Hi everyone. Happy Thursday. I'm Joe for jasperadescasebrakes.com coming at you with 2021-22 Panini Donner soccer 2 box random country break number one 17 spots all card ship some countries won't have autos like Norway won't have autos But Erlen Hollen is on there. So obviously his his cards are gonna probably be worth more than more than an auto Anyway, but that's the risk, but thanks for these folks here here for Giving a chance taking a chance on this break There are the teams are here and let's roll it and randomize it two and a five seven times one two three four five six and Seventh and final time after seven we've got will down to Robert and then same dice roll for the countries two and a five seven times one two three four five And then two more times six and seven We got England down to Spain All right, will with England Matthew with Italy Robert with the Netherlands Matt with Uruguay Craig with Portugal Robert with Poland Joe with Germany Robert with the combo Sweden Norway Croatia Will with the United States Robert with Belgium David with Columbia will with Argentina Steven with Brazil Peter with Korea David with France Robert with Mexico and Spain Let's order these by team and there is a trade of trade window feel free to trade right now While you're thinking about trades and I did drop the checklist into the chat if you're watching live There it is group rate checklist will drop the checklist again for you Now let's flip screens here full case. This is the correct case right Donner soccer. Yeah Heavy case here, so let's pop this open Let's see which boxes we're gonna do see how we're gonna do this first of all I guess we'll select these are two box breaks right I guess we'll select a column We'll go one two three four on the dice roll We'll use we'll use Liverpool red I'm a Liverpool supporter one two three four It's a six I'll roll again There's one so we'll do the left side right here For the next two box break. I'm just gonna grab random boxes So now we'll pick two boxes. We'll go one two three four five six on the dice roll and it's six one two three four five six and Then we'll go one two three four five six on the dice roll Three one two three top box right there and this will stick in this pile over there. All right, so This is what you're looking for Tons of cards autograph memorabilia cards a lot of parallels kaboom hunting which is really kind of I think the biggest Draw for this kind of break right here So I'm gonna pause the video when we come back. We're gonna see if there's any trades Then we'll have the break stick around be right back. I Welcome back No deals were done a lot of trade chatter, but no no deals were done. So thanks everybody For getting into the action. I think the next two box break is already like over halfway there So this could be our very next break. We can run this back pretty quickly big thanks everybody Once again for getting it on the soccer A lot of value in this soccer so keep getting it on especially if you lucky enough if you're lucky enough to End up with a kaboom card out of here. All right Chad. What's up? Yeah Check out that if you're just joining us lady and John check out our latest promo We got a we got a Got a wax party going on and a Lot of a lot of boxes a lot of wax being given away as party favors We are Mike check out the details on jasky's case breaks calm. All right first box. Good luck all card ship There's Liverpool's Roberto Firmino Like that they include some optic card near to these look really sharp There's a lot done Marvel's card Along with a press-proof Isak to 349 for Sweden And that's where Ibrahimovic will go as well And so both of those will be for Robert with the Sweden Norway Croatia Combo, there's a Pulisic believe it or not United States we've got a lot of got a late lot of youngsters coming up the ranks Be pretty interesting when we get the World Cup in North America and not too long It's crazy that the That's the non-press proof of Ibrahimovic It's crazy that the World Cup were in a World Cup year And it's going to be one of those weird That will I think not one of those I think it's the first one ever right is this the first Fall winter World Cup. I don't think they've they've ever done it in In the winter time There's Uriel Atunia To 149 for Mexico Robert It's Phil Foden De Jong there's Foden again craftsmen press proof And a silver Phil Foden nice He's pretty good The Manchester City Manchester City player and for the three lines, of course That'll go to Wilhash Look at these cards are upside down and right side up This guy's pretty good too. Let's see if we can find some. I know there's a lot of great Players here, but we want to try to find parallels of these Of these guys It's coming home, it's coming home, it's coming footballs coming home There's messy for will and argentina Now World Cup wasn't delayed there. I think they're saying that it's It's gonna be too hot in the summer for mostly for fans I guess players too. I think it's gonna be mostly it's it's all indoors And at night anyway, I don't know how much But I guess there must have been yeah jack saying it's not much change in the weather Is there I mean there must have been enough change difference in the weather from summer to the fall winter or they They moved the uh Where they moved the World Cup, but it wasn't delayed. It was just that's how they intentionally scheduled it It's going to be in the wintertime. So it'll be weird to see the World Cup During during the the festive season We got a gold right here. That is 6 out of 10 urv telmans for belgium That is going to be for robert. All right, so I haven't seen the autograph or relic yet in erling holland Serginio dest silver erling holland norway sweden norway-croatia combo robert I don't know what the uh What the market is on erling holland cards, but If he's pretty good I wonder if uh, how much his value would go up if he If he went to like the english premier league It's a possibility KC giloh saying kc's in the bidding for the hosting the next world cup Trying to find proper to rent out. I live next to the proposed area. Nice. Yeah, I don't think that they have decided on the Um, I think there's a pool of of Of cities and stadiums that they're going to use But that's not been set in stone yet I mean, I would imagine some of the bigger stadiums Like sofi and maybe where the jets and giants play. I think that's pretty much Guaranteed to host a match or two if not a If not a bigger match Like a knockout sage match, but But yeah, I think the other other cities are still up in the air There's kit kings nacer chadley for belgium Do nori get banned from the world cup? I've not heard anything like that Maybe they have I don't know. I haven't been super sharp on on soccer news lately This is for robert in belgium I know they're not that great. So I think they've had trouble qualifying different insert messy And I like a diago jota silver place for liverpool Pretty pretty fun player to watch Is it an autograph or or is it autograph and It's autograph and so we should there should be should be an auto in here somewhere And you know, this year is a great year to really collect hockey Hockey to collect soccer because of the world cup at the end of the year It's one of those things where you would probably You know buy into our hockey breaks throughout the year on jaskiescasebreaks.com And then, you know, just hold on to them just collect until uh Just collect until the world cup and you can you might see values Jump up as we get closer and closer to the tournament Especially the winter time pretty unique year for collecting There's the auto right there That's a familiar player Sort of an older school player And it's matzholz signature series for germany That'll be for joe joe felish one of those up-and-coming stars for portugal That's for craig. He may he may take the mantle from uh Cristiano Ronaldo. All right, that was the first box next box Yeah, I'm not sure how you're classified as a as a rookie in this my guess is Is this their first year they're appearing for their national team? Maybe I'm not sure But there must be there must be a rhyman reason All right, so what is going on in the soccer world? I know. Yeah, liverpool and arsenal drew in the caribou cup The cup of nations happening too so a lot of uh A lot of the top african players missing especially from my squad liverpool Mohammed salla and sari omane Cristiano Ronaldo aims to play until age 42. How is he now? And the next soccer break sold out nice Yeah, Cristiano is like the is like tom brady international tom brady maybe they're uh Maybe maybe they're using the same dark magic to like to stay To stay young these cards do kind of stick together a little bit Cristiano's 37 in a few weeks as robert and we've got Kit series ja motinho for portugal That'll be for craig now. It's one thing to say you're gonna play to 42 Sure, I guess you can play to 42 But where is he gonna play you think he plays until 42 in the english premier league? Or is a Is there a stop in in major league soccer as a lot of these sort of aging superstars tend to Uh tend to make a stop over the last couple years of the career in the united states Cristiano Ronaldo the United States playing regular professional games mls would love that Here's more christian polisic Here's wukai Osaka for england england Should have a pretty good world cup this year. I mean they they're probably be one of the favorites They could knock one out. They haven't won one since 1966 that country will go go nuts And there's jude bellingham. He's another good youngster there too. This will float in Ronaldo to sporting casey That stadium season tickets would sell out so fast