We are discussing about different microarray based platforms and how to perform some biological applications on these chips. In last lecture, MSAPURA Venkatesh showed you how to perform a microarray experiment using serum samples obtained from patients who are suffering from Falsipirum or Vyvex malaria. Today we are going to continue the demonstration and also show you the ways to do data normalization and how to do microarray data analysis, specifically if your goal was to look for a biological question of interest. In this case, we are going to talk about several ways of how to make meaningful data from the patients who are suffering from malaria using protein microarray based platforms. Let us have this lecture and demonstration session today. I am Apurva Venkatesh, your TA for this course and today we are going to talk about microarray data normalization and analysis. In the last lecture, we are trying to profile humeral responses of malaria positive patients using microarray technology. So, we are going to start from there. What we are going to do today is to see how to normalize microarray data using Excel. So, what we will do is we will start with the raw file you get from the microarray scanner, right. So, once you take your slide and you scan it in a scanner, you will extract the raw data and here is the Excel sheet you see. This is the type of data you get. I am showing you this one particular slide. So, I will just like to repeat that one slide can probe 8 patients serum. So, here in this one particular Excel which you see here, we actually have data for 8 patients. So, first of what I am going to show you is how to reorganize this data, ok. So, let us see first of all what kind of parameters are exported. Now, you will see that all important parameters are provided in this Excel. For example, start with pixel size is 10. The slide was scanned at a wavelength of 635 nanometer. Then you go down normalization method. This was not normalized yet. So, it says none. Then if you scroll down further, you can see the PMT gain which is 400 scan power 100 laser power 1.34. So, basically later on if you want to go back and check these slides again. If for example, for instance, if you forget the parameters you used, you can always go and open this Excel to see what you had done, right. So, now let us scroll down further. You will see block, column and row. So, this is very important. Again let us go back to the slide layout. One slide can probe 8 patient sera and one particular pad that is one pad which probes one patient sera has four blocks. So, which means that if I keep scrolling down, so every four blocks represents one patient data, right. So, now when I keep scrolling down and I go to block 5, a new patient begins. So, that is what I am going to talk to you about how to reorganize this. So, for example, if you see here four ends here, right with blank and you will see that there is an IGG mix 1 which starts again. So, this is basically your new patient. So, what we are going to do is we are going to first reorganize this, but before this let me tell you which are the columns which are important for us. So, now I am going to scroll back up and we are going to go through the columns which we have on this Excel. So, now apart from block, column, row, the name and the ID, basically this is your protein ID. We do not need any of the other columns except for column AH. So, you see column AH here. So, this is basically your F635 median minus B635 which is your basically your background signals, right. So, this is our the column which we actually extract and use for an analysis and we do not need any other column here. So, what I am going to do? I am going to first delete all unwanted columns to make this Excel less complicated. So, let us delete all of this and then we go and delete this as well. We also do not need these parameters for the analysis, I am also going to delete this. So, finally this is what you get. So, now you keep scrolling down and then you arrange all patients side by side. So, when you do that this is what you get. So, you also see that there are additional columns here. This is what you get from your GAL file and when you scroll right you will see that you all the patients are now placed next to each other, right. So, this is the kind of Excel you get first. Now what I am going to do is I am going to reorganize this Excel to make it easier. This is combined data for all your patients and now we will reorganize the proteins. So, as you will see the proteins are present in the same order as they are present in your slide. So, what we first do is now that this is common data for all patients we have already put them together. We are going to bring the IDG mix from all four blocks together. So, I will repeat this slide layout once more. Now, you have six IDG mix here this is present in your block one. Similarly, you have the same spots present in all four blocks of the same pad. So, what I am trying to do here I am trying to get all the IDG mix together. So, you will have 24 such spots one after the other. We will also do the same thing for your anti-human IDG mix and similarly we are also going to do the same thing for your next spots. So, when we rearrange our Excel this is how your Excel will look. What I have done here I have put all the 24 IDG mix of one pad together right we will go through all the columns once more. For example, these are all just your spot details now we will go to your ADI spot ID which is your GAL file. So, this is what you get from your GAL file I come to this in a minute before that let us talk about ORF. So, this column here is basically your ID this is also going to give you details about the fragment which has been printed on the chip. So, let us go to PlasmaDBID now if you look at PlasmaDBID these are all basically each and every protein has a unique PlasmaDBID. So, that is what is mentioned in this column here. If you go to the next column which is ORF fragment. So, this will explain your ORF your column H better. If you go here you will see that this specifies which Exxon segment is printed on the chip. So, basically as you know POTS which are printed on the chip were not purified proteins they were IVTT spots and basically not so, what is IVTT in vitro transcription translation. So, what was expressed the whole protein was not expressed here only a certain segment of a particular Exxon of a protein was expressed right. So, basically it is not really right to say that proteins were expressed on the chip instead it will be better to say that polypeptides were expressed on the chip. So, this particular column J gives us details about the polypeptide that was expressed and printed on the chip right. So, that is how you get this ADI spot ID which is a unique ID for each and every protein. What I mean here is that if you go to PlasmaDBID and then if you try to look for duplicates we will actually find duplicates here because it could be that for the same protein different Exxon fragments were printed on the chip. So, you might get duplicates here whereas if you go to your ADI spot ID you will not find single do any single duplicate because these are unique ID's for each and every protein which takes into account the Exxon fragment which was printed on the chip. So, that is what you see here. If you say for example let us look at this particular row if you say that this was the ID and this is Exxon 1 of 2 you will actually see the ID here and 1 O2. So, this becomes your unique ID for each and every protein. Why I am telling you all this is because this is very important for data analysis for all sometimes you might just start with an analyzing your I column and then you will figure out later that there are lot of duplicates and you do not know what you are actually doing. So, what we need to do is if you want to shortlist any antigens we need to consider the G column for analysis right. So, now let us move on to the next column which is your description. So, this we all know this basically described what was printed on the chip right these are just basically the names of them basically the names of the antigens. Now, the next column is your organism. So, as you know you have two types of spots here plasmodium falciparum and plasmodium bivax. So, basically this is going to tell you which organism does the antigen belong to. So, you have plasmodium falciparum 3D7 here for instance and probably and if you scroll down further you will see plasmodium bivax. Salvan right. So, this is going to give you details of the organism and then the next column which is M is going to talk to you about the preparation of the spot like for example you have the first few spots are basically your IgG mix right. So, then the preparation is basically your IgG mix it is not an IVTT spot.  