If you're watching that last episode, you know we started with about 75 lines of code, most of which was the same four or five lines repeated 10 times. We replaced that with a for loop. It worked very efficiently, worked really well, and it got the answer that we were hoping for. We were able to generalize it to other distance matrices, and it worked really well. Well, in this episode, I want to go about removing that for loop and seeing if we can do it a different way. Perhaps it might be a little bit more efficient, but ultimately the goal isn't to do it just a different way for the sake of doing it a different way, but to do it a different way to help us to learn to program better, to learn more things about programming in R. That's kind of like this current series we're working in, where we are trying to read in a lower triangular distance matrix, also called a file-up formatted distance matrix, into R. This is giving us an opportunity to really dig into the nitty gritty of functionality within base R. Sure, we've got the tidy verse and a lot of great packages out there that make using R easy, but I find that there are these kind of nooks and crannies of R, things like vectors, matrices, for loops. And in today, we'll learn about lists and how we can apply functions over multiple elements of a list and how we can work with lists. And you know what? Lists show up all over the place, you just don't realize it. So let's head over to our studio and we'll get going on today's episode where we try to remove a for loop from a perfectly good chunk of code. So as I mentioned, this is all base R. There's no packages being loaded in here, no tidy verse, nothing else going on. We read in this lower triangular distance matrix, it comes out as a vector. In the last episode, we went ahead and used the full distance matrix with like 348 samples. I'm going to go ahead and turn this back to the simple one. The smaller file makes it easier to kind of work through issues and understand what's going on, then getting the output for all those 348 different samples. From that vector that is produced by scan, we then looked at the number of samples, we removed that from the vector as you recall, we kind of chomped through the vector. And that's really what's going on in all of these different for loops. So what we find is that distances is a vector that's got 56 different values in it. We've done the accounting before in that that made sense. But anyway, when we ran scan, we separated all the values by a tab, right? And so again, if you think about this distance matrix, anywhere there's a tab or a line break, that demarcated where a different value of a vector was created. And so there's 56 values here. Our vector, as we saw down here, has 56 different values in it. I again, want to get away from what we have here with the for loop. And what I'm going to do is I'm going to go ahead and comment out all of that code because I don't want to use it. Perhaps I will use it for inspiration and thinking about things. So I'm going to start back at the beginning. And instead of separating on tabs, I'm going to separate on line breaks. Each line of my distance matrix is now a different value in my vector. So the reason we can't read these data in, say using read TSV is because there's a different number of values in each row. Again, the data are not rectangular, they're triangular, right? Lower triangular. And so a thought that I had would be, can we treat each line or each seat, each value of this vector separately, pull it apart by the tab. So again, we're working on each line separately. We want to separate by the tab. And then perhaps add values so that all of the lines have the same number of distances in it. Right. So that's what I'm going to do. So that might not totally make sense. And we haven't talked about the tools that we need yet to achieve that. And so that's what I'm going to go through with you today. So we can use a function called str split. And so str split will take, say distances, and let's give it like row four, or value four, I think of these as rows, because I have one value per line here on my screen, but it's really seat four, it's a vector, it's not a data frame. And we could then think about separating by a tab care. And so the output we then get, again, as we see, is row four, or value four for F3d125. And we get its three distances. And again, what I'm thinking is, it'd be really cool to then kind of add on seven additional distances, all zeros, or all blank characters, so that we could then have every value in our vector having the same amount of data. Now, something that you might notice is a little bit fishy with his output is that we have this double square bracket around a one. That is the telltale sign that you are working with a list. So string split outputs the splits as a list. So each value in the vector becomes a different value in the list. And then each value in the list is that vector of split values. To have that make a little bit more sense, let's go ahead and do string split on all of the distances splitting each string on a tab. What we then see is that again, each value of the vector is a different element in the list. And then within that element of the list, we have a vector which got which has the sample name, as well as the different distances that correspond to that. So I'm going to go ahead and assign the output of this string split to file split. And you know what, I'm going to go ahead and rename the red in file as file. And we'll go ahead and take that and that and that update everywhere else. And so let's run this and make sure it all works. And so now if we look at file split, we see that we again have 10 values in this list. And it looks just like what we had up above. So we've spent a fair amount of time in recent episodes talking about ways to get values out of a vector or out of a matrix. So I'm going to spend just a brief moment talking about how to get values out of a list. So we could do file split, and single brace with a one, we get the first list element out of the list, which was one, right? And so if we did like, say four, we would get the fourth sample, right? F3D125. But the output of this is still a list, right? It's a list with one element in it, right? If we had done say four colon five, we would then get two elements out of it. Well, if we do file split, with two square braces, and say two, so with the double square brace notation, we no longer see that double bracket one, or in this case, would be double bracket two, that it's outputting a list, right? So single brace, you get back a list, double brace, you get back a vector. Again, if we look at file split, the names of the different elements are numerical, right? They're not names, they're numerical indices, kind of like we saw when we were defining vectors without being named vectors, right? Well, I can assign names to the different values in my list. And so this is a bit silly. So bear with me, I can say names on file split. And again, those are the names for the list seats, so to speak, in file split. Now, I don't have any names, but I can assign names, right? So I could assign them say, let's try with letters, and we'll do one, two, and samples. And now if I do name file split, I get those letters of the alphabet, right? And if I do file split, I now see that instead of the double brace, one, I get a dollar sign a dollar sign B for seat two, right? And so if I do file split, let's say three now, I still get that third row. But another way I could have done that would have been file split dollar sign C. And so this is the way to get a named list element out of a list that you can use a dollar sign C. Now, some of you may be familiar with working with data frames, and know that you can get a column out of a data frame using a dollar sign, the exact same thing, a data frame is actually a list of vectors, where each column is a vector. But those columns are linked together with a list. For now, I'm going to go back to what we had up here with file split using the string split, and having our different elements of the list be numerically defined consecutively, each element of our list has a different number of entities in it. So the first entity has one in it. And I believe element 10 has 10 entities in it. And that again includes the name of the sample for sample 10. I'd like to create a function that will tack on extra values to make it so that everything has 10 elements in it. Actually, I would like them to all have 11 elements, because this one doesn't have a zero for the final column. So what we can do is let's start by creating a function that we can test and perhaps see how we can then apply that across every element of our list. So we'll create a function called fill in. And this syntax, I think we've talked about in previous episodes, but fill in is the name of the function, kind of like str split is the name of that function here, it's fill in, we have the function keyword, which takes arguments and these are the arguments that we're going to give our function. And then we also use the curly braces like we saw in the last episode for the for loop, this curly brace is going to define what happens within this function. So the arguments are going to be x and x is going to be the vector that we're feeding it. And then we'll also give it length, which is the length that we want to add padding to let me show you a little bit of my thinking here. If I do file split on six, I'm going to get that vector back. I can wrap that it as the argument to the length function, right? So that's six. So if I want to get out to 11 spots, that is going to be 11, or let's do this, we can do n samples plus one, because I have the sample name that I want to have minus the length of the vector, right? And so that would mean I need to add five extra spots, right? So this is getting a little bit