The following is a presentation of TFNN, the morning markets kickoff with your host Tommy O'Brien. Good Friday morning everybody I'm Tommy O'Brien coming to you live from TFNN we kick things off on jobs Friday 199,000 jobs added the unemployment rate how about 3.7 percent coming at you man a very strong economy yields pushing up a bit seems like we might not get all the cuts you were just thinking about that we would get just about an hour ago we'll break in some of the action but where's the markets we're right back to where we were prior to that number which is remarkable you come into the number 830 at 4580 you jump around we dive down low to 4562 we get back up to almost 4590 and just like that we're basically unchanged in the S&Ps NASDAQ 100 we're a little bit lower you got a little bit higher yield but basically a recoil action we'll see if the action carries through on the open NASDAQ 100 off by 78 points that's about half a percent right now in the red you jump over to the Dow off by 56 points right now 36,111 that's only about two tenths percent you see the dive load almost 36,000 and you get the rustle in positive territory in 1873 crude we got a 68 handle yesterday we're higher off that price point still sitting at a relatively reasonable $70.84 in the price of crude gold with some action yields some action currencies some action gold this morning been quite a week for gold you spike 2152 on the future Sunday night you hit a low of 2027 on Tuesday and we're approaching that low yet again in gold at 2030 you see the drop off there that'll make a little bit more sense when you drop over to yields now it's interesting right the market unchanged meanwhile you just said the 10-year yield go from almost 4.15 to 4.25 we'll pull it up in a moment but we got lower price higher yield coming at you that's part of the reason why you have the NASDAQ 100 leading the way to negative prices off about a half a percent you jump over to the dollar index this morning what are we gonna have right we're gonna have higher yield we're gonna have dollar strength the dollar back above 104 to 104 87 so you got dollar strength that's putting some weakness into gold you have higher yields you have a market that is actually above where it was at though when you came into that number okay so could you have higher yields then the market is maybe anticipating but the market not paying the penalty for that right just pointing to the fact that maybe the Fed is not going to cut as quickly as the market just continues moving forward at a very strong pace yeah we will see nonetheless interesting action we spike lower we get it back we're actually above where you came in at 830 now if you said that yields are gonna spike higher would you have the S&P also higher right interesting action sometimes you you could even know the news and you might struggle to predict the market action because the volatility you could have imagined if we saw those yields spiking higher that maybe the market would react poorly and you have seen that slightly in the NASDAQ 100 but you're only talking about negative by maybe 10 points where you came into that number and not even basically flat where you came into that number S&P is off by 6 and nonetheless let's get into the numbers so the economy adds 199,000 jobs in November that's the front page of Wall Street Journal dot com WSJ dot com and yeah I mean the trend is lower right you can't deny it it's a strong number this is going all the way back from 2021 2022 what we were pushing remember remember July 2022 that number that came in at 568 and everyone's like oh Fed's got some work to do since then we got lower lows and lower highs you hit a spike in January of this year 472 a little bit of an anomaly you hit a spike of 281 in May you dropped down to a level of 105 in June and yeah what the last six months now you're talking about 105 236 165 262 150 and now 199 today the most interesting part about it may be the unemployment rate at 3.7% and you're talking about labor force participation a huge part of that unemployment rate you're now at 62.8% that's matching a post-pandemic high and if you're talking about pre-covid levels of 63.3 pretty remarkable we're almost back to that level when you think about the amount of people that came out of the workforce right that was the whole rhetoric now make sure I get this headline right got a few headlines jumping around yeah I think this is the one I wanted yeah that's the chart we were just talking about there's your unemployment rate unexpectedly going back down to 3.7% on the wages front pay increases have become smaller in recent months compared with earlier in the year earlier in the year you're rising 4.7% wages and prices so you get the hourly let's zoom in on this and blow it up a little bit there we go hourly earnings at 4% weekly at 3.7 and we're gonna get CPI next week yeah so healthcare was a big one man added almost 60,000 jobs this month they're on 60,000 if you average it out and jump it back to some of what yeah a small net revision down 35,000 for the previous two months the unemployment rate at 3.7 you're gonna hear that 3.7 number a lot man you did see a little bit of a hot number and average hourly earnings I mean you got full employment man okay hourly earnings going up 0.4 percent right now that's a hot number we got the unemployment rate at 3.7 percent right now taking out some of the other numbers yeah healthcare was 77,000 it's a 60,000 average over the last six months government pretty much all local and state and they're talking about in the den interesting part of that not the federal government local and state 49,000 in the month is government what you're talking about there healthcare 77,000 manufacturing stuck in the mud 28,000 in November yeah now that's gonna be a big part of the auto workers there Bloomberg does a great job doing the live feed I was watching this right up till the program even if you're not watching their program you know just in terms of a live feed breaking down these numbers because it is interesting on a day like today where there's so many numbers that comes out in this yeah how about the household survey 747,000 is what it searched by after it declined 348,000 in October talk about variance man yeah so yields jump a bit they've held on to that a bit this one's interesting we talked about the employment okay people coming back to the workforce employment population ratio how tight the jobs market is you're talking about 60.5% just below the 60.8 average for 2019 prior to the pandemic everything is related to like prior to pandemic post pandemic we got the market holding steady down about 10 points right now yeah the 747,000 per thousand person in the household employment after the decline even they say in here variance right there's a lot of variance in some of these numbers the FOMC might not find that development either credible or durable right variance basically we'll talk about some more of it we got a lot to talk about we got 15 minutes till the opening bell stay tuned folks we'll be right back get ready Tigers Thursday December 14th Tim Mord is back to host another stellar live webinar from 4 p.m. to 5 30 p.m. Eastern time Tim Mord will delve into the secret science of market tops helping you the viewer with how to effectively call market tops in order to increase your success in trade Tim Mord has developed this understanding over decades of trading and is ready to impart this knowledge on you visit the front page of TFNN.com today to sign up for Tim Mord secret science of market tops TFNN educating investors if you're looking for potential trading setups in the stock market then rocket equities and options report is a newsletter you should try Tommy O'Brien delivers options and equity trades when the markets present them using a combination of fundamentals and technicals sign up for rocket equities and options report today with a 30-day money back guarantee so you have nothing to risk for all the details and to start your subscription 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11 points right now we get back to some of the discussion talking about the numbers unemployment rate 3.7 percent that is highly related of course to the participation rate at 62.8 percent right wage growth numbers 0.4 percent on a month-over-a-month basis I'm giving you kind of the biggest numbers out there the 747 thousand in the household employment that drove that a little bit suspect in the way that variance right it's either credit not credible or possibly not durable possibly on both sides of that but something to consider nonetheless all right we scroll up here talking about a different takes on this payroll gains broader than october's one take on there see how we make it through the holiday season when you talk about consumer spending right bonds did flatten a bit after that's first spike now what's great is you know this is a breakdown as you commit to about 853 that's pretty much where I kicked off this thing and started taking off getting ready for the program higher than expected hourly earnings that's going to be one of the Fed talks about man so check this one right right yeah this is the decline in the unemployment rate was led by teenagers in the month teenagers yeah and the unemployment rate for women over 20 hispanic americans and white americans also fell so that was who was coming back in the workforce yeah the feds in the feds in no rush and they're still looking for over a full percentage point of easing by the end of 2024 somewhere between four and five cuts right if you do a quarter point cuts they're looking for between four and five cuts by the end of 2024 never remember the fed meets about every six weeks they don't meet every month so if you got to get four to five cuts in that's a lot of cuts to put it lightly and that household survey is going to get a lot of attention next week man as they're saying it's basically a catch up for the variance we've seen in prior months variance is variance variance is one of the most amazing things out there folks if you deal with statistics probabilities all right some of you know I played a lot of online poker in my days and boy one of the things that really applies so much is different games have different levels of variance which lead to larger swings in the short term even though if you run the sample size long enough right everything should navigate back to the mean but boy variance can be a tough one sometimes I mean a great example right the biggest variance you can have imagine