All right, thanks everybody for making it out this week. Today we have our own Professor Lin-Wan Lu who will be talking about Richie Curvature and Richie Flow on weighted graphs. Go ahead and take us away. Okay, so thanks to join us in the seminar. Today we'll talk about the Rich Curvature and Rich Flow on weighted graph. This is a joint work with a bunch of courses and I'll explain to you. And these are two papers. The work was done by two visits at the Harvard University in the 2019. The first paper is during the May of 2019 when I attend the first conference of STER and the second one is another visit in summer to the Harvard University. Now everybody knows that STER, so I don't have to explain him. Surya and Tsu-Yu are my former PhD students at the university. Surya was a post star at Harvard University at that time. Now she is a tenure check assistant professor at the University of South East. And Tsu-Yu now is a post star at the Georgia Tech. Ah-Yang Hua is an assistant professor at Brandy's University in the post area. So it's very close to the Harvard University. Yunlin is a professor at the Tsinghua University. He is one of my long collaborators and he's also visiting Harvard University in August of 2019. So this is how the work will be done. All right, so let me give some background. Why we consider the, choose the weighted graph. So let's start with the big topic. So everyone knows that this is really most significant in the two decades. So the paramount in 2002, 2003 and last, is proof of the Pankani Conducture. The Pankani Conducture is stated at every single magnet across three manifold in a homophict of three spheres. Now there's at least three groups of people contribute to make the skills of his proofs and include the trainer, loft, and the child and Jew, and Mogi and Ken.  But I'm not a geometer, so I'm a commandorist. So we don't need to talk about that. So the whole proof is, the whole idea is the right, are the heavy things to reach for ideas. So let me try to draw and see what we can do for graph. So suppose that you have some manifold, okay? Let me just do a drawing. Okay, so I'm probably new too, okay? So yeah, so suppose that you have some bestian shape, you know, manifold, okay?   And it's through the rich flow. So that's why, you know, rich flow is so powerful right now. And it's also very popular. And what we want to know is that suppose I give you a graph, okay? Right? So, and here what we should do, okay? And we want to say, okay, okay, give me a graph and I'm gonna try and I try to morph it into a standard form. Now, the sphere you can think about is how part of, is how the constant curators. So maybe we could get a graph with the constant curvature and some format, right? So this is the topic, then why we want to study at a written graph because I want to change distance. If you're continuous or shrinking, moving, you want a lot of distance can be changed, nothing for the one, okay? So let me give you some background of the rich curvature on graph. Okay, so here is the background. Let M be a manifold, X is a point, Y is another point. And V, the vector in tiny bundle, okay? So suppose I have two ball, BX and BY, centered X and Y with Ypsom radius and I want to move the mass from the ball BX to BY. And what is the area distance between the ball BX and BY? That can be compute, oh, so maybe probably I just, okay, so that would be better on the high DSA. So it is approximately the DXY, the center distance between X and Y, but with additional terms, the rich curvature and the dimension will appear. And this is indicated that can be used, this idea can be used to define the rich curvature on graph. So there are some many variation of the, you know, coaches. Oh, sorry, so I accidentally, all right. All right, so I want to talk about the coaches based on L1 distance, okay? So early in 1985, Bakery and Emory have found a way to define so-called low-rich-coach ball. And that works all the magic space. Now, Fentra and Yale, they define a class of graph called the rich-coach thread based on frame. And they said from that, you can have very good, a lot of subtlety quality. A storm and a lot of the brineus, they throw the components of the curvature dimension bond. And all of the coaches are in the Markov chains, such as graphs. And in 2011, and Lee and I and Yale, we kind of adapted all of us ideas and to give a definition of the crucial graph. But our definition is slightly different from all of us. I will explain later.  So the width of graph we consider in this talk are slightly different from all, you know, the width of graph. We actually have two widths. So given NRI, a simple graph, and then you have two widths, one is called the distance function and the other one called the width function. And what did they do? One of them was for Ws, okay? So let me just try to draw. So suppose I had two vertex, okay, X and Y. The way we think about, we have Y is called WXY, okay? Y is TXY, okay? TXY, you can think about, TXY is the distance between X and Y and that indicates, you know, if you want to send information from X to Y, how long to take, right? And WXY is kind of capacity after the information you send X to Y. So that way, if you have information you want to send out to the neighborhood, if you have a large capacity and then you will receive more information. So that defines the random work. So the random work, the transient probability, okay, from U to V, you give it just the width, W. But the distance is the D, okay? Now, if we go back to the simple graph, the simple graph, we just said all the distance of U, V and W, U, V equal to one. And so that's all the definition that I come back to simple graph. Okay, so with these annotations, let's talk about probability distribution. A probability distribution is simply just function from vertex to zero one, and such that the summation is equal to one. And if we are, you can see about there's some mass distributed on vertex set. You have one distribution to other distribution, one to change with some mass to move from one to another distribution. And what's the distance? The copy is the way you can move from one with others. The copy of your definition is a function from A, from V cross V to zero one, such that if you send one index, you get the first distribution, you send another index, you get the second probability distribution. So the transportation distance is simply just the distance in optimal distance, and it's a conference, or possible conference. Now here the cost of mass from X to Y is the distance of X and Y. So if you take all the information and then it will just play distance, it is a real existence of probability distribution. So satisfy the travel if you call it. This is the solution of the optimal problem. So any linear open problem have a dual problem. So you can define another way using the dual problem. So dual problem is defined as the Lipsch's functions. So what's the Lipsch's function? It's a function of vertex to R, such that the difference between X and F, X and F, Y is that it's a most of times constant times D, X, Y. Now usually we put the C for the Y and we put the C for the C for the Y. So the transport distance also can express as a supreme among the FX times M, Y, X, M, M, or two X, that take the supreme among all one Lipsch's functions. So two definition is quite useful to appear. So one could give you the low bound, advocate up bound. If both of them achieve the same values and that is very much the optimal. So it's very useful to compute the transport distance usually. So now let's give you a definition of rich cultures or graph. So we firstly, what you find is so-called alpha rich culture. Alpha is the idonis that tells you you do random work, you do a really random work. So you start from X, you move to Y of the neighborhood, right? So with the probability of alpha, you just get at the vertex. We probability that one minus alpha, you move to Y of the vertex with probability proportional to the width of that edge, WXV. So here, WXV appears. And then we alpha equal to zero. This is the usually a random work. And that culture is very obvious or in the version culture. So we kind of interest idonis and then we can define so-called alpha rich culture here alpha. And finally, we take the limit. So this is some probability. Okay, just give an example. Here is the graph in the C full and the WXV, you have one alpha center at the X. And so you have alpha at the X and then you have one minus alpha here is one neighborhood. And then you want to move to this notation. Okay, this is the situation. And what is the optimal way you can do it is just, you move the one minus alpha over two units from U to V and move the alpha minus one more alpha