complicated, but bear with me, right? So what we could do is we could make a vector with the C function, that is file split six, right? And then I want to repeat, so I'm going to create a vector of the same empty character, right? So double quotes, and the length of that is going to be this. And so now when we run that, we get 11 seats in our vector, right? We've got seven, eight, nine, 10, 11. So that's cool, right? That works. So this is going to be my special sauce, right? That's going to be what my fill in function does. Now, we are going to take length, which is what we are going to pass in here. So number of samples plus one, right? And x is this vector, right? And so, yeah, so we'll place that, let's see, let's do fill in. And to double check that it works, we can do fill in on file split, brace, brace six. And the length we want is 11. And sure enough, that does exactly what we want. So we could always make this a for loop, right? But I don't want it to be a for loop because we've already done a for loop. And my goal here is to remove using a for loop. So what we're going to do is use a function called l apply. Now, this is a base r function that allows us to take a function and apply it over values of a list. There's a variety of other apply functions out there that allow you to do same type of thing, right, to perhaps apply something over different values of a vector, or over different columns of a data frame or different rows of a data frame for sure, right? And so this is very similar to what you might be familiar with those the map functions from the per package. Now, the per package makes things a lot easier, and perhaps a little bit more comfortable to work with within the tidyverse. But again, we're doing this all in base r. So I want to show you how to use the l apply function. Again, what we can do with l apply is that we can apply the fill in function over all values of file split. So again, we'll take l apply file split, and we will give that fill in. Now when we run that, we get an error that argument length is missing. And that is because fill in needs a value for length. And so we can give fill in that argument within l apply here, by saying length equals n samples plus one, right, because we want we used 11 up here online 18, because we had 10 samples plus a column for the name of the sample, right? So we're going to do length and samples plus one. So now when we run this, what we see is that we get 10 values within our vector, each of those having 11 elements in the vector, right? And so these now all have the same length. So I'll go ahead and call this filled. That's great. And we'll also go ahead and remove this test code here. So there's a couple of different approaches that we might try to use to concatenate those elements together to form a matrix. My go to instinct would be to do something like our bind on filled. So our bind binds rows together or barns things together as rows. What we'll find is that this doesn't work. This basically makes each list separate column. I'm not totally sure why that is. So this doesn't work. But that was my instinct. To get this to work, though, what we could do would be a function do dot call. And then we're going to do our bind over filled. So this is kind of like L apply, where we're going to call our bind on each value of filled rather than on filled all together. And what we get sure enough now is a matrix like we'd expect, right? Where the first column is the sample name, and then the 10 by 10 distance matrix. So that's one approach we could use. I think that's a lot simpler syntactically. But do call is a function that I only ever use in this situation. So I have to remember that function for just kind of a pretty esoteric situation. Alternatively, we could leverage some tools that we've learned previously, when we're talking about matrices in that episode. To do that, we could do unlist filled. And what that does is it unlists the data, right? So it takes those 10 elements of our list, and basically concatenates them all together to make a really long vector of 110 elements. We could then use that as input to the matrix function, right? We could then say n row equals 10, and call equals 11. And then we could say, by row equals true. And so we get back then the same thing. So instead of n row equals 10 call 11, I could of course do n samples. And so I'm not hard coding the number of samples in there. So both of these approaches work perfectly fine. I think I'm going to run with the do call, because that's a little bit more in the spirit of working with lists. But know that both approaches work, I'll call the samples distance matrix. So now if we look at samples distance matrix, we again get that output, I can then say samples equals samples distance matrix. And it's the first column, right? So I could do comma, I'll say dist matrix as samples distance matrix minus one, right? So that's going to be everything but the first column. Now if I look at dist matrix, I get my distance matrix, it's 10 rows by 10 columns, it doesn't have the samples. And if I look at samples, I see those 10 sample names. So what I'm working towards is taking this dist matrix object, and I want to make it a numeric distance matrix. If I ran as numeric on this right now, these double quotes would become NA values. And so it occurs to me that instead of repping double quotes, I should have wrapped a zero. So we can come back up here. And inside of the rep function, we can put our zero, we can rerun all this good stuff, because we're saving our code and making things reproducible. We can do this matrix. And now we see we've got those zero values. Now, if I want to make this numeric, I could do as dot numeric, dist dot matrix. And now I get numerical data, but it's a vector, right? It's removed completely all of the structure that I had up above from from the matrix, right? So to reform this as a matrix, we could of course feed this in to the matrix function as we discussed earlier, with n row equals n samples. And now we see that we have a lower triangular distance matrix, which is great. And so I'll call this dist matrix, basically writing back over dist matrix, which is good. But we want to get a full square matrix, we don't want only the lower triangle. So to get the upper triangle, we can use the t function, the t function is the transpose. So if we do t on dist matrix, we then get the upper triangle, right? So what would happen if we did dist matrix plus the transpose of this matrix? Sure enough, we get the square distance matrix, right? So I can then save this back over my dist matrix. And now I have read in and formatted my distance matrix without a single for loop. It did require us learning a little bit about lists and parsing things apart a little bit differently than what we saw before. But I think this was a fun challenge of seeing how we could read in a lower triangular distance matrix without using a for loop. And yes, it can be done. Is it necessary? No, it's not necessary. We had a perfectly good result before with the for loop. But, you know, trying to do things differently or imposing constraints on yourself is a great way to kind of stretch your mental muscles and to learn different parts of the programming language. If you always do things the same way in the strange strategy, then when you need a different strategy, you won't have those skills. And so again, I find it really useful as an exercise to impose these types of constraints on myself, and then see if I can solve the problem a different way. And you know, knows you might learn something, right? So again, we have done this completely in Base R without a lick of deep plier or any other package. I'm pretty proud of that. And I think that's pretty cool. We are learning a lot about Base R as we go through this. So the next episode actually won't be released until early January. I'm going to take the week off to spend it with my family as we celebrate Christmas. I really wish you the best of the season, whatever holiday you're celebrating with with hopefully your family and friends. And I really look forward to digging into more R and reproducible research practices in the coming year. So have a great New Year's and we'll see you in early 2022.


Okay, bear with me pray for me I want to see some encouragement in the comments because everyone's saying it's gross. I know it looks gross, but just please Please just Help me. It's quarantine. There is not going to be a better time to attempt to grow facial hair than right now So there's Ethan's beard checking for the day All right. Hello everybody I was having one of those days today where I just was so tired I'm just so tired today And so I just felt like kicking back and playing one of my favorite games of all time which is skate three now The wonderful people at skate and finally EA has done something right confirmed That another skate game is being made they didn't confirm that it was skate for necessarily Hopefully that is what it is and it's not like some shitty mobile game that's coming out or something Hopefully it's skate for because that's what everybody wants and it's what I want and I've been waiting for skate for since skate three came out back in When was that? 2010 I think I don't know just in 2010. It's all like that. But anyway, this is one of my favorite games of all time I have put in so many hours. How do I see that by the way? I've played it on multiple accounts I have played it also on multiple ps3's this game I got it when it first came out and it lasted me from 2010 when it first came out into 2013 on that ps3 my original ps3 and then That ps3 broke so I got a new ps3. I have no idea how many hours I have in this game How do I even check that? Couldn't figure it out guys If anybody knows how to check the amount of hours you've played skate three specifically on a ps3 Let me know how to do it because I'd be very interested to see how many how many hours I've put into this game Okay, we are back in game now playing game.  