The weird circle. In this cave, by the restless sea, we are met to call from out the past stories, strange, and weird. Bellkeeper, hold the bell, so all may know we are gathered again in the weird circle. From sea farrow, and as I've often said, I'm willing to learn, learn to do anything. As long as I can earn me enough to keep a roof over my head and food in my stomach, it was at lunchtime yesterday that I first came to this blasted town a new port in Monmouthshire and took myself to the nearest inn to see about a bite for the inner man. I stood at the doorway looking in and who do I see? But me old friend and pal, Grigsby, that greatest thief whatever lived. Yes, it was Grigsby. Looking as if he'd never slept on a London bench in his life, eating as full and arty a meal as I've ever seen. At the same time I see him, he sees me and he shouts, Battle! Well, well, well! Grigsby, my boy, you're looking as well as the Prince of Wales. I'm fit. Yes, fit farrow. Fit for what? Whatever you've got in that pig like mine. Sit down, farrow, sit down. I've been looking for a man like you. I want you to meet a friend of mine. Bender, this is Sea Farrow. Glad to meet you. Cheery sort of bloke. What? He ain't very cheery, but you get used to him. You have to. He's a friend of mine. Why not? Since you're asking the question, Mr. Bender, you're the one that must answer it, eh? Have you eaten lunch yet, farrow? Lunch is it now. Getting swayed, eh? I haven't had a morsel in three days. Roke again, farrow, eh? You'll never learn to make your way in this world. You've got to make some plans for yourself. I try, Grigsby. Honest I do. Bender. Yep. Show this bloke how to pick a pocket, eh? Well, it's simple. Look at the table next to the bar. See the old man there? That's old Peter. He's the cat-taker for the largest house in town. He always carries the keys to the silver closet, and his money in his left-hand pocket. Why his left pocket? He's left-handed. Live and learn, farrow. Live and learn. Now, all I have to do is walk by him, pass the time of day pleasant like, pick his pocket, and we'll all have lunch money, and my friend Grigsby will have the silver closet keys. A very important item. What if you get caught? Bender caught. Never. I taught him how to do it. Now, watch and learn, me boy. I'll make something out of you yet. I'm watching. Go ahead, Bender. Keep your eye on me. I'll be right back. See the way he walks across the room, just as if he doesn't care. Swaggering, swinging his shoulders like. Yes, I'm watching. He walks easy. Now, look. He stops at the man's table, talks a while, smiling all the time. What's he saying? How should I know, Fanno? No matter what a man says. What matters is he's saying to saying it. Now, what you saying there? Stealing into the left pocket there. You got it. He has for a fact. Yeah, he's got the wallet and the keys, too. It's quite a trick. And I taught it to him. If you want to learn something, Fanno, I told you before, stick to Grigsby and you'll make a mint of money. I got big plans for myself this night with old Peter Silverclotted keys. Should I count you in? As I've said before, and I'm saying it again, I'm willing to learn. So I stuck with Grigsby and Bender. We left the inn in the late afternoon. Grigsby had big plans on his mind, it seems. He was walking fast down the village road toward the biggest house in the town. Me and Bender was walking behind.  Get a move on, will you? Can't walk faster than the legs will go. I'm having you torn, man. So I'll never get anything done tonight. I'm sorry, Grigsby, but I'm hurrying. As best I can. What are you up to? Look at the end of the road there. That's the outside of us. You know that house belongs, too. How should I know? It belongs to the richest family in the old county. The family's out of town, are you? That's right, Bender. The family's out of town. And the only person living there is old Peter and his young granddaughter, Evie. Imagine. Old Peter along with a young child. Yes, imagine. An old man and a young girl without any protection. What are you thinking of, Grigsby? A simple plan, fellow. I've been working on his plan for days. A plan to steal the silver. It's worth a fortune, and it's easier than taking candy from a baby. Now I see why you stole the keys. Well, there's the house. We have to wait around here till midnight. When midnight comes, I'll knock on the door and old Peter will open it. Then Bender here clubs him over the edge. And I goes in, gets the silver and the money. And we leave town. What about the child? That's where you come into the plan, fellow.  Good at crawling through windows. What do I do when I get through the window? Find the child and get her out of the way before she starts to shriek in honor. I ain't no child, murder, Grigsby. I ain't. I gotta teach you the art way, fellow. You never learn it all.  I'll cut your throat for you. I'll cut his throat for him. You heard him, fellow. Bender's good at cutting throats. I taught him. Well, I got a wife and kid of me own.  Use the knife, Bender. Good idea. No, no, no. Wait a minute. Don't be hasty. I never was a man not to change me mind. Change is mind. So you'll change your mind, eh? I thought you'd come to my way of looking at it. Eh, look over there. Plain in the yard. It's a little girl herself all alone. Now, if you're smart, fellow, you'll take a word of advice. I don't think I need no advice right now. You might need it this evening when we start to work. Now talk to the little girl for a while. Find out what room she lives in. And you might even ask what kind of self are the family's hoarding. Ah, she's a pretty little thing. Look at her. Plain by herself. Nice as you please. Like a regular little lady.   Don't joke with a blog. He's getting pale. Go on, fellow. Are you going to top tour or do I have to convince you with me knife? I'll talk to her. Well, just sit over on the side of the road and watch her. Thank you. I don't need no watching. We'll watch anyway. Just walk right up to her and speak. Hello, little girl. Who are you? Who am I? Why, I'm a friend of yours.  You know, not personally, but I've heard of him. Do you know my name? Now, what is your name? Evie. I live in that house over there. It's a right nice house, Evie. Which room is your room? Don't you know? Should I? Of course. Everybody knows Evie's room. Grandfather says he says it's caused a special safe room just for me. Guardian angels watch over me all the time. Of course. Where is your room? On the second floor in the back of the building right over the kitchen entrance. There's a special porch off my room. A porch? Yes. Now I told you about me. What about you? What's your name? My name's C. Farrell. C. Farrell? That's a funny name. Who do you live? Any place I can find for me to live? On the grass or in a stake, in an alley? In a doorway or a shop? Is there a second floor for you to sleep on in all these places? I suppose so, Evie.  Wish I could stay with you longer, but I can't. Where are you going? Home. My grandfather told me to come in properly at five or I'll catch cold. He worries about me awful. He always does. Goodbye, C. Farrell. Goodbye, Evie. Bye. Goodbye. Goodbye, Evie. Goodbye. Ah, pretty little gal she is. Nice work, Fatal. Quite nice work. If I say so myself, I couldn't have done a better job.  One would, eh? Well, maybe she did. Ah, don't make no difference now. We just take a look around the grounds and then wait. And then wait till midnight. Sting around, Fatal. You're gonna learn something tonight. Till midnight. Might even get a few hours sleep. Griggsby led the way. And as I said, I'm willing to learn. As we walked around the ground, I kept looking at the second floor and my art sunk. Child-killing. That just ain't in my line. For now, if so, Griggsby studied the land. And then he led us to a little grove on one side of the field where we sat down to wait. Bender, the big fella fell asleep. Griggsby kept one eye on me and one eye on the house. And all of a sudden, I realized it was starting to rain. But rain and no rain, we just waited and waited until the church clock struck 12. It was fast asleep when Griggsby said, Bender, hey, Bender, wake up. It's midnight now. Oh, midnight. So soon. Blasphemethan ain't raining. It's been raining for hours. We ought to forget the old thing. We ain't forgetting nothing. Come on, Fatal.  I'm on my feet. Ready, Bender? I'm right behind you, Griggsby. Well, follow me both of you. The grand is wet, Griggsby. Not a night to do this job. I'm superstitious about the rain.  Well, I am. Well, gentlemen, there's the house right ahead of us. Imagine everyone's sound asleep. Fatal. Yes, Griggsby? We'll walk you to the bank of the house and give you a boost up over the ledge of the kitchen porch. Yes, Griggsby.  Get a move on. I'm moving. I'm moving. Both of you. There's the porch. You think you can climb it, Fatal? I don't know. Honestly, I don't. No, you'd better now. Climb up on that ledge and stay there until you hear me pound on the front door. Then what? When I pound on that door, climb in that open window and take care of that kid. She won't feel nothing. Just one quick jab in the heart and she's dead.  Well, go on. Take it. Yes, I will. Well, Fatal, are you ready? I'm as ready as I'll ever be, I suppose. Climb on me back and I'll give you a boost to the ledge.  