So, our control spots here were printed as purified proteins and not as IVTT spots.  So, basically this entire column M gives you details about the spot preparation. Now the all other columns here are basically your patient samples. So, if I just move this a little bit what you see here for example let us consider the first sample this is basically a positive control which was part of batch 1 set 1, slide 1 and pad 1. So, let me again take you back to the experiment this experiment was performed in four sets of two batches or rather two batches of four sets. So, you have batch 1 set 1, batch 1 set 2 then you have batch 2 set 1 and batch 2 set 2. So, basically what is this telling me this is telling me that this particular positive control was probed on batch 1 set 1 on slide 1 and pad 1 right.  So, this is basically probed on batch 1 set 1 slide 1 and pad 2. So, this is going to tell me my position of the sample. So, if ever I want to go back to the slides and check the real spots right the images of the spots then I know exactly where to go. So, for example if some sample is not behaving well and I want to go and cross check the intensity of the spots. For example, some sample is giving me very high intensity signals and I want to go back and check whether it is real then I will know exactly which file to open because I have all the details here. So, this is for the all other columns. So, this is I hope you have now understood how the excel sheet looks right. So, now what we are going to do the next thing is we are going to apply a color gradient to this excel right and now I will tell you why we are going to apply the color gradient let us first do that. So, for which what I am going to do is I am going to go to conditional formatting I am going to go to color scales more rules and I am going to choose a 3 color scale and then I am going to choose number type. So, I am going to say 0 and I am going to assume that my entire data falls you know in between say certain negative values and maybe around 80,000 is my maximum value. So, I am just going to assume that if my data falls in this range I am going to split my data based on 3 numbers 0 then my midpoint will be say 20,000 and my highest will be 40,000 and I am going to choose some colors here. So, I say this is maybe gray then I am going to keep this black and I am going to keep this red. So, what this is going to do is all my values above 40,000 are going to be in dark red and then around 20,000 will be black and the lowest or the least values will be gray and those which are in negatives will be almost white. So, that is how I am going to apply a color gradient here. So, you can see in this slide here basically what I have done is I have just minimized this excel a little bit. So, you can you will be able to see all 4 batches at once. So, I do not know if you can see a black line here. So, basically this is going to split your batches. So, in fact it is going to split your set, set 1 from set 2 of the first batch and set 1 from set 2 of the second batch. So, basically this is batch 1, set 1, batch 1, set 2, batch 2, set 1 and batch 2, set 2. So, when you minimize this excel a little bit and you apply this color gradient what you can see is that the signal intensities particularly for this batch. In fact, this whole batch, but batch 1, but batch 2, set 1 is really high compared to the rest of the batches. So, you will know that mainly from the IgG signals here.  So, basically this is your control which is going to tell you whether you need to re-scan your slide or not. So, if this is very high then all your signals by default for this particular batch will also be high right. So, that is going to screw up your results a little screw up your results later because all the patients in this batch are going to show high signals which will be which is which is not correct. So, this IgG mix printed on the strip is going to basically help you in deciding whether you need to re-scan your slides at different PMT settings and per settings right. So, what we will do here is we will re-scan the slides once more bring these settings down a little bit and bring these settings not as low as this, but a little lower because this is also a little high compared to this if you see right. So, later on we realize that this is because of the membrane thickness of the slides there could be other issues also which you might encounter later. So, to avoid this you need to first bring down the signals and then any changes after that will be corrected by normalization ok. So, now having re-scan all the slides as you can see in the slide the settings look pretty uniform though it is still not very uniform and you will still feel that batch to set 1 has higher signals, but overall it is ok because this will then be taken care of by normalization. So, now what we will do is we will proceed with normalization using Excel. Now, there are two strategies I am going to talk to you about today. The first strategy is basically a very simple normalization method which we will use only for visualization. For example, if you want to prepare heat maps then we will use this the first normalization method. However, if you want to perform statistical tests then we will use the second kind of normalization which I will talk to you about. So, let us first go through the first normalization method. So, what we are going to do in the first normalization is we are going to subtract the raw values for each of the IVTT spots from the sample specific median value of the no DNA controls. So, I am sure that this is a little confusing. So, what we will do is we will go step by step. First I am going to show you what raw values are and then I am going to show you what the no DNA controls are right. So, again we are going to come back to the same Excel. It is color coded and we have reached this stage. You also know that now in this data we have IgG mix, we have anti-human IgG, we have purified proteins. We do not need any of those right now for our analysis. We are going to directly go down to the IVTT spots. So, in fact what we will do is we will probably just delete those rows to avoid confusion. So, let us start from here. I am going to delete the first few, maybe what I will do is I will just zoom this a little bit. So, I have just zoomed this a little bit. What we are going to do is we are going to delete unwanted rows right now. So, we do not want IgG mix, we do not want anti-human IgG, we do not want purified proteins right now. Again, let me tell you