You can't disable it. And so therefore you couldn't use a capture card for it. It's so there Wow we Anybody else play this game still a lot because boy. Oh boy. Do I we'll do some we'll do some challenges We'll play some we'll play some skate challenge like actual skate We'll do some some hall of meat Maybe dude the opportunities that we have are truly endless Truly truly endless. Does anybody have one of these games though where? You just kind of never get bored of it And it's something that you just always can go back to because that is this is that game for me where I Always just play this whenever I'm just kind of bored and just want to Hang out for a second and chill and just pop a kickflip or two, you know, dude There's been so many other skate games like skateboarding games that have come out that I got really excited for like skater XL and What's the other one that that it was oh session was one as well But the thing about it is that skate just off the bat with like using Using the the sticks to control your board and stuff like that it's just so intuitive and it feels so natural and fluid and so Like any game that that tries doesn't have the same controls that skate does and so it's just it's just not the same Skate is truly they got it. They got it down right, you know, all right. Let's do some challenges Let's do one up. Let's do it. Let's do it. We have to do better than the others I guess maybe there isn't actual skate in this one Maybe that was just the first game in the second game driving played the first or second skate in a very long time. All right All right, Chris Cole your up pal you do that good good and you try and own the spot and Oh Justin Ollie, that's cute You've got 10 seconds Chris Cole master skateboarder Only 1800 points, huh? Wow All right. Well Let's go my character's name is yes all caps Hopefully I don't die. Let's do it 360 flip 360 off the first thing Nope Guys, I swear. I'm good at this game. I swear. I am all right Christopher. Go ahead.  Nice. Nice. Oh He bailed dude just 128 points. It's all I gotta be please don't die and come on, baby You can do it nice nice you That was close that was close I bailed but I got 1600 points. So Not too bad. I am leading the round. Yes Is leading the round? Going for the same line as the first time, eh? Oh Oh, baby. That's actually real good Embarrassing Chris Cole embarrassing. I can't believe it either. Let's go. It's just just a rolling. Is it? Yes, it is nice nice Nice She like There we go. There we go gamers 1980 Great here. Am I right? I have hair in my mouth. First call. Don't don't beat me please. He didn't even He didn't even ollie. How did he get so much air? That's really weird. What just happened. Please don't get more points, sir. Oh Baby One but barely one more round sir one more round. Here we go back to the beginning. Let's try and hit the hit the rail Let's try and hit that rail, baby Okay, not too bad Not too bad. Ooh with a little bit of it an under flip maybe a back flip.  Did you see that? Oh, I'd like to see you try Chris Cole None of the things that I did it were actually I'm very impressive at all Eek that was pretty good. That's pretty that's pretty. Oh That's a bummer dude. Well, wasn't that fun gamers? Wow. Wow Shit, let's do some meat training. Oh, am I going shit? I wasn't paying attention. I was on Instagram. I'm a dummy. Oh Oh, baby. Oh, baby. How love me to write on the skull There we go bail in a cannonball pose. I didn't I didn't do that, but it's okay Meat in training.  Isn't this a cannonball pose? I did a cannonball.  Sick, dude. How do I? Are There we go. There we go baby, so if you didn't know the hull of meat is um is a Way to You know, what am I talking about? It's just where you go to bail in the best way possible Adjust your body with L to land in different parts and break specific bones I know I know Break my head bone. Who's think about you ever think about like What would you do if you? Couldn't get hurt. I would do so much Stupid stuff, but it would be so cool if you couldn't get hurt I'd be so good at things because I wouldn't be afraid of getting hurt You know, you know where you could just practice endlessly because you wouldn't get hurt Don't I mean you ever think about that like I can try and grind this whole entire rail in real life And I wouldn't be afraid about it at all because I wouldn't get hurt Fear is the number one thing getting in your way from achieving your dreams. I want to see something sick. Oh Baby, dude. It's skate three is just Such a vibe, you know, I know that this isn't the funniest video I know that this isn't the most entertaining entertaining video, but I do know For a fact that it's a vibe just sitting here watching me Watching my no watching watching me do my my cake ease and my in my pop shove ease, you know Let's all let's hit this ledge real quick. Let's see. Oh, baby. Oh, baby. I love the way I love being able to set a session marker to and just go and hit a spot over and over again See what you can do, you know hold on. Let's uh Let's go to free play you guys ever want to Play as a piece of meat. I am a piece of meat Grinding on the ledge.   I love the spillway. All right Here we go meat man does the impossible.  Why is the hole? Such a so much more explicit.  That's when it becomes non-family friendly. Oh Baby, that's nice imagine doing this. I So sorry meat man. That's a dirty piece of meat right there. I Love the way he run hate to see you go, but I love to watch you leave meat man. Oh Oh Immaculate immaculate don't know if we'll have enough speed going into this or not, but we're gonna try Oh baby, oh baby Let's hit that again, dude. My god.  Loop it again. Don't mess up Jimmy's mom is watching and She might have the hots for you if you can hit it, right? All right, ready 10 60 in 420 out kickflip nose manual grind on the back end. Oh crooked Smith. Oh Man, he just got the ankles on the Who bad, you know, all right? Here's the line gamers nollie 360 in order to heal 360.  Oh This rail is going to be the one that is For me come on make it make them make them here at meet man Make them here it man.  Oh It means mom. I'm taking her out to a Denny's breakfast.  That's how you get the hot mom's so Hope you learned a thing or two because you know with this kind of facial hair How am I how do you not get the hot mom's? You know what I mean? I am going to end this one here Thank you guys for for just vibing with me, dude Skate around with me. I hope you enjoyed. Oh my little meet man. This is fun I love this game so much. Maybe I'll play some more if if you want me to but yeah Just a little a little bit more of a chill video. I don't know. I don't know was it I don't know. I just felt like playing skate and it I'm you know having a little bit of a lazy day I just wanted to play a game that I can sit back and chill with And I hope that you guys Enjoyed it as well. So thank you so much Bye


Today, I'd like to speak about the module a month, path.py. To install it, all you have to do is pip path.py. They have a special name because it clashes with the os.path module. Not in namespace, but just in identification. And in Google searches, which is very exciting. I really like this module.  So what you do is from path, import path, and you get this path object. This path object will instantiate with a string. So I say here is dot. So everybody's done UNIX, knows dot is the current directory. And here is this path object, but it's a string. So you can print it as a string. And also it does something very magical, which I really like. It has all the string methods and stuff. But it overrides the division operator for path concatenation. So if I do here slash there, it'll do dot slash there. Use the os.join. Here I'm on a UNIX system. So it does the forward slash on Windows. It'll do the back slash. I don't know what arcane system from the 80s might have another character. But it'll do it because it's always a path.join. So you can just have a relative path. So you can concatenate strings, other path objects. And they just stick together and they just work. So you can have relative path, concatenate it to absolute paths. And of course, they're all these things. So all these objects are strings. So they have all the string methods. So if you call any methods of a string, it turns a string. So if you need to do all the things you do with a string. Also what they've done is they've added to the path objects all the os.path methods. So you can say, in a sort of object oriented way, oh, does this see? So instead of os.path.exists, blah, a path, you just create a path object. Say if it exists, all the ones we know and love that we really find a pain to use, they have it in objects. So you can just string them together and call them as objects, right? This is, of course, a really like norm path. Simplify everything. What else is there? Also, oh, sorry, yeah. And so more modules like that. Also, what's interesting. So here I have the there, right? There does not exist, right? But they also implemented a lot of the os methods you can do on paths. So here I can make my directory. Suddenly I get a path called there, right? Does it exist now? It does exist. So it's true. I can change directory into it. If I import os, I show you my current working directory is in the there, the directory I just created.  So this is, again, my path object slash a file. I can just open it, right? And I get my open file descriptor and object oriented way. And so this a file is this path object and manipulate and do open blah, and I have my thing. And that's it. It's fun. So pip install path.py. I think there's a couple of border cases where it all goes horribly wrong. But I haven't had them yet, so I'm really like this module. Any questions?


