everybody forced here rip in 2022 luminance football hobby six box number one random team style i'll leave the results on screen for you right here and you can always find these late in sports cards dot com thanks again everybody for joining there's a link in chat for these as well so let's go ahead and get started first look at luminance football 2022 still weird saying 2022 for football or at all or in general we're almost at 2023 man man yikes okay let's find a big year one so you get three auto one men per box on average um i got the checklist up here it didn't say what the case hits where did it let me just look at that really quickly here yeah i couldn't find any info one checklist is very bland currently i didn't i don't even have names up yet so let's see what we got see we got herbert on the box herbert on the box the always cool looking cards cool looking inserts in uh in luminance fun stuff now let's find some big year one autos those are the real chase so some decent vet hits in here as well let's see what we got i guess you'll want the base there's a chase second here henry cool looking rogers uh rookie john mechie there texans cave on thibodeaux so you got base rookies here and boye may ma oh mafe i think mafe vestige panthers number to 100 christian mccaffrey vestige one of the cool looking inserts there oh nice and trevor lorenz gold 35 of 249 gold lorenz for the jags beautiful i love the foil on the on the luminance though i will say they look great first auto browns rookie um gerome ford 21 of 100 rookie auto there gerome ford browns god that's sick looking fusion zack wilson there you go jet zack wilson on the fusion inserts we'll go a little quicker than that but trying to get a feel get you guys a look at the cards here um seguan kyler there's alan taylor we've got rookies now vilas jones jr for the bears uh leo chenal yeah chenal and a kenneth walker for the seahawks we've got a josh jacob's green eight of 99 for the raiders donal mooney relic hard for the bears non-numbered memento swatches vestige insert titans aj brown titans aj brown so not an eagle in this product far out in moments thank you chris i remember reading that now that you say that far out in moments yeah that will also look like a super short print uh rookies jesse lucchetta Trent mcduffey and um i say a spiller niner's gold george kittle 218 of 249 got james cook bills year one auto nice one bills james cook good start today so blue will be the base on the year one autos good looking design this year too blue would be the base i don't know the exact hierarchy of of ink color the blue is base and savage insert cold starry slender does it glare that bad uh results are on the website late sports cards dot com link is in the bio again full streams are on youtube and twitch there's a unique willis rookie tennessee god uh cleal chicare bill's rookie a chris olave for the saints the rookie card there is drake jackson uh 85 of 149 for the 49ers rookie drake jackson we got kyle hamilton or ravens rookie auto numbered 28 of 100 nice one ravens and dynamic lamar jackson also for the ravens and man luminance cool looking product almost full on brakes today for luminance they got shawing with me lamook i'm trying not to have the glare but right hollywood chub there is um drake london falcons rookie photography and uh luminance is awesome uh travon walker for the jag is number one overall pick kyler gordon rams what's going on the moon sorry bears bears kyler gordon tyron williams i think is is ramp just one thing justin jepperson she six of ten what do you guys think of the sheesh answer and i am uh i'm undecided six to ten vikings justin jepperson sheesh um falcons tyler al gayer or algear 114 to 249 rookie cowboys rookie auto jake furgason 287 of 349 and sheesh uh diante johnson steelers probably the one time i'll say sheesh like that today what just happened so yeah brady mac jones second year got um jake furgason uh perian winery that's what it says winfrey sorry the the font is weird pierre strong mac jones green patriots 22 of 99 got jonathan taylor colds jersey card not numbered memento swatches and vestige niners george kittle i gave your cold cuts insert that'd be pretty cool yep luminance released today so we'll get moving a little quicker through the vet base here de marvin leal david bell um it was that tyler baity yeah tyler baity the ravens tight ends get dangle jones giants 47 and 249 in the gold auto for the giants wandale robinson year one your giant solid hit there for you wandale robinson not the best not the best looking signature but nice auto nonetheless nice nice hit that press got that in it for the cowboys and we got rashad white bucks rookie there who is this oh nice sam howell for washington the commanders uh andrew booth jr rookie kenny pickett numbered 127 of 199 steelers there he is kenny pickett first numbered card of the day kp and nice washington rookie auto sam howell numbered 67 of 100 what a pack box two nice sam howell s swiggle h swiggle double l respect on that dynamic garret wilson jets rookie uh this is all random team style i don't think we're gonna do luminance p y t yeah i mean to be fair nobody does anyone get taught uh cursive anymore like honestly i was gonna say there's no need to learn cursive except to sign your name by the time i hit middle school even me and i'm 30 i typed everything we didn't hand write anything really here and there yeah but my kids the same way uh kenneth walker uh john mechie cave on thibodeaux got javonte williams gold for the broncos numbered 58 of 249 relic card seahawks for shot penny nice patch card there 20 of 49 seahawks and vestige lions deandre swift i do remember in like fourth and fifth grade you'd have to write like essays and cursive here and there but world moves too fast i guess nowadays to be writing cursive off the top uh is he a spiller rookie for the chargers got delish jones bears uh leo chenal for the chiefs pier strung jr patriots rookie 97 of 149 on the blue got kyler gordon rookie auto red and gear one for the bears kyler gordon he is bears right pretty sure and double check that nice year one redding it has like a little hollow like you guys can see it there it's like a tv fuzz i'll call it a tv fuzz there yeah bears redding kyler gordon i think red is the step up from blue i think that's the next step he's got a nice signature and dynamic uh panthers c m c maybe they should just teach signatures in school not worry about the whole cursive thing like go over it a little bit but then just all right here's how you sign your name uh chris alive a saint's rookie uh jesse laketa trint mcduffy there is a bailey zapp patriots rookie 172 of 249 though the card was dusty and a bailey zapp patriots rookie auto 36 of 100 there you go pats and a jameson williams on the alliance dynamic rookie um always looking for year one autos dream that's what you're always looking for year one autos are probably the the bigger hits the on-card rookie autos and then for inserts it's i forget what the case hits are called already man i was just told this jaylin uh petry there's a malik willis rookie one of the titans we'll secure you got red rookie roger mc mccreary 13 of 25 for the titans roger mccreary uh rookie kavon thibodeau 73 99 giants and patriots auto again rookie auto tyquan thorton eight of 100 and savage insert dk for the seahawks oh players um kenny picket malik willis a lot of receivers chris alive jameson williams garret wilson um just want to breeze hall for running backs i would say he's probably the one chase some decent defenders a new hudgensen will be a decent chase trevon walker was first overall pick so he'll be there as well sauce gardener yeah there's a few tim howell for another quarterback not not a big chase but he might get playing time pretty soon wilson yeah it's jeremy ruckert for the jets um what was that justin ross for the chiefs got falcons tyler al dire or al jeer niners george kittle 52 of 99 relic for the bangles t higgins jersey card not numbered and a vestige bears justin fields vestige cold cuts uh cold cuts is no it's not mess with me now i have to go a little quicker going forward all right so yeah we're gonna pretty much get past the vet base vet base guys um asan haskens dable jobo channing tundle channing tindle yep christian harris rickie 42 of 249 for houston points cards 600 random to all at the end and she's kyler murray brian robinson jr i hope he's okay man i can't i was insane what happened to him scary to cameron thomas evan neal blue rookie lions james and williams 119 of 149 evan neal giants rookie auto 23 of 349 top 10 pick evan neal and dynamic trailing berks titans trailing berks another one that'll be a decent chase titans would be good getting good to get enough and other and 2022 products at least to start out uh nico bonotto roberson a karson strong derrius lennard colt's number 32 at 35 got granite cooks 23 of 50 or 25 of 50 for the texans davante white packers rookie auto 203 of 349 and fusion again jet stack wilson of a fusion those are sick looking two more boxes he was back in the building yesterday that's kind of crazy does he have a timetable or do they even get to that yet got kennedy brooks rookie that is james cook james cook and devin loyd green rookie uh for the texans derrick stingley jr 85 of 99 relic card for the giants plexico burris or oh no auto auto it was thick haven't seen a thick auto yet illuminated ink plexico bearish giants four of 99 and luminosity it's heighten's derrick henry that's a cool looking car luminosity i think as we got more luminance breaks today blink is in the bio for group breaks or personals personals will start after group breaks tonight adan hutchinson rookie choyce majai sanders chrysalovay saint's rookie numbered six of 35 there you go saints next up mccaffrey for the panthers orange 35 of 50 33 38 of 50 sorry this is a mere white rookie auto raiders 195 of 349 there's another auto here yeah rookie auto browns uh david bell 292 of 349 two auto pack for luminance of any year i think it's the first time i've ever seen that and vestige niner's george kittle these on our site 249 99 a box uh jaylen tulbert there's an elam penny picket rickie base for the steelers got 162 49 eagles jaylen hurts dak press scott jersey card cowboys non-numbered and dynamic dak press got cowboys that's crazy sean that's insane uh sion arsinae sauce gardener um tyler color goodson packers got colts michael pitman jr 23 of 149 blue color match washington jahan dotson rookie auto there you go washington 56 of 100 nice dotson and dynamic garret wilson jets it's a pretty solid autos in this first uh six box break come on let's see another year one here drake london for the falcons