Live from London, England, it's theCUBE. Covering Discover 2016 London. Brought to you by Hewlett Packard Enterprise. Now, here's your host, Dave Vellante and Paul Gillis. Welcome back to Discover 2016. We're here at the docklands at Excel London. This is theCUBE, the worldwide leader in live tech coverage, Brian Jaco is here. He's the worldwide UX architect for Hewlett Packard Enterprise. Brian, welcome to theCUBE. Thank you, thank you for having me. User experience architect, that's not somebody that we typically interview from HPE and theCUBE. What's that all about? There's not many of us. So my role is an interesting combination of having a technical background and having been at HP for quite a while now and doing software development. And then more recently I moved into the area of design and really wanting to improve the experiences that we provide for our users. And so I began studying the area of human-computer interaction and cognitive psychology and began to realize that there's actually a lot of improvements that we can make if we just focus on those basic fundamentals and the research that's been done in this field to improve our user's experiences. How does cognitive psychology relate to user experience design? It's a great question. So cognitive psychology deals with how the humans, how we interpret information, all the senses that we get, whether it's what we see, what we hear, what we feel. And there's a lot of research that's been done that for example, just the careful use of colors for example, like something that's red, not using red unless it really needs the user's attention. That sounds so simple, but in fact, that can be a terribly distracting technique that's used if it's used inappropriately. So you'll see in our interfaces when they come up, nothing is red by design unless it needs your attention. And so that's one very practical way that we use cognitive psychology to make sure that we're using the way we're programmed, it's nothing we've been trained or nothing we've been taught to do, but the way our minds and brains sense information to use that where we intentionally use things like color and sound only where they're needed to help our users be successful. When we were talking beforehand, you said that you're working a lot with the hyper-converged systems which tend to be used in branch offices by people who aren't computer scientists. What are you doing with the interface to make those rather complex systems easier to understand? Yeah, exactly. So one of the products that we're announcing today is this new version of our hyper-converged infrastructure, the hyper-converged operating environment. And we've done a lot of work to make sure that that interface is simple for generalists to use, that's who we refer to. They may not have been trained, they've not had any prior experience and they don't have to to be successful at this platform. So what we've done is we've designed this interface to be approachable and visually appealing, but it's also very approachable where they can come in and the data that's important to them is right there in front of them and the tasks that they need to perform are right there in front of them. The other thing that we've done is we've leveraged patterns that are familiar to them from their consumer devices. So things that they're familiar with on their mobile phones or other devices, we can take those and applying. So it's not something they have to learn, but they can still be successful at this interface even though they're applying that learning to a new area. And Brian, you sit in the software-defined group under Rick Lewis' organization, is that right? So you're aligned with the hyper-converged, the one-view, the hyper-converged, the cloud environments? Yes. Okay, so talk a little bit about, let's start with actually that portfolio if we could, you know that portfolio well. So set that up, it's composable, it's hyper-converged, Synergy, HP's made a big play in that space. Give us the update there as best you can. Yeah, absolutely. Let's tie it then back to UX. You bet. So it all really starts with the foundation of our infrastructure, including composable infrastructure, where we have built the user experience around that to be something that is usable by a lot of different users, whether they be more expertise in areas like storage or networking or in the server area, but then we provide an API on that. One of the key things with the Synergy platform in the composable infrastructure is having this unified API that lets this environment be used in many different contexts. And that's what we built upon. So our hyper-converged environments utilize those capabilities that are in HP one-view in order to be able to compose the infrastructure. We're doing maintenance on the environment. The generalists don't have to be experts there, but they can still perform these maintenance operations without having to be an expert. So they were using that technology inside, but it's not something the user has to interact with. And then we move into cloud environments. It's very much the same kind of experience. Today we're also announcing the combination of Cloud System 10 along with our Synergy platform. And those two together provide some really great capabilities where the cloud operators can program the infrastructure using a very simple interface. Underneath it's accessing those same capabilities that are provided by the Synergy unified API, but they get an experience that's cloud-like where they can on demand with a self-service portal and get access to these resources very easily. So we're seeing an evolution of what used to be called converged infrastructure. We still use that term, but it's sort of my mind. It goes all the way back to Teradata. That are the first instantiation of converged. And then Exadata was actually a project between HP at the time and Oracle. And that obviously evolved. And then sort of VCE emerged and HP at the time, which was essentially bolting together networking storage and compute. And now we've seen this sort of software-defined, hyper-converged infrastructure emerge. It's a huge Nutanix IPO.  So is that an accurate description of the evolution and how does HPE differentiate itself from some of those other guys that I mentioned? Right, that's a great question. So yeah, I think there is, there's a lot of elements that have been feeding into this all along where we've had, we kind of went from, if you look at it from a software point of view, we went from like runbook automation, right? And orchestration of tasks. But what we found is while you could be productive in doing that, it required a big investment in order to be successful with that. By having all the pieces that kind of our customers, when they were converging things and bringing them together, it became a pretty complex investment, pretty complex environment and a big investment to make that work. So as you've seen the transition into composite or first converged infrastructure, we began to put more and more of that into the platform. So we made the firmware more capable. We added in capabilities like HP OneView on C-Class. That was another, that was where the converged infrastructure really started coming to life when we started bringing those capabilities together. And then Synergy is the place with the composable infrastructure that really differentiates HP. We're the only vendor in the industry that has composable infrastructure. And by using this unified API, that really is what gives a lot of our customers the efficiency and the ease of use that they're looking for. Because then they can use whatever environments they're familiar with. They can use environments that if they're using a platform that's in our partner ecosystem, it's a growing ecosystem, it's thriving. If they're familiar with Salt or Ansible or Chef or Pop-It, there's all kinds of these places where they can be efficient using the environments that they're most comfortable with. And that's an important part from a user experience point of view. A user experience goes beyond just a pretty UI. It's got to be that, but it also has to fit into what the work practices are needed. And part of that means it might be that they're not using our UI. They're using automation in their own environments that they're driving. And that's a big part of what we're doing is making sure we meet the user's needs. Where do you take inspiration from in user experience? And outside of just Apple or other computers, what do you think products do you think define simplicity of user experience? Yeah, that's a great question. And there's so many examples out there that I enjoy using. And even if you follow some of the new startups like Uber has at their app, they just redesign their app completely. And that thing I mentioned earlier about making their app disappeared, I saw another writer write about it that way. And they simplified it. They made it all about just getting the writer and the driver together and removed a bunch of the information. So that's a big place that I look. I also look at things that my kids, I have kids who are in high school and look at the applications that they're using. Because obviously they'll gravitate towards what's easy, right? They're not going to make any investment. If it's hard to use, they're going to not go there. So I don't, so that's an area that I also find inspiration from. Another area that I look is outside of the whole looking at technology even. My brother-in-law is a potter. And I looked at how he designed his, and he doesn't actually start on a potter wheel. He starts with a sketchbook and sketches out ideas. And when I look at how we do the process we're doing design, that's often where we start on pen, paper, on a whiteboard and are sketching out ideas. And it's actually, there's a lot of parallels between the art disciplines and the design disciplines that we're driving right now. I also have looked at like music composers and how they compose music. It's a fascinating world on how they do it. They often, I've seen sketches on napkins of their, you know, they're putting together symphonies. It's pretty crazy how they, the parallels that we can see between doing design of an application to media that's completely different. And so each of those provide interesting points of inspiration for me. And your data points, I mean who did you interview when you started sat down to sort of develop the UX and the UI? Was it, you know, data center operators? Was it people in the branch offices? I mean, they have different needs. So how do you- Absolutely, that's a great point. And that's, there's two key principles with user experience that are important. They're going to sound obvious when I say them, but they're often overlooked. Number one is know who your users are. So you just mentioned data center operators. They're a different user than the developer who's consuming services. And so we can't extrapolate one from another. We have to actually know our users, whether they be operators or the generalists that we talked about for using those who are consuming hyper-converged environments. They're not the same users as an experienced IT