And I should still stand here on. Okay, welcome everybody to this webinar at links. And today we have eminent scientists from all the way from Australia, who came here to us and learned to talk to us about profiling, flocculation and sedimenting particles with neutron dark field emitting. And for those who doesn't know Chris Covey, he is a man with many instruments in his box. He's mainly a scientist at Anstor working with suns and so on, but the creative nature of Chris doesn't see any limits. And he also is a very imaginative, thinking when it comes to finding new application for scattering techniques. And we have some very important contribution in the field of cryoportraction and cellulose fields and cellulose interactions. And I don't dare to say polyethylene because then he will take up all the time talking about polyethylene, but we also actually made a significant contribution there to the environmental aspects of this. So we are very happy to have you here. And the Chris has been there for more than a year. More than a year. More than a year. And we are happy that he stay until Christmas. So yeah, there's some presence in it, is there? Yeah. Okay. Oh, please. Thank you very much, Tommy. So the title's a little bit of false advertising. I will actually get around to talking about it. But what I thought I'd also do is just give it an overview of what I think of the achievements of being here and also what the future holds. So, if that works? If it works. Okay. So just a little bit of a reflective moment here. Coming here after being an instrument scientist for about 20 years in Australia, he was kind of thinking about the new sort of, it was a new beginning for me, thinking about the new kind of things that I could do and some new horizons. Okay, so I arrived in Sweden. That's my toolbox with some stuff in it, some knowledge, some aspects of using small angle scattering as my trade for the last 20 years. And what I'm gonna talk about is something that I knew I've taken on as imaging, working in real space. And for people who are not used to scattering for real space, seem perfectly normal, but there's very much a dilemma when you're thinking about you combining small angle scattering and imaging. And this is the interesting area that I'd like to talk about today. Okay, so this is what I'm used to doing. It's, and I've shown this slide a lot. I think this is the most usual slide in one of my talks. This is my trade. This is what I've been doing for the past two years. Mostly I've been dealing with transmission sacks and it's a very good technique for recreating structure within a three dimensional structure within a volume of material. More recently I've been involved with what I call one dimensional techniques which are reflectivity and lamella diffraction. And we've used this to look at mostly in problems in membrane biophysics. And indeed I organized a school in Munich at the start of my time here. What I've been involved with more recently is what I call two dimensional scattering or gazing incident scattering. So the one dimensional looks at information in the direction normal to the surface. And for example, if you're interested in layers, you can get the thickness and the composition of layers. Or if you're interested in diffraction, you could look at the spacing between layers on the substrate. Maybe this is going to work. Yeah. I don't know. That's not going to work at all. So these are my layers on a substrate. And if one does grazing incidents, one looks at organization in the layer itself. So for example, if you have proteins with some periodic structure. So just to brief over what I've been involved with here and as Tommy said, I get sidetracked rather easily or unimaginative. I've had a long history of looking at scattering from cells, particularly with biometric aspects. I've started a collaboration here with Magnus Carl-Quist in the area of looking at cells and how they respond to the external conditions. We're looking at the organization in the context of yeast used to produce ethanol. I have quite some history and I actually collaborate for I think about 10 years now and create a stat or another thing. So I'm looking at how red blood cells regulate their volume. And recently or in the last few years I've been working with Ron Corpry looking at the organization of membranes within photosynthetic organisms and tissues. I'm not going to talk so much about that. What I will talk about a bit more today is looking at gel networks from the perspective of digestion. So a lot of foods are gels and how things get in there and digest them. And at a fundamental level we'll be looking at the statistical physics of gel formation and how that's affected by, for example, applying external food like shield. And there's some very pragmatic applications of this in food science. In the case of dairy gels, cheeses and yogurts, we've been looking at how the fractal dimension of the organization then affects the chemical properties of the food gel. And sort of expanding this a little bit more to vegetable proteins, this perspective of scattering on a structure and the transform the mechanical properties of food gels. I also dabbled quite a lot in complex fluids, mucins, which is a collaboration in Malmö. Meat substitutes, again, this is using foods, proteins to make foods, which are a bit like meat. And what I'm mostly going to talk about is sedimentation in sludges. So group chemical engineering. One of the things that I've had for a long time on soft matter is order and disorder. And this is fairly, it seems very abstract in terms of applying it to real systems, but things like looking at organization of skin livers, you can't approach this as a problem in organization, the organization of cholesterol in skin. I am going to talk about polyethylene, not only one slide, and various kinds of semi-crystalline polymers has always been a great interest of mine. And the stuff that pays the bills for me is the small angle of scaphing a person for a long time has been just structural paraprosization of samples in solution. And in this case, I've collaborated with Maratate at Malmö. I have a large background from the Australian Research Council to look at various kinds of medical nanoparticles and how they intercells. And another collaboration here in London is on the protein that is responsible for laying down teeth mineralization. And what the major part of the talk today is going to be about dark field imaging. And these are the particular aspects that I'm going to be applying this technique to. This is the new tool in my toolbox. Just coming back to the way I do things. A long time ago, 2015, I organized this meeting which was an ASS science symposium, looking at the fraction as a way of understanding our soft matter. This really nice, really old book. It's not really an original idea, but a lot of structural biologists, particularly people that do crystallography, think of, you know, you want an ideal crystalline structure and know where all the atoms are, but it's actually quite a powerful tool to be able to paraproach disorder in soft matter. And I think this is a huge role for the new instrumentation of the ASS and my syncopons these days. And I'll just give a few examples of this. Skin or the lipid skin are terribly well organized. Have this nice cartoon here. And it's a barrier. So things are supposed to diffuse through. And one can look at the transport properties of this, how things get from one side to the other in terms of the kind of disorder there. And there's a Knowledge Foundation grant with Emily Wilson to characterize the order in this. But to come back to this diffraction problem, one can use diffraction to reconstruct the structure of these things. And because the lipids are so strongly ordered, it's a very powerful tool for characterizing these lipids. Here's the polyethylene. Again, it's a lamel structure. So most of your packaging has this quite strong lamela organization. And this is typically a lot of this. These are small angle snapping patterns from different kinds of packaging. On the far side there is a package of Nesquik. And you can see this bump here and this is the distance from here to here. And if you go out into the, and this sample is actually collected from around the Caribbean, you can see the bump is disappearing. And as they get older, the bumps disappear completely. This work here, we just showed that what happens to polyethylene over time is that this lamela structure is disrupted. Now, if you think of polyethylene as a barrier material, it's supposed to stop things diffusing in. This is a very effective barrier for oxygen diffusion. So what happens when the thing's sitting in the sun is that it cuts these polymer chains more or less randomly. This structure is a kinetically frustrated state. And so when you melt the polyethylene and blow it into something, it forms this structure, which is an effective barrier. The polymer chains want to crystallize, but they can't crystallize any further because of this entanglement. So what happens when sunlight or UV comes along and cuts it, it starts to crystallize more and it causes this lamela structure to disrupt. And it's very clear from the small angle of scattering. So this seems to be the general process of degradation. But if you think of oxygen diffusing through this, it would seem to be much easier. And I think what's possibly happening is that this process actually catalyzes the further oxidation of polyethylene in least the marine environment. And it's kind of, and the story's here. And this work was supported by the CNRS and the University of Paris Circle. What it seems to say is that polyethylene pollution isn't quite as bad as first thought. These are very small bits of plastic. Little birds are not gonna choke on it. But what it seems to say is this is oxidized into carbon dioxide much more than people expect, least in the environment, marine environment. This is the second most common slide that I've shown since I've been here. And this is some experiments that I had planned that were maybe interrupted by the coronavirus. Simple idea is if one has a soft, self-organized system on top of a flat surface or even a textured surface, how does the order that this will, because these have made lipids on a formal millipase, how does that propagate into the bulk? And so the meaning of this is a hard interface with a soft interface is a really interesting question. And we have some experiments planned to do this, but obviously it will be the millipase here and as you go into the bulk, it will turn into a cubic phase. But also there's some biological relevance to this. If you think about what different kinds of peptides or