travon walker jags kyler gordon nicobi dean eagles rookie 39 of 50 nicobi dean got jeremy ruckert jets year one auto jeremy ruckert jets there we go solid hit there dynamic colts jonathan taylor kyle phillips who's that steelers uh calvin austin falcons desmond ridder first ridder we've seen um perry on winfrey perry and winfrey 107 and 249 for the uh browns or 149 the browns got jets relic card michael carter non-numbered vestige panthers christian mccaffrey two more packs we'll do a quick hit recap after this uh jake ferguson perry and winfrey pierre strung rookie going to the panthers is brandon smith to 249 jets brice hall rookie auto green color match 17 of 25 nice brice congrats jets that's a nice hit man jets are gonna be good to get these products too drake london dynamic rookie the falcons at least to start the jets will be popular with sauce wilson and um and brice it's a marvin leal david bell that's is tyler baity we've got chargers austin eckler number 24 of 35 next up is colt's derrius lennard or chiquil lennard as he'll be 99 of 99 and tyler baity ravens rookie auto number 15 to 50 had a good preseason that's a big place tyler baity ravens and savage nick job let's do a points random here and we'll do the recap all right 600 points random to all 600 snake eyes as a re-roll the illuminance looks good as always you guys six and four ten times there we go top spot gets the points one two three four five six seven eight nine ten j j there you go j yep j a y j all right quick recap everybody again if you're watching on tiktok link is in the bio we'll be live again in a little bit with some more luminance relic cards for the jets michael carter uh cowboys dak presscott bangles teahiggins seahawks for shot penny 2049 on that one numbered uh colt starvin taylor and bearish on our mooney autos now you had ravens tyler baity numbered 15 to 50 jets breeze hall 17 and 25 jamie ruckard year one for the jets bluing year one johan dottson washington 56 of 100 rookie auto um raiders zemir white one sec guys i need to fix my stack before we keep going here raiders zemir white 195 at 349 david bell browns 292 of 349 giants plexigovirus 84 of 99 davante or uh yeah davante wyatt 203 of 349 for the packers giants evan neill 23 of 349 tyquan thorton patriots eight of 100 bailey zapp patriot 36 of 100 red ink year one carla gordon for the um for the bears year one there sam howell washington 67 100 wandale robinson year one for the giants jake fergusson 287 of 349 cowboys kyle hamilton ravens 28 of 100 uh james cook year one for the bills and jerome ford browns 21 of 100 now it's gonna do it thanks again everybody more luminance coming up


Hi everyone, Joe for Jaspy's casebreaks.com here coming at you with 21-22 21-22, Panini, Prism, basketball, six-box, hobby edition, pick your team at number four, all-card chip And this is the second half of the case that we popped open. First half I popped open last night And here's the second half, two-night Very big thanks to this group for making it happen. I appreciate it here on Tuesday the 12th If you have a little rooftop next to your name that means you won that spot in the filler the team random But most of the teams were picked up straight up, which I appreciate Last spot mojo goes to ten of that with the Utah Jazz picked up those teams before we pulled them for the filler All right, so I mark those four so we know they're from the same case. Good luck a lot of summer league action happening So it's time to see some of the youngsters in action Summer league where hope still springs eternal has anyone been to Las Vegas for the summer league I feel like that's something that that I always wanted to do but I've never gotten around. I was kind of forget about it But I was like, hey, I don't mind going to Vegas. I don't mind seeing a little basketball I don't mind seeing seeing the up-and-coming the stars And then I forget I think like my cousin's a big basketball fan. He's he's gone a a couple times I think there's like fun like little ticket packages where you can Buy a pass for the day or something like that and see whatever random teams show up There so One of these days maybe next year. All right, Drew. What's going on? Yes? I would love to pull some big ones tonight Especially out of this prison basketball. All right. Good luck everybody box one of six Second half of the case that we popped open last night All-card chip it's Lou Gadanjic red wave Kauai Leonard hyper Davion Mitchell Steven P with the Kings got the Kings straight up 20 Ku Klux Silver and look at this drew holiday out of five nice. I Was like, I don't see these black and yellow parallels too often. That's got to be a low number. It's out of five that is for That is for Shaun T with the Milwaukee Bucks out of five out of fives and under get the train whistle All aboard the big hit express Good start Tim Duncan Speckle mindset Zion, let's see what kind of mindset he has this year by all accounts. He seems to be Ready to go LeBron James Silver Nice one for Adam and the Lakers Now I'm not sure I'm not sure what this goes for but I'm sure it'll be maybe more than you might think Jalen Brunson signing signing a big deal with the with the Knicks Good for him. Well, Moses Moody little Isaiah Stewart. Blue Reveals an elven haze old Washington bullet elven haze That's right drew you did call it Called the big out of five hit Alan Murdock won the Wizards in the filler Won a filler for a filler Got randomized the Wizards and you've got Alvin go on your way Moses Moody going to Sean Maddock Sean M and the Warriors Isaiah Stewart blue 106 out of 199. That's for Adam Kelly Detroit to buy his Harris red to 299 Through just watch another break live another break live. What do you mean other people do this? That we were the only ones Do this Duncan Robinson How about Kobe White and Patrick Williams? How about him? Kelly over a junior to 199 Got it. Got it. Got it. Got that treyland stained glass Gemma 10 love to hear it It's Patty Mills Clay Thompson to 299 Franz Wagner for Orlando. That's gonna go to Matthew Shara Clay Thompson goes to the Warriors Sean M You know what? I don't know. I've actually I haven't heard anything about him summer league-wise Rex I'm not sure what their what their status is Nice, Kate Cunningham, Stefan Marbury 21 out of 99 nice purple parallel a color match going on there for Phoenix will Kai Jones Hornets rookie hyper for Alan and There's his teammate Melville Kate Cunningham nice, Kate Cunningham Detroit that'll be for Adam Kelly. Maybe we can find some parallels of him some silver Actually, I think this is one of the best centered ones. I've seen two So pretty nice and a redemption To 149 Raptors No, not yet Rex not yet, but they will That's for Toronto. That's gonna go to Matthew and he guesses on this redemption I'll give everybody the time it takes me to open this The rest of this For you to guess who Bennett is hoping John Moran. He says he's a redemption Rex guessing Charles Barkley bonus points Rex if you guess if you guess the correct team Charles Barkley team Haines has got to be John and Rex saying son's edition of Charles Barkley. Good luck everybody. It is It is a it's a rookie signature. So not John Moran, unfortunately Bennett, but for somebody else. Congratulations Jay a M ES James James book night for Charlotte, I believe Book night is is Charlotte indeed. So that'll be for Alan and the Hornets Picked up the Hornets straight up Yeah, sorry Bennett did get the heart rate up a little bit of the lesson, but thankfully plenty of boxes to go Second of six. Good luck everyone Full of depot Larry Nance jr. I'm gonna Diallo 006 out of 299. That's right. Jopie Who says Rex doesn't hit Adam with Detroit? It's the Diallo. I like this. I don't know what the value is on this, but I Do like the photo of Yanis right here with the two trophies Milwaukee with that one. That's a good-looking card for Sean T Rudy go bear 109 out of 290 former former jazz. Now the jazz are thinking they will They're not actively trading Donovan Mitchell, but I think they're they're more open to it now. Oh Sensational signature Josh Giddy Okay, see Han picked up the thunder straight up nice And I think he did look good in a summer league game yesterday I want to say that I think I saw him and chat his new teammate being interviewed so far the it's just summer league but The people are liking what they're seeing here's like another Cade Cunningham as well Another one with some decent centering here. It's for Adam in Detroit other Davion Mitchell base for the King Stephen P and Juan Toscano Anderson red wave rookie This is warriors edition. He's with the Lakers now Sean Maddock. We'll probably get some decent playing time There's Isaiah livers as well Detroit Pistons if you're the jazz Do you do it do you pull the trigger on on trading Donovan Mitchell Miles McBride rookie silver That's for the Knicks Eddie in the Knicks 54 out of 299. There's a Pelicans. That's gonna be for Matthew Speaking of the Knicks Eddie, maybe we'll find him in here somewhere. I think in the first half we had pulled a Quinn grunt Quinn grabs, I think silver or something like that or was it not a graph I forget but But he actually had a pretty good summer league game last night I think like 24 points or something like that Mr. Main O'Neill Purple ice for the Pacers. That's gonna be for Jeremy Rex is asking a summer league spring training or is it certain players? Just certain players. It's usually rookies and maybe some second-year guys or G league prospect type guys So this is before regular training camp. I think People kind of consider it to be like a Like a rookie prospect training camp Before the main training camp later on The Andre Jordan that'll be for the next that's gonna go to Matthew the Bobon goes to Dallas and The Franz Wagner Orlando, that'll be for Matthew There's a base LeBron James There is Delano a Benton Toronto that'll be from Matthew S Matthew Silver scene that is to Matthew S is here Rajan Rondo Lakers edition in a Clippers uniform out of 60 It's for for the Lakers. That's gonna be for Adam Moses Moody for Sean M and the Warriors Got a deer and Fox Silver another deer and Fox Silver and an Ursan Ilya Silva That's for a ton of up last spot mojo strikes again 70% of the time last spot mojo hits 100% of the time. All right, another box Personally, I think that I think the jazz should just Pull the root cord I mean they got they got a huge haul back for Rudy Gobert. Just a shocking A shocking amount So if they're if they have all those assets You know, I say See if you can get another big haul for Donovan Mitchell Maybe start building a rebuilding To do a pretty quick rebuild with all those pieces But it says I'm changing your