unit to from X to Y. And then the calculator tells you that the kappa alpha is given by the piecewise linear function. And it's always like this. All right. So, oh, I forgot. All right. So now, because the n lips function, you can extend from service space to the whole space. And the transparent distance only depends on the distance and model vertex in support of M1 and support M2. So in other words, it's a local property. So, rich culture is really local. And it's a concave function in alpha that can be done and it's bounded. And so then we can really take the divider by model alpha to the limit. This limit always exists. So this is called, now people call it the Lin-Lu-Ya rich culture. And that is one of the popular rich culture on graph. And here's some examples. If the culture on the triangle, you get two. And if it's C4, you get one. C5, you get one half. And if the cycle is greater than or equal to six, the culture is zero. Somehow the culture will depend on the triangle, four cycle and the five cycles. All right. So this is the Lin-Lu-Ya culture. So now, this culture can be easier to return to their background. Just very recently, not five years ago, so it's 2017, almost like six years after our paper published. And then they give us Mutual and Trotsky first gave a definition of a limit free version. So it's very clever way that you define two operator, one for data. It's basically the average operator operation. And otherwise, neighbor X, Y is just the gradient vector, you know, the direction to reach some kind. And it turns out, it can define this way. So you are taking the if them among all the neighbor X, Y, F on this data Y, F, and among all the one lips of function such that data X, Y, F equal to Y. This condition is the same as the distance F X minus F Y minus F X equal to distance X minus Y. So yeah. The original version is really just for simple graph, but we can easily extend to the weighted graph.  So that's why we ask that, you know, what's the dual format of this formula? And in 2019, and we figure out, so we just call it the stock copy. Stock copy is like copy, but it's a match this, okay? So basically, let me just draw and tell us what that is. It's a match this. So index and I will X and then it's neighborhood. Okay, let's say for X, Y, X, two and so on, okay? And they have Y and then Y one, Y two and so on, okay? And between the X and Y, this is actually in the party, okay? All others are negative, okay? Some way. And if you sum up the row sum, one is negative of the probability version, other one is sum up with the column, you get the column sum is, except for the first one and it's another probability version. And summation of all edge together should be for zero. So this kind of very special coupling function we call the stock copy. I would like, this is the analog of like the combinatorial or the plastic versus edge is kind of relationship, okay? So you have one part of the entry or diagonal, but I think it's only one entry and all entry are negative. And then we prove that indeed, the little yaw furniture can have right as a supreme of the BXY times the cost of BXY and they are divided by the distance between the U and the V. So this format, you have no limit and it's just due to the previous formulas. So it's very useful because one give you, this one give you the lower bound. If you find a stock coupling, you immediately get the lower bound of the curvature. The other one give you an up bound. So if the up bound and lower bound, you pick equal and then you calculate this curvature. All right, so now back to the first part, the problem. So total curvature, what's the total curvature? Total curvature is simply the sum of the curvature of all edges. I have a quick question. So in the previous two slides, you have this info, I mean, one more. Info and soup, yeah. Previous slides, this one, this one looks like a continuous, you know. Yeah, yeah. So I think so, well, yes. So that's why, you know, there's some connection. It's nice. There's a geometry background, like every distance and average operator and also this gradient vector, you know, those are really geometric terms. Operators. So I think if it is continuous level, I will compute this operator, I will do this inf. I will end up at the curvature tensor. I think so, yeah. The Ritchie tensor, okay, something interesting. And I probably the second one also have like a continuous version, you know. This looks more complicated, but some says, you know, if y exists, the other one should be also exist. Okay, so now let's talk about the problem of total curvature. Total curvature is the sum of all the curvature on edges. Let's forget about the tree like, let's just think about this problem. So it's probably the way I would attend the YOS 70th birthday conference at the Harvey University and I meet Anhua. Anhua told me the problem because he's a physics collaborator, told him that they can show by the experiment. If they choose the weight to be the function of one, so if they choose the weight, it's like, let me get around here. So if you choose the weight of, if you pick the weight, okay, W equals to like a function of one over D squared or something like this. And then they found the total curvature is minimized at a simple graph.  This works and this asks why. So that's how the problem comes from. So now here's the solution. Let me explain what the tree like means. The tree like is this way. So I had vertex X and Y and X kind of a neighborhood, okay. I let's call it the K and Y has neighborhood equal to L, right? Now, it's basically say the distance is the distance of the path. If the super graph, this condition really said the gross is at this six. Oh, actually you can show the gross should be of this graph should be at this six, if the tree line. So locally like a tree, but the gross itself is not getting T. So you have, this condition is slightly stronger, but it was simple graph in the equivalent of this six. So we use the formula we have returned, you know, in previous slide, we have the, you know, you have the name and you have supreme. And then we can use to calculate a tree like graph. So we have to get this very nice curvature bond. So remember that if we want the lower bond, we can use the formula of a stuck-up ring. Use our stuck-up formula, we calculate a lower bond of the curvature. And we summarize it here, this is formula. It's a very nice formula. I know it's a two-v gradient Hw and Hw is with function, okay. It's a very nice functions. And now we can study, you know, when this functions achieve a maximum, achieve a minimum, and that should give us the result. So I will show you the next slide, okay. So the next slide, here is our mirror dot. So what, that's the result. So let's, G is this weight of graph. We have two weights, distance weight and weight for the random work. If we are weight and distance have some relation, given by any function F, F is a non-increasing function. As long as non-increasing function, then the total distance at the curvature, at least, two v times v minus two times v. And you call the holds, if and only if you need to treat it. And all this weight function F is just one number. So because the scaling that done that, the curvature. So that tells you essentially, it's a simple graph. And that's also the question, you know, asked by Huang. So, and then we realized, we also get, what if the F is a decreed function, not decreed function. And then you can turn the, you're quite a run, but this time we need a condition, if it's a tree line, then you can turn the encoder around. Now, as run F is a decreed function and the encoder at the most, two times the number of edges. And you call the holds, if and only if once again is just a simple graph or the weight as same on the graph, but distance could not be same. So this is the more general, because obviously this is a simple graph, but now it's right at the game with each edge equal weights when you do the random work and that gives you the total closure reaching the minimum. So this kind of I'm surprised with that class. All right, so this is the part of one. I probably the good time to ask the question, do you have any question so far? Okay, so let's move on and we'll talk about part two. The second part is about the rich flow. I have quite a quick question. For this kind of total curvature of this, do you compare this with the, compare the curvature with the entropy? But I mean, the entropy? No, we didn't think about the time. We had no real problem. We have this problem is really motivated by Hua Fonsi. His physicist friends, and as they did the experiment, and they found this and they saw that because maybe the function in the one over the square is very special. You