proteins might do, this one here, this is the protein, a peptide that we've shown that's not as to the cubic lattice at all, but this one turns it into a lamella phase. So even at the interface of biology with hard services, proteins have a very important effect on how they order. Looking at ordering in flow, we have a nice project looking at how if you put a particle into a particle that you want to stick to the outside of a capillary, we want to look at how it behaves in flow in what's affected the flow, which is effectively a pneumatic phase and we're using microfluidics through this. Looking as I said before, looking at how musons, when you extend them, the chains line up, they almost become a pneumatic phase. There's quite a strong collaboration with different synchrotrons, both Diamond in the UK and the Australian synchrotron. And this is quite a nice project looking at producing meat like textured food. And this is a lot of this has been funded by the ARC. So, sludges, but I've really been looking forward to talking about. I'll start off in this talk with really defining a very simple problem. Well, it looks like a simple problem, but it's not. And then I'll go to a less simple experiment and then I'll talk about measurements. Some experiments we did in Australia, looking at very low angle scattering and some imaging experiments where we use the small angle scattering experiment for the contrast, small angle scattering signal for the contrast. These are real samples. So, the typical kinds of applications that we're thinking about here is sewage engineering or any kind of sludge where you have particulates and you want to suck water out of it. You want to remove the water from the other stuff. Looking at cheese making, how dairy gels form. One can think of a cheese as these, what I'm talking about is these match produced cheeses. You get at the supermarket, but the cheeses where they're made of several microorganisms is kind of like an ecology that you have some on the outside which are quite happy in oxygen, some on the inside and they make quite different kinds of textures within the cheese itself. So, this is why imaging has really nice problem. A nice way of looking at this problem, is looking at digestion and biotechnology. And the underlying theme here is to do, of course, one can always put a sample in a neutron beam and make an measurement, but making this in a situation where we can apply this to the real situation. Okay, the simple problem. So, you have particles, maybe in solution and you have gravity and these are being, they may be, I've actually even made a bit more complicated but could be these are ellipses and one sprinkles on them on the top and they start to fall down. And at the top, the major interaction is gravity working on it and the interaction with solver. And as you get down to the bottom, as you have more particles, they start to interact with each other. And one arrives at the profile where there's more of the top and some of the bottom, more of the bottom and some of the top. The green is supposed to be a concentration gradient of these small particles. Now, one is not so interested in the microstructure here but in the gradient of microstructure. And this is what I mean by one dimensional profile. That if one takes a line across here, the structure's all pretty much the same but as you go down is a gradient. And the strength of that gradient or how the concentration changes is to do with this hydrodynamics, how long you waited and how the particles interact with each other. So as I'm trying to define this, what I call one dimensional imaging that there's only one dimension really that you're interested in defining the problem. But you have a modern or war or variable spatial resolution. So if one has a column that's very tall and things happen very slowly, you have a huge spread out of the interesting phenomenon and the spatial resolution that you need is much poorer to represent the spatial statistics of the structure. But everything happens much more quickly. And if you get large particles and set them in water, everything happens too quickly. You need to measure very quickly and you need very good spatial resolution. In context of X-rays and neutrons, X-rays gives you very good spatial resolution because you can make a very small beam and measure on a very small spot. Neutrons, because you never have many and generally the bigger the spot, the more statistics you have to measure and there's all other kinds of consequences. But generally the spatial resolution isn't as good except in the experiments that I'm going to talk about. So if you think of my picture here, what we're going to do is move this green dot which may be bigger or smaller depending on the problem that we're interested in or the kind of technique that we're using. And then look at the variation of structure down the column. The kind of problems that I'm going to talk about in this particular section are looking at de-watering of filter cakes. Basically my colleagues are chemical engineers who want to de-optimize this process so make the water suction as efficiently as possible. And there's a whole heap of different kinds of applications that one might think of. As I said before, sewage engineering, mineral recovery, so you make us ride up rocks and put them into water, water purification when one wants to remove sediment from water for drinking, that kind of thing. This is a collaboration of mine with Marcus at PSI and Miliana is also an instruments person who works on the USAN's instrument there. These are some really nice applications of it. This is a factory for making toilet bowls. So this is exactly the kind of problem I'm talking about.  This is really mass production but these are a couple of my papers, there's four of them there, very fairly high impact journals. They don't describe them as filter cakes but that's essentially what they are. You've got different kinds of particles, different kinds of interactions. You suck water out of them, you maybe put some salt in there to mediate the particle interactions but just by doing, considering this very simple paradigm, one can produce very advanced materials. So I hope I've sold you on that. That's a really interesting problem. But when you do sludges, when you do sludges, this is a typical sort of situation you might have. This thing might be meters across that you have a rake at the bottom for stirring it. You put in a feed of particles and you might put some polymer in there to aggregate them. And what one wants to do is the particles to go to the bottom and to collect more or less pure water on the top. If you make this as efficient as possible, you can collect these particles, they may or might not be valuable. If it's sewage, it's not terribly valued. If it's some kind of mineral bearing rock, it's a lot more valuable but certainly you want to separate it from the water as efficiently as possible. That's kind of an environmental problem. Now, really the problem for me, the thing that really makes me interested in this is, okay, it's a particle suspension and it's certainly that small and hooked scouting has a really good way of putting it but you cannot put one of these in a neutron beam. You cannot put one of these in the X-ray beam. So really the challenge is to have a representation of that industrial system. And if you were going to do a scouting experiment, you really need to think about the quality of the data. You need to think about properly normalised data. So one can get the volume fraction, enough information in the measurement to do the measurement as efficiently as possible. This object here is not uniform. There's different parts and different particle packing density and that's really an interesting problem as well. So spatial resolution, it also happens in time. So one wants to be able to measure quickly and one is never going to be able to put this in the beam line. So this is the ideal. This is what we ended up with. These are just qvets.  The model system is really very simple. It's just calcium carbonate particles. And one can buy these off the shelf in a range of sizes from about, I think we were at about two, normally average micron size to about 30. They're terribly irregular. If you look down a microscope, there's bits all, they're not spheres, by any means, and they're extremely polydispersed. When you sprinkle them on the top, you see that there's a cloud left behind, but it doesn't make so much difference to the sedimentation time. They have a slight negative charge by nature of the surface chemistry, and all this is done in D2O. The other thing I've done to this is, you notice if you compare this one here to this one here, this one's got a kind of lighter and fluffier texture. What we've done with this one is we've added a flocculant, which is a long, they're usually polyacrylamides, and just by the basis of the charge, you can actually control the linear charge density on them. But what they do is, they cause the particles to drop more quickly, but in this much fluffier, much fluffier sort of texture thing, there's exactly the same mass of calcium carbonate in this one compared to this one. But also, if you think about it, the water's gonna flow through this much more easily. It's a much more open structure. So what you have here is a much more open structure that water can flow through much more easily. We can do water more easily quickly, and it settles more quickly. So here's neutrons. I've been doing use-hands for a long time, at different facilities. In fact, one of the facilities recently died. These are sort of the BT-5 instrument instruments, it's quite a nice one. I don't think the Gundam have a use-hands instrument here. At the ESS, there's one at the RLL, quite a nice instrument in Pride. And the one that I'm going to talk about mostly today is the experiments we've done, and as so on Bookaboo, which is a really nice instrument. I'm also going to talk about this imaging experiment where we use the small-angle-scattering contrast, the small-angle-scattering signal as the contrast. We've done these experiments at PSI on two-beam lines, Icon, which is a specific beam line for doing cold neutron imaging, and Bower, which is a sort of general multipurpose beam line at PSI. In general, I'm going to show some curves, use-hands curves, and I'm going to assume that if you see something with use-hands, you're going to see it with this dark-field imaging, or the variation of the dark-field imaging under certain conditions, which is to say if something's possible with use-hands, then there's a capability to do this with dark-field imaging. And there's a hand-called transform, but I'm not going to talk about that at all, which