is well this isn't the biggest variance you can have I think are they one of the largest variance probability plays you want to play like mega millions or powerball or something like that right what are the odds to win something like one out of 225 million something like that okay now you have to consider when you're playing the lottery we're digressing here um but variance okay not a lot of people think about variance in the correct way there's a lot of times you may have so much variance that you can't withstand that variance even when you're making positive expected value bets wagers investments whatever you want to call them okay so let's just look at powerball first of all if you let's say the powerball jackpot gets to a billion dollars right well first of all you have to consider the fact that you might split that jackpot with two people three people four people five people whatever it may be okay but let's just say you knew you were gonna hit that jackpot for a billion dollars okay and let's just say you knew you're gonna hit it by yourself and the jackpot rules which is not the case uh meant no taxes or anything like that so basically that billion dollars was going to be yours tax free you don't have to share with anybody and it goes right in your bank account okay i tell all you gotta do is hit the number you hit the number you don't have to worry about sharing it you don't have to worry about paying taxes you don't have to do anything you're gonna win a billion dollars and i'd say to you well geez every dollar i put up i got about a one out of 250 million shot of winning and you're telling me that when i do win you're gonna pay me four times my odds for a billion dollars right say right say well geez that's that's that's easy money in the long term right how much money should you put into that wager uh probably none of it if you want to keep any of that money because there's so much variance in that bet that for you to reach the long term average mean in terms of for you hitting it one out of every 250 million times can you imagine the sample size that you would need to show that you're actually hitting the lottery one out of every 252 million times i wonder what that i wonder what we you know we could run that let's look at that on the statistics if anybody has that if you're out there watching you want to run this one right what's a good sample size that gives you and this goes back to statistics man gets me back to stat 101 what's a good sample size that gets you a decent confidence interval that you'll be somewhere within that range of the the actual long term odds of hitting the lottery it's got to be a big number point being right be careful out there because really that's the easiest money you'd ever make man you're guaranteed in the long term to make four times on your money basically three times profit right you bet on a probability one out of 250 million a hit and every time you hit you're going to make a billion dollars but guess what man sometimes it takes a while for those numbers to pan out so you know that household survey they're talking about they're just catching up yeah i digressed a little bit and went big picture variance but sometimes there's a lot of variance in a lot of the data that we get in a lot of the probability distributions that you see in things playing out so be aware of that one i like variance and that's the other part you know if you expose yourself to variance though right and you do it in a way that you can manage the risks okay as in you know powerball hits a billion dollars man you go join a pool with all your friends you spend twenty dollars right what are you doing you're managing the risk okay you're making a twenty dollar wager it almost approaches the point of expected value being positive it's real close because you got to take out the cash value of today which has diminished greatly because we got a high interest rate now and then you got to take out the taxes and then you got to think about splitting people so it's got to get really really high to actually be a plus EV bet even with everything else but you see that even in that issue you're managing the risk you're spending 20 bucks what a couple times a year when the jackpot gets big and um yeah but what do you do you open yourself up to variance and if it smacks you in the face on the good side um then yeah you bank that variance on the good side because you manage your risk gets me back into some of those poker days because i used to play i used to play big online tournaments folks that's what i enjoyed most and there is an extreme amount of variance all right we got two minutes to the break we're coming back we're going to talk equities on the opening bell but check this one out people like poker all right if you're in the stock market uh you may have played some poker in your day you like a little bit of gambling you like action as long as you can do it with a positive expected value so i always tell people people think about gambling and they have a misconception because they associate gambling with games that have an automatic negative expected value you go play blackjack this is interesting and later we have craps coming to the hard rock in Tampa pretty soon i think it's here like in the next few days if not this month that's a negative expected game for every time you walk in you put a hundred dollars on that craps table i'm telling you in the long term you're going to lose money not the case depending on how you're playing at poker not the case depending on how you're investing or trading in the stock market okay but i used to play tournaments and in tournaments it's very top-heavy you play a hundred person tournament right the winner of that tournament might take home 25 of the prize pool they pay down 10 of the field 100 person tournament they're paying 10 people 90 people are not getting paid at all you're supposed to have about 100 buy-ins for multi-table tournaments right that means if you're playing a 200 dollar tournament at your local casino and you want to do that by managing variants you should have about a 20 000 bankroll because there's so much variance in poker we'll leave it at that we're coming back we're talking equities folks we're going to take a look at lululemon they're trading low this the season for leveling up your trading skills basal Chapman is happy to offer all opening call subscribers a free subscriber webinar wednesday 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excuse me drop about 14 ticks you've now risen about six ticks from there see where our yields actually are on that 10 year 4.22 we'll call it we're sitting at maybe 4.15 when we kicked off the session so a little bit higher yield you jump over the dollar index dollar index hold on to those gains still above 104 we were as high as 104 26 on the dollar you check out the gold contract gold trading down by about 23 dollars right now we check in on the dollar yen a little bit of a recoil from yesterday's action as well a little bit of volatility on our number at 30 as you see as well in that dollar yen let's jump over to the euro see how we're doing as well as these markets fade in a bit and we're negative by eight right now with the euro trading at 107 47 all right we talked about lulu lemon so lulu out with their numbers last night disappointing numbers they're off by 2.2 percent that's a ten dollar drop now this thing man you talk about strong right yeah we're off by what two dollars this equity just traded up 150 dollars this year we're approaching all-time highs of 485 for lulu lemon but yeah they trade a little bit lower and the headline out there for lulu tepid holiday outlook despite strong start to the holiday shopping season is what they say there lulu comes in with earnings per share of 253 not sure what that is comped against revenue just above expectations by that would be 10 million dollars yes 10 million dollars you got your decimal points on billions man everything's in billions these days the company's reported net income for the three month period 249 million or buck 96 a share they made 255 a year earlier almost right in line right sales up 19 percent from a year earlier so it's interesting the sales go up 19 percent we look how much extra money they brought in and they actually make less money than they had costs man really you're talking about what 340 million dollars extra they took in on that quarter 340 million extra they made 255 million and 1.86 billion a year earlier they made 249 on 2.2 billion this year sales up 12 percent north america and 49 percent internationally holiday guidance is what they're talking about sending the stock lower though lulu said it expects between 3.14 and 3.17 for the fourth quarter they couldn't quite get into the 3.18 that the analysts were expecting so just below that number earnings 45 to 493 market was looking for a little bit higher as well 480 to 519 for the full year about 9.55 billion dollars is what they're going to be taking in i mean not bad when you look at some of the lower estimates and maybe that's just a cherry pick low 8.11 but the high end was almost 10 billion dollars decent numbers but not transferring to the profits and the expectation is a little bit dicey going forward total comp sales up 13 percent market was looking for 12.4 you got some lofty inflation numbers man nonetheless they're trading lower this morning on lulu was just reading through that but look at this as it saves itself a bit like it man look at this lulu up by 10 bucks on the open to 460 but be careful of that one man be careful because you know it's all about how much money you can make and boy they really crushed it on the revenue side but they can't bring it down to the bottom line and then you add that onto the outlook problems and that could be problematic if their margins are decreasing right we get the russell trading higher by 7.1877 let's check out some of these fang stocks amazon shares down a bit off about nine tenths percent you get the nasdaq 100 leading the way down you jump over to the big dog apple apple shares holding up relatively well only down about one tenth percent microsoft down six tenths percent how about the day for google yesterday man you give back some of those gains google off by 1.5 percent today there's some volatility for you meta shares today off by about four tenths percent we jump over to nvidia shares positive by half a percent for nvidia amd flat after their big day to higher prices yesterday and yeah let's talk a little bit of apple to apple let's