This one says, because it has a non-fluidine combustion product, which is water vapor, hydrogen gas is used for fuel above the space shuttles in earthbound cars with prototype engines in the reaction that's shown up here. So let's express the rate in terms of changes of hydrogen gas concentration, oxygen gas concentration, and water gas concentration over time. So how do we do that? Remember, the rate itself, in this case, with hydrogen. Can you guys help me out? What is it going to be? Negative? 1 1⁄2. 1⁄2. Very good. Times what? H. The change of the concentration of H2 divided by? Shift of time. Yep. L of T. Everybody OK with doing something like that? So what would be, what's the next one it wants us to do? Oxygen? So what would oxygen be? Somebody else help me? L of T, over the change. Times, yeah, a negative 1, right? Like that. Is everybody OK with that? So notice what I'm doing here. This is equivalent to that. So we can actually set these two equal to each other if we want it to 2. And that's what we're going to do in half feet of this problem. OK, so let's do water vapor. What is it going to be? So it's going to be also equal to what? 1⁄2. Yes. And the change in? The water vapor. Over the change in time. OK. And then it says when oxygen is decreasing at 0.23 moles per liter second, so the concentration of oxygen is decreasing at 0.0, 0.23 moles per liter second. How fast is water increasing? So how do we do this problem? Well, let's just erase this part here, because we have an equation here. So this, this. So we're looking for, so delta T is going to be the same. So we can cancel that out on each side. You guys see that? Delta T is equivalent to delta T. So effectively, what do we got? We got negative change of 0.2 equals 1⁄2 times the change of H2. Is everybody OK with that? OK, what do we know about the change in O2 concentration? It's going to be negative 0.23 moles per liter second. So what would be the change in water concentration? So negative 2 times the change in O2 concentration. So everybody OK with us doing that? When we do that? Negative 2 times the negative 0.23 moles per liter second. Would you expect the water concentration to be increasing when the oxygen was decreasing? Yes. So is this going to be the correct answer? That's positive. It's going to be 0.46 moles per liter second. Is everybody OK with doing something like that? Any questions? That's wonderful. Good job, guys.


I finally got back from my vacation I logged into Madden all-time team for the first time in like nine days. I know big deal calm down these Beautiful packs dude last time they let us open these they only let us open one And I was tweaking cuz I wanted more but I got to buy three this time with three fire fantasy packs We are going to do a pack and play with these three fire fantasy packs And then I'm gonna spend all the snow that I have and seven thousand eight hundred doesn't sound like a lot But it's actually a lot. Okay, so this is the base team. It's just 78 overall It's basically just decent gold cards. That's all it is the first card is always a full Legend or not ultimate legend full legend Jeremy shock bet the last card is a 93 or better zero chill Way we got to Beyond for one the antennas is a god for two that card is super expensive But this is a pack in place I have to pick based off of who's gonna fit into my team best the next one is just a decent It's gonna say these are like it is bear centers. This is kind of a tough choice three Do boop boop boop boop boop and this is our pot 93 or better And it's another Lions player Damon Harrison except is an ITT overall so I kind of sucks Okay, this is kind of a risky call, but I'm going with Dion Sanders. He's he's that good I know he's a 92 overall, but he's that good case of Dion Sanders My first fire fantasy back. Hopefully this next song will have a quarterback or a half back He's on Justin is the dude I got in my first ever fire fantasy back That's kind of cool. Our next one's not an ultimate legend like last one, but it is 93 overall much strange Sheesh next one's also a big legend Free safety Brian Dawkins. We're not gonna take him. This one's a frosty What 87 overall what me No keys on Johnson and what's it come on Michael big Is the brand new just came out best possible legend Paul Jerry Rice another full Moss you came out five years ago. Give me a 96 freeze-frame 96 Eddie George right here man Okay, so is 96 Bruce Smith, which is still awesome. I gotta do Bruce Smith, bro So what do we need out of position iron men mid elite 85 to 87 random player We're gonna open each one of these little puppies just to see where we can add players to our team And add one of these boys to the team.  So we have a half back. It's not the greatest hat back We have a half back now this one is an 85 to 87 So let's see how this how this adds to the team. Of course we get the punter Ben Routhisberger 82 cornerback Shaq Thompson. Okay, so we got Shaq Thompson and Deon Sanders at corners left outside linebacker Julius Peppers sure so I'm gonna take my zero zero chill mid elite Mike McGlinche. Sure, dude Okay, yes, we're stacking defense Wow What okay? Well this changes things actually no I got 3,000 coins. I'm gonna get to I'm gonna get to go some and future I don't know what these cards are. So I'll take whatever I get. Just give me two. Let's do it Oh There's my QB the final pay this is the final pack of the pack of play and then we're ready to hop in the team Give me like a middle linebacker.  Yeah, that's as good as that's so good strong safety Mike Evans Good, that's a good way to end it boys. That's good way to end it Earl Thomas 95 overall Mike Evans a strong safety Julius Peppers at linebacker Julian Edelman Bruce Smith at right end Shaq Thompson and I have Mike Evans at wide receiver in strong safety right now It's just pretty cool, but obviously Julio Jones needs to come in 84 overall team. We've got an awesome QB awesome wide receiver That's gonna be hilarious hackback.   Probably should take that up the middle, but all right Lamar Jackson. It's your time to shine. Oh, he's open. Oh Makes a tough catch shy playmaker why is a open?  He's coming Oh, it's Walter Peyton. It's Walter Peyton QD empty backfield first and ten My man is just looking to take off Second and nine I need to send someone. No, I knew that was there you can't It's just impossible to survive without middle linebackers in this game man Whoopsies, I let him score not really he kind of scored on his own, but two minutes 30 seconds left Who the old Jones is pretty much done? Everything. Oh, oh wait. No terrible.  I've traveled with him the whole way I think Julio Jones might be wide open right off the bat here. He's not Oh, there he is. Oh, he's not there. He's not Oh my goodness He just won him grab that ball.  Oh, boom Who we owe Jones right in the middle of the field. Okay. Got another time on into the belt That's a terrible pass, but I'm throwing it five yard line. All right. All right. All right.  He's been doing me to me with Walter Pate So this is gonna feel good to do. Okay, just no blitz. Don't blitz don't blitz.  Otherwise. I'm whipping this ball into the dirt and taking a field goal. Oh My god, that's open I didn't expect that to be open at all. Okay, I got my touchdown my Bro, even Deion Sanders can't play football right now. This is like so embarrassing Second in six. He's gonna run it Second in goal we got to make a goal on standard just take an L Go get this man. So that he That was the first pass. He didn't complete the run play actually third in goal That was his Lamar Jackson. Hey, that's a stop 17 to 10. This is a winnable game To me. It doesn't fake me Don't fake it, please. Okay Okay, I got two guys over here one of them is Jordan Howard. He caught it before he ours Yeah, like that. What where is Lamar Jackson? What is this? Why does this game do this shit? Hate it hate. Oh look at that wide open though. Okay Great block by my wide receiver, bro. His wide receivers will literally We'll literally set a basketball screen on me and my wide receiver ran past his man. I Want to look like I'm running the ball with Lamar Jackson so that I can hit him with this pass so that next time Okay, Sam Darnold There we go, there we go, I am not yeah, I kind of am used to I'm used to Lamar Jackson QB That's why I did that, but still pretty smart play Okay, we have to score second and six half back Wham. I don't know if this is the play call here But we cut it to the outside Seventeen beach. Did you hurry up to run it?  All right, Mike Evans. That's pretty good play Second and 11 QB spy on Bruce Smith or on Walter Peyton with Bruce Smith. Go get him. Go get him Tiger Okay, I just got a tackle to make no Farming okay Okay This is the part of the field. I don't like to cover. I need to be closer to the goal line So there's no like field Go get him. Go get him. Go get him. Go get him. Go get him a hundred percent Big stop Yo, that job, that's why we got the 95-year-old Thomas 20 to 17 with a minute 50. Are you kidding me? There's a game football Wait a second. Wait a second. Wait.  I was looking at my phone and I looked up.  It's clamp time Wow, he's gonna really Sanders like that 39 huge stock finds a great First and ten Thomas MVP.  Who is it? It's me 84 overall squad Bruce Smith Julio Jones and Mark Jackson That's it for now boys.  That's it That's all I got. Thanks guys. There's always peace out


Hey guys, it's Nate, aka The Foot Account. Welcome back to the channel. We have to talk about this loading screen and the content that came today in FIFA Ultimate Team and especially what is going on with this new pre-season loading screen. And as well, I guess that I had an issue. My issue. We have resolved your issue. Thank you. Yay. I guess I packed a card that was supposed to be a special card. Who am I going to have here in my club? Oh, Fred. And I still have my play up. Okay, sweet. Anyways, I guess EA gave us back some of the cards that they messed up on yesterday. But again, this is what we're here to talk about. What in the world is this pre-season promo? There's a lot that we can take from this and actually look at other promotions that have happened in other games, including Madden and FIFA Mobile, and talk about that and what this means. And also we have in the code, a lot of you guys know us already. We have a icon moments, SBC, which is prime icons or icon moments pack that was added to the code, not in the game yet. So we'll talk about that still. But FIFA 20 is not over. But this has a lot of promise. And this is actually really, really cool. The first thing that I noticed about this when I saw the loading screen today is this resembles the type of graphics and the type of art that EA is starting to put forth related to this FIFA 21 stuff, right? If you look at the, the Ultimate Edition cover, it's got a lot of white on it. It's got a lot of just square boxes and it looks really simple. And I think that's what they're going for with these new graphics and stuff with FIFA 21 is just very simple shapes, borders. And of course they have some of the FIFA 21 like lines and outlines at the top, like up in here. But then we still also have like a touch of summer heat as a part of this promo as well, because these color splashes are the same color splashes or the same design that we had during summer heat as well. So I'm just kind of interested to see how this all plays out. Again, we don't really know anything from the loading screen today.  