What's up everybody, Jason here for jaspyscaserays.com. We just sold out 22, 23, Panini, one and one basketball. To quick little one box break, random teams, the number one guys, then again guys, 32 of spots. Everybody gets a random team in the NBA. And again, just two cars guys, one base card that can be numbered or potentially like a case like a downtown. And then of course the patch or patch autograph at the bottom. So here's the dice roll, here's the customer names and the teams, let's click dice roll. It is a one and a six, seven times. Good luck. One, two, three, four, five, six. And seventh and final time, boom. Seven times, Francis down to Brian. And then seven times here. One, two, three, four, five, six, seven. Seven times, magic down to the Brooklyn Mets. All right, so Francis with the magic, Brian with the pastures, Hector with the bucks, Brian with the bulls, Anthony with the rafters, Brian with the Royals, a warrior, sorry. Michael with the rockets, Brian with the kings, Luke with the cats, Francis with the he, Brian with the nigs, Kyle with the jazz, two with the blazers, Brian with the 76ers, Justin with the spurs, Brian with the Celtics, Mark with the thunder, Brian with the suns, Alex with the pelicans, Brian with the Mavericks, Clippers, Hornets, Allen with the hawks, Michael Hughes with the Lakers, Michael L with the wizards, Brian with the Troy Pistons, Luke with the Memphis Grizzlies, Michael with the Denver Nuggets, and then of course, Brian with the Timberwolves and Brooklyn Mets. All right, trade them in the close guys, I don't think anybody was gonna trade. All right, fresh case here, guys. One, two, three, four, five, six, seven, eight, nine, 10. Let's see which number we generate here. Nine. One, two, three, four, five, six, seven, eight, nine. And these will be for future breaks. And again, just one box here, guys. All right, here we go, first one. We have a Josh Green, 10 out of 99. The cards look very sharp, guys. Very, very sharp-looking cards, as always. Dallas Mavericks. Mavericks would be Brian, last spot mojo. And there's usually a little blank here. Chet Holmgren, baby. Little napkin patch autograph to 99, but we'll take a little thunder mark with the O.K.C. Thunder. There you go, man. So again, obviously, I love Chet. I think he'll have the good chance of winning Rookie of the Year this year. But don't like the napkin patch where I feel like there could be still a big hit. And obviously there's still at least a downtown in there which could be a huge rookie downtown. Maybe even more Chet or more J-Dub for the Thunder, guys. So there you go. Those are the two hits there. And just like that, that is the break, guys. Appreciate it. Jaspyscasebreaks.com. All right, guys, come on. Let's get this jumbo super jumbo going, guys. Remember, guys, ace top prospect Jacob Wilson is our top load anything and everything to list, guys. He's a top, top tier one prospect. Rolls have a top prospect as well, which is Blake Mitchell. And then the Marlins have a prospect noble mayor, guys. Again, like I said, I got here at nine in the morning, guys, around 8.45. I'm working like 14 hours today. Please, let's get these two breaks going today, guys. At least with me, like I said, Joe's gonna be here a little earlier today to help me pack stack, but he's not gonna take over till three o'clock. And he's here from three to 11. None of these breaks sell out by three, then Joe's gonna be booked with just two breaks throughout the whole day, guys. Can't have that happen, guys. Come on, let's do this. A lot of great teams are available for a great price, guys. Remember, super jumbo's like a $700 plus box. You got six boxes in here. Chances of getting some big hits for all of these bigger teams, guys, like the Royals, like the A's, like I said. And then here as well, Marlins available still for 120. You got the A's for 239, the Royals for 150. Let's try to get these rolling, guys. I'm here early for a reason, guys. What's up, Tris? How you doing today, buddy? Talk good, Tris. Nah, you putting a lot of work on everybody.


Look, yesterday, Gilly posted something, and he said that he did this move in 94. Is there footage of that? Yeah, I'm going to post the footage tomorrow. You're going to post the footage. We got the footage. I'm going to need to see that. We got the footage. Cannon. Cannon. Did you hear about him in high school? No, I didn't. I laid Cannon up. Cannon, Cannon was a bum.  He was a big bum. I got an in one of them. Cannon, man, he already knew. Kevin Hart was a bum. Thank you, man. Kevin Hart was a swimmer.    We never heard. We never heard. We never heard. He went to the championship. He won the championship. Benjamin Franklin. Hey, Gil. Gil, Gil, Gil, Gil. Gil, where's the footage? Listen, Gil. Hey, Gil. Who's your star player? Who's your star player? Me. He was. But Gil, Gil, Gil, Gil, Gil. It's LeBron and the rest of them. Listen.