jump around here this one from the journal and i'm sure there's going to be multiple reports on this as we push forward but they're aiming to make a quarter of the world's iphones in india yeah the story out early this morning late last night foxconn plans to build more factories and give india a production role once limited most of the china apple's no fool man um no matter what g and biden talked about man things are ratcheting up in the next 10 20 and 30 years folks and time flies okay and apple man they're going to get it done like the next couple years yeah i saw by 2025 let's get the number where is it it's in here somewhere here it is uh combined with plans for expanded production at an existing foxconn plant near some city in india yeah they're going to be making 50 to 60 million iphones in india annually within two to three years quite a number man and there they've chosen india for its site for manufacturing stage for lower end iphones to be sold in 2025 the first phase of the foxconn plant under construction in the southern state of karnataka is expected to start operating in april and the plant aims to make 20 million mobile handsets annually manually iphones with the next two to three years yeah it's a smart move uh it's a long-term play but i imagine you see that happen a man why not right why not things uh looking a little dicey with china all right this article and that's interesting i was reading this this morning and i was thinking you know things are going to change at 8 30 and i'm sure they're going to change a little bit but we'll talk a little bit about this one when we come back double trouble investors fight the fed on two friends rate expectations have plunged for next year but further out they are still high the yield curve interesting to see how that one goes forward man but boy you talk about implied probability of interest rates above five percent in december of 2024 i mean look at this man october 18th okay there was a one out of three chance that rates were going to be above five percent in december 2024 that's the market implied probability october 17th by november 3rd that odd was two percent we got a flare up and today that odds are basically zero that we're anywhere near here we have cuts coming down the line it's just a matter of how many is what the market is saying we get cpi next week so we're going to talk about some of that action uh we'll talk a little bit about these numbers we'll be right back folks tfnn has just launched their new trading room the tiger zen hosted at discord tfnn has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours and now they are expanding their reach with the tiger's den available to all tigers and tiger's for just one dollar for the year there's no cash or added costs when you join our community of 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percent right now you jump over to city positive by half a percent wells fargo positive as well check out some of the airlines jet blue yeah what did they change their forecast yesterday something quite the day higher for them yesterday if you're looking to catch a fallen knife man gotta be careful anything i would be touching with this equity okay and i just flew jet blue used to love jet blue for the longest time a bunch of my friends can't stand jet blue anecdotally up in boston right now one of my friends travels a lot from the boston area just bought a home in the bokeh market so he's traveling down there he's on the flight saying it's the third flight that's been um delayed to an extreme amount that one they they had to turn around on the tarmac uh that one did have to do with the passenger in all fairness we said not jet blue's problem it's the third time this time i had to do a passenger now be careful if you put money into this thing just realized that you might be able to wake up some morning and it's a two dollars one dollars on its way to zero we just went from 21 to three okay just went from 21 to three so be careful but with that said yeah we got a little bit of a bit man finally in jet blue 549 we got markets we're there we got there it didn't take long it's 944 in the morning and we are in positive prices as you get the s and p is trading at 45 92 as we speak and what else do we have going on folks so we got a bunch of great stuff coming up man we have first of all a tiger doll holiday sale this sale is only running through next week folks okay you got one week left don't delay get some tiger dollars you can get up to a 40 bonus on your purchase okay three choices in terms of tier levels you spend 500 you get 600 tiger dollars you spend a thousand you get 1300 that's 300 free tiger dollars in your account you spend 1500 you get a 40 bonus 2100 tiger dollars uh 600 additional free tiger dollars you can use for any tf&n newsletter service and we got a couple great webinars coming up as well tim ward the secret signs of market tops how to identify market tops thursday that's six days folks december 14th four till 5 30 p.m eastern time okay you can check that out on the front page of tf&n and if you're a subscriber to the opening call you're all set already if you haven't tried out basil's outstanding newsletter folks his program is after my show every day okay the tiger technicians hour you can check out his daily newsletter the opening call great time to do it over the weekend basil usually puts out a video for his subscribers on the weekend on saturdays he has updates throughout the week every single day when warranted uh not when warranted every single day excuse me check out the opening call he's got a webinar coming up in 12 days okay that's uh yeah wednesday the following wednesday december 20th before we really come into christmas and new year's week where things will slow down a bit sectors and stocks just coming off major 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keep it you cancel it you get a 30 day money back guarantee those tiger dollars are put right back into your account and you can use them for the next thing that you maybe want to try out whether it's a newsletter um or you want to check out one of the one of the many webinars we have coming up the secret science and market tops we could be at all time highs by the time Tim Moore is talking december 14th we'll see how things go next week uh but yeah we're only what 200 points away in the s&p we got a cpi number next week which is an important one and we're talking about a recent high of 46 34 in the s&p's pretty interesting of where we base out that one to 1.618 expansion right 4607 we hit that number on the dot looks like we're heading back to that number as the market likes the numbers that we got this morning excuse me all right what else we got pulled up here to talk about let's take a look we talked about tiger dollars of course yeah talking about that same article back to the journal piece so this was talking about fighting the feds on two fronts right fighting it either on the short end of implied interest rates whether it's cuts okay now what it did talk about in here is that the most extreme investors expect cuts really soon 14 percent chance that we're getting the cut in january folks you think we're getting a cut in january that's a hefty percentage now this is prior to this morning's number that's probably going to ease a bit futures in in october okay there was a 40 percent chance of another hike that week that's now a three percent right now what they do get into is on the other side of that is the longer term rates okay here's where they talk about fighting the fed part two there's a big gap between long run treasury yields and where the fed thinks interest rates should eventually settle okay the strange thing here as they say i'm quoting this isn't that the market thinks rates will be higher than before it's that the fed does not the fed is kept persistent with that number of 2.5 percent right it's september forecast predicted long run interest rates at 2.5 percent and after inflation or real rate of 0.5 percent investors think the real rate in the long run will be close to 2 percent okay some of the gap between the treasury market and the fed's prediction is accounted for by the extra yield investors demand to compensate for the glut of bonds coming from the government as it borrows to fund the deficit but the rest is the market saying that the fed has got it wrong where's it going to end up right uh and ought to raise its long run rates prediction unless it's going to allow more inflation and raise its 2 percent target so what's it going to be uh we will see and they finish this up the fed next week could resolve both of those issues the most bullish case would be they say rate cuts are possible by the spring of next year and markets will be paying close attention to any sign that paul is becoming more dovish long run rates will get less airtime but don't forget that if the fed is right about that long-term interest rate then the 10-year and 30-year bond yields are far too high i'm thinking about those scenarios right so keep that in mind you know if you're believing the fed with where the natural rate of the long-term interest rate is going to be where inflation is going to be where the long-term interest rate is going to be then the market has some work to do and we get to see if that's going to be the case right markets today they're doing some work to the upside man s and p is right back to where we were at about 5 30 a.m this morning coming up to an area of possible resistance that's where we were yesterday afternoon to 45 95 we jump around to some of the commodities crude 70 74 this morning we check out that gold contract this morning gold down to 2018 we're back to 2025 you jump over to the dollar index pulling back a bit to 10403 folks don't forget check out those tiger dollars on the front page of tfnn.com 20 30 40 bonus you want to check out the gold report folks great time to do it my dad's got new issues on monday that come out you want to try out market insights you want to try out the opening call tim ord's webinar my newsletter lock in those tiger dollar savings one week only we'll be back to finish up the shows folks stay get ready tigers thursday december 14th tim ord is back to host another stellar live webinar from 4 p.m to 5 30 p.m eastern time tim ord will delve into the secret science of market tops helping you the viewer with how to effectively call market tops in order to increase your success in trading tim ord has developed this understanding over decades of trading and is ready to impart