mind. You're gonna take any Grizzlies card And thankfully we are not even halfway through the break We're only two boxes in Play to the whistle Bennett. Not not time to get nervous just yet Not even halfway through Only two of the six boxes in we're a third of the way through I feel like when we're in the final third, maybe these two boxes, that's kind of where Or maybe if you're getting nervous That would be the time to be like, all right. I gotta get something here. Good luck Where does the Andre Aiden end up? Mason Plumlee Silver the Sunmu Hyperparallel for EA and the Bulls Zach Levine and Franz Wagner Franz Popping early here. So we got that giddy earlier and another nice rookie auto Franz Wagner Wagner really technically That's for Matthew Shirah and the Orlando magic This is another team that could get good really quickly another young group Nice white screen Nicola Jokic Silver for Donghyun and Denver Bradley Beal red wave And behind OB Toppin. We got a nice Bill Russell Not a 25 mojo refractor Boston Celtics Chris Butler Won that spot in the filler Someone mentioned yesterday that this is kind of like an optic preview Baird inside this prism here Chris Middleton red wave and Vinsanity 64 out of 1 at 99 Speaking of Chris Middleton red wave, I think he saw a report. He had wrist surgery early July Feel beyond the men assume Trey Mann rookie silver. All he's good to see a rookie silver And base Scottie Barnes Toronto dyno DNA for Matthew Silversea reigning NBA rookie of the year No sophomore slump. Let's see him do well Rookie silver Trey Mann. Okay. See that'll be for Han Daddy is young to 199 There's a John Moran insert fireworks insert, which looks really cool Some of those can be lower lower numbered as well. That'd be nice to see Daddy's young for San Antonio. That's going to go to Jesse Jaylen Smith to 299 Phoenix That'll be for will Reggie Lewis to 299 for Boston Another Davion Mitchell for the king Stephen P Reggie Lewis for Chris and the Seas Some Kaminga And a Bobby Portis autograph For the Milwaukee Bucks. That's going to go to Sean T Nice, and I'll do a little uh Autograph recap at the end as well. Kaminga goes to the Warriors Sean Matic Kimalajuan red wave got a rookie hyperparallel Jason Preston for the Clippers It's going to be for ten of us All right halfway through this half case break three boxes in three more to go Good luck. NEM on ESPN top headline sources Jazz change tune listening on Donovan Mitchell Pelz rookie Lydell has a torn ACL out indefinitely That sucks There's Middleton has the wrist surgery NBA heightens the penalty for a transition take foul by the way Grizzly score 120 He's tying a summer league record Brogdon Gallinari willing to sacrifice to help the Seas On hoops rumors.com I don't know if there's any other Additional news here No, not too much some guy signing some deals nugget signing Deandre Jordan We're just waiting to see Youngster kind of get out of summer league without a uh Without too many injuries Then I think it's Maybe another month or so off before they start Before the NBA starts ramping up for the actual Training camp with everybody A lot of the rosters will probably be set at that point Another Jonathan Kaminga for the Warriors Sean Maddock Serge Abaka red wave those aren't numbered by the way nor the hyper is but They look really sharp To 299 So the Bay red wave moses brown 75th anniversary edition kind of see that diamond NBA logo print In the background Cool looking parallel Dallas that's going to be for Ryan Let's see some fireworks for Zion this year And very disappointing for Zion Collectors, but hopefully Hopefully we'll see him uh Start the season off really well Almost for a second there with the gold Corners, I thought that might have been an out of 10 But it is not Galen Green, Houston base card for Han. He's got some upside Do we like Cameron Thomas as well or scottie barns again in behind fearless? Giannis is Brandon Clark nine out of 25 Grizzlies That is for Bennett who got the Grizzlies straight up And on the board with an auto nice low number on that too Good young team. You can really call that a more of a complete team, right? feels like Another scottie barns for Matthew Silverstein and Toronto and I guess if Katie and Kyrie end up leaving Cameron Thomas might be Might be another one another one left. Yeah, let's see uh We've been seeing some of the scottie barns is we've been seeing some of the nice rookie names Start seeing some parallels matt silverstein saying wants a mobly parallel Or barns parallel would be nice. We've seen a couple barns base cards Eddie in the nicks to 149 purple ice Dramon silver lamello fireworks anthony davis on a graph And that sort of that vintage older school prism design Lakers adam compliment with my Lakers All he has to do is stay healthy I mean at this point Lakers fans like myself are being like We're talking about like man. If you only if you place 50 games, we'd be happy Maybe the back half of the 50 And the playoffs People forget that It kind of sucks people forget that when he's uh When he's on He's incredible Anyway, there you go adam Hopefully stays healthy knock on that that's not wood, but Best of luck. I'm rum room for anthony davis. I don't like seeing guys injured You know, I mean he can't carry it. He's not getting any younger High jones rookie silver Bradley biel blue to 199 Now those flashback autos look pretty sharp. I think matt Bradley biel for alan murdoch and the wizards Always good to see a rookie silver. Kai jones. That's for alan and charlotte Charles basie rookie red 239 out of 299 Matthew charl with the sixers Team act or lando addition Two boxes to go So math is saying a lot of redemptions on the flashback autos So Feel feel I feel a little lucky that we got that one live I think that's what that's what everyone's hoping. I think the people who have been bullish on xion rex Yeah, it's kind of like how otani's value dropped significantly after some injuries and then One monster season and an mvp later his cards. It's shot back up And he's having another strong year this year too. So that's definitely helped Keep them keep that value strong So let's hope we see the same thing with xion poor new orleans pelicans fans. They had anthony davis Who was pretty amazing went healthy? But just couldn't stay healthy And people just look up xion williamson in-game highlights When he's playing and he's he's pretty awesome And I think he surprised some people by being able to play different positions on the floor as well So He's just got to stay healthy Good luck gang. Here we go second to last box patty mills purple to 99 fearless insert ja marant some of these cards have like a weird Benton a lot of them don't Can't see a rhyme or reason just yet j crowder pulsar not numbered rj berry blue ice blue cracked ice is numbered 125 We had a nice year last year Another jalen green for houston. That'll be for hawn in houston And eddie has the nicks Right. Yeah, I think I think that makes sense. You can't really have too long of a layoff Otherwise the hobby may not follow All right, there's the autograph behind emmanuel quickly and Nikola Jokic is jalen suds nice. So we had the fronds wagner A few boxes ago All right a couple boxes ago and now his teammate jalen suds matthew chara Won the orlando magic in a filler nice And there's nikola jokic donkyun 107 out of 149 with the uh Another mvp for him Bradley biel fireworks silver grin fours car anthony towns lamello prismatic Evan mobily silver. How about that?  I think a lot of these centering has been a little rough, but It's better than most not as good as some there you go. Matt Matthew silver scene Evan mobily silver Cleveland this is for you And a lorry market. Yeah centering's been a little difficult here, but But uh, that's not as bad as I've seen some other ones Because I've seen some other ones where where that's pushed out almost to the end there, but I don't know if they're all like that then I guess that kind of evens out in the end when when the grading process happens Still nice though looking forward to a big season for him stuff curry silver Clint capello red 299 But hey parallel is a parallel Darius basely blue ice to 125 Rex is saying it's kind of like schwerber. He's been having a pretty good Years, but his card's still fairly cheap for how well he's doing well I wouldn't quite compare Kyle schwerber to Zion though Oro tonic She didn't dance to 199 They're definitely not at Schwerber's definitely not that level And we got some private signings kenyon martin jr on card autograph Houston hun with houston. Here's a scotty lewis as well different scotty for the hornets allen Private signings they're watching you they see your every T mac red wave jason tatum hyper And a base evan mobley. Oh man. See if we put that centering on this card And pop it on that silver Come on panini Right give me that centering, but this parallel It's a lot better So I mean still a little off, but it's kind of hard to get the the right angle, but I think you can kind of eyeball it But hey Mobley's nonetheless Matthew All right last box All right fingers crossed Good luck everybody Yeah, just have someone. Yeah, just have someone trim it easy. He'll never notice the difference Hey, I would not mind if evan if evan mobley turned out to be like chris botch People forget just how good I think people forget how good chris botch was Is he a hall of famer? Some people will consider him a hall of famer borderline at least If it wasn't for the uh, I think a heart condition, right? Kind of derailed his career probably stripped a good handful of Stripped a number of years a lot of years from his career. He might still be able to be He might still be playing now. I want to say If he was in tip top shape, how old is he? Poor chris botch chris botch is 38 years old So, I mean he might You know he's a year older than lebron. Um I don't maybe he might not be playing now, but he would have only maybe just retired You know or doing a carmelo anthony kind of role playing like 10 15 minutes a night on a On a playoff team or something like that. All right final box. Good luck Yeah, if al whore for still playing let's see chris botch. Sorry. I know it's the last box here, but Try making some comebacks But yeah issues with blood clotting and stuff like that But 2019 Or 20 2016 20 for the 15 16 season was the last Full season he played a foolish season that he played I mean heck there could have been another You know at least five seasons of High quality basketball There's kaminga to 149 purple ice For shon m and the warriors Stay healthy anthony, please capela Justin holiday to 199 for the pacers. That'll be for jeremy h shake shake shake shake milton bobon france For orlando. We