In this presentation, we will create an amortization table for the effective method of amortizing a bond discount. Support accounting instruction by clicking the link below, giving you a free month membership to all of the content on our website, broken out by category, further broken out by course. Each course then organized in a logical, reasonable fashion, making it much more easy to find what you need then can be done on a YouTube page. We also include added resources such as Excel practice problems, PDF files, and more like QuickBooks backup files when applicable. So once again, click the link below for a free month membership to our website and all the content on it. Our information will be on the left side. We'll put the table into this table, the data into this table, and then we'll calculate a couple different journal entries related to this information and post them to a worksheet so that we can see what the effect is, how would we actually use this table in context. So if we go back to the left, our data is over here. We have the face amount of the bond, 100,000, stated rate, that's the rate on the actual bond, and then the market rate, which is the rate on the market, this is not on the actual bond, that's what we assume the market will be, semi-annual payments, two-year bond, that means that there's going to be two years, four time periods, because we're going to pay interest every six months, and the issue price is 96,454. The difference between the 100,000 and the 96,454 is going to be the discount, we're issuing it at a discount. Now that discount can be derived from calculating the present value and the difference between the stated rate and the market rate, so that's what the effect is. Now because of that, we can use the effective method and these two rates to allocate between the amount of interest and the amount of reduction in the unamortized discount. So let's see this in terms of the trial balance, what's on the trial balance at the start and what's going to happen as we go through this. So when we record the bond first off, we have it on the books now, we got 100,000, or we got cash of the difference between 96,454, then the bond is on the books for 100,000, that's what we owe at the end, and then we have this discount, which is the carrying amount, the difference between these two, 96,454, the actual money we got now. When we make payments on the bond, we're actually going to pay cash on the bond, but we're only going to pay cash based on the stated rate on the bond, and we're going to have to deal with this discount here some way. That discount needs to go away by the end of this bond. So what we're going to do is we're going to reduce this discount and record it to interest expense as the bond goes. Why are we going to reduce it and record it to interest expense? Because really that difference is due to the difference in interest rates. So we're going to reduce it to really kind of put that difference between the interest rate, take it from the bond rate to basically the market rate, and record the expense as we go. The easiest way to do that is just to take a straight line method, just to take this and divide it by however many payments we have, and then allocate it out just like we would in a depreciation type of method on a straight line depreciation. But that's not exactly right because it should change with the carrying value of the bond. So we should be, just as we do for amortization of a note, we should be figuring out exactly what the carrying amount is and figuring out what the interest portion then should be. Now if it's immaterial, it might be easiest and okay to just use a straight line method. If it's material, then we'd want to use the effective method, the preferred method. So on a straight line method, all we would do is we would just say, well, here's the unamortized discount, which is just the 100 minus the 96. Here's the carrying amount, which is always going to be a 100 minus the unamortized amount. And then we would just take this original unamortized amount and divide it by the number of periods, four, two years, two times a year, to get how much we're going to reduce it by an even amount each time period. And then we would reduce the unamortized discount by that amount. And then the carrying amount would always be the 100 minus the unamortized amount. So if it was straight line, which is not the preferred method, but an easy method, right, we would just always have the same amount here. We would reduce the unamortized by the same amount each time period, and then the carrying amount would also be changed by the same amount here. So it always would be the 100 minus the unamortized amount and so on and so forth until we get down to the period four, the last period, where the unamortized amount is zero and we're left with the 100,000, just the bond amount that we can then pay off at the term of the bond. So now we're going to do that same thing, but a little bit more complex method, the effective method. And this is a better allocation between the interest based on the carrying amount in a similar way as a note is when we allocate between interest and principle. So we're going to start off the same here. We're going to say that the unamortized discount is the 100 minus the 96,554. The carrying amount will always equal the 100 face amount minus the unamortized discount. Now we got to break out how much is going to be allocated to the unamortized discount. To do that, we're going to calculate the amount of cash that we pay. So when we pay cash, this is actually what we pay on a stated rate basis. So we're taking the 100,000 face amount on the bond times 0.08, that's the rate on the bond. That would be 8,000 a year, but we pay every six months. So we have to divide that by two, 4,000. So the amount that we're actually going to pay each time period will be the same. It's 100,000 times 0.08 divided by two. So that's the 4,000. And then the bond interest that we're going to have, it's going to be based on the carrying amount. So we're going to say this bond interest is equal to the carrying amount times, and then we're going to multiply it times the market rate. But that's for a year, so we have to divide it by two. So in other words, we're going to take the carrying amount, 96,454, kind of like if it was a note that'd be like the principal. And then we're going to multiply it times the market rate, like the real rate, the actual rate on the market, times 0.1. That would be for a year. And then we're going to divide that by two. So this would be the amount of bond interest, the actual bond interest here. Now, of course, that's greater than the cash paid. So the difference, the amortization change we're going to have is going to be equal to this 4,823 minus the 4,000. So that's the 823. So then the unamortized discount then is going to be that difference. So we're going to say the unamortized discount is going to go down by that 823. So it's going to be equal to the 3,546 minus the 823, or the 2,723, and then the carrying amount will always be equal to the 100,000 minus the unamortized discount. So we'll go through this process again. So same thing for the next payment. Again, the payment is always going to be the same. It's always just going to be the 100 times the amount on the bond divided by 2, because it's semi-annual. And then the bond interest is going to be based on the carrying amount, which of course has now changed. So that's going to equal the carrying amount times the market rate divided by 2. As we can see, these amounts then change. So the amortization amount here is going to be equal to the difference, this number minus this number. And you can see these numbers are changing as opposed to these here. So if we subtract those out then, we're going to say this is the prior unamortized discount minus the 862, giving us the 1,859, and then the carrying amount will always be the 100,000 minus the unamortized. So we'll do this again. If we do this again, we'll say this equals the payment. We could just say it's the 4,000. It's the same times the stated rate divided by 2. I want to divide it by 2. And then we'll take the bond interest, which is always going to be the carrying amount times the market rate divided by 2. The difference between those two is going to give us the change or the unamortized. And then we'll take the difference between the unamortized before and the change gives us our unamortized discount. The carrying amount will always be the 100,000 minus the unamortized. One more time. That payment, we're going to have the 100,000 times the 8% divided by 2. The bond interest is going to be the carrying amount times the market rate, 10%, divided by 2. The change then is going to be the change, the difference. And then the unamortized amount is going to be the prior unamortized minus this number. And of course it should go down to zero. That's how we know it's working. And then we're going to say that the carrying amount will always be the 100 minus the unamortized, which of course is zero. So there we have that. Now you might be saying, what if I had a whole lot of payments, wouldn't that be a tedious process? We can do this in Excel a lot easier. So let's just take a look at that real quick. After we do the first one, I'm going to delete these and just redo them. So I'm just going to select these and redo them. And let's just copy this row down and see if it does what we think.  And let's copy this row down and see if it does what we think it should. And if it doesn't, then we'll see if we can fix it to make it just copy down. So I'll select these. We'll go to the autofill and copy that down. And we say, okay, this number looks right. This number, obviously something is wrong. What's wrong with it? This number is right, but then it pulled this down. I want that to stay at the 10%. So I'm going to have to use an absolute reference there. This number looks right. I mean, it would be right if the, and then this number looks like it's calculating right. And that looks like it's, it's doing something wrong again. It's pulling this number down. So we're going to have to fix two cells here. So if we delete this and then I've gone to the, to the bond here, this number, I don't want it to move down. So anything related to these data over here, we don't want it to move basically. So that means that this is B five. So within B five, I'm going to put my cursor right in there and put F four on the keyboard. That makes an absolute reference. You can also just put a dollar sign before the B and a dollar sign before the five. And then when we, when we copy it down, it'll, it'll copy down again. It has nothing to do with dollars. It's just a code telling Excel, don't move that cell when we copy it. And then the other one is going to be the carrying amount, which we pulled this number. And again, I don't want it to pull down when we copy down. I don't want it to go down to the 8%. So that B three, I'm going to put my cursor in the B three and push F four on the keyboard. Or you can just type a dollar sign before the B and dollars sign before the three. We only need one dollar sign typically, but two doesn't help, doesn't hurt any other. Yeah. Well, we'll use an absolute reference. So here we have that and then enter. And then we'll just select these again. And then I'm going to auto fill down and see if it does what we want. And so this looks reasonable. That looks like, right. That looks right. That looks right. That looks right. That looks right. And the check is that it went to zero. And of course, carrying amount is a hundred thousand. That's where we want to be. Okay. So let's record some of these. What would the journal entry look like? What are we actually doing here? Well, if we start off on our, our trial balance where we had, we had issued the bond and it's on the books for a hundred thousand discount here. And then we make our first payment in terms of interest and, and the discount. What's going to happen then is we're going to pay cash. So cash is affected. It's going down because we're going to pay interest. I'm going to right click and copy. I'm going to put it on the bottom and Q four, right click and paste one, two, three. How much are we going to pay? I'm going to put a negative to do the calculation. And we've already done it here. Right. We're going to pay this amount. I'll just pull it from the table. We're going to pay this amount, which is the 100,000 times the 8% divided by two. And then that's how much we're going to pay. And the debit's going to go to interest expense, but we also have to, to decrease this discount at the same time. So we do that typically at the same time. So this is a debit. We need to do the opposite thing to it. A credit. So I'm going to right click and copy that. And that's going to go underneath, right click and paste one, two, three. And we need the credit there. And that's going to come from our table. So now I'm going to say negative of this number on the table. That's what the table's for. And then we're going to debit the sum of those two 800 and 483 with a negative sum or our plug formula, some double click. I'm going to go from the bottom to the top, or you can just move this thing out of the way. And there's that. So our debits now equal the credits. You add them up, they add up to zero. And that's going to be interest expense. So we're going to put that to interest expense. Even though we only paid 4,000, we're allocating this discount amount. So our interest expense is 4,823. Right click and copy. That's going to go up top. Right click and paste one, two, three. So if we post this, here's the interest expense. Here it is on our trial balance. We want to be in the middle column, W12 equals, we'll point to that 4,823, bring the balance up out of balance now and the net income goes down by the interest expense, which is revenue minus expenses. That's income, not a loss. Cash is here. Cash will be in W3 equals, point to the cash. That's going to bring cash down. And then here's the discount. So we're in the discount. So in W7 equals, we'll point to that 823, bringing that discount down. So now if we compare this to our table, then we have a carrying amount on the table of 97277, unamortized discount 2723 after the first payment. So here's our 2723 and here's this minus this is the 97277. So then let's do one more payment. So I'll highlight this one. Now we're on the second one. And it'll be, in essence, the same journal entry, meaning the accounts will remain the same, but the amounts will differ as they do when we pay like a loan off that has an installment payment where we pay interest and principal. So I'm just going to copy the same accounts here, but then the cash that we're going to pay, we always pay the 4,000. That doesn't change the discount. And I'm going to put a negative of 4,000. The discount, however, is going to be this amount now. So it's going to be negative of this amount. And that'll change the amount of the expense, which will be the plug of negative sum. I'm going to go from the bottom to the top, or you can move this out of the way and enter. So similar, but not exact, just like when we make a loan payment, basically, because it deals with the same issue of this interest on the being based on the on the carrying amount. So we're going to say there's the bond interest. I'm going to double click on the bond interest expense to go there. Go to the end of it, plus 4,864 brings that up out of balance, brings net income down. Cash is here. Something's in it. So I'm going to double click on cash, go to the end of it and plus point to that 4,000 bringing this balance down. And then the bond discount is here. Here's the bond, the bond discount. Double click on it, go to the end of it and plus and we're going to point to that 864 and enter. So now we have this amount and this being the bond amount. So if we go to our table, here's the unamortized amount. Here's the carrying amount. And if we go back over to our numbers, here's the unamortized amount, which matches, of course, in the carrying amount being the difference between these two, which is 98141, 98141.


