Okay, good morning. My name is Garas Singh. I am product manager in OpenShift, and I have done it with me. If you would introduce yourself. Oh yes, my name's Darren Dunn. I'm with IBM Research. I'm the computational patterning team lead for Albany Nanotech, as well as the Thomas J. Watson Research Center. So I want to give you a view of what I'm hearing from my customers regarding Batch and this type of activity. So I've talked to a lot of customers. These are pre-government customers who come to me around Batch type of needs, right? So we talk to life-sense customers. They say we want to run genome simulations. We talk to manufacturing customers. They want to do computational food dynamics. We have very big presence in FSI or financial sector. They want to run their characteristics analysis. And then we have this roads is red hat data science, and they have this use case of running training jobs on top of a batch system. So when I hear all this customer, everything boils on into three characteristics. They come and repeat, you know, the correctness of the batch workload they're looking for. One is asynchronous run. What they say is we have this jobs. The jobs needs to run asynchronously and complete to give me the result. These jobs are computational heavy. You know, require GPUs, fast network fast storage, and these are massive scale. And since they're running on cloud, they need some type of elasticity. So listening to those customers, we are trying to address few problems either from red hat or open shift side or from community side. One is job Q is, I think the font is very small, but I'll talk through it. So what I've seen is what customers asking is, hey, these are my jobs, put it in a Q so before it goes and schedule. And that Q needs to be dynamic in the sense that the first job does not mean that it needs to execute first. I should able to prioritize things in the queue so that I'm gonna make sure my higher priority job is running all the time. I think community is working on something called Q. We have, we internally open shift is working on something called MCAT which is basically a app wrapper and dispatcher for queuing. Then a dynamic infrastructure, right? So we talked about Q. When a job comes into the queue, what customer wants is a cluster is a spinner for them, right? These are very expensive infrastructure. They do not want it to spin beforehand like think about GPU nodes and all. These are expensive. So they want whenever a job comes in the queue, they want infrastructure to be spin. And when there's no job, just deep provisioning the whole infrastructure so that there's no cost incurred. Second thing is they need to have specialized, specialized hardware into the infrastructure. For example, GPUs or you have a fast infrastructures or fast network that you want to use in. And there's a way to enable that within the platform. So things like operators. We use operator in open shift how to enable those operators within the infrastructure, looking at the job characteristics, saying that, hey, this job needs GPUs. So the infrastructure needs a GPU. Go install the GPUs and get the ready infrastructure before the job can be executed. Third thing is the gang scheduling. So what what customers says is, let's say I have 10 jobs and infrastructure has capacity for five to run five jobs. Make sure the jobs raise a weight until all the resources are available within the infrastructure. So all kind of all or nothing, run all jobs or nothing in the gang fashion. These are the trends that I'm seeing by talking to all these customers. So I mean, all these customers use cloud in one way or form for their application. Now they see benefit of it. They want to use the same benefit for their AI ML and HPC application. Multi hardware architecture. So what I've seen is what customers saying is, hey, to save the cost, I would be able to run my run my management on the arm architecture and compute on X86, right? So you need to have multi multi architecture in there. As raters, we talk about, they want to use GPU smartnakes fast network, everything that clouds present today. They want to leverage that into their into their workload containers. These guys now I've, I'm seeing a shift where customers of, you know, old HPC type of customer running on HPC workload on prem on a bare matter. They are transitioning, mostly a rehosting and looking forward to re-platforming their HPC type of workload into the containers. We talk about HPC a lot, but I've seen a very good use case coming on from AI ML site, where AI ML customers asking that, hey, I want this batch thing to run my AML jobs. So rather than HPC site, I'm seeing a lot of requests coming from the AI ML side of the business. Not only AML and HPC, but I've seen some another demand coming out from gaming, networking and telco site that they want to run on these, on a batch system. Next is Darren. He's going to talk about how we are running, which were EDA worker on OpenShift or Kubernetes. So I'll welcome Darren. Okay, so for my part of the talk, I'm going to give you a little bit of background about the two worlds that I bridge. I won't expect that anybody here knows the workload that I'm going to talk about. So I'm going to go into a little bit of detail so everybody understands. I will also go into a little bit of detail about why we want to do cloud burst. It's a very similar story to what everybody else wants to do and some of the benefits for us, particularly in semiconductor research and design for doing so. And then I'll go over some scaling results from our optical proximity correction workload using Red Hat OpenShift Container Service in the IBM cloud. So semiconductor research and development, my role is to span two worlds. I span process development and research. I also span hybrid cloud. And my sole role is to bring these two worlds together so that we can be more efficient and leverage more opportunities. So when you think about doing semiconductor research and design, you could boil it down to three main things. You need to be able to design new circuits and chips. You need to be able to transfer what you've designed to photolithography masks. There's no way you can do anything without being able to transfer a design to wafer with lithography and other processing processes. Once you've done that, you're not done. You need to develop processes that are capable of actually processing the lithography pattern that you have, transferring it to a workable circuit on the wafer. Today, what I'm gonna do is I'm gonna focus mostly on the transfer process. And I'm gonna talk about a project or process which involves what's called retargeting, which is taking a design shape and actually changing it slightly so that we can actually print it. And then using massively parallel, embarrassingly parallel HBC workload called Optical Proximity Correction to actually transfer the shapes to a photolithography mask, assemble it, and then write it to a photolithography mask and let the process development teams take over. So the tool set that I'm gonna talk about today, we use multiple tools. Today specifically I'm going to talk about a tool called Caliber, which is made by Siemens. These are commercial tools. Typically in engineering design automation workflows, most of the primary tool sets are commercial. So one of the challenges that you have is taking a commercial tool that has been written for a more typical HBC Linux cluster environment, containerizing it, and then finding efficient ways to run it with orchestrated containers on OpenShift or bare more vanilla Kubernetes services. So when we talk about this, what we're really gonna be talking about is taking this application. This application is meant to manage the transfer through the photolithography mask from EUV Lite or 193.2, an actual wafer. When you break this process down, actually, it consists of many different tool flows. When we finish with design, what ends up happening is we get a database file format, either GDS2 or OASIS. That file format contains a representation of design shapes that may contain upwards of 10 billion design shapes in a particular layer that we're going to transfer. As part of the process of figuring out ahead of time how we can take a design and print it on a wafer, we have to go through several steps. One of them is called design for manufacturing. That's actually taking the ground rules that were established for the technology, figuring out what actually has to happen, what's printable, what's transferable, and if we need to change design rules, go ahead and do that. We then need to use other shapes to fill the gaps in a design without impacting the performance of the design, and that's a tool and a technology all to itself. In many cases, what we're doing is we're decomposing a design into multiple colors and printing a color at the same time, and the reason for that is, is that as we get beyond the three-nanometer node, the five-nanometer node, even starting at 10-nanometer, we can't print the entire design with a single color or a single exposure. We need to find ways to break it apart so that we can actually pattern what we need to do. And then we talked about retargeting, where we take those shapes, we change them slightly so that we can actually print them. We will put insert features through code called sub-resolution assist features. Those are required to help the lithography actually print particularly intermediate to isolated shapes, and then we finally do optical proximity correction, and we also verify that the shape that we've created will actually print on wafer using modeling to within a certain tolerance or expected variance. So in a nutshell, what is optical proximity correction? You can think about this as an optimization problem. Prior to about the 90-nanometer technology node, you could actually take a drawn shape, multiply it by a scaling factor and transfer it to a lithography mask, and you'd have a pretty good probability of hitting the target for the design.  