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[Assistant] Now the letters W and E and the word whole are silent.


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[Assistant] This means that the word only has three sounds. The H that sounds like a weak R in some languages. O sounds like the O in Seoul.


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[Assistant] And the L is a dark L, so you need to roll your tongue like this.


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[Assistant] Moving on, we have the word world, which is always a tricky one to get right.


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[Assistant] So first you have the WU sound and you link it with the RU sound. Imagine that there's no vowel in between them. WUH, WUH. Like in the word WUH.


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[Assistant] Then you roll your tongue for a long, dark L sound.


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[Assistant] Finally, the tip of your tongue comes to the front of your mouth for the D sound. World. World.


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[Assistant] Putting the entire sentence together, it sounds like this. A whole new world. Repeat after me. A whole new world.


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[Assistant] The words knew and view end with the exact same sound. A long uuuh sound.


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[Assistant] Another interesting thing is that both A's in fantastic have the exact same sound. In open, eh. Now remember I told you about the schwa sound?


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[Assistant] Here we have it in the letter A again, in a new, a new.


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[Assistant] But the most interesting thing is the word of in point of view also becomes a schwa.


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[Assistant] Then the F is actually pronounced with a V sound. Of, of.


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[Assistant] Because of the way words are connected, we drop the T in point and connect it with of.


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[Assistant] And the V sound connects with the V from view. So what you hear is point of view, point of view, point of view. Putting it all together, it sounds like this. A new fantastic point of view.


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[Assistant] So far, we have these two sentences. Repeat them after me. A whole new world.


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[Assistant] Before we continue, let us know down in the comments below if you'd like to see more pronunciation lessons like this one. The next sentence is...


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[Assistant] So, tell and us are connected, and the words to and us become a schwa as well. So you hear to tell us, to tell us. Repeat after me. To tell us.


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[Assistant] So we saw what happens with to go in where to go. Now something important to pay attention to is that many people confuse the pronunciation of where and we're.


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[Assistant] They may sound similar, but they are actually not. The last E in the word wear is silent. So this word sounds exactly like the word wear, like in wear jacket.


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[Assistant] Now in the word we're, the E in we is a long one. We. And then you connect it with the R sound. We're. Whew. Pretty cool, isn't it? How about we try to put it all together and sing along?


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[Assistant] How'd it go? It's pretty amazing how much easier it gets to speak and understand English when you know how natives really speak, right? Well, we were thinking exactly about that when we created the fluent


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[Assistant] That's right! A course to learn English with your favorite TV series. In our PDF Power Lessons, we break down the pronunciation, teach you the most important vocabulary, and help you understand every joke. Sounds amazing, doesn't it?


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[Assistant] Well, you can try it out now for free by clicking up here or down in the description below. Now, let's get back to Aladdin and Jasmine.


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[Assistant] If you have seen the movie, then you know that Aladdin is poor and is always stealing so he's able to eat.


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[Assistant] Scraps refer to bits or pieces of food discarded by others. When he says he's always scrapping for food, he means that the only way that he's able to have a meal is by looking around for things other people left on their plates, discarded or threw away because they had enough or didn't want to eat. He also says that he's always ducking the guards. Now, what do you think he means by ducking the guards? He's imitating ducks to distract them so he can steal things. He moves quickly up and down or running away from them.


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[Assistant] When a person ducks, they move their body fast, like you do in boxing. Another way of saying this is to dodge or deceive.


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[Assistant] Both Aladdin and Jasmine find themselves unhappy in their current living situation by saying they feel trapped.


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[Assistant] This means that they don't feel entirely free and that there's nothing to do to escape the way that they currently live.


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[Assistant] Hey, what's your favorite Disney movie? Let us know down in the comments below so we can make a lesson on it. In the meantime, you can't miss out on our Disney playlist. We've done lessons on Zootopia, on Cinderella, Turning Red, and so many more. Now into the test of what you've learned today.


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[Assistant] A valet is a person who serves a country similar to a king.


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[Assistant] You are receiving glowing reviews from Whistle Down. Did you see?


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[Assistant] She knows everything about everyone, even the Queen, and yet we have her utterly convinced that we are mad for each other.


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[Assistant] In this video, you start to learn some concrete algorithms for learning word embedding.


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[Assistant] In the history of deep learning as applied to learning word embeddings, people actually started off with relatively complex algorithms. And then over time, researchers discovered that you can use simpler and simpler and simpler algorithms and still get very good results, especially if you have a large dataset. But what happened is some of the algorithms that are most popular today, they are


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[Assistant] So simple that if I present them first, it might seem almost a little bit magical. How could something this simple work?


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[Assistant] So what I'm going to do is start off with some of the slightly more complex algorithms because I think it's actually easier to develop intuition about why they should work and then we'll move on to simplify these algorithms and show you some of the simpler algorithms that also give very good results. So let's get started.


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[Assistant] Let's say you're building a language model and you do it with a neural network.


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[Assistant] So during training, you might want your neural network to do something like input, I want a glass of orange, and then predict the next word in the sequence.


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[Assistant] And below each of these words I've also written down the index in the vocabulary of the different words.


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[Assistant] So it turns out that building a neural language model is a reasonable way to learn.


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[Assistant] A set of embeddings. And the ideas I present on this slide were due to Yoshua Bengio, Regine Dujan, Pascal Vincent, and Christian Jarvin.


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[Assistant] So here's how you can build a neural network to predict the next word in the sequence.


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[Assistant] Let me take the list of words I want to gloss over.


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[Assistant] And let's start with the first word i. So I'm going to construct a one-hot vector corresponding to the word i. So there's a one-hot vector with a 1 in position 4 3 4 3.


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[Assistant] So this is going to be a 10,000 dimensional vector.


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[Assistant] And then we'll do the same for all of the other words. So the word 1 is word 9, 6, 6, 5, so we take that one hot vector.


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[Assistant] Multiply by e to get the embedding vector, and similarly for, uh, the other words. A is the first word in the dictionary, alphabetic comes first, so that's O1, gets us E1, and similarly for the other words, uhm, in this phrase.


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[Assistant] So now you have a bunch of three dimensional embeddings. So each of these is a three hundred dimensional embedding vector. And what we can do is feed all of them into a neural network. So here's a neural network layer.


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[Assistant] And then this new network feeds to a softmax.


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[Assistant] Which has its own parameters as well, and the softmax classifies among the, uhm, 10,000 possible.


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[Assistant] So this hidden layer here will have its own parameters, so it'll have some, I'm gonna call this W1, and it's also B1. The softback layer will have its own parameters, W2, B2.


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[Assistant] And if you're using three hundred dimensional word embeddings, then here we have six words. So this would be, uhm, six times three hundred. So this


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[Assistant] Layer, or this input, would be an 1800 dimensional vector obtained by taking your six embedding vectors and stacking them together.


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[Assistant] Or what's actually more commonly done is to have a fixed historical window. So for example, you might decide that


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[Assistant] You always want to predict the next word given, say, the previous four words, where four here is a hyperparameter of the algorithm. So this is how you adjust, uh, either very long or very short sentences where you decide to always just look at the previous four words. So you say, I'm just going to use those four words. And so, you know, let's just get rid of these. And so if you're always using a four word history, this means that your neural network would input, um,


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[Assistant] Feature vector, go into this layer, then have a sound max try to predict the output. And again, variety of choices.


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[Assistant] And using a fixed history just means that you can deal with even arbitrarily long sentences, uhm, because the input size is always fixed.


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[Assistant] So the parameters of this model will be this matrix E and use the same matrix E for all the words, right, so you don't have different matrices for different positions.


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[Assistant] And then, uhm, these weights are also parameters of the algorithm, and you can use backcrop to perform gradient descent to


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[Assistant] Maximize the likelihood of your trading set to just repeatedly predict, given four words in a sequence, what is the next word in your text corpus.


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[Assistant] And it turns out that this algorithm will learn pretty decent word embeddings. And the reason is, if you remember our orange juice, apple juice example,


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[Assistant] It's in the algorithm's incentive to learn pretty similar word embeddings for orange and apple, because doing so allows it to fit the training set better, because it's gonna see orange juice sometimes, it'll see apple juice sometimes.


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[Assistant] And so if you have only a 300 dimensional feature vector to represent all of these words, the algorithm will find that it fits the training set best if apples, oranges, and grapes, and pears, and so on, and maybe also durians, which is a rare fruit, end up with similar feature vectors.