this knowledge on you visit the front page of tfnn.com today to sign up for tim ord's secret science of market tops tfnn educating investors it's december tigers that means festivities decorating spending time with friends and family and the tfnn tiger dollar holiday sale don't miss your chance to receive a 20 30 or even a 40 bonus when you purchase tiger dollars once you apply your tiger dollars to your account you will be able to use them for any tfnn product purchase instead of your credit card visit the front page of tfnn.com today to purchase your tiger dollars don't miss your chance to receive up to a 40 bonus on your tiger dollar purchase this holiday season every tiger who purchases tiger dollars will also receive a complimentary tfnn tiger mug with their purchase act fast this sale ends december 17 happy holiday tigers tfnn educating investors a gold report as a precious metal gold is still king it continues to hold the most effective safe haven and hedging properties across the global major trading hubs of the london otc market the us futures market and the shanghai gold exchange the gold report tom obrien publishes his weekly gold report every monday morning for subscribers consisting of coverage of the xAU hui gdx the dollar bonds the south african rand as well as 25 different mining equities with specific buy sell recommendations the gold report new subscribers get a 30 day money back guarantee so you have nothing to risk subscribe to tom obrien's gold report newsletter now at tfnn.com you might think that if you want to be successful at trading in the stock market you're going to need a crystal ball after all it's impossible to predict the future right like any endeavor in life before you decide it's impossible get some advice from the experts you might find that it's not so impossible after all for daily market overviews that give you direction on the key indices selective stocks and commodities subscribe to the opening call newsletter at tfnn.com the opening call newsletter is written by basal chapman creator of the trading methodology known as the chapman wave the chapman wave up down sequence gives you an edge in identifying price turns finding the peaks and valleys and stock prices get the opening call newsletter by basal chapman and your inbox every day first time subscribers also get a 30 day money back guarantee if you're not satisfied let us know and you'll get a full refund within 30 days of signing up tfnn.com educating investors don't forget you can listen to tfnn live on your mobile device 24 hours per day go to tfnn.com then hit watch tiger tv that's tfnn.com then hit watch tiger tv welcome back folks we get the s and p's off by just two points right now we were just in positive territory we trade just back off where we were yeah keep your eye on that number right it's just a natural area of resistance about 45 95 we touched the area of 4607 46 0 46 34 the recent highs that we made earlier this year so keep your eye on this range we're in a range right now the next 40 50 points to the upside you break hard above that 46 34 area seems like all-time highs may be coming at you not sure you're going to be able to do that depending on where yields are going because you put yields back on a daily we're right at that 50% line and again kind of a natural area that's the area that was support going back to july of this year going back to august you get a bounce also the area that's resistance from september also kind of where you rolled over in october excuse me in august so yeah we're back to what what is that price point just over 111 what's the full point above we're at right now a little bit of a pullback but boy it's been quite a rally and yeah you could just see some volatility playing out all right what else we got we were talking about restoration hardware yeah this thing had quite a fall from grace right check out the weekly long term you make a high of 7044 you base for a while you trade lower and looks like we're probably on a way to test these recent lows of 207 as you get restoration hardware they missed on their numbers cherry picking what they did here but let's see yeah net revenue 751 million they were looking for 757 home furnishings market still being hurt by a slow real estate environment that's from cmbc over there and they narrowed its full year guidance range narrowing that range but yeah shouldn't be surprising man when everybody's stuck in their home it's interesting to see what happens with some of these equities if we do get that pullback and yield over the next year or two folks thanks for starting off your non-farm payrolls friday with me stay tuned basal chatments up next don't forget about the opening call subscriber webinar check it out with those time


Because I'm your Hey guys, it's Tilly and today I am back with a updated a bonds and noble leather bound Editions video so I will show you guys all of these beautiful beautiful books and hopefully not Drag it out for too long So two things before I begin the first one is that I am actually sick at the moment So my voice is very croaky and gross so please ignore that and Secondly, I'm going to put the camera on like auto focus so that it can show you guys the real beauty of all these covers So there might be a bit of background noise and glitching But hopefully it won't be too interfering with the video the Morty de Arthur by Sir Thomas Mallory And that has the gold trim pages a Treasury of classic poetry HP Lovecraft complete fiction So that is the call of Thulu at the mountains of madness the Dunwich horror the color out of space and more Complete Sherlock Holmes by Sir Arthur Conan Doyle The Hitchhiker's Guide to the Galaxy by Douglas Adams the Holy Bible Kings James Version The Chronicles of Narnia by C.S. Lewis the Vampire Chronicles by Anne Rice Interview with the vampire the vampire Lestat and the Queen of the Damned the complete tales and poems of Edgar Allen Poe The Iliad and the Odyssey Homer June by Frank Herbert Fairy tales from around the world by Andrew Lang The Adventures of Huckaberry Finn and other Nobles by Mark Twain So you have the Adventures of Tom Sawyer the Prince and the Pope are the Adventures of Huckaberry Finn a Connecticut Yankee in King Arthur's Court and Puddin Head Wilson Jurassic Park and the Lost World by Michael Crichton Wicked Son of a Witch by Gregory Maguire Selected writings by Abraham Lincoln The Wizard of Oz the first five novelist by L. Frank Baum So that consists of the wonderful Wizard of Oz the marvelous land of Oz Osmar of Oz Dorothy and the Wizard in Oz and the Road to Oz Grimm's complete fairy tales Jules Verne seven novels the Republic and other dialogues for trilogy John Carter of Mars by Edgar Rice borrows So you have a Prince of Mars the God of Mars the Warlord of Mars who be a maid of Mars and the Chessman of Mars Little House the first five novels by Laura Ingalls Wilder Anna Carina by Leo Tolstoy The Arabian Knights translated by Sir Richard F. Burton This side of Paradise and other classic words by F. Scott Fitzgerald So you have this side of Paradise the beautiful and damned flappers and philosophers and tales of the Jazz Age The complete works of William Shakespeare Charles Dickens five novels So you have all of the twist a tale of two cities David Copperfield a Christmas Carol and great expectations So you have the Sun also rises for whom the bell tolls a farewell to arms to old man and the sea by Ernest Hemingway a Christmas Carol and other Christmas stories by Charles Dickens Alice's Adventures in Wonderland and other stories by Lewis Carroll The Count of Monte Cristo by Alexandra Dumas The Divine Comedy by Dante Jane Austen seven novels The Foundation trilogy by Isaac Asimov So you have Foundation Foundation at Empire and second Foundation The Bronte sisters three novels So you have Jane Eyre withering Heights and Agnes Gray I have a second duplicate of Charles Dickens's five novels Grey's Anatomy the art of war and other classics of Eastern thought classic fairy tales by Hans Christian Andersen and Lastly we have Dracula and other horror classics by Brian Stoker. I hope you guys enjoy that video My arms are officially dead after like lifting those books off the top shelf and bringing them down here But hey, it's all for the love of these beautiful Barnes and Noble edition books if you guys want to search them up just search for Barnes and Noble leather band editions and they'll come up for sure They're absolutely stunning heavy beautiful books as you guys have just witnessed. Sorry about my lack of posting recently Hopefully after this video, I should get back into a routine things have just been super hectic for me lately And my laptop is currently in for a pair. So yeah, it's all been crazy, but I should be back And that is everything. I hope you guys enjoyed watching and I shall hopefully see you guys again soon And until then have a lovely bookish day and for great things to happen to you. Bye


Today we have a guest lecture by Professor Santosh Narona, he is a faculty at IIT Bombay, who works in the areas of big data analysis and various device development. Today lecture is about data analysis in evaluating the various hypothesis like searching for a potential drug delivery target or a genetic target in a disease context. When we are trying to search for a biomarker candidate or a potential drug candidate, it becomes very crucial to know how to make a good experimental design, in which manner our data should be reproducible. If you think about a test which has to be given to the patient, the test has to be reliable, reproducible as well as it has to serve the large community. So therefore, your number of patients to be included for the study has to be much larger. Your study design has to think about various flaws, various mistakes, various errors which might be happening if you do not carefully consider your end goals, your actual questions to address. So in this slide, Professor Narona is going to eliminate your understanding about how one should think about the good experimental design before actually planning and executing the Obelisk experiments. By now you are familiar that data generation using protein microarrays or using various type of NGS platforms or various type of proteomic technologies are quite straightforward as long as your sample preparation is good, as long as you know what exactly biological question you want to ask for. However, to really get a meaningful data, meaningful information is not so straightforward and that is where you need people who are