me were words that represent subgroups or minority groups, such as African-American, Hispanic-American, Asian-American and Native American. I decided to pursue the studies on minority health. What do I mean by minority health? I'm trying to find title word that has African-American, Asian-American, Native American or minority racial disparities in title. What did I find? The articles that have those words, name of minority group or minority or disparities in their title are like this. If you made a title to describe their study, these are the studies that I can claim that about minority health. So it looks like it's increasing. One thing I wanna explain is that I didn't double dip. If there's a study, a study that comparing African-American and Asian-American, it's not two study, it's only one study. But in terms of increase, if you put that in context, if you compare that with all the studies published, then it's not that impressive. If you look at the percentage, the proportion of minority health in a total and hence publication is decreasing. Again, when you look at data presented like nice graph like this, it may look very authoritative, but my method, I'm finding some flaws. So I have to work on this. I am thinking that it's not gonna be significantly different. It also was interesting looking at subgroups. So I looked for subgroup, Native American, Asian, African-American, Mexican and Hispanic Latino. When I compare this to full group, this is like this. Then here I doubled it. So if a study compared, let's say, Mexican and Asian, it will get one for Mexican and one for Asian. So I doubled it in this case. And what I found is that I have compared the study more about Mexican-American than any other ethnic group. And then African-American was number one until 2004 and then outnumbered by other group. The studies on Asian-American is McRoying and then native American, very, very minor. Another thing I looked was journal titles. So many journals have published their studies with top 20 journals in 1980 and 1989. But like this, if you remember from total publication from this period from 80 to 89, there are only 233. So there are not many. But if you take a look, journal, American Journal of Epidemiology is number one and you recognize these titles. And then there are two environmental health journals. And I said that cancer was, I was surprised to find and here it is, there are two cancer journals making top 20 in 1980 and 89. Point to 1990 and 99. Well, graph has become a little longer because we have 686 publication. American Journal of Epidemiology is still number one. You recognize JAMA and pediatrics. Slowly, Opicity Journal is making top 20 and then Environmental Health Journal is still here. More recent decade, we have a lot of increase. Out of the number one is American Journal of Clinical Nutrition. You still find the pediatrics in JAMA. New England Journal disappeared from top 20 last decade. And then you see ethnicity and disease. So this explained the increase of minor health studies. And again, obesity is making top 20. The Environmental Health Perspective, one of them. The last decade, most recent period. So we have a lot of publication over 7,200. Now, plus one is publishing a lot, becoming number one. This is very impressive given that plus one was created I think in 2006. And then obesity is moving up. And the pediatrics is still here. We don't see JAMA anymore. Another thing impressive is that we have a free Environmental Health Journal making top 20. Is there a story to tell? I don't know yet, but impression is that the number of journal have increased, not just article increased, but the number of journal increased, which you don't find is surprising because the journals are popping up every day. What I want to explore, and I had a lot of time, I wanted to explore if the number of journal actually lead to more and more specialized journal. Meaning that if the article is about disease, they tend to be published more in ophthalmology journal as opposed to other journals. And same thing with environmental health studies, if they are, they tend to be published more in environmental health journal. So free environmental health journal making top 20. And same thing with health disparities. This is significant issue in terms of leadership. If the article is published in certain journal, and then the leadership will be smaller. But I want to emphasize the new wake-up approach literature review. If we have 10,000 or 20,000 articles to review, traditional method will not work because it will get very old by the time it's finished. So that's what I'm trying to come up with, big data approach to literature review. And I also want to emphasize the titles are important, not just for my study, but when people search article they're looking for, whether it's a Google Scholar, whether it's a PubMed, title player, very big role when retrieving. And when I was looking at title, there are a lot of words that I found not very helpful for searching. So be very thoughtful when you article is accepted. Next step we want to think about is can people find it easily? So think very careful about title. So I didn't say much about this, but data must be cited properly in references. One of the reason why it's difficult to retrieve a publication using particular data sets, such as NHANES, is because many authors cite them in text, which is considered to be informal citation when they should cite in references that make it easier to retrieve. So here is the beautiful picture that I show in the cover, Dr. Harvest Data from PubMed. You can also use PubMed access through Python, so you may want to try that. And for mesh setting analysis, I got code from Blizzard Circus, the NIO. So if you are from NIO, she will be a great resource to learn about research metrics and data management. So that's from me. I am going to stop sharing and you will have questions. You know, in life, questions are guaranteed answers are not, so feel free to ask a question. So Yanju, there were kind of a question and a comment, so you may have adjusted in your last slide, but there was a question about the visualization tool you used for showing the mesh term analysis. Was that using PyMed? PyMed, maybe I didn't do it right. I couldn't import mesh from PyMed. If