there They would they would they would make Whatever they're paying him they'd make it back pretty quickly Yeah, then you might run into christiano ronaldo at your local grocery store There's edson alvarez 127 to 149 for mexico El three will be for robert Can you imagine? Can you imagine just wandering around your local grocery store? And turning into turning into the cereal aisle You know just turning the corner into the cereal aisle is this guy christiano ronaldo Yeah, I think that's what david. Yeah david b He Said that a little bit earlier too. Lorenzo insignia signed with toronto fc for tons of money. This is matias svensberg to 349 More in bapé for france le blu and we've got arthur 65 out of 99 for brazil that'll be perceived with flat All those in bapes will go to david. How old is uh insignia now? late 20s early 30s, maybe 32 so maybe on the on the on the back end of his prime, maybe Nice silver in bapé And a numbered leonel messi That's 13 out of 199 just a few off his jersey number 30 says jack so early 30s 30 can reconfirms risario leonel messi for argentina will hash last spot mojo another messy Rex, what's up joe john lister's first about hall of famer, right? Is he I don't know for you I don't know. He doesn't feel like first ballot hall of hall of hall of famer to me But uh, I don't know. I think he has Well, he definitely has the rings There's lucas martinez quarta But I don't think he's first ballot though. He's got the rings But he's never really has he ever won a scion I don't think he's for I think he eventually will get in Just for the just for the rings I think he has like I think he has like a sort of a Milestone win total, right? I'm pretty sure he has like 200 wins Which definitely helps I don't know. I don't know if no hitters no hitters are cool But I don't know if they're they're really part of the hall of fame conversation though. I mean there there are some Not good pitchers or just average pitchers who have twirled a no hitter before I don't think no hitters part of the conversation. I don't know. What does everyone else think? John lester recently retired first ballot hall of famer I say no I say maybe late ballot hall of famer And there's diego meridona. Wow Diego meridona autograph for will hash in argentina I thought it was just going to be a parallel Like I thought panini was just trolling me R. I. P One of the great Will is stunned shut up. He says That is awesome Will will's like blowing my mind right now. Congrats, man. That's awesome I guess the dice picked the right boxes Congrats I mean Yeah, all right. He is yeah, he's got to be in the top five soccer players of all time, right? I mean, I feel like in a lot of lists He's like right behind He's right behind pelle basically I feel like a lot a lot of pundits have him right behind pelle You know and a and ahead of like and ahead of like messi and christiano rinaldo Other rinaldos What a hit. There you go Thanks, everybody for watching. Thanks everyone. Uh in this break for getting in that was random country break number one I think another one sold out. So look forward to that in the next video. Jasper's case breaks.com. Bye. Bye


I... even by 19th century chemical standards that makes no sense. Here's another old textbook I've got in my collection and this one is Popular Errors by John Timms. It is going to be probably the oldest book I own for a while I think. Says here 1856 and I've got no evidence that this was made after so I think this is genuinely 150 years old so let's try not to break it. I don't really know much about John Timms, there's not a lot of detail on him apart from the fact he's an author and he wrote quite a few books like this. Curiosities, trivia books, misconceptions and a book called Things That Aren't Commonly Known. Basically kind of like maybe a 19th century QI or book of general ignorance type thing. So I don't know maybe you could think of this guy as like the Stephen Fry of the 1850s but we'll see. He had some wild, wild ideas. So this is just a book of just weird facts and misconceptions and you can probably see from the number of posts that I've added in. I've found quite a few interesting ones. So let's have a look at the back here for instance. We've got a pretty common one. This will pop up in trivia books. Wild beef eaters at the Tower of London called Beef Eaters and he's talking about whether they're named after the fact that they were paid in beef. Now the thing is we don't know whether that's true or not. A few others are really interesting ones. We're whalebone. This substance is improperly named since it has none of the properties of bone. Its correct name is baleen. Oh baleen, baleen. And yeah this is true. Whalebone is a very flexible thing. It has probably got about the consistency of maybe the plastic you find in a zip tag as whalebone was used in corsets and garments a lot and it's remarkably flexible. It's not this huge rigid, oppressive substance that you think of when someone says whalebone corset. But I'm surprised to see it in the 19th century when that substance was still common. Where else have we got here? Diamond. He's talking about the hardness of diamond and apparently there was a rumour or something that you could soften diamond by steeping it in goat's blood. He says that's not true. It kind of isn't. But he does say that diamonds can wear out, they are not indestructible. Obviously diamonds aren't indestructible. You can shine them and polish them down. That's what jewelers do. So where are we here? The sensation of heat this I think this is really good science this one because there cannot be a more fallacious means of estimating heat than by touch. Thus in the ordinary state of an apartment at any season in the air the objects which are in it will have the same temperature and yet through the touch they will feel warmer cold in different degrees. The metallic objects will be coldest, stewing and marble less so wood less so and carpeting and woolen objects will feel warm. In scientific terms we will think of heat and temperature being slightly different. Heat was really a energy or measuring energy transfer temperature would be kind of related to the internal state of the matter or the object. So an object that feels hot is transferring a lot of heat energy to your skin for your nerves to pick it up. You're certainly on the money there. Right let's have a look at some of the wilder ideas though. Let's see here. Air quality. Consideral error prevails respecting the cellular bill. Salibriti? I'll have to look that up. Of the air of our metropolis from ignorance of the fact ascertained by Mr. Cavendish many years since that there is no sensible difference in the constituent parts of the atmosphere under circumstances the most dissimilar. So the Mr. Cavendish he's referring to is obviously Henry Cavendish he figured out the constituent parts of air that being mostly nitrogen with some oxygen and then kind of this one percent leftover but in 1856 that one percent was not really well known. We suspected there was another element or another material making up that remaining one percent. It's mostly argon it turns out and what Tim's is referring to here with Cavendish is that constituent of air doesn't actually change the oxygen and nitrogen proportions in air really do not change appreciably anywhere on the planet. When it comes to human health it's that one percent that is really the problem that contains the carbon dioxide, carbon monoxide, particulate matter and so on. Things that maybe in the 19th century they weren't able to appreciably quantify and that is very different between the country in the city. Probably one of the weakest bits he does is the citations at the bottom it's really hard to pick up on what they are and in this case he simply cites a fact to the doctor. A book which tracks people how to physique themselves or to be entitled every man is his own poisoner because it cannot possibly teach them how to discriminate between the resemblance symptoms of different diseases citation the doctor. So that's what we might call today the kind of when doctors get really annoyed that you're googling your own symptoms I suppose so some things haven't changed there. Thirst this one was weird it says imaginary thirst the development of a certain morbid feeling is often mistaken for thirst to which there is a great analogy such as caused by the vicious habit of frequently drinking and the desire to taste some liquids is brandy and wine nothing produces thirst so much as quenching it or grows more readily into a habit than drinking. If you drink ethanol it is a slight diuretic and it does make you feel thirsty afterwards and causes you to want to drink more and if you do indulge in it you will become an alcoholic but he's not going to be using those terms because alcoholism as a word was only invented a couple of years before this book it may not have been popular enough for tins to have come across it. It would have probably been under the previous medical term for craving drugs the Dipsomania which was the craving for alcohol and drugs that was coined like nearly 50 years before this book so maybe that's just a code of reference to being an alcoholic and it is true if you indulge in that it will get worse. This is of really interesting once so coal is more valuable than gold so he's trying to say the natural supply of coal Britain among the nations is the most singularly favored very 19th century isn't it much of the surface of the country conceals under it continuous thick beds of that valuable material thing now the exhaustion of British coal mines the importance of coal is in this as necessary of life and to the degree which our