is the conversion of the two representations of the data. Here's Cookaboo. It's kind of, the people, so you have sand instruments, and they're typically the biggest instruments in the guide hall and most facilities. You think with Ultra, they'd be bigger, but they're not. They're really quite simple instruments. So you have neutrons, and you have a first monochromator crystal, and the neutrons bounce along it. And the point with this is that it monochromates and it collomates. It's all going in a very specific direction. And it's as usual with small-angle scattering, you put a sample in the beam, and the neutrons are scattered by the sample. And one measures the scattering as a function of rotation angle around on the second analyzer crystal. This is the kind of scattering hood you have. These are actually Dairy gels in D2O. So we measure a point at the time. Each of these points represent rotation of this crystal. So there's some tricks when you're trying to do kinetics with this because as you move it, it's obviously changing. So each of these curves represent a couple of hours to measurement. And you can tune the number of points you have. The more points, obviously, the longer you take to measure. So the instrument is quite compact. This is the sample area here. And on this case, we've got a sample changer. And because we're measuring things in the micron range, they tend to sediment. So these are rotating sample changers to stop it settling. This from the top to the bottom is probably about 80 centimetres. The beam is quite big. It's of the order of centimetres big. So you need big large samples. But in terms of my sedimentation experiments, this looks like a really nice space to put a column of things sedimenting. And indeed, this motor moves up and down so we can move it in and out. We haven't got round to that yet though. So this is the kind of sample we've used this is probably about five centimetres across. What we've done here is that as I said before, the beam is quite large and the larger the beam, of course, the shorter the measurement, what we've done is we've used some of the optics on either side to focus the beam in through a sample. So this is a sample that would be rotating and we've allowed it to stop. So you have a top level and a bottom level and we've scanned the beam through here. And with this, you can identify the interface between the particles and the D2R on top. And one can measure different kinds of scattering curves. So this is all intensity versus Q. There's a decay and they maybe change a bit. An important thing is that we always measure that there's a difference between the bottom and the top. In a normal kind of measurement, you just use a big beam in the middle. This is the other kind of instrument that I'll be talking about. So this is the kind of setup they have at PSI. Of course, in some respects, you still have neutrons, but it's a big neutron field and there's some gradings here. And we put the sample in the neutrons, of course, it's quite company neutrons scattering and we vary the distance between this gradient here and the sample.  Here's some samples. So here is one of these kind of images with bottom configuration. And what we've done here is plotted the DFI, which is some measure of the intensity on a pixel as we've varied the detector distance. So here you can see in the transmission image, it looks pretty homogeneous. And indeed, that's what one would expect for Solver. Here is the transmission image of, so we put some charged spheres in there and it looks fairly homogeneous. But if you look at the dark field image, there's contrast at the bottom. So what I have here is several of these curves where we've taken one cut here, position here, and we've put the DFI value versus something which is related to this distance here. And at the top, where the particles are fairly far apart, or even say that it's like a gas, one gets this kind of curve. As they sediment a little bit more, you have liquid-like behavior. And at the bottom, one has quite well-ordered material. It's actually close to hexagonal packing. So this is really quite a nice experiment. And one can show, this sample is actually the equilibrium. That equilibrium, there's a certain amount of crystalline material, some liquid material and some gaseous material at the top, and you might expect, if you're able to sort of perturb the solvent conditions, like put, for example, put a bit more salt to shield the interactions between the particles, one might shift the position of that equilibrium. Indeed, that's what you observe in that paper there. So this was the starting point for this work. And this has been lying itself. Again, coming back to thinking about putting this great big thing, the ideal measurement, this rate thing where you mix the sludge, that's really not going to fit in there. Neither is it going to fit into the use as instrument. So this is always the problem with these kind of measurements to learn something useful from a model system that you can put in there. So what I've got here is my camera shot. I always take a photo camera from my phone, probably still there. What I've got here is some particles of different sizes, with and without the flocculant, the bigger, fluffier ones tend to be the flocculated ones. So if you look in the regular transmission measurement, you can't see any difference between the two, between any of the samples, actually. So there's the measurements of the solvent, there's some particles out there, there's some particles out there. But because of the nature of the interaction of neutrons with this kind of sample, you can't see them. Here's a sample of just some vapors yeast. One can see it quite clearly. There, you can't see. There, this is a sample where we've laid, we have three layers of 35 and 30 particles. So let's just look at what happens. I didn't test out the capability to do, maybe it's here, maybe it needs me for a minute. What happens if I play that? This is fairly important. So I'll just take some time to, the whole talk was a bit flat, if I can't show you the video, I might do. Let's go to another viewpoint. Might be the cursor, the pointer. That's my interfere with that. Ah, I didn't need to show this because it is quite important. If you can get through that one. Point to the other side. Down here. If I can get rid of that one. I'm gonna complain. Ah, there we go. I don't have to describe it all. So each of these images is a slight change in the distance between the sample and the grading. And I'll go back through this again. But you can see that very clearly, compared to the transmission image, almost as clearly as my phone, although it did cost a lot more money to do this. You can see that where the sediment is, is really quite clear. Well, you can see in this layered structure, that it played again. So let's look at this layered structure here as we go to these different lengths. You can see there's a band appearing there and a second band. So it's able to image the difference between the different sized particles. Now, the other thing here is that this field of view is really quite small. It's about 10 centimetres by 10 centimetres. So the opportunity to do a very long sedimentation experiment where one gets very clear separation of the particles was fairly limited. But one can distinguish between the different size of particles. The other thing I like to, I'll play this again. So one can clearly see, not as clearly as these particles though, where the yeast cells are sitting on the bottom of the cell. And I'll come back to that later in the pool. But just from the perspective of the imaging, just from the perspective of being able to visualise things, this technique allows you to see the different kind of layers. One other observation I'd like to make about this is that, and what I've done is I've plotted the dark field intensity as a function of position. So you want to take one of these here and lay it on the side. And this value here, the correlation, is the different distances related to the different distances between the grading and the sample, is that for these fillings, these kind of samples over here, that it's fairly homogeneous, that this shortest length is all pretty much the same. As you change the length, it all varies fairly uniformly. So there's no change in the particle. Well, there's experiment-card-ciniest change in the particle distribution as a function of position in the sample. So in terms of the original aim of the experiment to see separation, clear separation of the particles, it doesn't work so well, but I think that's not surprising given that it's only about five centimetres from the top to the bottom. Okay. So that was a really nice proof of principle experiment. Works really quite well. In terms of what I want to do next, this is the thought process in my mind, that each of these DFI images represents about half an hour of measurement time. So for one DFI curve, you need to make a lot of measurements. And moving the sample. The use-sense is fairly similar, but it's easier to move between points. The spatial resolution for the DFI measurements is incredible. But I think that we haven't really used it in a useful way yet. Maybe in terms of being able to resolve the different layers of particles, which we couldn't resolve by any technique. And we can visualise the different kinds of particles and possibly the number of particles in there as well. The spatial resolution of use-sense is useful for certain kinds of experiments, but one also must think about the kind of acquisition time that you have to use. And how to put this into a useful context? Because beyond just imaging things, one usually extracts structural information from scattering curves. I mean, imaging people look at something and say, that's something wrong. We're not usually really looking at that. We can see the layers, but we can't see the particles in that layer directly. And that's where the analysis comes into. Now, these are very featureless curves, like a lot of use-sense curves, actually. And this is where we're sitting at the moment, is how to convert that DFI curve into useful structural information. And I think the key to this will be, with the use-sense curves, we put other information in, and this really hasn't been done for the DFI curves yet. For example, the following fraction of particles. And to get some simple numbers rather than trying to extract everything from modelling the data. We also have a nice collaboration of Patrick Judenstein, who painted these dark field imaging experiments. It's always good when you come to do an experiment at a neutron facility and you're collaborating with somebody who's doing a complementary