The last one when we skipped over we have analog data like my voice or music But we want to send it as digital signals And what we'll do in fact is and it's very common nowadays is instead of trying to send it as analog data The common technique is to convert that analog data into digital data digitize that data So we take our analog data Convert it to digital data zeros and ones and then transmit it as a digital signal If we have digital data, we know how to send digital signals We've had the tech techniques already and in fact if we convert the analog data Into digital data. We can also send it using analog signals using shift key So the approach here in this last one is how do we convert the analog data into digital data? and you Do that or you see it in in practice Probably every day because what your mobile phone does when you talk to it You've got analog data as input the phone converts that into a digital form into zeros and ones your voice and then that sent Across the phone network in fact using analog signals with a mobile phone a digital signals So we're going to focus on the conversion process analog to digital data How do we digitize the analog data? and the thing that does that or the process is called encoding and The opposite process is decoding. So the thing we that does this did Digitizing of the analog data. We call a codec in this course. We'll consider just one very basic technique called pulse code modulation But there are many other codecs Anyone know others other codecs? We'll go through PCM. What are some others? Does anyone listen to music before on a computer? right What format music is in and it's originally analog, but when we save it on our computer. What format do we use? WMV WMA Windows Media Audio mp3 Flak and many other Formats they involve specifying how to convert the analog data the music the audio and Convert it to bits So we can save it on our computer. That's all we're doing here, but PCM is the the very basic approach The ones that we mentioned mp3 and so on may may have some additional features So let's look at PCM And I'll briefly explain it, but then for the detail we'll go through an example The picture may not be so useful this stage. There are really Well four steps which will go through our example we have an analog data as input It covers a range of amplitudes an infinite number of amplitudes. It's analog mean it's continuously changing the amplitude What we do is we split The from the maximum to minimum into a discrete level of amplitudes We break it into a discrete number of levels That's the first step and each of those levels each of those amplitudes will assign a code number Like level zero level one level two level 255 we'll see that through an example then over time if you think of the Horizontal axis over time we take samples of the input analog data Every so often we record the actual input level from the first step find out what that level is and That level becomes a number It's got a special name in in in PCM a PAM value and once we have a number an integer We can represent that in binary And that's the last bit we get a n-bit binary number a code and that's our digital data so the key concept is we We're going to divide our amplitude into a discrete number of levels Then we're going to take discrete set of samples over time and each sampled value We'll correspond to one of the levels which will map to a binary number and that gives us our digital data So to illustrate that we'll go through an example Which is not in these lecture slides, but if you flick forward a few pages you'll find another handout on PCM Move forward on your handouts, and you'll find it this one. It's just a bit more detailed example of PCM pulse code modulation a very simple unrealistic example, but one we can follow through Everyone's found it So here's our Input analog data. This is what we want to send to our receiver and the way that we're going to send it is we're going to Digitize it. We're going to convert the analog data into digital data zeros and ones So it's just some made-up analog data over time. You can see the amplitude is varying Continuously changing So let's put some scale to that for this example There's an amplitude and the horizontal axis is time and I put some numbers on here so we can do some calculations We say that this scale just for this short period of time The analog data may keep going, but for this example goes up to about 18 milliseconds So this is think of it someone talking for 18 milliseconds We want to take that Audio and convert it to zeros and ones. How do we do it with PCM? The first thing we do is we divide the amplitude Into a discrete number of levels Usually the same width the same height The same height in terms of amplitude and in this example for the first case I chose eight levels So we've got eight levels, so I'm going to label them zero to seven so the the way to interpret this picture now is that From the bottom solid line to the first dashed line corresponds to level zero and Above the top dashed line Corresponds to level seven, so we have eight levels here and in this case We're going to set a sampling interval of four milliseconds That means every four milliseconds. I'm going to record a sample of the input analog data Let's do that We'll start at time zero So you think what I do I have this input data. I record at time zero What is the actual level and we measure this value whatever it is? Maybe it's one point one three four volts and I map it to the one of the discrete levels that I have Defined and at the start so in this case the way to read it instead of trying to me tell you what the actual Voltage or the signal strength is We just realized that at this point the signal is in level one If it fell in this range, it would be level zero up here level two But in this case it falls in the range for level one So the level What we sometimes call the code number is one and now let's convert that to binary So we have decimal integers for the levels. Let's convert it to binary and because we have eight levels Let's convert it to a three-bit number 001 so one in Decimal 001 in binary why three bits? Well, if we're going to have eight levels We need three bits to represent any of those eight numbers from the zero to seven Think of this as simply zero zero zero up to one one one. We could use four bits But it would be a waste To represent eight numbers, we don't need four bits We only need three bits and it turns out we'll see shortly. We want to use as few bits as possible Two is not enough of course. We can only represent four numbers with two bits That's why I get a three-bit Value here. This is the sampled value and Then with a sampling interval of four milliseconds at time four we do it again. I At this point in time. I measure what the value is here The actual value and I see it falls within the range of level six So we convert that to a three-bit number and we get one one zero six and we keep doing that and We get another three samples and We get a sequence of 15 bits. This is our digitized analog data So we've converted our analog data the blue line Into 15 bits in this case into our digital data and Those bits would be sent to the receiver or if it's Say for music and you want to save it on your computer. It may be saved on the hard drive Any questions on how PCM works at this stage? Discrete number of levels the number needs to be defined in this example. I chose eight levels and a Sampling interval in this case. I chose a four millisecond sampling interval. We take Measurements of the input analog data at each sample point and That corresponds to one of those eight levels and we get a three-bit binary number for each sample then We send those bits to the receiver Using whichever technique we want to we have digital data. We can use digital signals or analog signals Let's look at what the receiver does The receiver receives these 15 bits. You can think it receives three bits at a time at time zero the transmitter Generated zero zero one and then sends it to the receiver The receiver receives zero zero one Then it will shortly later at four milliseconds receive one one zero zero one one and so on What does the receiver do? the receiver is going to receive those bits and Then try to generate that same data the analog data and this is what it generates think when the receiver receives the first three bits at Relative to it from the receiver's perspective at time zero received zero zero one or level one so what it does is produces an analog output at level one For four milliseconds because our sampling interval is four milliseconds. It holds that level for a fixed duration Then at time four the receiver receives another three bits one zero Level six, so it produces an output at level six Then at three one and two didn't finish the picture here So this is the analog output at the receiver It's as you see an approximation of the input at the transmitter What it will call the reproduced data at the destination or the receiver? So let's compare them The blue line is the analog input The green line is the analog output One example may be the blue line is your voice when you're talking on your mobile phone and The green line is produced by the speaker at the at your friend's mobile phone That is the speaker on the phone at the receiver as it receives the bits it produces some analog output But it's not continuous because all that was it receives is these Bits every four milliseconds. So all it does is holds the level for a period of time Of course, they're not the same But you may see that the green line is sort of following the blue line It's low at the start it goes up to a peak and then slowly comes down a bit and then goes up a bit at the end What we care about is how close that green line is to the blue one We can think that's a measure of quality of the the analog output in terms of audio How good it sounds? We would like to get as close as possible to the original input When we reproduce it at the receiver How can we get closer? I want to get the received Analog output the green line closer to the blue one. What can we do? Right so we've got two options we can change the number of levels that we have here here We chose eight and or we can change how often we sample the other two parameters of our Our codec in this case PCM. I said in our case. It was four milliseconds and eight levels Let's go through three other cases with different values And we'll plot them and you'll hopefully visually see the difference between them. So let's do case two still with eight levels But now we use a two millisecond sampling interval Every two milliseconds we take a sample convert it to a three-bit number It's three bits because we still have eight levels We get twice as many samples half the sampling interval We transmit those 30 or so bits to the receiver When the receiver receives those bits every two milliseconds it produces an output at the the designated level So zero zero one for two milliseconds then at three for two milliseconds and then at four six and so on And this is what we get for two milliseconds we hold the level at one Then at three for two milliseconds then at six for two plus another two because there are two sequences of six received Then at three and so on What we will do at the end is I'll compare the different cases So this is comparing that for case two the input data and the output data Maybe you can see it's a little bit closer to the blue line But let's go through two more cases and then I'll show all of them overlapping on each other We can change the number of levels Here I use a two millisecond sampling interval every two milliseconds take a sample But use 16 levels zero to 15 and With 16 levels every sample must map to a four-bit number Because we need four bits to represent any of those 16 values from zero zero zero zero up to four ones for 15 So every two milliseconds we generate four bits and Those bits indicate the level We transmit those bits to the receiver and the receiver will generate hold for two milliseconds the output at that particular level and becomes this It's hard to compare with the other one. We'll do it shortly one last case One millisecond sampling interval 16 levels, so we've just reduced the sampling interval even further. We have four bits per sample But now every one millisecond we record a sample So many bits are transmitted this is what we Get at the receiver when we reproduce that Analog data, and I'll Try and show all four at once plus the input analog data It's not easy to see because some of them overlap and I haven't You can't see some are behind others, but first the blue line is the input That's what we want But what we're doing by taking discrete number of levels and discrete