the purified proteins basically we do not require in the analysis, but it is important when for example your slide is not worked at all. And or you have not got the signals you required. You can always go back to the positive control spots to see what their signals were right. So, this is basically used for such you know analysis just as controls. So, right now we are going to delete those rows and we are going to only keep rows which are IVTT mix right that is what this is. So, now we are going to have this way 500 Plasmodium Phalzipyrim IVTT spots and 515 Plasmodium Bivax IVTT spots. So, we are going to go down, give deleted unwanted rows there are few more rows below which we do not need. So, after these 1015 spots, there are few more like TTBS which is nothing but your buffer spot where only buffer is spotted and then you have some empty spots data then we have data for blank. So, this is also unwanted we are going to delete that as well. So, now what we have are 1015 IVTT spots and 24 No DNA control spots. So, now what are these No DNA control spots? So, basically these spots have the entire IVTT mix except the plasmid. So, basically what you expect here is no expression because you don't even have the plasmid here whereas the IVTT spots have the entire IVTT machinery just like no DNA but they also have the plasmid where you are going to express your gene of interest whereas you don't have that here. So, what is this going to provide is going to provide your background signal. So, what we are trying to do in the first type of normalization is we are subtracting our raw signals from background. So, now there are 24 such spots which you remember we have rearranged and that's why it's come together group together like this. The first thing you are going to do is take a median of this which I have already provided you here. So, this is the formula for it I have just done this the whole thing in Excel. So, now we have a median value here. So, the first thing what I am going to do is for this particular sample which is in column N this is one sample I am going to subtract the values for each and every IVTT spot from that particular median right. So, each of these spots which you see here I am going to subtract it and that is what is my median sample specific median normalization. So, let's scroll so probably we will do this in the same Excel ok I have kept place for that here. So, this is called IVTT spots minus median of IVTT control. So, that is exactly what we have to do we are going to say is equal to then we are going to go to that particular spot. So, say let's take the first patient and then we see minus and we go to the median value which is 7, 8, 4, 2. So, now because I want this row to remain constant throughout I am going to put a dollar sign in front of the row. So, this is what we get here and now I am just going to drag this across as well as down. So, once you drag and drop this is the kind of Excel you get I have just minimized this, but if you apply your color gradient this is how it looks overall. So, this is what you can use now to make your heat maps and what I have also done is I have sorted this based on the antigens as well as the patients who are falciparin positive and vivax positive I have split them completely and I have also made another Excel sheet based on age you can also split them based on age. So, this is how I have sorted them. So, I have put all your PV positive patients together and PF positive together and I have also sorted based on age. So, you have PV positive, PF positive as well as sorted by age. So, this way you can sort your Excel in different ways you can also use other softwares to make your heat map, but basically this kind of once you normalize it in this way you do not perform any statistical analysis with this data. For statistical analysis I am going to now show you the next normalization method which is your log 2 transform fold over control normalization. So, for this I am not going to show you the entire method again because now you know how to do it on Excel I am sure you all know. For this I am only going to show you the steps. The first thing what we do is we are going to set a floor of 100. So, what we are trying to say here is that all the samples which are below 100 is going to have a value of 100. So, this is going to remove all my negative values from my data. So, that is the first thing and I have done it here for you and we are going to keep scrolling right. The next step what we are going to do is to divide each and every raw value by the median of the IVTT control spots. So, just like how we did previously we subtracted raw values from the median of the IVTT control spots this time we are going to divide it. So, that is what is called fold over control. So, once you set a floor of 100 then you divide it and the next thing you are going to do is to convert this whole data into log values. So, you log to transform this entire data right and that is why it is called log to fold over control. So, once you do this, this data can be used for any statistical analysis. So, this because this normalization is known to be more stringent ok. So, now either you can use programming to do your statistical analysis or you can use different softwares which provide your statistical tests. But what you need to know is which type of test you need to use which is beyond the scope of this lecture. But you can always read about what you want to do and you can also decide on which software you can you want to you want to use. For example, graphpad prism is an excellent tool for preparing graphs. It also helps you in a lot of statistical analysis, but if your data is really huge like the one we have is not very huge, but still it is not very small for graphpad prism. So, for example, you can have graphpad prism can get stuck in the middle if you are using data of even this size. So, of course, if you have bigger data sets then it is very difficult to use softwares like graphpad prism. However, if you are going to have only 10 patients or 20 patients with 40 proteins or something, graphpad prism does offer you a lot of statistical tests. Apart from that, there are other softwares as well. You have metaboanalysts though it is for metabolomics data, you still have a module called significance analysis of microarrays in it, which you can explore for your microarray data analysis. But there are of course, R programming and Python and other things will definitely be much better for your analysis as you will save a lot of time as well. So, what I am going to do towards this we are coming to the end of the lecture. I am only going to show you very small analysis you do on. I have done on Excel. So, basically what my aim here is to identify most sero-reactive proteins in my from my chip, which means that the proteins which elicit the maximum antibody response in malaria patients, right. So, that is my aim. So, now just to get this whole list of best sero-reactive proteins, I you can also do this on Excel using a particular formula, which I will show you now. So, let us go back to the Excel. This is how our Excel was, right before we removed all of these rows which are IgG and anti-human IgG. So, retain them for now and probably zoom this a little bit. So, now if you see that I have retained all the rows. So, the first thing what we need to do of course we do not need this, but I have still retained the entire sheet from the beginning. You will see that there are these 4 patients which are deliberately kept out of the analysis. For example, there are, so if you see here there is PF plus PV and everywhere, right. So, basically these are my patients who are