got red to buy as harris to 299 Frans wagner going to orlando Matthew shura the other math us Tobias harris is going to go to math shura as well with the sixers kade cunningham silver desmond bain silver trey young and a joe weisskamp rookie autograph for san antonio That will be for jesse and the spurs and kade cunningham detroit Pistons and it'll be for adam kelly trivia. Who is the oldest player to ever play an mba game? Just to play Not even start I say bill russell Greg brown red wave. Excuse me rookie red wave for portland. That's going to go to tong kyun husky dolvin saying dav cohen's justin's guessing joan howard Rex better have an answer for us by by the end of that davion mitchell steven p and uh moses moody rookie silver That's for shon maddock and golden state We got a where does kevin doran go or does he go anywhere 78 out of 99 net's purple parallel math ushura josh giddy nice jalen suggs nice jalen suggs for orlando that's going to go to math ushura the josh giddy base Will go to han and o k c It's a cool. I just like the I just like the look of this card Sean with the milwaki bucks john collins cam johnson 33 out of 49 nice little color match the orange with the suns it's going to be for will in the last little bit here Bruce brown reveals a moses moody autograph Rookie silver auto warrior shon maddock. That's for the team. He bought straight up And there's bruce brown for the nets Right here and your answer to the trivia question. Thanks rex the oldest player ever to play in the NBA was nat hickey Like a like a little hickey on your neck who played one game in the 47 48 season when he was 45 years old 45 years And 363 days old he was almost 44 46 that is But I mean back in the 40s You were kind of playing against like milkmen, right? So Um, kevin willis johnathan kensing is the oldest modern day player. He thinks I don't know back in the 40s It was probably like someone was like, sorry. I can't I can't take my my meat packing job off They got they're making me work tonight so, uh Well that that wasn't out of five by the way, and I saw a recap right here So, uh, we'll get we'll get old nat hickey. He's got some time Let's get him into a game I'm joe. Oh he was a coach and he activated himself That's even better. He's like, you know what? I'm going in the game All right, everybody. That was uh figure team four. I'm joe. I'll see you next time for the next prison basketball break. Bye. Bye


It's no secret that a lot of little kids try to act like adults or do grown-up things before they're of age, whether it's using a fake ID to purchase age-restricted substances or just lying about your age so that you can play an online violent video game. But one of the most common things that I'm seeing these days with kids trying to bypass age restrictions is them signing up for social media accounts especially on Meta's platform. Now Meta actually has a rule all across their social media sites from Facebook to threads that a user must be at least 13 years old to have a profile there. So that's still technically a kid but it's hopefully old enough for them to not be so naive and know about stranger danger and the risks of them giving out their online or their personal information online. But despite these rules and Meta's supposed enforcement there's still millions of underage profiles and when I say underage we're meaning under 13 on Instagram and on Facebook. Now this is something that I actually knew a little bit about before I started the research for this video and I knew this from watching predator catching content on YouTube because many of those decoys who are posing as underage kids and chatting up these predators are doing so on sites like Facebook and Instagram.  Now of course that kind of evidence and observation is just anecdotal at best but now we actually have unredacted court documents showing that Meta was not only aware that there was a large number of under 13 year old profiles on their platforms but they actually coveted and actively pursued this underage demographic despite Meta's own terms of service. So if you scroll down a bit this is a chart that was created internally at Meta so you know this is public now because of the courts and discovery and stuff like that. But this chart was likely shown at meetings with top brass at Meta, top executives and it's showing monthly active people penetration okay so that's what MAP stands for in this case for different age groups. Now keep in mind this chart here is from 2016 okay so keep that in mind when we're looking at the year of birth here in the graphs key. So this clearly shows that Meta wanted to penetrate into these demographics of let's see I guess these would be 12 year olds for profit just as they do with every other age demographic and race and gender and everything else like that on the platform but things get even worse. So another related chart here again an internal chart from Meta that was shown to their top brass shows that in 2016 approximately 20% of people born in 2004 were using Instagram on a daily basis and another thing that I find very disturbing by this chart is that you can see how addictive the platform becomes for people that are using it at younger ages. So if we follow the trend of this chart here back in 2015 when these users would have been 11 years old you know people born in 2004 only about 10% of them were daily active users on Instagram but of course that doubled to 20% when they turned 12 in 2016 and then it doubled again to 40% by the time that they were actually old enough to use the platform at least old enough according to Meta's definition you know their own terms of service and you know this kind of stuff does not happen by accident in fact I would state confidently that Meta knows exactly what they're doing by getting users hooked on the platform hooked on social media in general when they're kids because this is the same exact kind of marketing techniques that cigarette companies were using back in the day okay so big tobacco they knew that addiction to their products was going to be strongest if people were trying them you know cigarettes and dip and stuff like that but you know mainly cigarettes when they were children and here we're seeing the exact same effects with social media the people that are getting hooked on this the most are the people that are using it before they're even teenagers and if we scroll down a bit more we can see that it's not just you know happenstance okay that a bunch of young kids started using the platform we also have some screenshots from ads that Meta ran so this one right here is a screenshot from a television commercial for Instagram that aired in April of 2023 and then this one is actually pretty easy to find on YouTube that they had in October of 2021 so both of these commercials you know both of these screenshots they appear to be showing well they're definitely showing children but the age is a little bit ambiguous right they could very easily be younger than what is allowed on Meta's platform according to their very own terms of service and with this one in particular I'm actually going to show some footage of this commercial because I was able to track it down you know I find it really ironic how in this commercial featuring the young hockey player it's showcasing parental controls on Instagram like yeah you could see everything your everything your kid does on Instagram you can see everyone they interact with and you can see how much time they spend on it ah parents it's nothing to worry about let your kids sign up to Instagram let us track everything they're doing and scrape their data and let us use targeted advertising against them but again we can infer from the data on the charts and just from our own common sense that most parents are not actually going to use these parental controls in Instagram or anywhere else on the kids devices in fact I would be surprised if most parents out there with teenage or younger children are even aware of what social media accounts their kids have in the first place or like even aware of what apps they have on their phone and honestly I think better parenting is really the only solution to this problem I realize that there's deeper societal issues at play here like single parent households or households where both parents work and so it's harder for them to watch their children this closely but please don't fall into this naivety that corporations are going to police themselves or that the US government is going to fix it okay don't believe just because we have this court document where 30-something states are suing meta because they're not protecting the children that anything here is going to change okay big tech and big government are in bed with each other and personally I don't even think it's a good idea for anyone who isn't a business owner and promoting their business to even be active daily on social media in the first place because of the risk of the scrolling addiction and the general exposure to toxic content okay I mean we can see again from the charts that it's not just kids that are getting addicted that are on Instagram every single day it's also people who are in their 20s and when you look at how many predators are on these platforms that are run by meta and how many cp traders are on there blatantly advertising like they'll literally show clothed images of kids that are known to the national center for missing and exploited children and then they'll like right in the description or whatever telling you to hit them up on telegram or some other encrypted platform to buy pictures and how meta just not only doesn't do anything about this but they actually feed predators more of these underage profiles and more of these cp trading profiles with their algorithm just like any other kind of content when you realize that this is what's happening on these sites I just can't imagine why any parent would still want their son or daughter using the same platform


Hello everyone, this is Amit Dhan and on this channel you will find financial travel and more videos, if you like what you see, please do the thumbs up and consider subscribing. Thank you. Hello everyone, this is Amit Dhan of AmitDhan.com. You are watching one of my Australia travel videos and today I am taking you to the Southern Cross Railway Station in Melbourne, Australia. The Southern Cross Railway Station is located in downtown Melbourne and it is Melbourne's third busiest station. 15 million passengers visit the station every year and this is expected to reach 60 million by 2050. The Southern Cross Railway Station has 16 platforms, 22 tracks and 2 concourse levels. The Southern Cross Railway Station is not just a station, it is a state-of-the-art mega station facility that has a railway station, shopping centre and a bus terminal. The shopping centre is made of over 100 shops, bars and restaurants and it calls supermarket. If you like what you see, please give it a thumbs up and subscribe because there will be more travel videos coming up so I'll see you shortly. Thank you.