see the very special function for the physicist. So they asked the question and we gave a solution and if you have a very general solution, when you call it holds, what condition is sufficient to set with the total curvature is maximized on minimum. Yeah, yeah, very good. That's a little bit. Okay, yeah, sure. So let's move on. The sixth topic about the rich flow, okay? So rich flow manifold is just by the simple equations. So here's GID is the real magic and RIG is the rich culture. So you solve this differential equation by the time T of the GID equal to negative two times the rich culture. And then, so that will change the rich culture or the remanifold and remanifold. And slowly, when to stop, it will become a constant culture and it became a sphere. Of course, the difficult part is you don't want to say, oh, you have to say, I'm security, you grow into two spheres instead of one sphere, but this is a really hard part. That's why those are working there. So, so important also significantly. In 2019, a group, actually there are two papers. And one paper is made by me, Lin, Luo, and Li Kao. They wrote a paper, trying to generate the rich flow to the graph. And they consider this iterate a discrete time and update the weight and the distance. And also they assume the weight and the distance is read by some fixed functions. And they have some special functions, they try all different functions. And then you say, oh, and some functions works well to be able to detect communities. So this is kind of interesting. And the paper is almost all the experiment dot instead of one mine, you know, straight or not. So that you didn't say why this, you know, the rich flow could exist. But just that it looks, works well. So we saw their papers and we want to give some mathematical theoretical result and what it can prove, whether the flow exists and if it exists, what the limit of it was like. This kind of things. So here is the approach. We'll pass the so-called normalize rich flow on the weight graph. So the first part is very similar to the second one, try to realize the normalization. So with this limit, you know, but that's too difficult, you know, we have to be take care of. One is that at some point, because the distance are changing, right? The weight are changing. And at some point, maybe the age is no longer the shortest distance between the U and V. So it's what I have 80 U of V. And because this distance will be increasing, increasing and no longer the, so this age is no longer needed. In that time, we'll remove this age. This is the time to not. And then we'll consider a sub-graph and it will continue this system. That another difficulty with some distance became too short. They're probably not for them to get zero. You cannot have a natural distance. So at that time, we'll just control that age. So U and V became one point. So we'll keep doing this and we'll hear the result. If we keep doing this and then this system will have a solution. The solution always exists for all the time from zero to infinity. So eventually we have some kind of a rich flow. But we don't know about this, whether the limit is equal to infinity, the limit exists. If it exists, if it converges and it will converge the constant curvature with graph minor of G. Why is the graph minor not graph itself? Because in the process, you might give some age, you might control some ages.  And that graph minor eventually will be have a constant curvature. This is not hard to see. So if I give you, if it converges, okay? What happened is, so as a tickle the infinity, okay? Okay, tickle the infinity. And this will become zero, right? If it converges, right? And you get here. So here you can solve that the curvature of K e of t equals to, you can solve this one, it's just summation of the other things, okay? And here is K of h of infinity, okay, it's infinity. And then the edge infinity. So support the limit exists, okay? So you can see it's a constant. That's why once the limit exists, you give it a constant curvature object. Okay, so this is kind of nice. Okay, there's some, you know, exists. And we also use a, if the limit converges, what it looks like. All right, aha. So this is after a page. I just want to give you the ideas after you run something. So I see online, you know, the web, you really change the way we present, we present, sorry. Okay, so I need to give it, okay, so, okay. So I want to give you the ideas, okay? Why?  So let's consider the way I have correction of this, all this, you know, the, okay, so here you are to this power, okay? So this is, give you, this is just the correction of all this, the ideas. D, U, V, okay? U, V, H. And the way you can think about, so something goes around. Suppose I have one cycle, okay? Your graph, okay? This is cycle C.  And this D, U, V might be longer. Sometimes, you know, so let's say this here is the U, 1, U, 2, U, 3, U, let's say U, A, okay? So there's something like, sometimes the time T, you know, D, U, V is the same as D, U, U, 1, plus E, U, U, 2, plus E, U, A, U, V, right? So that times, you know, this, this, this ages, it just is so long. That is no longer needed, right? So that's, there's a better situation. Another better situation is those D, U, V equal to zero. And those clenching, so think about it as the whole space, the whole partition by, you to feel, you know, okay? Just some readings, the connected readings. Now, you have a rich culture, okay? So rich culture is a flow, it takes the curve in this space, okay? So they go somewhere, okay, they grow here, they hit as well for the boundaries, okay? Either this or this, okay? And then, they will look at some space and they will approach on this one, and then you do it here. So somehow, this is not a criterion, I will say, okay? So suppose I have, you know, the high dimension, okay? So maybe this is a three-dimensional parameter space, okay? And then you have a rich culture, you move a sum of this, you know, on the boundary, okay? This is kind of boundary, halfway.    And that's the solution we're talking about. This is the rich flow culture, rich flow we are talking about. Okay, so then help you to understand what's the rich flow we are talking about. Any questions? This is just in the sense, you know, the system has a solution. And that's why we also have an underlying graph also changing. Could lose some edges, could combine some vertex, counter some edges to get a graph minor. Any questions? Okay. I have a quick question. Very, very, I'm sorry. So many questions. Yeah, this is very interesting question. Very nice. So, I know it's like eventually you're getting this constant curvature, you know, in this sense. So have you studied the dynamic of behavior, you know? Yeah, that's the next slide we're talking about. So that's all kind of, all kind of system will happen. Where the system could happen, okay? Okay. So that's the next one. Let's look at a very special example. It's just a P3. So just pass our three vertex and you put X, Z and Y. And AX, AY, you know, it's just give you the, from Z, you know, we're probably AX equals to X, you're probably AY equals to Y, okay? And XL, no choice. NMS, if it's X, it equals to Z, AY equals to Z, right? So, and so this ritual flow equation is very simplified, given by this line here. And we can actually study the behavior. Depend on what kind of function we choose. So the width depends on the distance. Support is identical functions. So gamma of X equals to X. So that means my width, WXY equals WXZ equals to DXZ. So is that a Y? It actually it converges. So this is the first example, it's really converges. And then just write something out. So this is why the limit object is like this. Just a simple graph of a piece of it. So each of the weight, you can see this, the D, which is one half and one half. And the culture is one. So it's just a standard, you know, piece of it. This is one scenario. And then the second scenario is more complicated. If we choose the square, okay? And then it's eventually claps, the three point. Eventually XZ, Y, right? So first it goes to XZ, Y and Z, what's the million? And then you get one point. So the whole graph just claps in one vertex. Now the most interesting part is the K3. So K3, we define this very special function. It's not trivial, trying to find this function, okay? And we define the gamma function like this, and the piece of Y is function. And with the initial value, you give the W of the weight is three eighths and five eighths. And then the result is spicy is a periodic solution. So basically, I tell you what it is, okay? So here is graph, X, Z and Y. And here is the distance, okay, D, X, Z, U, Y, Z. Now this is the focus, you can see what X, Y is fixed. And this Y is just