Live from Las Vegas, it's theCUBE. Covering Splunk.conf 19, brought to you by Splunk. Okay, welcome back everyone. Live in Las Vegas, we're here for Splunks.conf. I'm John Furrier with theCUBE. This is our seventh year covering .conf, but .conf's 10th year of their end user conference, their customer conference. I've been excited to watch the evolution of Splunk and a lot of it's because of their great products. And we have our next guest for you, Fayyapeng, Senior Director of Product Line Management for Splunk, Business Flow. Welcome to theCUBE. Hi John, thank you so much. Thanks for having me. Well, I'm glad to have you. One of the successes of Splunk has been great products. They never deviate off the core, kept building on it. You're in the Senior Director of the Product Line for business flows, analytics. All I see everywhere is dashboards and visualizations. It looks so easy. Tell us about what your products are doing. Yeah, definitely. And I think one of the places to start is just how we moved into this area and started the new product. A lot of people know us for IT and security use cases, but a lot of our customers are also using it to address business needs. So what they really saw was the value of Splunk to pull data from across different silos. So in a business sense, it could be, I have different systems for maybe my leads, sales, and closing the books, right? Those are all disparate. It's really hard to pull it together. And so they came to us saying, we'd love a way to stitch this together and be able to visualize it. And that was really where Splunk Business Flow was born from. So we actually simplify it by connecting all these disparate data points, creating a full journey view or process view that you can graphically see what's happening, and then point and click and drill in. So it's really opening up a whole new set of users for us with that, and a whole new set of use cases that way. So you guys are almost building a new abstraction around a holistic view of the data. Is that kind of what, am I getting it right? Conceptually, yes. So if you think about, we have tons of data, it's tons of events. If you know a common thread, like a user, and how they might go to the store and then do something online, and really understand the customer experience, if you could actually thread that all together, we would know so much more about their customer experience, and that's what we're able to do, and we do it seamlessly for the customer. Well the database guy in me from the old 80s college saying, hmm, I got to write a schema for that, I got to store the data. I mean, in the old way, it was really hard. It was hard. Exactly. I mean, compare the pain or even capability. You're hitting exactly the pain point, right? That's why it's been so hard to do that because it was so rigid. The beauty of Splunk is the schema and read aspect of it. So because we store all the data and then we can distract it as needed, we do the search on demand, and that's how we're able to actually stitch it together. You know, Faye, one of the things that I've been saying about to our audience around why this conference is relevant this year, and in the industry is that data is now like code, and it needs to be addressable. And also there's a lot of contextual issues around knowing what data is needed when and where, when to throw it away. Some data's not worth hanging around. So this is a real kind of programmatic issue. This has been, this is a big story. Yeah, yeah. And I think like one of the things has been the struggle of, well, people have made a lot of probably more conservative decisions earlier on in their data, and that's why they weren't able to get the information. And so the main pain point we always heard was, I got one piece of data, but now that I look into it, crap, I need to know what else there is, and then it's another three-week cycle, right, to pull that data in, bring it all in. Well, now that's all in Splunk, you can just pull it as you need it on search, on demand. It's a use case that from an operation standpoint, they're pretty comfortable with handling Splunk. They know what it means to Splunk the data. Exactly, and we really see as a partnership between the Splunk admin as well as the business users, the Splunk admin helps to get it all set up, and then the business user can actually investigate on their own, and they don't need to know SPL or anything like that to be able to use the product. Talk about the business flow constant again. I want to kind of understand that from a product standpoint. Is it a part of enterprise suite? Is it part of, how do people use it or buy it? Exactly, that's a great question. So it's a premium solution, so you do need Splunk Enterprise or Splunk Cloud, and then this stacks essentially on top of it. And so it uses the underlying Splunk data, but then it's also doing the additional work of doing the correlation across it, stitching it together, providing the visualizations, and then from there you can do things like A-B comparison mode, you can see conversion rates, you can drill down all the way into the actual event. So the beauty of it is being able to see the holistic picture, but then go down into the individual event and journey. We hear a lot of the target audience being the business analyst or the security analyst. For your products, who is the target customer? It's definitely the business analyst, and I think there is some crossover with IT and security as well.  So I don't know if you've ever submitted an IT ticket and you never hear anything back because it's gotten lost. We don't have an IT department. But yeah. We're all born in the clouds. Exactly. But one of those... We're lucky. Exactly, you're very fortunate. But it was one of those problems where you hear a lot of IT departments.  So they were actually able to use the product to see that. But it also applies to people within one example. We have, sorry, I'm thinking of some public customers that we have. So Domino's is a public customer that was a beta customer that used it for payment processing on Super Bowl. So that's another great example. Yeah, and obviously scale is huge here with the data. So I got to ask the cloud question since we brought up cloud. Is this service cloud enabled in the sense of is it an on-premise thing or does the cloud workflow kick into the analytics? How does the cloud play into all this? Yes, so it sits on top of both. So it works either with the Splunk Enterprise or Splunk Cloud Enterprise License essentially. And then the actual architecture of it is a hybrid environment. So we have a hybrid component that's in our own hosted cloud that feeds the UI. And the great thing about that is that we're able to update the product very quickly and push updates to the customers very easily that way. Take us through the history. What's the origination story behind this product? Was it a reaction to customers kind of tinkering around with it? Was it requested? Was it a feature? Was it an idea that came out of an existing thing? And how long has it been in the motions for? So we first announced it back in May of this year and have added additional functionality as part of COV. And it did come out of customers and them seeing the opportunity with the machine data. So there are a lot of great stories that we've had historically. I think to buy airports, you can see some different stories before people piece the journey together. And so out of those conversations, the idea was born. Every product line has a list that didn't make the cut on the product. It's called the roadmap. It's also new things. What are some of the things that you see, big picture areas that you're going to focus in on to extend out the capabilities and value of the product? We really see the product evolving the same way that you see a lot of the portfolio overall. So Doug has talked a lot about investigate, monitoring, analyzing and act, right? And so those same concepts apply into how you think about a process as well. So right now we're really helping the investigation and monitoring, but we'll also continue to extend across that spectrum with time. A lot of cloud services, microservices, observability, a big part of all this. Yeah, definitely in how we've built the product, but also I think it can sit alongside some of the other things that you're also seeing in that realm. Final question for you, for people that are watching that couldn't make the conference, what's the biggest story here for .conf this year? How would you describe it? Overall I really think it is our data to everything message that we're discussing. I think today you can really see how we apply in all these vast areas and really the power of being able to have access and make that data actionable and do something with it. Thank you so much for coming in and sharing your insights here in theCUBE. Appreciate it. Thank you so much. It was so nice to be with you today. I'm John Furrier here in theCUBE coverage here in Las Vegas with .conf, Splunk's annual conference it's their 10th year. Our seventh year covering them. We'll be right back with more day two coverage after this short break.