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[Assistant] So this is one of the earlier and pretty successful algorithms for learning word embeddings, for learning this matrix E.


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[Assistant] But now let's generalize this algorithm and see how we can derive even simpler algorithms. So I want to illustrate the other algorithms using a more complex sentence as our example. Let's say that in your training set you have this longer sentence, I want a glass of orange juice to go along with my cereal.


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[Assistant] So what we saw in the, on the last slide was that the job of the algorithm was to predict some word juice, which you want to call the target word, and it was given some context, which was the last four words. And so,


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[Assistant] If your goal is to learn a embedding, researchers have experimented with many different types of contexts.


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[Assistant] If your goal is to build a language model, then it's natural for the context to be a few words right before the target word. But if your goal isn't to learn a language model per se, then you can choose other contexts.


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[Assistant] For example, you can pose a learning problem where the context is the four words on the left and right. So you can take the four words on the left and right as the context. And what that means is that we're posing a learning problem where the algorithm is given four words on the left, so a glass of orange, and four words on the right to go along with.


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[Assistant] To try to predict the word in the middle, try to predict the target word in the middle, this can also be used to learn word embeddings. Or if you want to use a simpler context, maybe you can just use the last one word. So given just the word orange,


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[Assistant] What comes after orange? So this would be a different learning problem where you tell it one word, orange, and then also, well, what do you think is the next word? And you can construct a neural network that just feeds in the word, the one previous word.


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[Assistant] Or the embedding of the one previous word to a neural network and has to try to predict the next word.


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[Assistant] Or one thing that works surprisingly well is to take a nearby one word. So I might tell you that, uh, well the word gloss is somewhere close by. So I might say, you know, I saw the word gloss and then there's another word somewhere close to gloss. What do you think that word is? So that would be using nearby one word as the context.


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[Assistant] And we'll formalize this in the next video, but this is the idea of a skipgram model, and this is an example of a simpler algorithm.


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[Assistant] Where the context is now much simpler, it's just one word rather than four words, but this works remarkably well. So what researchers found was that if you really want to build a language model, it's natural.


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[Assistant] To summarize, in this video you saw how the language modeling problem, which causes the post machine learning problem, where you input the context, like the last four words, and predict some target words, how posing that problem allows you to learn a good word embedding.


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[Assistant] In the next video, you'll see how using even simpler context and even simpler learning algorithms to map from context to target word can also allow you to learn a good word embedding.


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[Assistant] Let's go on to the next video where we discuss the Word2Vec model.


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[Assistant] There is an elephant.


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[Assistant] Dexter, there is a rhino.


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[Assistant] Oh, you lose, dolphin.


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[Assistant] Oh, it skips! I can skip too!


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[Assistant] I dream. Sometimes I think that's the only right thing to do.


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[Assistant] And things you can only do with somebody else.


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[Assistant] When you come out of the storm, you won't be the same person who walked in. That's what this storm's all about.


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[Assistant] A profession should initially be an act of love and not a marriage of convenience.


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[Assistant] Do not forget that the work of a lifetime is not a business, but life.


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[Assistant] Those who want to enter my life, enter it. And those who want to leave, leave it.


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[Assistant] We saw that the clefs were the key to understanding their range and sonority.


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[Assistant] The same is true of the double bass, though in not exactly the same way as the cello.


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[Assistant] The bass clef is the predominant landscape, and bassists are most comfortable reading it 95% of the time and not bothered by a few passages in ledger lines here or there.


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[Assistant] Orchestration students are often cautions to keep the basses up.


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[Assistant] In other words, not write passages for double bass that are always utilizing the lowest pitches possible.


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[Assistant] There are several good reasons for this. Imagine the result on a violin or cello if those instruments were constantly playing their lowest string.


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[Assistant] It would get to be pretty dull after a while. The same is true of the double bass both in its own right and as an element in orchestral texture.


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[Assistant] The truth is that the double bass's best sound is to be found within the range of the bass clef, up to a few ledger lines above.


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[Assistant] The resonance of the instrument, the shorter span of the finger positions, and the response of the strings all conspire to make this register the most commonly used in the repertoire and the most comfortable to play.


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[Assistant] The factors I just mentioned are the reason why extreme low notes work against the essential character of the double bass. And here's one more reason. The design compromise of the bass's body being of smaller than ideal size for its strings.


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[Assistant] This becomes more obvious the lower the notes go. The lowest C possible doesn't ring with the same clarity as C in the staff.


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[Assistant] It's a pedal tone that doesn't have the same flexibility of function shared by higher notes. Save it for the right moment, and make those moments count.


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[Assistant] The tenor clef is sometimes used for higher passages, and even the treble clef.


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[Assistant] When scoring these clefs for double bass, it's important to remember that the same transposition holds in all cases, and the written notes represent pitches that are played down an octave.


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[Assistant] Use the same guidelines as the cello when deciding whether or not to use the tenor clef.


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[Assistant] If an entire passage is written for the G string, and centers around D or E above middle C, then use the tenor clef. If only a few high notes are involved, then stay in bass clef.


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[Assistant] It's mainly useful for notes higher than C above written middle C, which bassists will generally play as harmonics.


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[Assistant] As the instrument with the longest strings, double bass is capable of a huge array of natural harmonics. So much so that artificial harmonics are never written in an orchestral context, and would be extremely difficult to play anyway.


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[Assistant] Some of the individual sound of the double bass will depend on what type of bow is being used.


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[Assistant] The longer French-style bow is more like a cello bow, capable of more fluid legato. It's a common choice of soloists, and quite useful for certain passagework.


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[Assistant] On the other hand, the shorter German bow is gripped by its side and quite effective in shorter, more forceful strokes.


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[Assistant] Therefore, the choice of bow can be highly individualistic from player to player, or orchestra to orchestra.


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[Assistant] There are even more factors about the double bass's character, which are best seen through an examination of its role in the orchestra, coming up in the next video.


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[Assistant] This one's kind of interesting because I feel like in all the books I read in college, people are like, what you want to do is you want to build this beautiful slide deck with graphs going up and to the right.


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[Assistant] Bringing on venture capital as you're hiring part of your team. And who are the people you want to hire? And I think far too many people we talk to, even at an angel stage, are like,


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[Assistant] We need to optimize for like getting some ridiculous valuation right out of the door. And then they end up with some VC firm they don't have a lot in common with and they don't get along and like bad things happen. It's all about the people. And I think that's what you have to remember when you're going out to raise money. Find the people that believe in what you're doing that are going to give you the capital to achieve your goals. The other point here is you can go off and raise, you know, $40 million in a series a,


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[Assistant] But it turns out you don't need a lot of money to get off the ground these days. We spent like 60k to launch our first version of Instagram. 60k. We had raised 500 and we were like kicking ourselves the second day after, not after we raised, but after things started taking off, we were like,


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[Assistant] We have all this money left over and we got this far, like it turns out you can bootstrap yourself with Amazon web services, like you need like two engineers these days to do things well.


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[Assistant] It turns out that like, you know, you can get a lot done on a shoestring budget, especially with all these incubators and things that are happening these days. It's something to remember. So the main takeaway here is optimize for people.


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[Assistant] Bringing a prototype into a pitch meeting is so much more powerful than a bunch of graphs that say you're going to make lots of money in the future.


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[Assistant] Like prototypes are tangible. Prototypes are things that people can sink their teeth into and use and react to and ask questions about.


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[Assistant] Uhm, I remember, like, I have yet to create, we have yet to create a pitch deck for Instagram. We don't have a pitch deck anywhere. It was always a prototype that we brought in and we showed.


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[Assistant] I think that like, well, you should probably create a pitch deck and that's probably not like, you know, a lesson to learn from us. Uhm, what's more important is that I think people really attach themselves to prototypes.


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[Assistant] You learn about several algorithms for computing word embeddings. Another algorithm that has some momentum in the NLP community is the GloVe algorithm. This is not used as much as the Word2Vec or the SkipGram models, but it has some enthusiasm because I think it's part of its simplicity.


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[Assistant] Let's take a look. The GloVe algorithm was created by Jeffrey Pennington, Richard Solcher, and Chris Manning. And GloVe stands for Global Vectors for Word Representation.