diagnosed with mixed infection. So, I do not want any such patients in my analysis. So, I am going to purely have groups which are plasmodium falciparum and plasmodium vivax and I am going to look at look for their response to plasmodium falciparum antigens and plasmodium vivax respectively. So, I am not going to have any of these mixed patients I have kept them out. So, if you want we can also delete them, right. So, we need to basically start from row number 82 that is what we are interested in because these are the IVTT spots. So, the first thing I am going to do is I am going to take an average for each and every spot. So, for example let us write here average and I am going to say is equal to. So, I get an average value and I am going to just drag this down. So, you will have an average value for each and every spot for all the patients. What I missed telling you before is that the previous sheet had many more columns here that is because they we had a lot more samples which are probed on the chips. For example, we had positive controls which are nothing but samples from taken from a place which is a highly malaria endemic region. So, you know that those spots have to give you a signal, right. So, those are my positive control samples. So, do not get confused between positive control samples and positive control spot they are totally different. So, these positive control samples I have excluded them from this particular analysis. We also had healthy controls which are basically malaria naive individuals means patients who are detected were not detected with malaria at the time of admission. So, they were malaria negative. Those patients were also taken and probed on the chip just to see there is a difference in response. Such patients have also removed from the analysis. There are also certain samples which I have probed repeatedly in probably in duplicates or four times in all you know once in all the sets just to check for reproducibility. So, here are some scatter plots you can see where I am showing patient to patient reproducibility. So, basically I am showing reproducibility between my batch runs all of these patients also have removed from the analysis. I have basically now in this Excel 200 patients 100 Plasmodium vivax and 96 Plasmodium falciparin patients and 4 which are mixed infection also have removed. So, in this way you can choose to remove rows and columns based on what you want to study and you can make a Excel less complicated right. So, that is what I had missed mentioning, but now that is done. So, I have taken an average right now. Now I am going to apply this particular formula which you see here. If the average value for a particular spot is more than twice the standard deviation of the mean of the no DNA controls then that particular spot then that particular antigen is seroreactive. So, what this means is that if this is the average if this particular number is greater than this particular number which I am going to show you right now. If you take an average of the mean plus 2 times standard deviation of the no DNA spots this is my number. So, if that raw value or if any raw value is greater than this value then that spot is basically seroreactive then that antigen is sorry then that antigen is basically seroreactive. So, I am going to say if this is equal to if function this spot is greater than this then 1 else 0. So, I get 1 here then what I finally get is an Excel sheet like this where I have random ones and zeros right. So, all of these ones I am going to now say are my seroreactive proteins because they are greater than twice the standard deviation of my control spots. Now a lot of people may also use healthy controls in their analysis right, but we do not have them. So, what they do is they compare the signals signal intensities in a malaria group versus a healthy population, but since we do not have all that I am going to simply say that this is my these are the list of my seroreactive proteins which I can now take forward for further analysis. So, this is not a great this is not a statistical test this is only short listing my proteins I am here I am only short listing my proteins from 1500 to a handful which I can then take forward and study. So, this is what that sheet is now what I have done here is I have taken this for a group of patients, but now what if I want to check this for a particular patient.  So, what do you see here is that I have zoomed this for every single patient. So, basically the previous one which I showed you was the average for a single spot for a group of patients as well as the average for the no DNA control here I have done it for each patient which means it is sample specific right. So, in this way if I scroll down what you will get here this you will know the number of seroreactive antigens per patient which means that if one patient for example here is seroreactive to only 12 antigens whereas, there are some other patients which is seroreactive to 77 antigens. So, this is basically my antibody breath. So, these are the two basic kind of analysis like which I can show you in excel for now. Power of microarray technology is basically the fact that you can perform this experiment very fast probably in a day or two and then using any kind of patient data all you need to do is map this whole data which of microarray data to each and every patient clinical information that you have and then you need to you can perform any kind of statistical analysis and you can generate several results from the same single experiment. So, that is the beauty of this I hope you have got a glimpse of how to perform data analysis and how and how basic statistics can be done and how this is not the only way to do statistics at all you can do use softwares and programming and I still recommend that people do programming because if you want to make even a single small change you do not have to repeat the entire analysis also tomorrow if somebody provides you some other clinical information of the same patient population you do not have to repeat the analysis in excel you can simply write a code for it and then in a few minutes you will get results for that as well. So, that is all for now. Thank you. After going through this demonstration session and the insights of doing microarray based data analysis you must have realized that there are many ways of analyzing and representing microarray data of course there is no single way no correct way of telling you what is the best way of doing it analysis there are many considerations you have to keep in mind when you are thinking about how to make meaningful information out of this high throughput data there are several questions that can be answered using microarray data provided your data passes the quality control checks and it is properly normalized in such experiments your control features becomes very crucial both the positive controls and negative controls they guide you about how accurate and real your data is they could distinguish between real signals and background noise after proper analysis methods in the next class you will see another application of protein microarrays in a different application we will shift the gates to the cancer research and also the platforms. So far we have talked about self free expression based protein microarray platform we will not talk about how to take terrified proteins printed on the chip using human proteome arrays and then apply those to investigate a deadly disease cancer and try to talk to you about both experimental demonstrations as well as the theoretical concepts involved in performing such biological experiments see you in next lecture thank you.


