Hello everyone, here am I again for a short talk in this BioXL summer school about our efforts using Hadock for drug repurposing against COVID-19. And this is yet another view of Pula where we should all have been this week if it wasn't for COVID-19. So let's take first a look at SARS-CoV-2 and a key protein which might be interesting to target in a design of drugs to stop the virus. For this I'm going to use the following paper which was published quite recently in Nature and gives quite a nice and broad public overview of the key proteins, their function and how to potentially disarm them with drugs. And the first one of course of which you are probably all very well aware and some of you have even been working on it is the spike protein which sits on the surface of the virus and interacts with the angiotensin converting enzyme 2 to enter our cells. So potential target to prevent cell entry will be to block these interactions. Another critical protein of the virus is the main proteins. So when the virus enters our cells it delivers its viral RNA and the viral RNA is then translated into proteins, long strands of proteins in our cells. And one of the first protein produced is the main proteins also called M-pro but also 3Cl-pro depending on nomenclature. And this proteins is critical for the virus because it cleaves the polypeptide chain produced into functional proteins and these are the non-structural viral proteins which the virus needs for its further life cycle. So by blocking the main proteins we can in principle block this process. Now there are of course quite a number of structure of the main proteins already available. Basically we have homologous proteins from SARS-CoV-1 basically but also recently as a response to the pandemic the structural biology has been working a lot on those systems and now you see here a structure release on the 6th of May which is a crystal structure of the main proteins with an inhibitor bound to it. Now the third actor in this process is yet another protein which is further down in the life cycle of the virus. It's the RNA dependent RNA polymerase also called RDRP. Now what does this protein do? It actually produces viral RNA and this RNA is needed for the virus to increase the production of proteins but also to produce its own viral RNA material to be packed into new variants. So if we can block this particular protein again we're going to block this process, this RNA production which will again stall the virus. Now the structural biology community again has been working hard in the last months to generate structure of these and you see here one structure of the RNA dependent RNA polymerase which was published a few months ago in Science and this is a cryoelectron microscopy structure at 2.5 angstrom resolution which contain triphosphate form of remdesivir a well-known say antiviral or promising drugs in this case which we've been hearing a lot about in the news. So we have structures for all of those three key proteins. Now there is much more structure known about the viral protein. If you want to look more at the structural landscape of the virus proteins you can take a look at this covid-bioxcel.eu-proteins website which has been set up in collaboration with the more SSI in the US and collects a lot of information about the structure, the proteins of the virus but also all kind of information about simulations, molecular dynamic simulations, docking model and all of that. So it's really a hub for all the structural and modeling work done on the virus. Now how are we going to target the virus using HADOC? What are our strategies actually to perform small molecule docking in HADOC? We have three main strategies that have been used so far. So the classical one will be what we call binding site driven docking and that's pretty much in line with what I've been telling you in the first lecture introducing HADOC where we are going to define some information about binding site. The second one is what we call the template-based docking strategy. Here we need to rely on existing structure or molecular structure of the target protein with some other drugs. This is a strategy we developed along the Grand Challenge D3R, a Grand Challenge. This is a blind competition for the prediction of protein small molecule complexes to which we participate for three times actually. And the last one is a rather new and recent development in HADOC where it's also template-based so we need to have some structural information but we're going to use a shape, basically shape information as restraints to drive the docking. So now I'm going to shortly explain to you those three strategies before coming to actually the results of the screening that we have been doing. Now binding site driven docking is a classical HADOC type of approach. In case of small molecule binding to a receptor, we usually know the binding site on receptor. So we're going to target this binding site by defining the residue in the binding site as active in the HADOC setting. So HADOC means that those residues should make contact with the ligand otherwise there's a penalty. Now this is pretty similar like if you think of small molecule docking software like Otodoc there you define a box around the binding site.  