administrator. And so each of them, we have to know who the users are. And then we also need to know what their needs are. And so there's a discovery process that we go through. We use several different techniques. We will collect qualitative data. In fact, right now I have a survey going where we're surveying developers in the enterprise to get some qualitative data around that. But we're also going out and meeting what their customer is doing ethnographic studies so that we actually see them work. We see what their environment is like to make sure we're designing the right project. They're the right products. I am, one of the things I observe about UX design and UI is that oftentimes the UX leads function. In other words, what I mean is you'll envision something that the users want. Something that's obvious to value to them. But the product doesn't support that function. And so the UX and UI designers put it in there and it leads the roadmap. How do you square that circle? Is it just iterative? That's a good question. In fact, when I first started in this more focused user experience role, there was a lot of groups that I worked with that were UX was kind of an afterthought where if it didn't work very well, then let's put some UX on it and see if we can fix it. That's not the way to do it, right? That's not the way to do it. Instead, UX is actually a really strategic part and I'm really happy with the leadership that the software defining Cloud Group has in Rick Lewis. He is absolutely, and Tony O'Neary goes all the way to the top, are really supportive of having user experience be a strategic part of these products. In fact, when we first started these hyper-converged offerings, user experience was called out as one of the most important things. Now we're on stage. We don't talk about it in that sense. We talk about the capabilities and how it's going to give the benefits to our customers. We started from that lens so that we can make sure those capabilities make it into the product. Now, we also are pragmatic. We have to shift products and you could get into a cycle where you continue to polish and make it perfect and not shift product. So we do have to make some trade-offs, but that's one of the great things about user experience design. It's all about living within constraints. We have physical constraints. We have things that are possible in our technology today and things that are not that we would like to do, but we just can't yet. So we have to make the best experience we can given the constraints that were given and iterate and continue to make it better. But you have to have visibility on what those constraints are. Sometimes it's fuzzy. I guess you as a product, former product guy, have a pretty good understanding of it. That helps. That certainly helps. But there's probably still a gap there in terms of what's happening in the labs. I mean, you're always saying, all right, this is the goal and that's not going to work this time. You're not going to get into this rev. So the specific question is, are those development people part of the UX design? I see the UX process as being a three-legged stool. We have the design team and I lead a team of designers in the software-defined and cloud group. There's also the product management teams who are working with the customers. They know the technology area, the market that we're going after and the competition and then the development teams. All three of those are really key contingents to make sure we're doing something that's feasible. Because ultimately we need something that has business viability, is technically possible, and has a desire from our users. And when you get the union of those three, that's what's important. In my experience, software developers tend to regard UX as being kind of an afterthought. It's pretty pictures. How do you bridge that gap and get them to think of the experience from the outset? That's a really good question. I think that's kind of a position where we've been and we're continuing to move through that to a change that perspective. But we've come a long way and now we're at the point where our development teams are looking to us and saying, okay, we need to do this. Can you help us design this experience that's going to meet this need? Versus it being an afterthought. So there's still pockets of that, but I would say in general, we are definitely moving to a place where it doesn't work that way. Because when it's treated as an afterthought, it never looks good. The lipstick on a pig analogy comes to mind, that's just not what the end users want. Why do we have such an emotional reaction to design? And I think Apple introduces the menu bar and the net goes crazy with controversy with people arguing about whether this is a good or a bad thing. Why is this so visceral? You know what, that's a good question. I think part of that is it's our human nature that we all use applications and we all form our own bias and our own opinions. And so we can all therefore become our own kind of, if it was like in my ideal world, here's how I would want it to work. And if our desires don't match up with what the companies actually do or if they change something around, then it's disruptive and we tend to not like that. You can look at every vendor when they've changed something, you know, the transitions that Microsoft has gone through on their desktop environment, every one of those is met with pretty visceral responses, as you've said. Even I was reading a study that there's a major vendor out there, when they make a change to their user interface, they know that their customer satisfaction scores are going to go down and they expect them to be down for two months before they start to climb back up. So this is not just a perception, it really does. I mean, customers, any kind of change, it takes a little bit for our users to get over that. But if we've done it well, then yeah, there's going to be the initial shock of, oh no, you know, you've changed this on me. But if we make them more productive and make it easier to use, then even if they did know how to use it before, they can be successful again. Well, that too depends on whether you raise prices. I mean, they're going to sales force it. You know, introduce lightning and raise prices and everybody was upset at having to learn a new interface.  And so how do you practically do that? Do you have these sort of weekly sprints?  So I've got a project we're leading right now, kind of the next thing that we're working on. And I have multiple meetings per week with those three contingents to make sure we're all on the same page. So part of this right now, it's early, so we're in the research phase. So I mentioned that research project we have going. But the other thing we're starting to move into is getting more product definition around it. And it's all three tied at the hip. So it has to be intentional, it doesn't happen by accident. It has to be something that we set up and we are doing that to make sure we're aligned. Because otherwise, if you get those three legs of the stool out of whack, then you end up with not getting to where you need to be. And then when do you bring the execs in? Now when do you show Rick Lewis in Antonio? Because as Paul said, some functional design, they may say, I don't like it. When do you introduce it to the viewer guys? So I'm a big fan of being open and transparent and the leadership that we have in this group now responds really well to that. So we show things earlier than maybe people would be comfortable with. I would rather that and know if there's something that's really wrong with our direction versus getting too far down a path that's not going to work and be aligned with our strategy. I've talked to a lot of hyper-converged vendors over the last couple of years and I can't remember one of them that said that user interface was a differentiator. Is that going to be different for HPE? Well, you know what? In Rick Lewis's spotlight session that's coming up, he's going to be making the announcements about the hyper-converged environment that we're giving a really big update to that we're announcing today. And on the stage, he's not going to say it has a great user experience as far as those exact words, but he is going to talk about being simple, reducing cost for management, making the system easier to use and to maintain to lower those costs. So while the vendors and our competitors may not mention that, it is something that we're paying attention to and I'm convinced that the vendor that's going to win with hyper-converged and become the market leader is the vendor that has the great user experience because in the end, that's what's going to drive cost. Is it a great user experience? Absolutely. Yeah, it's world class. It is really, really good. And so the proof is not, you know, Rick or Antonio talking about it, it's your customers talking about it. Yeah, absolutely. I'll be showing a demo of it actually today on the stage. So I'm really excited about it. I think it's great and I think our customers are going to love it. We've already done some focus group studies and gotten early feedback on it, went through some usability testing. So it's not something I'm guessing about. We know that this is going to really be received well. What is the most effective method that you have found to study user response? Focus groups have their weaknesses, of course. Observing people's behaviors on the web is another way. What works? So I don't think there is one way to do it. You have to use multiple methods to be able to do that. One way that we do it that I find is very effective is once you have something that users can use, then we have a usability lab that's down in Houston and we'll bring in users and we'll record them. So they're outside the company, they've not been exposed to this before, but we record them, we can see their facial expressions, we can see their screen, we can record what they're saying, so we know what they're doing. That's a really effective way. Now there's downsides to that one because it's limited in the number of people that we can expose that to because they're pretty high overhead in getting all that set up. But we also know from research that once you get between five and seven people going through, which is not a big number, you'll get to about 80% of the issues. If you have major issues, you'll get to them, you'll discover them through that technique. So that one is really effective. If you want to drive up more quantitative data than using analytics that are built into the applications where they can send that anonymous feedback back, that's another good way, then there's others as well that we can use to make sure we're measuring it. But that's one way that has been very effective and we've been successfully using that for quite a while. All right, Brian, well, thanks. Well, we'll leave it there. Congratulations on getting to this point, and we're excited to hear this afternoon on the keynotes. Thank you, me too. It's been my pleasure. Okay, we'll be right back right after this. This is theCUBE. We're live from HPE Discover 2016's in the Docklands at London Excel. Right back.


