Open course ware

KRISTEN: So with that, I am going to turn it off to our first keynote speaker, Kris Clark. She also works at Lincoln Laboratory with me, but in a completely different field. She's going to talk about space cameras. KRIS CLARK: So as Kristen said-- I'm also Kristen, but I go by Kris-- I work at Lincoln Laboratory. And I want to say that this is an amazing program. So first, how many of you have ever done a program like this? Like an engineering kind of thing for girls. Come on, way up. I can't see that. OK, cool. Now, how many of you are freshmen? Sophomores? Juniors? Seniors? All right, no seniors. They're all busy doing college applications. All right, so let's get started. So today, I'm going to talk a little bit about me, just because we're kind of looking at what makes an engineer. It can be all over the map. So there is no one key picture of what an engineer looks like, or what a scientist looks like. Then we'll go into a little bit ab...

PROFESSOR: We start with the stationary states. In fact, stationary states are going to keep us quite busy for probably a couple of weeks. Because it's a place where you get the intuition about solving Schrodinger's equation. So the stationary states are simple and useful solutions of the Schrodinger equation, very nice and simple. So what are they by definition? Well, we begin with a definition. And the intuition of a stationary state will follow. See the word stationary is not the same as static. Stationary is something that maybe it's kind of moving, but things don't change. Static is something that's just not moving. Stationary states have time dependence. It is very simple, as we will see. So, your definition of a stationary state has a factorized space and time dependencies. So this psi of x and t is a stationary state. If you can write it as a product of a function of time times a function of position. And now, I will try to be careful about thi...

So, let's start with where we were. We were talking about exponential growth in populations. And, we said we could describe this as one over the dN/dt equals some growth rate, r. And, in this case, we're talking about, let me ask that is a question. As a model for population growth, what's wrong with this? What does this project? This is N. This is time. There's no stopping it. I mean, we'd be knee deep in everything if populations grew according to this model, OK, because it just goes off into infinity in terms of density. So, we know that this is inadequate. In fact, some people describe the entire field of population ecology as a field that tries to determine why real populations can't grow according to this model. In other words, the whole field is trying to understand what the mechanisms are in populations that limit their growth. So, they don't grow exponentially forever. So, in this case, this is really a maximum growth rate. We can call...

so we're on with lecture 26 where I'm planning now just to complete even though that 25 ran over it I still have a factorization to do for this polynomial and I still have to tell you what's good about that very particular polynomial the minus one zero nine sixteen nine zero minus one and what's better about the one that leads to so this there's the five three I'm dealing with here and then the nine seven that is even better okay okay so this fellow so this is our P naught and I factored it and the remarkable thing is and that's why it's so good that's why those particular numbers I mean the zeros everybody remembers the zeros had to be there zeros had to be there for the perfect reconstruction part but now we've got freedom on these other these nines and minus ones and so forth but so we use that freedom to get whole lots and i've instead of e to the i omega e to the J Omega I just wrote Z so just to make it simpler shorter so ...

It comes acquainted with different antigens. And recall that what we were talking about was the following, that there were several kinds of phagocytic cells. Phagocytic cells are cells that chew up other things, both macrophages and even more frequently, dendritic cells, many of which hang around lymph nodes by the way. They process antigens into oligopeptides. The oligopeptides get presented on the surface of these cells. Let's say, here's a macrophage, in the form of through the class 2 MHC molecules which are displayed on the surfaces of the cells. And, here's a typical oligopeptide that has been chewed up from one of the antigens that was previously internalized, eaten up by the macrophage, a dendritic cell, and then presented on the surface. Recall, then, we have an itinerant macrophage or dendritic cell. Could we turn up the sound just a little, just a notch? Thank you. And this dendritic cell or macrophage...