Okay, so it's my great pleasure to have Michael Levitier with me and he is one of the pioneers of founding molecular simulations as a field and we have him today and we're going to discuss a bit about where the field has been, how it's progressed and where it's heading today. So we can start a bit with how everything started, Mike. You can pretty much tell us, how did this journey start for you? Well Iman, it's great to be here. I love Sweden, at least it's light outside so you're worth it, that's very positive. So it started a long time ago, close to 50 years ago. It started I think, I thought it started by accident but I think it was actually directed by some very powerful people who knew where they should go and the idea was basically that once we saw protein molecules, had very detailed structures, it suddenly became why? It's not like grains of sand on the beach, we know why they're random. We had to understand the forces that stabilised proteins and that was the initial impetus. We started with minimisation, normal modes, then thermodynamics was an obvious thing and you know that is now 40 years ago that we've been doing simulations of various kinds. Simulation normally means molecular dynamics but I think simulation should include all the different techniques, Monte Carlo, normal modes, minimisation, overtaking to all parts of simulation. So it started a long time ago, in those days computers were very very weak, something like 10 to the 10 times less powerful than today and as a result we had to make things very simple. I guess we got the simplifications right because they're still being used today, which is scary. And when you started you had, I mean the application areas were limited due to the computational power you had and as you mentioned you worked with a lot of different techniques, big spectrum of techniques actually. So I think a wide range of techniques is very important because no matter where you are, you never really have enough, well if you could do a calculation just like an experiment, you'd still have to do all the analysis, there really wouldn't be any better than doing an experiment. So I think we always think about clever ways to capture the essence in terms of simplification or find methods that avoid injecting noise that you're going to take out and so on. So I think broad techniques are, it's surprisingly, a lot was very early in the business, the problems that we're sort of facing was one was how our protein folds, so this is a problem which is still with us. Another problem was how do enzymes work and again that one I think has been solved to a large extent thanks to computation. Often the computation is more quantum mechanical than molecular mechanical and then in between there's a whole range of issues but right at the very beginning it was clear we were worried about sequence giving rise to structure and structure giving rise to function. Other issues which I think were more studied by physical chemists in those days were things like pure liquids, water simulations were only done about the same time. So we actually did, I think we did a calculation on a protein before one had really been done on water which was kind of amazing but people were worried about solubilisation, free energies and things like that. So I think the problems have been with us for a long time, it's just taken a very long time to show that you could do something more useful. And what's really impressive with this field, according to me at least, is that it's not just one expertise that is needed. People have to know biophysics and computer science, chemistry, there's a mixture of different fields, physics, that made this realizable but I guess in the beginning it was also a lot of theoreticians that used this and experiments was pretty much orthogonal whereas today it's starting to merge. I think what's happened that has been very important is that these techniques have been come sufficiently alright, they're very multidisciplinary. I have a joke slide where I say we use methods of maths and computer science with forces from physics applied to molecules in chemistry, to biology and medicine. I probably left something out good for the economy and the rest of the world or something but basically you could hit every single thing and it's sort of true. I personally like that because I have very wide interests in this way, you know and you do many different things, maybe not an expert in any of them but I think greatly today these methods are very very widespread, they've become really accepted, it was a long time when computing was the ugly little sister of biology that's no longer the case and because they become widespread they are used more broadly and often by people who are not experts or experienced at a particular technique. Yeah so I mean in life science it's probably gradually becoming more biologist or I mean the application expert that is starting to look at this because they know the biological system, they have the questions and they want to apply it with one of these techniques and in your perspective what are the biggest achievements so far and what do you see? Well I'm just to relate to that at first point I think that if you think about a particular problem, for example now they're trying to work on the ribosome so calculating calculations on the ribosome is hard but its trivial compared to reading the literature on the ribosome which is incredibly hard, there are probably a hundred thousand papers you know people who have been doing this for all their lives really know it so what one really needs to do is either team up with what I call a domain expert, somebody who's doing experimentals, who else have the experimentalist do the calculation and the calculations don't really care what items they're