good knowledge for statistics who can work with you in designing good experimental plan. So if the aim is to look for biomarkers or to choose the drug target, your experimental design, your study plan, your number of patients replicates, all of these things becomes very crucial. So let us welcome Professor Narona to give you this lecture about how to really carefully plan a good experimental design for omic base studies. So what I see as a background of most of the participants here, students that too from a life sciences background, most of you. There are some researchers here as well I understand. So I thought it would be very useful and provocative both to bring up a discussion of data analysis as it applies to multivariate experiments such as the ones you are doing in proteomics. So a big issue with statistics is how confident are you about any insight you get out of any analysis that you do and while clearly you are spending 3 to 4 days learning about experimental protocols. The question is what are you going to do with this data that you generate and what hypothesis does it drive and for that matter how valuable is this data that you are generating in the first place. So I am not actually going to take a case study and the reason I am not taking any case studies is because I am not in this domain as a core domain. I am a biochemical engineer, I am interested in production of pharmaceuticals. I usually do not ask questions about what is the role of this gene in that particular disease context and so on. But one of the things as an engineer you are forced to do is to deal with large amounts of data especially in a process analysis context and this data analytics kind of need actually we find is very useful in evaluating hypothesis which is what most scientists are concerned with. So these are a collection of thoughts that I have had over a while which I think apply mostly to scientists less so to engineers but they have to do with the fact that we are generating data at very large scales, we are generating data in very large amounts and the question therefore is given this rate at which we are generating data and the assumption by the way is that experiments we are doing are well designed experiments which are therefore generating good data. But given this data the question is what can you infer and what are potential errors that one can make in trying to arrive at conclusions. So that is why I am titling this particular talk as one on reproducibility in particular and one flip side to this is as we see a lot of publications come out especially in the omics domains the question comes up which aspects of these are reproducible and if they are not reproducible what kinds of mistakes are people making which prevents good insights from potentially being leveraged into some kind of future drug development pipeline. So remember most of the reasons for working in this space of omics is that you are trying to find targets typically for drug delivery. So at every level here you can be asking the question have I statistically found a good target. Second given a target have I statistically found a good drug candidate given that there is a library of a zillion candidates. Have you found a good candidate to deploy as a potential drug and if you think about what's actually occurred out there the success rates are very low and success rates invariably are low because of issues to do with reproducibility and that's what I want to deal with. If you look broadly at why most published findings are false you can break it down into different possibilities which is that you have actually thought of a good research design for your experiment but given the fact that for example if you're talking about disease conditions given the fact that you might not be able to access that particular tissue type in more than one patient therefore you're not in a position to do replicates of this as a study systematically over a larger population. You might therefore end up with sheer bad luck and with an incorrect result it's kind of like saying you'll toss a coin hundred times and with your bad luck the one time you tossed a coin hundred times you might have ended up with 30 heads out of 100 which is theoretically possible and 30 out of 100 sounds as if you have a biased coin not a fair coin. Nothing wrong with getting 30 out of 100 it's just that it's a random event it has to do with the fact that coin tosses are random events and with your sheer bad luck the one time you did it you could have ended up with 30. Now your problem is you're trying to ask the question why is this coin behaving in this particular fashion you would have expected a fair coin therefore you would have expected 50 heads out of 100 and instead when you see a 30 you're actually pushed into this question as to whether what you're seeing is an example of an unlucky event with a with a fair coin where you should have expected 50 heads but instead you saw 30 or have you instead seen a biased coin so which way is this. So in other words are you looking for a particular hypothesis and you're saying that that hypothesis is not true or are you saying that an alternate hypothesis is true which way do we go. So this is a situation which instead of that fair coin now and biased coin think of whether you're looking at a genetic target for manipulation in a deceased context. So is that target something that you've pulled out as a random event or is it so significant to you that you feel that you should now go to follow-up experiments. The context of this is therefore that if you're trying to find targets and there's an investment by the way in both the experiment that you've performed to find targets and an investment subsequently in trying to validate these targets okay how confident are you in carrying out these experiments and of course the assumption in all of this that the experiment is designed correctly that you have done it with appropriate controls that you have randomized that you have got appropriate okay controlled experiments that you're doing and if you have not done that then things are only going to get worse in terms of the quality of data that you have and even more critically and this is a sad fact if you as an investigator are already biased towards one cluster of genes as being important to you then personal bias makes things even worse and the brute truth of this is that most published results are therefore irreproducible so much so if you go to the journal Nature there's an entire sub domain on that website which talks about irreproducible research and their concern is for all those papers trying to get published in Nature how do you guarantee that whatever insights or whatever results that somebody's trying to package in that paper how confident are you that those are reproducible enough that they're worthy of being published and being published not just in any journal but in Nature so they're so concerned that there's a subsite that they've created on reproducibility and the lack of it there are several causes for why there's this bias ultimately and I'm just I'm going to list a few as we go along and not all of these have to do with omics but I want you to appreciate the broad idea and finally towards the end we'll talk about how to control for some of these but the most important reason for why results are not a principle is something called a confirmation bias which is you already have a hypothesis going in your mind about what you think is the underlying cause for this disease and now when you're generating data you're only paying attention to those data points for that subset of data which you think confirms your hypothesis okay and in doing so because you're only looking at a subset of data points you're probably missing out something more important which might have told you something else about the disease condition okay the other reason is very common which is that people manipulate the analysis outright and a third situation is that the results are not independently tested you may have already had some discussion about this in terms of your omics pipelines but a typical headache for example is the tissue sample which you're processing for an omics study is not available to a different researcher so there's no way of validating whether your actual experimental workflow was executed correctly or not therefore whether your targets are therefore relevant or not so let's look at these one by one so the oldest example of confirmation bias is actually somebody you'd be surprised with Gregor Mendel to give you an idea why you know if Gregor Mendel were to publish in this day and age in fact he would not publish he would not be published because you would say that he cheated he plagiarized this data why you sit and do that pea plant experiment which is famous for where he has expected to get a proportion of three to one daughters with a certain phenotype you do that experiment starting with pea plants your proportion of plants of daughter plants which have which are carrying a particular phenotype will not turn out to be 25 25 percent to give Gregor Mendel credit or something which he realized intuitively was some number to round off to that the three to one proportion is probably a nice rounding off of numbers but if you sat and did the raw experiment yourself and you collected a whole bunch of plants and you categorize them by the length of the leaves and so on you would not get 25 percent of them having short leaves versus long leaves for example what does that tell you it tells you that he was already biased towards reporting a result of three is to one he wanted a number three is to one of course there was no statistician there at that time to challenge him of course these days there are when you send out an article for a review process so when he says three is to one whole bunch of people except three is to one as if it's the truth but here's the thing that experiment could never have been reproduced okay and he goes into this with a specific bias and the iron is the law of inheritance as he found it is an accurate description of how inheritance happens but inherently you have a problem that this is not inheritable the second reason why most results are not reproducible is that there is manipulation of this data and especially in large departments in the west you're starting to see a paradigm where there is sponsorship of the research in the department by some large entity a pharmaceutical company and that starts influencing because the whole process of which types of hypothesis