Welcome back to the lecture series in bioelectricity. So, this is lecture number 19. So, in the previous lecture we talked about the basic structure of the retina and the eye and there we talked about the complex layer of different cell types in the retina which ensures that the image which is formed on the retinal plate is conveyed correctly to the brain and the decoding takes place. So, our eyes human eyes can distinguish light wavelength from the range of 400 to 700 nanometers. So, if you recollect your previous lecture where I have shown on the retina on the bed of the retinal pigment epithelial cells you have these rods and the cone cells. So, the rods are the ones which could distinguish the different intensities of light whether it is in during the dark conditions or during heavy light conditions likewise and the cone cells are the ones which could distinguish the different colors. So, essentially cones have cones are of different types it could be a red cone, it could be a blue cone, it could be a green cone depending on the specific ability of that cone cell to distinguish a specific color type. So, today what we will do we will be talking about the electrical activities of these rods and the cones under one broad heading the photoreceptor electrical activity of the photoreceptors and then we will be talking about the current stage of the prosthesis where people have lost their complete vision or partial vision. So, essentially there are two kinds of blindness as I have previously described either the blindness could be at the level of the lens where the there is a damage on the lens and the lens has to be replaced by an artificial lens. Those are curable blindness, but there is another level of blindness which arises at the level of the photoreceptors where the photoreceptors gets damaged either because of old age or because of some both defects or because of some injury or some other path of physiological situations. So, let us first of all discuss the structure of the rods and cones and then we will talk about the electrical activity in terms of the dark currents which are generated and why these are called dark currents and then we will briefly talk about some specific aspects of the cones and then we will talk about the prosthesis. So, start off with so we are into lecture 19. So, basically we will be dealing with the photoreceptors and within the photoreceptors we have the rods and the cones and their electrical activities in the concept of the dark currents and then followed by that we will be moving on to the prosthesis. So, moving on to the next slide. So, let us talk about the structure of the rods and the cones. So, these are very unusual kind of a structure. These are almost like you know fairly elongated structure like that and if you could see a cone pretty much a cone looks like this. So, this is the part say for example, if you look at this pen. So, you could see that there is this kind of you know light black color or ash color body and there is underneath there is a lighter color. So, this is the part essentially where all the light receptive pigments are present and this is the cell body. So, if I draw it it looks something like this. So, for example, cones because of their whole structure they are more like a structure which similar to this and this is the classic structure of a cone like cell and within this structure you have these what you see this cap like structure what I have just now drawn for you. So, this is where all the light sensation or different wavelength distinction takes place. Whereas, if you look at the rod they are more like more almost like a rod only here you have. So, this is where. So, they are called cone they are for the color perception. So, these are the cones here for the colors and these are the rods for different light intensity. So, this is where they have the nucleus and the way it works is this is where the light falls. This is the one the cone is the one which could distinguish between you know the green it could be a green cone it could be a blue cone or it could be a red cone almost like you know R G V coding. So, the different wavelengths of these this could distinguish. So, these are the different wavelengths of the specific cones are of a specific type they could be excited. So, that they could emit they could they could absorb light at the blue wavelength or they could absorb light at the red or they could absorb the green wavelength. So, for example, this kind of color vision is only functional in high intensity light because if you walk in the dark you realize that you cannot distinguish color what you essentially see a shade of black and white. So, that means the cones are only active when you are in a bright light and where you could distinguish the different color red green likewise and there are people who suffer from color blindness where they could not distinguish there is a different kind of color. So, that means such individual suffers from certain genetic disorders where they are color distinction the specific type of cones which could distinguish color does not develop properly or mal-deformed or mal-formed. So, there is another kind of blindness which is called a night blindness which is essentially as it becomes darker your unable to really distinguish objects properly that essentially happens because of the rods and it is not a genetic disorder it is mostly because of the malnutrition of lack of vitamin A in the food and will come to that where it exactly happens in the case of rods if you look at this structure. So, out here any kind of like you know lights which are falling here and they respond to the light. So, essentially what happened when the light falls. So, these light or these photons binds to specific sites on the membrane and once they bind to the membrane this light energy is translated into an electrical impulse this is what essentially happens. So, here you have the light signal falling on photoreceptor which includes rods and cones depending on the situation it could distinguish the color component and if it is dark it is not going to do. So, and the intensity and these are coded as electrical signals electrical signals and these electrical signals are essentially sent to the brain for further processing and in between the rods and the cones are the whole network of amacrine cells horizontal cells which ensures that the exact depth of the object what we are looking at the exact the movability and all these features are being added to this. So, what the brain receives is a very complex electrical signal which it has to you know distinguish in terms of colors intensity depth frame and all those things. So, coming back again coming back to the structure. So, how this electrical signals are being received. So, it is very interesting that on the photoreceptor we there is one term which I am going to introduce now is called dark currents. So, what does dark current means. So, there is a common way we look at it. So, we believe when there is a intense some signal is coming our sensory cells respond to that and generate action potentials and this action potential eventually is conveyed to the brain and we decipher the object or what is over we are looking or we decipher the information fine. In the case of rods and cones this thing is just reversed something like that when the light falls on the photoreceptor layer they do not generate any signals and when the light does not fall they generate signals it is just the reverse it means when the light is not falling suppose my eyes are closed. So, it is actually conveying the signal. So, essentially what happens suppose you have a matrix like this this is how to conceptualize in your brain then only will be able to understand. So, for example, this is the matrix of say you know say for example, these are different matrix of say rods and cones which are setting there I am just putting different colors for your understanding. So, imagine this is a photoreceptor layer and these individual circles are different photoreceptors. So, say for example, when the light is falling here like this and this is the wave of light which is falling. So, when the light is falling differentially at some point this will one second at one point this one will get shut off then is the next point this one will get shut off and this one will get shut off this one will get shut off this one will get shut off, but this all shutting off is taking place if this is taking place at t 1 this will take place at t 2 this will take place at 3 3 t 4 t 5 likewise you know I am just randomly putting all the different numbers at different t stands for the different time. So, essentially what is happening you are getting a matrix over a period of time where initially this one switched off there is one frame you are getting where one say for example, think of a flood light one light goes off there is one image next light goes off you get next three light goes off you have another image next four light goes off you have another set of image essentially what is happening you are recording this frame by frame and eventually the whole image is formed in front of you or say for example, in a city you are looking from the top all of a sudden I you know switched off all the lights in such a way that you make a figure. So, I just selectively put certain lights on and switch off all the lights and then I change the figure I again switched off certain lights and gets a figure likewise I can really frame by frame I can actually convey the message this is exactly how some of these the retina actually this is kind of an analogy how it works. So, for example, at the frame one when if you talk about the frame let us let us try to you know see how the let us draw six frames what is happening. So, this is say for example, time t 1 this is time t 2 this is time t 3 this is say t 4 this is t 5 this is t 6 and this is let us assume that this is our. So, in the first when the first shot of light comes this one this one this one goes off the next frame. So, for example, this one sorry this one this one and this one goes off. So, now between these two you will see initially you saw a image like this what I am circling something like this with a contrast here there is a contrast here then you see an image like this with a contrast here you are seeing you are kind of seeing a shape now likewise frame by frame and this is all happening within a frame of you know nano or femtosecond you have lot of frames and these frames are the ones which eventually form the image this is very essential for you to understand before I go to the individual at the cellular level. So, it is a frame by frame images which are formed because of shutting off of say one photoreceptor or some population of photoreceptors the other photoreceptor layers are active then another set of photoreceptors is active the other set of photoreceptor goes off likewise. So, whenever you have to imagine think of a flood light in a in a velodrome or in a stadium where if one light goes off you have one frame then few lights goes off you have another frame then few lights goes off another frame or you can like think of it somebody is showing you the sky and slowly one by one you know showing you the star follow the stars it is almost like that one switch off switch on switch off switch on likewise something of that or suppose you are seeing a city from the top all the lights are on then somebody selectively switches off a light and make a figure then switches of the light makes a figure switches of the light make a figure and then that whole image will come exactly that is how the retina functions. Now, coming at the cellular level what is happening? So, I can I can use this. So, what we can do what I will be doing actually now I will give you the membrane structure here now let us see the structure of the membrane. So, we will be talking mostly about the rods at this time rods I mean the signal processing is fairly the same. So, here the here the rods. So, if you really have blow up this image it is almost like this this is the membrane structure which remember in previous one of the previous lectures we have talk about the membrane this is the lipid bilayer and on the lipid bilayer you have something like a protein sitting there which is called the rhodopsin protein the rhodopsin is a huge molecule rhodopsin which is essentially made up of two one is called opsin the other one is called retinal and the retinal fragment is sitting somewhere I will just put it in blue now somewhere here the retinal fragment is sitting. So, this is how it looks like. So, there is an opsin moiety and there is this retinal molecule. So, this is the key molecule which ensures or this is the one which receives the light. So, it receives the light. So, this has a light this is a light sensitive protein it is this molecule which ensures it has variants of course which ensures that at different intensities how it will function with different wavelength how it is going to function. So, this key molecule now we will be talking about what exactly happens with this molecule in the next slide the next slide what I will do I will talk about. So, let us again get back to the structure of let us take the example of a rod now. So, here you have all those rhodopsin molecules which are sitting here and here you have the nucleus and now say for example, you put an electrode inside the cell and you put another electrode of course the outside the cell like this and you have volt ammeter sitting here what will you see. So, initially what will you see when there is no light falling in this. So, at that time your volt ammeter reading will tell you round minus 40 millivolt. This is a situation when once again there is no light let me put that there is no light and at that point there is a flux of the sodium which is getting in as the sodium is getting in it is ensuring all the sodium ions are ensuring that this electrical impulses are being transmitted. So, this is the kind of the axon axonal end of a rod or a cone cells. So, because when the light is falling a light is not falling sorry. So, market light is not falling there is profuse entry of the sodium ions and this ensures that it generates axon potentials which are transmitted to the next layer what you could see. So, at this stage the electrical signal is travelling like this. Now when the light falls. So, this is basically what I termed as what I was trying to discuss with you is the dark current there is no light yet it is generating axon potentials. Now next let us go to the next situation the next situation what is happening here you have again the rod cells.  