Starting at 90-nanometer, that wasn't true. So if you look at this diagram, without OPC, that's the drawn shape, you could think about the lithography process as a series of transforms. One transform is to transform it to the mask, the other one is to expose it, and the other one is to etch it on wafer. What typically happened at 90-nanometer and beyond is the shape that we brought in as a design shape no longer was representative on the wafer. In fact, in some cases, some shapes wouldn't print at all. So there was a technique called optical proximity correction that was developed in which you bring in the design shape, you partition it into vertices and edges, you then expose that partition shape or calculate what the aerial image would be, which is a very physical process, and you apply an empirical resist model to it to predict what the shape will print on wafer, and then you iterate until you either hit a fixed number of iterations or a cost function has been matched, and you then output that onto the photolithography mask. So you end up with a lot of very complex serif shapes when you do this. So how is this represented from a compute process? So this is actually a two-part compute. You have a primary POP process that reads in the design layout, chops it up into tiles and creates a queue or a heap. Once it's done with that, it spins up a bunch of remote workers on a traditional Linux cluster, in our case on an OpusShift or Kubernetes cluster, and then it starts parsing out work tile by tile to all of the workers, and it does this in a round robin fashion until all of the tiles and the queue or the heap have been exhausted. There's no need for the remotes or the worker processes to communicate with one another. So it's a very traditional hub and spoke process. So when we talk about this in a traditional sense and bursting to the cloud, what problem are we trying to solve? What we're really trying to do is expand our opportunity horizons to work on more and more projects. You can think about this as a triangle. We have three main resources that we're trying to optimize. One of them is compute. One of them are commercial licenses for the tools, and the other one is people, okay? The most sticky component that you have are people. It's not easy to expand a design team or an OPC team or a patterning team in short periods of time. The two things that you can expand are compute and licenses. So if you think about the semiconductor industry as a whole, there are more opportunities than capacity to take care of. And time to market is huge. So the faster that you can realize an opportunity, the more opportunities you can work on. So people spend a lot of time trying to figure out how do I maximize my opportunities? There's two ways that you could do this. One is to increase your compute. The other one is to increase the number of licenses, and these two have to go concurrently, okay? So if we have a smaller opportunity horizon that's referenced in the yellow, but we've got a lot of projects that we really want to explore, one of the best ways for us to do this is to burst to the cloud. But in order to do this, as was mentioned earlier, designers and OPC engineers are not cloud engineers, and cloud engineers definitely are not designers or OPC engineers. There's a communication gap, and there's an expectation gap. So if you could come up with a way that people are submitting jobs to a compute resource and you can expand the compute license and people triangle to maximize your opportunities and deliver more in the same period of time. So our cloud burst strategy from the beginning is to use OPC or Optical Proximity Correction as a proxy workload. We want to create the same on-prem infrastructure as we would use in the cloud, and we want to target managed Kubernetes services. What that does is that lets us minimize the amount of configuration and tweaking that we do when we burst to the cloud and helps us realize this vision of same compute on-prem as we have in the cloud. We also want to centralize licensed servers in the cloud to avoid splitting pools or having gaps in time from when we can leverage the cloud or not, and also develop controllers and operators to make public cloud burst with OpenShift transparent to the engineering teams. So this is a pictorial diagram of the type of infrastructure that we're talking about. On the upper left-hand side, we have our on-prem compute. We have interactive compute to set up jobs to review results to also do job management. And we also have a number of OpenShift clusters that we maintain internally with shared distributed file space. When we burst into the cloud, we target a service called ROX, which is Red Hat OpenShift Kubernetes service in the IBM cloud. If you're used to AWS, you might think of this as ROSA. And in the cloud, what we do is we build some compute nodes, the job management nodes, and we want to run across as many data centers in a cloud burst geography as we can so we can maximize our capacity, leverage autoscalers where we can so we can scale up and scale down to save costs, and centralize the licensed managers in a cloud geography that's accessible to all. One of the key things that you could do in almost any cloud is connect different regions via networking that exists solely in the cloud itself. So what we've done is, within IBM cloud, we use transit gateways to connect different geographies and maximize our ability to burst anywhere in the IBM cloud from anywhere within IBM. So how does our workflow work? Based on the description I gave you earlier, how do we set this up in Kubernetes? And I'm sure people here could come up with many different ways to do this. We chose because of the nature of these tools to try to mimic as much as we possibly could the way that these jobs run on a native bare metal Linux cluster. And the reason why we wanted to do this was twofold. One, it solves a problem of OPC engineers or design engineers understanding how their jobs are running. And two, with very small tweaks to a controller or an operator in Kubernetes, we can build in resilience to this workload by making sure that we leverage inherent capabilities in Kubernetes to keep worker nodes, for instance, up if they go down. Make sure that we can checkpoint and restart jobs, scale them down so that we can prioritize jobs. So what happens is we use Kubernetes job types. We have a primary job type, which it starts initially. What it does is exactly what I talked about before. It reads in the layout, chops it up into tiles and creates a queue once it's done. It then talks to a distributed controller that then spins up a number of workers. And when I talk about workers in this case, we're talking about running at fairly large scale. Table stakes for an OPC run is typically between 8,000 pods and 16,000 pods. When you start talking to foundries, that's like a 20,000 pod run to make time to make most efficient use of time. So what we wanted to do was go out and say, for us to use this internally as research, we need to be able to get to 10,000 pods reliably. Let's go out, figure out how to assemble job flows like this and demonstrate scaling from 1,000 to 10,000 pods. If we can do that in a fairly linear fashion, then we have confidence that we can go and do this at more scale. One of the other reasons for breaking this apart, or breaking our job flows apart is it also lets us address the differences between running, let's say 4, 2,500 pod runs and one 10,000 pod run on the same cluster, and also lets us address some of the shared file system issues that we have. So one of the things to keep in mind is for many of these tools and many HPC workloads, a shared high performance file system is really key. All of the worker pods need to see the same file system, read and write to it and stat it that the primary pod sees. And they're gonna do this with irregular patterns. So one of the ways that we've made our work happen here is to explore both open source, high performance file systems like Ceph or internal file systems like GPFS or Spectrum Scale. And what we tend to do is offer Ceph Gluster or GPFS as persistent volume claims that are mounted by all of the pods in the run. And we keep those static, and there's also another advantage to that is when we have to go back and debug logs, everybody can see the same thing. They can pick up the logs in the same place in whatever cloud environment we're talking about and with asynchronous file management, we can bring it anywhere. So these jobs are heavily compute dependent and they're heavily IO dependent both at the network level and at the file system level. So one of the things that I get asked, I get some skeptical questions up front with new OPC engineers and new designers is, yeah, this is all great, it looks convenient, but is it gonna perform? How do I know that if I just give you a Kubernetes job that I'm gonna get it in the time that I need to do it? So over the last two years or so, we've been looking at scaling. And OPC is embarrassingly parallel, it should scale fairly linearly, but there are parts of the job that need to do IO, need to do some checking in the hierarchy management, the primary pod. So we don't expect it to be pure linear. So what I've done here in these results is plot a couple of things. I plotted the speedup that you would expect if everything was linear, which is really just this line, the black line that you see here. The next thing that we've added is a speedup, assuming you have a serial fraction of about 15%. That's something more attainable than pure linear given the amount of file and networking IO that we need to do. And then what I did was plot our speedup in the blue curve for OPC runs that span a thousand pods to 10,000 pods for a nano sheet node, which you could think of as three nanometers to two nanometers depending on who you talk about or talk to for a back end wiring level. Back end wiring levels, and I apologize for using jargon, are the first wiring level that's most important for signals. And they're called the thin wire levels and there's a couple of them, but the first one is the most important. So what we're showing here is that with in the blue curve, scaling from 1,000 to 10,000 pods, we're achieving a pretty close correlation with about a 15% IO or serial fraction, which is actually quite good. We're by no means done with this. We believe that these points that you see here around six to 7,000 pods, we understand how to fix, and we think that we can definitely, with some very simple tweaks, start to increase the slope of this curve so that we're somewhere between the 15% serial fraction and the actual hypothetical linear scaling result. So to summarize, we've successfully demonstrated running optical proximity correction at scale using a managed open shift service. We are targeting managed open shift services because it minimizes setup and tear down in public cloud infrastructure. And we're applying what we've learned using optical proximity correction to many other tools in the design change, this design tool chain. So we can apply this to the tools that we're actually using to do register transistor logic, synthesis, place and route, timing and enclosure and performance. We've got a lot of opportunities to build logic and custom controllers into this flow. We view this as the first step. We do use, for all of our mass delivery into Albany Anotec today, open shift and a managed Kubernetes service. So we're, in a sense, and this is something that we always have to do within IBM, our management team always insists that we eat our own cooking. So we can't go out and say, hey, everybody should use Kubernetes or everybody should use open shift. We actually have to do that and make sure that it works for us and that we can stand behind it and say, here's the data, I can scale from 1,000 to 10,000 pods, I get good results, I can do this reliably. And I think what this is also gonna do is enable us to make smarter use of public cloud services, the IBM cloud in particular, but also not to limit ourselves to that. We're also a hybrid cloud company. We wanna be able to enable this in other people's clouds so that we can make the greatest use of cloud resources and also deliver new technology on time. So thank you very much. Thank you. Nice talk. I was curious, it sounds like you have a lot of workflow management going on with this workload. Do you use any of the CNCF projects for like Argo workflows or anything like that? Not yet, we would like to. We started very simply because we had to solve some problems or some, I won't say problems. We had to optimize our containerization flow and in order to keep things simple on the compute side, we didn't try to do anything more sophisticated. That's one area where we'd like to go is use, particularly on the operator side and the scheduling side, better or leverage open source projects to actually do that better within OpenShift and Kubernetes. Just want to add one more thing. So also things like CRD changes, custom configuration on the node. So for that, we are looking at Argo CD, things like that, whenever you have a job coming and before the infrastructure is spent up, you have Argo CD comes in and you know, grab the infrastructure. So things like that, we're looking at the Argo CD in that way. Thank you for the talk, very inspiring question. You've been at this for two years, which means you