samples is we're approximating that and the red line and You don't see much but because it's actually behind I think the the orange line in many cases, so when you don't see the red line at this orange Line behind it because they're the same the red line was case one four millisecond sampling interval eight levels If you compare the red line with the others, I think you'll see that it's further away from the blue one especially at this point It's a long way away from the blue one But the others are much closer at that point and similar at this point It's not so accurate compared to the others the others follow the blue one a little bit closer So I may conclude that the red one is the least accurate representation of the input analog data in this case case one Case two and case three. It's hard to tell the difference. There's not much difference between them In fact again a lot of overlap. All right the green and the orange ones differ a little bit here The orange ones may be a bit closer, but at different points. They differ but here they're overlapping in Fact visually it's hard to tell the difference between case two and three But maybe you'll see the purple one case four is Again a little bit closer to the blue one compared to the first three For example at this point The red one is actually below these two case one two and three come down here The blue line is here. The purple one is the closest Now this is just approximating But if I hope you can see that the case four is the better approximation of the blue line Compared to the first three cases It's a more accurate reproduction of the original data It will depend upon what is the input as to how close they get but in general what we can Observe or what we'll discover is that the more levels you use and The more sampling or the more samples you take The more accurate you'll get in reproducing the original input So if we went up to 32 levels and we sampled every half a millisecond We'd get even closer to the blue line 64 levels sample every 0.1 milliseconds Then we'd be even more accurate. We use a million different levels and We sample every nanosecond Then the reproduced data will be very very very close to the blue one. You may not be able to see the difference In fact the blue one is just when we have an infinite number of levels and an infinite number of sample points That's what we get for a continuous line so More samples or smaller smaller sampling interval More samples in the same time and more levels gives better accuracy What's the problem? the more samples and the more levels The more bits we need to send to represent that same input analog data In all cases, it's the same data. Let's say it's the voice. It's the same voice that we're reproducing But as we increase the number of samples and increase the number of levels We need to transmit more bits to represent that same Analog data and that's the negative or disadvantage The the previous slides have some calculations, but they're summarized Actually, we'll jump back just to see the calculation Case one at least For case one We sampled every four milliseconds With eight levels we've reproduced three bits per sample So we get Three bits per four milliseconds every four milliseconds. There'll be three bits to be transmitted That equates to 750 bits per second So with case one to represent our data, we need to transmit 750 bits per second Case two we sampled every two milliseconds We still had three bits per sample. So if we sample twice as often Then we'd need to send it twice the speed 1500 bits per second and One of the later slides summarized the values of how much we need to send that and we'll use that to explain the trade-off In case one We need to send it 750 bits per second to transmit our analog data in case two we went up to Will we we halved the sampling interval from four down to two so we double the number of bits per second needed case three we had four bits per sample So equates to two thousand bits per second in case four we halved the sampling interval again to get four thousand bits per second Lower is better here. This is what many people get confused about This is not data rate of a link This is what I'll call to send the data. This is the data rate we require To deliver that data You can think about it if I have a link I've paid for a link and the data rate of that link is 3000 bits per second Then if I use the sampling interval the number of levels of case one then of that 3000 I would use 750 and the rest will be available for use for other users But if I use case two of the 3000 I would use 1500 bits per second I would require that amount to send my same analog data Okay, three two thousand bits per second. I couldn't do case four if my link supports three thousand bits per second But I must send it four thousand bits per second. I cannot do it My capacity is less than what I require so the point here is that This is not data rate of a link. This is the data rate we require to send this data So we would like to require as small as possible because it's still the same data Case one is best when we compare the data rate required case four is the worst but Case four was the best in terms of reproducing the analog data accurately We say in the accuracy or the quality of the reproduction case four is the best Case one is the worst We can see that visually here. There's more Accurate ways to measure that and that represents our trade-off with sampling and PCM more more samples more levels better accuracy But the more you must transmit the higher the data rate required and that's the negative To send analog data as digital signals or even analog signals one common approach We use is we digitize that analog data convert it to digital data and then send that and we Previously looked at using pulse code modulation PCM to do that conversion. So let's just Recap on what we know about PCM We went through an example in this example on this slide we have our Input analog data the solid line and with PCM. It's very simple our regular intervals record a sample That is measure the level the magnitude of or the amplitude of the input analog data Find out the actual value and Then map it to discrete value Which we call one of the levels in this picture shown the levels are shown as code numbers So in this case there are 16 levels from 0 to 15 but record at each sample point the Amplitude of the input analog data map it to one of our predefined levels and Those levels are then mapped to a binary value With for example 16 levels we need four bits per level to represent one of the number from 0 to 15 and We do that every sample point and we get our digital data and that's our representation of our analog data and the two main trade-offs that we care about with PCM and other techniques like this is When we send that digital data to somewhere or Maybe we save it on disk and then need to reproduce the analog output How good is that reproduction? What's the quality or accuracy in which this digital data can be used to reproduce the analog? input and we saw through examples in the previous lecture that When we reproduced a simple way we can look at it is that we produce a a step type wave we step up depending upon on the level and To get an analog output Which is as close as possible or closer to the analog input The two things we can do do to increase the accuracy We can have more samples Take more samples means we get more granularity on the horizontal axis and We could have more levels we get more granularity on the the vertical axis So more levels and more samples will allow us to get an analog output closer to the original input So that's another way to think of that the more samples the more levels the higher the quality of the reproduction But the trade-off here is that the more samples the more levels the more bits that we will get for the same amount of input and The more bits we need to represent that analog input that That is a disadvantage because we either need to send more bits across a link or From saving this on my hard disk. I need to use more space to save it So we'd like to use as few bits as possible to represent this data Few samples few levels to use less bits But more samples more levels to get better quality and that's the trade-off that we need to deal with is Summary that gives us an appropriate number of bits for the quality that we want to achieve We'll go through one more example of this some practical example, but before that so we need we need a Sampling interval that's going to be appropriate and the number of levels it depends on the analog input Maybe voice requires different than music and requires different from our other analog inputs For the sampling interval we often talk about the sampling rate. How many samples per second? so If the sampling interval is one millisecond Every one millisecond we take a sample then we say the sampling rate is one thousand samples per second Or we say one thousand Hertz one thousand times per second Now, what is a good sampling rate? for the moment forgetting about the number of levels or Assuming we have an infinite number of levels. What is a good sampling rate? Well There's a theorem that tells us what the optimal sampling rate should be It says if our input analog signal here. It's a noted f of t but our input analog data Remember any analog signal can be represented as a set of summation of sine waves and Each of those have a frequency so our input analog data has a Set of components from some lowest frequency component up to some highest frequency component so The signaling of the sampling theorem tells us if we sample at a rate Higher than twice the highest signal frequency of their input analog data Then when we reproduce we can get a perfect reproduction of the input analog analog data What does that mean? Let's take voice as an example Voice is the input analog data the human voice Ranges in frequencies from a bit larger than zero Hertz up to about four thousand Hertz so the maximum Frequency component is let's say four thousand Hertz. No one speaks with a frequency higher than four thousand Hertz So the highest signal frequency of our input is four thousand Hertz The sampling theorem tells us if we sample at a rate two times that at 8,000 samples per second then if we had an infinite number of levels and we reproduce From the sample data that analog output will get a perfect reproduction So it gives us a guide of what the sampling rate should be another way to think of that if my voice Goes up to four thousand Hertz if we sample at eight thousand times per second We will get good reproduction good quality at the output if we sample at ten thousand times per second We will not improve Eight thousand is enough That's another way to use the sampling theorem twice the maximum frequency component is sufficient To accurately decode and get the original data back Ten thousand is no better than eight thousand samples per second 100,000 samples per second very short sampling interval does not improve compared to eight thousand samples per second It depends upon the maximum frequency component Six thousand samples per second is worse than eight thousand samples per second We should try to get up to two times the maximum frequency component less and there'll be a Degradation in quality in the reproduction So if we have music for example, what's the range or the maximum frequency component of music? Music has a wider range of frequencies than the human voice What's the maximum frequency component? It's probably somewhere in your lecture notes Maybe the one of the very first lectures anyone remember You listen to music an orchestra or a band or something what freak? What's the highest frequencies that music will typically produce? I think in one of our first lectures We had a plot of analog data and digital data and music typically ranges up to about 20 kilohertz 20,000 Hertz So different instruments can have very high frequencies compared to the human voice So music let's say the maximum frequency component is 20,000 Hertz That tells us if we want to record music and save it in a digital form We should sample at two times that rate at 40,000 Hertz That's what the sampling theorem tells us if music goes up to 20,000 Hertz sampling at 40,000 Hertz is sufficient to to get Accurate reproduction less than 40,000 will produce lower quality reproduction More than 40,000 will not change the quality 40,000 should be sufficient So it depends upon the input analog data as to what sampling rate we should use We don't have to use that sampling rate, but it's just giving us what what we could use to get the optimal quality This actually assumes we have an infinite number of levels which we don't In practice we have a finite number