In the case of the SARS-CoV-2 proteins we know actually where the active site is so we can't define this information. Now in the rigid body docking stage again the ligand is defined as active. So what we have effectively is a set of restraints that are going to draw the ligands into the binding pockets at the rigid stage. Compared to our standard protein-protein docking we increase the weight of the van der Waals energy term in the scoring function to favor pauses that have a good van der Waals interaction. The default for protein docking will be 0.01. Now in the second phase of the docking where we're going to perform a flexible refinement of the pauses and final refinement in explicit solvent or not we change the restraints that we are using. So now only the ligand is active and the receptor binding site becomes passive. This allows the ligand to explore the binding site. It means that if some residues in the binding site are not contacting the ligand there is no more energy penalty for that. The only requirement is that the ligand should contact some residues in the binding site. So this gives freedom to the ligand to explore the binding pockets. We also change slightly our protocol for small molecule docking by removing the high temperature step in the simulated annealing part which is done here to refine the interface of the molecule. And we also decrease slightly the temperature which we do those simulations. Finally we change for small molecule this is rather recent setting. We change the weight of the electrostatic intermolecular energy term to 0.1 so 10% of the score instead of 20% and this protocol has been described in the paper showing our first participation to the D3R Grand Challenge. So this is a classical Haddock type binding site driven docking but adapted to small molecule. Now the template-based approach is a different one. So for this we need first to have available 3D model of related receptors, homologous receptors or ideally the same receptor that you are targeting but with over-ligands bound to it. Now to select which receptor we are going to use for the docking we first look at what kind of ligands are bound to those receptors and we are going to select the receptor which has the most similar ligand to the ligand that we have to dock. And we do this similarity measure by using the TANIMOTO coefficient and you see here two different ligands that share here a common substructure. So we select the receptor which has a ligand with the largest common substructure to the ligand that we have to dock. Now how to sample ligand conformations? We do some flexibility during our docking but often this is rather limited. So from the small string which are simple text string describing the chemistry of the ligand we generate 3D conformers up to 500 pair ligands so some of them don't reach 500 if they are only few rotatable bonds. And for this we use the OpenEye Omega Toolkit which is free for non-profit usage. So we find it quite performing very well to generate relevant conformations. Now we are going to... So the receptor that we have selected contains a ligand which has a given conformation it is not the ligand we want to dock but there is information in there. So we are going to compare the conformations, the shape of those 500 generated conformations with the shape of the ligands in our template receptor and we do that by matching the shapes basically and also matching kind of the pharmacophore model and this is done using OpenEye rocks. So we are going to select the 10 best conformers out of the 500 that we selected which have the highest combined score and these 10 conformers will be given to Hadock for ensemble docking. So we start from multiple conformations of the ligand. Now in this template based docking protocol which we use in D3R round 3 and 4 so we skip the first two stages of Hadock so we don't do the rigid body docking, we don't do the flexible interface refinement and we can do that because we superimpose the selected conformers onto the crystallographic ligands in the receptor that we selected and this superposition is done using the OpenEye shape TK software. And what we do is only run the final refinement stage of Hadock basically this water refinement that you see here. So this was a very successful strategy in D3R Grand Challenge 3 and 4. This strategy has been published in this paper so you can look up all the detail and what you see here are the 20 ligands that we had to predict for Grand Challenge 4. In yellow is the crystal structure and in blue are our predictions. So we see we did very well so the top one, the medians of the top one pauses for all those 20 is 1.5 and the best is 1.2. So this was a very successful case. Now we didn't follow this particular template-based protocol for our efforts against COVID but we follow a new protocol which is still template-based but now we're going to use the ligand information from the template in a different way to define a shape that we will use during the docking. So the identification of the receptor and with the more similar ligand is the same. So we have a template structure which contains a ligand which is the most similar to the ligands that we have to dock. We transform this ligand information into a shape and these are basically dummy atoms that are inside the receptor and then we define restraints from the ligand that we have to dock to those shapes. Now the way the restraints are defined depends on the size of the, so if the shape is larger than a ligand we're going to define the restraints from the ligand to the shape which will cause that the ligand should overlap with the shape and vice versa. If the shape is smaller we define the restraints from the shape to the ligand. And now we don't need to pre-select conformations, so we can use all the 500 conformations that we generated with open eye to dock into the receptor. So now we do again a full-fledged docking but the advantage of the shape is that it can induce, it will select the most suitable conformation to fit into it and it will also induce more conformational changes during the refinement process. So that's the protocol that we're going to apply to some of the cases for this COVID work. So now coming to our drug repurposing screen using Haddock against the three main actors of the virus. So what did we do? We selected approved small molecules from drug banks. So these are approved drugs that have passed clinical trial and are in use for all kind of different applications. We wanted to limit the system. We don't want to take very, very small molecule, there is all kind of stuff in drug bank and we didn't want to take too large ones so that these make reasonable drugs. We have an atom filter. We discarded any drug containing metals, organometallic compounds because simply our Haddock server cannot handle those. The way that we generate the topology