Now light is falling on this when light is falling on this what you see the membrane potential goes to minus 70 millivolt. Now what is happening here when minus 70 millivolt is here it means it is no more sending any signal out here just reverse to the situation where during no light it was sending signal and you see out light. So, basically the signals are being there is no signal what exactly happened this is a very very interesting phenomenon. So, let us dissect the problem at different level first of all when the light was falling sodium channel was not falling sorry excuse me when the light was not falling sodium channels are open when the light started falling sodium channels were closed. So, what is regulating the sodium channels first question and how that regulation is modulated by light because when the light falls sodium current stops. So, it means light and the gating of sodium channel is linked. So, these are the two things which now you will be discussing light and gating of sodium channels are linked and now what we will do we will establish that linkage what essentially is happening. So, there are two things which are happening out there. So, the first thing what happens is now let us go back to the membrane structure what is happening remember.  So, I told you said somewhere here is the retinal fragment is sitting and this is the protein which is with all its complex structure out there which is shown in green. Now, when the light falls. So, this moiety what you see here this retinal moiety this remain as 11 cis retinal with an aldehyde group this is in this stage and of course, you have the once again opsin moiety and all together it is a rhodopsin. So, now the light is falling at this stage there is no light. Now, here the yellow thing what you are showing showing my h nu is the when the light is falling when the light is falling what happens this 11 cis retinal what you see here transformed into 11 trans retinal. So, the cis bond becomes a trans bond when the light falls on it. So, now what you see there is a change in the molecular architecture of that retinal molecule what are the consequences of that change this is the first change. So, within the rhodopsin. So, rhodopsin is having this opsin and the retinal moiety attached on it it is a complex protein when the light is not falling on it the retinal moiety remain in 11 cis retinal form when the light falls on it 11 cis retinal become 11 trans retinal, but then what happens next let us move on to the next slide once again save it. So, the next thing what happens is this 11 cis retinal is formed. So, let us go back. So, here the photon is falling on here is the moiety sitting there. So, photon has formed now you have 11 trans retinal this 11 trans retinal then dissociates from this structure. So, what you are left with is now you have this opsin protein coming out plus you have 11 cis sorry 11 let me just rub it off and you have this 11 trans retinal now this opsin protein now remain it is pretty much orphan at this stage and whereas, 11 trans retinal through an enzymatic action again become cis ok. So, there is an complete enzymatic action where ATP is being used to convert it into 11 trans retis cis retinal now this cis form again assemble with the orphan protein or the orphan component which is the opsin and again form this structure. So, this is basically what is being called is a bleaching phenomena there is a kind of continuous bleaching taking place within the eye ok, but during that process something else has happened that is what we are going to discuss how that channel remains open. So, what essentially happens in a step 1 we come back to step 1 step 1 was opsin activation because when the light falls I told you the opsin becomes opsin comes out and the 11 trans retinal dissociates then what the opsin does the step 2 opsin does something very interesting opsin activates and second enzyme called transducin and this transducin which in turn activates another enzyme called phosphodiesterase and this phosphodiesterase then does something very interesting what it does is now just before that let me talk about little bit about the transducin what is exactly just giving one minute transducin. So, transducin is basically a G protein is a series of proteins G protein it is a it is a membrane bound enzyme and it activates by activates by interaction with receptor proteins bound in the cell on in the cell membrane in this case what transducin does transducin is of course activated by opsin and transducin in turn activates your phosphodiesterase what I have talked about and phosphodiesterase what essentially does is very interesting this phosphodiesterase disintegrates a molecule called cyclic GMP then where what is the role of cyclic GMP. So, coming back to the slide now you have phosphodiesterase I am showing by PDE this PDE then acts on cyclic GMP. So, what is the role of cyclic GMP in this whole context of things what I was trying to tell you. So, let us come back to the first question here out here what is the question what we ask once again let me go back to that slide where I talked about the dark current introduce it to the dark current yes when there is no light I showed you this picture when there is no light you could see the sodium channels are open up to this concept is clear then what essentially happens when the light falls sodium channels become closed. So, the fundamental question comes here we ask two fundamental question one was this one light and gating of sodium channels are linked we will take it up from here in this light two situation no light sodium channel or sodium current sodium channel closed no light sodium channel open fine. Now, the next challenging question was what keeps these sodium channel open at this stage as if a door is kept open. So, how you keep your door open if there are three ways how you can keep a door open think of the room wherever you are sitting and listening to this lecture you can keep the door open by using like something underneath the door stopper or you have something by which you keep the door like this. So, these molecules have something like a door stopper molecule and the door stopper molecule or door opeder molecule of sodium channel is cyclic g m p. In other word what is essentially is happening if say for example, this is let us take an analogy that you know this is the door of a room this is the wall and this is the door this is the wall and this is the door which is locked. So, this there is cyclic g m p if this is this is say for example, the sodium channel and this is in a closed state or this is the door. Now, this could be kept open like this by holding it with something like this or to hold it it could be a door stopper or anything in the case of channel here you have this cyclic g m p setting. So, cyclic g m p acts as a door stopper keeps the door open. So, now the door will only close when you remove that door stopper that door stopper cyclic g m p is being removed by the molecule called phosphodiesterase which is activated by transducin. Transducin is further activated by opsin and opsin is disintegrated in the presence of light does it make sense. So, when the light falls. So, let us summarize what exactly happened now coming back to the next slide. So, light falls on the retina what you essentially see no current no sodium flux. So, it means sodium channels are closed. So, what is happening at the molecular level during this whole thing light falls 11 says a retinal become 11 trans retinal on the rhodopsin molecule. 11 trans retinal dissociates out and this process dissociates the opsin molecule. This opsin molecule then activates a protein called transducin. This transducin further activates a molecule called a phosphodiesterase. Phosphodiesterase then dissolve the cyclic g m p which was keeping the sodium channel in open state. This is exactly what is happening in this whole process of signal transduction within the rods and the codes where when the light falls the electrical signal seizes when the light is not falling the electrical signal passes on. That is why this is called a dark current which is also called cyclic g m p regulated current. Depending on the wavelength of the light in the case of cones these electrical currents are being stimulated or you know stopped. So, this is a very fundamental concept which I wish or wanted that you people it is should be clear to you. It is this layer which is the most critical one. So, say for example, now some way or other if this layer. So, let us go back and see the global picture out here. So, out here just give me one minute.  So, the electrical signal moves like this left to right right to left. Now, at this level the first level where the image processing takes place is the PR or the photoreceptor layer which is essentially the rods and the cones. So, the electrical signals are traveling from the eyes all the way to the brain and the processing takes place. Now, imagine some way or other rods and the cones layer goes back what will happen. So, now there would not be any further image processing taking place. You will see it, but there would not be any further message going on. This is all blocked. What intervention can happen? One option is that if you could replace these eyes with a camera or with two cameras on two sides. So, you or you have a goggles like this you know something like this where you have the cameras sitting out here and this goggle is connected interface directly to the brain. So, camera instead of this now they are directly cross talking with the brain. This is the approach which inspire people for last one century that could we really bypass the whole vision by putting cameras instead of ISO. So, basically what is happening this circuit is no more functional. This is a defunct circuit out here. So, what you are doing you are interfacing the camera with the brain. So, images which are forming analog images are digitized and the digitize signal in the form of electrodes are being fed to the brain. So, in this context I will recommend you to read some of the papers and of course at the end of the course I will be providing all the references which I wish. So, one of the person who had made really good contribution is from university of southern California should read is Mark Humayun spelling may be wrong sorry Mark Humayun. This paper from university of southern California USC. He has made some very interesting cameras where he has interface them with the brain and this is really a very interesting piece of work which is going on. So, where the prosthesis has kind of a success of process, but there is lot of improvement which has to be done. So, what I will be essentially doing I will be giving you these references of Mark Humayun and I expect you to read through them because that is where lies most of the future of prosthesis. So, again to tell you what is the major challenge is this game in this game the major challenge how we interface the electrodes with the brain because you are putting some foreign material in the form of electrode. So, that has to be biocompatible not only it has to be biocompatible it has to be ensured that all the signal over a period of time continuously reaches because you cannot continuously open the system and you know fix the electrodes it has to be done. So, say for example, here essentially what we are talking about if this is the individual and if this is the brain. So, that is what you are saying is essentially this person has this goggles which they are putting. So, here is the retina which is receiving the light and this goggle which is a synthetic retina is essentially interface with the visual cortex out here in the brain. So, there is enormous amount of electrode implantation which has to be done to ensure all the message reaches the brain at the visual cortex and then of course, this person will be able to you know visualize if this person is unable to see, but this needs not only I mean of course, the camera technology is very well advanced, but the interface technology which is the whole area of neural engineering or you know you can talk in a much more specific way at the cell electrode interface or neuron electrode interface. This is in terms of the cell here this is the very very challenging area where lot of research is going on for last one century that how really we can develop extremely high biocompatible material which will convey the signal and help in this processes business. So, I will close in here with this and in the next class we will talk about the year and the year processes and so on and so forth we will finish this whole processes part with the basic structure. So, they are also following the same strategy. So, and I will be adding all these references for you and please kindly go through some of the work of Mark Hume and that is very inspiring pieces of work which is happening across the globe. Thank you.