working on, they don't care what system they're working on so the programs are much less domain specific than the experimental knowledges but I think it's definitely clear that any of these problems need to be linked to experimentalists who have a lot of experience in knowing where the hard questions are. I also think for the computer person it's a lot of fun to have this interaction so again I was lucky that all my life I was never in a purely theoretical department, I was always in a department of people solving problems and therefore someone would say is DNA stiff or flexible? It's a hard thing to measure but a calculation maybe can be useful so I think that is very very important to have this interaction I think today what has happened is that more and more as the programs become more widespread, easier to use, more and more the actual biologist is taking the program and using it and I think it's also very very good. Yeah that's very very I mean good for good for the field of molecular simulations you could say because it's no longer a niche area it's starting to spread and more people are trying to use it but as the complexity of the problems increase you need more types of different experts and also you need more computational power and there are a lot of different initiatives that try to make this applications accessible and share the knowledge and make sure that people can use them for applications of a relevant problem scope and yeah so forth and these are probably being done in each and every continent and what do you think there are? Well I think the applications I mean I would say the applications are increasingly bug free in other words not only that they probably also check the data so it's harder to do really stupid things one would hope without knowing about it so a lot of the expertise would need to sort of navigate through buggy codes I'm pretty sure that all the codes I know pretty much bug free the parameter sets if there's a missing parameter most programs will say missing parameter and not use the long parameter stuff like this I think this is being very important computers are such that you can now run quite large certificate calculations I think the hard part is when you want to do calculations that are sort of off the beaten path and I still believe that there's a lot of different ways of doing these calculations it's not simply a question of starting with a crystal structure and running it for a certain amount of compute time and analyzing the results and other things you can do those things require more expertise so I think there's definitely still room for certainly on the harder problems for collaboration between a computer person or a simulationist and an experimentalist in other cases I think experimentalists can actually do very valuable calculations themselves and you know one thing that's interesting about experiments especially in biology they're one of the really big achievements of the field is that they're very very good at worrying about controls and statistics because if you want to ask whether a certain cell line is toxic or not you've got to be very very careful so biologists by their nature are really good at looking at very messy systems and sometimes these computer systems are very very messy so a good experiment would say okay you got this calculation now let's see what happens if and they will do a control and that could be very very helpful I know this is something which should have been very obvious but it was only sort of in my mid-career when we were doing a certain test on seeing if it's full proteins and the results looked interesting but it was hard to tell whether they were significant and then I think my my student at the time had the idea to just randomize the sequence and as soon as you randomize the sequence all the signal went around we were thrilled but I mean we never thought it was necessary because we knew it was there but still when we saw that the signal did depend on having the correct sequence we were very excited so there's these kinds of tests are very very important and I think experimental biologists are really really good at doing this so I think one area that we can learn from them is how you do clean experiments on very messy systems and as you mentioned you often try to do things off the beaten path and in science you pretty much that's more the rule that you try to do something that is off the beaten path and start you know want to address larger problem areas that nobody still explore it's a personality issue I think you know in if you look at people there are people who walk on the beaten path and people who like to walk the beaten path and I think it's just my personality I don't know whether it's a good thing or a bad thing I know that earlier on people would say my grants are very unfocused and off the beaten path you know there's lack of focus I mean I think you need both things it's just that I'm very happy to do things where I don't know where I'm going and being off the beaten path is by definition there's no path but I think equally well there are there are other areas where you need both things I'm not saying one is better but I think personally you know I've enjoyed that breath and it's probably even getting wider than it used to be it's pretty wide so and as you mentioned also there is a lot of other fields such as like experimentalists that have a wide knowledge of how to validate something or do a do a type of anti thesis checking and you know it's difficult for example if you're sitting in a theoretical department who should you contact to get these types of you know you need to establish a collaboration to get this multidisciplinary I think also there's there's it's necessary to establish trust um you know a typically experimentalist