A lot of times what EA does with this promo screens, you guys know this, we might have another up, we'll probably have another loading screen, a new loading screen every for the next four days before this promotion comes out. We will probably have a new loading screen. I wouldn't be surprised if they throw a car design in there if there's multiple car designs, kind of like they did for the summer heat, they kept teasing different things over and over and over each day. And I feel like we're going to have some loading screen content for the next few days. But I really want to get into what is this promo like what is preseason because it's kind of like preseason that makes me think of FIFA 21 and especially with all the FIFA 21 graphics kind of put into here, it really starts to make sense. So I did a little bit of googling, a little bit of researching, and some of the guys that are involved with Madden and FIFA Mobile, this is actually a promotion that has been in FIFA Mobile for a while. This webpage right here is from FIFA 17 Mobile. So it's been out for a while in FIFA 17, but I kind of want to talk about this and what really happens with these cards. So of course it talks about what is going to be reset and not reset. When you move from FIFA 20 to FIFA 21, you don't keep your team, your players, your coins, your record, none of that goes forward. You keep your FIFA points, of course. And then this is some stuff that's just related to FIFA Mobile. But how this preseason promotion takes place and what happens in FIFA Mobile is basically there's these things called now or later cards. They have now or later cards where you complete an SBC or you complete an objective is how I think it would work in FIFA. And what you get is you get a really juice like a 99 rated or a super high rated card for FIFA 20. At the end of the year, super juice card is kind of what happens with that. And then you also get the later portion of that, which is, as it is described down below, carry forward an exclusive version of that item into the new season. So I think it also says somewhere that when it goes into the next year, so like, let's say you got a 99 rated, Ed and hazard as a part of this promo this year for the now or later next year, if you got this card as a later version into FIFA 21, it wouldn't be 99 rated, it would be a lower rated card adjusted for the the new year, I think is what it's it's basically just adjusted for the new year and the new ratings. So that's kind of like the concept that we're looking at here, we're looking at something that would give you a really juiced card right now in FIFA 20, and also give you the opportunity to have a card in FIFA 21 as well. So that's kind of interesting. And also a lot of people have looked at these colors and like, Hey, this is pretty similar to the color design and the color scheme, not exactly like the design itself, but just the colors are very similar to the ones to watch card design, like the purple and some of the yellow and the orange that's in there. So that's kind of the colors that are a part of the ones to watch design. So some people are thinking that we might be getting ones to watch items, like that's my and that might be what these are played toward. But that's some of the initial thoughts of this promo and what this pre season could really bring. So basically, again, it's either something that you would do an objective for. I mean, can you imagine this would be kind of cool, right?  I don't know, Ed and Hazard again, as our example, right? We have a pre season, Ed and Hazard that is in here in objectives. And you have to do three, four, five different objectives, unlock certain tiers of the card to get them to a 99. It's not super duper hard because it's, you know, August basically in this game. And then also the very last thing that you would unlock is like a token that would stay on your account that would then give you that card in FIFA 21 that would be hooked up to that specific account. So that's kind of interesting if that's what's actually going to go down. Again, we're going to see what happens on Wednesdays. It's another weird promo time. A lot of times they start on Fridays, right? But the summer heat promotion, that all started on a Wednesday. Same thing with this, the pre season promo as well. So I could see it being an objective or being SBCs, at least either of those two things we do not know right now. The other thing that I want to talk about is, and this is from a somebody in the FIFA community, Pistol Pete. I don't know if you guys know this guy. He's another FIFA streamer. He plays a lot of Madden as well. And I've not played a ton of Madden myself. I'll play it a little bit. But this is something that happens in Madden is a promo where you start. This is where he explains it right here. You complete an SBC in FIFA 20. So like the 99 hazard example that we had, you receive a nine line version of that card in FIFA 20 to play with right now. Then you also receive that player's wants to watch item in FIFA 21 at launch. So it's a rookie premiere. So somebody who is like a brand new player that has been drafted into the NFL and is playing is basically what this kind of is in Madden. So if we think about that in the FIFA sense, right? Let's think about like Timo Werner, right? Or Zee Etch or Thomas Moyniere, some of the guys that have just transferred. Could I actually look this up? Could I go Chelsea striker German? Would this actually show me Timo Werner? Because I know his card is in the game. Bang, there it is. So there's the Timo Werner, Chelsea card that is in the game.  And then you would get his wants to watch item at the beginning of the year next year, possibly. I feel like if they did that, that'd be kind of a problem though, because they would, that would affect the market a lot. Because if a lot of people in this game would have an untradable Timo Werner to start FIFA with, think about all the German links, all the Premier League links that people would go for right away if they're starting off with a card like this. Now, of course, they could give you a Timo Werner that's only like 80 rated, and it's still like a wants to watch card, but it's not the real wants to watch Timo Werner, because he's probably going to be around 85, 86 rated, I would imagine to start FIFA 21. So that's one way they could get around it is giving you like a low rated card. But then a lot, a lot of things back to Madden is when you get that rookie Premier card, if I'm not mistaken, I believe that that card starts as a really low rating, and then you can complete objectives with that card to level it up later on in the game. That is kind of what happened with the Summer Heat or excuse me, the Future Stars Academy players with like Daniel James and Osamann, right? Those two cards were objectives that were tiered, right? Whoa, my controller is fizzing out on me right now. Those were like tiered objectives.  Kind of the same thing with like the Bruno Fernandez and the Furlan Mendy as well.  So that could also be part of the story as well. Again, right now, it's all speculation. We will figure out more on Wednesday and as the loading screens get updated throughout the week this week. So that's going to be something to tune into. But again, this is massive for FIFA because if this is actually what's going to happen, if there's going to be something that you can prepare for and get FIFA 21 stuff in your Ultimate Team account early and do that work in FIFA 20, that's massive because that would bring a little bit of hype to their game and that would also kind of foster you to start thinking about FIFA 21 and getting your account ready and start focusing your mind on FIFA 21 as well while still playing FIFA, right? They want to keep you on the game, keep them interacted with their game because they want the interaction because that could possibly lead to them making money, right? That's what it comes down to at the end of the day. But that's something that we've never had before in FIFA. It's always just been a hard cutoff. FIFA 20, like FIFA stops, you know, like FIFA 19 to FIFA 20, right? It just stops. You pre-order FIFA 20, you buy FIFA 20, you get maybe some engagement rewards when you first log into the web app or first log into the console and then bang, you just are starting from a fresh slate. If there's stuff that they can add that you can do in the previous year's game to then carry over, that would bring a lot of hype. I think a lot of people would be interested in that and it would also possibly be a way where you could get ahead for the next year. But it's going to be interesting to see how they do it because again, as I mentioned, if they do something like this where they give everybody the same card or there's only a few select cards that can come out, we could already see like market impact in FIFA 21 and no, okay, there's going to be people have these cards in their clubs, link investment to these cards are going to rise quicker and earlier on if they're really good, pacey meta cards or popular cards even or easy just easy to link. It's crazy that we could have like a FIFA 21 market movement before FIFA 21 is even out like the concept of it is just kind of funny. But that's enough of me talking about preseason because again, right now it's all speculation, but I want to kind of bring you guys that information for today to just talk about it and to cover what that promotion could be like. I want to go backwards and talk about as well the content that we got on Friday. Again, EA did release the a prime icon moments SBC code and I want to show you that right here right now. Footwatch tweeted this out, it is not just moments though, right?  