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[Assistant] So previously we were sampling pairs of words, context and target words, by picking two words that appear in close proximity to each other in our text corpus. So what the GloVe algorithm does is it starts off just by making that explicit. So let's let xij be the number of times that a word i appears in the context


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[Assistant] So you can think of xij as being x subscript tc, but you can go through your training set corpus and just count up how many words does a word i appear in the context of a different word j? How many times does a word t appear in the context of a different word c? And depending on the definition of, uh,


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[Assistant] Context and target words, you might have that xij equals xji. And in fact, if you're defining


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[Assistant] Context and target in terms of whether or not they appear within plus minus 10 words of each other, then it would be a symmetric relationship.


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[Assistant] Although if your choice of context was that the context is always the word immediately before the target word, then Xij and Xjr may not be symmetric like this.


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[Assistant] But for the purposes of the GloVe algorithm, we can define context and target as whether or not the two words appear in close proximity, say within plus minus 10 words of each other.


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[Assistant] So Xij is a count that captures how often do words i and j appear with each other or close to each other.


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[Assistant] So what the globe model does is it optimizes the following. We're going to minimize the difference between


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[Assistant] J squared. I'm gonna fill in some of the parts of this equation in a little bit. But again, think of i and j as playing the role of


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[Assistant] T and C. So this is a bit like what you saw previously with theta, T,


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[Assistant] Transpose EC. And what you want is for this to tell you how related are those two words. How related are words T and C. How related are words I and J. As measured by how often they occur with each other.


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[Assistant] Which is affected by this, uhm, x i j. And so what we're going to do is


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[Assistant] Solve for parameters theta and e using gradient descent to


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[Assistant] Minimize the sum over i equals 1 to 10,000 sum over j from 1 to 10,000.


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[Assistant] So you just want to learn vectors so that their inner product is a good predictor for how often the two words occur together.


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[Assistant] Now, just some additional details, uhm, if xij is equal to 0, then log of 0 is undefined as negative infinity. And so what we'll do is we won't sum over the terms where xij is equal to 0. And so what we're going to do is add an extra weighting term. So this is going to be a weighting term.


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[Assistant] Xij is equal to 0, and we're going to use the convention that 0 log 0 is equal to 0.


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[Assistant] And so what this means is that if xij is equal to zero, just don't bother to sum over that xij pair, so then this log of zero term is not relevant. So this, so this means the sum is a sum only over the pairs of words that have co-occurred at least once in that context target relationship.


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[Assistant] The other thing that f of xij does is that there's some words that just appear very often in the English language, like this is of.


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[Assistant] And so on. Sometimes these are called stock words, but there's really a continuum between frequent and infrequent words. And then there are also some infrequent words like durian, which you actually still want to take into account, but not as frequently as the more common words. And so the weighting factor can be a function that


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[Assistant] Uh, gives a meaningful amount of computation even to the less frequent words like durian, uh, and gives more weight but not an unduly large amount of weight to.


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[Assistant] Whereas like, this is of, uh, which just appear a lot in language.


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[Assistant] And so there are various heuristics for choosing this weighting function f, uhm, that.


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[Assistant] Neither gives these words too much weight nor gives the infrequent words too little weight.


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[Assistant] So you can take a look at the glove paper that I referenced in the previous slide if you want the details of how F can be chosen to be a heuristic to accomplish this.


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[Assistant] And then finally, one funny thing about this algorithm is that the rows of


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[Assistant] Theta and E are now completely symmetric, so Theta i and E j are symmetric.


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[Assistant] In that, if you look at the math, you know, they play pretty much the same row and you can reverse them or swap them around and they actually end up


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[Assistant] Uh, with the same optimization objective. So one way to train the algorithm is to initialize theta and E both uniformly at random, run gradient descent to minimize this objective, and then when you're done for every word, to then take the average. So for a given word, W, you can have E final,


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[Assistant] Train through this gradient descent procedure divided by 2 because uh, theta and e in this particular formulation play symmetric roles unlike the earlier models we saw in the previous videos where theta and e actually played different roles and couldn't just be averaged like that.


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[Assistant] So that's it for the glow of algorithm. Uhm, I think one confusing part of this algorithm is, if you look at this equation, it seems almost too simple, right? How could it be that just minimizing a squared cost function like this allows you to learn meaningful word embeddings? But it turns out that this works.


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[Assistant] And the way that the inventors ended up with this algorithm was they were building on the history of much more complicated algorithms like the Neuralanguage model and then later there came the Word2vec Skipgram model and then this was, this came later and really helped to simplify all of the earlier algorithms. Before concluding our discussion of


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[Assistant] Uh, algorithms for learning word embeddings. There's one more property of them that, uhm, we should discuss briefly.


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[Assistant] Which is that we started off with this featurization view as the motivation for learning word vectors, where we said, well, maybe the first component of the embedding vector could represent gender, the second component could represent how royal it is, then the age, and then, uh, whether it's the food and so on.


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[Assistant] But when you learn a word embedding using one of the algorithms that we've seen, such as, uh, the glove algorithm, which you just saw.


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[Assistant] On the previous slide, what happens is you cannot guarantee that the individual components of the embeddings are interpretable. Why is that? Well, let's say that there is some space where, you know, the first axis is gender and the second axis is royal.


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[Assistant] What you can't do is guarantee that the first axis of the embedding vector is aligned with this, uh, with these.


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[Assistant] axis of meaning of gender, royal, Asian food and in particular,


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[Assistant] The learning algorithm might choose this to be the axis of the first dimension. So given a, maybe a context of words, so the first dimension might be this axis, and the second dimension might be this, or it might not even be orthogonal, maybe it'll be a second non-orthogonal.


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[Assistant] Access could be the second component of the word embeddings you actually learn. And one way to see this is if you have a sophisticated understanding of linear algebra is that


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[Assistant] If there was some invertible matrix A, then this could just as easily be replaced with A times theta I transpose A inverse transpose


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[Assistant] Because we expand this out, this is equal to theta i transpose a transpose a inverse transpose times e j. And so the middle term cancels out and you're left with theta i transpose e j, same as before. Uh, don't worry if you didn't follow the linear algebra, but that's a brief proof that shows that


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[Assistant] With an algorithm like this, you can't guarantee that the axes used to represent the features will be well aligned with what might be easily humanly interpretable axes. In particular, the first feature might be a combination of gender and royal and age and food and, um,


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[Assistant] The parallelogram math that we worked out when we're describing analogies, that still works. And so despite this, uh,


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[Assistant] Potentially arbitrary linear transformation of the features you end up learning, the parallelogram math for figuring out analogies still works.


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[Assistant] So that's it for learning word embeddings. You've now seen a variety of algorithms for learning these word embeddings and you get to play with them more in this week's program exercise as well. Next, I'd like to show you how you can use these algorithms to carry out sentiment classification. Let's go on to the next video.


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[Assistant] We've all had it, and it's the worst. Stomach cramps, diarrhea, nausea, not being able to keep any food down, maybe a fever, these are all symptoms of food poisoning, which means the consumption of contaminated food or drink. When it comes to stomach trouble, it's not always easy to identify what in particular is causing the symptoms.


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[Assistant] The term gastroenteritis refers to inflammation of the lining of the gut, particularly the stomach and intestines, and cases of gastroenteritis can be caused by viruses, bacteria, or parasites.


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[Assistant] When it's clear or at least likely that the source of an infection is contaminated food, we call it food poisoning. Let's go in for a closer look.


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[Assistant] It turns out that most cases of food poisoning clear up on their own without any type of treatment required. However, one of the most serious complications of food poisoning is dehydration, which can be especially deadly for those that are very young or very old.


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[Assistant] While food poisoning can be caused by a variety of pathogens including Staphylococcus aureus


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[Assistant] Vibrio, or campylobacter, today we're going to talk about one notorious cause of food poisoning in particular, the one you usually hear about in the news, shigatoxin-producing E. coli, or STEC.


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[Assistant] E. coli consists of a diverse group of bacteria belonging to the enterobacteriaceae family, which are associated with diseases ranging from UTIs to respiratory illness to meningitis.


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[Assistant] Pathogenic strains of E. coli can be categorized into what are called pathotypes.


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[Assistant] In particular, there are six pathotypes associated with diarrhea, which are appropriately named diarrheagenic E. coli. These are shiga-toxin-producing E. coli, or STEC, enterotoxigenic E. coli, or E.T.E.C., enteropathogenic E. coli, or E.P.E.C.,


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[Assistant] These bacteria are gram-negative. They're facultative anaerobic rods, which means they can survive both with or without oxygen present, and they contain lipopolysaccharide.