oscillating. Z is moving from left to right, right, left. And this function is, I tell you right, this function looks like this. So I would draw this graph. So here, this is a line. This is, I think this should be a quarter. And then you add one in. So it's a sine function, and then the top is three quarters, okay? This is three quarter. You come in like this. So this is the one half, okay? So now I have a sine function like this, right? I have another sine function like this, okay? So then function, X, Y, okay, something like this. So it's a one part of a sine function, then followed by another sine function, and then go down. So this is what it looks like, okay? And then similarly, the curvature is also oscillating. Yeah, the period, the period is two pi. So that means, it's never converges, it's just oscillating to each other. So I guess this is dynamic, I think Wu Chun is asking, you could have some kind of oscillating function. You don't have a gym down there. Okay, so maybe we should move on. Okay, so now, we can have this discreet algorithm. We can, so let's just kind of describe it. We have a distance condition, this condition, okay, at some point, the UV distance is too large, larger than other shortest thing along another path. And the multi-composition is that distance is too small, that's close to zero, and you have some actions. And the terminal condition is that, oh, you know, it's converges, the curvature, each ratio, let's say some arrow term. So we think about the stable. Okay, so now here is the average. So we calculate this AI, this is sum of all the weight, and then with the adaptation according to this formula. So, and then we'll check all three conditions until one company is certified. If we, the distance function in the virus, then it means that the DL-UOV is too large, and then it will continue. If the distance is too close to zero, we will merge the UNV, and it will self-continue. If the curvature is stable, and it will stop, we will output the graph. So that's no kidding, since every step, because like the example we show here, might be oscillating, they're not equal to any limits. But if it does, they'll give you something, okay? They'll give you the graph minor, and which is, have the constant curvature. All right, so the height of structure, it's a very interesting, you know, this I can repeat, then just draw something, and I'll help you understanding what we're talking about. So what I have a graph, okay? It's a huge graph here, right? And now, after I do the rich flow, okay, algorithm, I end up with a very nice, here's the graph minor, and it could out, okay? This is the graph minor. So each graph minor, it's a point, and it really corresponds to the radius, right? Okay, so this is, if you go back and get this, okay? So now, for this picture, so this is the V, or this is the V, this is the V on V, okay? It's the pre-image. And the pre-image itself is a graph, right? So this is an induced graph. And now we can, iteratively, we can apply this rich flow algorithm to the inverse of V. And we can apply here, we can apply here, right? And we'll continue. And then we'll get another graph minor, right? So, and then this actually, each one you will get to the community. And this kind of get the height of structure. So the right-of-way, just to deal with the algorithm that we can convert it as a graph into some standard drawing. So I will use the Google map as an example. The first you see the whole world, it's graph is so huge, you get the world map, right? And South Carolina is kind of like a one-dot, you know? It's not that big, maybe it's a small reading, just sort of in the United States. And then you can zoom in, because in South Carolina, if you look at it clearly, then you can see the cities, then you get the Crimea, and the one point. And then when you see the University of South Carolina in all kind of streets. So same as here, let's give it a natural way to present the information of the huge graph. This algorithm I can, each time I present you is a graph which has a constant culture, supposed to have very nice appearing, it's nice drawing. And then you can zoom in for each dot, and you get another subgraph, and then you get another map, another layer. So this gives a high, high, I could call left to Georgia. I think it's very useful. Okay, I hope I can explain this idea well. Any questions? Okay, so. Do you have any computational figures? Fast? Yeah, I think so. The ideas, yeah. Surya actually included some of the pictures in the paper. Okay. You can read it, he didn't, for this idea to work, so you have to have a huge graph in the very light. She wanted a text, a very small graph, and not really very good, but the way you want to really want to like, you have a hundred, so there's a lot of this, you know. And then you will study, see what kind of, different layers work, yes. But this is to give some, it's kind of like an idea about the rich flow in the geometry, right? You give any metaphor, you can get like, most of them into a standard version. Here, I give any graph, I give the most of them a standard version again, give like, you know, the constant rich culture graph, with a graph, and of course, the difference is that instead of already graph, you keep the project here to probably change, but you keep the graph minor. The one you got is a graph minor for the graph. All right, so let me just give some open problem. I think I'm down at the top. So we actually suspect that the chaos could exist if you have freedom to choose, you know, the function. So if I carefully design the function, I can, I should get some chaos behavior, like, you know, one initial value, you change the distance of weight a little bit, and the result is ending up in a very different or never converges, you know. We actually try some, try to embed a chaos system, the next system into this, but we didn't succeed. So we don't know. Even the example I show you, you know, the oscillating example, the function is not, not true, you know, it's a really need some time, but we think this chaos behavior should really exist. Now, if your gamma function is very nice, like an edit function, or let's say one over, you know, some part, you know, the D to some power or one over squares, for example, or just a constant functions. And maybe in that time, we can prove that this culture always corrodes. We don't know, but we hope we can get some of that. So, so this should start, you know, this paper is just starting, given mathematical foundation, so we, about the leech flow on graphs. There's a lot of question remains, we can out of them, I am sure some of the questions should be very deep. And then we, you know, we don't know how to answer them. Yeah, but anyway, I think this is all I want to say today. And thank you. If we have any questions, this is time for questions. Thanks, Lincoln. If we could all thank Lincoln in some way, and we'll go ahead and open it up for questions now. So do we have any questions for Lincoln? Yeah, if you wanna kind of see the body, so maybe I will exit and then they will move back to the room so I can see you guys. Any questions? Yeah, so the paper is available at outcome, you know, if you are interested in topics, you can choose to read the paper. Okay, and also you can ask about it closer, for them to be like here. Okay, well, it seems like there's no additional questions. So thanks again, Lincoln, and have a good weekend everybody. Okay, bye-bye. Bye-bye.