probably went to a number of OpenShift versions. I'm sorry, I'm hard hearing. You've been doing this for two years. Yes. You've probably been using several different OpenShift versions. Yes. What are your conclusions when upgrading? Did it really improve your performance or not? Can you repeat it one more time? I'm sorry. So you probably went from OpenShift 3 or maybe OpenShift 4, 1, 2, whatever, 10 or 11 or whatever you are today. Right. I assume there will be differences when you repeat the same test with newer versions. Can you share something about that? Oh, yes, yes, yes. We do see differences. We've seen an improvement. It definitely in the four series of releases, in particular with our network performance and our ability to opt to tune file systems. I think we need to be a little more rigorous about version control and how we benchmark different versions. We're not doing that today, but that's one of the things that's on our list. But I have seen some improvements definitely from three to four with networking and our file system performance and our ability to tune them. The compute is also better, but it's hard for me to quantify it because these workloads don't make very good use of hyperthreading. So when we do position workloads, we're looking more for physical cores and trying to drive the workload to physical cores. I think we have some work to do to optimize it and make better use of what OpenShift offers in other Kubernetes. So just one more thing. So OpenShift is 100% Kubernetes. We are also making improvement and upstream as well. So things like CRAN, CU Groups V2, these are coming, going forward, which will enhance the other performance. I mean, we have seen better results in the lab and we are going forward to implement this within OpenShift as well. So going forward, you will see good, I mean, I will working with Darren to see how the graph looks like when we implement those. Your slide listed GPFS and CIF running with OpenShift. Was that running in cluster or was that external? They are, so this is a managed service. They are running external. We're building them within the same VPC, but the control plane for the cluster is separate and it's different. So when we do the mounts, what we do is we're provisioning virtual machine nodes in the VPC and the control plane is managing all the connections. So if I understand your question as being external, the file systems are in one of the data centers that the VPC encapsulates. The block storage that we're building it on is usually local. So we'll build it over local NVME nodes or virtualized bare metal instances with local storage. So the operators aren't running in cluster and are like Rooksef or using CNSA for the GPFS layer? No, these are static persistent volume claims that mount this or the file system that we built within the customer within the VPC. All right, with the differences between Ceph and GPFS, did you see any performance differences in your workload? We did, so it's not really a fair comparison. Ceph performs much better than almost any of the other open source file systems that we've tried thus far. And truth in advertising, we haven't tried Luster yet, but between Gluster, bare NFS built over block storage, Ceph outperforms them all. GPFS is really optimized for HPC. And it has some features that Ceph doesn't have which allows us to tune metadata and just performance much more finely than we can with Ceph. So GPFS will outperform Ceph. But that being said, from an open source perspective, we're getting very good performance from Ceph. We still have a lot of work to do, but we're getting very good performance from Ceph. Thank you, great talk. You mentioned that for the Ceph, you have the something missing for the HPC. So I'm curious, what is your views that Ceph reserve is HPC? Isn't that necessarily something missing from Ceph? We're using Ceph file system. GPFS is really kind of like a Ferrari. I mean, there are things that it does very, very well and you can tune it to do certain things very well. So in some sense, it's not a true apples to apples comparison. It's more, if I'm willing to go out and get spectrum scale, take the time to configure it. And there are managed services for GPFS today or spectrum scale. But like anything else, you've got to tune it. And so it just has more capability under the hood than Ceph does out of the box. And I think that's the main way to explain it. It's, and it's often used in universities, national data centers. So there's this entire body of work where people have figured out for this workload, here's how GPFS gets configured. So it's, I expect it to outperform Ceph out of the box. Now maybe there's somebody who's better at Ceph than I am who could get very close or beat GPFS. But GPFS is really starting with a huge arsenal ahead of time, a huge priority advantage. All right, thank you.


around both the magnitude and the pace of the process, no understanding how and why decisions about the process and the substance of the process of change were made. What were the consequences of this particular process? And what are the insights that can be gained from this particular case studies? Well, the idea is that many states that undergo constitutional change have a choice whether to make a major change or at once with all the excitement that comes from doing that or whether to make the change more gradually. What are the factors that encourage decision makers or people to make one of those choices or the other? And what are some of the consequences that follow from those choices in terms of involving the people, in discussions about constitutional reform, in terms of actually feeling as if everybody owns the constitutional changes that have been made and in terms of implementing them effectively in the future? Most people would say these days that if major constitutional change is occurring, there needs to be an opportunity for public input and public participation. The fact that comes out of a constitutional process is necessarily grounded on the history, the norms, the values and the politics of the moment and the opportunities and challenges that are there at any constitutional moment that might arise to even discern the presence of that moment where white space has been opened up in order for a really substantial constitutional reform. Processes of constitutional building are increasingly global in character. These in terms not just of learning from the experiences of other countries but also because there are many global actors involved even in such a national project such as the building of a new constitution. Another lesson I think that's important is that even though there is this global aspect to national constitution building, you cannot ever play that international or global side because there are local or national constraints on any particular constitution building process. It's important to look at the experiences of other countries while you are doing your own charter change amendments. There is no such thing as one size fits all. That's true.  Positive experiences. I like to think of constitution building and constitution and minding process as something very, very positive. It is positive because even though political parties have different ideologies, at the end of the day the constitution is a living, breathing thing that we have to live with that should work for bettering the quality of life of all the citizens. For example, like Burma, we have many ethnic groups and then the people. So we want them to be involved in the process until and unless they are not able to be involved in the constitution building process, then they would not feel that this constitution belongs to them. Each country would have their own constitutional history. At the end of the day, people who want real constitutional reform build on creating that movement. People who want real change to happen in their country and to have a constitutional framework that embodies values that they hold dear, they contribute to that moment. But it cannot be them alone. They will have to capture the popular imagination of the people. So how do you discern that moment? I think you know when it's very right, but at the end of the day, again, things might not go the way you want it to be. So there's politics, especially elite politics that are more powerful than probably initiatives coming from the middle or at the center. But then again, there can be real opportunities where you have a significant coalition of powerful enough forces to really see through a meaningful constitutional change. In those countries, of course, the international aspect has been provided by the advice that international idea has been provided for the informative and participatory phases of the constituent process. Major problem in the constitution building process overall, it all depends on the political motivations of the drafters. Sometimes all those motivations could be very self-driven and self-ambitious manner. So that's why it is very important to build a good constitutional design and architecture generally, and to build a system that could filter out this personal ambitions. You put that in the constitution, you put that in the fundamental law, and no ordinary legislation can reverse that. So that's how important the role of the constitution in terms of maintaining, sustaining democracy, rule of law, and human rights. Well, the role of the constitution and democracy is very related, it's very important, because it is a repression of national consensus on the rules of the game in a country. The comparative approach enables one to locate one's country with some more objectivity and also some more priority from the lens of the issues that were considered important in other contexts or the processes that were undertaken. This could all inform the politics of the day that we have now or even the challenges of the future. Constitutional reform is an ongoing agenda. It will go away because no constitution is permanent. And so it's very important to continue with this kind of learning process. I think many of the choices made by different countries in terms of whether to choose incremental forms of constitutional reforms, or to go with the so-called Big Bang approach is a function of the political, his history, the culture, and the concrete specific local conditions, the dynamics between the opposition and the government. Well, I think the first lesson that comes across from we have 20 very diverse cases, but in all of them you can see that the modalities through which the constitution was changed, whether that was over time incrementally or at once, is, and also with regards to changing the substance of the constitution. We spoke a lot about limiting the powers of the president. The solutions to these problems have been very much driven by the context of the country in question, and it's legal and constitutional history. The key things that I've seen might be helpful for us in the future is trying to understand the different place, the different actors. Understanding how they actually influence the process. In this forum we've been talking about the processes, but those who engage the different actors, are we able to gather together and talk about those interests and how they influence the outcome of a constitutional amendment or a revision, or even a new constitution? People would say these days that if major constitution changes occurring, there needs to be an opportunity for public input and public participation. But depending on whether you're making a new constitution or amending an existing one, the opportunities may be different, and in any event there's a real question about how you properly involve the public.