of levels. So what's a good number of levels to have again? It depends upon the input with voice When people have done experiments especially for phones not necessary mobile phones, but home landline phones Then to get good quality voice reproduction The suggestion is that you have 128 levels So instead of zero to seven or zero to fifteen you have zero to one hundred and twenty seven And when you sample you map to one of those one hundred and twenty eight levels With 128 levels it means seven bits per level So a recommended value for for sampling voice to get good quality reproduction is you sample at 8,000 times per second because human voice doesn't go above 4,000 Hertz and we sample it twice that and We have seven bits per sample or 128 possible levels Sometimes we'll extend that to eight bits per per sample eight bits is a nice number because it's the same as a byte so often we'll see voice we talk about eight bits per sample and 256 levels still 8,000 samples per second eight bits per sample at 8,000 samples per second eight times 8,000 is 64,000 bits per second Good quality voice streamed across the network using PCM You need to send it a speed of about 64,000 bits per second 64 kilobits per second If you want to get music in there as well, then you need to consider that music has a higher range of frequencies than voice So for example if you wanted to stream a Radio station across the internet and There was only talking it was a talk back radio station. No music played very boring But still only talking then if PCM was used to encode then to get very good reproduction You could stream at 64 kilobits per second Because that allows us to take the input voice Sampler 8,000 times per second eight bits per sample all right We could use seven bits, but it doesn't change much eight bits per sample It's the one byte and that's equivalent to 64,000 bits per second and send that the receiver that receives it When you receive that stream at 64 kilobits per second Your computer converts it back to analog output and plays it on your speakers and It should be good quality because we've sampled at sufficient rate and the sufficient number of levels That's if you use PCM There are other codecs not just PCM that will allow effectively to compress that information Reduce the size but give almost the same quality So you don't have to go up to 64 kilobits per second even one last example I'll bring up a recording of voice again I'll open some recorded voice in a sound editor and we can see the the audio So this is five minutes of recorded voice in this case audio and it's just the showing that input and or that analog data in The time domain so from zero up to five minutes here And we see the variations of the amplitude over time What this in fact is is the software has is taking the digital representation of this audio the zeros and ones and Creating this plot to show us what the analog output would look like This is just me loading up a file on my computer. So it's the digital form of the voice, but viewed From what it would look like in terms of analog reproduction if we zoom in on some parts We keep zooming in a bit. We see it. This is our analog data It's that looks like a signal It's effectively if we break it down a summation of sine waves of different amplitude phase and frequencies It's more complex than the ones that we've seen, but you can see some variations There we keep zooming in In fact, what the software does is that it takes the digital representation of this audio Which is the set of samples if we keep zooming in we'll start to see in fact There's just a set of sample points the dots So I've zoomed right in each dot represents one sample this Analog data was sampled using PCM pulse code modulation and At a rate such that if you look at the time scale Each dot here is a particular sample some binary value which tells us the the level on the vertical axis and The time on the horizontal axis and then there is the next sample and the next sample and so on So you could actually map them back to the binary values What the software does to make it look nice is joins the dots So it looks like a solid line, but actually it's just sample points that's saved in the file The software just joins them together. How big is the file when I save it? So here's five minutes of audio How big is the file when I save it on disk? Anyone want to calculate or estimate? guess To start how do how big do you think it's going to be when I save it on this five minutes of of me talking? 500 gigabytes 500 kilobytes that sounds a bit better 500 gigabytes my disk is not that big okay 500 kilobytes Anyone else? It's not a bad guess anyone else 3 megabytes 5 minutes 3 megabytes Yep 200 megabytes okay, so we have three values 500 kilobytes 3 megabytes 200 megabytes We can calculate it. It's using PCM, but we need to know two things We need to know the sampling rate that was used So this is a recording for when I talk the analog input the computer is sampled at a certain rate and each sampled Sample was mapped to one of so many levels, so we need to know how many levels and I Forgot to set it, but I think I Checked before in the format It's actually 16-bit PCM And it's hard for you to see but the sampling rate is 44,100 Hertz That's the sampling rate every second there are 44,100 samples and Every sample is mapped to a 16-bit number So the number of levels is 2 to the power of 16 65,000 levels in our vertical axis and 44,100 samples per second in the horizontal axis Now find the file size Calculate how big the file is so we have a Write down the information you can calculate. We have five minutes of audio we're using PCM and The sampling rate 44,100 Hertz so when I recorded the audio that the software was set to sample at that rate and Each sample was 16 bits long. How big is the file when I save it on disk? How are you going to calculate? very easy 44,100 samples per second Each sample is 16 bits so then how many bits per second? 44,100 times 16 bits per second are Recorded on disk how many seconds? 300 seconds five minutes times 60 300 seconds just multiply and we'll get the file size in number of bits We sample 44,000 times per second 44,100 Hertz Every sample is 16 bits long. So that's the number of bits per second How many seconds well we have five minutes? 60 seconds per minute Problems with my keyboard Start again That's the number of bits for five minutes of audio and now let's convert the bytes Divide by eight because file sizes will see recorded in bytes 26 megabytes who was closest all right not many people someone said three megabytes someone said two hundred megabytes Well, maybe somewhere in the middle 26 megabytes is the file size 26.46 26,460,000 bytes to save that audio 26 megabytes about Let's confirm. I've saved the file on disk. Let's see the actual file size It's a the format of that file the codec was PCM the way that we save the data on disk We use the format referred to as wave the wave format. So the extension is Dot wave WAV Here's the file size in bytes 26,460,144 we were close we 144 bytes off Well, because the wave format adds a little bit at the front to tell us the exact structure of this file So it takes the 26 million bytes of actual audio Plus it adds 144 bytes at the front of the file to say this is a wave file using PCM encoded data 26 megabytes When you save an audio file what format do you use or what codec do you use? I think we said yesterday some of the different codecs instead of PCM. What could you use? How do you save music? What do you use? to save music mb3 anything else flak WMA and Many others. Okay, there are different codecs and all formats be careful. There is two different things The codec is how do we convert the analog to digital like PCM as a codec? mp3 specifies a codec But also there's how do you save that data on your disk or in a file? So there's also there's a format sometimes they are the same So mp3 has a codec and you'll see the extension mp3 here. We have a PCM as a codec the format is wave, but Often that's not the case Let's save that file or that that audio in different codecs It's actually saved as a wave file at the moment, but I can Export it to a different format first mp3 and then I'll do again to flak And then many other formats Okay for different systems or codecs If we open them those now I have three files dot wave dot mp3 dot flak If I open them up, they'll look basically the same and if you play them They'll probably sound about about the same. There may be some very subtle differences But I think in this case your ears will not pick them up very well, especially if we use this audio system But let's look at the file size The wave file was 26 megabytes. The mp3 is 4.8 megabytes Flak is 13 megabytes same original analog data Using different codecs to sample and to save in binary We get different file sizes. That is we need a different amount of data a number of bits to save that input analog data Wave is the biggest flak is then about half of that flak refers to lossless compression It really compresses the wave file because there are certain patterns in that PCM encoder data that can be replaced with smaller bits Smaller patterns of bits. That's what compression does really it looks for some patterns in the input instead of Saving that pattern that may repeat many times. It saves it with a smaller special code So we can have fewer bits to represent it So that provides compression in the same way that zip compresses your data files You take a text file. You use zip or ra or another compression Algorithm to compress when you decompress you get the exact same data back If we play back the flak and the wave the quality will be identical They'll have the exact same original sequence of bits. So the quality will be the same mp3 does a little bit different it compresses But it throws away some of the original data So when you play it back the quality in theory will be lower than the wave and flak files But in practice often the quality is hard to distinguish by our ears. It depends upon different parameters and the input So we make a trade-off between the quality of the playback Which one will be higher quality and the file size? We want small file size and high quality so there are different trade-offs to consider Similarly if you want to stream across the internet your audio the codec that's used Different codecs can produce higher quality, but will require more bits to be sent in the stream So that's why we want to keep the number a bit small so we don't consume so many resources It's not our intent to study the other codecs just an example there This software that or the software that I use to record the audio by default it uses PCM and it Uses 16 bits per sample and 44,100 Hertz 44,100 samples per second. Why does it use 44,100 samples per second? What's the significance of this number? We said with voice voice ranges frequencies up to about 4000 Hertz The sampling theorem tells us if you want to record voice you only need to sample at about 8,000 Hertz twice the highest frequency, but here we're recording at 44,000 Hertz Well, we said before music Music contains frequencies up to about 20,000 Hertz so the common standard is to sample just at Slightly above that and double So the the standard is 44,100 Hertz because assuming music contains frequencies less than 22,000 Hertz The sampling theorem tells us sample at double the highest frequency component So that's why we use by default 44,100 if you buy an audio CD How much music can you fit on an audio CD? Not one with mp3, but the original audio CDs. How many minutes of music can you fit? About about one hour Little bit more than 60 minutes. I think you can fit maybe 70 so minutes on a music audio CD How much data? How many megabytes can you fit on a CD? More than 500 about 700 megabytes 750 megabytes Okay, so a CD in terms of data has about 700 megabytes in terms of audio about 70 minutes The standard format for encoding audio on a CD It stores the indigital data, but the audio is analog. It uses PCM At 44,100 Hertz 16 bits per sample The only difference is the audio on a CD has two tracks It's stereo. There's a left and a right audio track so you can do the calculations and see that with about 70 minutes of music at 44,100 samples per second 16 bits per sample and Times by two because you have the left audio and the right audio Then that equates to about 700 megabytes if you want to save that on a disc So that's why an audio CD Fits about 70 minutes of music. You can calculate the exact number. It uses PCM But if you buy an audio CD that has MP3 encoders files, you'll get much more Many more minutes of music saved on that same amount of disk base Any questions to finish our topic on signal encoding techniques?