We feature the country of football, Brazil. Brazil, the fifth-largest country in the world by both size and population, is most known for their football prowess. Well, actually, Brazilians didn't invent football, but quite possibly perfected it. Football derives from sports created as early as the ancient Greek and Roman empires. The rules and general guidelines we know today weren't established until the mid-19th century and were introduced to Brazil by the Scottish in the late 19th century. In the inaugural FIFA World Cup of 1930, Brazil was even ousted in the opening round. Although an unimpressive performance by the Brazilians, the sport gained national attention and sparked the relentless dedication of young footballers around the country. Why? Football offered an avenue for the youth to escape poverty and the dangers of the third-world country. This sport became a great passion for Brazilians, who often refer to their country as the country of football. And the outcome for this passion? The Brazilian national football team is the only team to qualify for every FIFA World Cup. This led to their five championships, which are the most of any nation. They are also two-time runner-ups and two-time bronze medalists. It's not surprising that Pele, arguably the best footballer of all time, is also a Brazilian. He led the national team to three of these victories. The first was in 1958, at the age of 17. At the time, he was the youngest player to ever play in the World Cup. Pele has also been the all-time leader in goals scored, both in national matches and club appearances. Today, men and women in all corners of Brazil play football. From the streets to indoor fields, Brazilians dominate the sport with 10,000 professional players worldwide, most of any nation. Football fans of all nationalities love their skillful, creative, free-flowing, fast-paced style. When anyone thinks of Brazil, it is hard not to think of football. Thanks for watching. Drop a like. Leave a comment. Please don't forget to subscribe our channel, Design Hysterics. Get more updated videos from now. Click on the bell button below to get notified. See you people in the next video. Till then, it's a bye from Design Hysterics team.


Good evening, ladies and gentlemen, and I see Fred just tweeted about Sveta Smyrnova is ready for the challenge performance schema is 20 minutes and this really will be challenge because performance schema is big future rich and very complicated product actually. Okay, let's get started. My name is Sveta Smyrnova. I am support engineer for many years and through this years my work is becoming more and more easy to do and performance schema is actually main reason why. Today I will try to explain all the features of performance schema, all features I myself think are important, how it works, so you can, how to tune it, so you can tune it effectively, so you can get data from performance schema and you can also don't affect performance of your servers much. And finally I will show few tools, two tools which help to work with performance schema much easier. Okay, so why performance schema? Why it's needed? Performance schema is instrument for runtime performance collection and what is the difference? We always have in my school metadata collection, metadata collections and what is metadata? Metadata is schema like create tables, like tables, like databases, views, etc. It's like kind of support which doesn't change often. And runtime is information, this is resource usage, logs which are required, which queries is currently running. Metadata information always will exist in some very, very early versions of my school. First show comments, then last information schema tables. Runtime data, which you can examine, also existed in my school, but in very, very small amount of information, you can get really small amount of information. It was not perfect, it was just status variables, so you can see, for example, how many temporary tables server created, sign start, and how many temporary tables created your own session, but not more, you cannot identify where was peak, for example, for that. It's got some extension for information schema, but now it's like movement is here, so like information schema will contain data about metadata, and all about runtime will be in performance schema and actually are, and like, for example, in 8.0 to information schema in ADB logs to move it to performance schema as data logs tables. Why this change, why we are using performance schema for runtime collection? Performance schema has no design issues, which information schema has, no locking reads, no performance, almost, I put almost, because this is working QR code points to, and on slides, it has working link, it's points to current open bug report, which actually is about locking read, but I hope it will be fixed. It's optimized, it's MySQL engineering team put high effort for optimizing performance schema. When it first started, you probably read many complaints about like it work not as fast and that affects performance of the server, but they work it, and now it's almost all performance issues are gone. It's consistently extending, it's also, you may add your own extension for performance schema again, you can just go to user manual and read it, how to do it. I'm sorry, I do not like, I always have like kind of, what should I put on my slide? Long, long link, which is not readable, shorting link, which you cannot identify, which is this, or this probably QR code, so now I start with QR codes. And it's begin measure, it's begin most stable. So just like to show how it's grow, it's growth of versions. So first, it's, first the performance schema was cited in version 5.5, it has only 237 instruments, and now it's more than 1,000. I put here a Percona server, because it has a couple more instruments than MySQL server for Percona server extensions. It's what we can do with performance schema. First, it's all version 5.5, we can have insights on what's serving. It's like, it's very low level information, it's about like logs, mutics, it's about data reads. For this short talk, I put here information about IO, so like you can check for system tools, something which is written. But usually, even if it's dedicated server for MySQL, with operating system tools, you won't know what's written here. And with performance schema, you can clearly see what's written. You just query table called event weights history, or event weights history log, and you find out what's written, writes your data. Version 5.6 added information for statements, such like runtimes, you can have pretty good statistics of all your effective or not effective load. These tables called event statements are used by many graphical tools which collect data about your queries. Also, we have, in 5.6, we have event stages tables which show statistics by these things which you usually see in show process list. Like here, you may, for example, sort these tables and find out which stage takes more time and probably tune and maybe make adjustment for your server. Version 5.7, here in orange color, I put features which first introduced in performance schema. Before 5.7, we, once we created prepared statement, you could not find out if it still exists, if it's not to drop it, if it's not to be allocated, which, how good it's performed. So, with 5.7, you can see all current and existing prepared statements by querying this table. It also has same information, like in these event statements tables, like if you index use it, if other optimization used and just try it, and you find a doubt, watch it, please. 5.7 also adds information about storage routines, it's again, it's something which is, like in year 2006, I remember how people discussed on conferences, how to write even UDF to find out what storage routine doing, especially it has kind of flow control. And now, like you see, it's storage routine, it has this event name, it's called from flow control part. So, like, I didn't put definition of the storage routine, but it's, this part goes from handler part of the storage routine. So, you may find all these things, what is happening inside the storage routine. 