approached by random theoreticians oh god this is this I don't even understand what this guy's talking about he's talking about correlation functions and this and this and this I grew up and I did my phd in an experimental lab I was always in experimental labs right now at stanford I'm the only theoretician in a lab of 10 faculty so at least that way they trust me I mean maybe they don't trust me but they know me and what works both ways I actually have the two projects I'm working on right now one involves a collaboration on orally polymerase with Roger Kornberg and he knows that system backwards the other is a much more difficult problem involves a collaboration with another faculty member Jolene Puglisi on the ribosome now he is he just knows the ribosome really really well and one of the problems is it's when you have a very complicated simulation how do you bring the experimentalist into it and we've actually got very involved in in virtual reality so we have a couple of Oculus rifts and the idea was we haven't really succeeded very much but you could make you walk into the system and the great thing would be is that you know the domain expert next he would say gee that that you know nuclear tide that's moving a lot that's really interesting which one is it and then you know he looked it and said oh wow at a meeting I went to three weeks ago somebody said that's a very important thing for this function so this is something you'd never be able to find by any kind of research are you able to sort of but this project is you know we we sort of got it working but well all that virtual reality stuff is still a little bit early hasn't really prime time yet yeah and I guess actually in you know many labs these days they're very good at combining you know theory with experimental but there are many more that probably wants to do this transition and I mean for example in this building there is a wide variety of experimentalists and theoreticians but if you look at this at a larger scale on a continental scale or global scale where you want to make these connections and I don't know if these big initiatives help I mean the researchers are very good at collaborating still but is there anything that can you know make the push so you can make a bigger leap there so I think there are two ways of going and a lot of of the young people today maybe not so much in biological simulation but say in bioinformatics are doing experiments and calculations themselves I think that's good I think that probably only works when the experiments which often just involve a sequencing machine are relatively easy I still think that collaboration the great thing about collaboration is that it's a meeting of two minds if I meet somebody who's been working on say a membrane system all his life his brain is just teeming with this and I can interact with him and that is really it gets really really exciting so I I still believe we know we don't want everyone to become a mixed theoretician experiment this we still want specialization I think collaboration between people who have specializations and are different always leads to more than just having two people who are both doing the same things so I think that's still gonna be important I think collaborations you know one of the really nice things today is that because of the internet and and it's very accessible it's very very accessible that this morning my wife phoned me up on FaceTime without without the picture and the sound quality is better than any international call I think I've had in the last five years she was eating her breakfast and said wow you know I can actually taste it and she called me on FaceTime and said there's an answer you know there's all you have the channels I'm just saying these things can be very very powerful I think that the key thing is to have a wide net the reason you want to have an international kind of center is that you're able to capture a broader sense of people as I said the key thing here is collaboration between people who are not the same they've got to have a nothing common that they have a common but even I would say that they would actually be on a high scale biology and then for example but it has to be quite different if you have people who are very similar then it maybe seems easier it goes well at the beginning but the potential gain is less so I think you want to be able to have you know a broad capture area and then of course bring in experimentalists as needed I think it can be very very powerful and the last thought we would like you to share with us what do you think you know from today and onwards what are the greatest challenges that we can you know try to address and hopefully solve and what timescale do you think we can solve them I mean using both computational and experimental techniques together so I would say there's a general challenge which we make progress on that needs to be solved in a more specific one so I would say the general challenge is that it's quite easy to generate a simulation we can generate big data any size you want but actually analyzing to making a movie is is always easier and not not trivial but a good movie is a hard thing to make but it's one way but it doesn't it isn't enough we need to find various ways of analysis analyzing these things as I said virtual reality might be used for different ways of analyzing numerically different projections of the space etc I think that's something which is going to get better I think there needs to be maybe more emphasis placed on improved analysis methods I think the for me the big challenges are systems where they're so big that for example a ribosome and a box of water is something like more than a million particles maybe two million particles and what makes it so hard is that