So let's dive into unit testing now and the first question is what is the unit we are testing and very often we are talking about classes as a single unit in object-oriented programs and functions or operations so kind of a single unit of computation and there are different strategies on how do you test In order to find defects because if you remember defect testing is about trying to cover as much as possible And if you consider for example a class, so I just use you mail here you Well Have a class name You have a certain number of attributes for example you might have something that was called an ID Maybe a name so different kinds of attributes and then you have operations do a Do be and they might have of course parameters return values and so on So the strategy for testing a class if this is our unit is to say well We should try to set Modify and Read all of the attributes right can we read them? Can we somehow set them get them modify them? Is that possible in the way we would like to do and of course maybe some of them should not be settable then? We should not test that maybe for operations we try to test them well we should call all of these operations and We should try to test them with different input essentially, so if they have parameters for example, is there Anything we can vary there We can for example put regular Kind of expected input in there, but we can also do unexpected or simply wrong erroneous cases Input null undefined or so on and then finally if we look at the class as a whole Classes sometimes not always, but if you instantiate a class it sometimes might have a state That it can change over time. So then this is also something we would like to cover. So can we vary the state so that we actually visit the class in all different States and see whether it's possible now some heuristics in particular for for kind of setting values or for input parameters are a number of things that typically go wrong and Some of them is for example zero or one Length sequences so imagine you have as an input as a parameter you have an array for example that you put in Typical bugs include Issues where the the code assumes that there is at least one or at least two elements So for example in the code you might have a you might access the first element without even checking that there is one So by writing tests that that Include these cases you very often find errors So that's the heuristic that's simply based on typical mistakes that happen Other things are for example, buffer overflows. That's again a very common thing. So you try to simply put Basically put too much into an operation Your implementation expects that it will be a top thousand elements and you put something with ten thousand in maybe something goes wrong And you will get some errors very similar is To use elements at at the end of a range so for example Assume that you have an integer value What happens in most programming languages if you take the maximum integer number and you add one is that it Wraps around and it starts at the bottom. So the question is is the operation able to handle that and Well, this is again something that happens very often that people forget about that. So by using very large or very small Elements, this is something that typically gets exposed. So max minimum elements in a certain data type Is something and then finally this is something I have already mentioned a sort of the the Undefined things so for example null undefined Depending on the programming language you use it could be different kind of things that you could put in there So that's sort of trying out different inputs that from experience often lead to mistakes so these are mistakes we often make in programming and This often sounds trivial. So when you present this it's like, yeah, who wouldn't make these mistakes But these things happen and they even happen to professional programmers that have done these over the years Simply because it's sometimes so easy to forget. So it's it's really even though these heuristics are quite basic It's quite a powerful instrument to follow these So don't underestimate them Then the final concept I would like to mention here before we we get on to the next video is the concept of partition testing so very often you have an input That you can divide into number of classes for example again, let's assume we have an integer for example for minus 10 till 0 for 1 till 100 and For everything over 100 you expect kind of three different outputs For example for this you kind of expect that the the return value will be let's say true You expect that for this it will be false And you expect that here you actually get something like I don't know voyage you might get an exception or something else Again, maybe depends on the programming language And so basically instead of having to test all possible integer values You actually have Partitioned your input space into different groups into different boxes or classes And then the strategy would be to say well you choose a typical value from each of these classes So we write a test with input minus 5 10 200 typical elements And we check whether we actually get the expected results and additionally because that's a case where arrows happen very often We check the edges of our partitions the borders So how about we put zero in we put one in we put a hundred a hundred one in Simply because similar to this zero in one length it happens so easily that for example in your code instead of a larger than a hundred You suddenly have a larger equals Mistakes that happen very often but basically suddenly instead of having to write basically a number of Tests that is equal to the number of integers you have broken this down into three classes So you basically need to test Three different values plus maybe the edge values which is much much fewer tests than if you try to somehow go through there So that's a strategy if you can apply that by basically discretizing breaking down your input space into Boxes into different classes and of course the integer case is very easy This might be trickier you have a string as an input if you have a number of different parameters So of course in practice, that's not always easy But at least it helps you to think about what might be good cases that I can test So this is a concept that is called partition testing that is Something that can help you and overall these heuristics partition testing and the strategy how to test the class That at least gives you some kind of idea where to start if you're sitting and thinking about What should I write what kind of unit tests so overall this helps you to cover a large extent of cases And now in the next video we'll talk more about this coverage. What does it actually mean to cover a lot of code?


Welcome to another edition of Dog Talkin' Coffee with me Richard Hines. Fire! Where'd it come from? Sit. Down. Down. Sit. Welcome to another edition of Dog Talkin' Coffee with me Richard Hines. And today, I just want to show you what it should look like. Okay? When taking a protection dog or two to a whole new place they've never seen before. They don't know these people, the decoys. So all of it is new. These two dogs really haven't even worked in a year, year and a half. So they're a little rusty as well. But I just want to share with you a few different things about this. Okay? One, when you have great foundation and again these two dogs have not worked that much in a year and a half. Very little. Okay? So starting young, putting all this obedience and protection work on them. You should work your protection dog in different areas, not just your house. Okay? To really solidify that your dog is fluent. It is ready at any time, anywhere to protect you and do what it's got to do. Okay? So it's very important that you work those dogs in different areas so they don't freeze up. They go, what? Here? We don't do that here. What's going on? Alright? So that's the first thing to this I'm going to show you. Is these two dogs at this brand new place, they've never been, new decoy, they've never seen in their lives. The second factor is, and I've had this question quite a bit and I understand, what is the difference between what these dogs are doing and what a sport dog would do? And it's night and day. Okay? So first of all, we know that these two will protect for real. We've done hidden sleeves, we've done all that. We don't care. We've built them to not care about that stuff. And when it's asked for, they do it. No matter equipment is on, it doesn't matter who it is, who the decoy is, strange place, not, okay? The other thing is that this routine that you're going to see between the owner and the decoy, neither one of them knew what was going to happen as far as it's not orchestrated. They had no idea going into this what they were going to do at any moment. They just threw things, okay, at the dogs. So the decoy would just walk out and I'm going to show you the whole scenario in a second. The decoy would just walk out and start acting out aggressively. And the owner didn't, he just started doing things, right? And moving around and they would just, you know, at random the decoy went, well, maybe I'll attack him now. Okay, so there's no set pattern to what they were doing here. The dogs had no idea where the owner was going to move to, when the decoy was going to attack, if he was going to attack. So the whole thing is a realistic outlook on sports where the dogs know this is the routine, this is what we do, this is when you'll do this, okay? And it makes it very easy for the dog having the routine of knowing exactly what's coming and even in ring sport, they still, even in Mondio ring or whatever, even though they try to change things up, there's routines, right? The field might change, but he's still in heel, right? When the decoys come out to mess with a Mondio ring dog, it's the exercise now, the dog recognizes the due decoys, this is what's going to happen, wait for the touch, okay? I get on an object just like I do at home and I have to guard this, even though it's in a foreign place, I've never been here, but the exercises are the same, okay? There's not much for the dog to think about, okay? It's better than the other sports that have none of that, it's just a strange field, okay? But not even close to the same as these two dogs having no idea of anything that was going to be thrown at them today right here in this scenario, okay? And hold it together and not make mistakes, not go overboard with enthusiasm and aggression towards the person, losing focus on keeping control, all right? And the other thing is you're going to see back to back that the owner just, and the decoy, throw things at these two dogs from one thing to another to go back to guard there, to attack off verbal attacks, I'll go through it in a second, but so there's big differences, okay? Between what these two dogs are doing here and what a sport dog would do in its routines. These two dogs have to think now and hold their nerves in check, not knowing what is going to happen right now and whatever is going to go on and what the owner is going to tell them to do, okay?  So phenomenal perfection, okay? The guy comes out, the dogs weren't ready for it, they go into action, the dog stayed where they were supposed to stay on the owner when as soon as the decoy made a move, they just took off and hit him, and then you get things like when they're told to out, any little move from the decoy, the two dogs automatically are allowed to attack again, okay? And then you see differences where the one time the decoy slightly moves and the two dogs attack, tells them out, then the decoy there did a good job by now telling the owner with a head signal that he's not going to move, he wants to see on command now if they're fluent with the command of attack so he stays still and the owner tells them to attack and they attack right away instead of looking for a body movement, okay? So we have many things going on here of the dogs looking for signals, waiting for commands, and all in one total routine, right? of this, to this, to this, to this, then to guard them on the floor, okay? And then to go attack when he was attacked away. The outs, the returns, okay, all in one session they're consecutive and sport dogs do not do that much, right? It's not even half of that. So you're really testing these dogs with their fluency and their duration, right? Their stability to over and over and over, wait for this, wait for this, now we attack, now we out, we're told, now we were told to return. Then we, okay, wait for a flinch, there's a lot going on and the dogs did beautifully and they haven't worked in so long, right? So just wanted to give you that because a lot of people wouldn't realize what a big deal this is and it's not just one dog, it's two dogs working beautifully in synchronicity, right? As if they're one, so it doesn't get better than that. And again, not a routine, dogs have no idea what's coming or what exercise or to go by to return, it's all a mishmash, they have no idea what's going to happen in this whole scenario. So you're prepping the dog for its mental, right? Discipline its capability of thinking on the fly that if anything ever happens in life, these dogs can stay steady, can think, stay clear headed and not fail, bail out, get confused when the time comes for them to be called on for the owner at any moment, okay? So that was a huge test there for these two and the owner wanted to see how they were going to do, just throwing this at them, not knowing the place, the people, it's just a random attack, the dogs never saw that coming, had no idea that that was coming. So, beautiful perfection and that is what protection dogs should be, right? Know their stuff so well and have so many dynamics to their game that most protection dogs are not even half of what these dogs have, right? Or the control.  And these girls have much more to this as well that was not displayed here. So phenomenal and just a lesson, prep your dogs, prep your dogs, to get their skills really good.