Bender kneeled down and Grigsby helped me stand on the big man's back. I grasped one pole of the porch, which was just beneath the child's window, and then pulling myself up inch by inch, I climbed from the ground to the roof of the porch. The pole was wet and slippery, and my fingers were cut by splinters when I reached the porch. Painting and breathless, I just crouched his there, waiting. I see Grigsby and Bender walk slowly around to the front of the house. Then I noticed the window, right in front of me eyes, with little white curtains blowing back and forth, and it brings a tear to my eye. Then I noticed a little white bed inside the room, but the room's too dark and I can't see the child nowhere. It seems to me the old world's quiet, and my breathing's as loud as an elephant. When I is... I'm telling you to open up the door, Fatal! Are you dead? He sleeps like a log. Oh, he's playing the fox dog guarantee, pretending to be asleep. Wait a minute. If you don't wake up, I'll smash the door! Gentlemen! Gentlemen! Where is it, Grigsby? Up there in that window, right over your head.  Come on down and open up this air door. Gentlemen, why do you come to wake up on his people at this hour of the night? Stop playing the game, old Peter. Your time has come. You don't open up this door right away. My time? You mean to kill me? If you don't open up the door! Gentlemen! Gentlemen, I'm afraid you're too late. A cruel of faith and murder has taken the breath from my body. What are you talking about? You'll see. I'll open up the door, but you'll wish to hear when you had never visited this house. I'll be right down. What's he talking about, Grigsby? I'm not quite sure, Bender. Not quite sure. I wonder how that fool, Fatal, has done. Oh, he's done his job, I know, Fatal. I wonder how long he'll keep us waiting in this rain. If they don't answer soon, we'll break in the window on the side of the house. There he is. Come in. Come into the light where I can see you.  What are you talking about, anyway? Look at my face, gentlemen.  Do you have any idea what that is? Black rings, black splotches. Blimey, Grigsby, it's the plague. The black plague. Yes, the black plague. That's what it is. This afternoon, late this afternoon, my little granddaughter came in from play. Within four hours, she was dead. If he dead? Come in, gentlemen. Look. Look. There she lies on the stairs. Grigsby, look. Look well, gentlemen, because within the half hour, I should be lying by her side. Oh, oh, the pain. Oh, the pain. It's the plague, Grigsby. I've seen them before. Run, gentlemen. Run to the tanning factory and breathe in the healing fumes of the tannery. The smell of the tan bark and the leather have curative powers. It's too late for me. But if you wish to spare yourself the trouble-suffing of the plague, go. Go at once to the tannery and stay there as long as you can. Oh, the pain. My flesh. The pain. Oh, the pain. Help me, gentlemen. Help me. The pain. Help me. Don't, don't, don't touch me, Peter. Come on, Bender. The tannery's factory. As fast as we can run. As fast as we can run. Where's Faddle? Where's Faddle? Where's Faddle? What's the problem? He's gone to save us. He's gone to save us. It's the Black Plague and nothing can save us. Nothing but the air of the tannery. Let's get there. Hurry. Hurry. Bender. You sit here next to me on this part of the tannery. Right. Faddle? Yes, you sit next to him and all breathe together now. Odd. One. Two. Three. Are you sure the fumes from this tannery vet are good for the black plague? Touch someone who's edit. Oh, I never touch her, Peter. Did you, Bender? Never once. Not even tapped him lightly on the shoulder. But I touch the child. You? Yes, and right after that, I touch Bender. Remember Bender? Then I must have it too. Look at me, Grigsby. How do I look? Not so well as a fact. I don't feel well either. Not well at all. Yeah, your face does look sort of a, wouldn't you say, bloated, Grigsby? I've got it. I've got to play. That's what I've got. If there, I must have it too. But nobody touched me. Nobody. Nobody, Bender. I'm still all right. You won't be for long, Grigsby. What are you talking about?  But you ain't going to live. You've got to sin to this. I'll rub me hands on your face. That's what I'll do. Rub me hands on your face. No, you don't, you. Give me that knife, Faddle. He or Grigsby. Don't give it to him, Faddle. I've got it now. And if you come near me, Bender, you know what'll happen to you? What's the difference now? The plague of the knife makes no difference to me. I don't waste it. Stay away from me.  My hands are covered with a disease and it will soon be on your face.  I'll teach you. You teach me what? The knife. The knife. Don't you? That'll take care of him for a long time to come. Oh, Bender's dead. And we'll be with him in a short time, Grigsby. Oh, the plague. The black plague. What can I do, Faddle? I don't know, rightly, Grigsby. I'm just sitting here watching you. Waiting to see. Maybe I'll learn something. The leather scraps. Maybe if I rub myself with the leather scraps. Maybe for Faddle. I don't want to die. Not yet. I don't want to die. I'm the greatest thief I ever lived. Clever, too. Nobody ever beat Grigsby before, nobody. Nobody ever did before, Grigsby. Well, they ain't going to this time. No, they ain't. Maybe the black plague's got more power than you have. You can't fight the plague, Grigsby. Is my face turning black? I can't rightly tell yet. My eyes. How do my eyes look? Red. It's swollen. Any black rings around them? Oh, give it time. It takes time for the black plague to grow. Time. Time. If you'll sit still, it won't be half as painful to die. Maybe. Maybe if I breathe deeply. That won't do any good. What do I have to die? I guess you do. I guess you do. But don't worry.  The pain will tell you. The screaming pain. The pain. The screaming pain. And then Grigsby collapsed, crying like he was a baby. I sat there for one whole hour, watching Grigsby. Bender was dead. As the time slowly passed and the night became morning, I noticed a group of men, edding towards us, carrying guns. And I poked Grigsby in the ribs. Hi, Grigsby. Somebody's coming. What's the difference now, Farrow? You'll find out. Hello, over there. Hello. Mr. Grigsby. Mr. Grigsby. What's he want? I don't know. Ask him. I'm Mr. Grigsby. Oh, yes, Grigsby. And are you Mr. Farrow? That's what they call me. And the man with the knife in him is Mr. Bender, I presume. You'll have to take my word for it. You can't ask a dead man no questions. Gentlemen, in the name of the law, you're under arrest. Seize them, boy. Wait a minute. Arrest him. For what? For what am I arrested? For what? For outbreaking and plotting against the life of a child. And now it's for murder. How do you know all these things? How do I know? He told me. Oh, Peter? Yes, I told him, Mr. Grigsby. You? You told him? I thought you were dead of the black plague. I know you did. I wanted you to think so. It took a lot of trouble painting on black spots with kitchen charcoal. Charcoal? Let me go. Let me go. I wouldn't try that, Mr. Grigsby. But Levy, she was dead of the plague. I saw myself lying on the steps, dead as can be. Levy was down to sleep in her own bed. You saw a dressmaker's dummy wrapped in cloth and smudged with charcoal lying on the steps. And I thought I was dying. That's what I thought. Bender thought he was dying, too. You faddle. You were the one who told us we had the plague. You did.  You were. I always told you, Grigsby. I'm willing to learn. You?  Yes. You must have known. She wasn't dead? Yes. I knew she wasn't dead. She was lying in the bed. Smiling at me as I came through the window. Smiling and laughing. And she told me where to find her grandfather. Find him? Yes, find him. And the two of us got together and got an idea. An idea about the plague. Me knowing how afraid you always are of being sick. You double-crossed me, that's what. I promised I'd kill if you double-crossed me like that. Yeah, come along, Grigsby, and you're under arrest. And you, too, Farrell. Take them away, boys. Mr. Officer, don't take Mr. Farrell away. He farries my friend. I don't think there's any charge against Farrell, Mr. Officer.  Come on, come on, Grigsby. There's a nice jail cell waiting for you. I'll get you for this someday, Farrell, then I will. Drag them away, boys, and don't waste any more time with him. Oh, dear. Oh, Miss Evie. Yes, Mr. Officer. I have a special job for you. From now on, Mr. Farrell here is in your charge. Oh, thank you, Mr. Officer. I like to see Farrell. And I like you, too, Evie. Grandpa. Grandpa, can we ask Mr. Farrell to my birthday party today? I don't see why not, Evie child. I think he deserves a piece of birthday cake. Oh, well, Blimey, it hasn't been a long time since my mouth tasted birthday cake. And what do you intend to do in this town, Mr. Farrell? Shettle down here and get employment? Well, do anything, old Peter. Anything as long as I've enough to keep a roof over me and food in my stomach. If you've got any ideas, I'm willing to learn. I think I might have some ideas, Mr. Farrell. It's just possible that Evie and I both might. From the time-worn pages of the past, we have brought to you the immortal tale, The Great Play. Bellkeeper, pull the bell.