For some people, standing on their own two feet is hard enough as it is. So when Sasebo's Outdoor Adventure Center hosted a local ski and snowboarding getaway, there were plenty of trips, slips and falls. Eventually, most of the sailors got the hang of it after a couple of tumbles and some free lessons. The first attempt, like I think I only stood for like two seconds. And then the grounds happened. There's only one way down the mountain and you've got to get down one way or another, right? She's done really well. Got her down the mountain two or three times, now she's riding the lift and skiing seems to be easier for some people. I accomplished so much by just even coming out here and trying something that I've never done before. Petty Officer Sarah Villegas, Kujiski Resort, Oita, Japan.


The community personality thing that was in 1996, I wasn't aware that they're still making the rounds. But anyway, I think we benefit from the open internet and the strategy that we call open response and open recovery. The detail of which is in digitalminister.tw. But in short, I think the open internet allowed a participatory accountability system on masquerading, on making sure that collective intelligence surfaces the whistleblowing from Dr. Lee Wenliang from Wuhan starting last December, not this January, where we can get the SARS 1.0, which is the 2003 collective traumatic memory, sender operation procedures rolled on in January, even before we have the first local transmission cases. And from that onward, I think another key thing with the open internet is thanks to the social media, we share those very pro-social messages, such as wear a mask to protect yourself from your own unwashed hands, which connects hand sanitation and the mask use. And also, when we roll out physical distancing, we say when you're indoor, keep three Shiba Inus away, and when you're outdoor, keep two of those Shiba Inus away. And all of these ideas we're spreading have a much more joyful, higher transmission value, if you would, than the conspiracy theory. So everybody remains calm and connected, even in the time of pandemic. That's a great question. In fact, I think the digitalization, the campaign promises of Dr. Tsai Ing-wen four years ago, namely broadband as a human right, has really paid dividends in Taiwan in this particular COVID situation, because all the SMEs, all the pharmacists, all the convenience stores and so on, have a very good connection quality to the rest of the society. And everywhere in Taiwan, it's equal access. We designed so that even on the top of Taiwan, almost 4,000 meters high, you're still guaranteed to have 10 megabits per second for video, live-streaming both ways for just 16 euros a month, limited data. Otherwise, it's my fault. And so this basic promise made sure that even when we are during the pandemic, for example, people can see all the nearby pharmacies and so on. This is contributed by citizen technologists, is the masquerading map, so that people who queue in line even, can check with their phone of the mask availability of that particular pharmacy and see that people queuing before them, swipe their national health card and get maybe nine medical masks per two weeks during the ration. And then they can check whether the system is functioned as intended. And if it doesn't work, or if it breaks in some places, they would just call the hotline 1922 and be connected to a call center that will then through the idea of open government amplify their suggestions to the rest of the society the very next day. So we have this daily live stream by the quint, by the five medical officers.  I didn't want to wear it to school. The very next day that the quints, the five medical offices all wore pink medical masks. And because of this, this, this become a kind of fashion statement. You see that online SMEs and so on all adapted their branding and color their branding pink. And so suddenly the boy become the most hit boy in the class because only he has the color that the heroes wear. And even the heroes heroes because the medical officer, the minister, Chancellor Jones said, actually, Pink Panther was his childhood hero. And so it became a kind of fashion statement during our pride parade and things like that. And all this helps the SMEs to strive because they can reach through this kind of new normal fashion branding and so on and reach to a very wide audience and get assured that all of them have pretty good broadband access regardless. Yeah, we work with Facebook before the pandemic on another dynamic, the info dynamic issue because our presidential election was in January. So like the pandemic followed immediately after the presidential election. And what we have found in both the info dynamic which we countered with no takedown and the pandemic which we countered with no lockdown is that if we make sure that the social sector as the mask map example shows due to data collection and most of the data processing and also set the data norms. So that is like a distributed ledger, more than 140 different copies, including voice assistants and chatbots and so on are available to track the masks whereabouts. And then it will create a very strong social norm and the state will then work with, for example, with Facebook just ensuring those norms are observed in a truly multi-stakeholder fashion. On the other hand, if the state sees as our role to set norms or if the Facebook sees this as their role to set the norms, then the society feels that the data are being collected in the name of counter info dynamic or counter pandemic. And that's that never goes anywhere, right? People will of course very reasonably want to know the privacy as well as cybersecurity guarantees of those new data collection methods. So I think I do agree with the principle of this free flow of data. But I think add to that is the idea that those data are being collected and produced by people. And this has precedent as well because in a liberal democracy as we are, we don't say that journalists and their editors are just text collectors, text workers and text makers and text processors, right? There is a journalistic norm. And we say in Taiwan, starting last year, instead of media literacy in our basic education curriculum with a media competence, the knowledge and fooling that all the primary schoolers, maybe some of them have more Instagram followers than I do. And so because of that, our media workers themselves and need to be educated in the way of how a journalist approach text. And so if these ethics and norms around text could be transferred to the data realm, then we do have a thriving social sector that could then communicate the norm around data collection. And I think our COVID success is largely due to making sure that people understand where the data is going, how the data is processed and can co-create in the data processing ability so that we do not need to collect any new data that we were not collecting before the pandemic in order for the counter COVID work to happen. And so I think that is also one angle, a addition perhaps to Simon's main argument. So radical transparency, meaning transparency at the root. For example, all the meetings that I chair, including interviews with journalists and lobbyists, are recorded and published into the commons as either a transcript or as a video online. And so this makes sure that people understand not only the what of policies, but how of policymaking and why of policymaking. Why are we even considering these policies in the first place? And this also engages the collective intelligence in ways that we have not seen before because everybody can just call me and say that minister, you're wrong about this in your previous meeting, and I have a better idea. And so this also fosters social innovation. For example, when we're designing the triple stimulus voucher, the TSV, which is a simple idea to reduce the friction that people want to start re-engaging in the post COVID situation because we reached the kind of critical mass on the physical vaccine around June and people restarted their visit to night markets and those catering businesses and so on. So we want to encourage people to spend more on face-to-face gatherings. And because of that, we designed it so that if you spend using the credit card but only in a kind of outdoor shopping setting, 100 US dollars, then you can redeem actually two-thirds of that back on a nearby, friendly, automated tele-machine, which is in cash. So you're likely to spend it again. And this has been quite successful actually. The revenue of retail and catering this August and September has reached the highest since the 1999 actually. And so our production capacity also recovered. The export orders have grown 13% than last August, but this is because all the convenience store, all the supermarkets and non-markets and so on, just called me and say, we want to join on this too and just make the triple stimulus vouchers quadruple or quintuple or like many times more by offering their own discounts and by offering their own ways to, for example, you can choose to dedicate that two-third to a not-for-profits organization of your choice. So instead of drawing it from the ATM, you can type in the donation code for a charity and then the stimulus vouchers reward goes to that charity. And that is from a social innovation lab visitor to my office because my office is literally a park. Anyone can walk in and say, hey minister, I have seen the transcript of your previous meeting, but I have a different idea and I would really like the truth of the stone blotcher to contain this way to work with social entrepreneurs and so on. So this is literally my office and anybody can just walk here and have like 40 minutes of my time. In our national digitalization plan, digitization is just the first pillar as talks about the infrastructure problem as human rights 5G that goes first to the rural indigenous and remote islands, but that's just the first pillar. The other three pillars in the IGI program are innovation, governance and inclusion. And I see there is a kind of linear progression. We make the infrastructure of digitization work because we want to foster innovations and the innovations in turn foster this cross-sectoral partnership around governance and data normals. And finally, that is to include the people including women's entrepreneurs, mismies and so on. Social entrepreneurs, we call the social entrepreneurs who originally may be charities or co-ops or and so on, but through the training programs and the peer-to-peer learning, they enhance their resilience and become like very visible. And we do actually prefer like the buying power program if a large enterprise or large organization integrate those social entrepreneurs, products and services into their supply chain then I personally go out and give them an award. And for the presidential hackathon which encourages this kind of cross-sectoral collaboration and every year Dr. Tsai-ing Wen goes out and give five winning teams the trophy. The trophy is a shape of the island and then if you turn on the micro projector underneath it shows Dr. Tsai-ing Wen handing you the trophy, promising whatever you did the last three months it could be about improving water use efficiency, it could be about remote telecare and so on. That will become public policy in the next 12 months with all the regulation personnel as well as budget required. And this is a very strong incentive for the social innovator then to figure out a way to work with existing ecosystem players in a way that is kind of like a moonshot from an individual social entrepreneur's point of view but because of this power of the presidential hackathon then it gets translated like fast-track warp speed into public policy within 12 months. I think the executive power as a hackathon award that really excites me. Thank you. Thank you. Live low and prosper.