and the parameters for the small molecule is using pro-drug and we cannot handle organometallic compounds there. So this gave us about 2,000 compounds from drug bank. We added a few more compounds that are basically the active form of some of the drugs. Some of the drugs are pro-drugs meaning they need to be processed into the cells, into the active component. For each of those we generated up to a maximum of 500 conformations again using open eye so not all ligands have this and you see here the distribution of sizes in terms of number of atoms for the ligands that we selected. Now here is the first actor, so that's the main protease, main pro for which we have a crystal structure. All the work by the way has been done by Manon Reo and Panos Kukost, they are both postdoc in my lab and Panos was a former PhD student. So what did we do, the docking protocol? So they are quite a number of known ligands for this main protease also from SARS-CoV-1 and in the last months the diamond synchrotron released quite a number of small fragments screening data sets so they have been crystallizing the main protease with all kinds of small molecule fragments so we have a, we use a set of 58 fragments in the binding site and also some other structures with ligands. As non-structure, so they are two of dorms, there is an apo and hollow form plus they are many additional structures. So this was a starting point of information for the receptor and this is a starting point for the ligand and the protocol that we follow here is the shape-based docking protocol that I just explained before. We also follow the slightly adapted version of our shape base where we make use of pharmacophore information. So we're going to define a shape which represents the pharmacophore information, so what you see here are all the small fragments and ligands that we extracted for the main protease and from these we derive a pharmacophore model. So we can represent still this pharmacophore model as a shape consisting of fake beads but the beads now have properties that link them to a special type of atom and when we define our restraints we can make specific group of atoms overlapping with specific beads in that case. So we did basically two screening, screening 2,000 compounds twice against the main protease. So how did we do this kind of computation? It's a lot of computation, so we could do the entire screening of 2,000 compounds in about three and a half days using high throughput computer resources provided by the European Open Science Cloud project and also the EGI. The web server of Haddock has been operating on this kind of resources since more than 10 years now and this is what is powering all the machinery behind the server allowing us to serve a large user community. And you see here the week of, so this was April, in about a week time you could see the number of jobs running at different sites around the world. So this is more, a lot of sites are actually in Europe but you find the Open Science Grid here, you find sites in China for example, this is Beijing. So all those sites are supporting our efforts. Now what came out of all of this, so these are the results already. So you see here on the bottom left, the top 10 compounds that come out of the screen. So you see the score for the pharmacophore model, for the shape-based aluminum model and here we report the best score of the two and this is how we rank the model. Now this table can be sorted as you wish and everything is available on the bonnetlab.org.covid site. There is also a graphical view of the results, so you see the Haddock score in arbitrary units, this is important to remark because these are not binding affinities and if you click, if you hover a point here it will tell you which drug it is. So when we look at the results, so you have to be very careful when you look at the docking, it's in-cylico docking so it doesn't have to mean anything relevant, but when you look at your top compounds, it was already interesting to see in the top 10 here we have recovering 3 protease inhibitors and if you look in the top 100, we find several drugs that are actually in clinical trial currently in Europe. So this was already a good sign that our scoring function is able to recover drugs that are actually relevant for this. Now just a side note, Hydroxychlorine with chloroquine which was mentioned as a potential fantastic solution to COVID-19 is not ranking very well in our screen, it's around 800. Now the same was done for the INE polymerase, the entire screen here took about six days because it was a larger system, we actually to cut some domains that were not relevant for the binding site that we are targeting. So here we didn't follow a template shape-driven docking protocol for the reason that it was not enough structural information to follow a template-based protocol so we went back to our say classical hydro-combining site-driven docking protocol. Again you see here the top 10 everything is available online so you can go look at it and you see an interesting compound which is the active form of Remdesivir, Remdesivir triphosphate which scores at number five. Now here currently we are also targeting now the angiotensin converting enzyme receptor so the idea here is to block the receptor in a rather closed form which will prevent the binding to the spike protein but these calculations are running as I speak. Now if you have noticed carefully the ranking that I presented you then you should become aware that there is a potential problem with all this in-cylical work. So we have what we will call frequent haters. So the top rank compounds in both cases for the main protease and the RNA dependent RNA polymerase is septolazone, a beta-lactam inhibitor and this is a highly polar compound you see here the structure. So is this an artifact or is this a miracle drug? So one way of looking a little bit into that is to take a completely unrelated target and in this case we took di-refolate reductase which has nothing to do with the virus. We took the top 20 compounds predicted by