superiority in arts and manufacturers depend upon our obtaining supplies of it as a cheap rate has naturally attracted a great deal of attention to the question as to the period when the exhaustion of our coal mines may be anticipated he does write some very very long sentences even by 19th century standards but this this is a actually a key concern we know now we are running out of oil it is not going to last forever but his estimate here according to Mr Taylor and experienced coal owner the coal fields of Durham and Northumberland are adequate to furnish the present annual supply for more than 1700 years i don't think even if we were to extract all the coal it goes anywhere near that i would i would love to try and look that up but i'm pretty sure it's like 100 years okay i spent a while trying to get an answer to this one and obviously it's complicated because there's a big difference between the amount of coal that we think might be under there how much we definitely know is there and how much we could realistically recover and then you divide that by the rate of consumption which is currently being phased down to zero but has been significantly higher in the past in any of those cases we don't hit anywhere near 1700 years it is therefore quite idle either to prohibit or impose heavy duties on the exploration of coal on the ground of its accelerating the exhaustion of the mines yeah this this is where the kind of the mask is dipping about what this guy is on about he would you you could very much see him as kind of setting up one of those websites that says CO2 is green coal mine is great sod the environment it's all a con global warming is a myth you can you can kind of see him getting into that thing but 150 years early poor people and call oh yeah waste of fuel gilbert white has well observed that the very poor are always the worst economists and therefore must continue very poor the truth of which remark is strikingly evident in the mode at which the poorer classes use the fuel they have this is sounding very very familiar you put some of this behind your radiators it really works it makes the whole room nice and warm i just want to point out that this isn't actually true indeed poor poor persons make less of the little fuel they have than the richer classes still the poor must not be altogether blamed for the improvements in fireplaces by scientific men have done a great deal for the fireplaces of the rich but nothing for the habitations of the poor so you know at least he's blaming the poor for being terrible at fuel but he's least pointed out that they can only afford of the inefficient stuff okay let's go for the big one why don't we let let's have a look at this this is kind of hilarious um black skin strap in that the heat of the sun produces blackness of the integments is an opinion as all of the days are plenty buff on a search that climate may regarded as the chief cause of different colors of man and smithers of the opinion that from the poll to the equator we observe a graduation in the complexion nearly in proportion to the latitude of the country okay blooming back under the same impression devours to account for this black tinge by a chemical illustration somewhat curious he states that the approximate cause of dark skin is an abundance of carbon secreted by the skin with hydrogen precipitated and fixed by contact of the atmospheric oxygen our creals and the british inhabitants of india may have seen themselves particularly fortunate in not being subject to this chemical operation i i don't know even by 19th century chemical standards that makes no sense you have a darker skin because you secrete carbon and it reacts he's literally saying that they've burnt themselves black again we can kind of see it's here in the present days of boasted liberty it's more than probable that the benefits of the feudal system have been forgotten amid its abuses the system of servitude which prevailed in the earlier periods of our history was not that of unmitigated character that may be supposed no man in those days could prey upon society unless he were to war with it as an outlaw so basically if you are glorified slave labor for the local lord that's fine he and i'm not not convinced there's a lot of people who have have challenged whether the feudal system has been uh misremembered in some way and in the fabric of society as imperfect as it was the outline of rudiments of what ought to be were distinctly shown in some main parts now well now utterly effaced uh for anyone not remembering the feudal system was basically a no your place hierarchy kings barons lords and landowners and then workers who would basically be all locally governed and enthralled to those barons uh it is a very stratified system so moving between the barons and lords and and moving around the country was effectively impossible until the black death uh reduced the population so much that labor demands uh were really high and peasants could actually you know move around and get a better deal because they were in short supply for the work so the feudal system was kind of ended by that if one class were regarded in some respects as cattle they were at least taken care of oh it's very very good slave owners isn't it and while we're at it let's see the prophets of medical advisors it's a strange error to consider the prophets of medical attendance to be uncommonly extravagant because this great apparent prophet is frequently no more than the wages of labor okay so far so Karl Marx uh the skill of an apothecary is much nicer and more delicate matter than that of an artifice whatever and the trust is supposedly much greater importance his reward therefore ought to be proportionate to his skill and his trust for example the apparent extravagance of the charge of 18 pence for a draft file of medicine is obvious to many who do not reflect that the charge is in reality payment a professional skill the 18 pence may be fairly divided into two parts four pence for the medicine and 14 pence for the advice uh yeah so you basically he's defending massive medical markups on the ground that their doctors and they train a while fair enough but the medical markups he's talking about are huge basically it almost puts the american healthcare system to shame here in the 19th century you have to remember this is the uk this is pre-nhs this is even pre kind of the concept of medical insurance this is everything the worst medical system you can imagine for a society amplified up a little bit so this is uh this is common errors um a little bit well not a hundred percent accurate in terms of fact there's some screamers in here uh and also some weird kind of political motivation so he's clearly sat down and just scribbled his opinions down uh in a few ways we've got things on english gluttony uh carrying a dark lanth thorn the right of gleaning uh lots of laws and his opinions on laws it's kind of really weird to read through but fascinating insight into the mind of the 19th century


Our next speaker is Dan Sanchez, who is the Director of the Mises Academy, and his topic this morning is going to be on war and the government control of money. Real quick, I just wanted to announce that all the books in the back are free, thanks to our generous donors, and I also wanted to thank the Tatum family for making this seminar possible. You've been hearing a lot about sound money. Now I'm going to talk about unsound money, especially how it promotes war, and how governments love unsound money largely because they love war. A great thinker named Randolph Born once wrote, war is the health of the state. Now what do you mean by that? Well in times of peace people are more likely to ignore the government, even stand up to it. Rather than follow orders, they're more interested in leading their own lives and doing the peaceful productive things that humans do in civilization. Trading with people, working together, enjoying their free time, getting to know each other, falling in love, etc. But when people believe that there's a great foreign enemy threatening them or holding on to stuff that they want, they become obsessed with fighting that enemy and in a way they start acting less like humans and more like beasts. They act the way frightened herd animals do at the side of a predator or the way hungry pack animals do at the side of prey. They want to join together in unity for a single life-and-death purpose, like hyenas mobbing a wildebeest or a wildebeest stampeding away from hyenas. In order to have that kind of unity, they look to a single decision-making organization to direct the war, they look to the government. And the more terrifying the threat is, or the more tempting the prize, the more the whole life of the country becomes about the war and comes under the direction of government. So the more war there is, the greater the power and the glory of the government. People in government know this very well. That is why so many of them love war so much and work so hard to start wars and to make them bigger. But wars are extremely expensive. All those troops and guns and fighter planes and drones and bombs have to be paid for. So how to raise the money to pay for them? One way the government does this is through taxes. The government just takes the money from the people. But there's a major limitation with that. People don't like being taxed. They'll tolerate a certain degree of it and they'll tolerate more than usual if they're in that wartime beast mode. But at a certain point, even the most pigheaded rah-rah war lover will snap out of beast mode when he realizes that not only are his sons and friends' sons getting hurt and dying, but the