Welcome to MOOC course on Introduction to Proteogenomics. Today we have a guest speaker Dr. Joakim M. Schweng from KTH Royal Institute of Technology. Dr. Joakim will talk to us about affinity proteomics which is a field of proteome analysis based on use of antibodies and other binding reagents as protein-specific detection probes. We also talk about the study of human plasma proteome using affinity based methods which could enhance biomarker discoveries, validation and integration from basic research towards the clinical usage. He will then talk about the resources like Biobank Sweden and Atlas antibody. Dr. Joakim will also talk about mass spectrometry technique and how it can be used to study post-translational modifications, PTM peptides in a digested sample. He will then talk about PTM scan technology which allows identification and quantification of hundreds to thousands of even the lowest abundant peptides and provides a more focused approach to peptide enrichment than the other available strategies. So, let us welcome Dr. Joakim for his lecture. What I would like to talk about today is to give you a bit of a different perspective on what we do and what we understand by doing plasma proteomics and maybe there are some aspects of it that could be helpful for you and that sort of provides some ideas of either collaboration or you know for you just to get a new perspective on in your projects and how to move forward. So, this is my team so we are currently about 10 people and it is actually Kimi the person she is half Japanese to the left who made this painting. She is very sort of skilled in arts but I think it is also a nice way to sort of you know illustrating us in sort of one group of people with sort of the same phenotype, right? Even though you know we have Philippa, she is from the UK. We have Mungwan, he is from Korea. We have Ragnar, she is a post from Germany. We are a very international group and now actually we have a new person from Denmark. So, it is really sort of you know the mix of cultures and the mix of backgrounds that I think is really sort of important. So, I guess Fredrik has talked to you last week about these different aspects of the human proteome atlas. So, the tissue based atlas, the subcellar atlas as well as the pathology atlas. So, I am going to leave you with that and I hope you still remember some elements of it. So, I will talk a bit more about sort of what is actually outside of the cells. So, talking a bit about the plasma proteome as we see it and then how to use affinity based methods for studying the plasma proteome. I will give you some examples of how we use mass spectrometry, but the predominant part will be actually on looking at plasma proteins with affinity reagents. So, as Sanjeva alluded to, I am the current chair of the human plasma proteome project and whatever that means is sort of you know to be defined, but I think what it sort of is meant to be is sort of an organization that tries to give a global understanding of what are the initiatives that people are working on in the different areas of the world. And with a common feature of studying the plasma proteome. And I am doing this together with Eric Deutsch who is a famous biometrician from Seattle as well as Erich Natowicz from Australia. So, really trying to you know have this as a global initiative too. So, about two years ago, we published this paper in the annual special issue of JPR and we are actually in the process of preparing a new sort of review for the coming issue. Where we basically concluded that there are about 5000 proteins that we can detect using proteomics methods in plasma. Which is probably you know 25% of what the genome actually tells us there is. Of course, this is predominantly driven by the fact that these are the things we can measure. It doesn't mean that these are the things that actually are useful, okay? So, given that you know you have new technologies such as Somalogic who claim that they can measure 5000 proteins. This is within the ballpark of what we see at the moment. Mass spectrometry in combination with other affinity based assays can measure. And there is of course one intrinsic challenge to using for instance mass spectrometry. And that is one is of course you need to have a good detection system and protocols to increase the coverage. So, from around 1000 proteins which we could identify some 10 years ago. You know I think we have made a big step forward in detecting more. And this is also shown here by the charge this Venn diagram showing the progress. There are also interesting numbers here highlighted in red. Which are those proteins which actually sort of got introduced over the recent years. And it's particularly I think you have these two gaps between 2010 and 2013. But they're also about 700 proteins which disappeared from these lists. And meaning that these are proteins that have probably been misannotated. And probably glycosylation forms that have been sort of led to the false identification. So I guess you know we know that the end of sort of having the perfect system together. But I think we have a much better understanding of what the system looks like. Another challenge in mass spectrometry is of course the coverage. Meaning how many proteins do you in your single experiment actually can measure. And this is shown here again we have a time chart on the x-axis. And this is the number of proteins identified on the y-axis. You see there is of course a progress being made over the years that you can measure nowadays. Let's say routinely about 500 proteins in every experiment. But you can also see that as a quite substantial span and some studies people claim to have identified 2000. Where most recently you know some studies have only measured about 100. So it's really a matter of defining what you call a protein to be. And whether it should be identified in only one or in every sample of your measurement. And that brings me to the next sort of point is that if you look at the concentration distribution. So this is just basically here the ranked based on abundance. You see that you know predominantly sorry this is the rank of abundance. And this is the number of times we actually observe this protein in one of the 150 studies we looked at. Is there's a clear correlation between those proteins which are high abundant are seen more frequently than those proteins that are low abundant. Which also comes to the point that yeah now we can measure about two 500 proteins in every study. But really the question is how many times do we actually see this in every sample. I mean this is one thing to do with the concentration. But it also may have to do with the variants or the isoforms of particular proteins. It was quite interesting for me you know to see that for the four most most common or most frequent proteins albumin. I guess that's to be expected. Compliment factor four are from two Marco globin or Hapto globin. You did not have all the peptides seen in all studies. So which means that some peptides for albumin are in some studies so unique that they're not common and concordant with other studies. So again you know here we come to the point that there's much more information in the peptides that we actually currently so I think using. The next point we need to make is about quality.  And Sanjeeva and I discussed you know the really having the important connection with the clinician obtaining a sample. So you have actually control or at least a better understanding what happened to the sample when it was taken. Because if you just think about blood I mean basically you can sort of dissect it into three elements. You have of course the cells you have some some micro particles or so lipid vesicles. And then you have something which we call sort of the cell free component which is sort of serum plasma. And given that there are lipids that of course are also important to be considered you know if you just look at the proteins. The reason why you have the proteins in plasma could either be that they're actually actively secreted into blood because of the process that is related to it. Or they can be cellular cellular sort of cellular origin meaning either they sort of been leaking out when the sample was being prepared. Or they've been shed from the surfaces because of the certain protease has basically took care of it. But there's also an important element which means that samples could be introduced into blood because of the preparation. So meaning you even have intracellular proteins which by changing the temperature from 37 to let's say 23 or 28 in India. You may just you know introduce some of the inflammatory cells to to secrete cytokines because of the change of environment. And that may have nothing to do with how the system sort of has been before. And so what we really advocate importantly is in particular when you do these large scale projects is that of course you want to know about the patient. We call it the donor it's more of general. Of course you want to know how old what gender what the diagnose at the sample collection was. But you also need to think about what the sample comes from. So when was it collected the location how old was how long was the sample being frozen has it been freeze thought and so forth. So it's really about the standardization of the procedures and obtaining information about the samples that you're analyzing. So that I think is really a key. And a lot of initiatives in Europe that you know trying to understand and trying to develop a pipeline for sample processing and handling samples. Because if you just do plain statistics without even sort of been thinking about proteomics. If you want to claim sort of a significant finding. And this is here just doing a simulation where we I think took one of the major risk factors of cardiovascular disease with an power of 0.05 80 percent and an alpha of 0.05. In order to just measure that one analyte you need to have about you know 80 samples per group. So it's 160 samples in total. If you think about a proteomics experiment let's say 1000 or 10,000. You need to have up to 250 300 sample per group. So many you're studying your measurement to be sort of relevant. If I may use that word when you have 600 or more samples. So this is not always possible. You know they are not that frequent so it's going to be extremely challenging to get up to that number. But of course you know to get really understanding about the diseases that is one way of moving forward. So there's a whole science behind sample preparation variables or pre analytical variables as we usually call it. Where people really try to understand what is the quality of sample. Tiazman's group has a recent paper on bioarchives where they sort of you know looked at where they basically try to you know separate plasma and did sort of different centrifugation segments and removed