So, welcome back, so you are, you have already a question, Roa? No. Ok, so welcome back, so it's a pleasure to have Silvina here to give this seminar in spite of, well, she will tell us in spite of what. And Silvina is a scientist from, physicist from Argentina. I think she is a role model for also female scientist. And I think she will say something at the end about this. And today she will talk about her research on biological systems and information transmission in cell signaling. Thank you, Silvina. Ok, thank you Mateo for the invitation and thank you all of you for being here. I know that you just had an exam so it's not so easy. We can go through the talk without pain. And so I tried to prepare it. My name is Silvina Ponce doson, I am from Argentina. I work at the University of Buenos Aires. And I structured the talk in sort of two parts. The first part is the introductory because I knew this was part of the course. And then the second part is more about the work that we've done with Alejandro Coleman Lerner. And Alan Gibre, Alan is a PhD student who is about to defend his thesis in a few months. And Alejandro, he is a biologist who works on systems biology at the University of Buenos Aires. So what is this thing about information in cells? Basically cells have to sense their environment. All the time. And they have to react to changes in the environment. And in some occasions, on many occasions, those changes of the environment, they are related to changes in the concentration of certain substances. Let us call them effectors because they generate the response of the cell. So how do cells detect those changes in the concentration of the effectors? When they do that through binding, cells typically have on their membrane some proteins that are receptors with binding sites to which these effector molecules can bind. And those effector molecules once bound, they generate changes inside the cell. This is a scheme. So this is the receptor. The receptor is a protein that transverses the membrane of the cell that's called a transmembrane protein. So part of it is outside the cell, part of it is inside. And that's why it can relate what's happening in the outside to what's happening in the inside. And so once something gets bound to the receptor, the receptor can change its conformation and it can induce changes inside the cells. And that generates what is called a signaling cascade, which is further changes that induce changes and so on. This is an example that I don't want to go through, but sometimes you increase the production of some substance or you activate some enzymes or in other cases you repress the activity of enzymes. And on many occasions what happens is that something changes inside the nucleus, which in eukaryotic cells that have nuclei and that have their genetic content in nuclei. And so things change in the nuclei and that induce those changes in what is called gene expression, so the production of proteins, and that's how the cell mounts its response. One example is yeast cells, which is the system in which Alejandro works on, he does experiments. So yeast is with which you make a beer, basically. And when you have enough nutrients, those cells just divide, simply cell division and grow in that way. But if you deprive them from nutrients, then something happens with those cells. They become two types of cells, they start to differentiate two types of yeast cells so that they can mate and reproduce by mating. And so how do they mate, where they sense the substance that the opposite sex cell sends out to the environment? And so, for example, this mat alpha cell secret, this substance, which is called epheremone, that is called alpha factor, that is detected by the others that mat A cells. And mat A is cell secret and A factor. And when experimentally what they do is they grab this mat A cells only and they put them in a dish. And then they throw pheromone, the pheromone they react to. And so you induce the response, the mating response, even though there is not the opposite sex in the system. And once they start to sense these pheromone through binding to receptors, cells start to change, they change their shape. And for example, this is a picture where you see that the cell develops this, what is called the schmoo, which is directed under physiological conditions. It's schmoo that goes in the direction of the growing amount of pheromone because that is going to lead the cell to the mating partner. And so pheromone induces changes in many genes, in the expression of many, many genes, in the large percentage of the total genome of the yeast and they develop this schmoo. And then the cells come into contact, they mate, and they reproduce in that way. And as I told you how this is studied experimentally and you can modify genetically the cells so that some of the proteins that they express are fluorescent and you can look at them in the microscope. In that way you see which proteins are expressed in response to the appearance of pheromone. Sorry, so at the beginning you have two cells, right? Two types, yes. There are two types, like mat A and mat alpha. Then they mate and then they produce, what is the end state? Well, they mix up the genomics. So they become one? Yes, they become one. Because you can have diploid or haploid type of cells. And so they become one. And I guess they choose one of the two types. But essentially so this is not growth, no? This is from two cells you get one. But the others stay alive. I mean, well, then if you produce one, that's true, yes. You produce one, yes, yes, yes. The thing is that most experiments, as I told you, which is what I work mostly with, you don't have a partner. You just have a pheromone and what you see is that they develop the schmur and they stay. OK, so I don't want you to look at all the details, but the pheromone attaches to a receptor and you have all the signaling cascade and that induces the production of this schmur. And this induces an asymmetric growth because the schmur points towards the region where the pheromone concentration seems to be larger. And this is a chemotropic type of growth. The cells don't displace themselves. They just grow in that direction. And now, so this is not just detecting that there is something in the environment that wasn't there. It's also pondering where it is at the larger concentration. So it's not exactly measuring concentration, but at least it's comparing concentrations. And so how do cells do that? In this case, they would have to measure the gradient across their own length, for example. And what are the smallest differences that they can detect through binding? So one way to approach this is through information theory. And this is something that Bill Bialek has done for many years. He has an interesting book called Biophysics. It has a subtitle that I didn't write there. And our work has drawn from all the work of Bill Bialek. And so what I'm going to tell you about information comes from his book, basically. So let me see what information is. I guess most people here are from physics, but there are some biologists, so I wanted to introduce also this idea of information, quantitative idea of information. So let us suppose that we ask a question that can have any n-different answers. What is the level of uncertainty that we have before having the response? Once we have the response, we no longer have any uncertainty. So we gain an information which is equal to the uncertainty that we had before. And a way to measure the uncertainty that comes from statistical mechanics is the entropy, which is an idea of... It's related to with which probability the different responses occur. Because I know from starters that one of the responses is going to be chosen with probability one, and then I don't have any uncertainty. But if they are all equally probable, then I have a greater uncertainty. So the uncertainty is related to how probable the different responses are. And, well, all this quantitative theory of information was started by a seminal work that was written by Shannon in the 40s. And he proved that the entropy was the only definition of information that abides by some simple rules that I'm not going to discuss here. But the expression that the information has is similar to the entropy, is this one. So here, pn, I'm going to a little bit use this. So pn is the probability of occurrence of the answer. So if I do a sum over n of pn and whatever function of n of the state of the answer, what I'm doing is computing the mean of this function weighted by the probability with which the different responses can appear. So here, this function is minus log 2 of pn. Minus log 2 is the log base 2. This means that if I do log 2 to the log 2 of pn, then this is pn. This is and wraps 2 to the log and wraps the log base 2. And since the probability is less than 1 always or equal to 1, the log is always less or equal to 0 with the minus, this is greater or equal to 0. And then what we see also is that the smallest pn is the largest minus log 2 is because this is a negative number but the absolute value is larger and larger as pn gets smaller and smaller. And so this is telling me that what I am, when I'm doing this what I'm averaging here with the mean that I'm computing is of a function that is larger the smaller the pn is which is measuring the uncertainty because the responses that are less likely to occur are those that give me the most uncertainty. Ok, so this is the idea of the information. And then with this choice of log base 2 the information is measured in bits. And in one case it is particularly simple to understand. Please interrupt me if you need some clarification. If you have suppose that the number, n was counting the possible responses the lower case n and we had in total n responses and if n is the power of 2 then and all the answers are equally probable that means that each of them has the probability of occurrence 1 over n capital n which is the total number of answers then oh, that's my thing I think so when I do this pn this is n equal to 1 to n 1 over n minus log 2 of 1 over n which is 1 over 2 to the n and this is minus m because this is the same this is 2 to the minus m so if I take log 2 I have minus m minus it gives me a plus so this is m times 1 over n 1 over n also gets out and this gives me n so that this is m so I have m bits of information that I gain