5.7, there is also information about, like, anybody knows, like, this alter table, it's weights for metadata log, but anybody can say which thread holds the log. One, two, three. I cannot say either, actually. To find it out, I need to query performance schema metadata log table, which clearly says, like, go back here. Hour of read is number 95, which weights. And you see, it's weights, that's it's shared upgradable log, and it waits for exclusive log. And thread 96 holds the log. So, now you can just go, you can see that thread 96 actually does nothing. It's probably just not close the transaction, or you can either kill the thread or wait when the job is finished. What else, it's, like, my favorite feature, it's memory usage. For example, again, it's, like, because, like, I support engineer, I always receive these complaints, like, we are seeing something like this. But from this output, you cannot say what is my school doing. And with performance schema, you can just, like, run query and you can find out. This is, it looks maybe a little bit cryptic, but if you know my school options, it's very easy to understand, actually, like. This part, it's a join cache, it's probably a big join, big join used. This is part, it's about my NDB. So, you, like, again, like, you get usage to it, you can find it out. For replication, we have, always have pretty nice shows, status comment, issue with them. That's all the data was in the same comment, if you, like, if you have monitoring tool, you have to parse it. With performance schema, there is a different approach. It's many, many, many tables. But if you need all the data, you need to join them or still stuck with show slave status. For variables, before 5.7, it's, we have nice access for, good access for variables. But we only have access only for either global variables and status. And all, for variables and status of your own session. With performance schema, you can check all variables with all sessions. Like, for example, here, it's a pretty big joined buffer size. It's, you can find. 8.0 also has a summary about errors, like you see, like, for example, Worn-deprecated syntax, you may just check, what is your application doing and probably rewrite statements. And, actually, that's not full list, but we have limited time. Okay, how it works? It works, it's important to understand how performance schema works in order to tune it effectively, so it doesn't affect performance as a server. So, this is part of one of instrumented, the part of MySQL code, of which instruments this function. So, what's happening first? First, we call performance schema, make a call for this instrumented. So, we just tell performance schema, please start instrumenting our call. Now, we are actually doing a real call for the function. And then we call again this macro for performance schema, just telling it, please stop instrumenting, please record data. After that, performance schema stores recorded data. Here is events, performance schema has special name convention for tables, they call consumers, which stores the data. So, it starts, many of them starts with events, the name, like weights, statements, stages, etc. And then it goes currently, which is currently running history, which is last 10 per fret. And history long, last 10,000 per fret by default, but there's configurable. For events statements, it's table structure like this. And for tables, which are not events, which not store events, they have like try to make it again, it's very similar structure, very easy to understand. Return the data, it's again, you can just query it with normal SQLs, you're using MySQL, you use SQL, and you can query performance schema comfortably. Here, that's a picture, how it works, and what is important here, this one. This one is, you need to understand, that each instrumented part, it's wrapped into two calls of performance schema. And that means, for a rarely used calls, like statements or memory allocation, there is almost no performance impact. Because like, if you execute one statement, it's like two low level calls, it's almost nothing. But if this is a significant part, like for example, you are re-drawing, like you are reading 1 million rows. For this operation, you will always execute same code. So, for example, I have like kind of benchmark of table 5,000 rows. I truncated corresponding tables from performance schema before running. And what I am doing, I am just selecting all the rows. For events weights, you see that's pretty big numbers of instruments, and you see it's like, for example, this thing, it's true to instrument, this thing, it's expensive, because it's called it often. But for instrumenting statements, you don't know how effective, how it's like statistics, like if we index it, or if you need to turn the query, it's not expensive.  I often like see advice, like, if you have bad performance, just turn it off, but I believe this is wrong. You should turn it on, should keep it on, but just care about watching instrument. It's again, it stores the data into tables, which is again, what is important about this storage, so that performance schema must be supported by the company. So, if no developer wrote code for performance schema, it's not instrumented. It collects data only after it's enabled. So, like, if you started a server with memory instrumentation disabled, receive it memory leak, want to instrument it, you won't receive information right now, because memory allocation already happened. So, you have to turn instrumentation before the event happens. And after data storage into consumers, they never return it back. Okay, so how to tune performance schema? These are defaults for versions. And there are two places where you can tune performance schema. These are configuration options or setup tables. Startup configuration is always, like, performance schema on. It's now its default and recommended option. And then limits number of table sizes and number of instrumented instances. They are not, these options, not dynamic. You have to change them during restart. And also you can enable consumers and instruments, which you want to be at startup. You see, it's like a little bit different syntax, and I put it in orange color, so you must, it also supports wild cards, so you don't need to type much. For on-time configuration, there are setup tables. And you can, again, you can set up in various details. For example, you can just tell, you want to enable instrumentation only for particular user. Or for particular thread, for example, this is user connections, this is thread IO thread. You can just enable instrumentation only, like, for example, for tables or for events, and don't have it for something else. Also, these are enable consumers, which to store, which information again you want to store. These are instruments, which to instrument. You see, it can be time. It means it shows how much time. It was running. It can be enabled, disabled. Okay, so how to make it looks probably a little bit complicated for make it work with performance schema easier. There are two options. It's C schema. C schema goes, it's written originally mark lead. And now it comes as independent project. It goes to the version 5.7. It's practically views on performance schema tables and storage routines. It's just shortcuts for typical use cases. You do not mix this schema with something that doesn't add anything new. It just makes it much easier to work with performance schema. It's started. And just like to show you more useful views, it's views, which help you to find out which your statements are not effective and this is for memory problem. And like just example, here we are querying performance schema around tables. And much nicer with C schema. Same with configurations. So like you can just call storage routines to enable consumer instrument. Same for disabled, same for show. And C schema as well as performance schema supports wildcards. So you can use wildcards here as well. Another tool is called Percona monitoring head management. It's a tool, which is made by Percona. It has performance schema dashboard. Unfortunately, it doesn't use all the performance schema. But for this event weights, which is like locks, mutixes, IO operations, it has very nice graphic view over time. For example, you see here are some IO issues. You see like big peaks and you may probably want to know what happens here. And we can just move our mouse here and see like it was in a db data file read. Then we compare it with another point of time, we see there is no such reads happening. And again another point of time, it's another peak, but smaller. Okay, so here is more information on performance schema, which didn't exist in this 20 minutes. Here is my slide, share it with your GitHub and share I will put slides after the conference. Thank you.