Hi everyone. Sorry about that. I just had a crazy moment with my screen there But hopefully you can all see my screen. Can you give me a thumbs up real quick? Yay, thank you. So Welcome to the people ops functional group updates. I'm Abby for those of you who don't know me I'm the people ops generalist and I'll be leading this update this week so We have a couple of points on the agenda or standard thing We'll go through our accomplishments our plans and then if there are any questions In chat, I'll take a look at those If I can ask my fellow people ops stars to take a look at the questions in chat also And if they can answer those that would be really great as we go along if not I will attempt to answer them at the end Okay, so we have Our first big accomplishment is the rehire of Nadia who some of you will know She rejoined last Monday, I think and as a recruiter and we're really excited To have her back with us and we are going to keep her super busy lots of hiring going on at the moment So we're really excited about that Also, we have another news data in the people ops team Trevor Who's joined us as a recruiting intern. He's going to be supporting the hiring team With lots and lots of things initially Anything that they need help with in terms of screening and some other projects that we can get him working on as well In other news, we have a candidate who accepted An offer to be one of the people ops business partners Supporting product and engineering and they'll be starting on December 4th. So we're really excited about that And also some other changes Kirsten and Jennifer have moved Into the people ops team as well still doing their roles But it will be more It will be they will become part of the people ops team But still doing what they were doing before so it just makes the structure and a little bit more clearer and our other things that we've been Working on we have written some guiding principles on the hiring to reflect and get labs values There the page in the handbook is linked there. Please go and take a look at that Similarly, we've done the same thing for our benefits that we offer. We've tried to link those back To the get lab values. So please any comments on those? Please feel free to make a merge request also we've Asked for your feedback From the summits, it would be really useful for everyone to take a look at that. We've already got some really great feedback Coming from that issue. So please keep it coming. So we can make sure that we when we come to the next summit We can take what we've learned from the previous one and make it even more awesome and amazing that it was at the last one and So our forthcoming plans and We are working on iteration to the compensation calculator Brittany is heading that up and so some of the benchmarks and the formulas will be Updated as part of that. We also have an issue open on restructuring the team calls We've had some really great feedback and ideas on that, but we need some more as the team gets bigger We need to make sure that we are Trying to make everybody feel inclusive. I'm keeping the culture As good as it can but also we need some help In trying to make those better. So any ideas that you have please feel free and add them to the issue Another thing that we will be doing as part of the hiring is we'll be noting names of companies using CE and EE during the screening calls to give To pass on some of the leads to our S to the SDRs To help with that and we would also ask everyone in the company to do the same, you know, spread the word Because we really want to support the sales effort and in terms of Some other roles that we're hiring for within the people ops team We are on the search for a recruiting director another recruiter to replace Sasha and also another business partner for sales and marketing in addition to all the other roles That we are currently on the lookout for So that was very really quick, I apologize for my speedy speedy talking, but do we have any questions? I think I did see some in the chat What is business partner? Thanks, Nadia Yeah, people business partner HR business partner kind of the same thing just Different words. We're all about people not human resources Okay, any any questions from anyone, please feel free to unmute you unmute yourselves and ask away Okay, if not Thank you very much for joining. That's probably the fastest update ever I'll try to talk a bit slower next time. Thank you very much for joining and see you all on the team call


Butter it, butter it, oh so good, oh so good, oh! Let's do this, finally, finally a full review of the Hoka Carbon X full review after my 24 mile long run today in these shoes and I realized fully, really late to the game, really late, I think I saw a lot of other running YouTube channels publishing full review videos back in June, so you're probably, this is probably like water under the bridge for you, you already know a lot about this shoe, but as you know, I did a lot of trail running this summer and so I was unable to put these shoes through the paces but now we are at 76 miles, so we're well past the 50 miles, especially after the 24 today and let's dive into a few specs real quick as we get going, five millimeter drop, 32 millimeter stack height in the heel, 27 in the forefoot, so that five millimeter drop, basically right where I like it and then in the weight in men's size nine, we're looking at 8.7 ounces or 246 grams and in my size, we are looking at 7.9 ounces or 226 grams. Now, on the upper, it's a single layer mesh upper, all right, through the toe bot all around the shoe, single layer mesh upper and it's, what's coming to mind when I think about the upper is it has no bells and whistles, it's simple and actually I was a little concerned, you know, when I bought the shoe, if I was going to like the, how simplified the upper was, but sure enough, it's breathable, I feel locked down and yes, there is a gusseted tongue, so what does that mean? Basically on the inside of the shoe there, there's a piece of material, I'm guessing some sort of polyester synthetic material that connects the tongue just underneath the insole of the shoe and it's just perfect, it's just, it locks down over the top of your foot so, so well, so good work on the upper, no bells and whistles, now I do have a concern, I'm gonna bring up in a minute that does connect to the upper but we'll get to that in a second, all right, moving on to the midsole of this Hoka Carbon X, so it's that pretty solid stack height, okay, 32 and 27, yes, there is a carbon fiber plate, you can see it there through the outsole of the shoe, carbon fiber plate shoe for the Carbon X, now if you'll remember for my first impression vlog, I was a little concerned about the, basically how firm the ride felt in the carbon, it just felt a little firm, not too soft and for a 20 mile run, 24 mile run like today, I just, I'm, I have to be careful for myself because of my injury history, which is why I've actually been slipping into the shoe underneath the insole, this Spenco, which is just a piece of very, very light rubber, no, you know, there's no support through this Spenco but it just provides about another two to three millimeters of cushion, so for some shoes for my long runs, I do add this Spenco that you can buy from a lot of running shoe stores from Amazon and you cut it out with scissors into the shape of your foot, excuse me, into the shape of the insole, why not, I'll just pull this out right now, so you can see it fully, so here we go, so there's the insole and then here is the Spenco and basically you cut it out to fit the size of the insole, so you slide it right into the shoe to help provide a little more cushion, just because I did sense off of the first impression that it was a little firm through that midsole, okay, moving on the outsole real quick, not much to say, frankly, I like it, now I haven't raced in fast conditions in this shoe on a wet road, I don't know, I actually, if somebody has experience racing in this shoe in rainy conditions, I could foresee in your turning sharp corners in a city and you're going fast, like I could foresee a little bit of slipping happening or you know how you're on a road and then there's paint on the road, I don't know if you've ever ran into that before, but I've seen, I've experienced a couple of situations where the paint is a little slick, so anyway, no complaints about the outsole, it is smooth, you can see through to that carbon fiber plate and yes, I don't know if you can see that there right where my finger is pointing, there's a rock embedded right there, so not a major issue, but I did pick up a couple of rocks over the past 76 miles, okay, and moving on to the fit of the Hoka Carbon X, I went true to size, we're good to go, except for the one drawback I'm gonna talk about in a second, so fit was good and for the comfort, again, no frills, comfort through the upper, it's not like screaming comfort at me, but again, I'm feeling like that gusseted tongue is spot on for the Carbon X, for my positive and my drawback, the positive, oh my goodness, 24 miles today, I'm gonna go with bounce for the positive, I think it's that combo of the carbon fiber plate and the stack height and I don't know, I don't know what's going on, but I just felt a nice little bounce and I was very, I averaged 640 a mile for the long run and I'm just very pleased, so did this shoe help me get through the finish line today on today's long run successfully?  I don't know, again, if somebody else is having issues with some rubbing through the Achilles tendon, let us know down in the comments, and now how will I use the Carbon X moving forward? Long runs, long runs, now, Jim Wamsley, I just read, I just refreshed my memory, he has the world record now in the 50 mile and he wore this shoe and he averaged 548 a mile for 50 miles, so it's definitely a shoe I think built for the long run. I wouldn't race in this shoe for a half marathon or a 10, you could, but I think there's better options out there for you. Now, the price point, $180, hmm. I'm struggling a little bit, it's probably the mental game of me knowing that my heel is rubbing on the back of the heel collar there, or the heel counter, I should say, so 160, I'd be doing cartwheels to buy this shoe. 180 is like right on that brink, I don't know if I would buy it again at 180, so maybe just wait, I bet by the Christmas season and the shopping season around Thanksgiving, I bet this shoe starts to come down in price, I'm hoping for everyone out there that is interested, so I'm on the fence for the price and the score, 7.25, all right? I almost went 7.5, 7.25 is not bad for the score, again, it's just that rubbing and the first run, like it was almost debilitated, I almost had to stop and like, anyway, I didn't have any band-aids with me, but so not a bad score, but I'm excited, actually, let's just do it. Question of the day for all the Hoka fans out there, what are your predictions or your hopes for the Carbon X in 2020, all right? What will Hoka do to improve this shoe in 2020? I'm stoked, can I use that word? I'm stoked to see what Hoka can come up with in 2020 for this shoe, I predict definitely a drop in weight, I'm hoping Hoka is able to pay attention to the heel counter issue that I'm having, so anyway, I'll be excited to read your comments down below and that is it everyone, for my full review of the Hoka Carbon X, overall solid shoe, I'm excited again for the future and yes, we're gonna toss it back to, on the right-hand side, the Hoka Carbon Rocket and on the left-hand side, we're gonna go back to the first impression in case you wanna see my thoughts 76 miles ago for the Hoka Carbon X. All right, love you guys, thanks for being here, thanks for watching Seek Beauty, work hard and love each other, see you tomorrow.


