Hello, everyone. Welcome to this conference. I hope you're looking forward to a fantastic day. It's my first time at speaking at Open Source Summit, and I'm really excited about it. Today, I'm going to talk about Apache Kafka and the rise of a streaming platform. But before I begin, can I get a quick show of hands of people who have heard about Apache Kafka or used it? All right, that's most of you. It's still going to be a relevant talk. I promise you that. I've been working in this real-time data and stream processing space for close to a decade now. And during these years, one of the biggest shifts I've noticed is the rise of real-time data in production. And this is happening as part of companies becoming more digital. As part of my day job, I get to talk to companies that are natively digital, lots of them back in Silicon Valley. But I also get to talk to brick and mortar businesses that are transforming themselves into digital ones. And one of those trends I've noticed is that the modern sort of enterprise is moving to being digital. And this is the real impetus for the rise of real-time. So in my talk today, I want to put forth a simple thesis. I think we're witnessing the rise of a major new category of infrastructure software. And this new way of thinking about your data is not something we've had to do very often. If you've been in the industry long enough, you've seen this kind of thing happen with a number of major technologies, like database systems that shaped a whole category of business applications, data warehouses that shaped a whole category of business intelligence and analytics apps. I think this whole area of stream processing is going to be like that. We are watching the emergence of another category of infrastructure software, which is the streaming platform. But then what's the evidence that this is happening? Let's take a look at Kafka's journey so far. It started back in Silicon Valley several years ago. We created Kafka. We open sourced it. And very soon, a growing list of the most technically sophisticated Silicon Valley companies started to rebuild their architecture around Kafka. And after that, it turned out that this wasn't just a phenomenon that was limited to the Silicon Valley crowd, like a few things we do there. To give you an example, one of the things that we hadn't thought about when we created Kafka was this whole space of the Internet of Things. I think Jim mentioned that before. When I first heard about this, I was a bit skeptical. But then it turns out that companies are doing amazing things, connecting cars globally around the world, capturing streams of events from the car, feeding those streams back into the features of those cars, into apps that go along with the car, into analytics about the customer base. And this sort of thing is happening with a lot of industry verticals from manufacturing all the way to logistics. Having been in a web tech company myself, I have a pretty good idea of what it means when you can instrument your business at that level and optimize it. Not only is this happening with the Internet of Things space, but another industry that's undergoing this is the whole financial services and banking space. Now, this is an entire industry that's built on real-time data. But now, having a modern distributed platform like Kafka is making a lot more things possible. And being able to break down the silos that exist in these banks and get data flowing in real time, breaking the monolith into event-driven microservices, this transformation is making a lot more applications possible from instant credit card processing to real-time fraud detection, and so on. Even traditional businesses like the retail industry are adopting Kafka aggressively to compete better to become more efficient. And today, we know that about a third of Fortune 500 uses Kafka in mission-critical applications. And this includes the top banks, insurance, and travel companies. And so we all know that we are onto something here. I think all this data points to the emergence of this new phenomenon, the streaming platform. This is the thing that's powering these use cases. This is the architecture of these technically savvy companies. I think this is destined to be a major infrastructure platform that will exist in every company in the world. But then what's the role of this streaming platform? What is it supposed to do in a company? The role of a streaming platform is to sit at the center of a company, be able to interconnect all your microservices, be able to capture streams of events from applications, connect your data systems, and do all that in real time and at global scale. This streaming platform is what allows a company to have a central nervous system that allows capturing everything happening in your business in real time. But then what does it look like? There are a couple of core technical capabilities you need to have around streams of events. The first is the ability to publish and subscribe to streams of data. Now we've had messaging systems that have done this for a long time now. I think the real difference now is the ability to store these streams of events and do that in a distributed replicated manner at scale. And the final capability is the ability to process these streams of events, to act as a center for stream processing. Initially, what started off as a messaging system, Kafka today has evolved into being a full-fledged distributed streaming platform that embodies these characteristics of being one. So now switching gears a little bit. When people encounter this new idea of a streaming platform, like some of you might be thinking now, I think they come from a variety of different backgrounds. And each background lets them to viewing this technology in a slightly different way. The first lens is the enterprise messaging lens. There has been a whole category of software around real-time delivery of messages between applications. And many people would think of Apache Kafka and more broadly, a streaming platform is just an evolution of messaging. Messaging done right, if you might. I think there is some truth to this view. A streaming platform does support many of the kind of core applications that enterprise messaging systems support. And there are lots of initiatives out there to replace these enterprise messaging systems with Apache Kafka. However, thinking of Kafka as just a messaging system is overly limiting. There are at least three big differences. The first is that a streaming platform is built on a modern distributed systems foundation and can scale to the scope of an entire company, whereas enterprise messaging systems were built to support a handful of applications. Now, this might seem like a small difference, but it completely changes the nature and the scope of what this platform is supposed to do for your company. It isn't just the case that Kafka can handle more messages than enterprise messaging systems, but it's that Kafka can act as a backbone for not just a handful of applications, but for hundreds or thousands of microservices. And because it has proven ability to do that in some of the largest tech companies on Earth, it can act as a true integration plane for your company. The second difference is that Kafka is a true storage system for