Uh, everyone welcome Mark. Hi. Everyone, hi. My name's Mark, and I'm a neuroscientist. This presents a small problem for me when I am in bars or at cocktail parties, because people ask me what I do. And the most accurate thing to say is, I'm a neuroscientist. But people really don't know what to do with that.  Is that like a urologist?  You want to operate on my brain? That's not that kind of doctor.  And I say, well, I use fMRI to measure the mental state of people while they're doing things. But as Rick pointed out, brain reading has gotten a bit of a bad rap of late, because there have been a lot of studies with a little bit overblown conclusions, like looking for the neural correlates of maternal and romantic love, and looking for lies in the brain, trying to do lie detection via fMRI and General Lair, who, well, General Lair. And then in response to all of these, there have been a lot of people that have said, this is all bunk. This is baloney. We need to be very skeptical about all these claims made by these neuroscientists. And there's this rise of neuroscepticism and head talks about how to detect neuro bunk. And my personal favorite is this one, which is the seductive appeal of mindless neuroscience. It's such a wonderful pun. So is this really so different than something that happened about 150 years ago? Is this just phrenology reborn? Is this trying to assign functions to bits of the brain based on the shape of the skull, which is what they originally did? Of course, we have a little bit better measuring them now. But this guy, Gaul, tried to locate particular spots in the brain that were associated with particular personality traits based on a couple of people he looked at that had bumps on particular parts of their head who demonstrated a lot of a particular quality, for example, destructiveness or inhibitedness or benevolence. And well, that just didn't work. And it ended up being more than a little racist because he decided to look at criminals and people that he didn't like and draw conclusions about brain functions based on those people's brains and how they were different people he did like. But we're not going to do that. The point is, is what we're doing now any different or not. And the only way that any of you really have to make that judgment for yourselves is to have more information about exactly what we're measuring or measuring the brain.  So hang on tight, it's going to get nerdy. So I'm going to tell you about one particular way to measure the brain, which is functional magnetic resonance imaging. And I'm going to try and get at what each of those individual letters in the fMRI mean in the next few slides. So first of all, we're not going to go into order. We're going to go with magnetic first because it's kind of a weird thing to use magnets to measure a brain. You don't think that what's going on in your brain is actually magnetic. It is electric. And you know that electricity and magnetism are related if you take a physics class. But it's not even that satisfying a relationship. You're actually measuring the magnetic properties of different bits of tissue when you're doing an MRI scan. And you're measuring the concentration of protons if you're doing a standard structural fMRI. How many of you have had an fMRI scan or an MRI scan? Raise your hands. Wow, it's like a third of you, half of you. That's good. So you know what fMRI sound like. You know that it's kind of this tube that they stick you in. And I've got a picture of that later. And Rick had a picture of one already. But what that machine is looking for are differences in the magnetic properties of different bits of tissue in your brain. So some of them have more water and some of them have less water. And this is the presence of water determines the magnetic properties or to some extent determines the magnetic properties of different bits of your brain. And so if there's a difference, if there's a lot of water or a little water, you can read that out by just reading out magnetic properties. You can make an image that looks like this, which is actually my brain, which looks kind of alarming like a submarine sandwich. It's a little, it's like in the long end. It's just kind of, you know, people have different shorter, longer brains. Mine's kind of longer. But what you really want to measure if you're trying to read your mind, thank you. Thank you. Thank you. What you really want to measure if you're going to try and read your mind is what's happening from moment to moment. And so this measure of how much water there is in the given bit of tissue is really mostly determined by whether it's cell bodies or connective tissue, which is not going to change from minute to minute. The anatomy, it's pretty static. So you need a measure of function. And one measure of function is how much blood there is in tissue. Isn't that a great image? That's going on in such a head right now.  But you can see the effects of this when you're exercising because your skin flushes.  And the same thing happens in your brain. When your brain's active, the bits of it that are active need more blood and so blood gets channeled to those areas. So this is not a terribly satisfying measure of neural function because it's not actually measuring electricity, which is sort of the currency of neural communication, but it's correlated with it, which is kind of good enough if your measurement is slow enough. And that's where I is. So what you get is this measure of whether there's more blood or less blood in a given little bit of tissue over the course of your experiment. And then it's up to you to have something interesting happen during that experiment so you can make some conclusions about what that bit of the brain is doing and why the signal went up or why it went down. So this is what our scanner looks like. And have you, remember a third of you have had an MRI so you know that it sounds something like this? An MRI scanner. That is a bunch of people I think at MIT that decided to program their gradients in the MRI to make exactly that noise. Which is just great. But the point of these scanners is that they are extremely large magnets. They're very, very strong magnets. And I'm gonna show you exactly how strong right here. So the magnet that we use at Berkeley is a three Tesla magnet.  So this is three times as strong as those jump yard magnets that pick up cars. And this is what you're going into. Oh, I get an MRI. I can see a lot of open mouths and raised eyebrows. That's good. So the force we're reading on that guy right now just at that part of the magnet is plus 300, 280. That's like what a wrench would weigh on Jupiter. Yeah. So the point is this is a very, very strong magnet. And the best illustration of this if I can move my mouth forward which I'm not sure I can. Yeah. Yeah, I think I can. So we're gonna watch all of this. So this actually was done at Berkeley. And this was done when Berkeley changed over from the four Tesla MRI coil to the three Tesla MRI coil. So the guy that walked into that shot is actually one of our MRI technicians. And these are the awesome people that I work with. The last time I went to the MRI scanner and needed to change the MRI protocol I was working with, I wanted to change exactly the way I was scanning. I needed a dummy to test that on. And usually you test on plastic bottles. But then it's just so cool that I walked in and said, then I need to do the scanatomical scan. Do you have something I can do that actually has some variations such as the plastic bottle? And he said, yeah, hang on.  And it was, you know, I had like formaldehyde or something in it. And we stuck that in the scanner, not this scanner, the new one. And scan that to test our protocol because these guys are just great. So that is a chair. And the upshot of this is that that chair ends up weighing 2,000 pounds. And there's a really good shot of it kind of breaking. We are at 700 in this case. Yeah. 700 pounds. Oh, yeah. So the closer it gets to the center of the magnet, the more it weighs because the magnetic field is so strong. Yeah, it jumps. So this is what you're in. So that ends up weighing up to 2,000 and pretty much flat lining up on the ground. But we're going to skip ahead on that. Oh, sorry. The rest of the slides are just as good, I promise. So you have this incredibly strong magnetic field because you want to measure magnetic properties of the tissue. And you can't just measure properties of things in the Earth's magnetic fields. Earth's magnetic fields are very weak and you don't get much magnetization of the stuff in your head if you're just standing around it or it's a magnetic field. So you have this extremely strong magnetic field and then what an MRI scanner does is creates variation across that magnetic field. And this is really the great good of fMRI is that it can measure this property of your entire head, the magnetism of your whole head without actually opening up your head and getting into it. So this is totally different from any other kind of, well, almost any other kind of imaging. Head imaging, you have to have an injection of some radioactive stuff that you really don't want in your body. You don't want to do it more than once or twice. I have been in maybe a hundred MRI scans and if it turns out that it is bad for you, I'm pretty much screwed. But so far the evidence says it's just fine.  There's no problem.  