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[Assistant] The shiga toxin-producing E. coli cause disease by producing a toxin called shiga toxin. And here's where it gets more confusing.


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[Assistant] In the United States, the most common serotype of STEC is E. coli O157H7, which is the type they're usually talking about on the news, like when there's a widespread romaine lettuce recall.


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[Assistant] An infection begins when you swallow STEC, meaning that you've gotten a microscopic amount of human or animal feces in your mouth. That may sound gross, but it happens a lot more than you think. Common sources include raw milk, contaminated water, or contaminated food.


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[Assistant] Foods that are considered high risk for E. coli 0157 include unpasteurized apple cider, soft cheeses made from raw milk, and sometimes undercooked beef or contaminated lettuce.


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[Assistant] However, sometimes people get infected by touching surfaces at petting zoos, swallowing lake water while swimming, or eating food prepared by someone who didn't wash their hands after using the bathroom.


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[Assistant] In most cases, STEC infections have an incubation time of three to four days before symptoms set in. STEC strains, as their name suggests, express a shiga toxin that is one of the most potent bacterial toxins known.


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[Assistant] Sheega toxins consist of two major subunits, A and B, which work together to damage cells in the gut.


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[Assistant] In particular, these toxins disrupt protein synthesis in healthy cells and cause destruction of intestinal microvilli, which are finger-like projections that typically help healthy cells absorb nutrients.


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[Assistant] As a result, approximately half of patients experience vomiting, and a significant portion experience bloody diarrhea and severe abdominal pain. In some cases, shikatoxin can trigger an overreaction of the immune system to attack healthy cells as well.


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[Assistant] For the most part, STEC infections are diagnosed by testing stool specimens in the lab.


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[Assistant] In the case of big outbreaks, identifying the specific strain of STEC is important so that we can track when and where each strain was found and where it might be spreading.


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[Assistant] Public health officials suggest that anyone experiencing diarrhea for more than three days, especially if accompanied by vomiting and high fever, contact their healthcare provider to get checked out.


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[Assistant] Hydration is the most important treatment, as antibiotics are not typically prescribed for this kind of infection. In fact, antibiotics can make the illness worse, causing patients to be at higher risk for hemolytic uremic syndrome, or HUS.


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[Assistant] HUS is a complication for a small subset of patients that can cause renal failure and other deadly symptoms.


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[Assistant] Ultimately, the best thing you can do is wash your hands often, be careful about what you're eating, make sure to cook meat thoroughly, and avoid raw or unpasteurized milk or dairy products. Do your best to avoid swallowing lake or pool water, and make sure to keep food preparation areas immaculately clean. With any luck, you can avoid food poisoning for good.


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[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me at


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[Assistant] Finding the solution to the problem is the hardest part. I always thought like,


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[Assistant] You're faced with these problems that people have, right? You assume that you know exactly what you're gonna tackle and the hard part is finding that algorithm, right? The hard part is scaling that solution. It turns out, thank you Mike, uhm, that the hard part is actually finding the problem to solve.


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[Assistant] Solutions actually come pretty easily for the majority of problems. Not for every problem, but for the majority of problems.


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[Assistant] And in our case, what we did was when we sat down and we were deciding to work on Instagram, what we did was we wrote down the top five problems people have with mobile photos. Because we wanted to build a product that solved problems. We didn't want to just build a cool app.


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[Assistant] So what we did was we listed out these five problems, and I remember the top three that we circled. Number one was that mobile photos don't look so great, right? Like we've all had that experience, you're seeing the sunset, you take a snapshot, and it looks like, you know, washed out, you can barely see the sun, etc. And we were like, that's like the major problem we want to solve.


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[Assistant] Number two was that uploads on mobile phones take a really long time. So we were like, what could we do around that? And we were like, well, maybe if we start the upload way before you're done even editing the photo's caption. And what if we like size down the photo just to fit perfectly on the screen but nothing else?


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[Assistant] And that's like the small little problem and solution that it turns out really delights people because they press done entering their caption, it's already been uploaded, right? The third problem was that we really wanted to allow you to share out to multiple services at once. We felt like should you have to make the decision of taking a photo with the Facebook app, the Twitter app,


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[Assistant] Uh, problems that you want to solve. You need to verify that they're actually the ones


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[Assistant] And really the way to do that is get your product in front of people very quickly and test that hypothesis. I think too many people wait a long time, and I'm going to talk about this a little later, uhm, too many people wait a long time to see whether or not what they're working on is actually the problem people are having. And the last point is that


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[Assistant] Like I said early on, I think too many people believe you have to solve things in a really complicated way. And at the end of the day, if you delight people even a little bit with a simple solution, it turns out it goes very far. And that first day when we had something like 20,000 new users, I was like,


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[Assistant] Clearly there was a need for this that hadn't been done before and I'm so glad we tackled those simple problems. There's something about like, you know, in the tech community you always want to feel like you're working on the hardest problem in the world. It turns out that simple problem becomes very hard at scale and that's what's really exciting.


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[Assistant] We can't possibly hear little grey squirrel snoring from here. No, listen. There's no sound at all.


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[Assistant] I don't know. I've never seen anything like them before. What's that long line down the middle? It must be the creature's tail. Look what happens when I drag my tail in the snow.


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[Assistant] Well, this really big winter field mouse has got a lot more than four feet. It's got...


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[Assistant] Hmm, I've just noticed something little nut brown hair. What?


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[Assistant] By now, we've learned about the microbes that can be found in the world around us, the general ways in which we interact with them, how to classify them, and how they cause damage. Now let's dig into what's really happening on the human side of the equation, and how these diseases are circulated.


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[Assistant] There are several interchangeable terms we can use for diseases that spread from host to host.


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[Assistant] These are infectious diseases, contagious diseases, or communicable diseases. But how do these diseases develop?


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[Assistant] From the moment a microbe is introduced to a susceptible host to the moment that symptoms begin to appear is known as the incubation period.


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[Assistant] For diseases such as the common cold, the incubation period is typically a couple of days. But depending on the pathogen, the incubation time could be up to a few weeks, several months, or even years, like in the case of leprosy.


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[Assistant] The length of the incubation period depends on factors like the health of the host, the growth rate of the pathogen, and the number of infectious cells the host was exposed to.


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[Assistant] During the incubation period, the pathogen is multiplying at the port of entry, but hasn't caused enough damage to cause symptoms yet.


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[Assistant] Immediately following the incubation period is the onset of illness.


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[Assistant] In the early phases of some illnesses, you might experience what's called the prodromal phase, where you feel weak or achy before the full-blown illness comes on.


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[Assistant] Next is the period of invasion, during which the pathogen reaches peak toxicity, multiplying and establishing itself within the tissues.


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[Assistant] Common symptoms include fever, cough, rash, diarrhea, swelling, pain, or loss of muscle control, depending on the infection.


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[Assistant] The period of invasion can vary drastically depending on the pathogen.


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[Assistant] Next, as the immune system fights off the infection, the illness progresses to the convalescent period, which means a phase of recovery.


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[Assistant] Some patients may stop taking antibiotics during this phase because they feel better, but here's why that's not a good idea.


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[Assistant] As your body is recovering and fighting an illness, there are still pathogenic bacteria present, albeit at low levels, low enough to not cause noticeable disease. Those few bacteria that are left are the most resistant of the bunch.


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[Assistant] If you continue the full course of treatment, you take care of all of them and recover.


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[Assistant] If you stop taking the drug mid-course, those resistant bugs repopulate, stronger and more resistant than ever.


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[Assistant] Plus, that particular drug won't work next time, and no one wants that.


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[Assistant] Generally speaking, an infection is characterized by the progression of symptoms.


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[Assistant] In an acute infection, like a standard case of strep throat, symptoms develop quickly and then clear quickly, and the person typically develops an immunity to reinfection with the same pathogen.


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[Assistant] In a chronic infection such as tuberculosis, symptoms come on slowly and can last anywhere from months to years.


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[Assistant] In a latent infection, however, the illness never completely goes away.


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[Assistant] In the case of syphilis, tuberculosis, or typhoid fever, for example, a person might experience acute or chronic onset of symptoms, followed by a period of convalescence, then latency, where there's no sign of disease at all.


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[Assistant] However, the disease can come back from dormancy at any time.