streams of data. There are Kafka clusters out there that store petabytes of data. There are some that store data indefinitely. This ability to store streams of data in a messaging system, it didn't arrive in Kafka by accident. We added that to solve one big problem that we envisioned a lot of companies have, which is integrating the batch analytics world with the online request response world. This is the ability that's required to integrate batch analytics with real-time messaging to have one unified way of processing all your data in real-time. The final difference between Kafka and a streaming platform in messaging is the ability to process these streams of data. Not only is this possible through the native streams API in Apache Kafka, but there are lots of stream processing systems out there that are built to work with the streaming abstractions that Kafka provides out of the box. OK, so moving on to the second lens, this is about a real-time version of Hadoop, or even a warehouse. I think there is some truth to this view as well. After all, just like a data warehouse or Hadoop, Kafka can act as a place where data comes together from the rest of your organization in one central location. In fact, now Kafka also has the kind of rich SQL layer that you've come to expect from Hadoop or data warehouses. KSQL is the open-source streaming SQL engine for Apache Kafka. It allows you to do sophisticated stream processing operations from stream table joins to aggregation, sessionization, and a lot more. I think something like KSQL is a big step forward in enabling a streaming-first world. With months of history stored in Apache Kafka and exactly once processing now possible on it, KSQL on Apache Kafka enables a lot more things that were previously not easily possible. The first is enabling real-time monitoring and analytics, allowing you to shift away from batch analytics for things that are critical to your business. The second is making streaming ETL possible natively in Apache Kafka so you don't have to duct-tape together a bunch of batch ETL scripts to get data flowing in an organization. In short, it is a true bridge between the batch analytics world and the online databases world. So that means that there are some parallels between the Hadoop and data warehouse stack and Kafka now. But the difference is that, be it SQL queries or processing jobs or applications, things that are built with Apache Kafka are naturally built to continuously update with every single event that arrives rather than in a batch fashion. And that particular difference, it changes the role of the streaming platform in an organization relative to what you might think Hadoop and a data warehouse is supposed to do. Now, data warehouses are very good at solving the traditional domains of a warehouse and why they were created to act as a center for business intelligence and analytics. I think a streaming platform is unlikely to displace data warehouses for what they are built to do. But where a data warehouse at Hadoop might fall short is when you're trying to build applications that feed directly back into the business. After all, for creating reports, a batch ETL script suffices and works just fine. But for powering a much more real time and richer customer experience, it is a non-starter. Your customers do not understand 24-hour stale data. And the simple mechanics of building an application that depends on a batch ETL cycle feeding back into that application is extremely complex. So then I think the domains and use cases where a streaming platform truly shines are the kinds of examples that I showed early in my talk. These are not examples of things that involve reporting your business or analyzing it after the fact, but it is very much about directly powering it. So that brings me to the third and final lens for viewing a streaming platform. And that is about ETL and data integration. There have been a whole generation of technologies to handle data movement. We've had enterprise integration tools and enterprise service buses that handle low, quick, slow data. And then we've had ETL tools that handle scalable data flow, but not in real time. So this gives us a hard choice, scalability and flexibility on one hand and latency on the other. I think one view of a streaming platform is kind of a unification and up-leveling of this. In Kafka, the E and L are Kafka's connect APIs. They allow you to build and use connectors to a variety of different systems. And there are dozens of connectors out there that you can use today. And the T is stream processing, be it using Kafka Streams API or any other stream processing system available out there. So there are parallels between this ETL view and a streaming platform as well. But what the traditional ETL view misses is the use of this platform as an application development platform. A streaming platform isn't just meant for getting data from place A to place B and munching it along the way. It is a true infrastructure platform that allows building sophisticated applications on top of it. These lenses in isolation, they don't communicate the full picture. They make it harder to see the full power of a streaming platform because each group has their own use cases and their own vocabulary. But I think this is the process of understanding a new category of software. In fact, I think it is the hallmark of a new category where you have something that cuts across a number of use cases in a way that wasn't possible before. So what does it look like when you can use the streaming platform and put into practice in an organization? You have a real time platform that powers applications like a messaging system, that powers data flow, like an ETL tool, and that acts as a central hub for all data processing and analytics, like Hadoop or a data warehouse cluster. So what does the future hold? I think the future here is pretty bright. We are seeing tons of innovation happening in the stream processing space. There are lots of stream processing systems out there. And there are lots of streaming data services that are released by the public cloud provider. Confluent's role is to make the streaming platform more accessible to companies, make it something you can download and use and put into production quickly, make it something that you can use in a public cloud. The Confluent platform is meant to be a full open source streaming platform. We believe as people want to create applications around a platform that is completely open. So the Confluent platform is an open source distribution of Apache Kafka with all kinds of developer tools, clients in various languages, connectors, to lots of different kinds of systems. It is meant to get you started with Apache Kafka quickly. And I'll say something. I think this is not only important on premise, but also in the public cloud. It turns out that these two major trends, which is the rise of real time as part of digitization and the move to the public cloud, they're happening at roughly the same time. So what you want is a hosted streaming platform that is open, that has open APIs that you can program to. So you can preserve the optionality of switching between cloud providers if you choose to, without having to rewrite all the applications. So that's the thesis that I would