You'll see I mean it may I mean the The live feed using That both might be pushing it push push it real good I Hope this is awesome. We're going to try to make this awesome Everything is everything is cool when you're part of a team Everything is awesome When you live in the dream That's right See that's where the lights come in handy. I'm gonna come over here. It's super in shadow before and that's not I'm gonna come over here and test this one out is this one louder. Is it the same is this good real hot mic right here real Get some get some Fun stuff going on here people building robots People driving cars in a large building real fun stuff Scooters there's razor scooters. They're fun It's a good way to get around a place that's the size of four football fields Okay, that was the test I'm gonna go watch this now and see how it sounds


So first, I'll just explain what exactly OpenFlow is. So I'm sure you all know that Neutron uses OpenFlow these days with OpenVSwitch, but it's based on kind of a lower level protocol called OpenFlow, which is basically an open protocol for software to find networking. It's managed by the Open Networking Foundation. It kind of started maybe four or five years ago. And since then, a lot of vendors have shipped support for it in physical switches and other networking gear. There's also a software implementation, which is a Linux kernel module, so that you don't quite get line rate hardware performance, but it's pretty good. So OpenFlow was motivated by some researchers at Stanford. They were doing all kinds of experimental networking stuff with security, QoS, routing protocols, peer-to-peer technologies, things like that. And they found that it was really hard to do with existing networking gear, because usually these switches were kind of closed and proprietary. They wanted to run experiments at line rate, at high speed, with realistic traffic volumes. And they found that it was really hard to modify these networks programmatically. So I mean, certain vendors were already kind of shipping software APIs to their gear, but they were heterogeneous, and they didn't always support everything that these researchers found that they wanted to do. So they kind of stepped back and thought about how to solve this problem. And what they ended up coming up with was, well, there's a lot of commonality in all of this gear. They all have these kind of flow control tables. They all do forwarding in a similar way and packet inspection and all of that. So what if we can just make a common interface so that vendors can expose the functionality of their equipment programmatically without having to open up the internals in their secret sauce? So what they came up with was this. So the basic idea is to separate the control layer and the forwarding layer of these switches so that all of the stuff in hardware kind of below the line there is kind of the flow control layer. But above that is the control layer that kind of sets up the flows and decides which packets go where. And so the idea is that the hardware checks its flow table when it receives an incoming packet. And it can have matching rules against the packet to decide which flow table entry applies. And if it finds that it doesn't know what to do with the packet, then it sends it off over SSL via this binary protocol over to whatever IP address you specify. And you can give it more than one so that you can have high availability so that if it can't talk to its controller, it will try the next one. And then on the controller end, you can basically do whatever you want. You just have some software running that speaks this protocol. It can do all kinds of high level things. What it exposes to the controller are basically a lot of most transport layer and L3 attributes. So you've got source and destination, MAC and IP address if applicable, and VLAN tags, and a number of other kind of packet headers. And your controller can decide based on that, OK, this is what I want you to do with packets originating at this source MAC, for example. And so your controller can then push flows back through the secure channel into the flow table and say, basically, next time you see a packet with this destination MAC, send it out this switch port. And you can specify TTLs on it. You can do wild cards on certain headers. And it just gives you ultimate flexibility while still giving you the performance that you get with the traditional switch. I should have turned off my screensaver. So yeah, in the last few years, a number of vendors have put out switches with support for this. HP, Cisco, Big Switch, Larch, all of the big equipment manufacturers are supporting it these days. But if you want to just get started playing with it on your own, there's also a plug-in for the OpenWRT, like Linksys router operating system. So you can get started very easily on hardware just with a cheap off-the-shelf consumer grade router. Besides the hardware implementations, there's the software implementation that most people in this community are probably familiar with, which is OpenV Switch. It ships also a kernel module so that you can get better performance out of it. It emulates basically what the hardware switches do. And it's actually pretty fast with the kernel module, since you're not switching back and forth between user space and kernel space all the time. So a lot of you are probably familiar with diagrams like this, the way that OpenStack uses OpenFlow can get pretty gnarly. A lot of this stuff is still managed by Nova, these virtual ethernet pairs, like you see kind of in the blue and orange there. But with the latest developments in Neutron, they're all kind of plugged together with OpenV Switch bridges. And things like VLAN tag translation and things like that are all handled with OpenFlow flow rules. If you want to dig in and see how Neutron is using OpenFlow under the covers, it's actually pretty straightforward. There's these couple of directories in the Neutron source repo. It's very straightforward. You can see that it's basically just shelling out to some OpenV Switch utilities to enter these flows based on events such as network creation or adding a VM to a network or things like that. So that's all well and good. You can learn how OpenStack makes use of all this stuff. But for me, I like to get kind of hands on with things to really understand them. And playing with things in Neutron comes along with a lot of context in state and makes it really hard to just kind of experiment just because of all the things that are already there that you have to work with. So I set out to find a way to just kind of play with OpenFlow at a low level. And basically, there's a lot of great work out there that enables you to do just that. So basically, the basic idea is to implement a controller. It's just like in that diagram before where the Switch was calling out over SSL to the controller. To get started, you just write a controller. It's just a piece of software that speaks this protocol. You don't have to go off and reinvent parsers in an implementation for this binary protocol yourself. There's a number of client libraries out there for your language of choice. There's one for C called Knox, a re-implementation of that in Python called Knox. There's libraries for Ruby and other languages as well. So a controller is really simple. This example is for Pox, which is the Python one. This is kind of pseudo code. It's not filled in. But basically, you just initialize it. And then when the Switch encounters a packet that it doesn't know what to do with, it will send it off to your Python code to a method like this. And you can examine the headers, the source and destination MAC, all of these things. Decide in your code what to do with it, and then push that flow down into the controller. You don't have to do this on demand either. Like OpenStack, for example, already has a database of the network topology and what traffic needs to go where. So at startup, you can just proactively access your database, figure out your existing topology, and push flows for all of those network components directly into the Switch so that you don't have the first packet latency of calling back off to this thing so that the Switch will already be pre-populated with all of your rules.  So there's this thing called Mininet, which makes use of Linux namespaces, which is very good for that. So Linux network namespacing is something that was kind of pushed forward by OpenVZ and LXC. If you're not familiar, it basically is a way to create additional virtual network stacks that are namespaced within the kernel so that you have one running Linux system. And in a very lightweight way, you can just fire up completely independent network stacks and execute processes in them so that from the process perspective, they're on a different machine from the host system. You can fire up tons of them. I have a demo here in a second that you can run thousands of these things in a normal OpenStack KVM VM. So if you want to kind of test, if you want to experiment with things and test topologies of hundreds or thousands of nodes, you can do all of that inside of a single VM. And the performance is totally reasonable. So Mininet, again, allows you to programmatically set all of this stuff up. It's a Python library. And basically you can just have lines of code, one line of code to create a switch. You can create more and connect them. You can create hosts, which will fire up separate network namespaces. And so in just a few lines of code, you can create really complex network topologies to test out whatever you want to play with. So yeah, I guess I'll show some of this stuff. So I'll get this on screen here. So here you can see some Python code that sets up one of these Mininets. It's just adding a bunch of hosts, connecting them together into some switches. To fire this up, we can just run a script. I type it right. So here we've just fired up a bunch of network namespaces and all of these simulated hosts. It has some nice sort of syntactical magic where it sets up DNS entries for all of these things. So to run something in host1's namespace, you can just say host1 and run a command, say ping h2. Nothing will happen now because we haven't started our controller. So here we have some Python code that just basically implements a Layer 3 router. It basically listens for packets that are coming in from OpenB switch that it doesn't know what to do with yet. It emulates the Layer 3 router by basically doing ARP if it doesn't know about this thing. It maintains an ARP cache in memory in this Python process and pushes down flows that, OK, I saw this Mac come in this input port. So I'll check my ARP cache. If I don't find it, I'll send out an ARP on all of the other ports until I find this Mac address. And then tells the switch, OK, anything bound for this destination Mac, send it out port x. So we can fire that up. And now OpenB switch will talk to this thing. And if we rerun our ping, we should get responses, which is great. And we can test connectivity between all of the hosts. Over here on the right side, you can see some kind of debug statements that show what it's doing, which is basically what I just described, where we send out ARPs to find who we're looking for and push in and close into the switch. You can easily imagine doing more complicated things like this. You have access to TCP QoS bits, VLAN tags. People are using these tools to experiment with even things like alternate Layer 3 protocols, alternatives to IP. Really, the sky's the limit with this stuff. So that's really all I have. The sandbox stuff with Mininet and the pox router and all of that stuff is up on GitHub. If anybody wants to play with it, it's really easy to get set up with. I'd encourage anybody that's interested to learn more to read the OpenFlow white paper. It's very straightforward. It's a really great example of a clean, small interface that's just super flexible. Yeah, anybody have any questions?