In a few days a session on the gene and genomes at the time of next generation sequencing. So my name is Olivier Delano. I'm an assistant professor at the Department of Computational Biology of the University of Lausanne. And today I will be co-chairing this session with the help of Natasha. Hi, I'm Natasha Glover. I'm a postdoc in the lab of Christophe Desimo, also at the University of Lausanne. So a few remarks before we start. So far we've got 96 attendees, which is good, 97. We've got also four talks of 12 minutes each, plus three minutes questions. I kindly ask the presenters to stick to respect the 12 minutes as we are a bit tight on schedule. And for the attendees now, during the presentation, you will have an opportunity to actually ask questions. And this is highly encouraged. So you can use the Q&A functionality. So there's a button at the bottom of your screen, just have to press and it's a chat in which you can actually write your questions. Please do not forget to mention the name of the person to whom you address the question and also make the question as clear and concise as possible. If for some reason we can not ask all questions, we're going to basically transfer the remaining ones on the next session, the speaker session that is happening right after this set of presentation. So please join as well this session, make it dynamic. So Natasha, if you could present like the first speaker. Okay, so the first speaker that we have up in this genes and genome session is Abdullah Karaman. So he is the leader of the clinical bioinformatics team at the University Hospital Zurich. And he's going to talk about the pathogenic impact of transcript isoform switching in 12,009 cancer samples, covering 27 cancer types using an isoform specific interaction network. So without further ado, Abdullah, I'll let you share your screen. So you can hear me and you can see my screen, right? Yes, it's not in presentation. Okay, there we go. Alright, so thanks everyone for joining. So 110 people, that's a lot. So really fantastic to have so many attendees here. So yeah, I'm Abdullah Karaman. So I'm heading currently the clinical bioinformatics team in the Institute of Pathology and Molecular Pathology. And today I would like to talk about the project that I started off in the lab of Christian from Marion at the University of Zurich. And in this project, we tried to assess the pathogenic impact of alternative splicing disruptions and 27 different cancer types. And for that, we developed the new isoform specific interaction network. Because it's about alternative splicing, I mean, what I assume is that everyone knows basically what alternative splicing is, so I will skip an introduction on the splicing, but I would like to mention here an interesting fact about alternative splicing, which is namely, under normal conditions, despite having all the different isoforms encoded in the genome, actually, when you look at the expression, it's usually just one single isoform that is most permanently expressed. And all the other isoforms have actually very low expression. So and this actually also proceeds to other tissue types. And so even if you look into different tissue types, you'll always see the same major isoforms. There are certain exceptions, like in the brain, but the tendency is really that there's just one functional major isoform per gene. Now we can use this information now to assess the impact of alternative splicing and cancer and how can we do that? So we could simply identify the most dominant transcripts in normal conditions and compare to the most dominant transcripts in cancer conditions. And here, for example, we could assume that the most dominant transcript has to have at least twice as much expression as the second most dominant transcript. So if you have now this conditioning, you can now look for switches. So for those conditions where in normal conditions, you would have the green transcript expressed most dominantly. And in cancer now you have the blue transcript expressed most dominantly. And we would be now interested in our analysis to understand and to identify those most dominant transcript switches. But we had one additional actually requirement, which was namely that the blue transcript on the right, that this blue transcript is really never the most dominant transcript than any of the, none of the normal samples to which we compare our cancer samples with. And if this condition is also met, and then we actually call those most of the transcripts cancer specific most of the transcripts, because simply they were really very specific to that cancer sample, and we're not observed at all under normal conditions. Now, obviously, the first question is yeah, so how frequent do we see those cancer specific most of my transcripts? And in order to analyze this and to get information on this, we joined the PAN cancer analysis of our whole genomes project, which was initiated by the International Cancer Genome Consulting. And this project was really fantastic. Because we had lots of data available, and also wait a bit in advance. So this project, the major papers were published in February, couple of months ago. But we use the same data set. So and what type of data had me available? So first of all, we had 2800 whole genome sequences available. So mutational information, and this over the entire genome. And important for our project was that among those 2800 samples, 1200 samples with RNA-seq expression data. And the RNA-seq expression data we could now use to assess which transcripts are most commonly expressed. And the 1200 cancer samples with expression data, if you split them into different cancer types, you can see that we're covering more or less all primary tissue types. So we had samples from brain cancers, heart cancers, lung cancers, kidney cancers, prostate, and so on. There was just one caveat, but this is true for most of the consortium projects. And so the number of samples was in balance, then that we had available for the different cancer types. And so we had those cancer types with a very high number of samples like kidney renal cell carcinomas. But then we had also those samples or cancer types where we just had a few samples like cervix and adenocarcinomas. But nevertheless, you know, it was a fantastic resource where we could now perform our analysis. First, obviously, was the question how many most of the transcripts could be actually identified. And what we, I mean, the number that we identified in our case was that about 70% of the genes have transcript that is at least twice as much expressed as the second most commonly expressed transcript. And if you now look at the cancer specific most of the transcripts, then we can see that in 7100 genes, so which is about 36% of the genes that we have in the genome. So for those we could identify cancer specific most of the genes that we have in the genome. And in total, these were 122,000 CMDTs. And here at the bottom, I'm showing you here a distribution of the various samples and number of CMDTs. And so separated by the different cancer types and ordered by the median number of CMDTs. So it's a lot of data, but just I just would like to point out certain things that are very interesting. So I'm cancers of the cell right now in your mouth. I mean, the data set that we had available at least didn't have any alternative splicing disruptions. This was interesting. The second and third interesting point were on metanoma samples. Metanoma is usually a cancer type that is one of the highly mutated and really by far distance highly mutated cancer types. But here, you see very few alterations in the brain. Brain cancers are also interesting because the brain and tissues known to express different isoforms in contrast to other tissue types. But then brain usually express different isoforms from the same gene. But here, we also didn't see much alterations not to have to splicing between normal conditions and the brain cancer conditions. Interestingly, also for land cancers, and the nice thing with our data set, we had subtype information. So we had different subtypes of the cancers. And here, for example, we could compare the the CMDT between adenocarcinomas of another land and squamous cell carcinomas. Squamous cell carcinomas on those cancers that are induced by smoking mostly while adenocarcinomas can also be frequently found with non-smokers. So we have two different sets of or two different carcinogens that are causing those cancerous growths. But nevertheless, the CMDTs did not differ really extensively. And if you look at the very right side, then you see actually the highest number of CMDTs or the highest number of disruptions in our time to splicing and could be observed with female reproductive organs. And so here, especially leading with uterus adenocarcinomas. From all those CMDTs, 10 really stood out. And I'm listing here on those 10, because those 10 were not only cancer specific, most of the transcripts that were not observed in normal tissue, but they were also observed on all of the cancer samples that we analyzed. So really 100% all of the samples that we analyzed, all of them had this isoform that was not been found in normal conditions. And three in particular stand here really out. And so those three were even not only found in the cancer type itself and not in any other cancer type. So these are really cancer type specific most of the transcripts and those 10 are actually for us now potential diagnostic biomarker, which we really want to follow up now next, because these are transcripts that could, you know, in theory really be indicative of cancer growth in a patient. Yeah. So next is actually, so once we know, have the list of CMDTs. So the question now is, you know, do they have any impact? So what is the functional impact? Do they cause for example, any pathogenicity? And I will come to that in a moment. So for that reason, we actually developed an isoform specific interaction network that was based on the string interaction network and the string interaction network is the famous database that is developed by Christian from Mary and from Mereng's lab. So we combine the string database basically with a 3D domain interaction database. And the idea here was very simple. So we have a transcript consists of different exons. So an exon can now transcribe binding domain, where the binding domain is important for certain protein-protein interactions, as I'm showing you here with the red domain. And the red domain is important now to interact with the string