government is taxing him and his family into poverty to fund the war. When things reach that point, the people will turn against the war and if the government doesn't stop the war and he's up on the taxes, they'll turn against the government itself. So the government has a challenge. Is there a way for it to take the wealth of the people without them knowing it? And so without them resisting it. There is. If the government has control over the money. It's called inflation. The government just creates more money for itself that didn't exist before and uses that money to pay for the war. But how is that taking from anybody? It's easy to see how the government gains by doing that, but does anybody lose anything? Well, let's see. Let's say I'm the government. I have different departments. They're all part of me. They're all part of the same gang. But they each have their different roles. My right hand is the Treasury. It has my main bank account, which is on this notepad here. So I can just write the balance of my bank account, the Treasury's bank account on this notepad for my right hand. The Treasury includes the IRS, which is this finger. Now I use this finger to poke you and demand money. You guys are the taxpayers, the American public. You each have your own bank account, which is listed, the balance is listed on your own notepads. When you pay taxes, that money gets crossed off of your notepad and mine increases by the same amount. Now, my right foot is the Defense Department, the Pentagon, which controls the military. It's what I use to go around stomping on townspeople and goat herders around the world. It has a bank account too, but all its money comes from the Treasury. Again, as a government, I really like war because it turns you all into my obedient little beasties. But to have my war, I need my Pentagon right foot to have weapons for it. And that costs money. For example, let's say my Pentagon right foot really wants this fancy new fighter jet called the F-35. It's a real plane being developed and it's a piece of junk. It can't see where it's shooting and it can't fly in the rain. But it's a fancy piece of junk and I want it. But to get just one F-35, my Pentagon foot needs to pay $200 million to the company that makes it. Lockheed Martin. Who's good at making paper airplanes and wants to be Lockheed Martin? Anyone? Any Lockheed Martins? You can be Lockheed Martin? JP, we're even. So go ahead and make a paper airplane and come on up. Now, my problem is that Treasury doesn't have $200 million to give to the Pentagon to buy it.  I'm afraid if I poke you anymore, you'll bum rush me, drag me off the stage and throw me out that window. So let's say instead I use my inflation power. Come on up. My Treasury right hand just adds... just writes it in. Just adds $200 million. There. Now, I have $200 million and I know this isn't how it works in real life, but we'll get to that. I have $200 million just because I say so. And I can transfer that to my right foot Pentagon, which I can use to buy an F-35. So I transfer that to you, and then here is your big account. And now you can just add $200 million to your account, and I have my fancy new plane. Okay? Voila. Now, I didn't have to tax you all. So it's no skin off your back, right? Or is it? Remember, that's the question. Does anybody lose when I the government gain in this magical way? Now, when you think about it, somebody must lose because it's not really magical. Now, the true material wealth of society, what actually sustains human life and makes it more comfortable and delightful is the stuff we buy with money, not money itself. It's the food, clothing, housing, smartphones, mountain bikes, consumers goods plus the farmland factories, robots, raw materials and labor, and other producers goods used to make those consumer goods stuff. I covered this point in detail in my inflation talk last year, which is on YouTube and is linked onto the event page for this event. Creating new money doesn't create any additional stuff to go around. So if I the government am getting more stuff, it's necessary that someone has to be getting less stuff. Someone has to lose. It's a zero-sum game. So who loses?  Now, flying death machines generally don't have a lot of buyers outside of government. So if the Treasury hadn't been able to buy it, the highest bidder for the F-35 might have been a scrapyard, just as it should be. The scrapyard would have bought it, dismantled it, and sold it for parts and materials for peaceful purposes. So the scrapyard lost while the government gained, but the government isn't the only one who gained from the inflation. Who else gained? Who else gained from me creating $200 million? Yes. Yes, Lockheed Martin exactly. Very good. Lockheed Martin gained because he's getting paid a lot more than he would have been paid by the scrapyard because I inflated. So he gains. Now, who else gains by Lockheed Martin having a whole bunch of money all of a sudden? What's that? Oh, stockholders, which is Lockheed Martin basically. They're the actual owners of Lockheed Martin. Who else? Parts manufacturers who supply Lockheed Martin because he pays them more. He has more. He bids their prices up. Their prices get bid up. So does that mean that as the new money filters throughout society that all the prices get bid up? Well, yes, that's the definition of inflation that talks about price rise. But does that mean that everyone is getting wealthier because of that? Am I benefiting just all of society because of that? Remember, that's impossible because by inflating, I didn't create actually more stuff. So somebody has to lose. Now, the way it works is that the new money, there are early receivers and there are late receivers. Now, by the time the new money gets to the late receivers, all the things that they buy, those prices have already been bid up more than the prices of the things they sell. So they actually get poorer while the early receivers get richer. And the earliest receivers always include the government and its contractors, including especially the governments in what President Eisenhower called the military industrial complex. While the late receivers are usually honest working people who don't have lofty government connections. So these honest working stiffs are effectively taxed for the sake of the war and for the benefit of the government and its buddies. But the people don't realize that. They know they're poor, but they don't know why. They never saw a tax bill.  They just see their wages and revenue not really keeping pace with the rising costs of living and costs of doing business. And as a result, they see their ability to get actual stuff get smaller. But they don't see the government's role in making that happen or how the government and its cronies benefit from all that freed up stuff going to its wars instead. But that's what's happening. As Eisenhower said in a speech, every gun that is made, every warship launched, every rocket fired signifies in the final sense a theft from those who hunger and are not fed, those who are cold and are not clothed. The world in arms is not spending money alone. It is spending the sweat of its laborers, the genius of its scientists, the hope of its children. The cost of one modern heavy bomber is this, a modern brick school in more than 30 cities. It is two electric power plants, each serving a town of 60,000 population. It is two fine, fully equipped hospitals. It is some 50 miles of concrete pavement. We pay for a single fighter with a half million bushels of wheat. We pay for a single destroyer with new homes that could have housed more than 8,000 people. This is not a way of life at all in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron. Imagine Obama saying that. And this is true no matter how government wars are financed. So instead of obnoxiously demanding that the public hand over its wealth in the form of taxes, the government just quietly siphons it away through inflation. This way it avoids public outrage and resistance so it is able to take far more wealth from the people for its wars. Pretty sneaky, right? Well, it's not quite sneaky enough, actually, because even with this roundabout way of taking people's wealth, some people would catch on if the government simply kept just adding new money to its account, and even if people don't realize how they are losing, they can see that the government is obviously gaining, and that can look pretty suspicious. But the government has a way of even making that unclear, and it does so with the help of three new factors, private bankers, government debt, and the Fed. Now, let's say instead of the Treasury creating the $200 million for the fighter plane, it borrows it from a banker.  