will you look at what data will you generate what kinds of experiments will you actually perform and report and you're not free therefore to actually carry out certain types of experiments because you're using a farmer company's money you're not free to do what you want to do and therefore you're not truly reporting what you think is a is a relevant insight okay so this is an example where manipulation happens and this is a spin-off of this is at any given point in time one particular type of investigation suddenly becomes hot and everyone tries to do that and that's accepted as a standard protocol for data generation but there's nobody challenging okay because there's nobody capable of challenging given that it's not a kind of facility that's available to you everywhere okay what what the truth is of that particular data set or whether there's some prejudice involved in analysis and this is a simple example to point out what happens if you manipulate data outright by misreporting on in this case not reporting data so if you look at the plot on the right and you look at the reported R-square value it's a simple straight line fit which I'm reporting here you'll swear that it's a great line that R-square value if you just go by a published R-square value you think it's a good model but you see how you've arrived at a good model by just omitting a few data points and the risk associated with selective okay reporting of data in a publication and a final point on this line is that when you're talking about testing and independent testing the fact remains that we are all working in isolation and therefore there is no culture of materials being shared and being retested elsewhere if you're saying that something's a critical hypothesis a critical gene for example or a critical protein in some kind of a disease condition the first thing you ought to do is for this protocol to be repeated by somebody else by a different investigator who ideally doesn't have access to your raw materials and therefore is independently validating that your idea can be reproduced elsewhere okay all of this comes down to how you set up for the most part in especially an omics type of approach a hypothesis the entire statistical framework of how we evaluate a data hinges around the fact that you ask whether something's an important gene or not as a hypothesis and then you try to shoot down that process so the easy way for you to remember this is actually how the legal system works so how does the legal system work whether it is in India or in the west what what is it one tries to do somebody is what somebody's innocent until proven guilty and there's an additional clause there what is that clause you're innocent until proven guilty beyond the shadow of a doubt that's critical beyond a shadow of a doubt you're innocent until proven guilty so the effort is on somebody to try to prove you guilty not to try to prove you innocent so somebody has to try and prove you guilty and that to that guilt has to be shown beyond a shadow of a doubt that shadow for doubt is something that you would have heard of okay as a confidence level so I trust a result within a certain confidence interval and if I see a measurement beyond that particular range of confidence I say that this result that I've gotten is not consistent with the original hypothesis I now go with an alternate hypothesis one simple example here is if I toss that coin 100 times and I get 15 out of 100 heads 15 out of 100 as heads and instead of calling the coin a fair coin I say my outcome was so extreme 15 out of 100 that I would rather go with the hypothesis that it is a biased coin and not go with the hypothesis that it's a fair coin I got an extreme result beyond a confidence interval of sorts so therefore the entire hypothesis testing procedure involves on taking one particular statement asking whether our observations are extreme relative to a range within which that statement can be true and then therefore deciding on whether we are on the inside in which case that statement is okay or whether we are on the outside in which cases what we have seen is something extreme now if you think about how you identify targets you look at gene 1 what is its fold change in expression is that fold change extreme if that fold change is extreme then you say as you shortlist it and say that how extreme it was could not have been an event by chance therefore you believe that it's actually a good clinical target and for all the thousands of genes that you look at you ask is there been some random variation in their expression levels within what range could that random variation have been and you therefore assign some confidence interval and when you see a measurement well beyond that in terms of either fold up regulation or fold down regulation at this point you say I saw something so extreme it could not have been by randomness could not have been by chance therefore there's something going on there as to why this particular candidate is over or under expressed okay however what we know is that these findings these kinds of studies in terms of statistics they're limited for several reasons and and in principle the reason that these findings are less likely to be true is if whatever you're looking for is a small effect then odds are that you know you're going out on a limb by saying something's a significant gene when all you were out to do in the first place was to create a small effect okay odds are you're found a wrong candidate also if your effect sizes are small if your sample sizes are small then you have really really no business saying that something's an extreme result and if you're looking at many relationships if in in for example in the omics case if you're looking at many genes and trying to make the argument that this gene is important that gene is important and you've got a whole bunch of genes to analyze one after another the larger that pool of things being tested odds are you're making mistakes and I'll come back to that in a minute but take these points okay in the context of what threatens reproducibly I'm not sure you can see this very clearly some interesting buzzwords interesting terms which later on I really recommend all of you go look up okay so if you look at if you look at this cycle and in fact it's cycle because it's a publication publication cycle it's it's how one does research with publications in mind so if you look at the way you're supposed to operate you're supposed to operate at the top right there we generate a very specific hypothesis you generate that hypothesis you're supposed to know design experiments collect samples okay design a study and when you design a study by the way you're supposed to design what is called a powerful study I'll come to that power a little in a little bit but you design a study which is a protocol which is going to help you discriminate between a control and a test case that's that's what you mean by designing a study well and you then actually conduct the study and collect data so that's at the bottom okay notice that in red I'm putting down things which can go wrong at each level so when you're generating and specifying the hypothesis you might already be biased you're already looking out for a particular result okay and you might already be biased in how you're therefore trying to now carry out the study if you think about how tobacco companies operated for a while in carrying out trials as to whether nicotine is addictive and this paper after paper saying from a tobacco company saying nicotine is not addictive there was a bias because they cannot be in a situation where they report that nicotine is addictive and harmful because obviously they'll kill their own business so you can see they're going to tweak the way the results are and the study is done okay to end up with a result which is obviously good for them so you generate and specify a hypothesis you design a study and then you're worried about how capable you are of differentiating your control result from a test result you conduct the study and then collect data and usually a problem here is that you don't have good quality control in terms of your methodology and then finally you get down to what's called data analysis and some of you if not all of you have probably heard of something called a p-value how many of you have not heard of a p-value okay well heard of a p-value so let's discuss that p-value because a lot of omics critically hinges on the interpretation of a p-value and what you can do with it okay there's in fact unfortunately a phenomenon called p-hacking and again you really want to look up p-hacking if you've not seen this before which comes about where people use the p-value concept without having a true understanding of it and try to label some targets as being important and others as unimportant so the interpretation of this data via a hypothesis testing approach leads people usually once you get this data then you're going to do some follow-up experiment which means you're in the next publication cycle you're set up some follow-up activity and now maybe you're testing these targets or you're testing different tissue samples using this this particular protocol you would normally get locked into some kind of a cycle where if you've already published two papers on a particular cell type and with a particular subset of genes you are forced into publishing more just to offset the costing of the research that you're doing and this is where a bias can creep in because you're not in the position to say that what you've been doing all along is poor you'll notice that there's this double there's this line as an arc cutting across okay the circle and I've written harking there harking h-a-r-k refers to hypotheses after the results are known which is unfortunately what happens to most of us which is we generate the data and then we start asking okay what exactly are we trying to as an insight because at this point you've put in a lot of time and effort into your study you better come up with an insight so therefore you ask the hypothesis after the results are known which fundamentally is cheating because you know you can decide what you're going to call significant what you're going to call insignificant and this goalpost business if you're if you were ever going to have a goalpost to decide what's a good target what's a bad target that should have been done before you even looked at data okay otherwise you have the potential to cheat so this p-hacking business is the crux of most issues to do with