put forward, that this streaming platform category is really going to be one of the biggest and most exciting new categories of infrastructure software during our time. So as you think about your data, as you go back to work, think about your applications, think about how the streaming platform notion changes your view of using Apache Kafka in a company. Thank you very much. And we're done. Hold on. Stay up here. If you don't mind, I want to ask a couple questions of you. So when you first got involved with Kafka, were you at LinkedIn at the time? That's right. I was at LinkedIn eight years ago. And so in charge of streaming there. And so tell us how Kafka came about in your involvement. Was it one of these scratch your own itch open source moments? So I came about working on Kafka, even thinking about it, sort of by accident. I was hired to work on Search at LinkedIn. And the thing about Search is it's only useful if you have access to all the data in the company. And that was the problem that we had at LinkedIn, was there were two trends that were playing out. One is that we needed access to a lot more data sources than just the database feeds. And there were a lot of distributed systems starting to be put into place. Hadoop was one, but then there was Elastic, and there were lots of systems. And the question was, how do you solve this n-square data flow problem between applications of all sorts and systems of all sorts? And the thing was enterprise messaging systems did not scale, and the ETL tools were not real time. So we thought that there has to be a real platform that brought these two worlds together. And we ended up creating Kafka. Very cool. So you are a big leader in open source, and I know a lot of developers look up to you. What advice would you give to someone who wants to get started in open source or participate in a project like Kafka? Any advice that you give? Because I know there are a lot of folks out here who are just getting started. Yeah, I think the thing I like about open source is that it's fundamentally meritocratic by nature. So this is what I've found useful, is you can go read some docs, join the community, ask questions, and just take up some new visual and get started with it. I know that a lot of open source communities are pretty accepting, are inviting to new developers. I know Apache Kafka is, but you do need some patients to stick with it because it's all for free. Committers may not get to your patch immediately, but that shouldn't be the thing that discourages you. All right, well, good advice. Thank you so much for coming. All right.


So I want to talk about how the brain becomes wired up during development, because of course for the brain to function correctly, that depends on a very intricate and precise pattern of wiring during development. And I'm going to tell you three different stories related to how the tips of developing axons find their targets in the brain. So if we zoom in on the tip of a developing axon, we find that it's guided by a large number of molecular cues in the local environment. So at early stages of brain development, activity does not appear to play a role in establishing the initial pattern of wiring, rather there's a number of molecular cues in the environment. And one of the most important type of molecular cue are molecular gradients. So here we have an in vitro experiment done by Andrew Thompson in my lab, where we're releasing here a nerve growth factor from the tip of this pipette here. We're looking down from the top at a culture dish here. And this is a rat neuron, which is being guided towards that pipette, because there's a gradient being set up here. And the structure at the tip of the developing axon, the growth cone, is detecting that gradient and changing the direction of movement of the axon in response to that gradient. So there's three things I want to talk about today. The first is how a small sensing device, such as a growth cone, which is only about 10 microns across, might be able to detect a concentration gradient. So that's a difficult computational problem. The second thing I want to talk about is how once a growth cone decided which gradient points, how it decides whether to be attracted or repelled by that gradient. So that's a different kind of computational problem to do with understanding the signaling transduction pathways with growth cones. In the last part of the talk, I'll discuss some unpublished data where we tackle the problem of trying to understand the shape of the growth cone. So let me just play that movie one more time. As you saw in the movie, the growth cone shape is constantly changing. So the frame rate here is one per minute, and you can see that there's a very complicated dynamic morphology, which is constantly evolving in time. And how that relates to external cues and what kind of computational role it might be playing in axon guidance is still a bit unclear. So we'll discuss that in the last part of the talk. So how could a small sensing device such as a growth cone detect molecular gradient? Well, the way a cell knows about the external environment is by the binding of ligand molecules to receptors on its surface. And here we have a simple model of one-dimensional array of receptors, which are feeding into some complicated signal transduction network. And the output of that network is a decision about whether the gradient points left or right. Now, what makes this a difficult computational problem is that there are many sources of noise in this system. And the one I want to focus on is receptor binding noise. So the binding of ligand molecules to receptors is a fundamentally stochastic process. And it's only the probability of binding, which is determined by the external concentration. So using standard Michaelis-McMenton kinetics, we can say that the probability of a receptor being bound is the concentration divided by the concentration plus the dissociation constant. And so each time we look at this array of receptors, there'll be a different set of receptors bound. So given these continual fluctuations, this sensory uncertainty in the environment, how can we decide what's the best way to decide which way the gradient points? So we hypothesize that the growth cone may be thinking about the Reverend Thomas Bayes and in particular thinking about the optimal way to detect a gradient, which is simply to compare the probability that gradient points right given the pattern of binding with the probability that gradient points left given the pattern of binding. So using Bayes' theorem, we can calculate the probability of the binding given that the gradient points right and the probability of the binding given the gradient points left, which are easy to calculate based on what we know about receptor binding kinetics. And then using Bayes, we can invert that to calculate this ratio here and thus the optimal decision for deciding which way the gradient points. So we're taking an ideal observer approach here. So the result, oh, I should say that this was the PhD thesis of a student, Duncan Mortimer in the lab, helped by Peter Diane in London and Kevin Burridge at UQ and now at Oxford University. So I'm not going to show you all the maths, but essentially Duncan was able to derive this formula, which is