Come on up. This is your bank account. Okay. Now, my Treasury right hand, can I borrow your pen? My Treasury right hand, instead of printing new money, it writes on this post-it, IOU $200 million. This is a bond, or it's a lot of bonds. And you buy that from me, basically. So that's me borrowing money from you. So now, I'll add $200 million to mine, and you can write $200 million on yours, but then cross it off, because that's you paying me $200 million. And that IOU bond that I just gave you gives you the right to collect $200 million from me later, plus interest. Now I transfer the $200 million to the Pentagon, which pays it to Lockheed Martin for the F-35, and I have my shiny new plane, but I also owe $200 million plus interest. But that's when my left hand finally comes into play. What's my left hand? Can you guess? The Fed, the Federal Reserve. In real life, it's the left hand that has the power to create money. And the Fed says, well, I just so happen to be shopping for government bonds. I'm shopping for, does anybody have any? Oh, you. You happen to have $200 million in government bonds. I'll pay you $205 million for that government bond. Sound like a deal? That'll be a nice little profit for you, young man. So I'll take that. And actually, since I'm the Fed, since I have the power to create money, I can just do this. I can just reach in to your account, and I can just write to, with my left hand, $205 and 6 zeros. I just created this out of thin air. I just added it to your account, because I can do that. I'm the Fed. All right. Now, let's review what are our benefits. Goldman Sachs, which is who you are, by the way, Goldman Sachs has $5 million more, and my Pentagon has a shiny new plane. And what are our costs? The Fed has $200 million in Treasury IOUs, and that means that the Treasury now owes the Fed $200 million plus interest. That's my cost. That my right hand owes my left hand some money. There's no cost for me or for my buddy. It's all just me. It's the government. I didn't give up anything. There are no costs for me or my buddy. It's inflation simply made us richer at the expense of the public, just like it did in the simpler example. But to the average observer, it's not clear that it did, because of the little shell game that I just played. People think, oh, the Treasury's really just not really getting something out of nothing. It's borrowing, and it'll have to pay it back, and Goldman Sachs is getting new money, but that's not for nothing either, because it's selling a bond, and the Fed is not doing the money creation, but that's not going directly to government spending. It's just compensating Goldman Sachs for its investment, and it's also stabilizing the price level and keeping unemployment low, and it's all very complicated and technical, but they're the experts, and they're just looking out for us.  It's even worse than that. Government debt helps the government secretly enrich itself to finance war in another way. Inflation also involves a transfer of wealth from savers and borrowers. When prices go up, the money your grandma has saved for retirement doesn't buy as much stuff. At the same time, the government's debt burden is lightened because as the prices rise, so does nominal tax revenue, while the amount of debt doesn't change. This is another quiet transfer of wealth to the government. There's yet another way. Inflation masks the cost of war. Inflation causes the boom-bust cycle. Since governments inflate when they go to war, there's often an economic boom at the beginning of the war, so not only do the people not know who's making them poorer, they don't even think they're poorer in the first place because of the boom. In fact, they think they're richer. This causes them to squander resources, as Jonathan Newman will explain, which only adds to the waste of sending men and resources off to be blown up. Eventually, the inevitable bust comes, but often this doesn't come until after the war, as the familiar post-war depression sets in. This correlation promotes the myth that wars are actually good for the economy, which makes it even easier for the governments to wage them. Thanks to inflation, the U.S. government can take wealth to its heart's content in endless huge wars and globe-spanning empire. And that's exactly what's happened since the creation of the Federal Reserve in 1913. Governments meddled with money to support their wars long before central banks like the Fed. The Roman emperors did it, the medieval kings, but to the extent that people had sound money, gold and silver, there was always a check on that. Sure, you can clip and debase coins, but you can't conjure them out of thin air. It was with the rise of central banking that the government's ability to finance war with inflation really took off. In his book and the Fed, Ron Paul wrote, It is no coincidence that the century of total war coincided with the century of central banking. By total war, he meant the near-complete mobilization of an entire country's labor and resources into an engine of industrial scale, murder, and destruction. As Lou Rockwell wrote, the story of central banking is one step removed from the story of atom bombs and death camps. Without central banking, World War I, as we knew it would have been impossible. And World War I was one of the worst disasters in history. It ended a century of relative peace, freedom, and rapid economic progress. It introduced to the world total war on a scale never seen before. And it led to the rise of Nazism, communism, and the establishment of progressive statism in America. In particular, the creation of the Fed in 1913 made U.S. entry into World War I possible, which made the disaster even worse by making the peace totally one-sided and giving the Western powers a globe-spanning empire. Central banking caused the German hyperinflation, which helped lead to Hitler. It caused the boom that went bust in 1929 crash, which led to the breakdown of international trade relations, which helped to lead to World War II. It funded U.S. entry into World War II, which established Washington as the imperial capital of the whole world. Along with that went monetary imperialism, which means that after World War II, America used its military dominance to export its inflation and to extend its inflation tax to the whole world. So inflation promotes war and empire, which returns the favor by promoting inflation. Now, America's money machine imports over 700 foreign bases around the world and a global network of mass surveillance, CIA and NGO destabilization missions, drone campaigns and special forces deployments. It holds nearly every country in the free world in its thrall. It props up and arms brutal military dictators, royal despots and occupying armies in the Middle East, which along with our wars helps promote terrorism. Since the end of World War II, it has enabled the U.S. to launch 201 out of the 248 armed conflicts in the world and to bomb, sabotage or attempt to overthrow governments in China in 1945, Syria in 49, Korea in 1950, China in 1950, Iran in 53, Guatemala in 54, Tibet in 55, Indonesia in 58, Cuba in 59, Congo in 1960, Iraq in 60, Dominican Republic in 61, Vietnam in 61, Brazil in 64, Congo in 64, Guatemala in 64, Laos in 64, Dominican Republic in 65, Peru in 65, Greece in 67, Guatemala in 67, Cambodia in 69, Chile in 1970, Argentina in 76, Turkey in 1980, Poland in 80, El Salvador in 81, Nicaragua in 81, Cambodia in 80, Angola in 80, Lebanon in 82, Granada in 83, the Philippines in 86, Libya in 86, Iran in 87, Libya in 89, Panama in 89, Iraq in 91, Kuwait in 91, Somalia in 92, Iraq in 92, Bosnia in 95, Iran in 98, Sudan in 98, Afghanistan in 98, Yugoslavia, Serbia in 99, Afghanistan in 2001, Iraq in 2003, Somalia in 2006, Iran in 2005, Libya in 2011, Syria in 2011, Ukraine in 2014 and Yemen in 2015. It was looking back at World War I that Randolph-born concluded that war is the health of the state and around the same time, it was also looking back at World War I that Ludwig von Mises wrote, quote, inflation is an indispensable means of militarism. In other words, war is the health of the state and unsound money is the health of war. Thank you.


Next, we'll talk about pulmonology. Let's start out with some basic anatomy of the lungs. The first thing you see in this image is the conducting zone. The conducting zone are the large airways that consist of the nose, the pharynx, and then down further in the larynx, the trachea, and it branches off into the bronchi. The small airways consist of bronchioles that further divide into terminal bronchioles, as you see here. The purpose of the conducting zone is to warm, humidify, and filter the air that enters our respiratory system. It does not, however, participate in gas exchange. This is why we call this region the anatomic dead space. The conducting zone is made up of several different types of cells. First, cartilage and goblet cells extend from the beginning of the respiratory tract down to the end of the bronchi. After that, we have pseudo-stratified, ciliated columnar cells that will primarily make up the epithelium of the bronchus, and they extend to the beginning of the terminal bronchioles. At that point in time, then they will transition into cuboidal cells. The purpose of cilia in these cells is to clear out the mucus and the debris from the lungs. As we cough, and as we go about our normal respiratory function, that cilia and the mucus will pass any debris or any foreign bodies out of the small airways of the respiratory tract and bring it up so that we can cough it out. The respiratory zone is beyond the conducting zone and consists of respiratory bronchioles, alveolar ducts, and alveoli. This is going to be in the periphery of the lung and the very, very small airways that we see in those regions. This is where gas exchange occurs. Most of the time, these are made up of cuboidal cells, and specifically in the respiratory bronchioles, but we do get to simple squamous cells in the alveoli. The reason the alveoli are made up of simple squamous cells is because they participate in the gas exchange, which needs to occur on a very thin membrane. We do not have cilia in the alveoli. They terminate in the respiratory bronchioles. Any problems or foreign bodies that get as far down into the alveoli are cleared out by alveolar macrophages. We do not have cilia in the alveoli. They terminate in the respiratory bronchioles. The lack of cilia to clear out any foreign bodies or debris or dust deep in the lungs is the process and is the function of alveolar macrophages. These macrophages also will participate in immune response.