reproducibility so let me okay quickly go through that concept p-hacking also has a nice phrase called data dredging basically involves you searching through a large number of hypothesis typically with a single data set so the data set for example could be that you have one genomic data set or one omics proteomics data set and you're asking the question which subset of candidates are important as targets how does our hypothesis testing procedure work so if you look at this what what what does this mean we're expecting the fact that in an experimental condition this should have been the average value i should have seen if replicates were ever done now there's already a practical problem which is in an omics world you're probably not going to have the luxury of doing many replicates because it's a very expensive protocol that you're executing and you may not even have tissue samples to play around with okay if you had the luxury of doing many replicates you would see a range of values for the same gene that range of values would follow some kind of a distribution like this okay and with your luck you may end up with the result anywhere under this distribution just like when you toss that coin 100 times it is possible for you to get 50 ads out of 100 but it's also possible for you to get 30 out of 100 and for that matter 70 out of 100 these are all possible outcomes given that you're not in the business of doing the experiment again and again and again notice that statistics ideally says do things again and again and average them out because they're more likely to find the true value intuitively you know that that if you work with for example if i ask you what's the average height of a person what's average height of a human being either i can work with your one i can take one individual and use your height as a representative of the average but you know that's not smart because i might end up with a short person or a tall person instead the safer thing to do is what take an arithmetic mean of everybody out here and then use that as an estimate of the average you know that you know that's a better representation of an average okay so the whole notion that results can occur in under some distribution okay but if this is what you expect then we know that between these two boundaries here between these two boundaries we're in a situation where for example if this were 50 heads out of 100 this could be 30 this could be 70 then we are in a situation where any outcome we see the one time i do my experiment between 30 and 70 anytime i do the experiment once and i see an outcome between 30 and 70 what do i say then what do i infer as the hypothesis result i say that my result is close enough to 50 that i probably saw a result associated with the fair coin it is only if i see an extreme measurement sitting out here or sitting out here that i say look what i just saw is so extreme i saw 15 out of 100 or i saw 85 out of 100 that is so extreme it could not have been because of a fair coin therefore i should be considering this coin to be a biased coin okay that's how we interpret a typical hypothesis now if you look at this these two barriers which by the way your confidence intervals and for us typically what is the magnitude of this confidence interval what do we choose anybody has an idea what what magnitudes do we choose you'd have heard of a 95 percent confidence interval as a typical setting for confidence intervals so there's 95 percent okay of confidence what you're saying is 95 percent of the area under this curve probability which is probability is between this threshold on that which means the five percent sitting outside now the headache is how do you interpret this five percent sitting outside so what does therefore this dark red shaded area mean to you that dark red shaded area is a situation where you could have had a fair coin because the whole thing is associated with being a fair coin these are the range of outcomes you would see with a fair coin you could have had a fair coin but with sheer bad luck you see an extreme result like 15 out of 100 or 85 out of 100 and at this point you go with the other hypothesis that it's a biased coin but it could have been a rare outcome with a fair coin in which case as a technicality you've made a mistake in your hypothesis why because you've gone with the other suggestion or the other hypothesis then with the original hypothesis you have gone with the assumption that the coin is biased when you should have gone with the assumption that the coin is fair and what you've seen is a rare event with a fair coin so this five percent which is something we tend to take for granted is actually critical in an omics test because it represents an error it represents an error in an interpretation so this five percent is an error where we should have gone with the null hypothesis as this is called we should have gone with that original hypothesis but instead we are preferring to go with an alternate belief an alternate hypothesis so five percent of the time we commit an error in an analysis so we commit mistakes five percent of the time so that's the take-home message of that but how many times did we carry out a study how many of how many hypothesis did you test in an omics study how many genes are you testing if you're testing 10 000 genes in a study one after another and I just told you you're committing a mistake five percent of the time then have you committed mistakes in your analysis of 10 000 genes you would have somewhere in there you would have ended up with the wrong conclusion just by sheer bad luck because your error rate is five percent the error rate doesn't matter much if you're testing something once but if you tested something 10 000 times well you didn't test one thing 10 000 times you carried out 10 000 different tests one per gene and if you tested 10 000 things one after another you've made a mistake by sheer bad luck that is five percent of the time which amounts to a large number of candidates potentially being wrong therefore if you were looking at your null hypothesis being this gene is not important and you're trying to shoot down the null hypothesis so if my hypothesis is this gene is not important because its expression fold change is one and I'm looking at fold up or down regulation close to one then any range around one corresponds to my null hypothesis being right and what is the null hypothesis that gene is not important and it is only when I see an extreme measurement would I now say this gene is important but five percent of the time for a given gene I would make a mistake and I just did this analysis 10 000 times one after another which means most of the times I'm actually calling something important as a gene candidate which is up or down regulated it is not even statistically important to you it could have occurred by sheer chance sheer chance why because we did the experiment few times and with sheer bad luck we are seeing extreme results and we are being fooled into thinking that our insights are important when they are not okay so the p-value which is now so let's say our outcome is over here if my outcome is over here you then ask the question how far away from this 95 percent threshold was I and how close am I to changing my mind okay and this p-value if this so by the way if there's five percent of the area outside this threshold on the right there's slightly more than five percent of the area outside this threshold that's intuitive because I just moved the goalpost inside so there's more than five percent of an error that you're okay committing the question is how close are you to this threshold what are the odds of this error being extreme okay so I hope you are convinced that there are many minor but very crucial considerations about a good experimental design I hope you have learned how to analyze your data and to make best sense out of it while thinking some very minute aspects of experimental design and data analysis you also studied how conformational biases may lead to missing out some very important information from your data sets also there are various reasons like confirmation bias design issues data manipulation along with the lack of independent testing which could lead to the non reproducible experimental design and if you want to publish in a very good scientific journals you need to ensure that all of these considerations have been met I hope you have also learned about the importance of confidence interval in the selection of a potential reliable candidate we have also learned about how p-value could affect the interpretation of results on your data so you need to be very careful about knowing some of the technologies used in statistics how these tests could be performed and how one should really make a meaningful interpretation from the this data set which is available to you which is usually a very large data set from the omics experiment but what is the most significant lead out of that needs lot of consideration needs lot of considerable thought process and your understanding from experimental design to the various type of tests being performed and then only finally you can come up with a reliable list of the proteins biomarkers or candidate targets which could be meaningful for the future experiments so the next lecture would also be continued by Prasad Narona and he will further talk to you about various factors involved in good data analysis and experimental designs thank you.


Welcome to Global Public Health and Infectious Disease. This course provides an introduction to a wider way of topics and issues in public health with an emphasis on our global community. Some topics that we discuss include mental health, food insecurity, occupational health, healthcare systems, environmental health, and much, much more. This course also addresses a number of impactful case studies and controversies in health and biomedical ethics. Because public health relies on a number of systems to operate and serve diverse populations, students will also use system and design thinking in order to solve real-world problems. This means lots of discussion, teamwork, and data analysis. Students will be working in teams to research, analyze, and share their solutions with each other. All analyses will be evidence-based and student-driven. Students are regularly allowed to pick their own issue and choose to learn about it. This interdisciplinary course requires complex reasoning, critical thinking skills, extensive use of technology, communication, and problem-solving skills.