But honestly, that might make the price of this SBC cheaper. It might make the SBC requirements a little bit cheaper. It really seemed like they were going to drop that SBC today, but what we did receive is the leaked Musa Soco that finally came out. They didn't upgrade his skill moves or his weak foot, but this guy looks like an absolute rock. He's definitely going straight into my team. As a Spurs fan, I'm pumped to have this card in the game. I know a lot of people are not interested in him because they've already got Pogba, they've already got, you know, and Dombele, you've got Wine Oldham. So the midfield spot, especially another French card, people like, yo, where's the creativity from EA Sports here? But this is, you know, they, they said what it was. This is a foot favorite Musa Soco inspired by his full birthday version. So I'm stoked to have that card in this game because it is one of my favorite cards. One of my favorite players, IRL. We also got arguably one of the best right backs in the game as well. Nelson, Sameda with a huge boost, and it is very, very easy to do. It is all through rivals, but you only need five guaranteed wins right here, scoring five separate rivals wins using LaLiga players, and you have two weeks to do it. So that is a W. I'm glad that they put this out today. A lot of people are interested in this, you know, obviously green links to Messi at a right mid position and a lot of other, you know, Vidal. We've had a lot of Barcelona players recently with Langley with Vidal and players like that. So that is a GG from EA there as well. Some market movements that happened today as well. There was a lot of panic selling this morning because people thought the moments icon SBC was coming. And a lot of people also went out and bought SBC fodder, which I think worked out for some people today. And I wasn't following the market like super duper close. But I did. I do think some SBC fodder rose to today. What's going on here with this Jamie Vardy 260, 257 and 249. I think what I might do here is snatch that up because right now cards are rising because people are buying back teams for their week in leagues is what I'm seeing happening right now on the market. So I think if I slap that up there, I'll make a couple thousand coins and that's a GG. But a lot of stuff was panic sold this morning. Like this Jamie Vardy this morning was 210 thousand coins. And now he's back to 267. That was a very timely realist and a buy by me is bang. Now he's back to 267. But there was a lot of these team of the seasons that fluctuated today. A lot of icons that moved as well. And then of course you had market movements around some of the midfielders because of Cisoco coming out and then also the right backs because where is La Liga? Of course with Samedo coming out. That means that this card Carvajal was going to take a tumble. After rising yesterday at rewards, I mentioned this guy was a good buy at rewards. He was like a low 100,000 coin range, 106 during rewards peaked back to 123. And then now he's down to 70K today of course because Samedo. But a lot of stuff was just panic sold and then rebounded today. I don't really know too much about icons. I wasn't watching them specifically. I do know that this moment's block was right around like the 630 range, 640. Wow. Wow. So this card was what is this real? He actually rebounded. Wow, that's crazy. This guy's up like 200,000 coins from where he was earlier this morning. If that is true. Yeah, this guy was 650,000 coins this morning. And he just absolutely boomed today. Which is crazy how high this card is rebounded because he wasn't even down that much this morning. Check out his graph boys. I just think what happened was people just bought their end game teams today. This dude is literally up almost 200,000 coins. He was 650,000 coins yesterday. This morning he got down to 620 and now bang, he's up to 800,000 coins. So maybe that's just this card. But that's honestly pretty crazy that some of these I'm sure there was some panic selling on icons and some of them rebounded because of the SBC. I was not watching that myself. But I do know that a lot of the team in the seasons are now rising back up. Did I just get a sale on my already? Wow, GG's boys. Made some profit real quick, real fast, undercuts trading baby. We're trading in late July. Things you love to see. So yeah, like Neymar was 3.8 million coins yesterday at rewards. Now he's 4.2, 4.3 mil I think. Wow, 4.3, 4.265, 4.250. That's a wild rebound where some of these cards were at. I think Abape who was going unsold yesterday at 2.5 million is now around the 2.8 range. Is that right? 2.8 million coins were in Abape. So he's up a decent amount today. A lot of cards are up. If a prime moment's SBC does come and it's somewhat doable, the thing that is going to make the market drop even more, that SBC is going to make the market drop a lot. So if you think that icon moment is coming, which I do think it is in the next two or three days, I would be very careful with the cards that you own. I would probably cash them out. I would take the coins, even if you just bought a card and it went up, right?  Dynamic images are huge in this game. That will drop the market and the thing that's going to make the market drop more is the fact that there's no special cards in packs anymore. There's no upgrade packs to craft. So once people want to do these icon moments repeatables, the prime or moments pack, they're going to have to do them from the club at first and then they're going to have to start buying players. So you know, SBC fodder, if you haven't stocked up and done a club stock, some of the stuff is still kind of low. I would recommend doing a club stock on SBC fodder if you are potentially going to do any of these SBCs unless EA drop upgrade packs, which they could with these SBCs coming out or with that prime or moments icon coming out. It's possible they could drop upgrades, but I almost would just take the safe route and maybe stock a couple of these. Is this card up today? Moosa Dembele, 28,000 coins. I think Sasoka requires an 85 and an 86 squad. This guy was wow. This guy was 22 K 21 K last night and now he's up to 30,000 coins. So a lot of this stuff was really low last night and it got invested in since the code hit and now stuff has actually dropped down a bit because the SBCs did not come today. But I still think that if the icon or prime the moments prime or moments SBC does come, this stuff probably is going to go higher because it's not getting any supply on the market anymore. Unless they do drop some upgrade packs with it. So maybe do a club stock on fodder if you plan on doing a lot of those SBCs, but it's going to be an interesting next couple of days with new loading screens coming and with potential big SBCs coming and crazy market movements all around. Just thanks to EA Sports FIFA for continuing content and not just leaving us dead hanging. This preseason thing looks pretty dope. I'm excited to see what happens from it and hopefully they continue the new and hype fun things in FIFA Ultimate Team. If you enjoyed this video, smash thumbs up on it, comment down below if you have any questions and subscribe to training for new. It's been Nate with Quota Counting. Catch you guys later. Peace out.


With Animal Crossing New Horizons, the perpetually money-hungry Tom Nook is back to his old tricks again. This time around, Nook Inc is dumping unsuspecting tourists on a deserted island, charging them large sums of money for the privilege. Tom Nook has even invented his own made-up currency, Nook Miles, so that he can have complete control of the island's economy. Yet while many see Tom Nook as the villain of the Animal Crossing games, its developers really feel like the player has misjudged this tricksy Tanuki. Long-time series director Aya Kyogoku in particular feels the need to defend Tom Nook at every turn. Aya has insisted, time and time again over the past few years, that Tom is not evil, he's just misunderstood. In interviews dating back to 2013, she's expressed frustration that players often don't give Nook the benefit of the doubt. Indeed, when producer Hisashi Nagami was asked about his favourite character in Animal Crossing New Horizons, he said, If I had to pick one, I think it's Tom Nook. This is background information on Tom Nook, but he was unsuccessful earlier in his life. Because of this early failure, he has this sense of sadness to him. Time for a lesson in Japanese folklore. Tom Nook is a Tanuki, a real-life Japanese animal that's a little like a dog, but which is often misrepresented as a raccoon in Western localisation. Traditionally, Tanuki have a strong association with wealth. Metalsmiths used to use Tanuki pelts to soften their hammers when dealing with gold, and ever since, these creatures have been seen as a symbol of good business. Many shops and restaurants across Japan place Tanuki statues outside their doors in the hopes of attracting customers, so it's easy to see why a Tanuki would be a good fit as a businessman in Animal Crossing games. Tanuki are often seen as tricksters, not evil, but certainly mischievous. In many folktales, Tanuki transform into people or objects, mostly just to play practical jokes on humans. They'll also use this transformative power to turn leaves into money, spend them in shops, then run away before the illusion disappears. According to Aya Kyogoku, Tom Nook similarly isn't a bad guy. In spite of his brash sense of humour, he's fairly harmless. In an interview all the way back in 2013, she said, We think he is very misunderstood. He's just passionate about his business. He's not like a loan shark. He doesn't add a handling fee or anything like that. He can wait as long as it takes for you to pay back. He's not as bad as other people might think he is. This is technically true. While there's no mechanic within the game that forces the player to make a payment, Tom Nook will definitely threaten you to pay off your loan quickly. In the very first Animal Crossing game, he says, If you can pay off about a thousand bells a week, that'll work for me. Otherwise, I'll send for the raccoon goons. As with Mr Rosetti, Tom Nook's threats have been softened in Animal Crossing New Horizons, but players still remember these earlier outbursts. Indeed, Aya and the rest of the Animal Crossing development team have had to work hard over the years to fix Tom's reputation. Speaking about Tom's role in New Horizons, Aya said, Tom Nook, this time around, he has this place called Resident Services. He tirelessly works 24 hours making sure that all the residents on the island are happy and are living a good life. Whenever Tom receives those debts paid back from the residents and the players, I think that because these resident services upgrade as time goes on, I think he's investing that money back towards the resident service for the residents. More recently, Aya said, even if we borrow money from him and it takes time to pay him back, he never gets angry, he never gets mad, so if we could all try to be a little more responsible and pay him back, it might make him a little happier. But why make Tom's so money hungry in the first place? After all, Mr Rosetti's big gimmick was removed as the game series was refined over time, so why does Tom's still offer mortgage loans even when the player lives on a deserted island? The answer, according to producer Hisashi Nagami, is that Tom Nook is essential to give the player something to strive for. The concept of paying off a large loan is a gameplay device within Animal Crossing. In such an open-ended, free world with no limitations and no threat of failure, the player needs a mission to achieve and that mission is overcoming debt. Said Hisashi, it's one of the biggest motivations that users have to continue playing Animal Crossing, and Tom Nook is the man behind that motivation. I agree, I love Tom Nook. Even though sometimes he upsets me, I do have to admit that because of him and his hard work, I have a pretty good life in the game. So if you're mad at Tom Nook for constantly trying to trap you in debt, the developers of Animal Crossing New Horizons want you to cut the Tanooki some slack.  Tom Nook is not the problem. I guess the real problem is capitalism?