cells and re-spiked them into what is the contribution of cellular contamination in blood. So I think that's an important aspect because cellular contamination is a factor that can hardly be controlled. So what we mostly work with and this is what we're going to talk in the second part of my talk. But I have some more slides in between is how we're going to use different affinity based methods to study the plasma program. And I think this is predominantly driven by companies nowadays selling kids. It's a bit different to what the mass spectrometry field is doing where companies selling instruments and then this is academic environment that has to take care of them. So of course you have you know I think biogenesis or some other companies that sell or MRM proteomics that you know sell you kids. But I think there's no company that sells a kid for doing shotgun proteomics. So I guess you know it's a bit of a different ball game because you have you have a dependency on these on these companies. But the interesting thing about you know using affinity reagents in comparison to mass spec is that and here's a comparison about the proteins you can identify in mass spec as well as in immune asses. Is that you have a lot of the low abundant or annotated low abundant proteins that are actually measurable in immune assays compared to many structural elements which predominantly may originate from actually cells that are in your plasma that which you can measure in mass spec. Of course in mass spec most often and this is done purely on shotgun data is you of course take all information you get whereas in affinity based assays you preselect what you want to look for. So I mean these are really sort of conceptually different different approaches. And another aspect I mentioned before is really sort of the use of genetic data in combination with protein data. So how much information about your proteins is already given in your genome. Well of course we know that that is where the basic information lies. But how much of that information is actually being connected to what the proteins do at the end. I mean we have a whole machinery between the genetic and the proteomic information. But surprisingly and this is a study that for instance somalogic has been done. You know surprisingly there are a lot of indications that your genes or the variants of your genes tell a lot about the proteins that you measure in your sample in blood. So you know if you know somebody's genotype and if you know that genotype would be linked to a higher or lower risk of a certain disease. And you know that genotype is also linked to a so called PQGL so quantitative trait loci. Then you can say well that person always had a high risk of that particular disease. Always the protein level was low which was maybe you know and then a slight increase of a low protein level may actually mean much more than if you would have the inverse case where a person has a low risk but has intrinsically high level of a certain protein. So it's really important to include much much more data and I mean proteogenomics as one of the approaches but others I mean you know following the way so it's really. And this is actually one of the resources built by by. Collecting all this information so it will be I think a growing part in many of the proteomics study because if you don't know why a person has a higher level you know that might be one of the reasons for it. So I've been involved in a couple of mass spectrometry related projects. This is one that's led by Janne Lechjo's group. They have used this high reef system so they have basically isolectrically focusing as a pre-preparation concept. And then sort of did fractionation and what what we show in this study that we can you know detect 1000 proteins across all those 30 donors we've been using in this study. Interestingly we could also what what we have done is compare the baby newborn baby to the mother and we can see that there are many proteins are alike between the two but there are some which are only seen in the baby. Some are only seen in the mother and we also see that there are some proteins that basically traversely placenta so are being transferred from one location to another. And this could only be done using actually a proteogenomics approach where we know the variants that are sort of expressed by the baby versus the variants that are only expressed by the mother. So this I think will be a very important example and hopefully will be coming out in a couple of weeks time so we some resubmitting this as we speak. I think it will be a very important sort of element. What we also done and it's again using the protein atlas as a resource so the protein atlas had has of course produced these atlases. It has produced antibodies but it has also produced a lot of antigens. And I guess Peter Nielsen maybe some of you have met talks a lot about using the antigens for quality assurance of antibodies or using these antigens for autoimmunity profiling. What we're nowadays using is using these antigens as heavy standards for targeted mass spectrometry. And the reason is because we have these constructs so here you have the endogenous protein and then we select these unique regions we call breasts. And all these breasts by default carry a tag which we initially used for protein purification. But nowadays and this is basically a representation this is a fantastic tag to do quantification of that specific sort of standard. Because this is a common tag for all the standards we use in our system. And you can use this of course for all your mass spec retention time adjustments and so forth so what have you. And now we've done this for about 25,000 of these protein for these Q breasts as we call it. The paper is also on by archives and hopefully will be coming out in a couple of weeks time. But I mean you know using this as a pipeline that we can actually use that information to specifically build off the shelf targeted proteomics assays for the proteins we're interested in. And we've shown this for a couple of examples also in plasma now in this study that Frederick has been heading. So the main part of my talk is about sort of what do we do as sort of our core business if that would be sort of a pitch to the four investors. So the core is actually we use we do affinity based plasma proteomics. And that means that you know we use antibodies or different types of affinity agents if they are available for doing protein profiling. We care a lot about the study design. I think this is something I touched upon before. We care a lot about antibody validation. This has been a sort of a hot topic for us because antibodies have been criticized massively. There has always as in these constellations been some truth to it. But I think we believe that there are opportunities to change the perception and in parts that has to do is redefining or explaining more or less what the antibody is actually capable of. So an antibody is not an off the shelf universal tool that will solve all your problems. An antibody is something you need to know where to use it and for what to use it. So an antibody good for Western blood may not work and you use the chemistry or Eliza. That is an understanding that not many people have unfortunately. And so my lab currently runs three different sort of technology. So we use Luminex as sort of our go to platform because it's open access for us. We have all their equipment. We have 10 years of experience of using it in various aspects for protein as well as auto antibody profiling. It's for us sort of really easy to use. But it has some limitations in terms of sort of in the way we use it quantification and sensitivity. We also for about two years now have all ink as a technology that we run and we do this for different types of service projects or our own research project. We also have an interesting technology offered by ProteinSimple which uses microfluidics. And the nice thing with this system it's basically almost fully automated. So you don't have any user interference and that gives you a really excellent batch to batch precision. And it actually is a system that we think could be useful for clinicians because they don't want to think about how to run the experiment. They just want to get the data. We're also going to include Quanterix as a new technology, probably someone doing this year. And Quanterix is basically also a bead-based ELISA or a bead-based as a system just like Luminex. But they have a different way of read out in terms of that they use an enzyme to create chemo fluorescence. And they also have a different mode of detecting or counting their detection. Meaning that instead of measuring the sum of all signals that are sort of obtained they actually do, they call it digital counting. So they count the number of particles which actually emit a light at a certain level. And that's what they usually call sort of their sort of digital amplification range which is sort of then giving them a 100-fold improved sensitivity at the cost of using more samples and more antibodies which is not as well communicated. So of course then there's a whole portfolio of sort of options which technology to use. And this is usually when we have sort of meetings with users of the facility that I'm directing. What do you want to do? What is the specificity, the cost and the number of targets? Do you want quantification? How many samples do you have available? What are the volumes and so forth? So it's really sort of a ballpark of different features that you need to consider when you choose a certain method for your application. Which again is a bit of a different concept to MASPEC given that you can probably choose many systems for many applications. Of course you need to tweak them and some may be less suitable than others. But in theory I guess from all MASPEC you would get some data out. I mentioned antibody validation. This is really I think a key challenge that we are facing. But again what I've been preaching for some time now and this is sort of my tagline in this context is the performance and selectivity of antibodies application and context dependent. So it's the matter of the application meaning Western Blood or ELISA. It's the way that you prepare your sample, that you prepare your assay so that you actually get the best out of it. We've been working on using actually MASPEC as a readout for immuno-capture data. This is I guess most often done using cellular systems where you have actually an antibody towards a tag and the tag is fishing out a protein which has been introduced.  