And that is what I've spent a whole bunch of slides here, a whole bunch of ever trying to convey to you. So it can be one of what frequency is. Okay, so you're all familiar with high frequency sounds and low frequency sounds. High frequency sounds are like this, like my fiance's voice is up there. And low frequency sounds are like these, they're all those dark matter. And so it turns out that frequency can operate across space as well as across time. And so if you have a sound wave and a high frequency, it sounds high pitched. And if you have a sound wave, it's low frequency, it's low pitched. But you can also have spatial frequency, which is variation across an image or across something that you're looking at. And low frequency things are blurry and high frequency things are really edgy. So these are spatial frequencies that I have matched up with increasing the high sound frequencies. So this is an incredibly high frequency variation in space. And the earlier ones are low frequency variations in space. The point is that any image can be quantified according to those frequencies. You can take an image such as this handsome gentleman here and you can take only the low frequency components. And you can take slightly higher and slightly higher and slightly higher frequency components. And you can get basically every frequency component up to every single pixel being different. And all those make up that image. And this decomposition of an image into its constituent frequencies is called a Fourier transform. This is a bit of mathematics that was invented by the gentleman who was the ambassador to Egypt for Napoleon, who was also the first guy to predict that global warming might be happening because maybe the sun is progressively heating the earth because of some effect of the atmosphere. This is a very interesting man. Anyway, it turns out Fourier analysis is used in all kinds of things, including image compression, such as the one that prevented you from seeing Betty Page getting naked last talk. And I'm really sorry about that. I'm really sorry about that. So anyway, the point is that you can take an image and transform it into what's called frequency space. And these are all the frequencies that make up that image with the low frequencies in the middle. So the really low frequency things are there and these are progressively higher frequencies going out from the center of that image. And what this image in the center is, is basically a recipe saying, look, I need, you know, the brightness is the amount. I need this much of a low frequency and I need progressively less and less of each of these other frequencies. And if I combine those all together, I get Ryan Gosling. And that in the middle, that is Ryan Gosling. I swear, that's Ryan Gosling. It doesn't look like it, but you know. This is exactly how fMRI works. Is it collects the magnetic signature of your whole brain, but it collected at different frequencies and it collects them over time. And so that actually is my brain and I know it doesn't look like it, but that is, that's what I'm thinking. That's like my brain right now. That's like a two seconds of my brain. So this is how you collect an fMRI image.  And this, by the way, is gonna be the most hand-wavy part of my talk and I really am sorry for that, but I just could not squeeze into 20 minutes without this. You establish some spatial frequency across the brain.  And then you collect the measurement. And then you change the grid and you collect another measurement. So you do this to the brain, you read it out in an image like that and you transform that image into something like that. And so what I'm gonna show you now is a video of what it looks like as you progressively collect a single image in fMRI and it looks like this. Yeah. Okay, so the point is that you don't actually collect it pixel by pixel. This is really weird and really cool. And it is only because we don't have to actually measure a single spot at once that we can even make these measurements. This math, this like 200 year old math, 150 year old math is what allows it to happen because we measure frequencies. We measure one frequency and if you stop it partway through it, you get something that looks like that. Looks like shmoo.  So anyway, this math gives me very excited and I had to really restrain myself from putting the equation up here but I'm not gonna bother you with that. Oh, thank you. So what I hope you learned from these last couple of slides is that functional means we're measuring over time, we're measuring, so by the way, I didn't mention this but this is actually sped up 30 times to how fast it happens. So this takes two seconds to unfold and in fMRI, we actually take the whole brain in two seconds, not just one slice of the brain like this. So that's fast and the magnetic fields are changing very, very quickly and they're changing so quickly that the overall magnetic field exerts some force as they're changing and that's what makes them vibrate and that's what gives you the awesome soundtrack. So when you're hearing the eh, eh, eh or whatever your MRI sounds like, it's because the magnetic field's changing so quickly the wires are just vibrating at its frequency so fast that they're sound frequencies. Yeah. So it's magnetic because you're measuring magnetic properties of tissue, by the way, I didn't mention this but the magnetic property is that there is iron in blood and that that iron has a magnetic signature that you can measure and you can measure how much that there is from second to second.  Okay, fine but what can you use this for is what you really wanna know. I should mention before I go on actually that there are a couple of really important limits placed on how much you can measure a brain by the technique. This takes two seconds, two seconds is forever in brain time.   That's gonna break your computer really hard for one. And two, there's no way we can actually measure that. So what we're stuck with is either measuring a single neuron or a single individual bit of the brain, a single cell and what it's doing or maybe a couple hundred cells if you're really lucky and you have some fancy post-docs in your lab. Or you can measure the whole brain as you measure an fMRI and you can measure it slowly. And if you wanna measure humans, fMRI is pretty much all you've got because to get into humans' brains you have to cut open the skull and people don't really like that. So fMRI is what we've got if we care about humans and if we care about human cognition, which I do because I like reading brains that have something interesting going on in them. So now the question becomes, well, what can you read out with this kind of technique? And a really important consideration is this really boring first one that this doesn't actually measure any absolute quantity of the brain. It measures how much magnetization there is, how much blood there is. What's zero on that scale? So the scale here goes from two to negative two because there is no scale, it's arbitrary. You don't have a zero.  So every time you read an fMRI study, what you should be looking at is how creative were these people in determining what two things they're contrasting? Or if they did something even more creative and weren't just contrasting things that they actually made some kind of measurements. Oh, no, that's what our lab does, but that's what we're gonna talk. So the point is pay attention to comparisons because a lot of times, for example, lying, people, they're told to lie, does that satisfy you as a real lie?   It's not the same thing as, these are not the toys you're looking for, it's just different. There are, you guys aren't here, that's good. So what fMRI can be used for and can usually be used for is mapping studies. So if you've got something that's going on in the brain over the course of seconds, something that's not your decision of where to move your eye, your eyes move like three times a second, that's just, whatever computation is going into that, it's just too fast to measure with fMRI. If you ever hear an fMRI study talking about the influence of one area on another, don't believe it. Two seconds, there's like 2,000 things that happen in two seconds in the brain, but you can't make causal conclusions when there's 2,000 things that might have been going on. So anyway, mapping is what fMRI does reasonably well. fMRI makes map of the different parts of your brain that are doing somewhat different things. A really famous example from early fMRI, from a woman that I've actually collaborated with, who's a wonderful woman, is that there are some parts of the brain that are sensitive to faces that are particularly responsive to faces of other people if you show them visually to the person in the scanner and that some parts of the brain will respond really strongly if there's a face and not so much if there's not a face. So that contrast, I mean, that's a pretty clear contrast, that you can believe. But look, it's a face where it's not a face. It's a really responsive area. And look, every time I show a face, I show Jennifer Aniston, I show Halle Berry, I show Joe, I show Jim, and there's a response. And so mapping things like that can pick apart the components of perception really well. It's when you start getting into higher cognitive functions you start having problems. And also you start having problems because the mapping, well, it's only as good as the resolution you've got. And when you're changing those gradients in fMRI, the problem is that the gradients can only change so fast. And if you're changing magnetic fields around someone's head and you change them really, really quickly, eventually you give them the seizure because the magnetic field changes so quickly will stimulate the neurons in your brain. And you can't do that. So there are fundamental limits to how fast you can take these measurements.  So we can't really get, we're almost, almost, just from hacks and clutches, tacked onto this technique. We're almost down to what's a really interesting resolution, which is the resolution of cortical columns. And I cheated there. So what else can it map besides visual function, besides what visual categories you're looking at? Well, people would use this productively to look at mapping of the body. And one of the interesting questions, and I only put this slide in because I knew that any page shot was before me, was the mapping of the penis. Because there had been some speculation that, okay, I don't know how many of you know this, but there's very famous, if you've taken a neuroscience class ever, or read a lot of popular neuroscience, you know that your body is represented in a map in your brain. That your finger is right next to your hand is right next to your arm is right next to your shoulder, in this little tiny representation of a man in your post-sensual sulcus, right there. And the opposite sides of your body, some of you are reading ahead, the opposite sides of your body are represented on opposite sides.  