What's going on, everyone? Sam here with late sports cards. We're ripping 22 Top Scrums, Sonic Baseball, light eight bucks, break number five. Pick your team style. We're going to have a Phyllis of Art, Names, and Artiebs. Abrin A, or S with the Angels. Arthur Y, Blue Jays, Brandon A, Pirates, Rays. Brian D, Rockies, Brewers. Thank you, Brian, for selling us out. Daniel R with the Cardinals. Daniel H, Reds. Darren G with the Mets. Edward P, Yanks. Red Sox, Braves, Royals, Cubs, Rangers. Hung G, C with the Phillies. Josh S with the White Sox. Joshua W Dodgers. Ryan W, Mariners. Ryan B with the Nats and the Marlins. Sabrina S with the Giants. Shane W, Diamondbacks, Orioles, Guardians, Twins. Shu K with the Padres. We've got Stephen F with the Tigers. Tim W with the Athletics. And Todd D with the Astros. So good luck here, guys. Let's do it. Again, we got two, I think, in number six we've got three to go. It's going to be the Brewers, Athletics, and Rangers this time. Again, all $20 a spot. Brewers, Brewers, A's and Rangers. All $20 a spot. Good luck. Let's do it. Here we go. See some nice color come out of this. I want to see one of those, jeez, come on. I want to see one of those black, red, sonic pulses. Those are pretty cool. I like those. Then we got iMac at the end of the night, or end of the day as well, guys. I'm at random hit style for WWE. WWE. Refractor Juan Soto for the Nats. George Springer, Ray Wave. Josh, look. Try to never have a dull moment. Especially when it's you and I, and it's just chaos. Prism, you rise. Daniel Cruz, Rookie. Refractor Joe Adele and Joey Vada. Manning Refractor. Lester, and a Jackson Reets, Ray Wave, nice one there for the Brew Crew. There's Max Freed, Ray Wave for the Braves. Steamers, just steamers.  Francisco Lindore, Donnie. Yeah, me, Donnie, my brother, my brother and mom, myself. Oh boy, St. Paddy's Day term. There's Alex Kearlaw. Gonna be Lit. Ray Wave, Edward Cabrera, Rookie for the Marlins. Lit, lit. Oh yeah. Donnie's been practicing too, so that's good. Oh yeah, that's big. Blake Snell, Snell Zilla on the purple-yellow Sonic Colts to 299 for the Padres. We need him bad. Oh yeah. Cedric Mullins, Refractor. Youthquake is Bobby Witt, nice one there. I hate to be a member there. Nice, we've got a Wander Prism. How much smack you're gonna talk if you win it. Oh my God. Oh, it's the Lord. These guys would never, ever invite me again. Now, understandably, too.  Yeah, drink that might help. Nah, maybe. Cut down to two, maybe. Ha, ha, ha, ha, ha, ha. Man, I was lit when we did that last one. Bad move. Barely think I'm sober. Trevor Rogers, Refractor. Refractor, Max Muncie. Lance Lynn. Raywave, Nolan Aranada. Prism, Francisco Lindor. And Raywave, Michael Baranthly. Nicoladola, Miguel Cabrera. Prism, Hyunjin Raiou. Raywave, that's Prism, Dylan Carlson, Rookie Cup, Second Year. Justine Reigns. There's Pavin Smith, Refractor, for the Diamondbacks. Prism, Cedric Mullins. And Raywave, Brian Anderson, for the Marlins. There's a pink teal of Sonic Wave. Sonic Pulse 2199 is Patrick Wisdom. Too many words going on on those. Raywave, Stuart Fairchild. Refractor, Alex Kearlove. Youthquake, Tyler McGill. Joe Adele. I'll do box two on the box three. All right, guys, number six coming up next. We got, again, just the Brewers and Rangers. Very nice, we're down to just two to go. Brewers and Rangers on number six. Both 20 a team. That is it, that is all. No more, no less. Goldschmidt's base. Fractor, Lord Gurriel. Raywave, Mikey Stremski. Gold, Raywave, Paul Goldschmidt. Oh boy, 50 of 50. Nice one there, congrats, cards. That's a lot right there. That is gonna be Daniel R. Congrats, buddy, we'll mag that one for you. That's awesome, dude, Edward Cabrera. I have to imagine that's gotta be at least 200 with five. Gold, Goldie, very nice. Refractor, Jared Walsh. Base, J-Ram, J-Ram, The Guardians. Base and Judge on the back there. It runs in Contreras, Youthquake, Chris Sale. Chris Sale. There's Ryan Valleys, nice one there on the Raywave rookie for the Rockies. Raywave rookie for the Rockies. Is there a James baseball game today? Baseball? Yeah. Mookie Betts, maybe. I don't know what they're talking about. Freeing Raywaves and Andrew Bogarts for the Red Sox 34 of 99. There might be, like, free training. Jaren Duran, Prism. There's Raywave, Adam Frazier. Dijilme Hira, Refractor. There's Jaren Duran, Youthquake, Ryan Valleys. Presum, Salvi Perez for the Royals and Brian Anderson for the last pack here, everybody. Box three. Refractor, Patrick Wisdom, Rookie Cup team and Mike Yaz for the Giants. Case spot. We're sold out on six. Thank you, Brian F. Appreciate you, buddy, and The Guardian's gone now in seven. Seven, we are now down to three teams, Rockies, Marlins, Brewers. That's what we gotta fill up next. Again, guys, those are all just $20 a team. $20 a team. Let's keep it going. Thank you there, Brian, for selling us out on number six. Needless to just go right into the break after, which is awesome. We also need to, uh... Let me see. It's time. Okay, yeah. Refractor, Joe Musgrove. There's Raywave, Walker Bueller. And guys, we'll be a little behind going to the next one, Brandon Marsh. We had to sell out the last couple, but we can just go right into number six, which is nice. Keep it moving. O'Neil Cruz, Rookie. Wander Franco, Rookie. And Raywave, Honey Not Barrios. No autos yet, Eva. We hit a C.J. Abrams Green to 99, which is pretty big. Couple of goals, just hit a gold, Paul Goldschmidt, Raywave. It's big on the top's buyback. That is Robbie Grossman. Dylan Carlson. Fractor, Jake Berger. Presum, Louis Heal, Rookie. For the inks. And Chris Sale. Hey, thanks, Doug. There's Joey Vada. Gabriel Orias. Hey, picking up the Marlins in number seven. Thank you there, Doug. We're now at two to go in number seven. Thank you, Doug. Appreciate you, buddy. Everything okay? Yeah, no, she was showing me the order, Doug. Oh, Matt's. New Matt for gaming, and she wants us to look at them and see if they're really fine. Nice. She doesn't put her big in here. Alec Bohn. Oh, yeah. Oh, it needs to be all right. Raywave, Aaron Judge. It needs to be what? Did you guys keep hanging up on this little? Yeah. Yeah, that's true. There's Clayton Kershaw, Yellow Purple. The 299, Sonic Pulse. Riley Evans. Sold out on six guys. Two to go on seven. Appreciate you knocking these down. Evan Longoria. Anthony Rendone. Box number five. Also got to take a look at iMac Wrestling Random Hit Style. Again, everybody is guaranteed a hit. There are five base cards per case. Those get comboed with the lesser valued hits as well. Generally Relics to 99. Fracture Curtis Terry. Jake Berger. Jackson Reitz and Joey Vado Raywave. So everybody is guaranteed to walk away with a hit. Some people walk away with a hit and a base. You have probably about a 50% chance to hit an auto, which is good too. And usually there's one pretty big auto per box. Generally one big hit a box, I would say. Which is always a lot of fun. Raywave, DJ LaMahue of the machine. Teal Raywave, Vidal Bruhan. Two, 199. Nice one there for the raise. Manual Colossae. Hey, thank you. Andrew just took a spot in iMac. We're down to 15 to go on that now. 15 spots. Again, you are guaranteed to walk away with a hit. Guaranteed. 100% you are walking away with a hit. Refractor Max Freed. Chris Sale. Prism, Isaiah Connor-Felife. That is Hien Jin Raiute. It'll break around, I want to say, three o'clock, Selena. Or Sabrina, excuse me, Sabrina. Yeah, three o'clock. Three o'clock Eastern time. We'll probably be a bit behind. There'll probably be more like 315 would be my guess. 315, 320. Josiah Gray. But it breaks at the, it's the last break of the morning shift. The last break of the morning shift. Adam Frazier. Refractor Giancarlo Stanton. Green, Rodolfo Castro on the Raywave, two, 99. Yeah, so it'll break around Eastern time, that is, as well. Eastern time, no problem, Sabrina. We're very welcome. There's Patrick Wisdom. There's Max Scherzer. MJ Melendez, George Springer. Tony Santian, Ruby Refractor. Prism, Brandon Woodruff, and Nolan Arnaut. Casey, this is Topstrome Sonic. Topstrome Sonic Light. So basically it's like the light version of Topstrome with a couple different guarantees. Florian taking some spots. Joshua taking the Dodgers in nine, thank you buddy. There's Ernie Clement. Prism. Prism, Yadir Molina. Sayasizuki Youthquake. Someone called Florist Nerdboy. That was funny. It was during Almanac too, when we had everybody on, and he just looks at me when he's starting to break, and he's like, I just talked about Star Wars for two minutes. Someone called me Nerdboy. There's Clayton Kershaw. And Raywave, Cole Calhoun. That was good. That was good. There's Purple Is to two, 99. Corey Seager. Yeah, it is regular Topstrome base. Yeah, so yeah, you can get the MVP buybacks, yeah. Like we just got a gold Raywave of Paul Goldschmidt in this one, actually, it's a 50. Chris said it should be probably about this. What's up? Emmanuel Claasai, who said that? Chris. Did he? Really? Wow. Oh, out there, Chris out there. So it's up there, Big Money. Money. Shane Boz, Refractor. Connor Wong. Yeah, so you can get the MVP buybacks in this, which is cool. You can get them in color, you can get them in Raywave, Prisms, Refractors, and base. All the good stuff. Bryce Harper. What's up, Brian? Thanks for selling us out in this one, by the way, buddy. Or the next one, might be the next one. Forrest tonight, Forrest. George Kirby. Pimp. What's up, Brian? O'Neil Cruz, Rookie. Prism, Zach Lothar. Yeah, no problem there, Pimp. Yeah, Forrest, let me break in tonight. I can believe he starts at five. Ronesy Contreras, Refractor, and Mike Trout. Let me double check that. Makes sense if he did. Yeah, yeah, he starts at five. Two boxes to go, guys. Thanks again for getting this sold out. Again, six is sold out, which is awesome. So I do appreciate y'all for that one. Refractor, Louise Heal. Yeah, we hit a, so they have the silver-packed, super short-printed rookie inserts in this, too. Zach Pop, we hit a green Raywave Abrams to 99. That was probably the biggest we hit out of this, I would say. I think they're taking a couple more spots. Thank you there, Brian. Another two