So we've been putting all of this into plasma and this is a study led by Claudia. So here we've done more than 400 IPs and built sort of a library of data to judge whether an antibody specifically enriched a protein in plasma or not. And these are sort of these sort of enrichment plots. So to the left you have this crap-oam, the part that is commonly found in every enrichment and that may be due to proteins sticking to the beads. But then to the right-hand side we chose the Z-score of 3 as a cut-off. You see some on-target detection, you see some co-targets meaning proteins are co-enriched either because they have a similar sequence or they actually do interact which we find very interesting. We also see off-target interactions to proteins that are more abundant than the protein that we presume the antibody would bind to. And we actually also have cases where we have no target meaning there's no specific enrichment. So which I think in a way is interesting because either this could mean that if there's an enrichment that the target has not been sort of detected in mass spec or the protein that is simply too low abundant to be sort of reaching a Z-score that is of relevance. Yeah, as I mentioned protein interactions we find interesting. Here we know for this insulin growth factor binding family they sort of interact with another and as shown here in using the string database you have IGF-BP2 interacting with IGF-1 and 2. And as you can see here using three different antibodies we can see here is IGF that they actually interact. We also could claim new interactors with this BCHE as well as DERA as proteins that are relevant for these complexes. What we also do and sort of going back to our sort of most accessible technology is using Luminex and here RACNA has screened more than 200 antibodies. So 200 proteins using more than 600 antibodies to find which are actually suitable sandwich pairs so using both for capture and detection. This is also now a paper which you can find on bio archives and hopefully the reviewers will like it. So we've done sort of a long-term procedure doing two screening rounds and this is substantial amount of work with a couple of people involved. But at the end of the day it led us to this triangular chart here where we actually looked at longitudinal samples and the precision the assay provides in this context. So it's a bit of difficult to read but basically we looked at sort of what is the variance of the assay in terms of the technical position, what is the difference between the individuals that we observe over time and what is the difference between the individuals themselves. And as you can see we have a couple of nice proteins here those ones that we highlight in green which are those proteins that we can measure precisely that are stable over time but they vary a lot between the individuals meaning that there's probably a genetic component or some sort of personalized component to it that makes these proteins more interesting than others. Again we took a lot of effort to do validation and here's some sort of correlation chart where we compare the different sandwich assay data here in this case for a protein called CCL16. We have three different assays we developed in-house and this is the assay offered by O-Link so we have a pretty good precision in using completely different, so this is O-Link is a solution phase protein proximity extension assay whereas we have a classical ELISA where you capture on a bead, you wash and then you add your detection antibody. So our main workhorse has for many years been these antibody bead arrays so we really use the high multiplexing capacity of the Luminex system. We have a couple of liquid handling devices to do upfront sample prep. So here the idea is that instead of using two antibodies you basically label your sample like you do in different types of TMT assays and then you sort of have a bead array which has 384 different antibodies. You fish out the proteins that you can find in the solution and then you use biotin to detect whether the antibody has enriched the protein of interest. We've been working a lot on getting the data analysis and processing right and it's done by mainly Moon Guan who's a researcher in my group and we have a pretty good idea about the data, the precision and sort of the accuracy and these are sort of two T-SNE plots showing basically the same data but here basically these are the replicates where all the other data points are samples taken from the same individuals every third month over a period of one year and you can also maybe see that all these individuals actually cluster together. So using our data we know the phenotype or the plasma proteome phenotype that we measure is constant over time using our data. So we've been involved in a couple of larger and I think actually growingly larger projects which we try to have multiple study sets meaning samples coming from more than one location. Using more antibodies actually building our own sandwich immune assays. For those candidates we identify to use immunocaptor mass spectrometry as a way to validate. We still have Western blood sometimes as a go-to option but it's actually less relevant nowadays for our approaches. We do validation of antibodies using peptide or protein arrays. Sometimes this is helpful to certify the selectivity between different off-target candidates and more I think interestingly for us in the future will be to do this PQTL sort of the GWAS analysis to understand what is the genetic component behind these studies that we're performing. So these are two of these initiatives. One is the wellness project which is headed by Matthias and Joran Bergstrom where we've taken those hundred subjects, did all the different omics and clinical measurements where we looked at them every third month over the course of one year. I'm also part of a very large EU project with different pharma companies and clinicians from all over Europe to do basically the same or a similar type of molecular clinical and environmental phenotyping in the context of pre-diabetes and diabetes progression. And again an important aspect for us are these four elements. So it's a study design, how do we sort of proceed in terms of randomization? How do we get the number of samples right? How do we do the discovery? I mean we have to choose which are the interesting candidates because we make that pre-selection. How do we do antibody validation or actually building new assets for target validation and then to go back and sort of study new samples again to prove that our hypothesis is actually valid. So one of the studies we've done here on biotic surgery, so biotic surgery is a major type of intervention for people that are very obese and that usually are at high risk of diabetes. So the idea is that this surgery induces weight loss and along with weight loss comes that the patient is not longer defined as being diabetic. And we want to understand whether proteins in plasma can give us an indication about either will a patient be benefiting from that surgery? That sort of we're looking at remission which is done using a multi-omics approach by one of the post toxin my group, but also how do proteins change over time pre and post surgery. So because we have looked at the patients at baseline as well as following surgery. And we actually could see that there are a couple of proteins that are consistently increasing knowing that there's an individual variance consistently increasing post surgery and we looked at three months as a time window because between zero and the three months there's a lot of processes that are sort of overruling sort of the phenotypes we are interested in, in particular those that are related to wound healing. So if you measure a patient day after the surgery, a lot of the things you measure is actually the patient responding to the surgery, responding sort of on a metabolic level. And we could interesting to see some proteins actually do change also in the opposite direction which means that they are actually decreased in abundance. Another type of multi-omics approach we've done in the context of unstable atherosclerosis, so here you have basically the coronary plugs that some people develop and of course there's a risk that some are unstable, some are unstable which means that you might actually have a higher risk of stroke and heart attack. So with a group of clinicians who've done sort of microarray and QPCR identified a couple of candidates which they could validate in using mass spec in tissue we then took on this type of target and actually could validate the same sort of observation using either the suspension bideray, the screening approach as well as we build a sandwich assay to measure that same difference in plasma samples. So we really sort of brought from sort of early DNA, RNA detection down to sort of applications in plasma. We also did a large-scale study on mammographic density. So this is a study again we're sort of switching a bit sort of disease areas here is related to cancer and in particular is a risk factor for women in the western world. So if you lose density in your breast postmenopause it's actually a very good protective indication but if the stiffness of the breast stays after menopause there's a higher risk of breast developing breast cancer but nobody understands what is this density and we tried to find using association study on a cohort of about 1200 women whether we could identify features that are consistent and we found a couple of interesting proteins related to the exocellular matrix as well as two proliferation levels that could indicate that there's actually a loss and increase in density visible in the plasma proteome. Again sort of one aspect that we have been working on frequently is this longitudinal profiling and here I again want to bring up something I mentioned earlier which is sort of how consistent can you actually measure a protein. So here we've looked at basically these we correlated the data we generated for this protein across these four visits and we see that you know the protein is pretty the measurement is pretty stable over time but then if we look if you compare the data between the visits you can see okay here we have extremely high precision as well so meaning that protein can be accurately measured and it's very stable over time. The second protein here is again we sort of replicate this as a couple of times and you can see the precision of the measurement is very good but if you look at the correlation of the biological variation as we call it where you compare those data measured at visit two versus visit one and so forth there's basically zero correlation which means each blood collection introduces a factor which cannot be replicated and of course if you have a biomarker which looks like this on a technical scale but if it's impossible to replicate because it is and this protein we know is part of the smooth muscle system so we know it's actually coming from puncturing your skin and the vein but again you know it's the protein you would not have sort of considered but you can actually measure it. We also looked at seasonal fluctuations