Hey, space responding DRLs, the idea being that you're trying to increase your inner response time from said behavior.  I'd like them to shut up and like not talk so much. So we're totally going to reinforce them for having a lower rate per animal.  So every time they don't talk for a period of time, they're going to reinforce them. So you're lowering their rates through their inner response time and they won't talk so much. And it's going to be great. Bye!


Hi, this question is about second-order effects, which you very conveniently Removed from so can you give an example of you know where these second-order effects are gets more important and This exponential doesn't really hold absolutely So the second-order effects by the way the exponential is a primary thing Seiznality will oscillate that there's a wave on top of it Poisson on top of that with a Gaussian error Depending on the mood of the people applies so the second-order effect has a very strong effect on The inventory planning because in 15-day error 30-day error. They're very different. However If I look at what is the primary on the mean? That's what I do in mathematical modeling. You're not going to get anywhere with everything modeled out I mean first my big thing was the first-order effect. So I agree there are other effects anything else go ahead I'll come to you a company dealing in high-value products should be more careful Because if it's life and death you don't want any one person spreading that you know what I almost felt like I died Maybe it was safe, but he shouldn't even think he was unsafe. There's some other question in this quarter Go ahead, please did I did it answer your question? Fan out increase that fan out What are we doing to increase the fan out? Just taking an example if you know that some guy is not going to talk a lot or some result and he's making a big purchase So maybe he might not talk about the big purchase, but if a small surprise is packaged with the big purchase He might get talking that is a very good point. So Liking it is not good enough They have to love it to talk about it and love it a lot to talk more about it So freebies and gifts go very far Especially with a big thing. That's why in some of the top like Michelin three-star restaurants.  You absolutely have to do that anything else Cool. Thank you. Thank you all very much. I enjoyed. Hope you did too


So, hello everyone, so I'm Michael Brudno and I'm going to tell you a little bit right now about genome assembly and how genome assembly works in general and a little bit more specifically how it applies to hyperboot sequencing and the stuff that I'm going to talk about. You will actually have a chance to play around with in practice in the lab, which is just conveniently going to be held sometime tomorrow. So, okay, so that was true to the mid-federate flight and, okay, so I guess to start with all of you are here because you want to learn about handling mid-generation sequencing or hyperboot sequencing data and building genomes based on that. And there are some questions which you want to ask yourself before you actually go about doing this. And the first and the most important one is what do you want to learn about the underlying genome. If you really want to get the whole genome from first base to last base or if it gets a bacterial genome from first base to first base and assemble it using just hyperboot sequencing, odds are you're out of luck. You will not be able to do this. You will be able to get pieces of it. Sometimes you'll be able to get long pieces of it, but you will never or almost never be able to get the whole genome from start to finish unless it's something like a very small virus. It's just basically impossible with very short reasons. So the questions that you may want to ask, are you interested in comparing this genome to some other genomes? Are you interested in what kind of variation then? Snips, larger structural variations. Maybe you don't really need to do an assembly and all you want to do is detection of variation. If you're interested in predicting genes, can you somehow leverage other sources of information? You basically, you know, building a genome from scratch is going to be very difficult to impossible. Very important question is, do you have a similar genome? So very often you will actually have a close species of bacteria which was already sequenced via Sanger or some other technique, finished, high quality. And then maybe all you have to do is, you can use that genome in order to help you build the one that you're actually working on. Very important question is, do you have made pair data? So if you have made pair or pair and data, it's very good. If you don't, you're basically, you should go back and get some pair and data. It's basically, what? Frank, this has no break from my question. So, that should be easy. So do you have made pair or pair and data? And another important question is, do you have other re-types? So if you have both Sanger-type data and high-frequency sequencing, Illumina or solid data, you should be able to use them in order to leverage things to work together. And another somewhat important question is, what computer power do you have available? For many of these programs that we're going to mention today, computer power is not at all trivial. And actually there are various trade-offs. Some tools will be very memory-intensive. Other tools will be very CPU-intensive. So if you have something that is, you have a ton of data, but if you have a machine with 32 gigabytes of memory, which is not that much nowadays, but that you will be able to run it, but if you don't, then you're stuck using other tools. And by the way, for everything that I talked today, please, please ask questions. I initially thought that this was going to be a one-hour lecture. And thank you, Francis, for running over. It's been making my lecture a little bit shorter. But yeah, so yeah. Maybe this is just for me, but I'm kind of confused with the mage pair and the data. Uh-huh. Yeah, mage pair versus pair and data. It's actually a very, very good question. I'm going to be mainly using the terms interchangeably. That's not the exactly, Francis is shaking the card, that's not exactly the right thing to do. So I'm wondering if I'll have a good slide on this. And I don't think I will in this presentation. But basically mage pair data, you have a piece of DNA where you're going to be sequencing each two ends. You're going to be going in from the left and going in from the right. And that's mage pair data. Pair and data, you're grabbing a piece of DNA, and you will be sequencing sort of two pieces of it, one after the other, at some distance apart. The reason that the distinction is important is that the sizes of the distance between the two reads is going to be completely different in the two cases. If you're sequencing mage pair data, you have to basically take your DNA and bend it into a circle so that you can start sequencing out from the same location. And for the DNA to basically bend it into a circle, it has to be some minimum length, several, you know, around 500 bases. If you're doing pair and data, it can't be too big because you actually need to have somehow be able to sequence two pieces which are next to each other. Does that make reasonable sense? Yes, but what is the biological relevance of doing or adopting these two techniques? So it's not, so the biological relevance is not really much. Some of the technologies will support one or the other. Some of them will support both. For example, in the case of Illumina or Selexa, I'll use Selexa because I can't remember to use Illumina every single time. So in the case of Illumina, I think they do not support mage pair data and Francis can tell me if I'm wrong, but I'm pretty sure they only do pair and. In the case of solid, they actually only do mage pairs. They do not support reading two reads, some distance apart from each other. And this is basically a difference in chemistry. So it's more what your technology can give you. Although I've heard rumors that solid will soon come out of the pair and strategy. I don't know if Selexa's coming out with the mage pair strategy probably there. Can't remember if I know. Sorry? We're going to ask, well, we can ask them about the solid. I'm pretty sure that I've heard this from multiple people, so that probably it's true. So, you know, given this, what are your options? Well, it goes back to what you want to learn. You know, variation within the species or conservation between species. You can try to build a genome from scratch.  You can try to do reference assisted assembly where you actually use a related genome in order to help assemble. But you will miss some differences between the reference and what you're trying to assemble. Especially difficult are copy number variants, and we'll have a chance to talk about that later. If one of the genomes has two copies of a certain element while the other genome has a single copy, that's very difficult to capture with the references to assembly. You can also just map each reference genome and not even worry about doing any kind of assembly. This is great if you want to discover variation, but if you don't have a reference, then obviously this doesn't go anywhere. Also, same problem that when you're doing reference to assembly, you miss things which are not in the reference, or which are hard to find in the reference. So, mapping versus assembly. You have a reference genome. You won't find snips and indents. This is called genome re-sequencing, and this is great for somatic mutation detection, snip discovery, structural variation discovery as we'll talk about. But one of the problems is dealing with non-uniqueness. You ever read with maps to multiple locations, what do you do with it? Maybe it maps perfectly to one location with one change in the other one. Well, then you can probably send it there. But it's even more difficult if it maps with one change to each one and maybe different changes. So you really do not know where to send this route. In the case of denomogenome sequencing, it's great. You won't have these problems, but you'll get gaps. You'll get pieces which you don't know the order of or the orientation of. It's obviously easy to try to combine this first with de novo and then try to take the result in context and map them. And that's actually a very, very powerful technique which is used a lot. Okay, so just sort of a little overview. Whole genomes have got to be broken. Start with a piece of DNA. You have a sequencing machine which generates these pieces called reads. And these reads are very hard to see for whatever reason that the colors got destroyed. These should be red and blue, not red and red. But there are two colors and these are reads coming from the two strands of DNA. Computer scientists like me tend to think that DNA is a strain and they tend to forget that it's actually a molecule which has two strands. So, minor detail. Then there is an assembler which is basically a program written in your favorite programming language which will hopefully will generate three contents. And after some finishing, you want to be able to get a final genome. And finishing is usually expensive and time consuming. So, how well the assembler works really will dictate how well you can do with this.  This is what Sanger data may look like. Lots of, you know, relatively short pieces of DNA. And assembling these was considered a really, really big challenge ten years ago. And they talked about how difficult it was to build the human genome. And today we have next generation sequencing which gives you a ton more reads but they're even shorter. The problem is very difficult. And here is an illustration actually of a mate there which is what we talked about earlier. Two pieces of DNA which are some distance apart. And this was a pair of hands sequencing and they would be placed in both ways. So, what an assembler.  It takes a set of strings over A, C, G and T. And it gives output a common super string of the reads of these strings. So, for example, you have these three strings. And this is super string that contains all of these, these ones. So, are there any people in the room who consider themselves sort of computer scientists? A couple, a few. So, what's wrong with this definition? Yeah, it's not just many. There's as many as you want possible outputs because I never specify anything about that super string. You can have a super string by taking these strings and sticking anything you want in between them and that would be a perfectly valid super string. And while that may seem like a very pedantic point, it's actually a relatively important point because any way that you're further trying to constrain the problem may lead to problems in the assembly.  