a prediction from this ideal observer model, of the proportion of correct decisions this sensing device should make. So the proportion of correct decisions is just a half, which is the random value, modulated by a scaling constant, times the gradient steepness mu, so this is fractional change across the width of the sensing device, times a function of the overall concentration. So this is the dimensionless concentration divided by the dissociation constant. So this is a prediction for how chemotactic performance should depend on the gradient parameters. So this is a strongly hypothesis-driven modeling and this model has, I mean, this prediction has basically only two parameters that you fit to the data, which are the scaling constant and the dissociation constant, or you can look this dissociation constant up in which case you just have one free parameter. So it's a very tightly constrained model. So the predictions of the model look like this, I'm sorry this is going off the edge here, but this is just a measure of chemotactic performance. This is the concentration and the different colors represent the different gradient steepnesses. So you can see that this is complicated nonlinear dependence on concentration. At high concentrations and at low concentrations, you can't detect the gradient because there's either too many receptor bound or not enough receptors bound. Around about KD, you get the optimal chemotactic performance. So this is a prediction both for growth cones and in fact any chemotacting device. So we're interested in growth cones and interested in determining their chemotactic performance experimentally. And it turned out this was a difficult problem because the assays that have been proposed for studying axon guides have mostly been looking at the identity of the molecules involved and not very well set up for asking very quantitative questions about how a response varies as you make subtle changes in gradient parameters. So we developed a technology for creating very stable gradients in collagen gels where you print onto the surface of the collagen the chemotactic factor you're interested in. This diffuses into the collagen and creates a gradient which is actually stable for a couple of days. And this is the kind of time scale which corresponds to the time scale of which axons are being guided by gradients in vivo. And so using that technology we're able to map out this sensitivity surface how the chemotactic response of growth cones depends on both concentration and gradient steepness. And so this is the experimental data here. So the experimental data was generated by several people in the lab and this is collaboration with the lab of Linda Richards. And so you can see at least qualitatively the shape of these curves matches quite well with the predictions of the Bayesian ideal observer model. And I don't have a slide here but you can make more quantitative comparisons to show it's a pretty good fit. And so essentially apart from this scaling constant which when you plot the chemotactic, when you plot the experimental versus the theoretical data that goes away. So essentially all we're doing is fitting the dissociation constant and the dissociation constant turn out to be the same as what's being measured experimentally using more standard techniques. So this formula essentially applies to any chemotactic system and can be used to predict chemotactic performance in any system. And what we've shown here is that consistent with the prediction growth cones are only sensitive to gradients over a relatively narrow range of concentrations back to waters of magnitude. So this has consequences for understanding, for instance, therapies for regeneration. It's no good just to dump in a lot of some chemotropic factor. It needs to be at the right concentration in order to be effective in guiding axons. Okay, so that's the first story I wanted to tell you. The second one. Yes, so these are the gradient sleepinesses experimentally. And so this is over 10 microns. Yes, so these were very, very shallow, very shallow gradients. There's a lot more I could say about that but we'll come back to that at the end. So the second story is about how a growth cone interprets a gradient once it's decided which direction it points in. So here is exactly the same kind of neuron, exactly the same kind of gradient of no growth factor but now the growth cone is being repelled rather than attracted. And Mooming Poo and colleagues showed that you can get this behavior when you reduce cyclic AMP levels in the medium. So reducing cyclic AMP converts attraction to repulsion. That calcium also plays an important role. And so this is schematic summarizing many experiments of people like Mooming and James Jeng. So green here represents a chemotropic factor which is normally attractive. So when you have a normally attractive factor that produces a steep internal gradient of calcium, perhaps I'll use the point there. Steeping internal gradient of calcium and that causes the growth cone to be attracted. When you have a normally repulsive factor shown in red, that produces a shallow internal gradient of calcium. And that, even though the calcium gradient is still pointing in this direction, that causes the growth cone to be repelled. This is also dependent on the absolute level of calcium. So if you lower background calcium levels, then a normally attractive factor produces, okay perhaps I won't do that. Normally attractive factor produces repulsion. If you raise background calcium levels, a normally repulsive factor produces attraction. So you get this very complicated set of behavior in terms of how calcium and particle KMP levels determine the response of growth cones, how the growth cones interpret gradients. So in order to make sense of that, we proposed a very simple model of a growth cone consisting of two compartments. So this is the very simplest way of introducing a spatial dimension across the growth cone. And based on work of people like James Jeng, we imagined that there was a simple signal transduction network operating in each side of the growth cone. So the input is calcium and the output is the ratio of camkinase 2 to calcineurin. And so we hypothesized that it's the side of the growth cone with the largest ratio of camkinase 2 to calcineurin, which is the side to which the growth cone turns. And this is interesting because the output of this network is non-monotonic. And that's how you get lots of interesting behavior to do with differences in input calcium levels. So the assumption of the model is that you have the gradient produces a difference in input calcium level. And then we read out how that affects the output level. So this was work of Libby Forbes in the lab who did the modeling. And Andrew Thompson and Jaja Uanna did the experiments. So for the signal transduction model, we borrowed a model from Michael Grampner and Nick Brunel, proposed in the context of the switch between long-term pretentiation and long-term depression, which actually uses, they hypothesize it uses exactly the same networks that are able to borrow their model but have kind of two copies next to each other. And the results of the model look like this. So this shows the ratio of the outputs from the two sides. So if this ratio is above