you have any question about how I did it, I will be happy to share with you personally when I clean the code and then cannot move. I will share my code in ghetto, but for the code for using mesh to analyze article, that I need permission from Alisa because I borrowed from her. But eventually, eventually, if no one asked me any reference research question, I will have a lot of time and I will be able to work on this and make better plans. So I am over time, where I was, okay. We have a question and answer, a question. I would like to learn how you actually performed it. Oh, actually, I am breaking my rule. Rob, ask me a question. So I wanted to, yeah, so I see that one question and then there was a comment which I wanted to share about articles with non-English abstracts are harder to find. So they recommend having English abstracts. Although I guess in PubMed, those are represented typically, or there might be a translated abstract in PubMed where the titles and brackets, at least that's been my experience. Also, a comment about using standard terminology, which I know in other areas that I've worked with in terms of working with groups and trying to do systematic reviews has been a challenge. I don't know if you had any comments about the sorts of terminology you've come across or any of the other presenters. In terms of making these kinds of literature reviews that you're doing more systematic and easier to do. Do you have an answer, Rob? If you're the systematic review expert and Kerry, too. Well, I've certainly run across it in the toxicology realm that there can be some deficiencies in the mesh hierarchy in terms of describing some toxicology concepts. This really isn't, so I can speak to that, but not so much other ways of describing this data. But that was just a comment, not a question. So we can go to the next one, which is a question again about your data analysis tools and what you used, which I think what you're saying, what you just said was you'll share your analysis and how you generated those pictures. Yes, I can share my code. If you wanna know, if you want to learn how to do it, there are many sources that you can use. If you affiliate with Hopkins and also NIU, I looked up NIU, you have access to LinkedIn learning and formerly known as linda.com, so you can learn data visualization. TreeMap, I don't recommend using it. As I said, it's not very precise. It's difficult to find which one is bigger than which one and then the box gets really smaller. I try that just to get an idea once I know if something is going on, I will go for something else. There are many better resources than me because I'm just just studying. So that I can, if you're interested in learning, I can send you the link how I learned it. Now we have, we are done with the presentation. So I would like to ask the speaker in the final closing, if you can give a device to our attendees who are interested in using NIH for study but haven't very little experience because a lot of our attendees have very little experience, what would be your device? One minute, start with Megan. Yeah, great. My one piece of advice is always start with a good research question. And then identify if NHANES is the right vehicle to answer that question. So there are many criteria of a good question. I think it needs to be of interest. It shouldn't already have been answered and it should be something that is feasible to achieve.  That's my piece of advice. Itaka? Yeah, actually, before I go to my advice, I have to mention something. So I said, no, we don't, you know, places visited by NHANES are not disclosed. And there is an important exception to that, which is state of California and Los Angeles County have been visited many times in the past NHANES. And that fact are out. And also you can access the data necessary to do specific mission for California and Los Angeles. You can access those data through the RDC. Thanks. And so my advice would be similar to Megan. Yeah, it's easy to use NHANES, but it's important to be a good subject matter expert. If you keep studying a subject matter, there are always, I don't know, I shouldn't say always, but you can find something to study using NHANES. And in the last 15, almost about like 15 years of experience, things I think, oh, I wanna, I can do this. I wanna do this. Though the list is sort of getting longer and longer. And I think you invest your time and efforts. I think something like that would happen to you. Thank you, Itaka. And also, again, as I said in the beginning, I'd like to invite you to come back and give our full-length seminar because I know you usually give overview over an hour and you have to cut a lot of material. So love to have you come back. Now, next, Stella, one of the bikes, one minute. Yeah, I mean, I would echo what Megan was saying in terms of starting out with a research question and a conceptual framework. In particular, if you're interested in digging into some of the disparities pieces that I mentioned today in my talk, like maybe NHANES is this a best study design for you or maybe it is. So I think identifying and matching your research question to the data that's available and not trying to make a round peg fit in a square hole or vice versa. I think I mentioned this at the end of my talk, but I really encourage everyone to just think carefully about what representativeness actually means. I think don't be afraid to dig into survey documentation and see like, oh, how come native Hawaiian Pacific Islanders are not here or maybe like, how come who's in that other category? And I would say increasingly, over the last 10 years or so, there has been more and more attention placed on the other group, on the multiracial group. There's a lot of question marks about what to do with those individuals. I think past practices have been to delete them, but I don't think that that's gonna be the correct way to move forward. So, as many of you are newer to NHANES, not to say that you're necessarily young in your career, but newer to NHANES, I would say, don't be afraid to sort of dig in there and understand who you're actually representing and if that really is adequate to address the research question that you're trying to ask. Thank you, Stella. And also, I asked you and you said it right, yes, already. So