Hello, welcome back. Yeah, right. We're here. Are we ready? Lali, are we ready to start? Yeah, we're good to go. You're wearing a mask. So it feels like you're in a public place. I'm in my, in our office at the public, actually. Hey, it's watch my work. Y'all know what this is about with me. The title is you. We are back. We have been on hiatus. It was supposed to be September hiatus and my, my, my September is so long this month. No, we pick a September hiatus that turned into an October hiatus. Turned into a November hiatus, but that's all right. We're back. And basically we meet together and we work. We work together by this timer and then we spend the rest of the hour that we have until six o'clock Eastern time to talk with you about your work and your creative process. We have been doing this show for like, I don't know, 13, 12, 11, a while, we've been in the show for a long time. And it's still joyful and fun. And we get a lot of work done. So Lali will explain to you how to get in touch. And yeah, let's explain away. Yeah. So if you'd like to ask a question, if you're here in zoom with us, you can ask questions by clicking the raise your hand button, which should be either in the participants tab at the bottom of your screen or the reactions tab in the bottom of your screen. If you are having any trouble finding it, you can feel free to just private message me in the chat and I can help you out. If you are watching the stream on howl round, feel free to send us your questions via the public theaters, Twitter account or Instagram account or via watch me works, Twitter account, which is at much me work SLP with the hashtag howl round that's hashtag H O W L R O U N D. So that's how you'll ask your questions. Now, you know everything you need to know. We're going to put on the timer for 20 minutes and we're going to do some things. Okay. Yeah. Okay, okay. We are back. We are here. If anybody has this is the question part of the afternoon. Anybody has any questions. Comments, suggestions. We're here. Or we can just sit here. Yeah. Hey, Kimmy.  She's Kimmy's raising her hand. Oh, you got a good. Hi. It's so good to see you. Congratulations on all your successes. I'm so excited and proud. And I just love you. And I've missed you so much. So I wrote a play a while back. And I think we talked about it briefly where. It's loosely based on my life. And I talked to a friend and I said that. The character has. Her arc is that she had a lot of sexual trauma and abuse. And that. She starts, she has a lot of, in the beginning of the play, she's an alcoholic and drugs. And then at the end, she's not, and he goes. Well, you don't drink, right? And I said, no. And he goes, you don't do drugs. And I said, no, he goes, well, then it's not really. It's not really you. And he says, and it's not as interesting. We've seen that. Before. So he said, so how did you cope? How did, how did you. Survive all that. And I went back and forth trying to figure that out. And. And it was. To feel I was powerful was to not have sex. And then to feel I was powerful was to maybe be promiscuous. And I was wondering how I can maybe incorporate that without it being redundant or. Having too many people coming in and out of the play. Mm hmm. Cool. That's a good question. So you. So in order to make it more. And, uh, and, and. Sorry. And I guess. More what I'm sorry. Less pedestrian because we've seen the arc of drug addicts come to. Yeah. Honestly, Kimmy, it doesn't, it doesn't matter if we've seen it a hundred times. It doesn't matter if that's the story that you want to tell, then you're allowed to tell it. Number one. Okay. But if that's, if you're like, well, that was a cool note that my friend gave me. And actually it moved me to consider. Another take on it. That's cool too. Which sounds like you're, you're into now down the road of another take, which is great. How you can have, um, so how many, so you don't want too many people in the, in the play. How many people do you want in the play? What's your limit? Um, I don't really have a limit. I just thought, um, I don't know. I just thought it might get too muddled with so many characters, just a rotating door. Um, and you know, I consider that too me, like the, the same person because I tend to look, I, I tended in my past to have the same person. So I thought maybe I would have a revolving door in the, it's the same person, but just different outfits or something. Like I have glasses or something work. I mean, so they kind of look the same, but they're kind of different. Oh work. I mean, yeah. That sounds like a good idea. I mean, they're different people.  You know, one person can play more than one character in a show.  Not a vessel, but I, you know, just someone to hit the ball back to you.  So they deserve to have things that they want, things that they're trying to achieve, even if they're a bunch of different people.  We could say, you know. Yes. Thank you so much for that. That's wonderful. That's cool. Thank you. That version. That sounds cool. Thank you. I appreciate that. And I really, I really. I love that note that because you don't consider that, but when you do just now in my head, when you said that, I'm like, yeah, they might want something from me. I'm not willing to give, or they might be giving me something I'm not willing to accept. Right. This is why I freaking love you. Susan Lloyd parks is why I love you. Because I consider everybody a person. It's radical. I know. Yeah. So yes, I consider everybody a person. Who it should be considered. As a person. Oh my God. Yes, but that's, yeah, that's, you know. Yes, ma'am. You're so brilliant. Thank you so much. You're welcome so much. Also what's going to help you with Kimmy is it's going to make your main character and it's not you. Okay. It's a character to play or in a, in a novel, whatever you're writing, it's a character. It's going to make your main character better. So when you elevate one character, you elevate all the character. So when you elevate, you know, the sort of minor characters, we might call them, you're elevating all the characters. So it's. Yeah. You just shifted the weight so brilliantly. It deepens the, the protagonist then because. She's got to really, truly explore her own. She's got to really, really explore her own character. Like dislikes and fears based on the trauma that she hasn't been able to shake or deal with yet. And, and that's really a thing that we navigate through when we're going, you know, like, if we haven't had therapy and we're just trying to find our way. We can miss really good healthy relationships. Because we are not. Ready to receive. I think we're good enough to receive love. Thank you so much. Thank you so much. Thank you so much. Great question though. Great question. Thank you. Thank you. Lou, you're up next. Feel free to unmute. Thank you. Hi, SLP. How's it going, Lou? It's going so great. I'm not a writing residency right now actually in Massachusetts. That's why that's why I'm here. I'm not a writing residency right now actually in Massachusetts. That's why that background is like. Massive nice. Congratulations. Thank you. I'm that. Smoka. For two weeks. I'm really excited. Nice. Very nice. Thank you. I know. So I'm here doing my work all the time. I brought my timer. I've been channeling you and then I just logged in and was like, oh, snap. She's back. So anyway, I'm super excited to be here. I'm over the moon. So this is my question today. So I've been working on a memoir project. For a while. And experiencing the journey of. The relationship to my material changing from my own perspective as a person and then it changes my ability to deepen my writing. So it's just been a real interesting. Mind trick. And, um, And I'm writing about my career and advertising and, um, at these days and just, um, Sort of how it affected me as a person. Anyway, um, My agent, can you hear me? I'm hearing a little backlog of myself. Okay. There's a little clicky clicky, but I can hear you fine. Okay. Um, so this is my question. My agent, I spoke to her a few weeks ago. She's excited. And she said, I just wish. You put yourself at the center of your story and make yourself. The hero more. And talk about notes and what it drives you to do. So I'm looking at all my work and I'm finding paragraphs and I'm like, more me. More the Senate. And so this is maybe why I bring up like my evolution as a person along with the work because this is what something I've done, which is, you know, try to make myself smaller sometimes. So I think my question is just about. I mean, this isn't the nonfiction space. But the idea of seeing yourself remove from yourself as some kind of person on a hero's journey. Just the trick of that. If you have any thoughts on it. Mm hmm.  Um, I do a lot.  A lot. Um, so, um, as you're working on a memoir, yeah, think of. I mean, do you call it's the character in the memoir. Do you all have the same name? We have the same name. Okay, great. Great.  Mm hmm. You know, just what does she want? What's she doing here? What, what can Lou do right here to be Morris in the center of the scene or the story of this, this chapter? You know, can she drive this chapter? But just start talking about her. She's a character because Lou, she is because it's. Not really you anymore. You know, I mean, it was, it was over a year ago, right? I mean, you're looking back. How long ago was your career in advertising? I left four years ago. Okay. So it's almost a whole new set of cells, right? Yeah. But it generates itself seven years. They say, so you're kind of like over the halfway point. So it's not really you anymore. Right. So.  