a rank by Haddock for the main protease and RNA polymerase together these were 36 compounds and we docked against DHFR and you see that septolazone is also ranking on top for DHFR but DHFR has nothing to do with the virus. So that makes it very suspect. So it's a sticky compounds that always score well. So you see here the DHFR rank, the main protease rank and the RNA polymerase rank. So this one is one everywhere, all the place. But for example if you take the second compound for the main protease it's rank number two. It's ranking pretty poorly for the RNA polymerase which makes sense and it's only 20 for the DHFR. So by looking at this kind of analysis and data we can exclude some drugs for further testing. So where does this all take us in terms of conclusion and perspective? So we have been doing a screening of approved drug against the three targets and the last one for AC2 is ongoing and should finish actually this week. The most interesting compounds for the main protease are actually being studied by now by Molecular Dynamics in collaboration with Attilio Vargio and Giuliano Malocci from Calgary who are also co-organizing this by Excel summer school. So a strategy to block the virus waiting for a potential vaccine will be to design a cocktail of drugs as is done for HIV. We still have no vaccine for HIV and the therapies these days are using cocktails of drugs that are targeting different proteins like the three main actors I discussed about. Of course all of these predictions will need to be validated and we are planning to do that in collaborations with people in Utrecht and also in the context of a large european innovative medicine initiative project. What could also provide insight into the validity of our predictions are epidemiological data meaning that by analyzing patients that have suffered from COVID-19 and we might see that some group of patients have less affected by the virus than others and those groups were maybe already taking some drugs which are in the in the screen that we are doing. So if this kind of data become available we can cross-check our predictions. To finish with I want to acknowledge the people who have been doing most of the work so this is Panos and Manon in my group you've seen their pictures. I want also to acknowledge Ed Moritz from the pharmaceutical sciences department who has all the knowledge about all these drugs and he's collaborating with us in coming to some prediction of what could be a potential cocktail which will then go into testing and of course the entire lab for every all their contribution and offering for supporting all our work including of course bioexcel. Thank you very much for your attention.


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Welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of the CUBE. We have a great guest coming in remotely from LA, Christian Hernandez, developer experience lead at CodeFresh, CodeFresh.io. Recently they were on our feature at AWS startup showcase series, season two, episode one, cloud data innovations, open source innovations, all good stuff. Christian, thanks for coming on this CUBE Conversation. Thank you, thank you, John. Thank you for having me on. You know, I was really impressed with CodeFresh when I met with the founders here on the CUBE because GitOps, AI, everything's something ops. DevOps, DevSecOps, you got AIOps, you got now GitOps. Essentially operationalizing the software futures here and software's eating the world, as was written many years ago. But it's open source is now all soft. All things software is open source and that's kind of a done deal. It's only getting better and better. Mainstream companies are contributing. You guys are on this wave of this open source tsunami and you got cloud scale, automations right there, machine learning, all this stuff is now the next gen of code, right? So you're at CodeFresh and your title is developer experience lead. What does that mean right now? What does it mean to be a developer experience lead? Like you make sure people have a good experience. Are you developing? You figure out the product. What does that mean? Yeah, that's, and it's also part of the whole DevRel explosion that's happening right now, I believe.  What does that mean developer experience? What does that mean? So you kind of hit the nail on the head a little bit up there in the beginning is that the experience of the developer when using a particular platform, right? Especially the CodeFresh platform. That is my responsibility there at CodeFresh to enable end users, to enable partners, to enable anyone that wants to use the CodeFresh platform for their CI CD and get ops workflows. So that's really my corner of the world is to make sure their experience is great. So it's really what I'm here to do at CodeFresh. You know, one of the things I can say on my career, you've kind of become a historian over time.  And, you know, that was a workflow, right? Now, with the cloud, I was talking to one of your founders, you got new abstraction layers. Cloud has changed again, again, open source. So newer things are coming, right? Like Kubernetes, for instance, is a great example that came out of the open source, kind of the innovations. But that, and Hadoop, we were mentioning before he came on camera from a storage standpoint, kind of didn't make it because it was just too hard, and it made the developer's job harder, and then it made the developer's requirements to be specialized. So you had kind of two problems. You had hard to use, a lot of friction, and then it required certain expertise when the developers just want to code, right? So you have now the motion with GitOps, you guys are in the middle of, kind of this idea of frictionless based software delivery with the cloud. So what's different now? Can you talk about that specific point? Because no one wants to do hard work, have to redo things. Yeah, shift left and all that good stuff. What's hard now? What do you guys solve? What's the friction that you're taking out? What to become frictionless? Yeah, yeah, and