And people would say you're touching my arm, stop touching my arm. And they say we're not touching our arm, we're touching your brain. And people could not tell the difference. But in those original studies, I guess they were a little skittish about getting too close to the chunk. So they somehow concluded that the representation of the penis was actually near the feet, which provided a lot of fuel for speculation as to why people might have put fetishes and things like this. But sometime in the mid-90s, the Germans decided to straighten this all out and this is just my very favorite method section ever. And I've got to read it to you in case you haven't read it yet. So in contrast to previous studies that applied electrophysiological sensory stimuli, techniques, the stimulus touch was chosen to present a physiological stimulus. Bear in mind, this is just someone who's lying in an fMRI scanner, such as you just saw. With an ultra-sensitive tube brush, Dr. Best Flex is sensitive, the one that's so sweet, the one that's super healthcare. We brushed the needle aspect of the left halux that's actually the foot, the left penile shaft, the left pre-pupus, the glands in subject five, a worm, and the left lateral abdominal wall. So they're basically just saying touch, touch, touch, touch. And it turns out that the chunk is exactly where you'd expect it to be. So this was actually something we didn't know before fMRI. So it turns out you can read out maps of sensory function extremely well. But if you're talking about higher cognitive function, then you have some more problems and it really depends on partly the creativity of the experimenters and partly they're just fundamental limits and that you can only measure these chunks of brain that are a couple of millimeters across and you can only measure them every couple of seconds. So if you've got confidence that whatever you're trying to measure is some ongoing state of brain activity, then you're fine. Then you can draw whatever conclusion you would like.  The other problem with trying to figure out where the love center or the God center or the lying center is in the brain is that many areas have multiple functions. In particular, lying seems to have something to do with some areas in your frontal cortex that are involved in keeping many things in mind. And it turns out that if you're lying or if you're remembering the position of say three or four dots on the screen, the same area of the brain is more active. So remember three dots versus two dots. You get some contrast and you get some area that shows up as being more active for three dots than two dots. And if you show lying versus not lying, it's just easier to not lie. And so some of the same areas of the brain show up. So if you have a lot of confidence that that is in fact the lying area. And it's a bigger problem when you get things like emotions because there's a particular part of the brain called the amygdala. Who's wrote the amygdala? Excellent. You guys are up on your popular science at least. It's fantastic. This is a short list of things that have been found to activate the amygdala. In other words, you show this and you show something else. I mean, amygdala is more active to fear than to not fearful stimuli. Or to anger, things that might make you angry, then things that might not make you angry. And again, things that might make you angry, how do you define that? I don't know, what do you think? Let's show people with angry faces. Let's show someone you got in a fight with last week. Let's show, I don't know. And that is the part of the method section that you should really try and read. And that responsible reporters will report in the story if you actually want to evaluate the conclusions of the study. And also, it seems to respond to animals just for fun. I don't know. So, the other thing fMRI is really good at is that it takes a whole ton of measurements at once. It doesn't just measure one or two or three or four places in the brain. It measures 300,000 of them. And that's great if you actually want to read out the whole state of a person. If you want to read out their entire perceptual system, not just one little handful of neurons. And so some work from our lab, which I'm actually not going to show you because I show it in every talk, and I'm sick of showing you it's not my work. It was the work that convinced me to join the lab. But look up gal at lab.org and look up brain reading, and you'll find our video about reconstructing your visual awareness or what you were seeing based on your brain activity. And because we have so many measurements, we can do things like that because vision is really well mapped. But the point is that the state of the brain at any measurement, at any of these two second chunks that you've measured, is some pattern of activity like this. And you can look for patterns from one second to the next second. And you can look at an individual measurement and you can say, what was going on at each time? And you can say, why was it high and why was it low? And this looks like an easy problem if you consider just one little squiggly line like this. But remember that we have 300,000 measurements. And what we have actually looks more like this. So this is the cortical surface. This is the outside part of your brain. And this is what I am particularly concerned with in my research. We think that what's happening here is a pretty good reflection of what is going on in terms of what you're seeing, what you're smelling, what you're hearing, what you're thinking, dare we say. And these second to second variations are exactly the thing that my research is trying to figure out. Why did all of these signals go up and why did they go down? You can see the different parts of the brain are doing massively different things.  So that's like right around back here. And that is visual cortex.  And that seems to vary the most or vary the most reliably across all these images because what the people are doing is watching a video. And the emotional content of it, well, what do you think? Do you think you could rely if we came down to why one of these little dots turned from red to blue? I think so, then please go to grad school and you can go to our scientist. So, fMRI by no means the only way to measure the brain, there are a lot of ways to measure the brain, many of them cannot be done in humans. There are a lot of really fascinating techniques for making neurons glow, to manipulate the genetics of mice and fruit flies and their animals, to have them express things that every time a neuron's active, it glows. I don't think you guys have heard of the, used to be called the brain activity map with the BAM project, now it's called the brain project. You've heard of this? This is money for neuroscience, this is money for me. This is a good idea. It's a little bit of a vague idea because people, the goals of the project are not as well articulated as for example the human genome project or the human connectome project. So, the intent of this project is to create or one of the intents of the project is to create new ways to measure the brain, probably in animals, that will eventually be able to be used in humans. But if we're talking about humans and human brain reading, which again is what I care about, then one of the promising areas of research is in electrocorticography, which is actually sticking electrodes on actual brains and recording the electricity, which gives you about the same spatial resolution as fMRI. It gives you these couple millimeter chunks that you've put a little electrode to record over, but it gives you much, much better time resolution and you can measure things that happen on a millisecond to millisecond time scale. Unfortunately, well, perhaps fortunately, you can only do this on people that have epilepsy and are having surgery for epilepsy and to localize the spot where they have epilepsy, you have to put an array like this across the surface of their brain to determine which part of the brain is responsible for their seizures and which part you want to then cut out or burn out. But it turns out that there are enough people that have the surgery done, which is a very successful surgery these days, that you can get data and you can get data across pre-broad stretches of the brain. And this is incredibly valuable data and many, many neuroscientists these days are trying to cozy up to the MDs to figure out whether they can get access to some of these patients. And the thing that you may actually see in terms of brain reading over the next, I don't know, five to 10 years are better applications of EEG, which is actually a 100-year-old technology is by no means the cutting edge. But it's getting good enough and our signal processing techniques are getting good enough that we can actually make use of EEG scans in things like video games. So there are a number of things, including these people at MOTIVE, that are trying to use EEGs that you can just buy for a couple hundred dollars, stick on your head and use this input to a video game. And you can do things like use the force if you calm down.  So it's not really the most fancy brain reading. That's why I don't do EEG research. So, so what can we read out of the brain? Well, we can read out whatever we're creative enough to come up with a contrast for. We can read out differences. We can read out large scale patterns.  Absolutely not. The best way to mess up an MRI is to move two millimeters and do it regularly. So just swallow a lot or sneeze and then pretty much the whole thing is shot. So I guess I bring you a message of comfort. And I hope that I have taught you a little bit of skepticism. So the title of my talk was neophrenology or window-engaging movies of our minds. And that last was actually a quote that was attributed to my advisor after one of our brain reading papers came out. And it was attributed to him by the press office at UC Berkeley. Somebody wrote it and said, you said something like this, can we say this? And he was like, no, no, window-engaging movies of our minds, really corny and it's not really an actual reflection of our research. But people just want that, they want to believe. There's this vertigo, like looking at brain reading and saying, oh, I want to fall in love with you. I want to fall into this, this has got to be true. And we're, you know, and that's basically the end of my talk is we're coming here.