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[Assistant] The pathogenic microbe might linger in host tissues without causing disease, waiting to act when immunity is low.


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[Assistant] Also worth mentioning are carriers, which are individuals that carry infectious diseases in their tissues for months or even years, spreading them to other people without suffering any signs or symptoms themselves.


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[Assistant] Generally speaking, infectious diseases can be transmitted at any stage of the infection cycle. It just depends on the microbe.


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[Assistant] Some are more commonly contagious during the incubation period, while others like shigella are more infectious during the invasive period.


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[Assistant] Before we move on to detailed information about diseases, let's get some more terms out of the way.


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[Assistant] Infections can be localized to a particular region in the body, like an ear infection or a boil.


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[Assistant] Or systemic, which means they affect the entire body, like chicken pox or measles.


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[Assistant] Bacteremia implies that bacteria are circulating in the blood.


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[Assistant] While toxemia means that toxins are circulating in the blood.


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[Assistant] As you may have guessed, the suffix emea means in the blood.


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[Assistant] Septicemia is the term for large numbers of microbes multiplying and circulating in the blood.


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[Assistant] Finally, some diseases cause sequelae, which means long term or permanent damage.


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[Assistant] You might be wondering, how then do these diseases find us?


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[Assistant] Infectious microbes have to have a home base of sorts. This is called a reservoir, which is the natural habitat of a pathogen.


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[Assistant] Reservoirs can be anything from human or animal carriers to plants, soil, or water.


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[Assistant] Let's say we have a living reservoir in the form of an animal harboring a pathogen. This pathogen can be transmitted either directly from the animal to a human


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[Assistant] Some pathogens, such as the causative agent of tuberculosis, can survive for great lengths of time in nonliving reservoirs, such as soil, air, or water, and infect an unsuspecting human who comes in contact.


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[Assistant] Asymptomatic carriers are in fact living reservoirs. You might have heard of Typhoid Mary, one of the most notorious asymptomatic carriers of all time, who unknowingly spread Salmonella typhi around New York in the early 1900s, before we understood such phenomena.


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[Assistant] Zoonosis refers to infectious agents that are indigenous to animals, but can be naturally transmitted to humans.


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[Assistant] In these cases, the human is called a dead end host, because they cannot transfer the infection along.


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[Assistant] However, zoonotic diseases account for almost 70% of emerging infectious diseases around the world, especially affecting those who regularly work or live with animals.


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[Assistant] To make sure we have it all straight, let's talk about transmission.


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[Assistant] Vertical transmission refers to the transmission of a pathogen from parent to offspring, either through the placenta, milk, ovum, or sperm.


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[Assistant] Horizontal transmission is the spread of a pathogen from one infected individual to another.


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[Assistant] This can be direct through skin contact, a bite from a vector, an injection, or exposure to contaminated droplets, such as a sneeze.


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[Assistant] This can also be indirect, from an infected surface, let's say a garden hose or fork that a sick person touched, or perhaps food, air, or soil.


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[Assistant] A separate category that often gets overlooked is the oral-fecal route, in which feces is ingested, either due to poor hand washing during food preparation, or by touching a surface that's been contaminated with fecal matter and then touching your mouth.


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[Assistant] Now that we've got all the language we need to understand diseases, let's do a deep dive into some of the most medically important diseases.


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[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me.


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[Assistant] Uhm, I just love it. Like, I've been playing video games in general for a long time. Uhm, and then getting to obviously play an incredibly, to me, an iconic female who's a com- Wait, are there children in here? Can I say certain words? Is, is badass acceptable? Badass is acceptable, right? Okay.


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[Assistant] It's a similar situation, cause, so I had a moment with Mr. H, like the producer.


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[Assistant] So, I don't know, I can't, I don't know. I really can't figure it out right now.


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[Assistant] The VC route is that small is a beginning. Small is a stepping stone. Small is what you go through to get to, to the major leagues. Small is, is,


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[Assistant] Inconsequential, it's a sort of a path to the billion dollar business. Uhm, and everything is designed around that pipeline. Uhm, most VCs who invest


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[Assistant] Are not going to be happy with a business that throws off a million dollars a year in cash flow, if that's sort of the scope of the business. That's not interesting. They need an exit that's five, 10, 15 times, whatever it is, preferably 100 million plus. All right, we'll take 20 million, whatever. The fact of the matter is a million dollars is a lot of money. Uhm, when it goes straight into your bank account. So if you can build a,


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[Assistant] What do you think your odds are of building a billion dollar business versus the odds of building a million dollar a year business? This one, the billion dollar business, is about the same as


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[Assistant] Going into a casino and putting it all on Ret5. Alright, roll it. Uhm.


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[Assistant] Trying to get a two million dollar a year business, a one million dollar a year business, half a million dollar a year business.


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[Assistant] It's more akin to being a fairly skilled poker player and going to a table. You have a chance.


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[Assistant] It might still involve luck, it might still involve unknowns, but at least you have a fighting chance. You're not just relying on the, uh, buyout lottery. You're not just relying on the fact that


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[Assistant] Uhm, the Google gods will favor you and buy you for X million dollars.


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[Assistant] You're actually playing, uhm, for skill and not just random timing. So,


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[Assistant] I think you're much better off trying to build a small business. And the fact is that most big businesses started as small businesses.


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[Assistant] But the problem is that there's just enough that we believe the story. There's just enough YouTubes and whatever else that gets, uh, that trajectory. Which is also just enough that all the VCs get.


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[Assistant] All sharky and wants to get in on that action. Like of course, who wouldn't want to invest, uh, $50,000 and get, uh, 200 million back?


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[Assistant] So they try. And when they try, it sort of becomes that hit-driven business where, alright, let's, uh, fund these 20 guys. Just one of them makes it? Awesome. The other 19s? Meh. Collateral damage. That's fine. I mean, if it didn't work out, it'll blow up. Their time bomb will reach its, uh, duration and it'll blow up. No skin up our game. Skin up all those 19 guys' games.


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[Assistant] Skin of all the game and time that they lost and they wasted. And the missing dent that they didn't put in the universe.


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[Assistant] And gonna as well, but the pronunciation stays the same. Wanna, gonna. Not gonna, it's gonna. Not wanna, it's wanna.


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[Assistant] Now I hope that clarified your doubts about the reductions wanna and gonna. Remember, not gonna, it's gonna. Not wanna, it's wanna.


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[Assistant] Hello bluebird. What a wonderful family you have.


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[Assistant] But you are very cute. Hm, kind of.


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[Assistant] I'm going to miss him, Bluebird. Me too.


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[Assistant] Well thank you, my little nut-brown owl.


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[Assistant] Later that night, after all their friends had gone home,


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[Assistant] Today we're going to practice 12 phrases. So I need everyone to listen and repeat with a big voice. Here we go. Brush, brush, brush my hair.


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[Assistant] Catch, catch, catch a ball.


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[Assistant] Drink, drink, drink apple juice.


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[Assistant] dry, dry, dry my hair


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[Assistant] Fly, fly. I can't fly.


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[Assistant] Hop, hop, hop around.


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[Assistant] Jump, jump, jump up and down.


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[Assistant] Very good. Now let's try that one more time.


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[Assistant] Here we go. Brush my hair.


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[Assistant] Thank you very much for watching everyone. This has been Elf Learning. We will see you next time. Good night.


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[Assistant] For example, like, I was still, like, working, and I said, a job where Kevin was like, I am going to quit my job and raise money and, like, live maybe on savings and was ready to, like, you know, get money from his folks back east or something if it, like, fell through, you know. That's, like, crazy uncertainty. When I joined, I was like, we have some money, we have no idea what we're building. Like, that's crazy uncertainty. Like, after we launched, like, this thing has legs.


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[Assistant] We need to work on it for a really long time to make it successful and it could still, you know, you know, be hurt by all these different factors. That's still uncertain. You're always dealing with that and I think equity splits are often, you know, should probably be a reflection of those different factors and what kind of uncertainty and what kind of questions you're, you're answering at every level.


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[Assistant] So, a question for all three of you. What would be more threatening?


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[Assistant] Best ones, I think. Although I really love what they did with sound assigned with Mr. X with this. Cause that, that made you paranoid. Uhm.


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[Assistant] Kinda gets me a little bit, so I think sometimes just like the classic ones like that work for me, so.