Hey guys, this is Chad Halverson from when I work and I wanted to go over quick the new bulk open shifts feature that we recently rolled out Those of you that have used open shifts in the past You've probably run into Situations where when you're setting up an open shift schedule you get a lot of the same shifts stacked on top of each other You'll end up putting a putting a shift in and if you've got five shifts from seven to three like this You'll get them all stacked up and it'll push your schedule down and it can kind of be clumsy to work with So what the bulk open shift feature does is it eliminates that wasted space when you have multiple open shifts so for example, I've got my 9th or 7 to 3 shift here and Normally when I would add more of that same shift it would just stack But now you'll see that when I add more of that same shift I need three people let's say to fill seven this the seven to three shift I get one visualization of it and I get an indicator of how many there are to fill so that is going to help manage a Visualization of the schedule and then what you can do from here as far as scheduling goes if you're the the kind of scheduler that Puts your your shifts that you need an open shifts and then drag some down into the to your to assign them to people all you do is drag them off of the open shift and Drop them on to the person that you want them to be scheduled for and as soon as you run out It'll disappear from there and now you've got three shifts scheduled for those three people If you're adding them in like this and you want to make some edits, so let's say I add three of these Maybe what I really want is like ten of those and I don't want to click ten times I can click edit and change this to ten how many do I want? I want ten save it and now I've got ten in there and I can publish that and when I published it now That open shift is available To anybody qualified for it and the first ten people that sign up or pick it up Get it and then it goes away from the open shifts. So that's really quick the the bulk open shift feature. Hopefully It's something that you find helpful and make scheduling easier if you have questions or comments or suggestions Please email us at support at wheniwork.com and we'd love to hear from you


These are 5 Linux facts that you might not know about. There are just over 25 million lines of code in the Linux kernel, and despite recent efforts to make the kernel smaller, that number keeps growing. More than 5 million lines of code have been added since 2018. Only about 1% of the Linux kernel is code written by Linus himself. As of this video, there are nearly 1 million contributors to the Linux kernel. Those million devs have contributed over 1 million commits to the Linux repository. Leave jobs almost ruined Linux by offering Linus a job in the year 2000. The catch was that Linus would have to adopt the development of Linux, which obviously he did not do. Originally Linus wanted to name Linux freaks, and only because of a co-worker eventually agreed to change the name to Linux. If you like these 5 Linux facts, make sure you hit the like button and subscribe for more.


If you've been following along in my recent episodes of Code Club, you know that I've been building up to a reveal of a new R package my lab's developed called MicropML. MicropML allows you to easily implement a variety of machine learning algorithms to identify features that allow you to make classifications, as well as to predict continuous variables using regression. We're going to get going learning about Micropel right now. Hey folks, I'm Patch Sloss and this is Code Club. One of our motivations for MicropML was that in my lab we have started to use machine learning algorithms a lot as a way to identify markers within a microbial community that we can associate with health and disease. One of the problems is that in every paper we seem to be using different pipelines. And so with an eye towards reproducibility, I told the former postdoc in my lab, Big Oom, hey, Big Oom, it'd be awesome if we had a single pipeline that we could release and that we could use ourselves to improve the reproducibility of our machine learning practices. That's what MicropML is and over the coming 10 or so episodes, I'm going to tell you all about MicropML and we're going to use it to analyze the data that I've been showing you in the past few episodes from a project that a former graduate student in my lab named Neil Baxter originally developed. Anyway, let me take a step back and tell you why I think machine learning is where it's at. As I showed in the last two episodes, really, we can identify individual features such as microbial populations within a community that are significantly different between healthy people and people with disease. In my application for this current series of videos, it's looking for screen-relevant neoplasias or special type of advanced lesions within people's colons that typically is indicative of colon cancer. We can identify these populations that are associated with these SRN, screen-relevant neoplasias, but at the same time, when we then use those individual populations to say, can we classify individuals as having an SRN or not, the answer is no. They actually do quite poorly on their own. One reason for that is that the distribution of microbial taxa across people is very patchy. As we've seen, the number of people that might have any one of the populations associated with SRNs or associated with health is very low. Generally, it's less than 50% of people have that biomarker of interest. That causes problems for our statistical tools and then also for thinking about classification. It's also a problem because it plays into a very reductionist mindset that is growing within the microbiome field, that there is one bug or maybe two that is associated with disease. The reality is that we're most likely looking at dozens of organisms that are associated with disease. For heaven's sake, we're looking at a community of bacteria. And so to look at an individual and say, this is the individual that's driving disease just doesn't sit right with me. The benefit of machine learning is that we can then look at collections of organisms, collection of features, biomarkers, whatever you want to call them that you can then use to make predictions or make classifications of different types of samples based on what's in there. The example that I like to use in my talks is of an email spam filter that in the back end, my Gmail account has some type of algorithm running that allows it to classify individual emails as being legit, which goes to my inbox or a spam, which then goes to my spam folder and I never see it again. And so if it sees something like Nigeria, million dollars, bank account, it says, that's probably spam. But if it sees million dollars, well, that could be legit because that could be an email from NIH announcing some great new award that I might want to apply for. And it might even have the word Nigeria in there because it's for collaborations with Nigeria. But there's going to be a lot of features that Gmail has to use to figure out what's spam and what's legit. Now, that's very much like what I'm trying to do with the microbiome, that I'm trying to build a cancer filter, if you will, I'm trying to build a filter that looks at different components of the microbiome to determine whether a sample comes from somebody with colon cancer, or from somebody that is healthy. And so I think there's a couple of benefits to this. So first of all, again, it takes the community kind of an ensemble based approach, and it allows for that very patchy distribution of the community. The other thing that it allows us to do is to get somewhere practical with the microbiome, right? I mean, basic science is great. And I do a lot of basic science myself. But at the end of the day, we need some type of deliverable from the microbiome field in the place that I think we are going to make the first advances in the human microbiome, getting something deliverable to improve human health is with a diagnostic, right? If I could take a stool sample from somebody, run that through my sequencer and say, aha, this person is at a high risk of having colon cancer, we need to get them to have a colonoscopy, that would be a win, right? We already have some really good noninvasive diagnostics like the fecal immunochemical test fit, which I talked about in the last episode. And then there's others that have kind of built upon fit. Well, what if we were to add the microbiome onto fit? Could we make an even better predictor or diagnostic of colon cancer? Of course, getting a colonoscopy is what you should be doing if you're over the age of 50. But only 30% of people adhere to the recommendations for screening. And part of that is because colonoscopies, they suck, right? They're pretty awkward. They're expensive. It's just not fun, right? So if we can make a better noninvasive diagnostic, that would be a win. So those are the reasons that my lab is really investing heavily in developing and applying machine learning pipelines and algorithms to microbiome data. So again, that we can study our communities as communities and as collections of organisms rather than individual organisms. And so that we can perhaps get somewhere practical in coming up with a deliverable from the human microbiome. Now, a lot of what I'm going to be talking about over this episode and coming episodes is based on work that my lab did in this paper, published in Ambio. So we published this data because we're really interested in trying to have a conversation with the field about using machine learning approaches to study the microbiome. People are doing all sorts of things that are probably really just not appropriate. So things like neural nets that really require thousands of observations to build an intelligent model. Nobody has that many samples. So what we really wanted to do is to communicate and have a discussion, start a discussion with the field about how we should be using machine learning with microbiome data. Now, we then went on and took the the code that we used to write this paper and put that into a package called microbe ML. Although our genesis was with microbiome data, that doesn't mean that it can only be used with microbiome data. People have used it with genomics data. We've used it with publishing data with all sorts of different types of data. So what we think is unique about microbe ML is that it implements this pipeline. And so I want to spend a little bit