once I read one of the responses when I have certainty about the response and the way that we can think of is let's take small m for example I don't know 2 so let me imagine this is a the 0 1 interval this is a segment and and have I have a 2 to the m 2 to the 2 these 4 options and they are all equally probable so that I'm going to divide the segment in 4 segments of the same length which is 1 4 and so when I learn I can have this response or I can have this response or I can have this response these are the 4 responses and so a and when I hear the response one of them I gain 2 bits of information and also 2 bits is what I need to identify each of these segments because I could say well when I divide the segment by 2 the half of the segment to the left I call it 0 half of the segment to the left I call it 1 and then if I further divide them by 2 then this I put another digit this is 0 0 this is 0 1 this is 1 0 this is 1 1 this allows me also to identify each of the 4 responses so the entropy is not only the amount of information that we gain when we listen here the answer but also is something related to how much space I need to write down the answer to identify the answer anyway now let's apply this to something more biological and in biology noise and variability is unavoidable and usually it's not that you get the full answer you ask something and you get what is this what is this when certain probability is with another probability is more complicated and so the idea here is that for example if you sense the environment you might think that the environment is in a certain number of possible states let's assume that you can count the states and we call them W the states and then the cell observes some data that are called D and so what we want to quantify is how much information the cell gains when it measures this something D which could be the concentration of an effector how much it learns from the state of the environment so so this is the idea of this information gain you have on one if I forgot the idea there is this thing here so before we have we have the entropy the the probability distribution of the possible answers and when we hear one of those answers we simply gain as much as information as the initial uncertainty now we gain partial information somehow and so what we are saying is we have the entropy related to the all possible states of the environment and the amount of information is subtracting from here the entropy of what we can know about the state given that I measure I measured something so this is this here is a conditional probability is I measure for example in the case that we have four responses related to four segments maybe I could I can measure with this level of a resolution and so I measure here I know that I have to be the first digit is a zero so I can cancel out these two and the conditional probabilities this occurs with probability one half given that I know that zero and this is conditional probability one half I kind of constrain the universe of possible answers that's the idea of the conditional probability and then what you do is you are this is what happens when you learn one you have one answer but then what you do is you average over all possible answers of what you measure with the weight of each of those answers so with their own probabilities and that's this expression here so it's the I have the entropy of all this is kind of difficult to point I think here the probability of the word let's say this is the word given that I measured d and this is the probability that d occurs this is copied from the book and this should have been lowercase d but anyway you can work it out the quantities I don't care but I wanted to write down that I don't want you to actually follow the calculation anyway so mutual information is something that is useful to quantify the transmission of information also when you have noise because as I told you maybe because you have noise you cannot detect this with this level of resolution you can only detect it with this level of resolution and that's something useful in in biology so anyway so an interesting example that Bill Bialik and collaborators applied these ideas to is the development of the of the fly the fruit fly, drosophila melanogaster and so which starts with a fertilized egg and then it goes to a embryo and then from something that doesn't seem very differentiated full organism here which is the fly so well things are not so homogeneous they are these drawings these are schematic depictions where the different colors show you some substances that are involved in the expression of genes that are not distributed evenly in the embryo and they will be involved in telling the fate of the cells that will be in those regions and whether they will become what part of the fly they will become this is called positional information how does the embryo know that these part will become the head and not the one with the red thingy anyway so how the process works is that the positional on the embryo which is what is going to become the different parts of the full body of the organism is codified is represented by the concentration of substances that regulate gene expression and so there are questions that Bill Bialek asked what is the mutual information between the concentration of one of these regulators of gene expression and the position along the embryo and so they did it with various of these regulators of gene expression in particular in the book he discusses this one which is called Hunchback is a protein is a transcription factor and so well this is from Bill Bialek's book we will see a more complicated picture of transcription basically this is the idea you have DNA that has the information on how gene gene is and that is going to be used to make a messenger RNA is not plotted here and the messenger RNA is going to be used to make proteins and so and this enzyme reads the information along the gene and makes or catalyzes the union of the nucleotides that yield the messenger RNA and whether the polymerase is going to work or not depends on whether there are some transcription factors bound to the site where the reading of the gene information starts so that's the way in which you sort of regulate gene expression anyway and so what they did is they measured they expressed this protein with a fluorescent label so that they could look at it in the microscope and they looked at many embryos and this is what they found here is you put the plot is in terms of the position over the total length of the embryos in that way you can compare embryos of different lengths and also this uneven distribution stays while the embryo grows so in that way the total length of the embryo and this is the fluorescence basically of hunchback and this is a here is the explanation so there are some small dots that for me are impossible to see but those are the individual measurements I saw one once but basically what you see more clearly is the circles which is the mean over 51 embryos and then the standard deviation around the means this is the plot and this is the fluorescence image he is showing two proteins hunchback is the one depicted in red there and so if you associate this to the gene expression that you call G and you have the position X then you can compute what is the distribution of the gene expression what is the probability of the gene expression for each position along the embryo which is this conditional probability that the fluorescence are equally probable and then you can compute the probability G not conditioned I'm not going to go through that but then you can compute the mutual information that's basically what I want to say and what they got was a over two bits which is different from what used to be thought before because this seems very much like kind of distribution with hunchback high here in the first half and zero in the second half in the posterior half of the embryo but there is a little bit more information than one bit anyway so here in this example all the quantities are stationary although the process is pretty dynamic but there is a separation of time scales and so you can assume that what you are comparing here is pretty much stationary but that's not always the case with transcription factors and so for example let's go back to yeast which is a typical model system because it's very cheap and it's you have a zillion mutants, I mean it's easy to introduce mutations and observe a zillion proteins anyway so what they did was look for transcription factors that enter the nuclei in a pulsatile way and they found many that showed pulses of nuclear localization this is the images of where they took that from so it's not just that transcription factors are either there or not there in a stationary way they can also pulsate and many of them in the case of yeast and furthermore in some other experiments what they showed was that this is another example of yeast in which crazy one is a transcription factor that enters the nucleus in a pulsatile way in the presence of external calcium and what you see is that if you look at the concentration in the experiment you vary the concentration of external calcium and you look at the burst frequency and you see that it varies with the concentration so in some way you are encoding the concentration of the external effector in the frequency of the nuclear fraction of the transcription factor so this is another example and this is an experiment of a student that we co-directed with Paolo Aguilar, Nawel Tarkovski and we've observed pulses of calcium in the presence of pheromone in yeast so there examples in which you encode the strength of the stimulus the concentration of the effector into a frequency and in other cases what you do is the stimulus strength the concentration of what's in the environment is encoded the increasing concentration and increasing concentrations of something that varies more stationary in particular the transcription factor and so the question that motivated our work was why what was the advantage of having these different ways of encoding the external the environment and when one of the encoding mechanisms was better than the other and so something that is interesting is that there are some transcription factors one transcription factor that it can enter the nucleus and stay there up for a while enter in a pulsatel depending on the type of external stimulus and so it seems like this is an example also I'm going to discuss a little bit in more detail in yeast so here is what we call information multiplexing because you have one transcription factor one regulator of gene expression that depending on the external a effector which is on the stress that you put on the cell