Hey welcome back to the channel. So in this video it's gonna sound a bit hypey but I'm gonna talk about how to get rich fast or fast and do it in a legitimate way. And so I've broken it down into three simple steps to follow not easy but simple steps and I'm gonna share what those three steps are today and how you can do it too to get rich and make a lot of money fairly quickly in most people's standards. Now if you are new here my name is Sean and on this channel I talk about how to make money from writing online. So if you want more content around that make sure you hit the like and subscribe button and also the bell icon so that you don't miss any of my future videos. And let's just jump into those three steps. How do you get rich quick? What are the three simple steps that you can follow to get rich quick online? Now before I talk about those steps I want to first define what I mean by quick. Most people are trying to become millionaires by the time they die right by the time they retire at age 65 they want to have millions in the bank and saved up so that they can live on on retirement. Now what I'm saying is not you're going to get overnight riches but if you follow these three steps you'll get rich a lot quicker than waiting your entire life until you're 65 or retired. So the exact timeline I can't give you but I can say that if you follow these three steps you will get rich a lot quicker than if you just pinch pennies and invest a small amount and save for your entire life. So with that said let's get into these three steps right now starting with step number one and that is simply to do something cool in your life. Now how do I define something as something that's cool? What has to be something a result or an outcome that you do that there are many many other people that want to achieve that same outcome? So for example my cool thing was I was able to quit my nine to five as a corporate project manager back in 2016 by building up my online businesses multiple online businesses. So I was in e-commerce I built up an econ business I built up a services and consulting business now I'm running an info product and education business and so I've grown all these businesses using email and by writing online and that's the cool thing that I've achieved that I can share with people. So that step number one is to do something cool in your life it doesn't need to be business related it could be health or fitness or relationships whatever else and do something cool where many many other people in the world want to do that same thing and want to get the same result as you. Step number two is to build an audience of people by sharing what you did that cool thing that you did and documenting and sharing how other people can achieve that same thing. So for example I share a lot of content on Twitter on YouTube on my Facebook group my email list about how to build an email-based business. So every day I'm writing content about online businesses and copywriting and email marketing and I'm sharing basically things that I've done in my journey that cool thing that I did and I'm sharing it to other people and building an audience by sharing what I know and so this is why step number one is so important and why step number one is in order for you to build an audience in order for you to actually have people interested in what you're saying you need to have done that cool thing in your life first. So once you've done that cool thing once you've gotten a result that somebody else wants or many other people want then you can go to step two which is building your audience and sharing what you learned. I'm sure you've seen all these influencers nowadays on YouTube and TikTok and Instagram and they've done cool things in their life and they're basically just documenting it they're sharing to their audiences and sharing to people what they've done and they're building massive groups of people and massive audiences from that and finally step number three on how to actually get rich quick is to create a product and sell it to your audience on how they can achieve that same cool thing that you did step by step you're mapping that out and you're selling it as a product to people who want that same result. So I have multiple different info products and educational products now and the way that I create those is just mapping out A to Z the process and steps that I took to achieve the the cool thing that I did that other people want to learn about. So for example one of my products is called the email side hustle program and in that program I teach you how to get paid 1k to 5k per month per client by writing simple emails for other businesses and all I'm doing in that program is teaching exactly the steps that I took to achieve that cool thing to achieve that cool result that other people want and again this does not need to be just around business I've seen this in health I've seen this in fitness I've seen this in relationships I've seen this in all types of sub niches and each of those so it does not need to be just make money online it can literally be anything as long as it's a cool thing or cool result that other people want to achieve as well now how much can you actually make selling a product how are you actually going to get rich selling a product to your audience well depends on how many people you have in your audience multiplied by the price point that you're selling your product at so there's actually a girl who is on tiktok I can't remember exactly what the price point is but she had a million or so followers and she makes up to a hundred thousand dollars a day by selling a course on how to do microsoft or it wasn't microsoft excel was google sheets she sells a google sheets training course and she makes up to a hundred k a day and she posts tiktok videos to build her audience around google sheets and she sells a product on how to use google sheets and so just imagine that right if a girl can make a hundred k a day selling a product on google sheets what kind of product can you create and build an audience around you wouldn't even think that google sheets is this sexy thing that people would want a product about but that would prove you wrong so if I don't know what her name is you can google it a hundred k a day from a google sheets course tiktok influencer I'm sure you will find her but that just goes to show you can create a product pretty much around anything as long as other people want to learn about it too so to recap those three steps again number one do something cool in your life that other people many other people want to do as well step number two build an audience by sharing and documenting your journey and sharing that cool thing that you achieved and step number three is to create a product and sell it to that audience of people who want to do that same cool thing as you and how much money you can make is going to depend on how big you grow your audience and how much you price your product at and how big that problem is that you're solving with your product but there's a huge potential there depending on what industry you're in and depending on other factors like your pricing and the people in your audience how engaged they are but those are the three simple steps these are the steps where you can go from making basically zero dollars online to making six seven even eight figures in a pretty short period of time relative to waiting your entire life these are also the three steps that I basically follow to build my different businesses and to make my living online as well so this is not just me giving you theory this is exactly what I do you can see me posting content you can see me sharing what I've done the cool things in my life and you can see me selling products around how other people can do that same thing so hopefully this was helpful for you hopefully this gives you an actionable plan that you can follow and remember these three steps again do the cool thing share that cool thing to build your audience and then create a product to help people achieve that cool thing as well now if you got value out of this video make sure you hit the like button let me know in the comments if you have any questions and I'll see you in the next video take it easy


There we go. So this presentation gives you an overview of some of the most frequently used cell types in biometrics related to next generation sequencing. So the cell types we will discuss will be the fast-fell. You've already seen a bit of it. So that's just a fail to store, for example, genomes, but nucleotide sequences in general also amino acid sequences can be stored in the fast-fell. Then the fast-q-fell, that is where sequence results are stored. So usually you store them the actual sequence of that read together with the base quality in the fast-q-fell. Then the sum slash bomb format, so that's where you store the alignment. So we will spend quite a bit of time on that because it's important and it's not super easy to understand that first was all in there. There's really a lot of information in there which you have to understand a little bit how that works. The fast-fell, which is a relatively simple fail, specifying regions in a genome, for example, GFFL or GFFL stores annotations and the VCFL stores variants. So GFFL has, for example, the positions of different genes and transcripts in there, VCF, for example, snips and indels. So a fast-fell is one of the most simple fail types, most frequently used fail types in biothermatics. So it stores a plain sequence and with dot fast A or dot FA can store both nucleotides and amino acids in there. And it looks like this, what you see on the bottom right of the slide. So the title starts with a greater than sign and you can have any kind of characters in there. And then there's a new line, so an enter and then a sequence and then you can have multiple new lines with sequences. And only if there is a line starts with a new greater than sign, a new sequence starts. So you can have multiple sequences for in a fast-fell. A very useful command that the decider can be used for a fast-fell is grab minus C. So grab find screen in a file in any file minus C means counts. So you count the number of matches and this little part over here means if the line starts with a greater than sign. So what this command reports is the number of sequences or at least titles in a fast-fell. So that can be quite convenient to quickly have a bit of an overview of what's in your fast-fell, at least how many sequences are in your fast-fell. So I think most of you have seen a fast-fell before, have a pretty good idea of what it is, then a fast-fell. Fast-fell contains of records of usually sequence reads and each of these records contains of four lines. First line is the title of the sequence reads. And in the title, you can store quite a bit of information for Illumina data that is typically, for example, where the spot was on the flow cell among others. And also you usually store the barcode in there. More on the title in the slide after this. Then the actual cold basis, so the nucleotide sequence. Then usually an empty line spreading with a plus. That line is there probably for backward compatibility. So when fast-q-fell was designed, people thought that would have been a good idea to use that line. But nowadays it's not used very frequently anymore. So it contains room for an optional description. And then the fourth line, that's also an important line, stores the base quality. So what you want to do in a fast-q-fell is to store the quality of each base that is called in our nucleotide sequence. But if you remember well, our FRET-based quality ranges, for example, from zero to about 40. So sometimes that quality is a single integer, so something between zero and nine. Or it's an integer consisting of two numbers, so somewhere between 10 and 41, for example. So if you would store these integers together with our nucleotide sequence, that will become a little bit of a mess. So people thought of a way on how to store that in a smarter way. And that's by storing these integers actually as an add-key character. So that means that each add-key character represents a certain integer. So in this case, for example, a exclamation mark represents a zero, a semi-column represents 26, an add-time 31, an i41, for example. For now, for example, that's how it's usually stored in a fast-q-fell. So that means, for example, the A over here is a B. And maybe somebody can tell us what kind of base quality that would be, what kind of base quality we have for this A over here. 33. 