experiment for them to actually come and do the experiment with you. I think this is a really useful interaction. And indeed, I've been doing the NMR imaging experiments with him virtually via Zoom. So that's quite nice. Okay, so proof of principle, and this is where it's going, applications in third technology. So as I said before, gelation, and I'll do a bit more of it to convince you that it's useful in the next couple of slides. Bary gelation, plant gelation, what I've called undulation, which is what is effectively digestion, pulling that network, that gel network apart. I've really covered this topic fairly well at this stage. And I'll talk a little bit more at the end of the presentation about applying this to cell biology. Or industrial cell biology. So gels, if you think of what a gel is, at one level, if you gel milk, you can't pour it out. It sits in the top there. And the reason for that is that you have some structural element that's aggregated to form a network, continuous network across the container. So in the case of a glass of milk, it's a gel structure from one side of the milk to the other. It may be more or less fluid if you've got cheese and you do that. It's probably okay. Yogurt's gonna get a bit messy and a bit smelly eventually. This network has a fractal dimension. And this fractal dimension has often been used to describe the mechanical properties of colloidal gels. But recently, not that recently actually, this is 1989, but not much has been done about it because I don't think there's any real way of measuring it. One can understand the texture and the mechanical properties of a dairy gel or the different kinds of yogurts or cheese just on the basis of this fractal dimension, how the aggregates are put together in space. Now for colloidal gels, it's the fractal dimension. This has really been related quite extensively to the Canadians, how fast they act, it's diffusion-related aggregates, how fast they add together. And the really appealing thing about Usand's is that it acts as a direct characterization of the fractal dimension of this 3D network. And this is just some experiments that we've done in the last couple of years that we're in the process of writing up. This is skim milk, so there's no fat. So in my cartoon here, the green blobs are the fat and the red dots are the case in myself. This is skim milk. So there's no fat in there. But one of the cool tricks you can do with neutrons is contrast out the fact so you just see the network, but that's not done here. So what you start off with then is just this form factor from casing myself or little spheres. And what happens as they aggregate together is you get this up here and the slope of that is like the fractal dimension of the network. This is some work I've done on the sacks on a pea protein in NALMO. It's not terribly good data. But one of the interesting things about it is that it was just the case of you have to heat them up to 90 degrees in a capillary. It's very hard to do that uniform. In fact, I'm hoping to do that tomorrow. And this is, again, you have unitary bits that aggregate together and as they gel, they form a network with the fractal dimension characterized by the slope here. Another really quite cool thing about doing with neutrons is the part in the contrast variation is that you're actually able to put microbes in there and do it directly. This is something you can't do with x-rays. We have similar plots here. So this is an enzyme, reddit, comes from a calves tummy. You can put microbes in there. You can look at what the casing, myself, are doing with the microbes in there because we've adapted them, the microbes to live in some kind of D2O mixture. And the neutrons, they're perfectly happy. And so one can see the formation of the network directly in a microbial process with molecules. I think that's quite cool. So it looks remarkably similar to the last slide.  So in the gelation, it's the aggregates and it happens more or less uniformly over the entire sample. But if you want to put some enzymes in there and digest it, they're of course going to work from the outside in and this network may provide an obstruction to free diffusion into the network. And this is where quite likely that looking at this with the DFI will be very helpful to one be able to at least some experiments we did in ICON a couple of weeks ago. We weren't able to go to digest but one can clearly see where the gel is here. And if you have a solution of enzymes on the outside and they're attacking in and one has a scattering curve for every point in here, one can look at the relationship between the structure and how easy it is for the enzyme to access inside the structure. So again, one can use, look at really quite nice applied biological problems. So we're into the higher straight in. What I'm finally going to talk about is red blood cells. And this is, so at one level, I've been working on this for a long time. I've been interested in the connection between the biochemistry of red blood cells, how they metabolize, how they use ATP because this is how they maintain their shape and the rheology of blood. But it turns out these are remarkably simple system for looking at the effects of shape cell volume and cell shape and volume on use ends. And we've just sort of published a nice little paper in the last couple of weeks. So it's a very, very simple system. So it has no nucleus. So there's only protein inside, about 30% volume fraction of hemoglobin. Come back to that in a second. About 50% of the volume of blood is erythrocytes depending on what kind of organism and how much you've had to drink and that sort of thing if there is a bit. As I said before, they actively regulate their volume. They use metabolism. They use burn ATP to keep that shape. And what we'll do is in a minute, look at what happens by changing the shape to the scattering. But in terms of the scattering problem, biochemically it's a very complicated thing. But because most of this stuff, 30% volume fraction inside the cell, there's a lot of other proteins there that are no doubt biochemically important, but scattering is a fairly coarse view on structure. One only gets a view on that. The cell membrane, which is really important because if you don't have cell membranes, you have hemoglobin drifting through your blood and they clog up your kidneys, it's really important from a living perspective, but that's not the same as a scattering perspective. There's very little of it. So it doesn't contribute to the scattering signal at all that you can measure. So in some sense, the UCNs experiment dumps down to the problem so you can make some useful information. So I got a student to do this. I like saying that. We got some cells, a lot of cells, and we put them on a slide and we're interested in what happens to the shape as they run out of glucose. As I said before, they consume glucose to heat their shape. They turn that into ATP. Now, apart from the fact that the red blood cells will interact with the glass of the slide and change their shape, the student counted cells for a long time. Actually, I think what they did was they took pictures of a long time and then they looked at that. And you can see the error bars here. So from my perspective, looking at the effects of the rundown of metabolism on the, you know, by the end of this, the red blood cells are sitting in their own metabolic poisons and they get more and more unhealthy. It's not a terribly good way to do it. We don't have good statistics. They're reflected in the noise here. And I think if we've got several different students to do this, depending on the expectations they had on the experiments, we might get quite different results. Not a very good way in biology. But to do the use-hands experiment works really quite nicely. And I'll talk a little bit more about the analysis at the moment, but at one level, we've got cells which are isotonic. This measurement took about four hours on the instrument, the ILL. So we had to put some sugar in there. All these have some sugar in here. So there's no metabolic effects. I've got another dataset with metabolic effects. But what we've done is we've put them in more salt. So they shrink, less salt. So they blow up a bit. And this one here is something that's been metabolic poison. You put sodium fluoride in there and the ATPases that work very well. I'm not going to go into this in a great deal of detail, but I'll make some sort of general comments on it. The first thing to realize in a red blood cell is when it changes shape, the surface area stays the same. Now, the bicarbonate discs, you put them in a less salty, more than 100 millimolar sodium chloride, they blow up a bit like balloons. If you put them in 200 millimolar sodium chloride, they shrink down, they become quite compact discs. So the surface area is staying the same. What we're trying to get at is at the border. The contrast, as I said before, all the Scattering Institute is the hemoglobin. So if you have a one cell and you blow it up, hemoglobin concentration inside of dips goes down. It's got a bigger volume, same number of hemoglobin molecules per cell. That's an important point. So the point I'm trying to make is that this intensity here reflects the volume of the cell. And indeed, I'll just come back to that. This is a well-known law in Scattering. It should be a minus three slit smear, not in whole, but it's minus four. But if you plug it like this and you get the intercept there, you can very clearly extract the volume per cell of the average of the number of cells in the neutron beam. So it's a very good measure of the cytopyrid. It's a very good measure of comparing it to metabolic to see how they maintain the volume. We did actually normalize it to something that we did know. So there may be a little bit of rubberiness in this measurement here. And for this one here, there's spherocytes where we put it in metabolically. What's interesting about this is that they lose surface area. So they shed some of the surface area and they become more dense. It works quite nicely. So we've shown that, and in principle, the DFI, and if you look at image things, we can measure the number of cells and their concentration or the cytopyrid, which is mills inside a cell per unit volume. I wanna talk about this. This is a lot more speculative. We have some published results that I'm not all too happy about going on a limb. We can tell the difference between cells that are long ellipsoids, that's, or robots, that's what microbiologists call them. We can tell a spherical, we can tell the difference between different shapes. That's not too much of a stretch. Some cells are ranging chains, and again, we believe that we can