protein here. Now, if you have now in cancer transcript that is most of an express that lacks this red domain, so it's sliced out, then that protein will not be able or will not express the binding domain and will not be able to perform the interaction. So you will have an interaction loss. So and this isoform specific interaction network basically was now used to assess how extensive interaction losses observed with those cancer specific muscle and transcripts and are those interaction losses somehow pathogenic. So in terms of so to put the number of interaction losses in context first again at the number 7100 cancer specific muscle and transcripts that we observed from those 7000 CMDTs. Unfortunately, we don't have high quality domain-domain interaction information for most of them. So for 2500 at least of those genes, we indeed had high quality domain-domain interactions within the protein-protein interaction network. So those we analyzed further and we could identify around in 55% of CMDTs that there are either all interactions lost or there are some interaction or there are some interactions lost due to the expression of this cancer specific muscle and transcript. The pathways and the molecular processes that are disrupted in those cases. We did an enrichment analysis using the geo-molecular process terms. And it's like a heat map, but I just would like to put your attention on the top six of those molecular processes that we find most often we disrupt due to alternative splicing disruptions. And these are they fit basically to alternative splicing. So here we see translational termination, which is affected in 22 of the 27 cancer types. You see translation initiation transcription, nucleotide biosynthesis, protein-protein RNA splicing as those processes that are most often just erupted by splicing induced interaction disruptions. In terms of pathogenicity, we wanted now so now knowing which interactions are lost, the next question is, you know, are these interactions somehow important for cancer? And here what we did is we use now the interaction network to measure between our instance. Sorry, just one minute left, Abdullah. Yes. So there's a CMDT, basically those genes and the distance we measured distance to known cancer related genes. And those cancer genes came from cosmic and you can see most of the CMDTs are actually either interaction partners or interaction partners of interaction partners. So they're really related compared to a random selection of random selection of proteins. Unfortunately, in terms of mutations didn't correlate much with alternative splicing, but what we saw is we saw an enrichment in CMDTs if you had the mutation in the splicer. So as you can see here on the right. So with this, the take home messages, brain cancers and metanomers and surprising have very little CMDTs, female reproductive organs have high CMDTs. And if you have the cancer type from the same primary tissue, then you tend to have similar CMDTs. They induce 55% interaction losses. And very often actually the interaction partners are cancer related. And if you have a splice of some mutation, you have a higher number of CMDTs. And I would like to point out here poster where tonight, my PhD student will present further information and also get on my GitHub account to get the code for the analysis and treatment is available. And with this, I would like to thank Christian for the help and demand for the support environment. And Josh, you and from the people project for all the constant help. And with this, I'm done. And if you have one or two questions, I'm happy to take Thank you Abdullah for your very nice presentation. A very impressive piece of work. So we don't have much time for a question for this first talk, maybe I'm going to just ask a quick one that has been voted. So from Antonin Thiebaud. So you found cancer types, specific CMDT, but on the opposite, did you also find any CMDT common to a majority of cancer that could maybe even be used as global cancer markers? So let me think.  But we could check this up. But I don't remember seeing one that really was found in all of the cancer types. And it's also like a balance. Would you like to have one? The thing is, the interest in CMDTs are the biomarker so that really appear in all of the cancer, of a certain cancer type. And this is now put in contrast then to a CMDT that might not be found in all of the cancer types, but that is found in different in all the different cancer types in a few cases. There is none that really that was found in all of the cancer types. I guess it's my job to introduce an X speaker. Jeremy Breda from the University of Basel, going to talk about realizing the Waddington metaphor, inferring regulatory landscape from single cell expression data. So the virtual floor is yours. Thank you. And so first thank the SID for accepting my my talk. So I'm Jeremy Breda from the groups of Mihara Zavalan and Eric Van Nenvegen at the Bio-Central Neurosythe Basel. So the sort of question we're interested in the group concerned the high variability of cell that we can see in the higher eukaryotes. And in particular, I was interested in how cell types are defined, established and stabilized. So to approach such questions, Waddington introduced his famous analogy in 1957, where stem cells are compared to marble rolling down the landscape and following values. So analogously, the gene expression of cell would be guided by an epigenetic landscape and cell with developed through differentiation time, developmental time until they reach stable minima that we then call cell types. So I wanted to see how far we could go with this analogy, try to give it a rigorous meaning and try to infer such a structure from single cell RNA sequencing data. So from physics, we know that every system can be characterized from an energy function. And so in that case, due to this in the study system, due to the high degree of freedom and the inherent stochasticity, we can use a well-developed framework of statistical physics, which states that the probability to find a system in a given state, knowing only the energy of state is given by the maximal entropy distribution, known as the Boltzmann distribution. So what this question says that an energy of state defines the density of state and vice versa. So if we can estimate measure density of gene expression in a cell in the space of gene expression, we can infer landscape and this landscape would have different application, like defining cell type as local minima, defining differentiation path as value of the landscape, and would allow us to ask what's the minimal perturbation needed to bring a cell from one state to another similarly to an activation energy in a chemical reaction. So as I said, we want to infer such structure from a single cell RNA sequencing, but there's a caveat in that the number of, I mean, what RNA sequencing RNA sequencing measure is not exactly gene expression, but number of mRNA per gene and per cell. So let's take a gene with a constant transcription rate and the constant decay rate. So it's expected expression is going to be the ratio of those two rates. I'm going to call that transcription activity. And now that's only an expected number. But due to the biochemical process of transcription and decay, we accept that the number of counts in a cell is going to follow Poisson distribution with that transcription activity as a mean. On top of that, in the protocol of single cell RNA sequencing, only a small fraction of the total mRNA is captured. So adding another layer of sampling. But fortunately, the combination of those two Poisson process stays Poisson with a mean being this transcription activity times the capture probability. And then finally, we of course, don't expect transcription and decay to be constant across cell. We expect heterogeneity transcription activity. So in other words, the total variance that we're going to measure in a DMR account is going to be the sum of two terms, the Poisson variance due to Poisson noise that doesn't have interesting biological meaning, that case, and the variance of the transcription activity that we want to keep. So to solve that problem, we developed a Bayesian model that removed the Poisson noise. And to do so, we first estimate the mean and the variance in the transcription activity for each gene. And then in each cell, we estimate transcription activity in each cell separately. So we we made that algorithm available in a GitHub page. And we detailed the derivation and made a benchmarking in this preprint. So let me show you one of the main results. So I said, the main goal of this method is to remove the Poisson noise. But to test this ability, we took a set of mouse marine stem cell consisting of 80 single cells and 80 aliquots. So the aliquots were smartly designed by pulling together the mRNA content of several cells and then sampling back single cell equivalent content of mRNA. And the nice thing with those aliquots is that by construction, they have only Poisson noise. And this Poisson noise is well characterized by the strong negative dependence between the coefficient of variance and the mean. So if we first look at simply the row count, without doing anything, you still see the strong dependence of the CV and the mean. Now, a common normalization in for bulk RNA sequencing is the transcript per million, which kind of normalized for differences in sequencing that per sample and per cell in that case. And as we expect, these dependence is not removed. Now, as I said, we benchmarked with several other normalization methods. And so those are the different methods we tested. And they remove these dependence to some extent, but only sanity, which is here in the middle, almost completely remove that dependence while at the same time, finding the same very low base level of variance for both the single cell and the aliquots, as we would expect. Okay, so we believe that I mean, it's sanity, we have a good estimation of density of cell in a space of gene expression with this transcription activities, we can go back to our problem how to infer this landscape. So let's think at what this epigenetic energy can be. And in so in 1957, when Wellington introduced this idea, imagine that this landscape would come from a complex system of interaction underlying the epigenetic landscape that would be due to the chemical tendencies with gene produces. So in fact, we don't want to infer this landscape in the whole space of gene expression, but only in the space of regulators, gene regulator. So