And this is, you know, before we even consider such things as sequencing errors. You know, there's as many outputs as you want. So, initially when people thought about this, they said, well, why not the shortest common super string? And, you know, there's multiple ways of putting these reads together. Why not find the one which is the shortest? Well, there's two problems with that. One is sort of a computational problem and the other is a biological problem. The computational problem is that this is what's known in computer science as NP-hard, which means that it's very, very difficult to do computationally. A bigger problem was that this is biologically not very sensible because it leads to something called over-collapsing of the repeats. Imagine that you have some segment which is present in genome multiple times. There is absolutely no reason for you to use it multiple times in building the shortest common super string. In the end, you will get all the many copies of the repeats collapsed into a single location. So, this was the first earliest approach to this and it turned out to be exactly the wrong thing to do. There have been alternative approaches which have been proposed and these are based on something called the brewing graphs which I'll talk more about in the string graphs. These two have been... So, if you read Dulliver's Travels, they have this war about big Indian versus little Indian.  So, this was meant to be a parody of religious wars in Renaissance and early modern Europe. So, the brewing graphs and string graphs are very much like that. There really are two sides of the same egg and the groups have been... I wouldn't call it a religious war but close to it about which of these approaches is right and there will be people who will add them and we say our approach is the brewing graph approach and that's the exact right way to do it versus the string graph approach. There really are two sides of the same egg. One of the key things is that both of these formulations are also NP-hard. They're formulating these approaches. If you read the papers, both of them try to hide it. The fact that they make arguments about that their approaches are computationally easier for one reason or another, it's not true. They're all NP-hard. There is no free lunch. Well, if they're really hard to solve computationally, can we, you know, why try to do it like this? If this is going to be computationally really difficult? Well, an answer is that there is a big difference between theory and where it's NP-hard in practice and whether it's actually can be implemented. To explain this, I sort of have a little roughing joke where biologists and mathematicians were asked to predict the outcome of force races. So, you know, a biologist decided to look at all the forces and study their evolution and study their lineages all the way back to the time of Genghis Khan and came up with a model which was able to predict the winner of a force race 50% of the time and was very happy about this. And the mathematician drew some integrals at some complex formulas and the tan said, hey, I have a model which predicts the winner of a force race is 100% of the time, but it works for a spherical horse in a vacuum. So, this is the difference between theory and practice. In theory, it's NP-hard. It should be very complicated. In practice, we have very good ways of making the things work. So, what are these deploying graphs, these magical contraptions which supposedly make things easier? And imagine that you, again, have a set of strings and nodes, so we're going to build a graph. Hopefully, it's a graph that doesn't scare any of you. A graph that has nothing to do with a graph paper. It's a thing with nodes and edges. So, nodes in the graph will be k-1-mers from the set of strings. So, this is a set of strings that I got. Every single k-1, so this is a k-mer, a string defined k, k-1 would be 2 in this case, and every single one of them should be somewhere in this graph. Wow, I managed to get the buttons. Okay, so AG is right here, you know, PC is right here, and so on. And just sort of a little bit of terminology. The set of k-mers in the genome is called the k-spectrum. And finding the shortest string with a given k-spectrum is something called the Chinese Postman problem. This is a classical problem in computer science, and Fez 189 sort of showed the way to do this, that if you had every single k-mer that was present in the genome, you could find really easily, computationally, the genome that has all of them, the shortest genome that has all of them. And it works by something called Eulerization. And this has to do with a classic problem in computer science called the Bridges of Königsberg. So the question, there was a map of the Russian, now Russian city of Königsberg, where the city had islands and bridges, which connected the islands. And the question was, could you traverse all of the bridges of the city without going on any bridge twice? And the solution turned out to be, you know, whether this is possible turned out to be a really simple problem. You can do it as long as from every single, and return to the same place. So traverse every single bridge and return to your original location. You can do it if and only if every single island is bordered by an even number of bridges. If there's an even number of bridges from every island. If this is the case, you can do it. If this is not the case, you cannot do it. So the same thing is true here. If every single node has an even number of edges going, or equal number of edges going in and going out, you can do it. Otherwise, you can't. But in this particular case, you know, you cannot do it. This graph had initially two edges coming in, but only one edge going out. The second time you come into this node, you will never be able to leak, which is why you wouldn't be able to do it. So, but there is something called, there's a technique called Eulerization, which is to make me graph Eulerian. And Euler was from this, lived for a while in the city of Kenningsburg, which is why, you know, it's called an Eulerian tour. It was one of the first to formulate this solution to this problem. So, to make the graph Eulerian, basically you add edges to it, so to make it balanced, so that you have an equal number of edges going in and going out. So, this edge, this red edge, actually two edges, two here and then two there, will make it balanced. Yeah. Most of it. So, when you talk about K minus one more, like from their team, other than your particular tumor, is there any specific way to do it? Yes. So, what I did is this. I should have been a bit more specific about how I did this. So, every single K minus one more became a node. Every K more becomes an edge now, between two nodes. So, if you have a greener GCA, that becomes an edge between GC and CA, so that this edge really is called GCA. C is common there. C is common, exactly. So, actually K minus one letters will be common. So, GC to CA. So, this edge becomes GC. So, yes, thank you. That explains why everything else I said afterwards, which is that, you know, every single edge becomes an original tumor on the graph. And now you want to go through the graph so that you traverse every single tumor, every single edge at least once, but as few times as possible. So, that's exactly the formulation. Yes. Thank you. So, this polarization can basically add the purest number of edges to make the graph Eulerian or balance. And once it's balanced, you can go through every edge exactly once. Okay. So, another important thing about DNA is that, as I mentioned, it's a molecule. And the graph which I just showed you, one of the things I had was that everything was a string, actually. Right? Everything here was just a regular string of DNA. A string of letters, ACG. How is DNA different from a string? Well, it's got the two strands. So, this is actually what DNA looks like in a cartoonish way. So, the question you may ask, how can two DNA molecules overlap? How can, if I have two DNA molecules, how can I join them together? And it turns out that there are several ways of doing this. So, imagine this AC and TCTD. They could overlap by this letter C right here. Or, imagine this AC and this TCG. I'll have to flip TCG in order to get the G to align with its C. Right? So, in the reverse trend. And, in this case, I actually have to flip both of them. So, AC gets flipped and, well, no, I flipped the first one. Sorry. Flip neither, flip the second, flip the first. So, these are the possible ways of having things overlap. And this can be modeled by something called a bidirect graph, where I actually have a direction on, not on an edge, but on a node. And there's three types of edges in the bidirect graph. They are called sort of, there's a regular edge, an out pointing to an in, but there's also an out pointing to an out or an in pointing to an in. These model the ways that DNA molecules can overlap and the fact that DNA is actually not a string, in reality, double stranded. And bidirect graphs actually turn out to be really similar to directed graphs in that a walk, you can define something called a walk on it. So, a very valid way of traversing it. But this is going to be, there's going to be a little bit tricky. For example, this is not going to be a valid walk. Why? Well, because, here, you start at an AT. And then you go into a TT, right? From an AT it will overlap a TT by one T, so you can go into this node. But a TT does not overlap a GT or an AC. Only AA does. So if you go through like this, that's not really a valid string of DNA, which you cannot never parse a valid string of DNA from here. So the key thing in order to figure out walks and bidirect graphs is that you always have to balance your edges. So if you go into a node on an, something just pointing out, you have to leave on something that's pointing in. So from here you can go down or you can go, but you can't go straight. And from here you can go down as well, but you can't go straight. And you can see this will actually spell it out string of DNA. This will be GT, TT, TT, GT. So the cool thing about bidirect graphs is that they completely let you forget about double-strandedness once you sort of get them into your head. Take a look at this. If you start from here and do this walk this way, you will spell the following string. G, G, C, A, A, T. What if you take the exact same walk and walk it backwards? You will spell A, T, T, G, C, T. And what's the relationship between the two strings? They're the same string of DNA. They're the same DNA molecule, just the two strands of it. So the two walks on the bidirect graph, whether you walk it one way or another way, will actually give you the two strands of DNA. This is actually a great example of theoretical computer science inventing something and then finding an application for it. So bidirect graphs, as they relate to computational biology, were sort of invented, but this was found by Kechecheoglu in 1992. And then we were doing some research in bidirect graphs and decided to Google them. And Kechecheoglu in 1992 didn't have Google. That's a great research tool. So we Googled them and it turned out that bidirect graphs were invented by theoretical computer scientists back in the 1960s. The problem is, you didn't know what to do with them. So they were basically forgotten. He invented this sort of formulation, but who needs it? And nobody used it. And then Kechecheoglu used it but without realizing that they were known back in the 60s. So this is, for example, the bidirectness using the brewing graphs. So imagine that you had this DNA string. These are all of the camers which you would generate from it. And you would build some graph which looks like this if you were dealing with regular strings. Unfortunately, what you really have is for every single camer, you have a traverse complement as well. And for some of them, the reverse complement is itself. If I have any examples here. And the graph would actually look like this. And what you need, right? Once you add all of the reverse complement to camers. And what you need is now two walks which are reverse complements of each other. And this is obviously somewhat, you know, doesn't seem as obvious how to do anymore. But by going to a bidirective graph, it's very easy to see that there is a simple walk through these nodes which uses these two twice. So bidirective graphs are really an easier way of thinking about DNA sequences. And almost all of the assembly tools now use these bidirective graphs. They're really elegant from a computational perspective. So what are the downsides of the brewing approach? What is the first sort of, you know, the first thing they do is they take the DNA sequences which you've got and split them into camers. Split them into strings of length tape. And there