오케이, 저는 다음 주의 주인공의 작가, 덕선 미의 키아스입니다. 어서 오세요. Can you hear me? Right, so thank you very much for inviting me to this nice workshop. 장봉, 정효, and Antonio Salani. In this talk, I'm 덕선 미 of 키아스 here. And today I want to introduce a simple model for the co-evolution of the cellular metabolism and also the phylogenetic tree of species. The motivation is that we wanted to explain the origin of some empirical features that we found when we analyzed the large-scale dataset of the cellular metabolism across many species. So before telling you my research problem, I'd like to introduce first the background. So this shows you the metabolic reactions. In our cell, we have many thousands of chemical reactions that convert chemical compounds. And this is, when you collect all of them, we see a very complicated map like this, a map of biochemical pathways. And this can be also represented by a network like that. This is a bipartite network connecting metabolic enzymes and reactions and chemical compounds. So the left one is from E.coli and the right one from humans. So we see that although the cellular metabolism is expected to be almost identical across species, but when you check out their differences then we see they are different from species to species. So we wanted to understand the difference. So we downloaded the data from Biocic database and we used this version and the numbers are. So it covers about over 5,000 species and over 10,000 reactions. And the starting point of all of our research is this species reaction matrix. It is a matrix element of 1 or 0. That means that whether a reaction R is present or absent in a species S. And the main quantity we are interested in is these two. So the first one, both are obtained by summing the elements in a row or in a column. So R to the S represent RS is the number of reactions in species S and S of R indicates the number of species containing the specific reaction R in the metabolism. So the number of reactions in a species they are quite similar. So its distribution is almost Gaussian distribution. On the average, 1,500 reactions are found in the metabolism and the standard deviation is large, but anyway the distribution takes a normal distribution. But the problem is not problem but one interesting thing is that when you check out the distribution of SR that means how many species has the specific reaction R then it takes a power law. And when you feed the data to a power law then you find that the exponent is 1.0. So that slope is to be exact, I mean to believe. So it's a power law with exponent 1. So those things were identified in the analysis of the empirical data. So our question is this one. Why is such a clear power law observed in the metabolic network? So we checked out the metabolism across species and it takes a power law. So we expect some principle to be underlying that power law. But I don't know. Would you think this finding interesting? So one possible answer to this question would be that this distribution simply reflects different biochemical importance of different reactions and for liveners. And the importance of different reactions happen to be distributed in that way. So this power law just reflects quite different degree of importance of biochemical reactions and no surprise, right? But of course I don't think so. We thought that this distribution comes from some specific way of the metabolism evolution over a long time scale on Earth. So if we think about evolution of the metabolism then this answer could be the case but we need to be more quantitative in digging into this idea. So that's the main theme of this work. So we need to check out the previous studies about the model for the metabolism evolution. There are many models as far as we can check and these two are the most famous one. So in the left one the main idea is that the goal of the metabolism is to produce A material and then if it is available in the environment then everybody is happy but if there is normal A then we need to produce inside a cell from B and suppose that B we are run out of B then we need to produce B to make A then we use the available resource C to produce B and then A and so on. So chemical reactions are added one by one it's a backward evolution. In this model in the primitive time long time ago one enzyme catalyzes every all the reactions so it has a broad specificity and then as time goes on it takes a long time to catalyze every reaction So by mutation and application there are many enzymes that are acting on selected reactions So along this idea if we draw the metabolic network then metabolic network changes in this way. Given that the structure of the metabolic network is characterized by a broad degree distribution people were interested in proposing the metabolism evolution model that can reproduce the broad degree distribution power degree distribution and this is based on that idea In the right study the people introduced the organism degree this is exactly equal to the number of species having a given reaction R, S of R in our study So they found that the organism degree is distributed broadly but they didn't plot it in logarithmic scales And then they proposed a natural growth model where they consider one metabolic network and it is expanding by recruiting a reaction one by one from a large pool of reactions So they were able to reproduce many properties Including the fact that At the center of the metabolic network you find more likely the reactions having a large organism degree So the way they define the center is based on the natural properties like the bitenin centrality and so on Also Maslob and Snapple and this statistical physicist was interested in explaining specific scaling between the number of transcription factors and the genome size There is a quadratic scaling So they introduced a similar model This was published before the previous one So they proposed a two box model It's also similar So taking reaction from the pool and metabolic network is expanding And then the main idea is that they add a sequence of metabolic reactions And they assume that this pathway segment is activated by the same transcriptional factors So they were able to explain this quadratic scaling And also Borenstein and their colleagues was interested in explaining human microbiome So their idea, the main difference is that they consider the interaction between multiple species metabolism to explain the human microbiome Right, so this is a brief introduction about the previous studies And so, let me tell you first our model result So the main difference of our model is that we consider both network expansion and also speciesation So we consider both the growth of the network and growth of the species tree This is the animation So circles are species And in every species the metabolic network is included And new species are born And network is also expanding So this is one of our model Before telling you the details of our model I would like to advertise that this model explains a lot of empirical features So here yellow circles are from the simulation of our model And blue triangles are empirical data So you see their agreement This one is the Gaussian distribution of the number of reactions in a species This is the problematic distribution The power law distribution of reaction popularity And here this popularity I forgot to mention this The popularity f of a reaction is simply equal to the number of species containing the reaction r divided by the total number of species So f is ranging between 0 and 1 Also, the lower two panels were generated to test Demonstrate the predictive power of our model So it reproduces well the power law distribution for compounds in the metabolic network And the last one is the characteristics of the species tree generated in our model So we measure the distance between species based on how similar The two species are regarding their least set of metabolic reactions So we use the Jacquard index I'll explain it later So among these we will focus on this feature And our original motivation is to understand and explain why this power law distribution emerges So why such a power law emerges can be understood by considering a much simpler model than presented in the previous slide So please think about this toy model So initially, suckers are a species and scares a reaction Suppose imagine that there is just one species having one reaction And then next time this species obtains another reaction number 2 Also at the same time new species born it inherits the reaction number 1 from A So A is a parent of B But simultaneously species B obtains a new reaction 3 This is repeated So at time equal to A obtains another reaction 4 but and give birth to another new species C C has reaction 1, 2 and additionally has reaction 5 and so on So these yellow color reactions are the newly recruited reactions at a given time And then this is the situation at time equal to 3 So you see that reaction number 1 is found in all the species On the other hand number 2 reactions are found in 4 species number 3 is found in 4 species And reaction number 8 is only in species A So This can explain the origin of the power law distribution So we can compute everything number of species is doubled and number of reactions is doubled as P and R T can be obtained exactly And also we can compute how many species will have a given reaction R So it is given by exponential function with tau R the birth time of the reaction So the earlier a reaction is born then as R will be larger for the reaction And popularity is obtained by simply dividing it by the total number of species So popularity of a reaction decays exponentially with its birth time or first recruitment time So here reaction Number 2 was recruited at time 1 So if we look at the situation at tau equal to 3 then the time interval is 2 So 2 to the minus 2 That is one force is the popularity of the reaction So popularity distribution can be computed in this way So if this is the definition of the popularity distribution We check out every reaction whether it has the popularity f And then we change variable to birth time tau And then this is Jacobian And we know that how many reactions are born at given time So R tau plus 1 minus R tau gives you the how many reactions are born at given time And it is the 2 to the tau in this submodel And this Jacobian tau tau decreases with tau So the derivative of tau with respect to f So this increases with 2 to the tau So the birth time distribution behaves as f to the minus 1 And this Jacobian also behaves as f to the minus 1 So its product gives us f to the minus 2 It explains why we see the power load distribution This summarizes the result But this model is satisfactory but has a problem So the exponent is 2 Not 1 So sadly And also Every reaction is recruited just once Of course it is inherited But for instance the reaction number 4 is recruited at this time by species A And then it is no more recruited by any species at any other time And also in this model Spassation and metabolism expansion Here metabolism