you mentioned a very interesting point about how things that are coming out almost makes it seem harder nowadays to develop an application. You used to have it to where, kind of a sort of a waterfall workflow where you develop your code, you compile it, right? I guess back in the day, Java was king. I think Java still has a large footprint out there where you would just compile it and deploy it. If it works, it works all right, cool, and you have it, and you kind of just move it along in its process. Whereas I think the whole idea of, I think Netflix came out with the fail-off-in, fail-fast, release-off-in, the whole Atlassian CICD thing, Agile thing came into play where now it's a little bit more complex to get your code out there delivered, to get your code from one environment to the other environment, especially with the advent of Kubernetes and cloud-native architecture where you can deploy and have this immutable infrastructure where you can just deploy and automate so quickly, so often that there needs to be some sort of new process now into place where to have a new process like GitOps to where it's frictionless, meaning that it makes that process a little easier, makes that complex process of deploying onto a cloud-native architecture easier. So that way, as you said before, returning the developers back to what they care about more, the most is just code. I just want to code that. You know, the other thing, cool thing, Christian, I want to bring up, and we'll get into some of the specifics around Argos, specifically CD, is that the community is responding as a kind of, it takes a village kind of mindset. People are getting into this, they're saying, hey, we can get our act together around some de facto workflows and de facto capabilities. Everyone wins. It's a rising tide, floats all boats kind of concept. CNCF certainly has been a big part of that. You've even seen some of the big hyperscalers getting behind it. But you guys are part of the founding members of the Open GitOps working group, Amazon, Azure, GitHub, Red Hat, WeaveWorks, and then a ton of contributors, okay? So this is kind of cool. This means that there's like people behind this thing, look, we got to get here faster. What happened at KubeCon this year?  Can you take a minute to explain two things? One, the open community vibe, and then two, what you guys announced at KubeCon in Spain. Yeah, yeah, so as far as Open GitOps, that was, you know, as you said before, CodeFresh was part of that founding committee, right? A group of people trying to figure out, define what GitOps is, right? We're trying to bring it beyond the hype word, right? Beyond just like a marketing term to where we actually define what it actually is because it is actually something that's out there that people are doing, right? A lot of people, you know, remember that the Chick-fil-A story where it's like they are completely doing, you know, this GitOps thing, we're just now wanting putting definition around it. So that was just amazing to see out there in KubeCon. And like you said, in KubeCon, we, you know, we're taking some of that acceleration that we see in the community to, and we announced our hosted GitOps offering, right? So hosted GitOps is something that our customers have been asking for for a while.  And, but with all that comes like the feed and care of that platform.  And on top of that comes like the scale aspect of it, right? And so with scale, so a lot of people go with like a hub and spoke, others go with like a fleet design, in either case, right? There's a challenge for the feed and care of it, right? And so with CodeFresh, hosted GitOps, we take that management headache away, right? So we take the management of Argo CD, the management of all of that and kind of just offer Argo CD as a surface, right?  And so they can concentrate on, you know, their application deployments, right? And you also get things like Dora metrics, right? Integrated with the platform. You have the ability to integrate multiple CI providers, you know, like GitHub actions or whatever existing Jenkins pipelines.  Yeah, I mean, that whole single source of truth concept is really kind of needed. I got to ask you though, the popularity of the Argo CD on GitOps internally, right? That's been clear, right? Kubernetes, the way that's going, it's accelerating fast, people want simple, it's scaling and you got automation built in, all that good stuff. What was the driver behind the hosted GitOps solution? Was it customer needs? Was it efficiency, all the above? What was specifically, and why would someone want to have the hosted versus say internal? Yeah, so it's, it was really driven by, you know, customer need, right? It's been something that the customers have been asking for. And it's also has been something that, you know, you have a process of delivering an application to, you know, a fleet of clusters. In a traditional, you know, I keep saying traditional GitOps practice as if GitOps was so old, in, you know, when people first start out, they'll start, you know, installing Argo CD on all of these clusters and trying to manage that at scale. It's, it seemed like there was, you know, it'd be nice if we can just like be able to consume this as a service. So we don't have to like worry about, you know, best practices, we don't have to worry about security. We don't just, all of that is taken care of and managed by us at Codefresh. So this is like something that, you know, it's been asked for and something that, you know, we believe will accelerate, you know, developers into actually developing their applications. They don't have to worry about managing the platform. So just getting this right, hosted managed service by you guys on this one. Correct, yes. Okay, got it. All right, so let me, let me get in the Argo real quick, just to kind of just level set for the folks that are leaning into this and when kicking the tires, where are we with Argo? What, why was it so popular? What did it do specifically? Did it just make it easier for developers to manage and