of time going through this with you, but know that we're going to come back to this periodically as we go through the coming episodes. In general, we have this bigger, big outer box that you can think of as representing all of our data. So in the current data set, we have 490 samples. Let's just say we have 500 samples, okay, for just making round numbers. So what happens is that we initially split the data into a training and validation set that's 80% of the data, so 400 samples, and then we hold out 20% of the data, about 100 samples as our testing data. And so we're actually going to repeat that 100 times, but we'll hold on to that, right? So we have this 80-20 split. With this 80% that we're holding out for training and validating, we then do another 80-20 split. And so this is called cross-validations. We do a five-fold cross-validation on that initial 80% split. And we take that second 80%, the 80% of the 80%, so it's about 320 samples, I think we're at. And then we train this using a bunch of different parameters, different characteristics or weightings or elements of the algorithms to build the best possible model. And then with all those different trained models, we then attempt to validate it on that other 20%. So at this level, it's about 80 samples, right? And so we then repeat this 100 times, right? So within that initial 80%, we do that five-fold cross-validation. And we'll repeat that 100 times. And then for each parameter setting, or what we call hyperparameters, we then get an average cross-validated area under the curve as a metric of our performance. And so we then come up with that mean, that mean AUC and the best possible hyperparameters. And we have then trained the full 80% with those parameters. And then we take that 20% and we then run it through the model, each iteration through this big outer box, we get one test AUC. Now, we repeat this 100 times because we could get lucky on that initial 80, 20 split. And so by doing it 100 times or 1000 times or whatever you want to do, you can then get a mean test AUC, as well as a standard deviation or a confidence interval or whatever you want to do. So this is what MicroPML really excels at. So each iteration of MicroPML does one iteration of this initial 80, 20 split. It then goes in and does the five-fold cross-validation 100 times or however many times you want to do it. And then reports the mean training AUC, as well as then the mean or the actual the observed test AUC. And again, then what we'll see as we go over many episodes is how we can then repeat this 100 times to get our 100 test AUCs to model and look at performance. So I know that's a lot to take in. Don't worry, we will see this flow chart many times over the coming episodes. This is really a framework, right? This is a pipeline. There is no model baked into this, right? And so one of the benefits of MicroPML is that it allows you to use many different machine learning algorithms, whether that's logistic regression, SVMs, random forest, XGBoost, decision trees, whatever, that you can then plug into this framework to get the best possible model. In fact, one of the arguments that we made in this paper was that authors really should be evaluating a range of different models to figure out which model gives the best performance. And so that's again what we're going to be doing, not just today, but over coming episodes, is to build machine learning models with this framework and to find the model that does the best job of modeling the data. Why would you do that? Well, because as we'll see, there are trade offs in the interpretability of the different models, something like logistic regression is far easier to interpret than something like random forest. And so if we can get by with a simpler model, we should. At the same time, if we need a more complicated model, then we should definitely do that as well, even though it might lack some of the interpretability of the model, right? Interpretability is kind of like, you know, when they ask people at Facebook, you know, why do I see what I see on my feed? And they say, well, we don't know. That's interpretability. They can't interpret their own model. They just know it works, right? They know that people keep coming back and so it works. And so interpretability is a tradeoff. It's not everything. But as again, basic scientists, we want to know what are the features or what are the characteristics of the community that's driving the classification? Because I would like to think that, you know, if those are associated with a good classification, that maybe we should follow up on those and better understand their biology and any role that they might have in driving someone's, you know, normal colon towards something that has something like colon cancer on it. So my goal for today is to introduce us to micropml, maybe run a few commands and kind of get a sense of the inputs and outputs, as well as get our project set up so that we are ready to use micropml. The documentation for micropml is off of my lab's website. So Schlosslab.org forward slash micropml, micropml is on CRAN. So you can install it directly from R. You can also get the bleeding edge version of micropml by downloading it from GitHub. I'm going to be using the version that is in CRAN because that's the easiest for everyone to get. If you are interested in reading more about the package itself, it has been published in the Journal of Open Source Software, which you can see here. We even have a nice hex sticker, which makes it like a legit package, right? Anyway, it's a very short paper that if you use micropml, we kindly ask that you cite this paper. So all the great people that helped us with working on it get the credit that they deserve. As always, I'm here in our studio and I need to get micropml installed. So I'll go to packages and then install micropml. So what does micropml mean? So Begüm is Turkish. And so microp is microbe in Turkish. So go ahead and install that. There might be some other dependencies that we need to install along the way, but should be pretty straightforward to install if you've been kind of following along in recent episodes. I'm going to go ahead and create a new R script that I will save into my code directory as genusml.r. And so we are good there. I'm going to come back to my genus analysis.r script. And I'm actually going to create a new R script. I'm going to go ahead and take some of the code that I generated for genus analysis.r and put that into another R script to help keep my code dry. And so you'll recall that I have these libraries I read in the shared file for mother, my taxonomy data, my metadata, and I generate this composite data frame. So I'm going to go ahead and cut that and plop that into this new untitled R script. And I will call this genus process.r. And so now what I should be able to do is a source on code genus process.r. And this genus analysis.r script should all work. So this is where we generated the strip chart as well as the rock curves in the two previous episodes. So I'll go ahead and source that. So that all runs smoothly. So I'm going to go ahead and rename this genus analysis.r script to be genus by genus analysis.r to indicate that this is really comparing each genus separate from the others. And then I've got my genus ml R script separately. And I will go ahead and put this source code genus process.r into my genus ml.r. And so again, running that loads and gets me this composite data frame, I then get my data frame that has the group or the sample ID from each participant in the study, the taxonomy, the relative abundance, the fit result, which is the amount of blood in the stool, and a bunch of other metadata on all of the subjects. So looking at my git tab up here in the upper right, I see that it's got a D for code genus analysis.r because I renamed it. So it looks like it deleted it. But then I replaced it with his code genus by genus analysis.r. So if I click staged for both of those files, it then shows that it's renamed it. I'll also then go ahead and click on that staged for genus process.r. And I'll go ahead and commit this to say reorganize code to make dry, right? Because otherwise, I'd be taking that same code that was in genus process.r and copying and pasting it over and over again. Again, we have this composite data frame, and we're now ready to feed this into microp ml. But I need to do library, microp ml. And to get composite to work with microp ml, I need to have each of my features as a separate column in the data frame. So when I say features, that's anything that goes into the model. So I might decide to put in the fit result, I might put in the person's sex, I might put in their age, but I'm also going to put in their like genus relative abundance data, right? So for now, I'm only going to look at the microbial features of that of the community. So again, we'll do composite. And I'm going to pipe that into a select where I can get the group taxonomy relabund. And I also need then SRN, because I'm going to be predicting whether the person has a screen relevant neoplasia. This of course gets me now my data frame with the group taxonomy relabund and SRN. And I'm now going to make it pivot wider, right? So we'll then do pivot wider. And the columns that I'm going to pivot wider with are going to be the taxonomy and relabund. So I'll do names from equals taxonomy, and then values from will be relabund. And so this now gives us a fairly wide data frame, where we've got 490 rows for 490 subjects and 282 column. I also now want to get rid of the group column, because I don't want that to be a feature in my model. And so I will then do select minus group. And again, we now see that we've lost that one thing that is kind of particular about the machine learning tools is that I can't have true and false as levels for the predicting variable for the output variable, right? So I need to rename the values in SRN from true and false to something else. I'll go ahead then and do mutate SRN equals if else. And because SRN is already logical, if SRN is true, then I'm going to put in quotes SRN. Otherwise, if it's false, I'm going to say healthy. And this again, then gets me healthy and SRN in that SRN column. And SRN is already the first column, but I'm going to go ahead and for my own peace of mind, I'm going to use the select function to determine the order of the columns. So something you may not have noticed is that the order of the columns in that select function determines the order that they are outputted. So I'll do SRN and everything