one, we hypothesize that leads to attraction. If it's below one, that leads to repulsion. And the three curves show different cyclic AMP levels in the model. And so black corresponds to normal cyclic AMP levels. So you can see there's a critical range of calcium for which you get attraction for low calcium or high calcium when you get repulsion. And then this blue curve shows the effect of reducing cyclic AMP levels which shifts this curve to the right on the calcium axis. Now one of the most interesting things about this is the predictions it makes. So these points here correspond to experimental data, which was already available, which we use to help validate the model. But then the model makes a number of novel predictions. And perhaps the most interesting is that at high calcium levels, so if you raise calcium and normal cyclic AMP, you don't get attraction anymore. And the way to recover attraction in that case is to lower cyclic AMP levels to put you onto this blue curve here. So contrary to textbook wisdom, this suggests that there's a complicated interaction between calcium and cyclic AMP. And sometimes to promote attraction you actually need to lower cyclic AMP levels rather than raise them depending on background calcium levels. And again, this has implications for therapies for regeneration. People have been very interested in cyclic AMP levels, for instance, in the regenerating spinal cord. And it's not just as simple as raising those levels. In some circumstances, you may need to lower those levels to induce attraction again. So we recently reviewed the role of calcium in axon guidance. This is in the current issue of trends in neuroscience. And I apologize for the cheesy picture. OK, so in the second half of the talk, the last part of the talk, I want to discuss some unpublished work we've done on growth cone morphology. So this is rather different. The first two parts were sort of hypothesis driven. Here we're taking a more neuroinformatics approach and generating a large data set of movies of growth cones and trying to extract information from those movies in a more sort of bottom up way. So the basic questions we were trying to address are what are the basic shape primitives of growth cones? How do these change over time? And how do these relate to growth cone movement? So for that, we had many people in the lab. Richard and Daniel here did the computational analysis and the others did the experimental work and this is a collaboration with Ethan Scott's lab. And so here is the basic set of data we were working with. I won't go through all these different conditions, but the main point is we had several hundred movies, time-lapse movies of growth cones growing in vitro, and all together about 50,000 individual frames of growth cones. So there's been some beautiful prior work trying to understand growth cone morphology based on looking at just a very, very small number of growth cones and using the intuition of the observer to try and classify what kind of shape patterns those growth cones are going through. Here we used a more sort of big data approach where you just take a whole bunch of data and you try and extract in an unbiased way what are the key sort of statistical principles underlying this data set, in this case, growth cone morphology. So the way we did that, so first we had to automatically extract the outlines, of course. We can't have a student sitting there tracing 50,000 growth cones and so Richard wrote some image processing software to automatically capture the outlines of growth cones from these movies. And then we performed what's called an eigen-shape analysis. So if you haven't heard of eigen-shape analysis, don't worry because it's just principal components analysis in a space of shapes. So essentially we take each of these 50,000 frames, we extract the outline, and then we parameterize it. So you need some way of parameterizing the shape. So we parameterized the shape by putting a lot of dots around the outside of the growth cone, so 250 dots. And each of those has an X and a Y coordinate. So in other words, we have 500 numbers which describe the shape of the growth cone there. So you can think of that as being a point in a 500 dimensional space. This is at this point, in my experience, biologists head start to explode. But here are two dimensions and you just have to imagine the other 498. And so that's a single shape in that space. And when we take our 50,000 frames, you get a whole cloud of points in that space. And then we perform principal components analysis in that space to extract the dimensions along which there's most variance. So this is trying to ask the dimensions of the shape space which have the most variance. So here's the answer to that question. And the way you normally represent eigen-shapes is that you show the mean shape in green. So each of these is one of the principal components. And the mean shape is the same in each case, that's in green. And what we're showing is the shape, one standard deviation in each direction along that shape axis. Because you have to try and represent how shape changes as you move through that axis in the space. So here are, so here's the first principal component captures 37% of variance in the data set. And very neatly that represents bending right versus bending left. So this is saying if you had to characterize growth cone morphology by one number, the one number which preserves the most variance is just telling you whether the growth cone is bending to the right or bending to the left. The second principal component, 20% of the variance, is basically measuring thinness versus fatness of the growth cone. So moving one way, you have a thin growth cone.  Then moving further down, you have these more subtle variations. So here you get a bend to the left, then a bend into the right. Here you get another version of thinness versus fatness. And here you get bending to the right, then bending to the left, then bending to the right again. And so you get this kind of hierarchy of these kind of shape primitives which capture the most important sources of variance in growth cone shape. OK. So the next interesting question. So this is a property. These shapes are property of the entire data set. The next question is how the projections onto these shapes vary through time for an individual growth cone. So here is a growth cone doing its thing. Here's the outline. And this will start again in a moment. Here are the projections, measured by a z-score, onto the top four modes. And so this is saying that this growth cone is bending one way, then bending the other way, and so on and so on. Similarly, go through variations in thinness versus fatness. And you can hopefully start to see here there's a hint of some periodicity in this behavior. So to analyze this periodicity, we measured auto-correlations and Fourier power spectra. So here is a set of auto-correlations and Fourier power spectra for the top few modes for one growth cone. These movies are a few hours long. So here we have, you can see, oscillations in the first mode, the bending right versus bending left. You can see very strong oscillations in the second mode, which is thinness versus fatness. And so on, you see oscillations going down in all the modes and seeing our sharp