I don't know where you are in the memoir. I don't know memoirs. I don't know how to fictionalize for dramatic effect. Or if you're doing anything, I mean, I don't know what you're doing. Or you're just telling it exactly as it happened, as you best remember it. So I'm not sure. But you can bring yourself more into the, yeah, just what's she doing right here? Why is she in the back? Why is she on the sidelines? Treat her like a character because she is. At this point. And that's a great note. And I hope you think so. Do you feel like it's a helpful note? Yeah, I think it's actually, I mean, I've come a long way in my writing. I'm really proud of myself and a lot to owe to you in this space and the work I've been doing. But this feels like a final frontier note. You know, like, I think it's a good note because I think if I can crack it, I just feel really optimistic about what it will mean for the project. Right. Right. It's just a challenging sometimes to, but you're getting what you're saying. Yeah. It's not about you anymore. Right. You're you who we're talking to right now. And in the, in the, in the memoir is a character. Yeah. Telling a story. I mean, I will talk about, I rarely do, but I will talk about something that I've done recently plays for the play gear. First lines in that show where I play the writer. It's not me. I'm playing a writer. So, you know what I'm saying? And I don't know, and I'm sure people with degrees and these, these fields can make more of a meal of it than I can, but it's not me. Okay. Yeah. So think of yourself. Yeah.  Yeah. And I was to answer something that you sort of said out loud. It's like, I don't see it as reported or journalism. So I mean, of course it's got to be based in truth. It's like, that's essential, but it, there are, I'm taking leaps and putting a few moments together. And I think that's okay. And you, I'm putting a few moments together here. I'm taking a leap here. I mean, I started with the truth and then I bent it.  I mean, are you right? So you are allowed to tell the story.   So there's a whole, there are a host of things that you need to be mindful of. And yet if you say, you know, reader, I'm straightening from the truth here, some version of that. Or I think that's allowed. Yes. You know, but really go for it. Yeah. That's a great note. And I'm glad you're taking it. Me too. Thanks for the insight. I'm going to get in with getting, getting it, getting the new draft. What are you, where are you with it? So I need to deliver in mid January, sort of an evolved version. So what is it? You gonna, you gonna make it? Yeah, I'm going to make it or make it. Good. Oh, I'd like to hear that answer. Hey, I know Kimmy's clapping. I'm clapping too. Hooray. Well done. Thank you. I'm on the road. I'm on the road. I'm going somewhere. We'll see where it takes me, but I'm not giving up. I can't stop. I can't stop. That's a thing I can't stop. So. Very good. Yeah. Thank you. Thank you. Thanks, Lou. Thanks, Lou. Thank you. Anyone else have a question? Some of you were in places where it's still light outside, or is that just like artificial lighting where you're faking? Where are y'all? Kelsey and Louise, where are you guys on the, where are you? I'm in Vancouver, BC and Canada. So West. Okay. Okay. Oh, that's just a photograph. It's a photograph. It's a photograph. It's a photograph. Oh, okay. Yeah, it's. I'm going to say it's made up, but it's a photograph of. Another city. Oh, no. It's just my background. But since I'm talking. Yes. Let me just. Let me just say. I'm working on a couple of things, but I. In participating. I've heard other participants talk about. The work, like a memoir of. Their parents, like the mother or the father. I've heard that in this group. And I've been. Starting out doing, I had taken care of my mother. For. Actually, five years. And. Now bear with me. I'm going to try to keep it together. She passed about a year ago. So. I had start writing and I was okay. A friend of mine actually told me about your workshop during the pandemic. And I was like, oh, my God. Everything was fine. I was just, you know, the memories were coming up. Because as I was taking care of her. You know, I would write off in the one. I took pictures, videos, all of that. And then at one point. I was writing and. It was just. All of this emotion. Just kind of. And I was just, you know, almost like. In shock. That. It was just all of these things kind of. Came up. So I just hit the pause button. I mean, I will eventually go back to it, but I was. Just kind of quite amazed. The whole experience of writing. And I was like, I don't know what to say about her. So. So you're pushing, you're pushing pause.  It's like, if you're, you're just walking along and suddenly you find yourself in water that's up to your.  And you're like, stop. You know, I don't think I'm going to keep walking because I, I don't think I'm going to keep walking. I don't think I'm going to keep walking. I don't think I'm going to keep walking in danger. And it's not a good kind of danger. It's not like an adrenaline junkie danger, you know. So you're pushing pause. You're taking care of yourself. And I'm guessing, if I may. That you. Deciding to share this with us today. Means that maybe you want to find a way to. Go forward. Resume. So, but it was kind of interesting because. You know, in the community, I live in Harlem and. I guess. Maybe there are at least five people. You know, five different women, I know. Mostly older, but they could be younger women. And we were all like. Taking care of our mothers. So my neighbor that lived around the corner. She was she and her husband were taking care of the mother. Their mother was the same age as my mother. So it was just kind of interesting. I mean, I guess these characters. In my orbit. We're kind of dealing with the same thing. But I guess I am ready to. Jump back in and start, but it was just. Oh, it was just overwhelming. You know, it's just like kind of overwhelming. And. You know, I don't know anything about the light in the background. Ha, ha. Interesting. There's light in the background, Louise. You said, you just said that there's light in the background. You know what I mean? And. I, I don't know. I mean, I feel like you're talking. You know, I feel like you're too overwhelming to, you know, spend five minutes with it today. You know, or tomorrow. I could. Yeah. I mean, because I know I'm years ago. I know Alice Walker had. Said written something or made a comment. How it whatever's going on in your life. And this I really believe if it's something unhealthy or whatever. She said that writing. Could. Make it all right. That you could actually feel through writing, which. I believe you can. You know, I believe you, you can get to a certain space. And I know that I was recently I was. reading a work by James Baldwin and how he grew up in Harlem and his family. And I hadn't read it in years, but it was just like, unbelievable, you know, that he kind of went through all this and was able to put it down, you know, it was just quite remarkable. So, but, you know, I feel I will get through it. And I think the suggestion of like maybe doing five minutes, just five minutes.  It's for you. So pause this act of self care and now maybe restarting in a modest, mindful, loving way is an act of self care as well.  I love this. I love my time. Five minutes and then just sit there. I don't know what you write on a computer or a notepad actually, like a like an open, like an open notepad. Yeah.  I usually use a pen. You just sit there.  Just sit there. It's a very wonderful way to spend five minutes. I mean, what else are you going to do with those five minutes? Like do scroll, watch the news, you know what I'm saying? Like watch a binge watch your favorite TV show, right? Five minutes. You know, so okay.   