to do this, to do that, to infer the say to regulators, we used a model that was developed in the group of Eric van den Beggen about a decade ago called Mara and for this activity response analysis. So very quickly, what this model does is to first predict binding site of transcription factor on the promoter of the genes and binding sign of micronames on three on the three prime and three prime UTR of the transcripts. Then it interprets gene expression as a linear combination of the number of motif on that gene times the activity of the corresponding motif. So if you just predicted binding sites and observe gene expression to infer a motif activity per cell. Okay, so now we can reconstruct this landscape. And one of the first application of the rise is that it defined cell type as a local minimum. So to do so, we start from the estimated regulatory activity of each cell, we follow the constructed landscape descending down the gradient until we reach a minimum. Then each minimum we call it a cell type and we assign each cell type to the valley it's located in. So we apply this framework on a published data set of 2000 human pancreas cell. So here I show you each point each cell projected in the first three principle component space. So you apply that framework and could retrieve the main known types of pancreatic cells. I show you in different colors here. Now each of those cell type have the precise location in the space of our gene regulators. So we could ask which regulator is most distinguished those different types. And I show you here those different factors projected in that same space. And we could collaborate from literature that those factors as important regulators of pancreatic cells. Another one last more complex and dynamic example. We considered mouse embryonic stem cell during neurogenesis. So in an experiment made in collaboration by Tangsila Mukhtar from the group of Verden Taylor in the department of biomedates in in puzzle. So she fact sorted some neural stem cell at day 13.5 from the forebrain cortex of mouse. So the the forebrain cortex of both human and mouse are created in several successive layers of distinct neuron. Those neuron come from the acymetric division of neural stem cell. Those neural stem cell can also undergo symmetric division and proliferate to give rise to more neural stem cell. So again, we apply this framework inferred that landscape and I show you here this landscape projected on the first two principal components. And you can see a sort of main valley connecting different minima showing red. So I constructed the minimal energy path connecting those different minima where a minimal energy path is defined as a path connecting any two points of the landscape along which the energy is minimized. So we have a path that is defined in the whole space of gene regulators. Again, so we can ask which gene are most variable along the path. I show you here the top 16 most variable motif and you can see sort of three groups of gene having similar behavior. Now, each of those factors that have predicted gene targets, so I could do gene oncology and entities on those target genes and ask what biological process are being regulated by those factors. You can see a first group that is highly active on the right hand part of the trajectory, which are all related to neural differentiation process. Another group of it where factor high in the middle, which are all related to S phase in general replication and finally, a few genes high in the left hand part, which are all related to mitosis. So this corresponds to what is known about the system, which is that those neural stem cells undergo asymmetric division. So cells stay in a cell cycle and proliferate or can alternatively undergo asymmetric division, give birth to base of progenitors that can further differentiate towards different neuron types. Okay, so to summarize, first showed you here a Bayesian model to remove the Poisson noise and infer this transcription activity from a single-cell RNA sequencing data. And I show you how to map this transcription activity to a space of regulatory activity for each cell. And finally, I showed you a few simple examples on how to use this constructed landscape to define cell type as local minimum, identify regulators as they distinguish those different cell types and finally identify developmental path with associated regulators. So with that, I want to thank my supervisors, Mihail Zavalan and Eric Van Niemwegen, as well as both their groups. And thank you for attending and listening. Great. Thank you, Jeremy, for that nice talk. So I think we have time for one or two questions. So let me see in the chat here. There. Okay, here's one from Pierre-Luc Germain. I'll just ask one of them. He said, how reasonable is it to take density into the gene expression space as a proxy for stability? In particular, doesn't the abundance of the cell type influence density in a way that doesn't relate to stability? I mean, I would assume that no matter how big, okay, if we take one cell type, we assume that we have a group of cells that have all the same state. I guess that the stability is going to be defined by how heterogeneous they are in their minima. And I think that should not be, that should not depend on the principle on the number of cells that we have in that group. So I assume that, I mean, the more cell we have, the more precise we are going to be, but the kind of shape of this landscape around a minima defined by a group of cells, I think should not. I mean, in the limit where we have a really low number of cells, then I guess we won't be precisely probably need a few cells. But otherwise, I guess we can estimate a sort of variance from any number of cells. Okay, thanks. Maybe just one more quick one that's kind of general. This one's from Frederick Bastion. He asks, if I'm only interested in determining cell types, what is the added value of your method as compared to simply using expression of few marker genes? I mean, I guess you could also try to infer a landscape in the space of transcription activity, but then I also believe that there would be other method that works better to kind of cluster the cell in this one. I guess the added value would be that I mean, the additional information at least would be that those cell types are defined in a space of regulators. So you could ask which transcription factor or microRNA are distinguishing the different cell types, rather than how those cell types are different in terms of gene expression. Okay, thank you. And so with that, I will wrap up this session on genes and genomes. I want to say thank you again to the four really interesting talks from the speakers this morning. And also thanks for all the attendees to join this session. And now that it's over, all of the audience members, if you want, you can now join the Meet the Speaker session. So all of the questions that were that you put in the chat that we didn't get the time to answer, they will be answered in that session. So you can just join that by leaving the current session and then clicking on the camera symbol next to the Q&A session of the genes and genomes. And also, if also about the questions that get unanswered, there is a slack channel created called follow up discussion. So follow up one word, then dash tier a discussion. So this is a channel, you can also post some follow up questions to the speakers. But for now, we'll be in the Meet the Speaker Room. So I think that's it unless I miss something, but no, I think it's it's it's all good.  Okay, everybody. Thank you. Yep.


Boo! We mean again, um, I didn't upload last week and I'm sorry about that, this one's gonna be on Sunday, sorry about that too. But it's because I'm on spring break and I got my hair dyed. Now to most of you this is probably gonna be a huge surprise because only my friends and my close YouTube fans know about me dyeing my hair.  Two, they thought I'd become a criminal.  But with a lot of begging and guilt-tripping and telling them that I have really good grades, I got my hair brown or dirty blonde. I don't know how it looks in the light.  But let me tell you, this wasn't easy to get done, okay? Two months ago, I convinced my mom to let me dye my hair only one time. My mom told me, fine, but you're only going to do it once. Which, not sure how I'm going to get by that, but that's another day. Um, and my dad didn't even know until the day of, because he saw me after I got my hair dyed. So my employment was at 11.45 and my dad happened to forget his phone off he was driving me there. So he said, all right, give me a time where I'm going to be right here and you have to be here on the same time. And I didn't know how long it takes to color hair. So I just went with an hour and 15 minutes. And then I told him to pick me about one. I also brought a hoodie with me because I wanted to see how long I can hide my dyed hair from him. The max I've gone without seeing him was five days. I got out of the salon at 1.25. So I walked out, I put the hoodie on, and I go to his car and he's not there. But then I hear him beeping from Verizon across the street and he told me to come in.  His reaction wasn't even about my hair. It was about being 25 minutes late.  Then he's going to take a picture of this and show it to me as 20 year old Frederick. Or when I'm 35 years old. But I like it. Let me know what you think. You can be honest, I really don't care. What's done is done and there's no going back. And I'm not going to pay for black dye. Because I think the moment you take a shower, it's not going to be black anymore. It's going to be really dirty, brown, gray. And this is the first time I've ever dyed my hair. And I think probably in the future we can go like, I don't know, pink, cotton candy. Maybe a Melanie Martinez style. Silver is another color that I really want to try. I don't know. We'll see. But for now, this is the Frederick you're going to be seeing for maybe two months or three. Depends on how fast my roots grow out, which I'm hoping it doesn't do that so fast. So this is just an update video. Once again, I'm sorry I didn't post. It was spring break. I was really lazy. But I want to see how many more videos I can do once a week because I've messed up like three times this year already. Last year I messed up like six. If you enjoyed this video, give it a like. Leave a comment down below. Tell me your opinion about this new hair and subscribe because it puts videos every Saturday, every Saturday. I love you guys and everything is less than three.