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[Assistant] So I'm usually like the person that, I mean it's not really that interesting what I'm about to say, but the reasoning I think is interesting. But like anytime I've ever played games or anytime that I'm like paintballing and stuff, I'm usually like the sneaky one that gets like down and like I'm like all muddy and disgusting and I just snipe people off. So I would love a sniper.


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[Assistant] I mean for us in this field, uh, we have to be able to put up with anything and especially with mocap because we get stopped all the time.


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[Assistant] Oh, the battery went out on this. Oh, this happened here. Oh, uhm, it's not picking up data. Can we stop? Let's T-pose again. Start over. Uhm, oh, you have your gun, right?


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[Assistant] I would just love a new IP, to be honest. I would love to fresh start. Like, I would love to be able to create something that hasn't been done yet.


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[Assistant] And like, create a character that's completely new. Personally. Because I got to do mocap, like I was a huge Tomb Raider fan, and I got to just do like some mocap as Lara, and I was like, you know what?


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[Assistant] If history were taught in the form of stories, it would never be forgotten.


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[Assistant] Is much more accurate than a man's certainty.


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[Assistant] A man can never have too much red wine, too many books, or too much ammunition.


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[Assistant] Take everything you like seriously. Accept yourselves.


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[Assistant] I keep six honest serving men. They taught me all I knew. Their names were what and why and when and how and where and who.


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[Assistant] You must learn to forgive a man when he's in love. He's always a nuisance.


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[Assistant] Gardens are not made by singing, oh how beautiful, and sitting in the shade.


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[Assistant] There is no sin so great as ignorance. Remember this


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[Assistant] The world is very lovely and it's very horrible and it doesn't care about your life or mine or anything else.


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[Assistant] We have 14 million reasons for failure, but not a single excuse.


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[Assistant] Buy a pup, and your money will buy love on flinching. That cannot lie.


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[Assistant] How can you do anything until you have seen everything or as much as you can?


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[Assistant] He travels the fastest who travels alone.


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[Assistant] The noblest form of fear known to people is fear for the fate of another.


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[Assistant] The choice of adults can be as random as the choice of toys by children.


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[Assistant] Create problems for yourself, if it's your nature, but don't create them for others.


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[Assistant] The glory of the garden lies in more than meets the eye.


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[Assistant] Excessive courtesy is sometimes no better than rudeness.


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[Assistant] Then the importance of the world justifies.


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[Assistant] It does not matter what people think of a man after his death.


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[Assistant] At twenty, the things for which one does not care a damn should, properly, be many.


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[Assistant] Upbringing is the highest of blessings, but only when it is of the first grade. Otherwise, it is useless for anything.


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[Assistant] People in essence do not grow up. They only get old.


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[Assistant] The most stupid woman can cope with an intelligent man.


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[Assistant] Better to be torn apart by beasts than killed by people.


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[Assistant] Hello, this is Scott from Optics Realm. This is Zmax tutorial number 7. Today I'm going to show you how to locate the cardinal points in an optical system using Zmax. Now,


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[Assistant] If you want more background information on what cardinal points and principal planes and nodal points are,


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[Assistant] I have another optics tutorial. I'll try and provide links somewhere in this video screen using YouTube's tools to get you there.


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[Assistant] Today I just want to show you how to get that stuff in Zmax. Not that one would often want that, but...


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[Assistant] I'm going to use, and I'm going to record straight through all my mistakes as you just saw, all my guffaws, and try and talk through all the shortcut keys and hotkeys I'm using.


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[Assistant] We're going to use a sample lens file. So my documents is going to sh- when you install Zmax, Zmax is going to have a subdirectory under my documents. This isn't where the program is, but all the supporting files that go with it, like glass catalogs.


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[Assistant] It also includes this sample subdirectory that has a bunch of sample lenses for starting points on lenses or to see how things are modeled. We're going to go into sequential, objectives,


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[Assistant] And I like this 28 degree. Let's do something different. Now let's do the double gauss 28 degree.


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[Assistant] Bunch of things here I don't really care about. RMS versus field, matrix spot diagrams, don't care about. Comment, I don't care about the comment column. Just kind of clean this up, I don't care about conics.


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[Assistant] I'll clean up our user interface, hope, in the recording process as well. Here's our lens layout.


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[Assistant] I'm gonna insert a dummy surface out here to show the rays coming in.


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[Assistant] So we can see our ray is coming into the optical system.


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[Assistant] So I'm going to go to surface one, highlight surface one, and just hit the insert button, or you could go to edit, insert surface, and this shows you all the hotkeys here.


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[Assistant] Now that we have this, let's put, I don't know, 5 millimeters.


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[Assistant] So now that this is here, if I double click, I'm not drawing that. Well,


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[Assistant] This window is drawn from surface two.


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[Assistant] To surface 13. And you can see that if you do a right click or you go to the settings. First surface, surface 2 to 13. Let's go to surface 1.


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[Assistant] And you can see now we have rays coming into our lens. I'm going to get rid of this surface here. Just kind of clouds it. Come to standard, double click standard.


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[Assistant] The draw we're going to highlight. Do not draw the surface. Click okay and then I have to update. Double click or hit the update button. Here's our optical system.


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[Assistant] There are a couple ways to find the cardinal points in an optical system. The easiest, well the first way is under reports, prescription data.


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[Assistant] This gives you a window of a bunch of relevant information for this particular prescription.


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[Assistant] So for instance, radius and thickness just like you get in the lens data editor. I also happen to have index data. If you go to settings, or I like to just right click.


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[Assistant] When I get rid of index, we're going to turn on cardinal points.


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[Assistant] And you can see there's a bunch of wavelengths here. So for 486, you've got object space and image space.


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[Assistant] I don't really care about all these wavelengths. I'm going to change the wavelengths. I can either hit this wave button or control W, get rid of wave length 1, wave length 2. In this case, now we're just at 587. Click OK. Double click to update.


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[Assistant] Focal length, focal planes, principal planes, we've got all the data we want here for the system's cardinal points.


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[Assistant] But there's another way to find it. In case, another way to get cardinal points, and it's more generic, it allows you to get on particular lenses or lens groupings. Go to analysis, uh, not this.


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[Assistant] But now it's saying we're referencing from surface one. Object space is referenced from surface one.


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[Assistant] And remember, we added this 5 millimeter air gap here, so that's changing what we're seeing here.


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[Assistant] And we're, and we're going, we're referencing image space from surface 13, the image. Now if you want to get rid of this, you can just right click and go to surface 2 and all these will change by 5 millimeters.


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[Assistant] Let's find the back principle plane. Back principle plane, as referenced, the image, image principle plane relative to surface 13. So,


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[Assistant] It is saying minus 99 millimeters. Now to show you where that is, I'm going to insert a couple dummy surfaces. I highlight the last surface image, I hit insert, insert, I gotta do two image planes.


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[Assistant] We're going to come to this thickness, we're going to say minus 99.


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[Assistant] Because the last surface is here. If I hit a spot diagram, not an empty spot diagram, these spots are huge. We're talking, you know, on the order of meters.


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[Assistant] Millimeters? Oh, this is microns. Millimeters. Tens of millimeters.


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[Assistant] Well, we want to go back to the image plane. So I'm going to come forward. So at this plane, that's the surface 13. This is surface 13 here. Now I want to come back to the image plane. Instead of typing that again, I'm going to put a solve in. I could either hit enter from here or double click. Double click. Uh, we're going to select pick up. Pick up.


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[Assistant] This is surface 14. We want to pick up the surface from 13. And we want a scale factor of minus 1.


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[Assistant] Now we're back to our image plane and to prove it we go to our spots. We've got our nice spots again.


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[Assistant] That are confusing the issue, but let's just trace them to make sure we understand what's going on. Let's take this top marginal ray, this blue ray right here. We trace it through the optical system. It gets to the edge of the aperture stop, actually does it?


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[Assistant] Uhm, ray aiming on. So hit control G or you could hit the general and it's not. Paraxial, okay. And when I double click this and update, you'll see our marginal ray will go through the edge of the stop. There we go.


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[Assistant] Control end. So we trace this marginal way through, goes through the edge of the aperture stop, through the back optics, and then focuses down.


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[Assistant] But we have two dummy surfaces. We come back, we go backwards through these lenses. It's not tracing through these lenses. This is a virtual ray back here. And then we come forward again to the image plane. I want to get rid of these virtual rays. I'll show you how to do that. Come to surface 13. I'm going to double click. Hide rays.