actually the transcription factor dynamics is different and the response it generates is going to be different because the cell responds differently to the different stresses so it's you use one transcription factor to modulate different genes depending on its dynamics well this is a the example in which they did the experiments with three types of stresses and they observed different types of behavior of the same transcription factor this is averages over many cells these are some individual traces and this is increasing increasing stress so glucose limitation is more limited here than here and what you see is a different behaviors and for example for oxidative oxidative stress is very different and what they saw was that a depending on whether they induce this glucose limitation or osmotic stress oxidative stress the transcription factor either had these pulses or not or it had a prolonged elevation in the nucleus whose duration lasted more depending on the intensity of the stress so there are different ways of encoding the external way the external word this example says anyway so let me discuss a little bit more about transcription to introduce what we did it's 20 minutes left sort is it? so so this is from another book it's a short version of a very famous book molecular biology by Albert et al this is the short version of it and so you have the information in DNA which is a double helix and you have these very tight bonds between the nucleotides of one strand of the helix and the other and this is what is called the central dogma which is you have the gene some sequence has the information to make the proteins so first what you do is create a template which is the RNA and then from the RNA you translate it into what is the protein now and then you have the polymerase which I've already talked about and so you have to bring apart the two strands and be able to read and this is done by the polymerase but in eukaryotes there is a question here is where does this reading start what has to find something that is called the promoter which is a sequence on the DNA that is where you have some accessory things that will indicate to the polymerase that they can start to read and but this is not so easy in cells with the nucleus because DNA is packed associated with zelium proteins and you have to unwrap a bunch of stuff to be able to read and then you have some molecules that bind that are called general transcription factors that assemble on the promoter to allow the polymerase to read the genetic information and there are also regulatory sequences that are used to switch on and off those transcription factors that we were talking about and then you have to change all these packing and this stuff is very, very complicated and so you have all these transcription factors effectors et cetera to regulate gene expression and all these mess we model in a very simple way for what we did which is what we wanted to study the differences between the 3 encoding strategies that had been found in these experiments in yeast which was the transcription factor stayed elevated for a while inside the nucleus with a concentration of increasing amplitude with increasing strength of the external stimulus or it stayed for a longer period of time with increasing strength of the external stimulus or it had these pulses inside the nucleus so we call that encoding by amplitude duration and frequency and then we used a very simple model we started with the transcription factor assuming that it could have different amplitudes or different durations or different interpulse frequencies and then produced some mRNA and which is the blue curve here and then this is the simple transcription factor that I'm going to discuss in more detail in a bit and we did not model the mapping from the external word onto the transcription factor that's something that we've done after this work and then we treated three types of inputs for what we call amplitude modulation it was inputs in which you had one pulse of different amplitudes in the case that we call duration modulation it was one pulse of variable duration in the case of what we call frequency modulation is bunch of pulses and then well we integrated the mRNA produced by the model we tried different integrals but the ones that I'm going to discuss today is when we integrated over the whole time of the simulation and and then well let me go back a little bit and then we computed the mutual information between the integral of the mRNA and either the amplitude the duration of the frequency of the transcription factor to see if there were differences so I think this I'm going to skip a little bit how to make sense of this simple model of transcription I'm going to give you a flavor of this which is we can give it we took it from the literature and they explained it in terms of their fit in their experiments but you can give a mechanistic explanation the idea is that you have your promoter in four possible states and the transition from some of the states are related to binding and unbinding of the transcription factor and then there are transitions that are not related to the transcription factor that switches on of the promoter basically so that's the way in which you can model all that mess with specific regulators of that gene the factors that are general to let the polymerase know that it has to start reading there the restructuring of the chromatin etc. we have it in this way and assuming that there is a separation of time scales so that this binding unbinding of the transcription factor acts on a faster time scale than these other transitions which are related to their remodeling of chromatin or other things that might be slower well then doing that separation of time scales is that you can come back to this simple model in which the promoter is in only two states and the transitions are these saturating functions of the transcription factor concentration so I had all the I'm not going to discuss this which is all the how you can derive that which comes from the from the separation of time scales but the idea is to make the transition you need two things on one hand you need to bind the transcription factor and also you need to activate the promoter through other mechanisms and that's what's here and also the production of messenger RNA is also modulated by so you also need to have the transcription factor bound and the promoter active to be able to make the messenger RNA anyway so we computed the mutual information and we looked for the parameters that maximized mutual information for each of these modulation types and our expectation was that we would be able to find like separate values of the parameters these parameters are the ones that are here these transition rates these KD the exponent how many molecules of transcription factor are bound and stuff like that and that would be like identifying which promoter I mean because we were thinking maybe when the transcription factor enters in pulses then it activates a certain type of promoter characterized by these parameters and when it enters as only one pulse it activates another type of promoter and we didn't find much of a difference actually we found that the parameters that maximized the three ways of encoding the external word were more or less in the same ballpark of values and also the maximum information was not that different, it was between 1.5 and less than 2 bits and so then we said well maybe well how could multiplexing work if the optimal parameters are more or less the same for all the modulations and so sorry looks like this is a large mutual information you have a four state system, right? no no no this you mean because I plotted here oh you mean the transition of the promoter? the promoter can be in four states no no it's not that a because and what do you mean by we actually clamped in two so I mean what I mean is you have four states of the promoter and so that would seem to limit your mutual information to at most two bits and you get 1.37 which seems rather large yeah I don't see how the state of the you say that the state of the promoter will limit the information that we can transfer I don't know the details but say I think this is definitely an ingredient it's a very simple model of course then in the experiment they measure more or less this amount of information and okay so maybe I never thought in that way I have to think about that anyway so let me skip this a slide because I don't want to go and so then we said well maybe there is a different sensitivity to parameter changes of the different of the different modes and there we did see differences we started to change one by one the parameters and for the different modulation modes from the the values that gave the maximum information transmission and so then we said well if the if the sensitivity is different why don't we allow the parameters to vary so that we are within 90% of the maximum information transmitted for a one of the modulation types and then within that set of parameter values we looked for those that minimize another transmission mode and so how much can we turn down the information transmission for the other mode and that well these are projections of these sets of parameters which is what we did was we compare duration with amplitude maximizing duration, minimizing amplitude then maximizing amplitude minimizing duration and so on so with six possibilities it's like having six options of promoters and and then what we found was that this is the information the mutual information as a function of the six promoters that we picked each promoter is plotted with a different color and then we computed for each of those promoters the mutual information when you have an input that is encoded in the duration this is a circle or the amplitude for these two in this case we compare the mutual information when you encode the input in the duration and in the frequency here you compare when it's the amplitude and duration here is amplitude and frequency and what you can see is that for example for duration no matter which promoter you pick you always more or less get the same information while for frequency when you minimize the transmission for frequency modulated inputs you get small transmission of information and if you are in those promoters that maximize the transmission by frequency you have a much larger transmission of information what we found was the duration was the least sensitive and frequency was very sensitive so that you could have some multiplexing in the sense that you could turn off the frequency modulated inputs for some promoters so we found promoters that could be blind to frequency modulated signals and could transmit good information for