peaks in the Fourier power spectra. So that's one growth cone. That's nice oscillations. Here's another growth cone. It also has nice oscillations. But these are a different set of oscillations from the first growth cone. And basically, each growth cone seems to have its own unique set of shape oscillations, so this periodic behavior. Now these don't actually predict which way the growth cone is going to go. So these growth cones are generally going straight, but they're kind of wiggling back and forth as they go straight. They're not doing that. They're just kind of doing that. And I should say the time scale of these is about, there's a variety of time scales, but the basic time scale is about 20 minutes of this oscillation. So we come back to try and understand where that time scale comes from in a moment. So turns out these mode scores and oscillations actually predict the movement. So if you look at how far a growth cone has traveled over an entire movie and ask, are there any properties of the oscillations which predict the amount of movement, the strongest correlations are with the score here, which is a measure of the strength of the oscillation. So the strength of the oscillation in this mode, which is thinness versus fatness, there's a correlation with the frequency of the R1 mode, which is bending left versus bending right. And if you put the top few modes into a regression prediction, trying to use the strengths and periods of those oscillations to predict the distance moved, you'll find that there's actually gives quite a strong prediction. So in other words, growth cones with strong, fast oscillations are moving more quickly. And I challenged the lab to come up with a catchy way of remembering that, and Daniel came up with this. Okay, so I'm almost done. So we found that similar modes and oscillations were present in vivo. So far, all the data I've shown you is in vitro. And of course, one might say all this behavior, this dynamic behavior we observed is just an artifact of the in vivo environment. In vitro environment. So in collaboration with Ethan Scott, we use some of his zebrafish, where there's a very small proportion of retinal ganglion cells, which are labeled with green fluorescent protein. And we did time lapse imaging of the movement of those growth cones across the tectum as those axons try to path find. So this is just a static picture, but you can do time lapse imaging of these growth cones. And then you can do the same analysis, right? And what we found from that analysis was that we get exactly the same kinds of shapes, the same kinds of oscillations. So it's not just a property of the in vitro environment. So just in the last couple of minutes, I want to discuss a possible explanation for this. So I don't know if anybody's had any thoughts about what kind of processes inside the growth cone might lead to periodic behavior on a time scale of 20 minutes. It stumped us for a very long time until we had a revelation that it might be something to do with dynamic microtubule instability. So he's a schematic showing microtubules invading the peripheral region of a growth cone. And you see sometimes they extend quite a long way into the growth cone. And microtubules undergo these characteristic phases of growth and retraction. And so maybe this has something to do with the oscillations. So to investigate that computationally, we discovered that there was this fantastic model from right here in the Netherlands from General Vesius, my apologies for the pronunciation, Van Pelt and Van Oyen, where they considered microtubules growing together in a limited volume where they're competing for a supply of tubulin monomers. So the basic equations of the model are like this. So you have a growth phase. So this is the length of the microtubule and it's related to the concentration of free tubulin. And then you have a shrinkage phase where they shrink and then they switch between these growth and shrinkage phases with a frequency which also depends on the concentration of free tubulin. And these authors showed that this model produced a very nice fit to the behavior of microtubules measured from real data. So we looked at a very simple version of this model where we just have two microtubules competing in a small volume in a growth cone for tubulin. And this shows the length of those two microtubules as a function of time. So blue is one and red is the other. You can see as one starts to grow, the other one doesn't tend to grow at the same time because they're competing for the same supply of tubulin. And so if you plot, if you look at the length, the ratio of the lengths as a function of time and then plot the autocorrelation, you get an autocorrelation function that looks a bit like this is a very preliminary data. But essentially the period as measured from this is about 20 minutes. So it exactly matches the period we've measured in vitro. At the moment we're pursuing the idea that at the root, these oscillations may be driven by the dynamic instability of microtubules. Okay, so that's all I have to show you. So to conclude, I discussed how a Bayesian model predicts the response of axons to molecular gradients, how calcium and cyclic MP interact to determine attraction versus repulsion in axon guidance, and then how eigen shape analysis can help reveal new features of the dynamic morphology of growth cones. So thank you very much for your attention. So the first model, it seemed to me, the Bayesian model sort of assumes a kind of static position of the head and then you show that the oscillations. So how do you, you've got good data for the first one. Right, so the first one sort of simplifies things into you have a series of snapshots of the gradient. Obviously you'll improve your performance if you average over time. But essentially it's just looking at the most reduced kind of idea about gradient sensing possible. But you're getting at a very important point which is maybe these oscillations, especially waving back and forth, are in some way helping to optimize gradient detection. And that's a very interesting idea which we are kind of thinking about how to pursue at the moment. The shape changes in the growth cone and the redirection are strongly mediated by the actin mesh work in the Lamelli podium. So the dynamic behavior of depolymerization and polymerization, can you make a link between your chem kinase two and cyclic MP model with respect to the behavior of the actin mesh work? Right, thank you. So let me just answer the question which I thought you were asking first to start with which was aren't we ignoring actin in this model of microtubule dynamics? And well at the moment we are but we're not saying that actin is not important in driving these things as well. But in terms of putting actin in the picture in the calcium and cyclic MP model. So we haven't really thought about how to do that. So we're basically looking at the signaling at a particular level. So we're assuming the receptor signaling has been converted into calcium concentration and then we're not considering what happens downstream of those chem kinase two to calcium urine levels in terms of rack and rower and CDC 40 and so on. I mean that would be an interesting thing to do but we haven't attempted to do that. Thank you.