Autolight and its 96,000 dealers present. Suspense. Tonight, Autolight brings you Mr. Dennis O'Keefe in Very Much Like a Nightmare. A suspense play produced and edited by William Spear. Yeah? What spectacular event heros the approach of summer. Why, getting an Autolight-stay-full battery. The battery that has three times as much liquid reserve above the plates as batteries without stay-full features. The battery that needs water only three times a year in normal car use. Come on now, Wilcox. I mean for fun, for pleasure, for excitement. Well, so do I have. What could be more fun than driving a car that has an Autolight-stay-full battery to give you extra starting dependability? What can give you greater pleasure than the knowledge that your Autolight-stay-full battery needs water only three times a year in normal car use? And what excitement when you find out that SAE life cycle standard tests show that Autolight-stay-full batteries give 70% longer average life than batteries without stay-full features? The circus is what I had in mind, hollow. You should have seen the menagerie. Every kind of animal. Camels? Naturally, wouldn't be a circus without camels. Well, no camel can tea-total water like the Autolight-stay-full battery that needs water only three times a year in normal car use. Just remember, friends, you're always right with Autolight. And now, with very much like a nightmare and the performance of Dennis O'Keefe, Autolight hopes once again to keep you in suspense. It started out like nothing more than a small freaky accident. The kind of thing you could tell a little sheepishly as a joke on yourself. By the time it was over, it was very much like a nightmare. A dream you wake up from drenched in cold sweat shaking all over with the clock pointing at 3 a.m. and the covers on the floor. It was only my second day back on the job after a short bout with the flu germ and I'd been up practically all night working on a job. My boss, old hard rock Gilman, had been kind enough to say, especially for me. As I walked into the lobby of the Nugent building, my biggest worry was simply whether the little pills my pharmacist May Hermey had given me would keep me alive and jumping through a rather long day. Eddie, the hawk-faced kid who ran the elevator, was just about to take it up as I came in. Hold it, Eddie. Oh, I am Mr. McClang. Oh, I'm sorry, Mr. Pray. Didn't see you. Good morning. Good morning, Mr. Pray. How are you? Anybody for three? Down to God, seeing you around lately, Mr. McClang. No, I've been playing hooky. Lucky you. Far ready.  Mr. Pray, though not precisely my type, was one of the rare plus qualities to be discovered in the Nugent building. She was one of the few girls I've seen around office buildings who actually looked as though they belonged in somebody's penthouse apartment. She usually tagged along with Faris, a cocky-looking character who also worked at Circassian brothers' importers up on fire. Hi, Bob. Watch your step. Thank you, Eddie. Mr. Pray. Yes? Is everything all right? What? Oh, yes, I think so, Eddie. But maybe you'd better check with Mr. Faris. He may want to send out for a sandwich around eleven. Okay. Seven, Mr. McClang. Watch your step. Thank you, Eddie. The morning went slowly, even with Hermes' pills to help, but at noon I resumed a pleasant custom which had been temporarily interrupted by my illness, out of having lunch with Miss Catherine Warren, a delightful young lady on whose future I had very definite, if not especially dark design. Now for the gruesome lecture. What time did you go to bed last night, Bob? Who, me? Last night? Well... All right, then. It was this morning. It was this morning, wasn't it? Such a charming girl to be so snoopy. Would you like to hear about a very interesting conversation I had going down on the bus this morning with a young man named Hermes? Hermes? How a guy like that can run a prescription shop, beats me? What would the doctors do if they knew he went around blabbing professional secrets? It's you not Hermes I'm concerned about. And I'd like to know what makes you think it's smart to start taking dope? Oh, that Hermes and his melodrama. All he gave me was something to... Something to keep you from getting to sleep you ought to be getting. At this rate, you'll be back in bed again before you know it. They're just little white pills. Perfectly harmless if you don't try to swallow the whole box. What Hermes says, here. Well, let's take a look for yourself. Oh, that's it. It's a wrong box. What do you mean wrong box? All right. So I'm a fugitive from a drug counter. These are the, uh, one of the other ones. The other one? Well, the fact is, darling, at first I was as leery about these stay-awake things as you are. And I started worrying. Suppose the stuff was slow-wiring off, or suppose I developed insomnia or something like that. I heard me steady dollars as that would happen, but just to make sure, he fixed me up with these, uh, sleeping pills. More dope. Oh, look, honey, it's not Gilman I'm doing this for. You know that. It's us. I wouldn't have tried to come to work with you. I know, I know, boss. I'm sorry.  If you don't, I'll come over and toss tear gas through your window. I made my way back toward the Nuget Building. Wedged is very appropriately near Narrow Dead End Street. About halfway down the block, I heard footsteps behind me, and then a voice. Familiar enough. Explain. It's Ferris. Marie-Dupri's boyfriend. Hi, McLean. Haven't seen you around lately. Been out of town? No. Just had to stay in for a few days. How's it with Circassian brothers? Never better. Never better. And with you? No complaints. Say, something was said in the elevator this morning.  