the other modulations but we couldn't find the other option and well these are some examples of how much RNA you produced but I'm not going to discuss that and this agrees with some experimental computations of mutual information done with this transcription factor in yeast that can have the different types of behaviors and what they did was they what they can change the amount of the transcription factor and so they manipulate that experimentally they can produce a prolonged accumulation of different amplitudes of they can produce pulses of different frequencies and then they computed the mutual information and they obtained and what they compared was two genes was good at frequency modulated signals and the other one that was good at duration amplitude modulated signals and what they saw was that the one that was good for a frequency when you look at the amplitude when you compute how much information it transmits for amplitude it's pretty good the amount of information it transmits and it also well is similar how much information it transmits for frequency but in the other case which is the one that is tuned to work for amplitude it doesn't transmit very well for frequency which is what we found in our theory so you have a promoter that is blind to frequency and transmits well amplitude but the other way round is not found and in these experiments they also did experiments with mutants that show that you can transmit better with a mutant so the cell is not optimizing its promoters basically it's what they say and our answer is maybe it's not optimizing the promoters because it's trying to make those that are good for amplitude to be as blind as possible to frequency anyway so let me go to a summary in the first half I hope you could follow the second half I don't know if you follow so what I showed was that mutual information subs to quantify the ways in which cells make representations of the world around them and also I showed that not only is under stationary conditions that you do that you can also use the dynamics to encode the external world and that motivated the question of our work in the examples I mostly focused on the regulation of transcription so what is the dynamics of the transcription factor and we discussed the case of the transcription factor that one transcription factor that could be modulated by could be stayed in a elevated way for a prolonged way or have these bursts encoded different stresses in different dynamics and and so one of the maybe this multiplexing is the reason to use these different encoding strategies so you save in transcription factors you have one transcription factor and you can transmit many different signals using different dynamics and well for a future talk how does the mapping from the external world onto these different dynamics operate and then from our work we used that simple model of information transcription we looked for the promoter parameters that maximized the mutual information and we found that the main differences for the different modulation input types was the sensitivity to parameter changes and we could find promoters that are blind to frequency modulated inputs but reverse situation and so frequency encoding is more sensitive so it requires a finer tuning of the parameters that characterize the promoters that respond to frequency modulation and also they need higher whoops, slightly higher binding affinities which is something that is related to other aspects of transcription so maybe the fact that some wild type promoters are not optimal from the point of view of how much information they transmit it might be due to the fact that they sell evolved to a situation in which they are not optimal they are a little suboptimal but they are blind to another way of relying information so this is my final words that I am very glad here many women and basically it's important to challenge gender stereotypes and make everybody feel comfortable doing science I mean it's not a fight here it's a collaborative way of working and also in gender violence there is a very strong movement that started in Argentina but now it's in all Latin America called Nuna Menos so not a single less I say goodbye with the picture of my granddaughter who was born yesterday here in Trieste Thank you Questions? Yes, good morning and thank you for your presentation so I don't know if you understood very well so you said that to encode the information these three words to encode the information frequency and duration and you said that it depends on the type of format that we are going to use so I would like to know if maybe I have a problem and I want to translate information so how do I behave am I going to check all the three types of the translation or they are a certain type of property and I know that in this case I am going to use this type of translation What do you mean from a theoretical point of view because this is actually with the experiments you find that with the cells you see that when you subject them to different types of stimuli different types of stresses and you look at the transcription factor it is a different behavior and this has been observed also in mammalian cells and other transcription factors so it is not a unique example of yeast which is a very simple organism so if you want to say something about a particular system you have to understand experiments I would say then we can come and try to explain why you see what you see in the experiments that's what we we were wondering what were the advantages of one over the other if there were some situations maybe by collecting information from all these observations you might eventually say I can expect in this particular case that the signaling will go through frequency because frequency usually happens when for example promoters have slightly binding affinity for the transcription factor but it's I don't know it's a little it's not so predictive the theory you know you always need to go back and forth between the modeling thank you for the talk and congratulations on your birth of your gender so my question is how did you incorporate a noise in the model I didn't understand how did you take the noise and put it in your model because I started to feel that you were getting a little tired and it was a little late and I didn't go into all the details of the model some steps are stochastic I forgot to mention that we added noise to the amplitude of the transcription factor because in the model what we do is it's like manipulating what's going on with the transcription factor it's not that we are manipulating the external word and expecting a model to say what's going on with the transcription factor we go directly to the transcription factor so we add a little bit of noise on the amplitude of the nuclear concentration of the transcription factor and in the case of the frequency modulated signals what we fix is the mix interpulse frequency but then we choose the time between two subsequent pulses from an exponential distribution with that mean and so there is that's the stochastic part of the whole thing the noise that we have in our model then the rest goes deterministic and then the production of messenger RNA is one by one we count the molecules and we just decide when we create a new molecule so that's also stochastic that step is stochastic so one part is deterministic and then there are other parts that are stochastic and the noise that we add to the amplitude is important in the sense that's the reason we have there I went through a little bit fast but the transition from these two states that we call inactive and active of the promoter this was K1 and here is the concentration of the transcription factor which is an exponent which is called the cooperative exponent and then it's a constant here that is called the dissociation constant that if you think of the if you go from well if you think of n equal to 1 and you have one transcription factor binds to something and this gives me the transcription factor bound to this and this has one constant and this another the dissociation constant this which has units of concentration and so it tells you when this function which if you plot it as a function of the concentration of the transcription factor is a hill function and where this changes behavior is this Kd the dissociation so the dissociation constant in the when you look for the parameters that maximize the transmission it has to be relatively large because if due to noise if this is too low then you can cross it very easily and you don't want that and in the case of frequency you can have it smaller and the dissociation constant is the opposite to what is called the affinity which is how strongly it binds to the the binding site that they call be here Hi, thank you for your great talk I have a question on the K parameters and it is nice you mentioned about the work of Bialek and Gregor I think if I recall they developed their work from Drosophila embryos and later on to pseudo embryos in mice wherein there is this notion of K on and K off in the transcriptional bursting dynamics in your case the case you mentioned could you give us an intuition on what they physically mean on the different the different parameters as I told you you can get them from that a little bit more mechanistic kind of description and so here is like binding and unbinding of the transcription factor is super fast and so it is sort of in equilibrium so this thing here is sort of the fraction of a if you are in this first date which I don't remember how I call it I think like this and I don't know it wasn't like that it wasn't zero I think it was bound and unbound and here you have the binding and here you have the binding so you could and then here comes over here like that and so the transcription here you assume that it's very fast compared to the other transitions and so the fraction that you are here is more or less a constant and this is related to the fraction of bound promoter either in the active or in the inactive state and then via this K1 would be more slightly slower transition which is the other kind of changes that you need to produce to put via a promoter in a situation that allows the polymerase to start reading the information and then the other then the other step that goes to mRNA which is also something that goes like this this is not a transformation from P1 into this the pace at which the transcription occurs and it comes from assuming that it can only proceed from this state when it's bound and active and that's when it's bound you need this factor and then this is the transcription rate from this state basic thank you you're welcome any other question so if not we thank again Silvina and now we are free