really isn't any huge motivation in order to do it. There's actually very often very few disadvantages to doing it. When you're dealing with really, really short reads, your key will almost be the size of your read. And then you don't lose much information. But if you're dealing with longer reads, then there's obviously room to lose information by splitting your read into subsegments of length tape. And all of the brewing based approach, they will do the split into length tape, and then they will add really complicated computational steps in order to go back then and to reconstruct the reads. Because you could theoretically get a path which is not compatible with a read if it's longer than tape. Because all that we asked about is that you go through every single camer. It's also, well, so this is a big argument, actually. So it is very going to be very significant to sense the sequencing errors. Because as soon as you change one letter, well, you destroy that whole length tape and you will get multiple paths for the same thing. This one is actually quite controversial. Not memory efficient. People who believe in the string graph approach will say that the brewing graphs are not memory efficient. Because you have a node for every single string of length tape. People who do the brewing graph approaches will say, no, no, no, we are the ones who are memory efficient. It really is a trade-off. If you have really, really, really high coverage and really short reads, the brewing graphs are more memory efficient. If you have lower coverage and longer reads, they become much less memory efficient. So there is a trade-off. So the goal of people who believe in string graphs is that there should be one node per read or better. There should be a way of reducing the number of the memory that's used by the graph. That the division into camers, since it's really arbitrary, you shouldn't have to do it. And there should be flexibility in the presence of sequencing errors. And this has led to sort of work on string graphs, which are based on something called an overlap graph. Here you will start with your sequences, your reads, and you will not split them in any way. You will just directly build the graph from them. The nodes of your graph will be the reads. So every single node goes through it. The edges are overlaps between them. And, for example, the weights of the edges are lengths of the non-overlapping prefix. So, for example, ACG to ACG-TAC overlaps TACAT, right? And three letters are shared. The prefix is ACG. So that will be the weight of this prefix. Here ACG-TAC versus CATAC. There is no overlap. Well, there's no overlap by one letter, C, right? And so the prefix is five letters, ACG-TAC. So... So this is... Usually they are... It really depends on the actual assembler. Usually indels will... It's porcanger reads and 454 reads. You definitely need to allow indels. For things like Selexa and solid reads, you usually don't allow indels. But, yeah, that's... They could be allowed. For what? Oh, basically... So whenever you have an overlap, you have two pieces of DNA, which you actually have read, you know, from two different reads. If there is a sequencing error, which is an indel, you want to be able to model it and hence you will allow for overlap, which are not perfect, and including indels or sequels. I guess I also wanted to ask what's the reason or why you would want to allow indels for Selexa and solid because of a deep coverage? Yeah, so for solid and Selexa, the reason indels are not allowed, typically, is that... Well, for solid, because of color space, indels are relatively tricky. And if you ask... So the A-B, people that I've talked to claim that there are no indel errors in their read data. I think that may also be due to the fact that the tools that they've developed for working with color space data do not allow one to find indels. But, you know, the relationship between them is somewhat murky. So for Selexa, the fraction of indel errors is very, very small. And as a result, because of the high coverage, you're basically better off discarding the read with such an error and dealing with everything else. So, okay, so I'm making this a directed graph. In reality, this would be a bi-directed graph. The exact same way that I showed bi-directed graphs earlier. So in reality, all of these, so ACGTAC, the same exact node is also GTA-CGT. And that will overlap with other screens in whatever way. And this would be a bi-directed graph in practice. I'm just making it directed for simplicity. So, and then what you do in this graph is basically try to find things like cycles and paths, and longer paths that could be supported. So for example, this could be a circular walk, for example, ACGTAC. It would be TACATAC, and then GTAC, and then back into this one. So this is, for example, a circular walk on this, in case you're assembling a bacterial genome. There is something called transitability and permeable overlaps. So for example, ACGTAC, TACAT, and then this TACAT. This walk will exactly spell this overlap of one letter. So this overlap is something which is called transitability and permeable. It can be inferred from these two edges, from this edge and this edge. So these are overlaps that we'll want to remove from our graph, because they're actually redundant. Okay, so to build a string graph, you start with an overlap graph and remove these transitability and permeable overlaps. So you start with something which will look potentially like this, and you remove all of these red edges because they can be spelled directly. And you end up with a graph which looks like this, and I apologize if I can't see these. There are also nodes between these, but for some reason they're not coming through. The next thing you do is collapse chains. So from here, there's this read followed by this read, by this read, by this read, and then to this read. You're going to start walking along this path. You've got to finish it. You have no ways of leaving it. So all of these can be collapsed. And then all the edges get separated into two classes. There are required edges. These are edges between two nodes, between two nodes that had internal vertices before. So all of these, this path had internal vertices, so this is going to be a required edge. This is this node. This is going to be a required edge because there was an internal vertex right here. This is going to be an optional edge. The reason, since it doesn't contain a read, you're actually not sure that it's part of any path. So this is basically all that there is to string graphs. You build these, you find these required and optional edges, and then you output the required ones as your assembly. So the way that Myers formulated string graphs initially in 2005, the goal is to find the shortest path using all of the required edges and any optional ones. He had some suspicions that maybe polynomial time may be easy to do computationally, turned out to be hard as I mentioned earlier. Well, once you're done with this, so at least the slides are out of order. I'm going to do them backwards. So from paths to context, do you have reads when they overlap each other? This is what a path in your string graph may look like. But there could also be a repeat region in which case you will have multiple reads in here where other reads are coming from a completely different genomic region. So this is going to actually lead that there is an edge going here, an edge going here, an edge through, and then edges would split. So this is going to be what your shape of the graph looks like. So you will typically merge reads up to potential repeat boundaries. This is like collapsing these paths. But what do you do with these? So, sorry, the two reads were out of order. Well, the way you can think about this is that you have an edge is going in, then a repeat region, and then edges going out. Well, in order to figure out what is the right way of joining these together, you can use mate pair data. And this is why mate pairs are so important for assembly. If you have short reads, all of these edges will be relatively short because you very often encounter repeats which will collapse the paths. But you can use mate pairs by looking at this. So you have red and green things, and they're somewhere nearby each other. There's some piecing between them, but it's not, hopefully, not too big. But then they should have mate pairs, right? So this read will be paired with this one and this read will be paired with this one. And the question which you may want to ask is, well, is there a short path between A prime and B prime? Because if there is, that implies that these two edges should be next to each other as opposed to these two edges. So you can do something called extra sort of path algorithm to find where A prime and B prime are close to each other and then join, if so, join these two edges together and as a result, resolve the street. So you've got a reading region. You've got unique edge going in, unique edge going out, unique edge going in, unique edge going out. So you want to know which of these unique edges sort of belong with each other. The way to do it is you look at the reads which are only in unique regions near the reading and look at their mate pairs which are located some distance away from over there. And then you ask, well, are the pairs close to each other? Because if the pairs are close to each other, that means these two regions are also close to each other and hence the right path through here would be like this as opposed to like this. This is only for mate pair, right? This is exactly. So this is why pair and data is so important for mate pair data. Because unless you have these paired reads, there's no way. As soon as you get to any kind of a repetitive area, and oh, this is something I'm not mentioning, but when people talk about assembly, so when biologists talk about reading, they usually think transpose on something like that. When computer scientists or people who do assembly talk about repeat, there is a very simple definition of a repeat. This is a piece of DNA that is longer than your read length that appears multiple times in the genome. A repeat is defined relative to your read length. The shorter you read, the more repeats your genome has. So if you have reads of length 25, then even short homopolymer stretches are repeats now. So making assembly very, very difficult. Okay. And the final stage of assembly is consensus calling. You get these reads. They got errors, for example, sequencing errors like indels or in the letter changes. You do multiple alignment of these reads. And at every single position, you follow the most common letter. Or alternatively, you can take the best quality letter. So all of the reads have quality values. It's sort of the likelihood that they're mistake. I'm actually not going to talk much about quality. Mike, are you going to talk about quality values? Yes. You will. Okay, great. So you'll hear more about quality values. But you can use the best quality value or a consensus of some kind by weighted voting or any other way of predicting the best letter here. And by now there are many, many, many assemblers which are out there. For example, one of the most popular ones and ones you will see used most for, especially selected data is velvet. And this is a comparison from the velvet paper which came out a year and a change ago. They claimed that they were able to do a much better job than all of the previous assemblers and they were. I mean, now there are other tools which do almost the world velvet. Still sense to be the tool that everybody uses. In bioinformatics, there is, and I think in all of science, there's a huge conservative mindset. Once you get a tool which works, you don't care about better tools which come along for better or worse. There's a new tool which is 5% better. Who cares? If it's 50% better, probably people will switch. So here they claim that their error rate is 0.02%, 97% coverage and M50. So whenever people compare assemblers, they talk about something called M50. The M50 is the length of the content in which you have half your genome. So in contents of this length or more, you will have gotten half your genome. This is the typical number which all people who compare assemblers work with. You typically don't want to look at your average content length because that will be very, very short and not very meaningful because what you end up with is lots of long pieces and lots and lots and lots of really short ones. So people will typically talk about M50 score which is half the genome is in pieces this length or more. So 8.5 feet or I think that's the least. And the previous assemblers were available for really a lot of work. There is by now, so by now there is a huge set of assemblers available for next