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[Assistant] To the surface and skip raise to the surface.


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[Assistant] So, we, we got rid of the rays that went from...


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[Assistant] Skip raise, hide raise, two, skip raise, I'm just going to do both.


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[Assistant] Okay, so that got rid of them. We're back to a normal looking lens.


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[Assistant] Now to prove to ourselves this is indeed the back principle plane, we're going to


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[Assistant] Copy this image and take it to some drawing program and do the real ray traces. I like to use PowerPoint because it's simple, it's easy to use. So I do CTRL C. Well first I make sure this window is highlighted. CTRL C. I'm going to go window, copy the clipboard. I'm going to go to PowerPoint.


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[Assistant] Ctrl V pastes it. It's huge. Let's give it a height of, I don't know, six. Of course it's outside the field. So Ctrl, uh, trackball on the mouse changes magnification.


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[Assistant] So there's our lens. I'm going to draw


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[Assistant] This back marginal ray, we're gonna start right at the image and go right to here. Oop, and I've got grids on. Sorry, gotta turn grids off.


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[Assistant] Now we're going to backwards raytrace, graphically backwards raytrace. I'm going to hold the shift key down and grab this.


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[Assistant] And you can see the slope of this line isn't changing, but I can do that virtual ray trace there.


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[Assistant] Change the color, we'll go purple, we don't have purple yet. Let's make it really thick so it stands out three point. Let's do this marginal ray here on the input. Let's get it right there, okay. Change the shape.


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[Assistant] So it looks like that really is indeed the back principle plane. Okay.


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[Assistant] What if we want to do it on an individual lens?


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[Assistant] Let's pick just this lens, this back lens here. I'm going to get rid of these two because we don't care anymore. Double click. Those are gone.


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[Assistant] Ah, I didn't need to delete those. I should have kept them.


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[Assistant] Again, I'm gonna do a pickup, I hit enter, I hit P to jump to pickup from surface 11, tab, to the next entry, minus 1 for scale factor.


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[Assistant] And again, now we're seeing all this nastiness here. I'm gonna get rid of, uh, hide rays, skip rays, that surface, hide rays, skip rays, okay.


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[Assistant] So the rays aren't nutty, crazy, but we can see, uh, the extra dummy, dummy surfaces.


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[Assistant] So, we don't want this dummy surface. We're going to hide, do not draw the surface.


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[Assistant] So now you can see this lens here, there's surface 13, there's surface 14, there's the principal plane. It's 3.186 millimeters from this front vertex.


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[Assistant] So this is a simple way, in case you want to understand cardinal points, measure cardinal points, or if you're doing some homework on theoretical cardinal points, and you want to check your work, you can now do this in Zmax.


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[Assistant] Hope this was helpful. I know we don't do a lot of cardinal points very often as when you do lens design, but cardinal points are so important and I wanted to show the power and flexibility of Zmax and how to do this, these sorts of calculations.


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[Assistant] That's it. If you need to get a hold of me, you can email me at scott.sparrow.gmail.com. I'm on YouTube. Please, if you have comments, leave them down below. And my website opticsrealm.com. Thanks for tuning in.


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[Assistant] So far, we've learned what viruses are made of and how they're classified. Now, let's talk about what happens when a virus encounters a cell.


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[Assistant] When you get down to it, there are really only two basic types of interaction possible between a virus and a cell, and those are called nonproductive and productive, the latter of which means that infection of the cell occurs.


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[Assistant] It turns out that lots of things have to happen the right way and in the right order for infection to result, so let's get a closer look now.


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[Assistant] As we might guess, a nonproductive interaction means that the virus is not able to replicate itself. However, there is some nuance here.


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[Assistant] There are three possible outcomes for a nonproductive interaction. The first outcome is that the virion and its genome is completely destroyed.


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[Assistant] 99% of the time, if you take a random cell and a random virus, they won't be compatible, and the virus just gets destroyed.


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[Assistant] The second outcome which is more rare is oncogenic transformation.


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[Assistant] This means that the virus isn't able to make copies of itself, but it affects the cell in such a way that it triggers uncontrolled cell division, which we typically call cancer.


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[Assistant] This DNA virus induces primary brain and bone cancers, malignant mesothelioma, and lymphomas in laboratory animals.


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[Assistant] The third outcome is a latent infection, where the virus enters the cell but remains dormant with little or no gene expression observed.


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[Assistant] In a productive virus cell interaction, on the other hand, new viruses are made.


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[Assistant] These infections can fall anywhere on the spectrum between two extremes. On one end, 100% of the cell's resources are channeled into making copies of the virus, which ultimately causes the cell to die.


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[Assistant] When the cell dies, it essentially explodes and releases a slew of viruses that can then infect neighboring cells and the cycle continues.


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[Assistant] This is called a lytic infection, and we looked at this process in some detail in the biology series.


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[Assistant] On the other end of the spectrum, the cell is continually making viruses, but it doesn't divert as much of the cell's resources.


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[Assistant] This keeps the cell alive with the rate of virus production being much slower. This is called a persistent infection.


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[Assistant] Some viruses fall somewhere in the middle of this spectrum, depending on what is optimal for the lifestyle of the virus.


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[Assistant] Now let's move on to the stages of the cellular viral infection cycle.


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[Assistant] Typically in a productive virus-cell interaction, infection happens in the following stages. Attachment, penetration, uncoating,


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[Assistant] Let's start with attachment, which just refers to when a virus sticks to a host cell. Viruses use proteins or anti-receptors to attach in a highly specific manner to particular receptors on the surface of the host cell.


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[Assistant] If a cell doesn't have the right receptors that will allow for binding, it will be a nonproductive interaction.


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[Assistant] We should be aware that these receptors which the viruses bind to also have a normal function in the cell. They don't exist solely for the benefit of the viruses that hijack them.


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[Assistant] For example, things called CD4 molecules that sit on the surface of T cells in our immune system help the T cells carry out their normal function, which is interacting with things called MHC molecules, or Major Histocompatibility Complex.


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[Assistant] However, CD4 molecules are the primary receptor for the HIV virus, so they need these proteins to get inside the cell.


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[Assistant] Next up is penetration, which refers to entry of a virus or viral genome into the host cell. There are three known mechanisms for penetration.


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[Assistant] The first is called endocytosis, which literally means to bring something in from the outside or to go into a cell.


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[Assistant] Both non-enveloped and enveloped viruses can use this method.


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[Assistant] The second is direct injection of the viral genome into the cell, as is done by bacteriophages.


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[Assistant] The third is direct fusion of the viral envelope with the plasma membrane of the cell. Only some enveloped viruses do this.


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[Assistant] We should note that in direct fusion or injection mechanisms, penetration and the next step, uncoating, are combined into a single step.


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[Assistant] With endocytosis, we have a separate un-coding step, which refers to changes in the virion structure that are necessary to allow for viral gene expression.


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[Assistant] For some viruses, this just means removal of the protective protein coat around their nucleic acid.


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[Assistant] For more complex viruses, encoding may occur in several more involved steps.


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[Assistant] The next stage of the infection cycle is gene expression.


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[Assistant] This is where transcription, if necessary, and translation of viral mRNA occurs.


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[Assistant] After gene expression, there will be genome replication where progeny viral genomes are generated.


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[Assistant] Next there is assembly of progeny nucleoproteins. The protein capsid must be formed, and then the genome and any necessary enzymes packaged within it.


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[Assistant] Second to last, we've got release. For some viruses, release takes the form of lysis, in which viruses accumulate in the cell and the cell bursts. For others, the nucleocapsids of enveloped viruses push out through the lipid bilayer of the cell, acquiring an envelope on the way out. This process is called budding.


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[Assistant] The final step is maturation, which refers to the changes in the virion required to make it infectious.


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[Assistant] Note that not all viruses have this step, and that maturation might occur before, during, or after release.


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[Assistant] Now that we've covered the virus-cell interaction from the perspective of the virus, let's move forward and discuss things from the perspective of some different types of hosts.


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[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me at


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[Assistant] London is an amazing place all year round, but at Christmas it is just unbelievably beautiful.


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[Assistant] But when December comes along, you can see all of the busy Londoners making time.


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[Assistant] To take a step back from their routine and just appreciating what the city has to offer.


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[Assistant] One of the things I really enjoy doing at Christmas is buying amazing gifts for my loved ones. One thing I love doing is window shopping.