I'd like to invite you back to give a talk in full length for those of you who are still staying with us. If you're interested in knowing all these new classes coming up, I will talk about how we can get in touch with me. Now, Josh. I would echo what Stella said, check the documentation, read it, reread it, and it'll answer a lot of your questions. They put a lot of work into it. You should put a lot of work into reading it and take its recommendations to heart. And if you are going off the beaten path in terms of analysis, I would highly recommend collaborating with a statistician or epidemiologist who is skilled in these particular practices. We can be very helpful in helping you make the most out of NHANES because it's an excellent resource for learning many different fields. So. Thank you, Josh. And, okay, so I'd like to introduce Gayan Yanokian, Executive Director of Biostatic Center. Gayan, could you give us a few words how, when they need help from Josh, how they can get in touch with me or about your centers, how we can help with our researchers at Hopkins? Great, thank you. And I promise I'll keep it under one or two minutes is because we're just finishing up. And so just to reiterate, so my name is Gayan Yanokian and I'm Josh and I are colleagues from the Biostatistic Department in the School of Public Health at Johns Hopkins, but we also belong to this smaller group called Johns Hopkins Biostatistic Center. And we're an applied arm within a department and we support, provide biostatistics and data management support. So it's a wider group of us who essentially do consulting, sharing of best practices, education, and essentially at all stages of research from study design and different types of study design, observational, clinical trials to data management, setting up databases, data quality control to analysis and publications. So for those of you who are within Hopkins, we're also partners with the Institute of Clinical and Translational Research, ICTR. And so if you just want to quickly find us, it's ICTR biostatistics program. And so we offer both virtual now walk-ins as well as abilities to submit short requests for smaller projects. And then of course those might develop into larger projects and maybe even long-term collaborations. We also do work with researchers outside Hopkins. So we have a mechanism for that. So if you actually find us on the website, the easiest way is to go to biostatistics at Johns Hopkins. And then there is a panel on the left-hand side that says Biostatistics Consulting Center. And so there are lots of questions explained both our combined experience as well as the type of projects we do as well as how to work with us, especially for those who outside of Hopkins. And then last small thing is the email would be johnshopkinsbiostatisticscenter. So JHBC at JHU.edu is another way to contact us. So I'll just stop here. Thank you, thank you. Maybe 30 seconds, could you talk about our walk-in clinic that you offer? Of course, and we actually offer two types. So one is for faculty and staff. And so those are three per week, Tuesday, Wednesday, and Thursday. And it's a nice split between different software programs. So Tuesday, we have SAS. And Keith Carson is actually one of the participants. So she's the one who runs the clinic. It's the full hour. And she can answer others as well, but her primarily software is SAS. We have Leah Jagger who works in R and that's a Wednesday clinic at 11. And so the one on Tuesday, 1.30, Wednesday at 11. And then Thursday at 11 as well. And that's me and I primarily work with Stata. And again, any other type of questions, this is primarily for short walk-ins. Just imagine if you just have a quick question about something. Again, sometimes it's hard to determine. So we might advise you to submit the request and work more extensively on your question. But those are the three days, Tuesday, Wednesday, Thursday. Thank you. Kevin, could you reopen the third poll? So regarding the voting for next year's data topic, I was told that there are many people who didn't get to vote. Maybe they were left. So maybe we can just get it later. But if you, I would like to say a few things. If you are on Twitter, I will be tweeting information for upcoming classes, any event using hashtag enhance 2020. You can also follow Megan and Stella and me. And also for all the residents, I will be sending the survey tomorrow. So if you fill out your email address, then you will receive information. Well, you can email me. I will include you when I send out the information. So I think that's it. So this has been really wonderful. I don't know what else to say. I didn't really expect it when I was talking individually. So thank you so much for coming. Hopefully we will meet again, really meeting again on site to meet our attendees. Thank you for all the work you did. Thank you, Yonju. Thank you, Yonju. Thank you so much. Thank you, Rob. Thanks everyone who came. Thanks to everyone. Wonderful job. OK.


was like on the wrong spell for half the fight. I gotta play this game with like a heart rate monitor. I'm getting a little bit better at dodging them but it's been a good part of that fight out of magic. I'm not tempted to try my sword but if I get close because my sword's like plus four I get close he's gonna wail on me but it might be worth a try. And I have that flask that gives me plus 20 strength with that mix. Let's give that a try. Probably not the best. When he had me in the hug I thought he was just gonna like rip my head off man.


So one of the pieces of work that we worked on in this sprint was jobs widget, I guess, on the user profile page. This is something that we significantly improved. Now, in this screenshot, it's a bit extreme case when a person has the multiple jobs and reports to three people. This is usually not going to happen. This is just an example of all the different things we show on the user profile page. So this is now significantly easier to read and we show people who report to you and people who report on the screen. So as I mentioned already twice, bulk actions and web services are almost done and will be completed after 4.3 rebates but before our 4.3 release. And people's tab is fully completed in March. This is something we showcased last time.