IMAX spots came in, too. Thank you, guys. That's awesome. Appreciate y'all. Thank you so much. Yeah, that was probably the biggest. We hit an O'Neil Cruz to 299. There's a manual closet. Hit a couple of the gold youth quakes. We hit a Elliot Ramos and a George Kirby. So a couple of decent rookies. Pavin Smith, Roanze Contreras. We hit a gold Raywave of this card right here, too. Roanze Contreras. A couple other gold Raywaves. Hit the Goldschmidt in this one, which is really solid. Youth Quake, Juan Soto. Hit an Exposé of Acuña, which is like one in every four cases or something, the Exposés. Refractor, Jaren Duran. Miguel Cabrera. Who's the biggest steamer here? Refractor, Mitch Hannigan. Lepidani. Pretty wave, Sammy Long. Give me a top three. Stani. Alex Bregman, Refractor. Julio Rodriguez. Right in the jig. Steel Raywave, Jackson Coar to 199 for the Royals. I'm missing my Nobius one. You. Yeah. You're only on the top of the list. Here's Bobby Dahlback on the prism. You and Donnie have a fight head-to-head. You're number one on the top. Yeah, you're always bad luck. He's second order food. Yeah, that's true. I don't know, I haven't seen you in anything. You got Frank Tank, but he's just soiled. Hey, thank you there, Edward. Ed. Taking a bunch as well. Troy D taking the Twins in 10. Let's keep it going. It's pretty solid overall, Doug. It's pretty solid. The way it breaks down is generally, so the way the statistics are with this, six cards a box, one base, generally two relics, and then three autos. So that means there's what? 15 autos to the break. And then we combo the five lowest relics with a base card as well, and the base are all numbered. So basically there's only a couple that are going, a couple people going home with just a relic card, which even sometimes the relics are huge. Like sometimes the relic card will be by far the biggest card out of the box if it's a good patch of a big wrestler. The secondary market's been very hot for them so far too. Map Manning. Secondary market's been, there's a lot of sneaky value for WWE stuff. Jose Ciri, Lord's Gurriel, Hugh Darvish, and Kevin Gaussman. This is probably the highest end product for wrestling that Panini will make as of right now. Sami Long, Ryan Mountcastle. Yeah, secondary market's super strong for this too. Spin Fond Man, pulled Stone Cold Steve Austin, John Cena, Redemption, Hulk Hogan Redemption, Quad of a bunch of the Divas. There's Byron Buxton, nice one there on the Pink Teal Sonic Pulse to 199. Bunch of the Divas, what else did we pull? Mandy Rose, Patch Auto to 10. Becky Lynch and I think Bayley or Alexa Bliss, Dual, Patch Auto to 10. Bunch to five, Kofi Kingston to eight. I'm trying to think what else? Like just Wahoo's break alone. Jack Blairty was nuts. Couple of Goldberg Redemptions. Refractor Wilson-Andrerra's. There's Jerry Lawler in there, which is cool. Yeah, Quad of the Usos. There's Jerry Lawler in there, which is awesome. Miss Hannigarh on the railway. There you go, Doug. Let's do it, buddy. Good luck, man. Trevor Rogers, Prism. Castro, Alex Bregman. And there's like, it's a strong collecting base too. People love their WWE. Refractor CJ Abrams, Youth Quake. We'll just recap the big stuff from now on, I would say. Yeah, Braun Breaker, RPA, two Kurt Angles. Miggy on the refractor. Prism, Joey Gallo and Harrison Bader. Hi. There's Corey Seeger, Clayton Kershaw, Blake Snell, all to 2.99. To 1.99, Patrick Wisdom, Byron Buxton, Jackson Coar and Vidal Bruhan. To 99 is Ridolfo Castro and Xander Bogart. And a Gold, Reyway Paul Goldschmidt to 50. Let's do it for the break, guys. Thanks again for the fill. Next up, we've got number six.


The Sable District Court of Baku arrested Aziz Orijov, the head of the internet television, Canal 13, for three months. When he was removed from the courtroom, he faced violence. Orijov linked the criminal case with his journalistic work. The investigation accused Orijov of illegally building a house on state-owned land. However, thousands of houses have been built on the same territory, but there are no claims against their owners. On the other hand, the land is owned by the state, and 11 citizens who are not related to this land file the complaint against Orijov. In general, there is no crime in this case. The maximum that government agencies could do is to demolish the building, the lawyer agile Lage said. According to him, the defense intends to appeal against the arrest.


So by now, I'm sure that most of my audience is kind of tired of these Caitlyn Jenner gubernatorial run stories, especially considering that she barely got one person of the vote but I want to talk about this story because It's just one of those stories that I enjoy so much for no particular reason. Maybe it's shoutin, Freuda Maybe it's just that I like seeing dumb people get proven wrong when things are pretty obvious either way she basically explains that she Had a lot of meetings with Republican politicians and many of them did not want to be seen publicly with her They were embarrassed of her. I wonder why why would Republicans who are transphobic not want to meet With somebody who's transgender. I wonder why even if she's a Republican perhaps it's because They're bigots But either way, uh, what she says is uh, it's fascinating to me to say the least So LGBTQ nation reports. Caitlyn Jenner said that during her failed California gubernatorial campaign Some republican party leaders and elected GOP officials refused to be seen in public with her despite supporting her in private I had elected officials and party leaders who would gladly take private meetings with my campaign team and me But would balk at the mere notion of being seen publicly with me Jenner wrote in a recent USA Today column She would have been the first transgender governor in the united states had she won to a point I understand they have to protect themselves from their voters and base who might not be as open-minded as they are She explained noting that the problem for someone like myself is partly generational But leadership means standing up for what is right and if you thank me privately for running for office You should be able to do it publicly despite association with her being treated like a dirty secret Jenner assures readers that she is still a republican and will fight for inclusivity in the republican party That's gonna go well She even complains that coming out as a republican was more difficult than coming out as transgender So I just love that even as she explains to all of us How republicans were literally embarrassed to be seen with her in public even if they privately supported her She's still a republican. She's still loyal to the party She still is making sure that that republican victim narrative is is a thing No, it's it's harder for me to come out as a republican than it is to come out as transgender. Is it though? Is it though? Really? Okay, maybe that's true in her case because she is a multi-millionaire And she's older and she's not living with her parents who you know, you may rely on for housing as is the case with trans youth But I just we're to a point where I can't do anything but laugh at this. What did you expect katelyn? What did you expect? This is a classic leopards eight my face situation where somebody was totally surprised That a leopard ate their face after they voted for the leopards eating people's faces party I just I don't understand how you would expect something like this. It's like You are running as a democrat and your your number one thing is that you want to ban abortions The party kind of is against that. So what are you trying to do here? I mean, perhaps when it comes to economic issues. She is conservative because she's rich and she's looking out for her own self-interest But still I mean you can't pretend to be surprised that this transphobic party that made it their mission, especially in 2021 to demonize trans youth They're against you. How is that surprising to you? This party has always been bigoted this this party has always used Social issues and culture war issues to get elected. They offer voters nothing So what do they do? They prey on people's bigotry. They prey on racism and xenophobia And transphobia and homophobia. That's what they do. They've been doing this forever So for you to be shocked To have a surprised Pikachu phase when they're like, I don't want to be seen with you in public What did you expect Caitlyn? What did you expect? Jesus christ. She's like a less self-aware version of Blair white.  I want to remind everyone that it's not just that she was transgender As part of the reason why she lost and didn't even barely reach 1% or she did but she barely got 1% either way Remember how bad her campaign was and that also tells you a little bit about why she wasn't successful So during her campaign, she pulled at 6% for public support Lied about not voting for trump in the 2020 election had an inconsistent stance on COVID-19 mask wearing Complained about seeing homeless people in the streets expressed some support for texas has bounty hunting anti-abortion law Left her campaign to go record a reality show in australia and ended up having to fund her own impoverished Campaign, I mean you might think that she's just a really bad candidate But i'm sure she'll use the footage that she shot from her campaign to make some reality shows She might already have a reality show that i'm unaware of maybe it's like in its ninth season. I don't know But she's just so insufferable so clueless so Insufferable I don't know what else to say.  This party doesn't want you katlyn Jenner So you have to choose do you actually care about transgender americans? And they're suffering and the bigotry perpetuated by the party that you identify with or Do you care about your own personal wealth? So what do you care more about class solidarity or solidarity with the lgbtq plus community? Well, I think that she's already made that decision. She's already stated her position loud and clear So now she shouldn't be surprised when her party rejects her because they're against you They don't support you having the freedom to be you they're against you their bigots This is kind of obvious.