Hello everyone if you're experiencing some deja vu and thinking you've seen this before that's because you have this is a Reupload of a previous video that had some issues This is just a video announcing that I'm releasing Frightfully forgotten the music volume 2 on digital download on CD and also on cassette tape the link will be in the description for this video We'll take you to band camp where you can also if you don't want a physical copy You can get the digital download which of course will be cheaper and no shipping involved So this is the music composed for this YouTube channel between seasons 3 and 6 There will be 13 tracks available for digital download The CD will have an extra 4 and the cassette will actually have an extra one song because there's only so much Space on the cassette and the CD to actually but I was able to squeeze 17 tracks onto the CD Some songs included are the Freightfully Forgotten 8-bit version and the trash or treasure theme song Which a lot of people seem to like and this is a full song. It's got vocals You know a verse chorus and an awesome guitar solo done by a very good friend of this channel Mark Miller aka Corruptor he is a great musician I'm gonna put a link to his channel as well in the description to go check out some awesome Progressive instrumental metal and if you like there are still some physical copies of volume one available on CD And of course on digital download as well now that we have that out of the way I also want to take the time to give you guys a tour of my home studio to see where all the magic happened So keep watching if you want a studio tour So here is the studio tour welcome to my new studio For you gear heads and people are into music production and synthesizers and drum machines and all that kind of fun shit So I'm a little panoramic shot here before I get into Details what everything is here in the studio starting here with my guitars. I'm not a guitar player I'm a hack at best. So I don't need anything fancy. This is my Fender Squire Telecaster which Fred squires is pretty damn good There's a saying that not all squires are built equal and this one is one of the better squires that I've played in I remember everyone in the stores like yeah, that's a killer squire over here is my PV Bass which again PV sometimes is considered a bit of a joke when it comes to especially basses and guitars but I gigged with this thing for years and This neck has never been adjusted that neck is solid. It never moves It rarely ever needs a setup or intonation done on it So it's a great bass and I got a killer deal on it and it looks pretty badass, too Over here is another bass. This is the epiphone hollow body Jack Cassidy bass and There's a bit of a story behind this guy actually my good friend Ian found this in the garbage Enrichment BC it was stripped of all the hardware and electronics and he always knew that I wanted a hollow body bass, so he shipped it to me here in Winnipeg and We put it together me and the guitar tech that works at the store that I run put it together So this basically cost me parts and now it's it and Basically the bass I've recorded with now Doesn't play great. It's a bit of a beast play, but it sounds good really good recorded So over here is kind of drum machine and Sequencer land so starting over here on the right Is your cheerio beat step pro and if anyone has watched my synth jams that I do This thing basically sequences Anything I'm not playing with my hands. So it has two sequencers on it plus third sequencers specifically to be run to a drum machine, so The drums when I do the synth jam come us from the this guy here, which is just a sampler There's a ton of bray intro So anything that's drums is triggered to this guy and I have an Akai rhythm Wolf here, which is another analog drum machine and Very lo-fi sounding. I don't use it much, but if you need like a lo-fi Vintage type Drum machine that's kind of a go-to for me for that kind of stuff Next to it is the newest addition to the studio the Archeria mini freak Haven't much haven't had much time to really use it But I do plan on using it a lot in the future especially for anything that really needs to sound Kind of modern and futuristic as opposed to the usual retro stuff I do so that's where this guy will fit in Down here is kind of drum machine land So I got the Volca series here by Korg all very small, but very powerful drum machines Like the Volca beats here is an analog drum machine and that kick drum Sounds so huge. He may not be able to tell here, but um It's worth price of admission for that kick drum sound alone Up over here is the Volca drum and again a super underrated little drum machine This thing could do wonders for really weird wacky cool sounding electronic drums I've used it quite a bit and over here the Volca sample, which is a sample-based drum machine The only thing is the panning ass to load samples in doesn't use like an SD card or anything You have to do it with like the mini jack This is pain in the ass, but the built-in sounds aren't bad Over here is the Waldorf blow fell which I don't use tons, but when I do my synth jams This thing is being triggered by the beat step for a lot of the sequences because This guy can actually do like eight parts or more Maybe I think it's even 16 parts at once So if you wanted to you can trigger 16 different sequences from Another source into this guy Over here is my rack Nothing too fancy. I'm not really into super expensive or vintage gear if it gets the job done it gets the job done really So basically just a bunch of mic preamps That's mostly what's in here little compressor there headphone amplifier for every need to record a Band which is basically never but I've done at the odd time down here is My pedal board from back when I used to gig I busted it out. It's been in hibernation for about seven years So those are all bass effects down here. I don't know if you can see it But I have a another rack mount synthesizer Yamaha Tx81 zed, which is a FM synth so for any retro type stuff That comes in really handy over here to studio monitor land I got ns-10s and care k vxt fours now the ns-10s you might recognize they are kind of the staple Studio monitor used for decades and decades and they're you're basically the studio monitor and there's actually a story behind how I got these I was doing a PA install at a place and they were using these ns-10s as PA speakers, which is fucking ludicrous So I asked the guys you mind if I take these home because they weren't using them So I got a free pair of ns-10s that being said I had to replace a tweeter and a woofer on on each one of these So they weren't free free, but still not nearly as expensive as Having to buy them so over here to computer land. This is the laptop.  