firstly there's just a lot of data but secondly there's a lot of noise so you need to understand how you reduce degrees of freedom and I think one of the big challenges in any mechanistic study we don't have to find motion we want to find functionally important motion and you know most biologists think in terms of a reaction coordinate you know go from product reacting to product if one dimensional so one dimension might be too simple we need to get very complicated systems actually have a two dimensional picture but we're taking a space which might be six million dimensional like ribosome and projecting it down to two and we don't know yet how to do that very very well so I think there's real challenges in choosing the right reduced sets of degrees of freedom and that's a very general challenge so I think that also on these very large systems we have to find a way to reduce the noise we you know you can't run a full ribosome simulation for enough computer time to get rid of all the noise so we have to try and do the calculation in ways that don't generate the noise in the first case and these are techniques which I don't know we have the answer for but I think are interesting and I think that these machines are really interesting because they have moving parts and and it's not just the movement if it's productive movement when you think about a molecule vibrating it's vibrating because it has no choice when a ribosome does what it's doing it's part of the action so it's very interesting well thank you very much Michael and thank you for sharing your thoughts with us and we hope you have a great continued stay here in Sweden thank you very much your money it was really nice talking to you thank you


Hi, I'm Molly Jane, Managing Editor here at Cointelegraph and I'm talking to Maddie Greenspan this week about Bitcoin's price. Hey Maddie, so Bitcoin's price right now is trading between about $7,500 and $8,100. What's going on there and is there any sort of trend that you can see? Yeah, so at the moment we're consolidating gains, right? We've had a huge year so far, so from the lows in mid-December, several ramps already. February was a notable one. Early April, and then that massive one in mid-May. That one in mid-May is actually, it happened over several days, which was a really good sign. So it's not, you know, like some people are waiting for a Bart Simpson pattern, right? Where we see those huge surge and then a consolidation and a huge surge back, or a huge plunge. When you have that type of sustained rally, that's much less likely to happen. And what we're seeing now is a consolidation pattern up at the top. So basically, after you have that type of surge, it's very normal to get a little bit of a pullback, or at the moment it's basically cementing in people's minds, okay, this is what the price is right now. So we're trading in a range at the moment from around $7,500 to $9,000. Of course, there's that notable resistance point right in the middle, which is around $8,100. But the big one is $9,000. If we go past $9,000, if we get a big breakout of $9,000, that's one that's very likely to cause more FOMO. And obviously on the downside, if we get a break below that $7,500, but there's a lot of support here too. So $7,000 is a support, $6,400, which was the great stabilization range you'll remember from last year. $6,000 is a support, $5,000 is a very heavy support. So that's more or less where we're at at the moment. So now kind of going on to the news cycle. The Facebook coin news, the fact that they have 100 people working on it, the fact that the white paper is supposed to come out June 18th. Do you think that that's going to be influencing the market, or is that something that has already influenced the market, and if so, is the price indicating that influence? So the thing about the Facebook coin is that we don't know very much about it right now. All we really know is that Facebook has put one of their top players, David Marcus, in charge of a blockchain division, and that they're hiring like crazy. At last count, it was estimated that they have about 50 employees working on their blockchain section. After that, what we get are a lot of leaks, right? So we had an article in The New York Times, we had an article in Bloomberg, and the latest one here is from TechCrunch, which was just last week. And when you get these type of leaks, they always say sources say. So they are not saying who is telling us what's going on, but we can trust that because these are big name news sites, they verified their source. So the best thing that we can indicate is, or the best guess that we have at the moment of what's going on is that Facebook is making some sort of a stable coin, which is going to be pegged to the price of several different fiat currencies for stabilizations. They don't want to just peg it to the US dollar, but actually a basket of different currencies. And it's going to be integrated with what's that Facebook, probably Instagram, or a holistic integration throughout the Facebook. So this is similar to what Tencent has done in China, where they have WeChat, WeChat Pay, and stuff like that. So Facebook is very likely looking to do something like that. Now, question that we all want to know, is this good for Bitcoin? Is it good for the crypto industry? And I actually ran a poll on my Twitter account, which is just closing within the next, so by the time this video is published, it'll be closed already. Facebook coin is blank for the crypto industry, 55% indicated that it's good for the industry, and then 10% other, 35% said it's bad. So overall, what it looks like is people feel that this is going to be a big on-ramp for crypto, whereas people are going to realize that the Facebook coin is some sort of a centralized