monitor Kubernetes, keep them updated? What was the specific value behind Argo? Where did it come from? And why is it so popular? Yeah, so Argo, the Argo project, which is made up of a few tools, usually when people say Argo, they meet, they, they're talking about Argo CD, but there's also Argo workflows, Argo events, Argo notifications, and like I said before, CD with that, and that is something that was developed internally at Intuit, right? So for those of you who don't know Intuit is the company behind TurboTax. So for those of us in the US, we know, we know that season all too well, the tax season. And so that was a tool that was developed internally. And by the way, Intuit, we've done many here, they're very huge cloud adopters. They've been on that train from the day one. They've been, they've been driving a lot of cloud scale too. Sorry to interrupt. Yeah, and, and, and also, you know, they were always open source first, right? So they've always had, you know, they developed something internally. They always had the, the intention of open sourcing it. And so it was really a tool that was born internally and it was a tool that helped them, you know, get stuff done with Kubernetes. And that's kind of like the tagline they use for the Argo project is you get stuff done. They wanted their developers to focus less on deploying the application and more right, more than on writing the application itself. And so the, and so the Argo project is a suite of tools essentially that helps deploy onto Kubernetes, you know, using GitOps as that, you know, that cornerstone in design, right? In the design philosophy. It's so popular because of the ease of use and developer friendliness aspect of it. It's, it's, it's meant to be simple, right? And simple in a good sense of getting up and running, which attracted, you know, developers from, you know, all around the world, you know, other companies like Red Hat got into it as well. BlackRock also is a big contributor, thousands of other independent contributors as well to the Argo project. Yeah, Christian, you bring up a good point. I'm going to go on a little tangent here, but I want to get your reaction to something that Dave Vellante and I and our CUBE team has been kind of riffing on lately. You mentioned, you know, Netflix earlier, you mentioned Intuit. There's a kind of a story that's been developing and with traction and momentum and trajectory over the past, say, 10 years, the companies that went on the cloud, like Netflix Intuit, Snowflake, Snowflake not so much now, but in terms of open source, they're all contributing lift, they're all contributing back to open source, but they're not cloud providers, right? So you're seeing that kind of first generation, it's a massive contribution to open source. Open source has been around for a while, I remember the early days and we all participate on projects, but now you have real companies building IP, going open source first, because they're on a hyperscale cloud, but they're not the cloud themselves. They took advantage of that. So there's kind of this cycle of flywheel of cloud to open source, not from the vendors themselves, like Amazon with services or Azure, but the people who rode their CapEx and built on that scale, feeding into the open source and then coming back. This is kind of an interesting dynamic. What's your reactions to that? Do you see that super cloud kind of vibe there? Yeah, yeah. Well, and also I think it's indicative that, open source is not only a way to develop applications, a way to engineer your project, but also kind of like a strategic advantage in such a way, right? You see companies like, even like Microsoft has been going into open source, they've been going to open source first. They made a huge pivot to using open source as like a strategic direction for the company. And I think that goes back to a little bit for my roots. I always talk about, I always talk about Red Hat, right? I always talk about, I was in Red Hat previously and Red Hat being the first billion dollar open source company, right? We always joke is like, well, internally, like we were a billion dollar company that sold free software, how does that happen? But it's really built into the community, built into being able to tap into those expert resources. People love using software, people love the software they love using and they want to improve it. Companies are now just getting out of their way. Companies now essentially it's just like, let's just get out of the way, let's let people work on what they want to work on. They love the software, they want to improve it. Let's let them. It's interesting, a lot of people love the clouds of all this power. If you think about what we're just riffing on and what you just said, the economics and the organic self-governing has always been the open source way where commercial value is enabled if you play ball right, like Red Hat for instance. And now you're seeing the community kind of be that arbiter of the cloud. So hey, if everyone can create value on say AWS or Azure, bring it to open source, everyone benefits across all clouds hope eventually. So the choice aspect comes in. So this community angle is huge and I think it's changing a lot for the better. I think this is where we're seeing a lot of that growth and you guys have been the middle level with the Argo project and GitOps specifically in that sector. How have you seen that growth? What's some dynamics have you seen? Power dynamics, organic, is it governed well? What's some of the successes? What are some of the challenges? Can you share your thoughts on the community's growth around GitOps and Argo project? Yeah, yeah, yeah. So I've been, you know, part of some of these communities, right?  You know, I've seen things like GitOpsCon, right? So, you know, being part of the GitOps open GitOps community, you know, one of the things we did was we did GitOpsCon. It started as a meetup, you know, a couple of years ago and now, you know, it was a, you know, we had an actual event at KubeCon in Los Angeles.  