Thank you. Yeah, sure. Thank you, Louise. Oh, it looks like MC has a question. How are you doing? Good to see you. I saw you the Sunday before. Thanks for coming out. Thanks. Jim was there too. I was singing to Jim. It was amazing. And there are so many memorable things that that stayed with that. I got from it, but including the the one line where he said, Oh, Americans, they want a happy ending. This is America. That's one. And I love how you conjured up and gave place to the people that meant a lot to you. And so I'm thinking of that. And Louise, I just want to tell you, I'm so sorry you lost your mom. And I know that's so hard when you've been taking care of people. I'm your loved ones and it's really hard watching them go. I took care of both my parents and I was the only one of four siblings who are who was there when they actually passed. And I don't know if that's good or bad because they always say, well, a lot of times they let go when the person they loves the most leaves the room. So it's okay. I bear witness, but I just wanted to give a little to Louise and also to myself. After my father passed, I'd been writing about him before he passed. But now after and it's been four years, I really felt this freedom to write about him and go places where I wouldn't dare when he was alive. You know, and it and it's really liberating sometimes to think about that because he was a very complex person. And then my mom who died quite young, I don't want to make this all about me but I found that, you know, she died real, she died of a stroke and she was younger. And I just feel like in my writing I'm going to give her the life that she deserved. So it really, you know, that's why I'm going to call myself a writer, the writer right now, junior writer and give them the lives that they tell their story because they didn't tell it themselves. That's really beautiful. So that's really, really thank you so much for sharing that. Oh, you're free to say more. That is so very beautiful. Well, you inspired me. Everyone here does, you know, there's so many universal experiences when we lose somebody we love. And then what can we do? You know, I can't paint. I can't sing. You know, I'm not a Taylor Swift can write about every breakup I've had. But you know, I can write something and I wrote a few things and I read them and I don't know unless people are lying to me. They were really moved by it and then it brought up their experiences. So I'm going to do it because I want to. And they deserve it. So Louise, I'm going to take five minutes. I'm going to take 10 to your five tomorrow. Yeah, that's so beautiful. And MCU deserve it too. Like, like in a way we're going to like take what Lou is taking as a note for her written work and we're going to plow it into the field of our lives. Remember, you you are also at the center of your story. You know what I mean?  And that's that is a beautiful thing. It's not a it's not a selfish thing. It's not a bad thing.  Yes. Yes. Yes. If we if we are full of ourselves and loving and generous, then wow, look at what we get to give, you know. Yeah, Lou, go for it. Right. Right. Lou. Everyone. And Jim, I'm sorry. I got to ask. Is that a gingerbread man sideways on your wall and back of you? No, I actually was a magazine editor for several years and that came with a screener for a play about voodoo and the film that we were supposed that we in the magazine was supposed to review. It was not a good film but they fell in love with the doll. Okay. The doll flies on my wall. Okay. I thought, oh, it's a cookie. That's no. It's they said earlier we could make a comment. This will be a quick comment. Okay, bro. I know a lot of you are not in New York. So this was the flyer they sent around for top dog underdog. This is the program and it was raining cats and dogs when I came home. So perhaps you can see, even though I had it in a pocket of a plastic raincoat, it got wet. This is the flyer they sent around for plays at the for a place for a plague year. And here's the here is the program. But you outside of New York can buy the book. It is so it was so wonderful to see and feel that in some weird way I'd been a part of it for the months of the gestation but also as you say to see how weirdly sometimes it had changed even from little incidents that you had told us over the year. And if you're going to be in the area I hear it's coming back for a few weeks in the springtime. So go to the public theater website and see if you can see it because it's at Joe's pub. It's a perfect cast. It's a perfect production. And I mean, everyone was so elated and drinking all that. But yeah, so that's the comment. I'll be quiet. You're such a you're such a sweetheart and it was great to see you in the audience and to sing to you. Hope you didn't mind. I was like, get a drum. But what a joy it was. And again, that's just about making, you know, or just making something beautiful out of scraps and bits and things that, you know, sometimes are wonderful and sometimes aren't. And just continuing going forward, you know what I mean? I mean, that's really what this is about. It's like, how can we just get through the day and into tomorrow and keep working? Because I feel like if we continue to work, if we need to do our work, and I don't mean drudgery, I mean our work, our thing. If we continue to show up for ourselves and for our communities and for the world, we win. We're gonna win this. We keep showing up and we keep getting our friends to show up. We keep getting our families to show up. We're gonna win. You know, the glory is ours. We will manifest the beauty of the spirit. So that's all we're doing. That's all we're here for, you know? So, thank you, Jim. Thank you. And thank you, MC, for coming and anybody else who came. I really appreciate it. Super fun. Super fun to do, you know. Thank you all. Rebecca, it looks like you will have our last question of the day. Hey. Hey, Susan Laurie. I had tickets for the week that got canceled. Well, somebody named Susan Laurie Park got COVID. I apologize. I was hoping it wasn't you. So, like, who got COVID? People, you know, it went through the cast. We kept putting in our understudies and then too many people breathed on me. Yes. I know. Anyway, but we'll come back. I know. I'm going to be away the week they opened the advanced tickets for those of us who missed that week. And I made my partner promise. She put it in her calendar.  You have to do this. Are you going to be on a writing retreat? I'm actually on a two-week meditation retreat. Oh, yeah, of course. Of course. Yes. You go on those often. Well, I hadn't been, but again, this is sort of, this is both taking care of myself and taking care of my writing. I've been trying to go more often in the last year since the end of COVID. It was just like, I feel like I really need it. You know, I've been, since we're talking about parents, I've been writing about a promise my father made me make for, it's going to be 14 years now. And that issue of who I am in this has, it comes up all the time as I get to completion. And completion was being able to fulfill the promise, not so much to finish something in a way that makes me look good, but to do what he asked me to do. And the way I solved the problem of the writer in this, the daughter, the niece, I put her in a car and sent her on a trip. So that's how she gets to show up. She likes to drive. And, you know, father liked to drive. Everybody in the family liked to drive. So I had, it was like when I was young, first like actually me, learning to drive, I couldn't learn to drive until I was 20, which in Akron, Ohio is a problem, because I didn't have enough to do. So they, they, I would get bored. So I got an old Volkswagen with a stick shift and that solved that problem. Giving, giving the writer something to do, the daughter is also the solution, I think, for the problem. Whether I'm in the car with with Virgil or not. And he's not my father. He's, I mean, he's my father, but he's Virgil in the, I mean, I just use his name and, and that's, has given me a great sense of freedom to just mostly call him Virgil. So, so I really appreciate how difficult it was in the beginning. I had these great long periods of time where I was just like, I'm done. I'm done and I'm not done. And it's been feeling much more partly, you know, certainly because of watch me work, but also allowing myself to have a self in, in the piece. Because we have many selves obviously. And, and there is one that that lives in, in the manuscript. So, but, you know, I consoled myself with missing, missing out on plays for the plague, play for the plague here with top dog underdog, of course. And that was the happiness. It was a really good time in a really, you know, intense kind of way. So thank you for, thank you for being so present this year with your own work. Yeah, thank you. I love you, Sarah, Becca, allowing myself to have a self in my work. Would we take off the work part? Allowing myself to have a self. Oh, yeah. Everyone thinks meditating is about losing yourself, but you can't actually give up a self if you don't have one. So yeah, and then, yeah, and it's that self that's happy and the good fortune of all of you. And so that's, I mean, that's one of the things I try to work with in, in my writing practice and, and my sitting practice. Thank you. Thank you. Thank you all so much. We are just at time. We will be back same time next week, so please join us. Yeah, love you guys. Thank you, everyone. Thank you so much. Bye bye.