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[Assistant] I'll spend an afternoon looking at all the window displays without really buying anything, just trying to gather ideas and inspiration.


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[Assistant] London is so great for shopping. The most famous street is Oxford Street, but I really like King's Road as well. The luxury brands really compete with each other.


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[Assistant] Another lovely place to visit in London at Christmas time is Covent Garden, the centre of which used to be a fruit and vegetable market. And then in the 1980s,


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[Assistant] It changed to a shopping centre. There's lots of lovely little shops and you can also see some street performers if you go at the right time.


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[Assistant] I enjoy heading down south of the river and appreciating the views of the River Thames from one of London's many bridges. It really does make me feel very, very, very small in such a big city.


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[Assistant] One of my favourite lunch spots is one of the largest and oldest food markets in the whole of London, Borough Market.


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[Assistant] If you do head over to Borough Market, don't forget to try some of the hot cider. Perfect to warm you up on a cold day.


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[Assistant] So that was a little taste of my beautiful city at Christmas time and I'd love to hear your recommendations. Bye for now.


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[Assistant] Hello kids, this is Elf Learning with My Home. Can you chant with me? Let's go. Apartment. Balcony. Bathroom. Bedroom. Carport. Closet. Dining room. Doorbell.


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[Assistant] Apartment, balcony, bathroom, bedroom, carport, closet, dining room, doorbell. That was fast! Door. Downstairs. Elevator. Front door. Garage. Hallway. House. Kitchen.


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[Assistant] Door, downstairs, elevator, front door, garage, hallway, house, kitchen. Woohoohoo, wow. Living room. Mailbox. Shower. Stairs. Toilet. Upstairs. Window. Yard.


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[Assistant] Living room, mailbox, shower, stairs, toilet, upstairs, window, yard. Did you do it?


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[Assistant] This has been ELF Learning with My Home Vocabulary.


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[Assistant] Thanks so much for watching. We'll see you next time. Bye bye.


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[Assistant] Hello! This is ELF Learning with School Subjects.


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[Assistant] Some things we learn at school are interesting, and some things are fun. Some are difficult, and some are easy. Let's practice. I think art is difficult. I think art is difficult.


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[Assistant] I think English is interesting.


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[Assistant] I think English is interesting.


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[Assistant] I think geography is difficult.


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[Assistant] I think geography is difficult.


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[Assistant] I think history is interesting.


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[Assistant] I think history is interesting. I think math is easy.


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[Assistant] What do you think about your school subjects? Tell me.


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[Assistant] Thank you very much. We will see you next time. Bye.


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[Assistant] Measles is an infectious disease caused by the measles virus, and it has been around for a very, very long time. According to CDC, one of the first written accounts of measles popped up in the 9th century.


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[Assistant] The thing about measles is that it is incredibly contagious. It's considered to be one of the most common and most unpleasant viral infections in existence. Historians believe that transmission of measles increased dramatically as trade routes expanded and worldwide travel became more popular.


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[Assistant] And even though measles is notorious for causing childhood disease, it can also cause severe symptoms in adults. In fact, there were over 21,000 reported cases of measles on both sides of the Civil War in the 1860s, resulting in over 500 deaths.


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[Assistant] By the arrival of the 1900s, it was expected for nearly all children to get measles by the time they were 15 years old. And those cases usually involved rash, high fever, cough, inflammation of the nasal passage, or conjunctivitis.


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[Assistant] Millions of people in the United States were infected with measles each year, some of whom required hospitalization, and a smaller number suffered encephalitis as a complication, a word which refers to a swelling of the brain.


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[Assistant] With a brief history covered, let's talk more about the virus itself.


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[Assistant] The measles virus is part of the paramyxoviridae family, and within that, the morbidly virus genus.


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[Assistant] The other two genera in this family are paramyxovirus, which includes the parainfluenza and mumps viruses, and pneumovirus, which includes respiratory syncytial virus, or RSV, and metaneumovirus.


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[Assistant] Paramyxoviruses are made up of negative-sense single-stranded RNA that's packaged in a helical nucleocapsid and surrounded by an envelope. In the case of paramyxoviruses, the envelope is pleomorphic, which means it can vary slightly in size and shape.


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[Assistant] Paramyxoviruses are similar to orthomyxoviruses like influenza, but they tend to be larger and don't have the segmented genome that influenza viruses do.


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[Assistant] Within that pleomorphic envelope surrounding paramyxoviruses, there are two glycoproteins that facilitate viral replication within a host. One of these glycoproteins is a viral attachment protein that helps the virus attach to the host cell to begin with.


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[Assistant] The other is a fusion protein that supports the process of the host cell membrane and viral membrane fusing together, which allows the virus to enter the host cell.


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[Assistant] The process of fusion involves a series of intricately controlled steps carried out by viral proteins, the blueprints for which are packaged neatly into the viral structure. The fusion process basically shepherds the virus into the host cell to start replicating in the cytoplasm.


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[Assistant] As we mentioned, measles is highly contagious. The measles virus is spread from person to person through respiratory droplets, so all it takes is inhaling droplets that an infected person has exhaled, talked, coughed, or sneezed into the air.


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[Assistant] Once the virus is inhaled, it sets up shop in the respiratory tract, where it replicates for a bit before moving on to spread through the lymphatic system and blood.


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[Assistant] The virus is thereby widely distributed throughout the body, infecting the respiratory tract, blood vessels, urinary tract, lymphatic system, and the central nervous system.


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[Assistant] Ultimately, this multi-system attack causes the characteristic maculopapular measles rash, along with fever, cough, and conjunctivitis.


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[Assistant] Compared to other viruses, the symptoms of measles are fairly easy to recognize, so laboratory tests aren't always needed for an official diagnosis. Most people recover from measles and develop lifelong immunity to the virus, though some may develop pneumonia or encephalitis, which can be deadly complications.


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[Assistant] It's also worth mentioning that there is only one serotype of the measles virus, and as luck would have it, it infects only humans.


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[Assistant] To put things in context, measles is considered to be one of the five classic childhood diseases, along with rubella, roseola, fifth disease, and chickenpox.


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[Assistant] These diseases are also called exanthems, a word that refers to an eruptive disease or symptomatic eruption of disease.


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[Assistant] Since the development of measles vaccines, and especially the live attenuated measles vaccine developed in the 1990s, fewer than a thousand cases have been reported in the United States.


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[Assistant] The measles vaccine is often combined with mumps and rubella, which is called the MMR vaccine, or combined with mumps, rubella, and varicella, and referred to as MMRV.


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[Assistant] Worldwide, however, measles is still one of the most prominent causes of disease and death in places where the population is unvaccinated.


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[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me.


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[Assistant] Chickenpox is a very, very contagious disease that you've almost certainly either heard about or experienced yourself. The virus that causes chickenpox is called varicella zoster virus, which is why chickenpox is sometimes referred to as varicella.


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[Assistant] This virus is also part of the human herpes virus family.


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[Assistant] Varicella is one of the five classic childhood diseases along with rubella, roseola, fifth disease, and measles.


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[Assistant] Virus-wise, VZV shares a lot of characteristics with the virus we covered in the previous tutorial, herpes simplex virus. Like HSV, varicella zoster virus is able to establish a latent infection of neurons and cause recurrent disease.


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[Assistant] When that happens, it's called herpes zoster, though the name for this that you've probably heard is shingles.


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[Assistant] Other similarities between VZV and HSV involve cell-mediated immunity and the characteristic lesions that look a little bit like blisters.


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[Assistant] One thing that sets the viruses apart though, is that VZV is spread by the respiratory route. Once a local infection is set up in the respiratory tract, the virus is spread through the blood, which leads to those characteristic skin lesions all over the body.


[User] Please transcribe this text

[Assistant] Compared to other human herpes viruses, VZV has a smaller genome. VZV also replicates a little bit more slowly and infects fewer types of cells than HSV. Once the virus is inhaled, it sets up shop in the lungs, with a primary infection beginning in the mucosa of the respiratory tract.


[User] Please transcribe this text

[Assistant] Similar to HSV, infection goes through a fairly complex stepwise process. After the virus replicates in the lungs, it spreads through the bloodstream and lymphatic system, and then spreads throughout the body and to the skin.


[User] Please transcribe this text

[Assistant] The virus causes a real bummer of a rash, and someone infected with chickenpox may develop as many as 500 itchy blisters. They may or may not develop a fever, headache, and fatigue as well.