Hi everyone, Joe for JaspeysCaseRakes.com coming at you with 2019 Panini Prism Football Hobby Pack Break number 81 with six of those 2018 Immaculate Football Spots being given away. Also, 10 Prism Spots being given away within this break as well. So there's three different dice rolls happening in here, so let's do the first of three. First of all, we're only selling 22 spots outright, almost half of you will get extra spots. Top 10 after eight, six into two, eight times. One, two, three, four, five, six, seven, and eight. I'll read you that in clear over. Got the top 10. I'm in the middle of doing a filler over. So you got top 10, we'll get extra spots. Oh, boy, we got walkie talkies, folks. And then let's put little rooftops next to your name. And let's do that. And now we can run the break itself. So for the top 10, you got a little buy one, get one situation happening here. So congrats to you. Now let's do the break itself. Roll it, randomize it six into one, seven times for names and teams. One, two, three, four, five, six, and seven. And then six into one, seven times for the teams. One, two, three, four, five, six, and one, seventh and final time. Cardinals down to Ravens. All right. So here's the first half of the list right there. No trades in these little filler packs, as you know, by now. And there's the second half of the list right over there. Now let's do the break. Let's see what we got here. Maybe a little parallel. There's Kittle and we've got to Ed Reed, Red Waved 149. That'll go to the Ravens. That's going to go to Joe Clanton. And a DK Metcalf rookie card. That's not bad. That'll be for Siano. Let's be for Sean. All right. Now let's see who's going to win those, those Immaculate 2018 Immaculate spots. Let's gather everybody's names again. Third and final dice roll. It's going to be the top six after 12, top six after a six and a six. Good luck, everybody. One, two, three, four, five, six, seven, eight, nine, 10, 11, and 12th and final time. So from seven on down, sad times, especially Joe, very close, but no cigar. Thanks, everybody, for giving this shot. There is another one in the store. So if you want to try again, jasperyskateswakes.com. Top six, happy times for you. Andrew is happy. So is Eugene, Eugene, Jared, Eugene and Jared. Congrats to the top six. We'll see you in that 2018 Immaculate football break. jasperyskateswakes.com. Bye-bye.


Gold contract up $17.20 trading at 18.21 an ounce. We have Silver up 33 cents, $24.25 an ounce, Light Sweet Crew flat $79.57 a barrel, notes and bonds. A 10-year note right now trading down 23 ticks at 112.11, the third year is up a full point plus 26 ticks at 125.11 and right now we have the 10-year, we are yielding, there she goes again man, 3.854, bottom line is at the end, you know, for a slow week, let me just see this for a second. Do we have volume on the way down inside these contracts too? No, we don't have volume, there's only $593,000 contracts on the 10-year. But you get the size of break and you're inside the strength bar, the strength bar that came across on the November 10th, top of that bar is $112.27, the bottom is $110.22, they're going down to $110.22, so these rates continue to go higher. Euro is out here, trade down, well, $king dollar, $king dollar is down 159 ticks, trading 104, 153, Euro is at 106, yen is at trading at 133.45 and the British pound is at 120 to US dollar. Well, you know, let me take a look at the spy, bottom line, you have the spy, the small caps, plus the NDX100 and a confirmed ABC structure down. Right out here, you know, look at the spy, it's down a buck and a half, that's really a sideways move. I suspect, you know, the B point on the spy also focuses $377.85, so what we did here, you know, last week we went down, we broke it with volume, we broke it with 100 million shares versus the 74, and now you go back on the other side of the B, when you do that, that is a complex ABC structure down, and the way you understand it's a complex ABC structure down is that when you are going higher, you have contraction of volume, that's exactly what's happening out here. We go into the NDX100, now that broke, broke on volume but can't even hold price, so this is going to be something that's very quick, you know, the NDX, bottom line broke with 60 million shares, it was breaking that B point with 54, and the price projection inside that Q is lower, it's 244 actually, and October low is 254, so you can expect more action on the way down. I go over to the gold contract, bottom line, gold contract, again, failed the whole price and volume wasn't bad but not great, you know, we take a look at this, you're going to see how this shakes up, and, you know, bottom line, we got to 1841 today or 1821, you get volume out here, 152,000 contracts, you can see the high that was established, and it got into that high today, it got above it, we had that first high had 231,000 contracts, then last week we went up at 197, you got one


In this episode, we'll be talking about how to transform transactional organizations into ones that are customer centric. We'll be talking about the power of an insight. And finally, we'll ask the question, are we all designers? And here is the guest of this episode. I'm Jose Mello and this is The Service Design Show. Hi guys, my name is Marc Fontaine and welcome to The Service Design Show. The show where you get to learn what some of the world's best service designers are currently thinking about. So you can use that knowledge to transform services and businesses all around the world to become more human-centered and eventually more successful. We bring you a new episode every two weeks on Thursday. So if you don't want to miss anything, be sure to subscribe. My guest in this episode is Jose Mello. Jose is the head of innovation at Liberty Mutual in Brazil and he also teaches about service design and design thinking at several universities. For the next 30 minutes or so, we'll be talking about three topics. How do you transform transactional organizations into ones that are customer centric? What is the power of an insight? And finally, are we all designers? If you'd like to fast-forward to one of these topics, check out the episode guide down below in the description. Or just stick around and enjoy the whole episode. So, let's jump right in. Welcome to the show Jose. Oh, very good to be here Marc. Jose, we just had a small chat before the show and you told me that you've been into service design for quite a long time. And I'm super interested. Do you remember the first time that you actually heard about service design or got in touch with it? So that's a very good question. Actually, I think the first time I got in contact with service design was in the 80s because I was with my mom on a bank. And we got a huge line to withdraw some money at the bank. And it wasn't not only one line, but it was like three lines, so I get to go from one line to the other. And at that point, I didn't know what service design was and I was just a kid. But 30 years later, I met a bunch of people who were speaking and talking about service design. I was looking for a way to understand more how could I deliver and design better services for people. I've been doing that in my entire life. And I think at that point was the moment that I realized what service design was. It was my first contact, like 2007, 2008, almost 30 years later. But I have been experiencing service design for my entire life. Or the lack of service design. And maybe that experience on the bank made me the professional I am today. Because it was so frustrating that I wanted to do something to change that type of thing. Alright, interesting that the lack of service design is the first memory. I can relate to that. So Jose, you sent me three topics that we can talk about in the show and I have them next to me. And you have a few question starters that I gave you. And we'll be co-creating the questions as we go along for the next 20 minutes, right? Oh, perfect. So I'll pick the first topic and I'll surprise you. I'll just pick a random one. I won't do them in the order you gave them. So I'll pick this one. And this one is called customer-centric organizations. Do you have a question starter that goes along with this one? Yes, yes. So let's start with this one. How can we? How can we make the organizations more customer-centric or how to make that? I've been working on that for, I would say at least the past 10 years. So combining my experience at Itaú and then my experience now as the head of innovation at Liberi Mutual here in Brazil. And I think one thing that is key to turn a transactional organization into a customer-centric organization is people. Most people believe that it's a matter of processes, so making better processes, but it's not about processes. It's about people because are those people who deliver the processes? And for example, here at Liberi Mutual, we've been working for the past three years and a half to create a culture of customer-centricity into the company. Because we strongly believe that it will deliver our differentiation in the market. We'll bring this differentiation to the market. And we've been doing that, starting by the people, starting to bring people together and making them understand why think about the customer is so important. And you mean the employees probably, right? The employees, yes. I'm speaking about the employees. So people who are out there speaking with the customer every day and following a script to deliver one type of service. And most of them, they do that, but they do that without understanding what are the real needs of the customers. So using it as an example as well, last year we did a project here. I'll show you just the big bookcase. Wow. So in the past two years, we did a project here where we brought people on the street to live exactly what the customer lives every day when they need the service from our company. So we sent people to see the tow trucks helping a customer, a person who had an accident, how they deal with that problem, and how we deal with the process. And we engaged something around 100 people on the project from several different parts of the company just to make them see what's the level of services they deliver to the customer. And that was a game changer for us here to get people involved and engaged in the process. Was it hard to get people involved? It was hard because people don't have time, they never have time. They are always busy because they have something to deliver, they have an operation to run. But that operation just exists because of the customers. If customers don't buy, the operation doesn't exist. So I think in a nutshell, I think engaging people in the customer mindset is really key to move companies from a transactional company to a more customer-centric company. And if you would have to give, what has been your biggest learning for the past three and a half years and if you would have the opportunity to do it over again, what would you do differently? Well, very good question. I haven't thought about that, but I would say that maybe I would get people on the street sooner than I did. Because I worked more than one year, one year and a half almost, to do this type of project to get people from inside and go outside with them. I started to create this change by the inside. So bringing people on meetings, speaking about the customer importance and who is the customer. And it took me one year and something to realize that it was not working well. If I didn't get people and put them on the street to see what happens in real life, we would be speaking until today about why focus on customers is something key for the