Hey everybody, Dr. O here. This is going to be the first video in quite a long series where I'm going to cover the different types and classes of antibiotics. This is not a pharmacology class. I'm not going to cover all the details of every brand name, but we're going to focus on the major classes of antibiotics and I'll certainly give you examples. We're going to start here with the group of antibiotics called the beta-lactam. So this video specifically is going to be about penicillin, the natural penicillin and the semi-synthetic penicillins, but I'll do a separate video on cephalosporin and in a separate video where I cover both monobactam and carbapenems together. So they're all very significant. Hopefully you can see from this top image why they're similar because they all have the same beta-lactam ring, which some people would call the nucleus. So they have the same nucleus or they have the same beta-lactam structure. The difference is they're going to be primarily where you see the R's. The R groups, the slight changes in these R groups make these antibiotics different, which is some can survive the trip through the stomach, can be taken worldly. Others are going to have extended spectrum. Others are going to resist some of the resistance factors that bacteria have come up with. So the fact that they're similar is important, but their differences are primarily why they all exist. The other problem though is the fact that they're also similar means that some microbes have evolved the resistance to combat all of these. So if you develop an enzyme like penicillinase or more often called beta-lactamase, it doesn't mean that you're just penicillin-resistant. You would also be resistant to the rest of the group. So the similarities are kind of a problem too when you look at it from an evolutionary standpoint. How these work, the reason I have these all clustered together, is these are going to be your cell wall inhibitors. So they inhibit the production of cell wall. So the first thing you should think of whenever you think of a cell wall inhibitor is, number one, these are going to be way more effective against gram-positive bacteria, typically, than gram-negative. And that's because gram-positive bacteria, their big thick cell wall, is their primary defense. Gram-negative bacteria have an outer membrane outside of their cell wall, so they're not as concerned. So some of these certainly are effective against gram-negatives. I'm not saying that, but the first thing you think of is they're going to be a bigger effect on gram-positive. So here I just have a quick picture of what the peptidoglycan cell wall looks like. It's called that because you've got glycan. You have these carbohydrate backbones. That'd be the blue and orange structures there being held together by proteins. So most of these antibiotics are going to inhibit something called penicillin binding protein, which is a trans-peptidase, but they're going to inhibit the connecting, the cross-linking of these peptidogly, these carbohydrate backbones, which means you're going to have very weak cell walls. So again, I won't go through all this detail in the separate videos on this topic. These are usually going to be bacterial, they are going to be bactericidal. So you think about every time there's a new generation of bacteria, they're going to have a weaker and weaker cell wall until they reach the point where they either, it's so weak that they disrupt your or it disappears completely. So give after a few generations, this will be fatal to these microbes, bactericidal. All right, let's go ahead and jump in. So we're going to focus on all these groups here. It's going to be a little bit longer video, but bear with me. Super, super important in microbiology to understand antibiotics. So the first two we have are the natural penicillin. So these would be the ones that we actually found from the penicillium, you know, from the fungus, from the penicillium mold. They believe it was penicillium chrysogenum would be where they found this. Interesting story, they were looking, when they first found penicillin, they couldn't find strong enough strains that would produce enough of a potent antibiotic. So they put out a call for, you know, if you have anything that has a green or blue mold, like, you know, bring it to us, let's take a look at it. And the story, the legend is anyways, that a moldy cantaloupe is where the penicillium came from that was powerful enough and potent enough to make the antibiotic penicillin. So that's just kind of an interesting thing. It was first discovered in 1928, commercially used in 44. I covered the history back in a separate video. So the natural penicillins are the ones produced by these microbes. So penicillin G versus V, here's how I remember them. G comes before V in the alphabet, oral comes, or injection I comes before O, right? So penicillin G has to be injected. So G comes before V, I comes before O. Penicillin V can be taken orally. So that's going to be the key difference here. Penicillin G is primarily effective against gram-positive bacteria, small handful of gram negatives, but must be injected. Penicillin V, that slight change you see there with the R group with the addition of the oxygen, makes it more stable in stomach acid so it can be taken orally. So those are your two natural penicillins, penicillin G versus penicillin V. Let's move on then to more changes to the R group. You see, adding the amino group to the R group of ampicillin and amoxicillin created a semi-synthetic penicillin, which means you start with that natural penicillin and then make some changes to it. And now it's semi-synthetic. So part natural, part synthetic. So these are called the amino penicillins, ampicillin and amoxicillin. And what that primarily does is it made them extended spectrum, which means they still kill gram-positive bacteria, but they can kill more gram-negative bacteria than regular penicillin can. So that makes ampicillin and amoxicillin extended spectrum. But hopefully you'll see, you know, looking at these R groups, again, these side groups, you see one more change. Amoxicillin has the hydroxyl group added to it. And what that does is it makes it more stable in acid, which means it's going to have better oral absorption. So you can take ampicillin orally, but amoxicillin will be better absorbed. All right. So that's going to be the amino penicillins, ampicillin and amoxicillin. I do want to talk about one more that's not shown here, and that would be augmentin. So if you take amoxicillin, which is really the top of the line, you know, semi-synthetic penicillin, as far as we've talked about so far, you add one more compound to it. I like to call it potassium clavulinate. It's a hard one to say. Some would call it clavulinic acid. But what that does is it's actually a penicillinase inhibitor. So we mentioned, let me go back. We mentioned that penicillinases or beta-lactamases are going to be enzymes that some bacteria develop. Think of them like molecular scissors that go in and chop this beta-lactam ring. Well, our solution has been to create antibiotics that are that are penicillinase resistant. So that's what I would say augmentin is really the top of the line cell wall inhibitor, in my opinion. All right. And then the last group here, before we're done with this video, is going to be methicillin. So you've probably heard of methicillin. If you haven't, you've heard of MRSA, which is methicillin-resistant staphylococcus aureus. So methicillin, they totally change the side group. As you can see here, this is a dimethoxyphenol group or something like that. It's not important. But the wild change to the side group is what made this the first penicillinase-resistant penicillinase. So it made it resistant to this beta-lactamase enzyme that was trying to chop up these drugs. The problem is, of course, that we're trying to fight an evolutionary arms race, an evolutionary arms race, with microbes that are better at it than us. So we released penicillin. They came back with penicillinase resistance, or penicillin resistance. So we used methicillin and they developed methicillin resistance. So that's why methicillin is not even used anymore. It's been discontinued because there's just been too much evolution around it. Our next response would have been oxacillin, another antibiotic that's not on here, but it's another semi-synthetic penicillin that did replace methicillin in clinical practice because methicillin became worthless. And then we'll do other videos about vancomycin and some of those responses. So oh, you think methicillin isn't strong enough? Well, let's use vancomycin and then we'll wash your hands of it. Well, no, because use of vancomycin created Versa and VISA and VRE. So the microbes are going to continue to evolve around our antibiotics, just how evolution works. So all right, so these are going to be your natural and semi-synthetic penicillins. So watch for the other videos where I cover different cell wall inhibitors and then I'm going to go through all the other types of antibiotics as well. So I truly hope these help. Have a wonderful day. Be blessed.