see that with the u-sands and we've begun some preliminary measurements with the dark field imaging. So with this toolbox, we actually have a nice way of looking at how cells behave in a mixed culture, how they organize, and really the next challenge is to put these kind of cultures into a beam line. In some sense, we've done this already with cheese, but in the case of cheese, these are these fancy French cheeses where you have different kinds of ecologies depending where you are in the cheese. It's mostly the network of the meal gel that we see, but I think this is a really nice and useful way to go. So in my toolbox, I've packed some more tools, and I'm ready to go off to the next place to learn about something new, and that's about all I've got to talk about. So thank you very much for your attention. Chris, you want to talk, Chris? Hey, Chris. Steve here. Can you hear me? How you doing? Yeah. Good, sorry I couldn't be there. Very nice talk. Thank you. I'm here in spirit, I'm here somewhere, yes. I think it's really nice. I think extending the imaging to the scattering signal is fantastic, and there's been some stuff done with X-rays where you take the real space image and you hit Fourier transform, and you can get similar information, but I guess up to scale above. That's kind of nice, but what I was wondering about is one thing you do if you do the Fourier transform of the real space images, you can get the anisotropy in things directly. So I guess with this DFI, can you rotate the gratings and start to get the anisotropy out and things? I mean, I guess the time scale, the measurement goes way up, but it was a stable system, could be interesting. Absolutely. And I think they've recently published something like that from the group at PSI, imaging texture. I was kind of interested in terms of imaging texture in wood. This has always been a huge interest of my many interests, but I think the big problem is how measurement time is, and in the case of wood, it's much easier to do it with an X-ray beam. It's just a very simple, quick measurement. So I think it's going to be a case of what kind of systems this is useful for. Flow might be a very good one. Yeah, I guess when it comes to wood and things, I guess, or that type of product in Sweden, people are very interested in certainly those flow and things. I guess that would be a very nice application. Indeed. What you're talking about. I guess if you have a steady state system, so you say flow, then you could actually image it while it's flowing and you measure over a long period of time. I guess the flow would be long, but it could be good. Your practical example at the start with the big, I don't know what it was, hopper, rotating hopper thing. Yes. What was the scale of that? I didn't get, because you said you can't put it in the beam line, but what is the scale and how much could you scale it down? The smallest one they have in the Cammage department at Northern Union is about a metre across. And they get way big. In real situations, there'll be 20 metres across, I think they're really big pieces of engineering, but I think there's a nice challenge to have something that has the essential physics, well, the essential fluid mechanics of it in there. And I think that's a question of talking to somebody who does this kind of work. In fact, that's the reason for the collaboration. Yeah, that was nice. It would be nice to discuss this a bit with granular mechanics stuff as well, that would be good. And we'll get back to you, I'll get back to your wood at some point. Yes, I'll put my supply. You can only see that. Thanks. Oh, so I have two questions, actually, related to this.  And that is, as I understand, it has to do with the hemoglobin. Yes, yeah. And have you thought of that? And the other thing is that hemoglobin, if I, I mean, I was surprised to learn from the appeal of others that this is a really attack molecule. It has a very large penetrability and then, as I understand it, then we were trying to apply for a ground where we used it as a torpedo for, torpedo for a drug delivery and so on. Oh, okay. Well, to come to the, to the, so the reason I got involved with red blood cells to start with was I was working on a project where we were using yeast to digest pig waste. Wow. And the lab that I was working on was specifically working in bio-energetics. They had a long history using, using NMR to study phosphate tablets. You can see the ATP peak really well and you can understand the bio-energetics. Anyway, I was a small angle scarer. So I said, let's put some hemoglobin solutions or red blood cells in X-ray beam and see what we get. It's one of those kind of experiments. And you see a very clear interaction peak. I was really excited. I was really enthusiastic. And then I looked back and Guinea published this book in his, this word in his book. So it's been done a long time ago. And there is a fair precedent for rediscovering things because people tend to forget this, but it's not really where you go to do the new stuff. So the story is with the sickle cell is that it's related to the cell volume. And this wasn't just sticking a piece of red blood cell in a beam line. There was actually some scientific rationalization for it. But what you do see is that this interaction peak disappears. It's kind of like my gels that the hemoglobin molecules form a gel inside the cell. And it is related to resistance to malaria. Nobody's really quite worked out how it works. It kills people, but it's actually an adaptation which protects people from dying from it. Yeah, that's the story. It's a weird one. Yes. So I was wondering with your pulling ethylene with the danger of sidetracking you here, but you mentioned that it happened in saline conditions that did degradation. So what is the mechanism? What is the effect of navigating in seawall, for example? This is something we want to see whether it's specific or within polyethylene for a very long time. Mostly from industrial processes. So there's a company that makes big containers for transferring sulfuric acid around the world. It's a straining company that you see them here. When they make them, they make them completely without strain. So it's really very resistant to degradation.  This is one of the big things with it. When it goes in the ocean, it seems that this structure breaks apart and they oxidize much more quickly. We think that this lamella structure is protecting oxygen diffusion and oxidation is what really drives it back into the carbon cycle. We're not quite sure about what specific about the ocean. When we've done a lot of land-based degradation where you just put it under a UV light, we see quite different pattern. We see this lamella structure actually crystallizes more and it stays there. So we think there's something wrong. There's something special about having it in the marine environment that causes it to oxidize more quickly. Right, but it doesn't necessarily have to be the salinity. It could be other things, it could be organic, yeah. There's an awful lot of stuff there. Articular processes are... Yeah, yeah, and I always read this that... And I'm not suggesting that we go through all our waste in the ocean. Like it's stopping there. But I always read this and I read it this morning. Plastics in the ocean are worse than first thought. There's a whole industry of science and I think it's really... It's to the detriment of science funding in general that money gets put into this kind of activity to show that things are much worse than we first thought. I think we really need to take a much more introspective view on particularly environmental problems. That's my flag waving. It's a really interesting thing and it's very difficult to get funding to study this sort of thing. That if you show it's really, really bad, funding bodies will give you money. If you show, well, it could be this, that's rather interesting. You're not going to get any money to do it. And we were kind of lucky that the CNR supported this for... I don't think they were paying attention. Actually, what we asked. So there's a really interesting questions about the way science is funded everywhere in this. And I think it should have huge effects on policy because just to show putting huge amounts of money into things to show that they're worse than first thought is a complete waste of money because eventually you're going to be shown to be wrong. That goes without saying. Unless there's enough clear thing in the ocean that the world explodes or something. There was a recent case from Australia where someone got fired because he had a... He published something about, I think it was coral reefs. He said the state was much better than it thought and then eventually he got fired. It's a huge problem. In Australia, we had... There's the CSIRO, everyone knows the CSIRO. We had a huge division that did environmental science from a farming perspective. They were modelling climate and showing what the effects of global warming were. And it was very useful to the Australian economy. Farmers could plan things and then they got caught up in the global warming industry to show things which were worse than first thought, which is fine. I think it's all together good to be cautious but what happened was a government came in that weren't very receptive to this idea and the whole section got caught. And at the end of the day, we don't have what we're near in Australia which is to model the climate from a farming perspective. And that was a great shame. Any other questions out there? Nothing from the people online. Oh, blown away. What can't you say about sludge? I think we should thank Chris for this very exciting talk. And it really shows that Chris's asset to links and I think he has provided so many connections that sometimes when he talks about them, my head is spinning, but that's me. So thank you very much. Thank you. Thank you. Thank you.


Not troops have fought an attack by suspected terrorists at Ongwan Garama, located in the Maraban-Rido General Area of Chikunloko Garbantaria of Kaduna State. The State Commissioner for Internal Security and Home Affairs, Samuel Aruan, confirmed the incident and said that the operatives also rescued seven persons who were kidnapped by the terrorists in the Maraban area. He said the operation followed a distress call received by security forces who swiftly responded by engaging the bandits. He added that the superior firepower of the troops forced the criminals to flee in disarray after an intense exchange. He stated that all the rescued persons have since been reunited with their families. Kaduna is one of the not-west state most affected by banditry where terror groups kill and kidnap residents at will.