generation sequencing. So velvet was the first one which had really spectacular results for short pre-assembled, all paths as well had showed very good results. On the other hand, I've never run all paths and I've never seen anybody outside of Wiped who has run all paths. So the results reported that the paper are absolutely spectacular but I have an extra theory. Euler is the original sort of Eulerian assembler based on the brewing graphs out of the Kemsner's group of nine papers, ultra short reads, USR, this is beginning to sound like airplane names but they claim results better than velvet in that paper. And the paper, I think the paper title is something like this length matter in the name. So all of these tools which I talked about work with letter space and it turned out that assembling in color space becomes a little bit trickier and as Francis promised I'm going to now actually tell you a little bit more about color space. I'll explain color space to you again and this is what color space reads look like and this is what a consensus may look like when you're trying to call consensus. Here I have reads and they start with a letter and then have a whole bunch of numbers and if you didn't get what they meant in the first time we'll try this one more time and you could try calling consensus based on the actual numbers while it's got screwed up the fonts are completely wrong. This should be all courier so that you can actually see consensus. Sorry about this. Should I edit this? This is courier. What's going on? It should look something like this. I'm not sure why it got screwed up in translation between the PC and the Macs. Something went wrong. But you can see that these should align and they should give you a consensus of some kind of logic for this. Sorry? The binders. The printout of the binders is fine. Yeah, but see, that's why you know we should settle on one kind of technology either if it's newer or Mac. Sorry, consensus is tricky. Okay, well, sorry. What could be a PC's fault? It's a Microsoft. Okay, I'll just blame Microsoft. That's a lot easier. Ah, great question. What are the three to try to fill out? So these are reverse complemented. Color space works tricky. There is no reverse complement. There is no reverse. But you have to push the letter through. So what I have actually done is I've just threw the reverse the whole week. So you don't, it's actually great for those of you who are unique tax and if you're not, you will become one by the end of my lab. There's a great command in Unix called Rev which will take a string and reverse it. So it's great for color space. So it's, so you just take a string and reverse it. So you can read from the opposite string, which is why the letter is at the end. So this is basically to be a multiple alignment of these three and then you call consensus and dot and get some string which has a letter and a whole bunch of numbers after it. And you can then, as Francis pointed out, since you have the first letter, you can translate it through. You can take and apply these rules and translate it. The question which you, which you may wonder about, what happens if you get one thing wrong? Well, if you have a regular string of DNA letters, you screw up one letter while one letter is wrong. It'll have no effect on the next letter or the previous one. That's how consensus works. You call every single position independently. So what happens in the case of color space? So let's go back and try to explain color space again. I took me probably a month to figure out color space. So if you guys didn't get it the first time, it's probably fine. So this is how I explain color space in a slightly different way because that's how I understood it. So this is what the color space read looks like. The letter followed by a whole bunch of numbers. The letter is part of the letter which joins the DNA to the slide. So it's actually known. You know what it'll start with. And then you have a whole bunch of numbers. Well, Francis tried to explain it to you using this table, which is what EDI gives you, the stable to have it figured out. And in case you haven't gotten this from the first part of my talk, I like graphs. So instead of using tables, I prefer to think of graphs. So this is what's something called in computer science state automata. And you have letters. And then you have numbers on the edges between them. And what you have is you're at some letter and then you see a number. And that tells you where to go to. So you start at the T and you see a zero. Well, it tells you you should stay at the T because all the edges coming out, there's a zero, one, two, or three edge. So you stay at the T. Then you see a one. Then a one goes to G. So the next letter should be a G. G, followed by a two could be an A. Back to G, C, G, D, D. So this is what color space, this is a simpler way I think of thinking about color space. You're at some letter, you see a number and that determines where you go in this sort of little state diagram. This also it'll become a lot clearer now what happens if you get something wrong. What happens if as you were reading this read you got one color somehow wrong. Let's say you got this one where the zero is wrong, you should have gotten a two. What will happen? Well, we'll start at the T go to a T to a G to an A and say it's an A. So we'll get that letter wrong. So what will happen with the next one? We will go to a T an A a G a G and A and we get this string and if we compare them it's not so this I'll blame on the Microsoft as well it's not showing the second part of this but you can see that actually the two strings are completely different after the color space error. So if you were to call color space consensus by just taking the consensus of all the colors and then translating through if you get one letter wrong anywhere you'll get the rest of the genome wrong. So probably not a very good idea. Will not, that will not really work. Well, actually there's ways of getting around this problem and this it has to do with the fact that these things called translation. Let's forget about the first letter and let's just think about the colors. What string of DNA could this represent? Four different strings and how will they be different? By the four different first letters. And let's also think about the sort of string with an error just in parallel and these will be different from this if they started with a T. But for every since there's four possible strings depending on where you started when you started to know you'll never the two paths will never agree. So for this string with a zero there's four possible translations starting from A or C or G or a T. So if you start at the T which is where you're supposed to start because you know the first letter of a linker you will actually be able to go through and translate up until the error and you get the right string you get from G. But the string after that is going to be completely wrong as I pointed out. But do you see anything interesting about this sort of set of strings compared to what we expected to see if there were no sequence in errors? One of them has the right sequence. One of them has the right sequence. Which one? The second one. Yeah this one's wrong but this one this is wrong but this part actually matches it exactly. So if you basically when you have a sequencing error it's not that the rest of it is wrong. It's still right but it's just in a different translation. You have to figure out which one of the other translations to use starting at that point. So you can compensate in some way and jump to a different translation. So how would we know when we need to jump to a different translation? Really you have an additional piece of information. The piece of information is the first letter of all of the subsequent strings. Because you got that letter at some point I'll tell you what the right frame should have been later on. So you can work your way back. So this leads to something called the hidden markup model which you know I'm going to skip the details of this. If you're really interested in hidden markup models I'll be glad to talk to you offline about it. But I'm definitely so the key thing is you shouldn't just call colors because colors are redundant. If you're calling consensus for example 00 could be both an A or a TT. What you should do is you should call things both the colors and the letters simultaneously. Let's consider two positions on the genome.  Let's call it X, Y and X, Y and Z. So three positions. Well X, Y is a pair. So it should correspond to a single color. So this is these are all the possible pairs of nucleotides that you could have at that position. And YZ is another pair. But the two of them actually overlap by a single letter. So the middle letter has to be the same. So what we will do is we will allow one to give a walk sort of this graph where every single position you have this, you know, a set of 16 possible nucleotides but only allow it to follow from where the second letter will match the first letter of the next. So similarly anything which ended with a T can go to a TT or to TA to a TC so on. So this, if you're familiar with hidden Markov models, is a hidden Markov model. If you're not, don't worry about it. So then you have something called emissions. And for, you have color reads. So for every pair you have a set of colors which would be emitted by the pair. But you also have letters. If you have a T followed by two, you will know that the next letter is going to be a C. So this tells you which of these four frames you should be in. That at this point you should really be in the C frame unless this two was wrong. So in the hidden Markov model you have these emissions. You have color emissions where an A is likely to generate a zero with high probability and everything else with a low probability. TT, exact same thing. With high probability it will generate zero and with low probability anything else. These are basically the probability of the sequencing error. But as far as the letters go an A, A is likely to generate an A. That's the second letter. And so that's the letter that you want to generate. And everything else with low probability while a TT will have with high probability will allow for the generation of T's and with low probability anything else. So what this lets you to do in the context of a hidden Markov model is to say for every single position what is the likelihood of the state that you are in. So for example you could say I'm most likely in the state AT. And hence call the letter T at that position.  So this this is completely screwed up for a reason which I do not understand. So what this shows is an output of a program which we will use tomorrow is you get a set of colored face readings and you get the genomic sequence but also for every single letter you sort of get a confidence. So a 9 indicates a high confidence a 0 indicates very low confidence in that letter. And then at some point the program just said I can have no more confidence to call these letters. As far as I'm concerned so it will output so this is a program called I Here Which You Guys Will Play With Tomorrow. So just to recap most key points of what I've been trying to tell you over the course of the past hour. So there is for different types of data you need different assembly approaches. So for shorter reads the burning graphs actually capture most of the information that you need to read data. Velvet and other similar assemblies will do a great job with short reads. Velvet is a debroying graph based assembler and so is Euler-USR. For longer ones string graph like approaches are better. So approaches so I here for example actually uses an overlap string graph based approach not a debroying based approach. For longer scope assembly when you're trying to really do the genome from scratch read length is really key and in the absence of read length you need makers. So and because read length is really key there are many efforts to combine various types of data so there's 454 reads are now the long reads. There are almost 500 letters now where Sanger was 10 years ago so there are many efforts which tend to combine 454 data with either solid or selected data mainly selected because color space is trickier and people are just sort of beginning to figure it out from a computational side. But at the same time color space is tricky but can handle with the right computational tools. So as a biologist so basically if you're a biologist and that's the goal of the computer scientists in the room and of the bioinformaticians in the room you should be able to hide color space away from the biologist by making everything look like letters. So and the key to this is the right computational approaches. Okay so thank you and I'll be glad to take any more questions.