33. Yes, exactly, 33. So we go to the add-time is 31, then the A would be 32, and the B would be 33. So we have a slash over here. So the second base has a bit of a lower quality. I would really have to count what it is, but it's somewhere that looks like 12 or so. We have a smaller dance time, so then B would be 27 and so on. So that's how base qualities are stored in a fast-q-fell. Then the fast-q header contains usually quite a lot of information that can also be relevant to you. For example, what it usually contains is a first part that depicts the position on the flow cell and the instrument and the run. The flow cell identifiers the lane and the tail within the lane and the coordinates within the tail. So that exactly depicts the position of the spot in which run on which machine. So you can always trace back where you came from. As a second part, there is information about which read it was in a pair. So if it's a one, it's the forward read, if it's a reverse read, but it's filtered out. Yes or no, that's not used very frequently anymore. So in principle, you can store a fast-q-fell and then use this second part of the entire title to store whether you want to fill it out or read it just now, but it's not used very frequently. Control bits also not used very frequently, but there is also at the very end the barcode. And that's also a very relevant part of information that you often store in your fast-q title. So that is the identifier of the sample. So the barcode related to the sample that you have sequined. I have a question for you. I'm going to start a new share and try to find my browser. Here we go. So question. There's a bit of more of a technical question that's also related to how these base qualities are stored in a fast-q-fell. So what we have learned in the previous slide is with grep minus p and then k-read-created enzyme, you can count the number of sequences in a fast-q-fell because you're just counting the lines that start with a k-read-created enzyme, which is typical for the fast-aid titles. In principle, you could also say, okay, the title of the fast-q-fell, so with all this information about the position on the sequencing lane, it always starts with an add time. So we can also use grep minus p and then not the line starting with a k-read-created enzyme, but with an add time to call to calculate the number of lines in a fast-q-fell. Why is that not going to work? Okay, so most of you have answered the add time can also occur elsewhere in a fast-q-fell. And indeed, that's the correct answer. The add time is not special for a regular expression. You can just use it directly in a regular expression. And there is no specific start to the voice also, but we always start with an add time. So if I go back to the presentation and if we just go to the previous slide. So the example of our fast-q-fell, so we have our title that always starts with an add time. So in principle, you could start counting the number of titles with lines, but in an add time. However, as you can see over here is that the add sign is actually one of the asking characters that can be used to specify base quality. So let's say if this A would have a base quality of 31, then this line would also start with an add time. Well, it's not a title, but it is the line for the base quality. So you would count extra lines there. Usually you have really millions of reads here, fast-q-fell. So it's very likely that at least one or a few after read start with a base with a base quality of 31. So you would count them as an individual record, which would not be correct. Therefore, you cannot use that line of code to count the number of reads in a fast-q-fell. Using yesterday that we have been counting the number of reads in a fast-q-fell. How about we just did that by counting the number of lines? Because we know that each record consists of exactly four lines. So we just take the number of lines in a fast-q-fell divided by four. And we have our total number of reads in a fast-q-fell. I see something in the chat appearing. The Gabriela tried to grab add signs in a fast-q-fell and give zero as output. And that's because it's quite likely that there are no add signs in a fast-a-fell, of course. They will be in a fast-q-fell. All right, then the sum format. Sum stands for sequence alignment format. And the aim is to store alignments, obviously. So the sum file is like many other files and bioinformatics, just a regular text file that starts with a header. And then there's a tab that links this part. And the header already contains quite a bit of information. And it's quite nice to have a bit of an idea of what kind of information is stored in there. So for a sum file, the header lines always start, again, with an add sign. So you will see later on that other file types, for example, start with a hash sign. But some headers start, all the lines start with an add sign. And different type of informations are stored there. So you have these different tags that we have, HD. And that gives you basically, I don't know where HD exactly stands for, but something like header gives you a virgin name of the sum format and information about how it is sorted. And that's already relevant information. Because if you do an alignment, then the sorting of your alignments are according to the order of the FASTQ file that you use as input. However, usually you are mostly interested, not so much in the order of the reads, but that was used as input, but the order according to their position on the genome. So if you see over here that it's sorted by coordinate, then the reads are not sorted according to the order in the FASTQ file, but their position on the genome. And you will learn later on how to be able to actually do that. Then there are usually quite a few lines describing the reference genome to which your reads were aligned. Each chromosome or each individual contact has a new line in this sum header. We're now looking at the E. coli reference genome. And E. coli reference genome in our case has only a single chromosome, so that chromosome has a certain name and a certain length. And that's stored over here. If you have multiple chromosomes, you have multiple chromosome names and multiple chromosome lengths stored over there. Then another very typically used part of the sum header is the addPV spec. And that is information about which programs were run on this sample in order to come to the sample as it is right now. So usually you can have multiple programs doing different calculations on your sample. In this case, we only have information about the aligner that was used. So with this addPG tag, we can figure out what kind of options were used for the aligner. So what you see over here is this program we use. This called Botide, a version even of is stored. And then the original wrapper command is actually written over here. And the index and the reads are specified. So the entire command is stored in your sample, which is quite nice. Because if you, for example, receive a stem file from a colleague and you have no idea where it's from and what the history is, you usually can figure it out by just looking at the PG tags in the header. Then after the header, the tab, the limited part starts. And the tab, the limited parts consist of multiple columns of specific information regarding the alignment. Each line represents a single alignment, usually a single read. First column specify the read name. So that is exactly the title that it got in the FASTU file. Then a flag and a flag represents binary information about the alignment. For example, over here you see an integer and that integer represents binary information. So whether it aligned property, for example, yes or no, or whether it is made, is mapped, for example. How this binary information is converted into a flag and how you can convert a flag back to the binary information you will see later on. Then the reference to, so let's say the compact or chromosome to which the read was aligned, the start position of the alignment, the mapping quality. So that would be the information that is used of how sure you are the mapping position is correct. And then the SIGAR string and the SIGAR string specifies whether there are matches or mismatches between your reference genome and your read. So how you can read this SIGAR string is like this. So over here we have a read of, I'm not exactly sure how long this read would be. So that's always a bit of calculation. But our read starts with five exact matches. Then two deletions, I think those are deletions in the query. So deletions in the read. So we have the aligner deleted the part in order to optimize its alignment. Then again seven matches. And then three as this and then add me thought clip. So three bases are clipped off of the alignment. Based on the start position and the SIGAR string, you can also define the end position. Daniela has a question. Yeah, sorry, two questions. One, what was again the S? Sauce clipped. That means that three bases were sauce clipped from the read. So it can work for the alignment. I know it. Okay. And then the start position is the start position in the whole genome or in the chromosome. In the chromosome. Okay. So this read aligned at position 12,513. So based on 12,513 in contact or chromosome U 96.3. Exactly. And then based on the SIGAR string and the start position, you know the exact alignment of the read. I think Gabriella has a question. Yes. For me, it's not clear. How does it look? For example, how the aligner can figure it out when it's a deletion or a soft clip? I mean, because in both will be like a gap in both. It's like there is no alignment in the query seconds to the reference genome. So how is like they are named deletion or soft clip? Maybe for me, soft clip, this is not very clear. How does it look or how the alignment? It will make this. Yeah. So what an aligner tries to do is to optimize the alignment itself. So if you allow for stuff clipping, that means that the aligner is allowed to remove a part of the read and ignore that while calculating the penalty, the alignment penalty. So if the aligner would say, okay, I'm not going to soft clip, but I'm going to call this a deletion, for example, that would mean that it would add up to the penalty. And therefore it might not have a significant alignment at that position. So if you tell the aligner, okay, do the soft clipping. So ignore that part completely for the alignment. Try to optimize the part that does fit. Then that soft clip is just ignored for penalty calculation. And therefore you might get significant alignments with soft clipping. Well, you wouldn't if you would do, if you would not allow for soft clipping. So if you would call that a deletion. Okay. And what is the difference between soft clipping and hard clipping? I forgot. I have to look it up. Hard clipping is not used very frequently. Okay. So the alignment, the aligner just use more soft clipping because what would be that is soft because less bases were ignored? So soft clipping means that the information of the wreath itself is retained. So for example, what you can do with soft clip bases is you can display them in the alignment if you visualize it. And then you can see which wreaths are exactly clipped off. Okay. That's the option in IGV when it says, see a soft clip. Okay. Yeah. Yeah. Exactly. She knows the question. Yes. I was wondering in our case, we have a reference of only one chromosome, right? So we know that that starting position is of that precise chromosome. If we would use a reference with multiple chromosomes, do we see which chromosomes were in the reference name in this case? Yeah. Or in the starting position? No. So that would be reference. So this is a bit confusing. So you would say, okay, reference means the entire genome, but so that's just how it is named in the sum manual. So reference here means contic. So basically the false A title. So the thing that