Hello everyone, welcome to the next webinar about the Jupyter notebooks. My name is Stanisław Krzyżanowski and I will have the pleasure of introducing you for the next 60 minutes to Jupyter notebooks on Kriodias. Firstly I would like to suggest a couple housekeeping rules. First of all, if you'd like to ask, if you have any questions or remarks or you would like to share any thoughts with me or with the rest of the viewers, please type them in the chat. I will answer all the questions in the end of the presentation. We will have a short Q&A session. Secondly, today's presentation will consist of two parts. In the first part I will introduce you to, I will answer the question, what is Kriodias for those of you who are not familiar or would like to refresh their knowledge? Yes, there is a question whether the video will be available after the workshop. Yes, it will be available. It will be available on the CloudFerre's YouTube channel. You can find it from our website. You can find it by typing CloudFerre into the YouTube search engine. Coming back, today's workshop will be divided into two parts. During the first part I will answer the question, what is Kriodias and what are the most important components of it? I will try to show you how and why it is a very useful platform in this day and age. During the second part I will walk you through the Jupyter notebooks on Kriodias. I will show you what the notebook is from the very beginning. I will guide you how you can utilize it and we will hopefully end up with a very simple example of data, let's say sentinel data processing using the notebook. Let us begin. First question, what is Kriodias? Kriodias is one of the five at all and four commercial Copernicus Dias platforms. A Dias is a data and information access service. It's each of the Dias and Kriodias in specific is a cloud platform that brings processing to the data. What the data, you may ask, I suppose, that you know because you are here. We host lots and lots of observation data. Our main data broker is the Copernicus program. We will come back to it later. But now let's focus on what are the major components of Kriodias as a platform. The first very important part is the EU data repository and catalog. As I've mentioned, Kriodias is a platform that is hosting and disseminating Earth observation data. A majority of the data is sourced from the Copernicus program. We are talking about Copernicus program, which is one of the biggest Earth observation data brokers in the world. We are talking about influx of data at a range of 25 terabytes daily. Currently we are sitting at, I think, above 21 petabytes of Earth observation data hosted online in the Kriodias and the EU data repository and catalog component is extremely important. It's our backbone when it comes to handling and disseminating the data, downloading and disseminating the data to the users. It consists of a couple of substantial elements. Firstly, we have the data repository in which we keep the online products. Then we have the data catalog, which stores the information about the data. In order to be able to access the data, you have to be able to look for it when we are talking about such massive quantities of it. We have a set of tools called ingestion engines. Those are applications that automatically download and index new data into the system. We have another set of tools for accessing the set of the repository. We offer a wide variety of those tools. From NFS through HTTP, we also offer, we also host the web map service-based applications. I will talk about it a bit later via our cooperation with Sentinel Hub. This is the data component. The second important component is the cloud services component because only having the data online would be not enough because in order to derive added value from Earth observation data, usually you have to interact with not only two products, but bigger sets of products. That will require you to download it and then to have the processing power. That's why we offer cloud services, which are placed within the same environment as the data repository is. Our users can access the data immediately and process it and then share it with other users. We have the cloud services that allow for it. We base our cloud services on the OpenStack technology. We offer all classical cloud services such as computing, meaning virtual machines, networking security appliances. All those things you may require when building your own application and value chain on our platform. But it's not all. We've built also a set of tools and applications and platforms to make it easier and make it possible for Kreadias users to interact with the data. We have the Kreadias portal, the Earth observation data finder, EO browser, cloud dashboard is the graphical interface management tool for the cloud services and other third party applications. Today we'll be talking about Jupyter and notebook, but I will show you also the Earth observation finder to be able to compare different means of accessing the data and interacting with the data. I was talking a lot about the Earth observation data themselves. So let's talk about them a bit more. Here you can see a very pocket table listing some of the products that are available in the Kreadias environment. As you can see, a very backbone of our data offer are the products produced by the Sentinel satellites. Depending on the type of the product and on the processing level of the product, they are either available fully online. That means you can access them immediately from the environment. Or there can be orders to be generated for the user or accessed from ISAS long-term archives. Additionally, apart from the Sentinel data, we also host the coverage of Europe for the Lancet satellites. We have some additional satellites such as the ANVSAT. We also recently started offering data, very high-resolution data from a number of satellite operators. If you're interested in that, you can check out that data on the Kreadias portal. We'll go to the Kreadias portal so you will see how it looks like. Additionally, what is not listed here, because I wanted to make the presentation short to switch to the hands-on part as quickly as possible. Additionally, we also host a number, I think four out of six. Copernicus services on the Kreadias. Copernicus services are quite wide, wide sets of data collections. Those data are usually derived from satellite data themselves. Sometimes they're combined with on-site data or there was some processing performed on it. So this is our data offer. It's wide. Currently, we're sitting on over 21 petabytes of data. As I mentioned, we are growing by 25 terabytes of data daily, on average, of course. We are doing our best to hold all the data online, or as much data as we possibly can, because it's the easiest for our users. It makes the data the most accessible in all cases. And as I mentioned, when I was describing the platform itself, we have developed a number of user tools that are helping our users to manage the environment, to utilize the data, to leverage the cloud services that we offer. The main tools that we have in Kreadias is, first of all, the Kreadias portal, which serves as an information and management hub. You can find links to all the other tools from the portal. You can find our extensive FAQ base and knowledge base and number of guides on how to use different parts of the environment. And there is also a forum using which you can interact with our user support, our user support, which is very responsive and will gladly help you with any questions. Then we have aforementioned Cloud Dashboard. Cloud Dashboard is a graphical user interface tool for managing your cloud resources. As I mentioned, our cloud is based on OpenStack, so you can manage your cloud resources not only through the dashboard, you can also manage it through the API. It's a standard OpenStack API and through the common line interface. Then we have two important tools for browsing and data search. As you heard a couple of times by now, we have lots of data. In order to interact with the data in an efficient manner, one needs proper tools to do it. First of all, we have the browser, which serves as, let's say, the easiest tool for accessing the data. It allows users, even non-registered ones, to choose certain data products and visualize them on the map. It uses Sentinel Hub Engine generating web map services. Then we have more advanced EU Finder. This is our main tool for data search. And we will take advantage of the EU Finder API today because we'll be looking for some specific products. And finally, we have the Jupyter Notebook, which is the topic of today's workshop. Jupyter Notebook is available only to registered users. However, it is free of charge. It is hosted on our virtual machines. It offers each user up to two gigabytes of RAM. It offers up to two gigabytes of storage. And it's probably the easiest and the first tool to use when you want to try the environment, get to know the platform, get to know how it works. And when you want to actually learn something, how to process the data using different programming languages, I will now break my own housekeeping rules because there is a question very in line with what I'm talking about. Paulina Bartkovic is asking, is it possible to use our language in Jupyter Hub? In fact, yes. The CreadIaaS Jupyter Notebooks support our language. Today, we'll not be talking about it, but it's fully usable and it can be done. So please follow me to the CreadIaaS portal. As you can see, we have now ended up on the main site of the CreadIaaS portal, which should be your hub for anything you need within the environment. Today, we'll be talking about the Jupyter Notebook. So we will head over to the tools and then we'll select Jupyter Hub. And as you may remember, I have mentioned that in order to use Jupyter Hub, you have to be registered. Because I was pre-registered, so there was no registration step, but should you try it, you'll have to set up an account and then log in. So first of all, I would like to talk a bit about the notebook in general. What you can see here is the main page of the notebook. We shall now create a new notebook. Today we'll work with Python 3, but as mentioned before, you can see there is a number of available programming languages for the notebooks. You can also use the R language. Also, Julia is supported as well. So let us go into the notebook. First of all, what you can see here is a standard notebook interface. You can work in two modes in this interface. You can either interact with the cell itself. This single line is called a cell, or you can interact with the notebook. The difference is indicated within this color here. When it's blue, you're interacting with the notebook. When it's green, you're interacting with the cell. We have two main types of cells in Jupyter notebook. We have code cells and so-called markdown cells. Markdown cells are plain text, basically, with some formatting options. Our code cells, that's where you prepare your code to be compiled. I will now very quickly show you what you can do with markdown. From my experience, it's very useful to learn a couple of shortcuts. First shortcut is M. This way you can change your cell type from code to markdown. You can also do it using this drop-down menu. This is code, this is markdown. We show what we are talking about. As I mentioned, we have two cell types. We have code, we have code, markdown. I'm using different formats to show you what you can do with markdown as a whole. In markdown, you can do a couple of things. You can list things, you can bold text, you can italics text. You can create also code blocks. An example of code block would look like this. As you can see, I'm now typing and formatting the text. In a minute, our notebook will process it. Now I'm showing you an example of code block. We can do the most typical hello world. If you want the cell to be processed, you can either use the run button or use a shortcut. You can push control, enter. Then we have one cell ready with some description of what we are doing. Next useful shortcut is a shortcut for creating additional cells. You have actually two shortcuts. If you want an additional cell below the one you are in currently, you use B. If you need a cell above it, you use A. Easy as can be. If you want to delete a cell, you use X. That was the markdown cell. Now we have a code cell, and this one is a code cell. It's indicated with this parenthesis here. We'll once again use the most basic code to show you the basic principles of Jupyter Notebook. We have created a variable X, and if we process the cell, it will say hello world. Additionally, we can also create another cell with our B shortcut. We can ask the notebook to