So it's not bad for video.  I'm just waiting for it to die on me Over here is more synthesizers. So the Juno 106 is a classic classic 80 synth Very sought-after these days and if you want that 80s sound, you know Juno 106 I use this pretty much on tons and tons of the music made for the Channel because it just has that horror movie retro sound Down over here is another oldie occurs. Well k2000s. This goes back to like the early 90s And it takes floppy disks.  So I don't move this thing unless I really have to these days Over here is the Novation KS4 again, I used to gig with this thing all the time Pretty badass synth is a virtual analog synth, but you can do four parts on it So it's good for gigging because you could split it or layer it Four different places if you need to over here is the Novation base station, which is a Fantastic monophonic analog synthesizer again. I use this on almost every song that I made Since I got this thing It's just got such a huge beefy analog sound I love it and it's great for building sequences because the onboard sequencer is is really damn good And that's kind of about it You know over here on the floor I got a TASCAM forward track tape recorder that I'll probably use to make the tapes when I release the frightfully forgotten volume 2 on cassette and That's pretty much the studio in a nutshell if you got any questions about any gear here You know feel free to let me know in the comments because I am a bit of a gearhead if you're still tuned in Thank you very much for sticking around for the studio tour Like I said if you have any questions about the gear and stuff and how it works Let me know in the comments because I love this kind of stuff Just a reminder to keep your eyes peeled in the next couple of weeks We will be making another physical media release announcement so keep your eyes peeled and stay tuned and Until then feel free to go down to bandcamp and pick up the album either digital download or physical copy if you like


Hey there, Mighty Max, and welcome to a very fun field edition of Ask IU. Woo! Today, we'll be meeting our accepted students and potential future Mighty Max. Let's go. Where are you going to college next year? Immaculata University. That is such a great answer. I love that answer. Rumor has it, you're coming to Immaculata. Duh! Duh, obviously. I think I have a pretty good, uh, pretty good idea. I'm going to Immaculata University. What are the odds? That's fantastic to hear. Where are you going to college next year? Do you know? I'm not sure yet, but Immaculata is definitely a top choice. It's okay. You can say Immaculata. Your other schools won't rescind your acceptance. I think I have a pretty good idea, too. I like these ideas. These are really good ideas so far. Here? That's amazing. I love that. So, why are you coming to Immaculata? Um, the nursing program is really nice, and the new college they build with it is beautiful. I know that. I have heard that rumor. Looking at the campus, it's a really nice environment. Everyone has been so nice, so chill. Because I really like the school. I like how everyone's so nice. I feel very comfortable, too. That's so good to hear. All the teachers are really awesome, and it's really pretty. The soccer team, you know, you did a fantastic job with recruiting. The teacher-student ratio seems nice, and just the community. I just love the campus. I've seen, like, the classrooms, and it looked pretty interesting. Everyone I've met here has been really nice.  What are you going to miss the most about high school? Probably my friends, too. My best friends go into Florida College, so. That's so silly of them. Tell them to come to Immaculata instead. Probably my friends. Probably because we all live so close to each other. It's like you're leaving home, you know? You've been there for four years. It's not going to be easy. Probably my bed, honestly, and my house. Probably my friends. Are they going far? Yeah. That's so silly. You should just tell them to come here. Yeah. Probably the friendships, but I'm happy that, like, I'll probably most likely be able to meet friends here, so. Most definitely. The friends that I have now and not seeing them every day. Well, they all come to Immaculata, then. Probably all of the friends and memories I've created there. Totally get it. But what are you most looking forward to at Immaculata? Doing the same thing over again. Meeting more new people and creating more memories that will last a lifetime. Creating a new home. Definitely the professors. A lot of them are really nice. They're like talk jobs. Yeah? Yeah, which ones have you been talking to? I'm kidding. No, they're all fantastic. I'm playing field hockey, so I'm pretty excited about that and, like, meeting new people.  You know, making new money, making new friends. Everyone seems to know each other, and it's, it feels like it's like a family that you know nobody, but then you're already a part of it. That's such a good answer. I love it. It's tearing me up a little bit here.