occurs after the greater than time in a false A file. So if you have multiple chromosomes, that would be chromosome name. And then a position in a chromosome. Great. So if you have paired and reads, you would like to store that, of course, as well in your stompbell. So which read belongs to which reads are paired in the stompbell. So for example, one thing you would like to store is whether the made. So the other one, the other read in your pair is actually mapped to the same chromosome or to the same contic. And of course, usually that's what you would expect, right? If that's not the case, either something went wrong or your reference is just broken up into multiple context. So if the mate is mapped to the same reference, you get an equal sign over here. If it's a different reference or different contic, I should say a different chromosome, they get the chromosome name over there over here. Then you get interface about start position of the mate. So exactly know where it starts. And therefore, you can also calculate the fragment length. Sorry about that. The fragment length. So that would mean the complete length of the unknown part that you have sequenced. So between, let's say the first days of read one, and that's also the five prime base of read two to the end of your fragment. So that's also calculated the total fragment length. Then we get the sequence itself that's also stored in the sample. So that's always there. And the base quality string is also stored over there. There we have the sequence and the base quality and the optional text that depends very much on the alignment. You are using what kind of information is stored over there. So that's basically extra information related to the alignment. So then a few words about trigger strings. So the letters that are most frequently used are the m, the i, the d, and dn, and also ds. So the first five. So the m specifies a match, the i, an insertion to the reference. So in addition, you have something inserted to read a deletion. It's a deletion from the reference. So it is deleted in your read. An n is skipped from the reference. And that is actually used for, for example, specifying intronic or spliced alignments, I should say. So if you then you skip an entire intro that's specified by the n, and then the add is used for soft clipping. However, what you can also store, which you see, for example, at the end, so this number seven and eight is a sequence match and a sequence mismatch. So in principle, you could store in the sticker string also individual snips. So if you have a mismatch between your reference and your read, for example, in your reference, you have an a, in your read, you have a g at that position. You can specify in the sticker string, for example, an x. However, this is actually stored as an m in the sticker string. So these are almost never used. And it has a reason. So let's say this is what I just explained. So let's say your reference is over here. And so on the bottom and your alignment is at the top. So you can say, okay, we have seven matches. So that's how it's actually stored. Which can also say, okay, I have three sequence matches, one mismatch, and then three sequence matches again. So we have a longer sequence string, but we have stored information about this variant over here. So the reason for that is that also the read itself is stored. So the sequence of the read itself is also stored in the sum file. That would mean that if you would store these mismatches in your sticker string, you would store the same information basically twice. That you have to read information. So the actual basis and you also have the reference information. That means that if you would store those individual mismatches in your sticker string, you would store information about a mismatch twice, both in the reference and the actual read and in the sticker string. And people said, okay, we're not going to store information twice. So we are going to say, okay, if there is a single mismatch, meaning for example an S&P, we are going to store it as a match and you can figure it out from the read itself, where that actually came from. Or to figure out what the actual nucleotides were in the read. So I have a different question. So I'm sorry, before you made the question, so it means that the mismatch are stored as SNPs. The mismatches, they are stored, so let's say the axis, they are just stored in the read themselves related to the reference. Meaning that you store the actual sequence in the sound file. So that's what you see over here. So the sequence itself is already stored in the sound file. And because you know the matches and deletions and insertions based on the sticker string, based on the reference sequence and the read sequence, in combination with the sticker string, you can figure out what the nucleotides are that are different from the reference and the read. Yeah, okay, so let's go to the actual question I want to answer to ask you. So now we have a bit of an idea of what the sound file looks like, what kind of information is stored in there. And let's say you have only the sound file. And my question is, whether you would be able to regenerate the fastq file, so the original fastq file out of the sound file, or at least the read that were in the fastq file, or and or can you generate the reference sequence, so the original what they felt from the sound file. The only the sound file, you have nothing else. Yeah, most of you have answered.  So most of you have answered, or the most answered answer is only the fastq file, and that's also the correct one. So indeed, you cannot regenerate the fastq file from the sound file, but you can regenerate the fastq file in order to explain that. Let's go back to the presentation. So if we look at this step, the limited part of the sound file again. So everything that is in the fastq file is stored there. So we have the read name that is stored, we have the sequence, and we have the base quality, and all of those are stored in the fastq file. So you could again, and it's often actually also done, regenerate the fastq file from the sound file. So that's pretty good. The next question. But you don't have anything on the instrument information in the sound file? Yeah, so that's that would be the stored in the read name. That's this part. Everything? No, I shortened it. Oh, the read name is the entire title. The line. Yeah. So if the information about the reference that is stored is mainly stored in the header, and that's only the on takes or the chromosomes that you have in reference and their leg, other than that, that there's nothing stored in the sound file. So if you want to do, for example, variant analysis, you always require the sound file and the reference, you know, in order to find mismatches between your reads and the reference. Okay, then about some flags. So these are these the second column in the tab, the limited part of the sound file, and they are stored as integers. And the integer, they are a sum of different bits that specify a certain characteristic, whether that is true or false. So meaning that let's say if we look at the third one, so with bit number four, segment is on map means that, for example, your read is not met at all. So you couldn't find a place for a written genome, it's stored, still stored in the sound file. Then you get a four. And let's say you have another characteristic, for example, that your read is spared. So that this, I'm pretty sure that's this one template having multiple segments in sequence things, but it basically means that your read is spared. So if your read is on map and it is spared, you get a four plus one. So you get a five at that position. So at that second column in the sample. So all of these characteristics, these, how many are there? So about 10 or 12, they can be stored in the sound flag. For example, you can also store in the sound flag, whether we have a PCR duplicate, just know whether it's marked as a PCR duplicate, or whether it was the first or the last segment in the template, meaning is it the fourth or the reverse read, and so on. So how does it work? I've put together a small example. So let's say your read is spared and is properly aligned. So the bit that is, the specify whether the read is spared is the one. And the bit that specified properly paired is the two. So you add them together. So you will get a flag of three. Let's say your read is spared, not properly aligned. And the mate is on map. Then you get a flag of nine. If your read is spared, it is on map. And the mate is also on map. Then you get a flag of 13. And so, and you can, because we have, we're working with bits, you can always translate back these flags into the individual binary information. Question for you. She knows question. Yes. So this means that actually the points are always the same. So just go back. So like the read paired is equal to one. Give it like a point to that is always the same. So if you, so for example, you can also have stone bells where you combine paired ends with single end reads, for example. So then some of the alignments would not have this characteristic. So you would not have the one in there. See what I mean? Yes. I don't understand what are the one, two, four, and eight, the bottom.  So there's one, two, four, and eight, meaning template having multiple segments and sequencing that basically, if you translate that, which basically means was your read paired, yes or no. So that's this one. Okay. So these, these are always the same. This code is always valid for some flash, right? It's always the same. Yes. If we have this table here, then we're sure that if I have a total of two, then I can go back. Okay. Yes. So the two always means each segment is properly aligned. Yes. The four always means the segment is aligned, the read is aligned. Thanks. All right. Then a few more examples of types of data. So we have the bedfell, and bedfell stands for browser-extendable data. And what it just stores are usually regions in a genome. So what it always stores is at least three columns. And the first column would be the sequence title. So for example, contact or chromosome. The second column would be the start position of a specific region, whatever a region of interest. And the third column would be the end position. Then you can give it a name. For example, you can store information about axons in there. So in the example, we have axon one, axon two in there. You can store a score for whatever reason. For example, how sure you are there is an axon over there. And you can store the strength. So whether it's in the positive or the negative strength relative to your genome. One thing that is very typical about bedfell is that the numbering of the start position starts at zero. And numbering of the end position is, let's say, standard. That is inclusively called. So if you would convert it in, for example, a way we also specify regions, starting with the chromosome name, a colon, and a start and an end, you would convert this bedfell into regions. So we would specify it typically, for example, also in ITV, we would start at the 1700 and two. Well, in the bedfell, it would be 1700 and one. This is a little bit annoying. So this really starts at the 1700 and second base. That's where your region starts. So basically in a bed, it starts after the specified start sequence. This is a bit confusing, but it is what it is. So at some point, people have thought, okay, this is what a bedfell is. And she just tickled that because every bedfell is formatted in that way. Then the GFFL is quite similar to a bedfell, but we have some additional information. For example, what kind of feature we are looking at. And GFFLs are typically used for storing information about genes, for example, about where transcripts are, where coding sequences are, where genes are in a genome. And there are a few columns. The first column is, again, the chromosome name, the contact name. The second one is the source. So where the source, where the annotation actually comes from. Third one is the feature. So what kind of feature are we looking at? So is that message RNA? Is that an axon or coding sequence or whatever? Then we have a start and an end. So the start and the end is different from a bedfell, if I'm not mistaken. So that would be really starting counting at the one instead of counting at the zero and just coming for the bedfell. Then we can store a score. So how sure you are that certain feature was actually there, depending very much on the software you're using, whether you store that kind of information. The strength, same as a bedfell, minus or plus the frame you can store there. So whether the translation of an amino acid actually start at that position or whether that's irrelevant. So if it's missing, so if it's irrelevant, so that would be the case for messenger RNA and axon. You get a dot. The coding sequence, of course, is you can use that to translate that into a amino acid sequence and then where to start this frame. If it's a zero, it really starts where the coding sequence also starts.  