It was a second largest co-located event at KubeCon. So that just seeing that community and, you know, from a personal standpoint, you know, being part of that, the community, being the event chair, right? Being one of the co-chairs was a moment of pride for me being able to just stand up there and just seeing a sea of people. I was like, wow, we just started with a handful of people at a meetup and now, you know, we're actually having conferences and speaking of conference, like the Argo community as well, we put in, you know, we put on a virtual only event on ArgoCon last year. We're going to do it in person today, you know, this year. You have a date on that? You have a date on that ArgoCon 22? Yeah, yeah, yeah, ArgoCon September 19, 2022. So, you know, mark your calendars if it's, you know, it's a multi-day event, you know, it's part of something else that I've seen in the community where, you know, first we're talking about these meetups. Now we're doing multi-day events where, you know, in talks of the open get-ups, you know, get-ups can also make that a multi-day event. There's just so many talks and so many people that want to be involved in network that, you know, we're saying, well, we're going to need more days because there's just so many people coming to these events, you know, and, you know, seeing these communities grow, not just from like the engineering standpoint, but also from the end user standpoint, but also from the people that are actually doing these things and, you know, seeing some of these use cases, seeing some of the success, seeing some of the failures, right? Like people love listening to those talks about postmortems. I think they're part of my favorite talks as well. So, seeing that community grow is, you know, on a personal level, it's a point of pride for me. It's like CSI for software developers. You want to get to know what happened, you know. You know, it's interesting you mentioned about the multiple events at KubeCon. You know, the vibe that's going on is a very festival vibe, right? You have organic groups coming together. I remember when they had just started doing the day zero programs. Now you have like almost like multiple stages of content at these events. It feels like like a Coachella vibe or some sort of like festival vibe. Like a lot of things going on and you pick your kind of area, but you can move around. I find that the kind of the format du jour, I think is going well these days. What do you think about that? Yeah, yeah, no, for sure. It's, and I love that analogy of Coachella. It does feel like, you know, it's there's something for everyone and you can find what you like and you'll find a little, you know, a little group, right? A little click of people.  Like the security guys, they, you know, you see them all clumped together, right?  It's a code party. Yeah, it's a code party, for sure, for sure. Nerdfest on steroids. Hey, I got to get, I want to wrap this up and give you the final word. Christian, thanks for coming on. Great insight, great conversation. This is a huge, you guys are in the middle of a hot area. Obviously large-scale data growth. Kubernetes is scaling beautifully and making it easier at managed services is what people want. Machine learning is kicking in and you get automation building in, all favoring the developer and CICD pipelining and all that good stuff. People want to learn more. Can you take a minute to put the plug in for CodeFresh on the certification? How do I get involved? Where are, is there levels?  Yeah, yeah, for sure. So CodeFresh is offering a free certification, right? For GitOps, or Argo CD and GitOps. The first of its kind for Argo CD, first of its kind for GitOps is you can actually go get certified with Argo CD and GitOps. Their level one is out right now. You can go take that codefresh.io slash certification. It's out there to sign up. You don't need to pay anything, right? It's a free course you could take. Level two is coming soon, right? So level two is coming soon in the next few months, I believe. I don't want to quote a specific day. But soon. But soon, soon as in months, right? So we're counting that down where you can not only level one level certification but a level two more advanced certification for those who have been using Argo for a while. They can still, you know, take that and be able to get another level of certification for that. So also, ArgoCon will be there. We're part of the programming committee for ArgoCon, right? This is a community driven event, but CodeFresh is a proud diamond sponsor. So we'll be there. Where's it located? It's set for September 19th, multi-day or one day? It's a multi-day event. So ArgoCon from 1920 and 21 in Mountain View. So it'll be in Mountain View in the Bay Area. So for those of you who are local, you can just drive in. Great, I'm gonna write that down. I'll plug it. I'll put it in the show notes. Awesome, awesome, yeah. And you will be there. So you can talk to me. You can talk to anyone else at CodeFresh talking about Argo CD. You know, find out more about hosted getups, codefresh.io. You know, you can find us in the Argo project, open getups community. You know, we're deep in the community for both Argo and getups. So, you know, you can find us there as well. Well, let's do a follow-up when you're in town. It's the only couple of months away. Getting through the summer, it's already, I can't believe events are back. So it's really great to see face-to-face in the community. And it was responding, you know, KubeCon in October. I think that was kind of on the, that was a tough call. And then get to see your own in Spain. I couldn't make it. Obviously I had COVID, came down with it, but our team was there. Open source is booming, continues to go the next level. New power dynamics are developing in a great way. Christian, thanks for coming on and sharing your insights as the developer experience lead at CodeFresh. Thanks so much. Thank you, John. I appreciate it. Okay, it's a Kube conversation. I'm John Furrier, host of theCUBE. Thanks for watching.