so this is of course interesting you know in a sense that what are the differences if you measure your protein during winter compared to during summer and may just be the seasonal have an effect on your protein levels and just assuming let's say this would be sort of a cut-off level for this protein here you know here you would be actually above cut-off and the doctor may say well we may need to check up on you a bit more while as during summer you know you actually have a much lower level so of course these parameters which also I mean relate back to the time point and the age of the sample are important things you need to consider when you do your measurements. I'm going to finish with this project so this is also something which hopefully will be coming out in a couple of weeks time we've received some very good comments from the first round of review that will be certainly able to manage to handle this so what we've done here is basically this is a study this is a bit of a hobby study actually because in most of the projects we have we know the age of the person which donates the sample so we just started to collect a couple of studies and actually now it ended up to be it's actually 4000 in total where we actually looked at the same protein over and over again and as you can see here in all these studies the slopes may be different but in all these studies we could see a consistent increase in trend over time so basically using this as an additional passenger in the different studies we sort of as a byproduct more or less found a protein which is associated with age and we've been validating this and this is the meta analysis that we did so the p-value is I think far better than most studies you've seen because it's really consistent across time and what we also looked at is what it's one thing that this protein HRG tells you about your age but it also tells you much more about the risk of dying so it seems that elevated levels of this protein increases your risk of dying compared to low levels specifically up to eight and a half years prior to death and this is sort of on an all-course mortality so it's not linked to any particular cause of death like cancer or cardiovascular disease of course we did a lot of validation of the antibodies we actually also did GWAS and this is actually the protein array that we've run with Peter's group so these are 20,000 spots and you can only see a single peak of this antibody which was quite surprising but we know we can measure HRG but then what we actually also did and this is fairly new for us is again we took this genetic information we had about these individuals and there was another antibody against the same target and when we correlated the slope so meaning what you do in this peak at ELSA is you have a box plot with three different groups so it's the AA, TA or AT genotype and then you just superimpose sort of a trend and what we could see that these two antibodies they have the exactly same list of peak at ELSA they have an opposite trend in their association and that's also seen here by these distribution plots meaning the red genotype is lower for this antibody whereas the red genotype is higher for this antibody so it's completely new data nobody has done something like this before but what it says we have not really fully understood but what we believe right now is that every person has a particular variant of that protein and that the antibodies have a particular affinity to that protein variant so we think and it's likely that many proteins we nowadays study they don't actually in reality differ in concentration they just differ in the variant they are and that the different methodologies may be mass spec or affinity based ethics just think or just report different signals because it's a different variant and I guess it's a particular challenge for both acetypes in mass spec because when you look at the libraries that you use to match your data this is done on canonical sequences of course you can do proteogenomics approaches but that's not always possible but it will be in the future because you need to have that understanding to know what to look after and it's the same thing for affinity acids if a small variant if you have an exchange let's say you would change a hydrophilic amino acid and you have a non-synonymous mutation meaning that that suddenly becomes from serine you change to a proline you will change the behavior of that protein either in the way it's been recognized in your test or how it actually interacts with other proteins and thereby may be more accessible for let's say different types of measurements I'd like to thank you for your attention and of course all these people in summary today you have studied the human plasma proteome using affinity based methods which could enhance biomarker discovery validation and integration from basic research towards clinical usage how at least antibody from HPA project can help us in getting detailed understanding and background of the affinity based methods Dr. Joakim also provided a brief understanding about GWAS and how patient information is important to understand data set variations in the next lecture we will listen a clinician Dr. Sachin Jadho who will talk to us about clinical considerations for ohmic studies thank you


The study examined the behavior of a GRS wall with static footing loading above it while varying the positions of the footing. Finite element modeling was used to analyze the behavior of the wall and the effects of different parameters were studied. The results showed that the settlement of the footing was reduced by up to 36% and the wall's ultimate bearing capacity was increased by up to 42%. This article was authored by Ananya Srivastava, Savar Jaswal and Vinay Bhushan Chauhan.


A narcissist can only do this one thing. Whenever a narcissist interacts with someone, they really only have one option. They have to lie to people. They have to deceive people. They have to deliberately cause them to believe something that is not true. They have to give them a mistaken impression because narcissists have a lack of character. They lack the mental and moral qualities that are distinctive to an individual. They don't believe in anything. They don't have values or principles. So they have to pretend like they have these really strong beliefs, feelings and opinions because they see it as though that's what good people do. But they don't really value good people anyway. They take them for granted. They use them for their own personal gain. And then they throw them under the bus. But the narcissist wants to look like they have good intentions. They present an illusion to the world. They're not actually doing anything good. They're just making people think they do good. Because all they really care about is if it looks good to people. Which is why it can be inadequate. It can lack strength and solidity because it's only designed to fool people. The narcissist is very lazy. They don't want to work or use energy. They want to do the bare minimum to fool people. And if it works, it's enough. But if it doesn't work, they don't really care. Because they also enjoy negative attention. Especially if it doesn't threaten the illusion. Because sometimes negative attention can help them. It can make it look like people hate them. It gives them the opportunity to play the victim role. And then they can gain sympathy from other people. Because other people may believe the narcissist is kind of supportive. But if the narcissist is such a good person, why do they lack character? Why do they lack the mental and moral qualities that are distinctive to an individual? The only thing they have other than their manipulation and deception is their sense of entitlement. They have right or privilege to do whatever they want. They're like a child. Because there's no responsibility or consequences for their actions. But despite everything they do to people, they always find a reason to complain. Everyone around them becomes their parent. Who needs to do more or needs to treat them better. Their boss becomes their parent. Their relationship partner becomes their parent. Even their own children become their parents. So that the narcissist can then complain and justify their actions. So that they can play the victim role. Because it's very rare that someone will draw attention to their unacceptable actions and behaviour. But it's also an unconscious desire. For their own destruction. They're depriving themselves of the ability to function normally. Which then prevents them from accomplishing anything great. But they are very active. They're always doing something. They're always ready to engage. But it's usually the result of some deep rooted pain. They have to be in control. Because they're very insecure. They always have to be involved in everything. They're very anxious. They're constantly worried about something with an uncertain outcome. Or they have a strong desire or concern to do something or for something to happen. They're in all places at all times. They're always watching. They're always listening. Because they need to have an effect on something. They're always involved in the illusion. Because they have nothing outside of it. Which is why control is so important to them. Because the illusion is their legacy. It's the only thing they're committed to. The only thing a narcissist can do is manipulate you. They can only deceive you. They act like they're good people. But they lack character. They don't have anything. They don't believe in anything. They don't care about the results of their actions. As long as it supports the illusion. Because it appears to be the only thing they seem to value. But even then, they don't really value the illusion. They just hold on to it out of fear. Because they fear being exposed. They're very weak and afraid. It's just what they then try to project on to you. It's the reason why everything has to be a game. It's why they constantly have to draw your attention. To things that are of little importance. It's why they're very competitive. But they do not care about doing anything good for people. They just want people to think they're good. And they want to one up you. Whenever they see an opportunity to do so. They want to gain an advantage at a feeling of superiority over you. Because it makes them feel like they've climbed in mountain. It makes them feel like there's top dog who has conquered you. But even if you ignore it. They will continue to do it. Because they're getting something out of it. It makes them feel better about themselves. Thank you for watching. I hope this video resonates with you. Please like, comment, share and subscribe. If you would like to donate. My PayPal link is in the video description. Coaching Inquiries. You can email me at coaching.nagcify.uk Thank you for watching and I'll talk to you soon.