Usually that's a pretty long line of, for example, the gene identifier, for example, the ensemble ID, the gene symbol. And of course, also often, for example, the parent. So if you're talking about an axon, the parent would be the transcript. If you're talking about a transcript, the parent would be the gene and so on. And that's also stored in attributes. So you know, which kind of features belong to which. Then the last format I want to discuss, Teba. Sorry, I'm not switching on the video because my Wi-Fi is not good. Sorry about that. Yeah. So my question was about the difference between the information that's stored in a bed versus a GFF file. So was it that in the bed file it was only about axons, whereas the real annotation of the gene and transcript is coming from the GFF? That's a bed file can store any region for anything you want to annotate at the genome. So it doesn't necessarily specify icon, but pretty whatever you want. Then it could also, for example, specify promoter region or, you know, something that is enriched somewhere or. Okay. Then what additional information is a bed file adding apart from what is already, what could already be in a GFF? Well, it's more that. So a bed file is much more flexible. So you can store anything there. And a GFF file is really there to store information usually about genes. Okay. Genes and transcripts. So in principle, you can store the same information in GFF file also in a bed file. You can actually convert the GFF file to a bed file. It would have a few additional columns over there, but pretty much for all information that is in a GFF file can also be stored in a bed file. But not everything that can be stored in bed file can also be stored in GFF file. Because in addition to genes, you can also store, for example, I don't know, primer position, for example, in a bed file. Okay. Okay. Thanks. And a second question is GFF and GTF, are they similar or different? That is a bit of a complex story. They store mostly the same information, but there are multiple versions. So if I, just by heart, I think so you have GFF, GFF2 and GFF3. And I think the GFF2 is exactly the same as the GTF. So that's by heart. I'm not entirely sure whether that exactly true, but that is how they a little bit relate. So there are GFF versions that are exactly the same as GTF, but for most applications, you can almost use them interchangeably because the difference in how things are stored are very similar between the GFF version and GFF file. Okay. Okay. Thanks. So then the last file types. That's the VCF. And in VCF, you store information about variants. So like a song file, a VCFL also starts with a header. But other than, so where the headers, the song file starts with add signs, the header of VCFL starts with hash tags.  And this step, the limited part, not alignments are stored, but variants are stored. So basically what you store in a VCFL, we'll go into what's exactly there in later slides. I'm not mistaken. Yes. So we have our reference and we do the aligned reads. And at some points, you want to use some software, for example, to figure out whether, for example, at this position, you see sometimes a T and sometimes a C, which is the reference, whether that is a variant, yes or no. And that is stored in a VCFL. So what you want in a VCFL is a position, you want information about the reference allele and information about the alternative allele. And for example, also information about the genotype. So the genotype for this position over here with only seeds is probably homozygous C. In this case, homozygous alternative because it's different from the reference. And at this position, it is heterzygous for the alternative, meaning half of the reach probably or half of your genome is a C and the other half is a T. So if you're looking at a diploid, then of course one allele is a T, one allele is a C. Also, what you want to be able to store in a VCFL is, for example, insertion and deletion. So that's also possible. So over here, we apparently have a insertion of two bases. And over here, it seems to have a deletion of one base. So probably the T is the latest in the reference. So basically what a variant color does is converting our SunVal into a VCFL, specifying specific positions and alleles for our variant. It looks like this. So we have our header starting with the hashtags. And in this header, there is quite a lot of information. For example, what kind of version we have, the date, often also what kind of software was used to create the VCFL, not specified in this example over here. And there is information about what is specified in the tab delimited part. So the tab delimited part starts with, again, the chromosome name or the contact name, the position. So very similar, again, to other formats. Then we have an ID and identify for the particular variant. So if you call new variants, you do not know the ID on the forehand. So this is usually then empty. Sorry. Then we have the reference allele at that position. We have an alternative allele at that position. So these are only the positions where we actually call a variant. The quality of the variant in general, whether there's a filter specified over here. So if there's a pass, it is not filled out. If there's a Q10, it is filtered out. There's an info field giving information about the variant as a whole. And then there is also information about particular samples. So you can store information of multiple samples in a single VCFL. And what is stored in these particular samples is stored in the format column, specified in the format column. And then all these different codes we see over here, in both the info field and the format field, they are described in the header. So we have this part in the header where the info field is described. For example, the NF. So what kind of, how information is stored in there and a description is also stored in there and over here says, okay, NF means the number of samples with data. So we can check that in the tab delimited part and we see number of samples with data equals 3. And we actually see two columns per samples. And that is because this VCFL was a little too long for the slide to cut off the third sample, but there are three samples in total with data. We have CTP, for example, total depth, total depth of 14, and so on. So the VCFL is super flexible, which means that the software you're using, or the end user, can decide what kind of information is stored in this info field. Thing counts for the format field and the filter field, by the way. So first, let's go to the filter. So if the filter is not false, then you might specify why a certain parent is filtered out. In this case, you see Q10 over here. And the script in all is that there's a quality below them. And that's actually what we see over here, that the quality is below them. So again, also the filter can be anything, very flexible. Then the format part, the format part describes what kind of information is stored for sample, so not information about the parent as a whole, but what kind of information is stored for sample. And that is what you see over here. So for example, we have VenoType, so GT, that's a very frequent type of information that's stored in the VCFL. And so the order of the format column over here should also be, or is always the order of the different characteristics that are stored in each individual column of the different samples. They do not have to be exactly the same for each variant, because for example, for an indel, you might want to store different information than for a SNP, for example. So we have the GT over here. So the first part of the column of a certain sample should be the genotype. And the genotype is always specified according to the number of the ploidy level of the organism. So in this case, we are looking at human data. So it's deployed. And the numbers in the genotype refer to the reference and alternative. So over here, we see the zero and then a pipe symbol and then a zero. That would mean that this specific sample is homozygous reference. So the zero always specifies the reference value, while the one specifies an alternative value. Sometimes you even have multi-allelic variant. For example, over here, where we see the references in A and the alternatives are G and a T, they can also even have two specifying a allele. So the two here specify the T and the one specifies a G. In this case, this individual is heterozygous for T and T, and it doesn't contain the reference allele. And so you can also store other information, of course, about the specific samples, for example, in genotype quality. So how sure the variant color was that the genotype it has called is the actual genotype. Again, this is also in a threat-based core. So very similar to mapping quality and base quality. These are examples that was already clear. That is it in terms of file types. Everything clear regarding that. I have a question. I would like to know, for example, this upload type quality, what does it mean or what it explains? So there, so PCFLs are very flexible. That also means that you can store any information you would like to store about a certain variant. So hepatotype quality can be something that is completely used by only one very specific type of software. For example, software that does haplotype calling that uses haplotypes. So to understand what haplotypes and everything are, I think that goes a little bit too deep for this course. But you can, for example, link multiple variants together and calculate, for example, a score about how sure you are that the gold haplotype is actually the haplotype. So that's probably what this would represent. So whether the haplotype that is gold, so haplotype information can also be stored in a VCF. I am not going to explain it now. But so this haplotype quality would be depicting how sure the haplotype color was that the haplotype reports is true. In the sample column, which number is the haplotype quality and which is the read depth and which one is the quality, genotype quality? Okay, so that is specified in this form of column. So here we see GT, which is genotype or genotype GQ, which is genotype quality, BP, which is read depth, HQ, which is haplotype quality. And in that order, it is reported for the different samples. So we have the genotype quality over here or the genotype over here. So the zero zero, the genotype quality over here. So 48 for the sample, the depth. So read depth one, apparently. And the haplotype quality is 51,51. So there are two haplotype qualities. I have no idea why you would have two haplotype qualities. But apparently haplotype qualities is reported with two numbers. So you can already notice that haplotype quality is not a quality that is reported very often. Because in the, for example, in the bottom position, that is only like the genotype, the genotype quality and the read depth, and then the haplotype quality is not reported, no? No, indeed. So because you can report different characteristics for different variants. So in this case, the variant caller has decided, okay, I'm not going to report haplotype quality over here. And that is because it's not based. So if you see a pipe symbol over here, then a variant is faced. So meaning that it's part of an haplotype. If there is a slash, it is not based. So it's not part of a haplotype. So because it's not part of haplotype, also haplotype quality is not reported over here.