company. And for one example for you, last year Liberi was awarded the insurance company in Brazil that most respects the customer. So it's a work that we've been doing not for so long and we start to see the changes. So you're saying it's really all about being in touch with the customer. Get out of the office, get onto the street, talk to the people that are using your service, right? That's what you're saying. Yeah, that's it. And for everyone from the top director inside the company until the lowest levels on the company, they need to know who the customer is and be in touch with them. That would be my advice for someone who wants to go down that path. Let's keep it at that for this topic, I shall say, and move on to the second one because, right, I'll pick this one and keep the most controversial for the last. The second topic is the power of an inside. I guess it relates to what we just talked about. Do you have a question starter? Oh, yes, yes. So let me see here, let me see here. The power of an inside. The power of an inside. Don't make it too hard for yourself. Yeah. What if? Yeah. So maybe the question here is, what if we could think of an inside more as an interpretation of effect? Rather than the real fact. All right, that's something you need to explain. Yeah, that's a trick question. But let me try to explain that. I've been doing research for a long time, especially research to understand customers and to design services for those customers. And we designers, most of the projects that I see, we designers, we go there, see effect. So we see that people wear yellow shirts, and then we write down an inside saying that people wear yellow shirt. The problem with that is that it doesn't make any sense to create something just knowing that people wear yellow shirts. So we should think of an inside more like an interpretation of that rather than the fact itself. So we should try to understand why people wear yellow shirts and how that relates to the service of the project that I'm doing right now. And it's so hard to see people taking that step ahead and trying to create a point of view about that specific topic that they saw on the street. That I think that's why I chose that topic to speak about. So it's like, for me, it's the difference between almost like quantitative research versus qualitative or design research where we as designers are or should be intrinsically motivated to understand the why. Yes, totally true. And so how do you break that? So I can imagine that if you go on the street with employees and hope that they will find an inside and you see that they get stuck at the yellow shirt, how do you help them to move beyond that? So we usually do several rounds of discussion. So one thing that we always do when someone goes out to do some observation is to list what we call their top insights, which really are not insights, they are just observations of what they saw on the street. And when we get together, we put everyone together who did the same research and we start a discussion of why that is important, why that specific observation is important for us. And that's how we come from like 50 observations on the street to three to five really driving insights that can help us create a better service for our customer. So the discussion is a very important point of the process. And is it also do you use evidence from the field research because in our projects, for instance, we do photo studies and then just hanging up the photos and having the studio filled with these photos. It just helps you to see the patterns and to have a better discussion. Are you using some techniques like that? Yeah, yeah, we use some techniques like that. We use a lot of photo research and because that's very immersive. So even the people who didn't had the chance to go out for a research, but we need them as part of the discussion to being that immersive space helps a lot to to gain the right discussion. Yeah. And I think from what I've seen, for instance, what helps is forcing people to actually look through a camera, you know, because they see other things during the research. So if they don't use a camera and they don't have to make a photo, for instance, during observations, they will see past the things that they are used to, right? And with a camera, they will have to make a photo of it. Oh, yeah. Oh, that's a very good one. It just forces people to pay attention to the things that are common, I guess. Yeah. Oh, that's a very good one. Yeah, I'll try that next time. I haven't done that, but I'll try that next time. Yeah. And so the power of an insight, anything else you'd like to say or add about that? Anything? So I think we spoke a lot about how to gather information from our observations and research and how to make it turn it into an insight. Maybe we could speak a little bit about what to do with that insight right now. Sure, yeah. So which I believe is where the power of an insight is. So once we have all this discussion and we could write down one really powerful insight for that project and one insight should be something like a very short phrase, something that really... is really unique to that project. We start to understand the boundaries of the project. And one thing that is really hard for someone who is designing is not what the project should be or should address, but what the project should not address. And I think the insight, it creates the boundary of the project so that we can understand what are we not going to solve with that project. Because when we start, you'd like to solve the entire project and all the problems that the world has. Once you define the insights you're going to work with, you are creating the boundaries. You're creating, this is what we're going to solve. I'm not going to solve anything else. And I think this is why we come up with those insights, just to help us frame the direction of the project. And the frame is also super useful, I guess, during a project to see if you're still within the scope, right? Oh really? Yeah. Yeah, doing the things you should be doing. Yeah. All right. Make sure you're not going to stray to a different path and start solving an insurance project and end on a pharmaceutical project. So yeah, insights are very useful to create boundaries. That's interesting, as you'll say. Time is flying by, so let's move on to the third topic. And I think a Swedish company once said this, right? And the third topic is called We Are All Designers. Do we have a question starter for this one? Yeah, I'll pick this one. When will? When will we all become designers? Aren't we designers already, as the Swedish company said? Yes, we are. But we don't realize we are. And I think that's the problem. Because everything that is not nature in the earth was designed by someone. And for those who believe in a God, even nature was designed by someone. So if everything was designed, it was designed by a person that has or doesn't have a design background, but they need the design ability. And that's why we have a lot of services that doesn't have any design in it. And that's why it's so frustrating for the regular people. So I strongly believe that everyone has the ability to design and everyone is innovative in their ascension. But we are not trained for that. And if we had, maybe in the early stages of school, very simple and soft skills for design would help a lot for us to be able to design things better in our world. Alright, so I have two young kids. Now as you'll say, help me. What soft skills should I help them with in these early years? So I'll list you some of them. So one, for example, a spirit of participation. So it's the willingness to collaborate with other people to build something. Because in the world we live today, we cannot build something alone. We don't do anything alone. We need people. The second one I would say is experimentation. So it's learned by doing, not by only thinking. Because when we use a lot our thinking process, but we don't make it tangible, we are not really testing how people are going to relate to that specific thing. So I would advise you to teach your kids to experiment and experience more things. And learn and fail and move ahead. We are very afraid to fail. And the interesting part of course is if I look at my young kids, they don't even have the notion of failing. They just do stuff. It's not failing for them. It's just another experiment. But along the journey that we take through school and through university, we get influenced by their notion of failure. And we begin to, we become afraid of experimenting new things. Because we don't want to fail. So I'm not going to test because I don't want to fail. And actually failing or the experimentation process lowers down a lot the risk that you are going through. So how do you help people that are, I don't know, in their 30s, 40s, 50s to rediscover their ability to experiment? How do you do that? So I think something that is very important, I think, is first help them with some very maybe basic skills so that they can understand that they have the ability to experiment. Because sometimes we hear from people that they, I just don't know how to do it. I know you're right. I think you're right, but I don't know how to do it. So we have to help them understand very basic skills from some techniques to do that. So observation techniques and some building techniques to help them do that. So I would say a common language of what is to experiment. And the other thing I think is more related to the environment where they are working in. So the environment needs to be friendly for experimentation and it also has a lot to do with the rewards or the reward system that you put in place. So if people work alone on a cubicle, they will never collaborate with anyone. So you need open spaces, you need the environment to be friendly for them. If they stay on the same seat for the entire day, for the entire week, they're not going to collaborate. They need to move around. They need to have that mobility. And also the reward system. You should reward not only the results that people bring, but also how they made it. So what's the process that you took to get there? Let me understand that and reward them for each step of the process. And I guess this also strongly relates to the first topic we talked about, about customer centricity. If we look at the process, if companies would reward employees that take their time to go on the street, talk to customers, it would probably create a totally different atmosphere. So putting those measures into place is really essential. Yeah, totally true. So you'll say, final question for you. And this is your opportunity to ask the people who are listening or viewing this episode to ask them a question. Is there something you'd like to ask? So I'll ask them what was your most powerful insight listening to this, watching our show today? Right, yeah. That's a good one because that's also something I ask at the end of the episode. That's a good question. What was the biggest insight from this episode? Leave a comment. We are really curious. So Jose, thank you for your time. Thank you for sharing your thoughts and ideas and it was my pleasure. Oh, thank you very much, Marc. So what is your biggest insight from this episode? Let us know down below in the comments. This show is all about helping you to become a better service designer by sharing real-life stories of people that are currently shaping the service design field. This is your first time here and you'd like to see more interviews. Check out some of the past episodes and be sure to subscribe. I'll see you in two weeks time with a new episode and for now, thanks for watching.