give us the output, so now basically in this cell, I've asked the notebook what is X, and it tells me that X is a variable with such string. To summarize it, maybe not to summarize it. Some more information about the markdowns. We can have bigger headers. Now I have forgotten to change the cell type, and I can use my shortcut M to change the cell type and process it, and then I have the header. We can have smaller headers. Now I will use the drop-down menu. We can have smaller headers, header 2, and if we process it, then it goes. There is much more to the markdown and the means of formatting. If you need anything, I would suggest you should just Google markdown cheat sheet, and you'll get a comprehensive list of all the functions that are available in markdown. It's very useful. It's commonly used to make a comprehensive understandable for everyone notebook. Now I would like to talk just a bit about kernels and variables and Jupyter notebooks. How do they act? Let's talk about kernel and variables. First of all, all the code cells are interconnected. They are only a front-end. Behind them is one Python kernel. Should we ask for a list of methods and variable for this Python, we'll get a list with our X at the end, because we have defined it previously. I will show you something. We can also define another variable, let's say we'll have variable Y, defined edgily as goodbye world, and we'll process it. Now, if we perform the same action as in cell number 4, we shall get a list of variables, including Y. Now we come to an important point about keeping your order in the notebook, because you can possibly add a cell above it and then you can actually delete one of the variables, if you like to, and should you do it, despite deleting the variable, the information in the next cell still has it, because it was processed before the variable was deleted. That's why this command should be at the very bottom. I hope it's quite clear. Another useful trick for working in the Jupyter notebooks is collapsing. Let's say we will list a long result, and now it prints numbers from 0 to 100, and it starts to look basically unreadable, so a notebook allows you to collapse a certain cell and scroll through it, as it's a neat little trick. But I promise you that we'll be talking about not only collapsing variables, but also that we'll talk about kernels. So kernel is the programming language that is behind the notebooks frontend, and you can do a couple of things with it. First of all, you can interrupt the kernel, and why would you like to interrupt the kernel?  So let's say that we give a command to print ones every half a second. This command would eventually crush our server. Not very quickly, but it would. So if we... I'm sorry, that's embarrassing. Of course, while true, I'm sorry. So if we give that command, it starts to print ones every half a second. So if we want to stop it, it will never end. So this indicator, because this indicator is important, in the parentheses you can see that the cell is still executing. So unless in this parenthesis we have a number and the cell is still executing, and we don't want it to execute forever, so we can select kernel, interrupt, and now it has stopped. It has printed quite a large number of ones in there, and we can collapse it as well. So finally, the final thing I want to show you about Jupyter notebooks is that you can actually use bash terminal commands from the node level. How you do it, how you can do it, you use the exclamation mark. Let's say we want to see where are we. So we can see that we have, in our current directory, we have the untitled notebook. This is the one that we are working in right now, and the TIFF Creator, and this is the one that will be our example. In a minute, we can also finally get to the Earth observation data at last. So let's say, and now it's important. This notebook is set up on a virtual machine that is connected directly with the EOData repository, and that's why I will now list all the satellites that are available, or the satellite data that is available from this notebook level. It will take a bit. I was hoping to list it. Let's give it another try. Well, I will show you this in the terminal. But with this, we have went through the basic activities in the notebook. I would like to talk just a bit about the toolbar, what you can do with it. So you can do all the basic stuff, such as saving, renaming, making copies. You can download the notebook, the standard notebook format is here. All the others require some changes. But it's useful, it works. You can transfer it to Latak and then share it. But the most useful way is to share it as a notebook. You can perform all the activities that we have performed using shortcuts. You can perform also using the tools from the edit toolbar. You can toggle some things, such as line numbers, quality of life changes, whether you like them, you don't have to, it's as easy as it can be. You can manipulate the kernel. Now I can actually restart the kernel. And this clears all the variables that we have put in. I can also restart and clear all the output, which has deleted out from the cells shown here. I have mentioned that shortcuts are useful, so you can learn them from the Hub button because there is a whole table for keyboard shortcuts. I wouldn't invite you to learn all of them, but some of them are really useful if you spend a bit more time in the notebooks. Now we can exit this one. We have come back to the view of the directory in which we set up our notebook. As you can see, there is a difference in the indicators between those two notebooks. The TIFF Creator is black, this one is bright green. Bright green means it's still running, so the kernel behind it is still running. We can turn it off, switching to the running tab, and we can shut it down. So yeah, now it has been shut. I promised you that I will show you an evidence that this programming environment is connected to the EOData repository, and I will show it to you using a terminal. So now we have opened the terminal. The main directory of the Earth Observation Data is called EOData, so if we go there, we can ask the directory to list itself. As you can see, we have the satellites listed. In each of those folders, you have all the satellite imagery produced by that selected satellite. Today, in my short use case into which we will go shortly, we will be working on this in the middle of two data. So let's go to the actual use case, what you can do with the notebook in regards to the data. I hope now it's a bit more readable. We have prepared a very short example of what you can do with the notebook to process the data. So first of all, we shall select a single satellite image. We will be working on the Sentinel-2 L1C image. It's a standard Sentinel-2 image in the indivisible range. But to do it, we have to interact with the CreoDiance EOData Finder. The Finder is an engine that allows us to search for products accordingly to some defined parameters. In our case, we shall query, as you can see here, we shall query for imagery covering Warsaw during the last winter, actually, in 2020. And we allow our cloud cover to be between 0 and 5%. And we shall then list 10 of the products with the lowest cloud coverage and select the one with the lowest. So let us do it now. You can see we have listed the products. And now what I've mentioned, the collapsing and decolapsing, I would say, of the results. We have listed products and we have selected one. If you are familiar with Sentinel-2 data structure, you can see that first of all, you don't have to be familiar to recall that. I have mentioned that all our data sits in the EOData repository. And the output of this cell is the path to a certain product. Now we have selected it, we have to open it. For this case, we've decided that we'll open bands 2, 3 and 4 to create a close to visible image as a geotiff. In order to do it, you have to familiarize yourself with the structure of Sentinel-2 data. It's available in ESA's documentation. Pretty straightforward, we use it here in order to be able to open selected bands. So using the product identifier, we open the bands we would like. It has been done. Then we shall create a geotiff in which we will write down our bands. To be later displayed as an image. This geotiff shall be saved in the parent directory of this notebook. So after we execute aforementioned cell, in this directory, we shall see truecolor.tiff file. Which will take approximately, which approximately will take probably 30 seconds. As I mentioned, the computing resources assigned to a single notebook are a bit limited. So if you start using Jupyter notebooks on Creadias and come up to the point when you decide, well, there is not enough resources for me. I have used, I have done my max with what is given here. You can set up your own Jupyter notebook on virtual machines in our environment in the Creadias Cloud. And they will have same access to the EO data as shown here. But in the meantime, we have produced our geotiff. As I promised, here is the truecolor image, in this case, file. So the last step for this very simple use case will be displaying the image. You can do it in main number of ways using number of tools for this. We shall use rasterio, simple and pretty simple library for this use. And it's done. We have selected an image from our Warsaw, we have processed it. I mean, we have selected the product, we have opened it, we have saved it as a geotiff and now displayed it. And it was what is most important probably, it was all done within the Creadias environment and it didn't require us to not download the data anywhere. So we basically skipped that big obstacle of downloading lots of data. We have done it with one product. It can be done with a dozens of pictures. You can make time series with it. You can do it in the free to use notebooks. You can do it using virtual machines, installing notebooks on virtual machines. It's a really powerful ability. So this summarizes my hands-on part of the workshop. Now I shall stop sharing my screen and we'll come back to the presentation. I'm sorry. That wasn't intended. Here we have the Sentinel-2 product structure. That's what I mentioned. When I mentioned that we are opening the certain bands, that's what we have done. We went from the product to the granular to tile folder all the way down to the image data. We opened bands 2, 3 and 4. Having this in the background, now I can answer your questions because we still have 15 minutes to do so. We have a question. Is the notebook online to let us follow your example? No, this notebook was not available online. However, it can be made available to you and we can share it to you after the presentation, I'm sure. Next, we have a question. What is the memory limits for Jupyter notebooks? So for the RAM usage, there is a strict limit of 2 gigabytes. As for the storage, there is no strict limit per se, however, should we notice that there is too much storage being used by the notebooks, we will probably limit the storage of the user. You can freely expect to be able to store a couple of gigabytes of data there. It's not a problem. Do you have any other questions regarding either the Jupyter notebooks or what we have been talking here or any other matter that you think I may be of help? I would consider, is there a way, okay, there is another question, is there a way to import other data? Yes, there are a couple of ways to import data. You can either upload the data into the notebook directly from your working station. You can interact with external databases within the web, however, this ability might be limited depending on the database you selected. But should you face any limitations, you can also contact us and we can probably lift them. And you can interact with the notebook from a virtual, from your virtual machine within the creator's environment. So it is possible to set up a virtual machine that will be storing certain data sets and sharing them with your Jupyter notebook and it can be done in a similar manner to how we have accessed the EO data. I can see that somebody is also typing, it was thanks. You're welcome. It's my pleasure. There is another question, is it possible to analyze data free? For example, NDVI, if I understand your question correctly, yes, you can prepare a script that will calculate an NDVI of a Sentinel to a product in the Jupyter notebook. It should be fully doable. We have the next question, is it possible to share the notebook with other users on Creeodias? Unfortunately, currently it is not possible to share notebooks, however, it is being planned to create a shared space or shared library for Creeodias users to share their work notebooks as well. Thank you so much. Have a good day. Till the next time.