as a function. And so the every function, everything function gets everything else, right? And so again, this doesn't change the output. But to kind of give you a sense of what might have changed, if I had have done on this second select here on line five SRN comma group taxonomy relevant, then what you'd see is that the first column is my SRN, right? Whereas if I moved this back to the end, then my last column would be SRN, right? I'm going to go ahead and call this SRN genus data. Now I'm ready to run the run ML function from microbe ML. So I'll do run ML. And then I'll give that SRN genus data. And I will say method equals GLM net. So GLM net will do a logistic regression. And we will we'll see other algorithms that we can use later in future episodes. Logistic regression is fairly quick to run and generally does a fairly decent job. So we're going to do a bit of work with logistic regression to help kind of learn some of the nuances of how micro ML works. And then I'll do outcome call name equals and then in quotes SRN. And that tells run ML what it's trying to predict. So it's trying to predict the values in the SRN column by default run ML will use the first column of the data frame. Again, as you've seen in past episodes, I like to be crystal clear in my code about what's going on. So I think specifying outcome call name equals SRN is the way to go. And then I'll also set a seed. Again, this is using random number generator when it makes those splits in the crossfold validation. So by using the same seed, I'll get the same output every time. So I'll do 1976, 0 620 at a birthday a couple of weeks ago. Some of you remembered. Thank you for wishing me a happy birthday. And what we'll go ahead and run this then. And again, what this is doing is that first iteration of the 80 20 split with that 80%, it then does another 80 20 split where it builds a model develops kind of an intuition about the parameters, the hyper parameters for the model, and then trains it on that 20%. That was held out that five fold cross validation will repeat that 100 times. And then it'll take the best model and apply it to that 20%. That was held out. This is going to take a few moments to run. But I'll show you what we get when we come back. Very good. So it took about two minutes to run. I'll go ahead and maximize this output window. And so one of the first things to notice is that I got a couple of warning messages. Again, the warning isn't the end of the world. But it tells you some information about how the algorithm or how the function ran, right? So it says that this warning usually means that the model didn't converge in some of the cross validation folds, because it's predicting something close to a constant. And so this generally suggests that the parameters that we use the hyper parameters weren't chosen very well. So again, we'll come back and we'll deal with that in another episode. But I want to kind of scroll way back up here to the top of the output. And so, yeah, so here's what I entered. And it again gives us some information. It outputs information about the trained model that it used GLM net, the number of samples, the number of predictors, the two classes it was trying to predict that it used five fold cross validation. Remember, the cross validation happens within that second 80 20 split. And so what it's doing with that 80% is that it's building models using different hyper parameters. So in this case, lambda, which is the regularization parameter that's used between one to 10 to the minus four and then 10. And that we then get a variety of AUCs. And so what it's trying to do is maximize the AUC according to lambda. And so what we try to do is pick hyper parameters that will bracket the maximum of the AUC. And so what you can see is that the AUC keeps going up. So what we would like to do is perhaps modify our lambda to a larger value, so that our lambdas allow us to see a peak for the AUC. Again, we'll talk about this in another episode, but this is showing us the output of those cross validations. And that also tells us that the tuning parameter alpha was held constant at a value of zero. So the alpha of zero is for L to regularization or ridge regression, we could have also used alpha equals one for lasso regression. And if you use a value in between zero and one, then that's elastic net regression. Anyway, again, we'll come back and we'll talk about that another time. This then shows us the test data that were inputted into run ML. And then we get some performance metrics, right, we get the cross validated AUC of 0.636. The test on that 20% held out was 0.714. So that's good. But again, that's for only 180 20 split. Ideally, we would run this 100 times, so we can get a distribution of both the CV AUC and the test AUC, then also, there's the ability to do feature importance. And as it says, we skipped feature importance, adding the feature importance, which again, helps with that interpretability of the model makes the model run a lot slower. So I'd like to figure out all the other things upstream. Things like figuring out the right hyper parameters and can we maybe pre process the data bit to make the model perform better and faster before we do something really time consuming like feature importance something I should have done was assign the output of run ML to a variable name. So I'll do SRN genus results. And that way, then we would have SRN genus results as a variable that we could work with. And I'll show you how we might do that once this reruns. Again, I can look at SRN genus results, and get all that output, or I could do SRN genus results, dollar sign. And then I have a variety of different things that I can get access to. So if I do performance dollar sign performance, I then get out that table for performance. If I do trained model, I then get all that information out. And I should also add that if we wrap names, the name function around SRN results trained model, we'll get a variety of different information generated from a package called caret caret's actually what micro ML is running under the hood. And so there's all sorts of other information that we could extract from that trained model part of SRN genus results. I want to show you a few ways that we can customize how run ML works. So we have k fold, by default is five. So that's the five fold cross validation, we can do CV times, the default is 100. And then we can do training frack equals 0.2. So that's the 20% 80% 20% that we had at that initial separation of the data, right? And so these are the defaults. You know, we could change it to be say two fold cross validation. And maybe we only want to do 10 cross validations. And perhaps we want to do 50% of the data goes into the training fraction, right? So running this should run a lot faster because we're doing fewer iterations of everything, right? So that was like, bam, while I was still talking, it finished. And if we look at SRN genus results, again, we see all this great stuff. I think our performance on our areas under the curve are fairly comparable to what we had before, but perhaps not as robust as using the defaults of 500 and point two. So I'll go ahead and put that back to what we had. Okay, I'll go ahead and save this and then commit my code genus ML and commit it. And I will say roughed in logistic regression code with microbe ML and commit that and go ahead and push both of those commits up to GitHub, and we'll be good to go as a reminder of what we've done so far with microbe ML. Again, microbe ML allows us to do this full framework for multitude of different machine learning methods. So we can do things like logistic regression, we can do decision trees, we can do SVMs, we can do random forest, and we can do XGBoost random forests. But whatever method you use doesn't matter so much as this framework. And so what we just saw actually was that we can change the amount that's held out in the test. So, you know, I did 5050, but the default is 80% 20%. And then with that 80%, what's happening within run ML is that it then splits the data further into an 80% for training and 20% for validating. That's the five fold cross validation. So again, if you made it two fold cross validation, then it would be a 5050 train validate data set. And there then we are training the model with different hyperparameters. And so we saw how we could change that regularization parameter, the lambda, or I guess we didn't show you how you could but just that it does. So all of the models come pre installed or pre packaged with a range of hyperparameter values that microbe ML will try to evaluate for you. Later on, we'll see how we can expand that range. Because as we saw with ours, the lambdas didn't allow us to see the maximum area under the curve value. And so we'll have to kind of modify that range a bit. And so again, that is what's happening in run ML is kind of what's going on within this outer gray box. And as we'll see later, we'll want to iterate that say 100 times to get a 100 test AUC values. So we can look at the distribution there, as well as the distribution for that mean cross validated AUC. And we can then compare those values and ultimately compare models and ultimately then like change the world and save lives, right? Isn't that what we all want to do? Anyway, I hope you find this really interesting. And I strongly strongly encourage you to be sure you thumbs up this video as well as subscribe to the channel and click the bell. That is the best way to make sure all your bases are covered. So you know when the next video drops, again, we are going to be working with micro ML for maybe the next eight to 10 episodes. I'm going to show you all the ins and outs of how we would use micro ML in my lab to build a better, more robust model of classifying people's as either being healthy or having an SRN. My hope is that after you've seen how I do it, you can then take what I'm doing and apply it to your own data, so that you can use it in your papers and you can cite my paper and then we're just all happy, right? Anyway, again, it doesn't have to be microbiome data. It can be a multitude of different types of data that you would like to use with machine learning. And really, the glory of the great thing about micro ML is that it provides us this framework for fitting hyper parameters for training and testing and validating our models. And what you put into it isn't so important as that you you do it as you use this framework. Anyway, keep practicing with this and I'll see you next time for another episode of Code Club.