An echocardiogram is an ultrasound of the heart. It uses standard 2D, 3D and Doppler ultrasound to create images of the heart. Pre-procedure, you will need to remove your ornaments, accessories, purse, belt etc. During the procedure, you are given a hospital gown to change into and settled comfortably in a private area throughout. The technologist applies conducting gel on your chest, stomach and neck and uses an ultrasound probe to get different views of the heart. In case of female patients, the procedure is always performed by a female technologist. The technologist will then remove the electrodes and wipe off the excess gel. The procedure takes about 15 minutes and is completely painless. Post-procedure, you may proceed with your routine daily activities and the report will be issued within 13 minutes at the reception.


Okay, so let's try this structure. What kind of compound is this one? A bicyclo. A bicyclo again, okay. So, I'm going to write bicyclo because we're trying to name this. How many carbons are in this thing? So, what kind of bicyclo is it? It's a decking. So, it's a decking. And in all bicyclos, right, we're going to have these brackets. And in there, we're going to have how many numbers? Three. Three numbers, okay. And those three numbers are going to be the numbers that are the number of carbons where? The bridge head. From the bridge head, right? From the bridge head. And we're going to label them in what order? For them. Largest to smallest. Okay, so let's try that, okay. So, what's going to be the largest number that we'll put? Four. Four. In fact, there's two of them, right? Two of them, right? So, let's count them. So, the bridge heads are here and there. Am I correct? Yes. One, two, three, four. And one, two, three, four. So, we put four dot four. But I thought we were going to put three numbers. What's the third number? Zero. Zero, very good, right? Because there's no carbons this way in between the bridge head. Okay. Does anybody remember the common name of this molecule? Anybody remember? Not a single person, huh? How about if I do this? Does this help? Trans-deckling. How close? Trans-deckling. Deckling. Trans-deckling, okay. Remember, it's trans, right? Because this carbon here is up and this one here is down, okay? So, it's trans because of the carbons. Trans-deckling. Any questions on that? Wonderful.


PDA is not caused by inadequate parenting or attachment. Oh, this is so important. This is so, so, so important. You know, much of the crisis that we find ourselves in as families and those bottoms that we hit, a lot of it has to do with the parent experience or the family experience along the way. I mean, part of my experience as a parent was the trauma of consulting with various other professionals who gave us all different kinds of labels and reasons. And a lot of it had to do with blaming me as a parent, saying that I was too anxious and I was making my child anxious or I wasn't being hard enough or I wasn't being consistent or I wasn't being flexible enough or I wasn't being a good role model. None of those things had anything to do with it at all. There are so many PDAs who have grown up in homes where they have been nurtured and loved and cared for. Their families have been responsive caregivers. They have been supportive and kind and flexible and accepting and the children are still PDAs because PDA is a neurobiology. It is a way of being in the world. It is a profile of autism. It is an autistic person. Poor parenting does not cause autism. Poor parenting does not cause PDA. And let's just dump the whole poor parenting label anyway. But please know if you are not yet convinced that your child's behavior or their inability to comply with demands or directions or instructions or to ask for help, if you are still not convinced that that is not your fault, stick around because this is so important. We as adults and carers need to be able to sit with our own trauma, our own fear, our own grief, our own sadness and process that for ourselves. And if we're walking around in the world thinking, my child could be a different person if I was or my child could have an easier, better life if I was a better parent. Oh, I can't encourage you enough to give that up right now. I really can't. We are all just who we are doing the best with what we have in any given moment.