version, but people are hoping that it's going to be easier to convert Facebook coin into Bitcoin, and vice versa, so that it'll just basically open up the on-ramps. As far as my personal opinion, this is a tweet that I sent out, Facebook coin isn't a competitor to Bitcoin, it's a competitor to the US dollar. I don't know if you agree with that statement, not agree with that statement up to you, but I feel that this is more or less where we stand at the moment. All right, so with that news maybe coming out this summer, and I mean just other news, price surges, price falls, what do you expect to happen this summer? Do you see any sort of price cycle coming up? Yeah, so the price cycle is very much in line with what we've been saying all along, right? So that we had that, you know, here's the summer, right? Let's say from September till end of 2018, and then we had fall, right, until February, very short fall, and then crypto winter, which was a prolonged winter this year, and then a capitulation phase. So at the moment we are very much in spring, so this is where we're at, and personally I think that people should really enjoy from the spring. Summer is very hot, people get burned. I don't know if you remember what happened to Bitcoin and some of the crypto industry during last summer, there were a lot of websites that just couldn't handle the traffic. Some exchanges basically just said, okay, we're not accepting new customers at this time, because there was just so much. The Bitcoin blockchain itself got really clogged, transaction fees spiked, transaction times were out of control. So let's not try and look forward to summer at the moment. We're in spring, let's just enjoy the spring. Okay, my last question has to do with Litecoin, which is up a lot, 7% today. Can you speak about the reasons as to that? Oh yeah, definitely. So Litecoin, we're going to be seeing a halving in Litecoin, as we know that means that every new block is going to see half the rewards, and this is basically influencing the supply, so we're going to be seeing half of the new supply coming, so inflation is basically goes in half. Litecoin has been seeing also, as far as the price is concerned, I believe that Litecoin is really what kicked off the current bull run that we're seeing, so since the mid-December lows, Litecoin has been one of the best performers, second only to really Binancecoin, as far as the large cap altcoins, and it's not seeing a consolidation. Just yesterday we saw a new, fresh, yearly high, very nice trajectory in Litecoin, as far as the technicals are concerned. What's more important, what people are missing, I believe, is that yes, we have the halving event coming up, but Litecoin's fundamentals are extremely strong. If you think about what gives a crypto its value, a lot of that is derived from the network effect, so how many people are actually participating in the network and how many people are using it. This is something that I feel on social media all the time, because every time I write anything about Litecoin, I always get very positive responses, and everybody's always extremely happy about it, but if you want to look at the numbers, you can actually see this is the number of transactions that happen on the Litecoin blockchain, so there's the obvious spike in crypto summer, last crypto summer, January 2018, but what you'll notice here, this is something that's incredible, is that the transaction level never really came back down to previous levels. It just had this great stabilization at around 20,000 transactions per day. What that told me at the time was that we basically created a floor on the transaction level of Litecoin, which created a floor for the entire crypto market, and basically held it up, showing that yes, there is actual usage going on, even when there was apathy in the crypto market capitulation. People wanted to puke from their crypto holdings because they got so wrecked, and Litecoin was still seeing those 20,000 transactions per day, which was much greater than anything that it had ever seen before that. And then, of course, we can also look at the hash rate of Litecoin, which has been on the rise lately, no kidding, look at that, all-time highs right now, and just for comparison, we can overlay Ethereum on top of that. So Litecoin is the blue line, Ethereum is the red line. Take a look at that. Litecoin is one of the most secure crypto networks, I believe, second only to Bitcoin. So this is, if you're familiar with Crypto51 app, which shows how secure each blockchain is by how much hash rate is available to be rented. Bitcoin, obviously, zero, you cannot nice hash. Bitcoin, Litecoin is at 3%, which is less than Ethereum. The only other one is Bitcoin Cash ABC, which has also seen other stability issues according to BitMEX research. So Litecoin has two of those primary factors that contribute to valuation, which is the network effect and the security of the network. So I believe this is very likely the reason why it's been outperforming lately. That was my last question. So thank you so much for speaking with us today. Yeah. Right on. Anytime. It's a pleasure, Molly. So that was me, Molly Jane, speaking with Maddie Greenspan. And remember, this is the month of movie premieres for Cointelegraph. We're going to be premiering Multi-Blockchain Ireland Part 2, Crypto in the USA, and Sex in Crypto. This episode is sponsored by Trade Santa. Trade Santa is a cloud-based trading bot. Set it up in less than two minutes, trade multiple pairs, choose between long and short strategies, check analysis indicators, and see your results in real-time. Trade Santa works 24-7 to get you the profit you set. The platform is already integrated with Binance, BitTracks, Bitfinex, and HitBTC. The link is in the description below.