Meat Boy is back and today we are making Swedish meatballs carnivore style. The original recipe for Swedish meatballs is actually just about carnivore. Both the meatball component and the sauce is fairly close. We do have to omit and substitute certain ingredients and this also depends largely on your dietary restrictions. You know depending on whether you want to use certain spices, seasonings, small amounts of plant-based foods will be up to you. Let's take a look at the ingredients. For the meatballs, the carnivore ingredients are beef or pork, two pounds combined. Most recipes call for equal parts beef and pork. Today I'm just using pork that I have from a local farm, better omega-3 to omega-6 ratio, higher nutrient content. This is some raw heavy cream that I got from a local farm. Traditional recipe is half a cup of milk and one ounce of heavy cream. Since my pork is so wet, I'm just going to add heavy cream to it instead of the milk and the heavy cream. This is one egg that is traditional in the recipe. You know again from a local farm, soy-free, higher nutrient content. Here we have some Parmesan cheese that is actually substituting the breadcrumbs that you would normally put in the recipe. So this adds a lot of salt. We don't have to add salt to this. This has like a fermented umami flavor and it's trying to replicate some of that texture we get with the breadcrumbs. Normally you would add like fresh onion, fresh garlic diced up to the meatballs. If you'd like to do that by all means, but that is not carnivore. Seasoning wise, this is up to you. Some carnivores do add spices. Grilled Dica is a Swedish grilling spice. This is really similar to that. I found this at Whole Foods, $7, completely ridiculous. I felt the pain in my heart when I purchased this. You could also use allspice. This is basically just like paprika, garlic, black pepper, chili pepper. You could look up just Grilled Dica Swedish seasoning and see if you have the ingredients at home. The meatballs are going to be sauteed in butter. We are also using butter for our sauce. So the sauce today is going to be beef broth, heavy cream, butter and fish sauce. Normally you would add flour to the sauce to thicken it. Since flour is obviously not carnivore, we are going to reduce the sauce a little more and add some more heavy cream to make it thicker. Sometimes you'll actually see soy sauce, mustard, even parsley in these sauce recipes. So I'm going to use fish sauce. This is by no means traditional, but this is going to add more depth of flavor and I think it's going to make this really, really tasty. Some of you might know about colatura, which is the Italian fermented anchovy sauce, similar to the ancient Roman seasoning garam. Keep in mind that the measurements for the meatballs can be consistent, but the sauce varies subjectively because I have some very thick, heavy cream here. This broth is different than the broth you're using. One thing I will mention about the broth is this actually has fat in it, which is hard to find. If you guys are looking for a beef broth or a chicken broth or anything, the fat is where the flavor is. That is what you want. You know, this is an organic beef broth. It has fat in it. This is probably the best thing you're going to find taste wise in any market. Again, for the meatballs, we have the two pounds of pork. I'm adding half a cup of parmesan. We have the one egg and I'm going to do about half a cup of heavy cream and you guys can see how thick this is. Just a couple tablespoons of the grilling seasoning. Maybe like two. We got everything nicely mixed up. Probably eat this raw if you wanted to. Tastes pretty good. This is what I wasn't looking forward to because normally when I make meatballs for my family, I just make these giant meatballs so I don't have to roll out like 30 tiny meatballs. But no one likes big balls. This smells so good. I just want to take a bite out of it now. I could have just done the raw carnival version. We would have been done already. You could even spread this on some sourdough bread. Oh man, that would be so good. Put it on some raw cheese, some slices of raw cheese. You know, this is a great recipe if you want to feel like a vegan and spend half the day in the kitchen, but yeah, I think it's nice to do once in a while. So we got the pan on medium heat. I'm just adding some butter to it. I don't really want to add too much because the meatballs are so fatty. They're probably just going to render out. I'm going to even take this out of the pan. These are so moist, I'm going to try to be really delicate when I'm handling these in the pan and try not to crowd the balls too much. You know, this isn't like a New York City gym or anything. All right, never mind. We're going to go to Equinox. We'll crowd it up. While we are browning the meatballs, we want to reduce the stock. Frankie Boy is always prepared and ahead of time. So I already reduced four cups of beef broth down to about a cup and a half. Again, I used, you know, one container of organic imagine grass-fed beef broth. This is four cups. Put this in the pan medium heat, you know, simmer down for about 35, 40 minutes down to this nice, reduced, heavier stock. So we'll add the fish sauce now, maybe about a tablespoon of fish sauce. Now we got to get these brown as soon as these are brown. We could take the meatballs out, deglaze the pan with the reduced beef stock.  If you like brown balls, you can eat this cooked. There you go. You can appeal to everyone. So these went for about four minutes on that side. I'm guessing it's going to be, you know, maybe five to six minutes. And then we'll take them out. You don't have to get too crazy with this. But I do like to cook the meatballs on as many sides as possible. Now in the pan, we have that beautiful brown fond that is super flavorful.  So we definitely don't want to get rid of this. This is really simple. We're just going to add the reduced beef broth into this. As you could see, there's this thick layer of oil on top of the sauce. You know, it's not emulsified at all. It's completely separated. We're going to add the cream. This is going to thicken this up a little bit, as well as cool it down. But we're still not going to have the sauce emulsified. So this is where flour would make things a lot easier. What we could do, cool down the sauce a little bit, and then we'll add in cold butter. As we add in the cold butter and the cold butter melts, it should help emulsify the sauce together. The butter has to be added cold, and it has to melt slowly while you're whisking it. That's why I have this off the heat. I actually want to put this in the other pot to cool it off even more, because the pan is still hot on the bottom. I was able to emulsify the sauce without breaking it. You know, but as you can see, this is not the easiest culinary technique.  And if the sauce gets too hot or the butter is not cold enough, the sauce is going to break, and then the fat and the water is going to separate. And you're not going to have a nice, smooth sauce like this. What did make this more difficult is the large amount of fat that was in the pan from the rendering process. But let's give this sauce a taste and see what we need. This is textbook Swedish meatball sauce. Very good. Let's plate some up. Not bad for first attempt, right? So the sauce is pretty thick. You know, to get this sauce any thicker, you'd have to reduce the beef stock to a glaze and then just add the heavy cream to it. You know, without having flour or like a traditional thickener, very, very difficult to get a super thick sauce without using an incredibly large amount of beef stock. That looks great. The meat's nice and medium rare in the middle. This is slamming. This is pretty good. You know, nothing really stands out. It's like a nice balance of flavors.  For those of you that haven't met my lovely sister, Gina, she's been following the carnivore diet for, what, like two years now? I think it's been three years. Three years. Not quite that long, but just about that long. I thought it was two years long. Today I made some Swedish meatballs. And I wanted you to try them. And for the past week or so, she's been having like regular meatballs with red sauce. And she just really eats the red sauce. She doesn't like the actual meat. The sauce is good. This one's cute. Wait, I'll drop it. Come on, get it. It makes more sense if I just eat with a fork. Meatball's hard to stick. Have you had Swedish meatballs before, Gina? No, not as good as this. How do they compare to the other meatballs? These are more fresh and tasty. That one tastes like dry hockey pucks. I would, okay, well. Please order from Frankie's Free. Yeah. They were just shorting it. So for those of you guys that don't know on Frankie's Free range meat, we do offer high quality, nutrient dense animal foods at an affordable price. We don't exactly have ground pork at this point in time, but you can definitely try this recipe with our ground beef. It's taste-tastic. If you guys could please like the video, subscribe, hit the bell icon. Of course, above all guys, please do share the video. As I just said, definitely check out frankiesfreearrangemeat.com. You can also check out Frankie's Naturals for minimal ingredients, minimally processed hygiene and cosmetic products. Thanks again for joining me, guys.  Just so you know, everyone, all of this is all natural, no makeup. Well, I guess we're gonna have to break out a towel for that one. And I'm not wearing makeup either, which, you know, give me one of those things. So there probably aren't too many vegan fairy boys watching this video because most of them just watch the vegan videos, but. But out of all of them, you're the hands-missed one, Frank. Out of all of what? All of the guys. In general, right? In general. Yeah, in general. You're pretty good looking. So as you know, we can tell you guys that we look alike. He's the hotter version of me. Why wouldn't say that? You just need to get a little sun. Come on, you're like a Greek god compared to me. I wouldn't say that, Gina. But thank you for joining us. And let us know what videos you guys wanna see in the future with Gina. You know, if you guys haven't seen it. I'm giving him to anything. If you guys haven't seen. Nothing weird. The, you know, we did a day of eating with her. I'll link that at the end here. We also did a couple of other videos, you know, showing her diet, talking about our diet, talking about her weight loss journey on the carnivore diet. So definitely check those out, too, guys. And please buy his hair care products. Buy him now.