This was a random collaborative project between Adnan, Rahim, and a few other people. Actually, a lot of people helped out with this one. We originally targeted for Make a Fair, but we couldn't make it because we had lots of issues. But let me go through it. So it's an experimental physical interface for the incident algorithm. So it all started with Magenta. Magenta is a Google project for using the machine learning power rest to do art. So about a year ago, they posted this. It's a neural audio synthesis. So it's a neural network that makes audio. I was like, cool. Sound plus neural networks, what could possibly go wrong? Of course, the default answer to that is Mac. So this is the way you actually run this thing, which is an iPad and notebook and you need GPUs and stuff. The basic idea is you can take a sound and sort of extract it into a model. And then when you run that model, you generate the sound again. So that's the very basic idea of what this does. And I was like, wow, this is super cool. And there's no internet. Yeah. And there is some really cool things. So when they made this website, that actually allowed you to play with this algorithm online. So the basic idea is they have a trombone that they have recorded and made a model out of. And if the internet works, I should be able to play them. Let me try it again. So this is machine learning to make sounds. And this is all cool, but one quick thing about that algorithm. So normally, when you mix two sounds in audio, you will basically hear both of them together. So if you mix a clarinet sound and a trombone sound, it sounds as if both of them are playing together. But what this does, and especially the demo earlier, you kind of had a combined sound of the two. And you could sort of add the two together and come up with a new instrument almost as if a trombone and a clarinet had a baby. And that was the thing that totally blew my mind. I was like, that's super cool. You can do so many things with it. But then nobody's going to make music on a laptop. That's just boring. Nobody's going to be typing in a keyboard making music. Although they're a lot of orchestras. And so another group at Google called Creative Lab took the sounds from Ensign and created a musical instrument. We call it Ensign Super. Wow, that's great. So this came out, and I was like, wow, this is super cool. I want one of these. And then in the website, you go down and say, all the technology and design you used to create Ensign Super is available as an open source project. When you go and look at the actual website, it's a proper get-up thing. When you look at the actual website, it's a proper get-up page. It's got all the documentation. I'll go through some of the stuff they actually have. So let me actually just open the page up. So this is the PCB part, which I'm guessing most of the people will be excited about. So they actually have the entire documentation on how to build the PCB. The first thing you'll notice is they removed the touch screen bit, because I'm guessing it's a bit too expensive to get that big a screen. They're way for this form factor, so they have a PCB that's this big. There's a touch pad, so there's no screen, but you can still touch it and it actually does the whole morphing bit.  The actual music synthesis happens on a Raspberry Pi behind. And they have really nice instructions to tell you how to put it together, to tell you how to solder it, all the things that are involved. There's a touch interface. This is an audio bit, so the audio is generated with an external DAC because they don't trust the DAC onboard the Raspberry Pi. So they kind of shrunk the display from something really tiny. But it's super cool. All the documentation is there. They had the documentation for the software. So it's using something called Open Frameworks, which is a framework, C++ framework for doing audio stuff. They have the firmware for the microcontroller on there. And they have all the samples for the audio to just do a quick demo. Unfortunately, it's 64 GB of sample audio. It's huge. It's a lot of file. So I was downloading it the first day and a similar time, 6 hours. Left it overnight. I had the 64 GB. It's the next day. But it was painful to get it down. But at the end of the day, I got it. Just a quick setup for the whole thing for anybody who's interested. There's a Raspberry Pi that runs the Open Frameworks bit. They use touch sensors. They use all the shelf touch sensors, two of them, on that touch panel. You just get the touch. Audio data comes in as MIDI. That's again taken in by the SDM32 and transferred to the Raspberry Pi. The LED is driven by the Raspberry Pi. And the DAC is also driven by the Raspberry Pi. So very simple, straightforward. PCB, very simple, straightforward circuit. Not very hard to make. So of course, the schematic is also online and so are the Gerbys. So everything is there, literally. All you need to do is just take them send them to your favorite PCB manufacturing hub. And you get PCBs like this. I got it in green because that was the choice of color of the people I asked. The bomb is also online, thankfully. So I can go and find all the components. Unfortunately, it's all UK RS electronics links. So half of them will work and the other half are not even available in Singapore. So we had to do a lot of swaps and a lot of hacks, but we managed to get it all soldered up and are all working. On the back side, you'll see this is a mesh for the touch panel on the back. This is SDM32. These are the touch sensors. This is the MIDI input, the display, the audio output bits and the Raspberry Pi itself. Lots of funny things happen, though. What's a random display from AliExpress? Realize that pinouts don't work. So of course, you've got to dodge all the cables there. Usual stuff. So we had this working. This is the first trial of it working. The video is kind of horrible, but when I shoot a demo later, you can check out the display. The smaller OLED is beautiful and the stuff they have set up to make it work. You kind of practice your finger and the full corners give you different instruments so you can sort of morph between them on the full corners. It's super cool. Thanks to Terrence from UWC Tampines, I got a laser-cut box made. They also have the box designs online. So literally, they've done everything for you. All you need to do is compile the software and make it run. It's a nice box. It's got a MIDI input, the audio output and the power. Of course, none of my MIDI keyboards actually output MIDI anymore because these days everything does USB MIDI. So, well, Bodge Live again. I had to cut a hole in the nice case and open the Raspberry Pi MIDI USB port to plug in my MIDI device. And a couple of USB sort of open frameworks hacks to get the MIDI from the USB instead of the MIDI from the SDM32. Otherwise, you ask, where do you do the cool thing? The machine learning, right? So I figured out that all my synthesizers set up and I was like, I have my 64 GB samples. I want to make my new samples now. How do I set it up? So I read about the audio pipeline, which is how you create new samples, how you sample sounds, make models out of them and you can morph them into things. And then I saw this bit. They set it up with 8 NVR, 8 K AD GPUs, or how? I have no idea, but I don't want to know. So I'm stuck at this part now. I have the whole thing set up. I'm now trying to figure out how to make new models for this stuff. Any ideas, any help would be appreciated. Or I could have access to 8 NVR K AD GPUs or something similar. We could talk and see what we can do with it. But overall I could spend a lot of money and that's probably a good idea. I should have gone to the TensorFlow meetup now instead of Google instead of coming here. It's at Google right now, right? But that's a quick sort of adventure that I did with the whole thing. I have the whole thing set up. I probably don't have enough time for a demo but what I'll do is I'll set it up. You guys want to come over and play with it. It's super fun. I have a keyboard and a small loudspeaker or we can plug it into the AV system later. We'll have some fun with it. The question is, are we German or not? It's all.


Hi, I'm Dr. Sunil Kini, senior consultant joint replacements surgeon and arthroscopy surgeon working at Manipal Hospital's old airport road, Bengaluru. So I specialize in the area of joint replacements both primary and revision as well as arthroscopy or keyhole surgeries of the hip knee. So as we know that we are amidst the COVID-19 pandemic for a while now and this uncertainty may remain till we get a vaccine or herd immunity is developed. At the same time it's extremely important not to live in fear and to get on with our daily course. So at the same time in relation to medical healthcare it's important that you do not disregard your physical illness for a disproportionate amount of time. So we have been working all through these couple of months through trauma surgeries and emergency surgeries but for the last one month we have restarted all our elective surgeries as well. We are full-time running our outpatient departments in the hospital. I'd also like to comfort you by telling that we have a separate fever clinic for suspected COVID patient and as well as an isolation ward for the same. So we take utmost care to make sure that there's no mix-up for all the other patients. Thus I would recommend you to consider visiting us for all your health related problems. You can make an appointment with us through our landline numbers or through our Manipal Hospital website or you can always call us through our helpline phone number for any assistance in this regard.


Now, to health care costs under Obamacare, changes made to the landmark law could open the way for states to offer cheaper plans that cover less. New Jersey representative Frank Polone's introduced a bill to shore up what's left of the Affordable Care Act. Breonna Vinosi reports. People actually thought the ACA was gone, that they were able to dismantle it. Actually, the ACA is still alive, though many suspect it could be on life support sooner than later. But Trump administration's rollbacks, more specifically the repeal of the individual mandate, is having a significant effect here at home. We've had 20,000 fewer individuals in the open enrollment period at the end of 2017 signing up for this year for the individual market. So Congressman Frank Polone is pushing his federal bill that expands and protects the provisions in the ACA. His legislation gets rid of the cap on eligibility for tax credits. That means everyone enrolled gets something. This is the amount of those tax credits and cost-sharing subsidies, and perhaps most important to the folks on the ground restores funding for outreach and advertising. It'll also keep requirements in place for essential health care. In other words, no junk plans allowed.  You know, we believe in personal responsibility. Everybody has to take responsibility for their plan. How many of the people that are actually buying these things know that it doesn't include hospitalization, or it doesn't include maternity, or it doesn't include pediatric care. They don't know. That's the problem. If you lower those standards for certain plans, what's going to happen is that healthier people will go into those plans, and sicker people will have to go into the plans that we already have, which will get higher and higher. So it's going to have a huge impact on individuals who are the sickest in our state. The risk pool is compromised when healthy people drop out, when it's harder for insurance plans to stay in the individual market, and then, of course, premiums rise for people and then access to health care suffers. Health care advocates shared stories of patients who are suffering from the initial changes to the law. New Jersey saw a 23 percent increase in premiums this year. If you're making $60,000 and you're an individual, you're not eligible for any subsidies at all under the federal rules. And they're bearing the full 20 percent increase, and they just can't afford that. State Senator Joe Vitale has a dual bill package to support the marketplace, allowing instead the state to impose an individual mandate and setting up a special security fund to collect the money and redistribute it to stabilize the market. What we would do is have our individual mandate kick in when that ends, so sort of a seamless transition to both, to the new one. And then the reinsurance element of this package takes time to develop. And so we would apply to CMS for that waiver, like other states have done. We're trying to sell this on a bipartisan basis. We're trying to get it even into the omnibus of spending bill that we're going to hopefully adopt this week. I can't imagine that all of it will get in there. Maybe some of it will. So with little help from Republicans in Washington, Congressman Frank Polone and his Democratic colleagues are counting on state leaders to keep what's left of this law intact. In Perth, Amboy, Briana Venosi, NJTV News.