is considered as the set of reactions Its expansion occurs simultaneously So their time scales are set to be equal And if we consider the network structure of metabolism then the recruiting or reaction will be more selective It should be able to be maintaining non-zero flux when it is added to the metabolic network It should be considered So we want to address these points further So this is an improved model In previous slides I showed the result of running this model So we consider universal pool of reactions And here RSA is the standalone reaction set They can be activated standalone They can be activated Even if they are inserted into the metabolism alone Because it uses the substrates that are externally available So this summarizes the main steps of the model So this is the algorithm So initially the beginning is the same as the time model So one species having one reaction exist Then at every time step Every species undergoes one of three transitions Expansion Rest means doing nothing or specification So expansion or rest specification is divided depending on the reaction selected for insertion So every time Every species selects one recruitable reaction from the universal pool And then examine whether the species already has a similar reaction to the candidate reaction So the meaning of the similarity The similar reactions are the reactions sharing substrates or a product So if there is a Suppose there is no similar reaction in the current metabolic network of the considered species Then the new reaction new candidate reaction is just added to the metabolism Suppose that that reaction new reaction has a similar reaction already in the metabolism Then rest or specification happens with probability mu So this is the only parameter of this model Then R new replaces the similar reaction and then new species is born So Please see this one So this is the parent species And new reaction is this one So this new reaction replaces this one The parent species is still there But new species is born And comparing these two metabolic networks This one is replaced by this one And this one was dropped Because it cannot be activated anymore Right? So mu controls the specialization rate So we repeat these steps Until we obtain over 5,000 species The same value as the empirical data The same number of species So I showed you the result in the previous slide This is a snapshot Another snapshot of the simulation of this model All right So how many minutes do I have? 8 minutes, all right So From now on What I want to do is to do the similar calculation as I did in the toy model So I'll compute the number of species and number of reactions as a function of time to derive how the popularity distribution behaves as the popularity So in this model there is one parameter specialization rate And depending on this parameter result are changing But please look at this line So we know that mean number of reactions per species is empirically 1,500 And we ran our model for these four values of mu And among these four values mu equals 0.02 is gives the most similar numbers of reactions per species 11,100 Right? At mu equals 0.02 Right? So I'll present the result with this value of mu in the slides that I'll show you from now on One remarkable feature of this model is that Yeah It shows a crossable behavior in the popularity distribution So when mu is equal to 0.02 that crossable is not so clear But when you use a larger value of mu with this value of mu the metabolism size is smaller than the empirical one Then you see that two regions are seen So slow decay and fast decay of this popularity distribution And the exponent this region the exponent is 0.7 or 0.8 And the fast decay region the exponent is 1.5 or 1.9 So I want to say that to me it looks that the larger exponent is close to 2 of the toy model the wrong exponent And 0.78 is close to the empirical value And when we plot the simulation with the empirical data together then this slow decay looks similar to the empirical one So my point is that there is a crossable behavior And let me denote this crossable scale of the popularity of the AppStar And AppStar also depends on parameter mu And with such a small value of mu as 0.02 then that crossable is not so clear So the argument with the empirical data is better So the first term is the number of species as a function of time It was 2 to the t in the toy model And it's similar So we can write down the equation for the number of species And mu is the spatial ratio and alpha In our model new species appears only when the candidate reaction has a similar counterpart reaction existing in the current metabolic network So alpha is a probability that the candidate reaction has a similar reaction already in a species And if if it is constant then we can expect that as grows exponentially Right? And this is the result So it really grows exponentially and the exponent is measured as 6.31 Here We introduce the normalized time So t tilde runs from 0 to 1 dividing by the total simulation time Right? And we divided the time scale from the crossable popularity value So f star was 0.4 with mu equal to 0.2 t tilde 0.4 Right? And Such an exponential growth was observed for different value of mu So next is the number of reactions And this is the equation for the evolution of the number of reactions It also expected to grow exponentially but there is a constraint So 1-alpha is the probability to be expanding And 1-3-mu-alpha is the probability of spacation Either of the 2 happen then new reaction will be recruited But RT counts the number of distant reactions So newly recruited reaction may not be brand new setzen to indicate the new candidate reaction is really new brand new then our measurements So it says that it's better 이를 적용하는 기준입니다. 그래서, 만약에 적용한 기준의 function을 적용하는 기준의 1을 적용하는 것입니다. 이 부분은 거의 계속되면 그의 반응이 이번green restaurant는 동면들에 대해 만드는 상황입니다. 이еты와 groans 가ing in the previous slide, it is given by this factored times bet time S. And our expectation is that beta is almost constant in the initial time, early time, and later time, beta decays S to the minus one, so it is almost constant. So this distribution will be almost constant in the later time regime, 이 시각의 가장 중요한 차이가 있습니다. 이 시각의 차이의 차이의 차이점은 성장적이 성장되어 있습니다. 그런데 이 시각의 차이점을 그의 정지와의 정지의 정지와의 정지에 관해서는 하지만 어리타임을 사용하는 것에 대한 영향을 가진 것입니다. 그것은 중요한 부분입니다. 블루, 어리타임, 그리고 레드, 레드타임을 사용할 수 있습니다. 그의 정지의 정지의 정지의 정지와의 정지의 정지로 불타임을 제공하고 있습니다. 우리의 리액션을 통해 리액션을 제공하면 다른 시스템을 제공할 수 있습니다. 만약 우리의 리액션을 제공할 수 있다면 그 위치의 반응을 제거할 수 있습니다. 이 위치의 반응을 유지할 수 있습니다. 그리고 이 위치의 반응을 제거할 수 있습니다. 그리고 그 위치의 반응을 제거할 수 있습니다. 그리고 이 모델에 놓여서 푸짐을 깨는 것과는 조립기의 정치기의 정치기의 정치기. 그래서 벌써는 전기적으로 확산기의 정치기의 정치기의 정치기의 정치기는 정치기의 정치기입니다. 전기와 전기적인 정치기의 정치기의 정치기입니다. 다� pins if the late-time regime, the Borscht's distribution is almost constant. So if you remember this formula, then p tau is almost constant in the late-time regime. So it cannot give the f to the minus 1 contribution. That's why we observed, in the early time regime, we see p f is given by f to the minus 2, and late-time regime we have, pf가 f to the minus 1으로 적용되어 있습니다. 다시 적용할 수 있습니다. 그와의 차이점이 다를 것입니다. 그리고 이 두 quantities, 드리버티브, 버스타임, 이 자코비아는 f to the minus 1으로 적용되어 있습니다. 이 exponents are 0.7. 1의 차이이의 차이자 이 exponents essentially determine this slope 0.766, etc. 그래서, 이 exponents of the popularity distribution is essentially determined by this Jacobian thing. 알아요? 그리고 마지막에 이 월에 대해 공개한 레트로가 있었습니다 그리고 그는 우리의tim e elastic power of this mother so we checked out the properties of the species tree generated by our model. distance between species based on the dissimilarity of their set of reactions. So we define the distance as 1-jacquard index of their set of reactions. And it's the distribution, sensibly dependent on the parameter mu. And luckily, we found that when mu is quite small, 1-jacquard as the point of 2, then this black one is the empirical one, and the yellow one is from simulation. So this distribution overlaps with the empirical data. So it is a plus of our model. Right. So in this world, we wanted to understand the origin of the popularity distribution, taking a power law. And we found that birth time of a reaction essentially determines its popularity and its distribution and dependence of popularity on the birth time essentially determine the statistics of popularity with that we observed in empirical data analysis. All right. Yeah, thank you very much for your attention. Any questions? Can you tell what the rate of new reactions being recruited is? I mean, this. Right, so mu is the rate of giving birth to new species. So mu equals point of 2 means that when you expand your network 50 times, and then you have a chance to give birth to a new species. So yeah, it's the rate of specification, right? Any other questions? If not, let's start here. And let's thank you Professor Lee. Right.


Adding links to your CESAW post or activities allows teachers to make resources easily accessible to students. There are so many ways you can use links to share even more great content with your learners in CESAW. Let's see how. CESAW makes it easy for teachers to share pre-made resources with students. By tapping the green add button, tapping post student work and selecting the link icon, teachers can share videos, links to articles, or websites for students to reference. Maybe you are teaching a unit on animal habitats. Add a link to a kid-friendly article about forest and freshwater habitats for your students to compare and contrast. Students then have the opportunity to comment on the resource in the journal to engage in a focused classroom discussion. Another option for using links in CESAW is to add links to resources that enhance student learning directly within a CESAW activity. While in the creative canvas, select the three dots, then tap on the link icon. Copy and paste the URL you want to include. After the link has been added, adjust the size and position of the thumbnail within the activity. To add more context, use the label tool to add information about the link. Teachers can also attach a link to any image or text in the canvas by just tapping the three dots associated with the image or text. In addition to providing links to articles, teachers can provide students with links to online read-alouds, videos, blogs, podcasts, you name it. Students can also share resources that they create or curate with the link tool. If students are conducting research, they can cite their sources on the canvas by including links to the articles or other resources they find. In addition, students can link to Google Slides, Reports, or any other learning created outside of CESAW. Simply copy and paste the URL from the resource to include a clickable link on the canvas. Don't forget to make sure the share settings allow for unrestricted viewing. Adding links to posts and activities unlocks learning opportunities and organizes resources all within CESAW. Your challenge this week is to plan and create a CESAW post or activity that incorporates the link tool. Share the creativity you capture on Twitter using the hashtag CESAW me a minute.