I was wondering if that's just an arrangement with the people on your floor. Sandwiches? I don't get it, Mr. McLean. Eddie's never picked up any sandwiches from me. Somebody's kidding. Oh, here we are. Calling up. Hi, Mr. Ferris. Mr. McLean. Eddie. Eddie, what's this about some sandwich service you've got and holding out on me? Well, I, uh... Well, Eddie, I just thought I overheard you and Mr. Ferris say something about handing out for a sandwich, so I... Oh, no, no, that was... No, you got us wrong. I never said that. I told Mr. McLean, Eddie, that it was probably some kind of kidding you and Mr. Ferris were carrying on. That goes to show you should never kid on the job. That's a good motto for Eddie, huh, Mr. McLean? Never kid on the job. Um... The three of us stood silently in the rising elevator. It seemed to me that Ferris had pushed the sandwich topic a little farther than necessary. Eddie was pretty upset. Why would he? Oh, well, it wasn't any of my affairs. I went back into my cubby hole and waited till three o'clock the hour for which Gilman had called a conference with Fisher, Barton and Fisher. Our big account. And it was in this conference that the sleep I hadn't got started creeping up on me. Out of a feisty fog, I suddenly heard Gilman barking at me. McLean! Yes, huh? Heaven's sake, what's the matter with you? You didn't get out of bed too soon, did you? Oh, no, no, I'm all right, Mr. Gilman. Oh, good. I'd appreciate your getting Mr. Fisher's brief finished up for me. I hate to ask you this, but I wonder if you'd mind getting it done before you go. He's an apology and stood up. My eyes burned and my neck muscles seemed to be dragging my head down as I went back to my office. It had been 10, maybe 12 hours since I had taken the last couple of stay-awake pills. If I could borrow enough energy for the next 45 minutes, I told myself I could clean up this last job and the whole blessed night would be mine for sleeping. This time I'd cut the dose in half, only take a single pill, sir. I shook one of the pills out of the little box and followed it with a glass of water. Then I sat down at my desk and unclipped the sheets that Gilman had given me. When it was that I noticed that the words had started running together, I looked at the papers and it was like peering through water. A bell was bonging somewhere far off in the distance and I seemed to be lying across a bed. Slowly I forced over my eyes and blinked while reality swam into focus for a moment I really didn't know where I was and it was just silence and the lost feeling of being in the wrong place. Then suddenly I realized the bed I had seemed to be lying across was my office desk. Was it clear? What was I doing here? The outer office was nothing but darkness. Stiff and cramped I stood up and I looked at my watch and it was 215. 215, but that's impossible. Only a few minutes ago I looked at my watch and it was, oh no, it couldn't have been a few minutes ago where Gilman was here then and Miss Tyler and the others, it can't be two in the morning. Oh, bright boy McLean. Hermes pills.  When I went into the washroom I'd taken the wrong pills with me. The sleeping pills. The others were still in my top coat.  Somehow it seemed too hollow and then suddenly standing there in the middle of the floor seven stories up in an empty office building the darkness moved in on me. I got that funny feeling as if the darkness wasn't all as if I had something worse to contend with.  Weird as it seemed all of a sudden it was like being on the island again with the Japanese somewhere near you and not daring to let a match and holding your breath for the darkness so they could choke you.  Auto light is bringing you Dennis O'Keefe and very much like a nightmare. Tonight's production in radio's outstanding theater of thrills. Suspends. One of the circus men told me that elephants drink 10 gallons of water a day. 10 gallons a day? Wow. And an auto light-safe full battery needs water only three times a year in normal car use. It's the super battery that has three times as much liquid reserve as batteries without staple features. Yes but the elephant is a mighty powerful critter Well, I'll talk about power. Man, the auto light-safe full battery is amazing. You see, there's a fiberglass retaining mat at every positive plate to hold the power-producing material in place.   Yes, but half think of the lifespan of the auto light-safe full battery while according to SAE life cycle standard tests it gives 70% longer average life than batteries without staple features. And the auto light-safe full battery needs water only three times a year in normal car use. So friends, see your friendly auto light battery dealer. Remember, you're always right with auto light. And now, auto light brings back to our Hollywood down stage our star, Dennis O'Keefe. In very much like a nightmare, a tale well-calculated to keep you in suspense. I felt my way out into the darkness beyond the door. The hall stretched out like a cave to the stairs and the faint red glow of the exit lights. The angle of the stairs at the landing there was a window and I pressed my face against the cool of the glass. I had to wake up. And suddenly a light found out over the loading platform down below. Somebody must have opened the door down there. I saw a truck and three or four men dwarfed by the distance. Light from the door was gone, but I still see the stabbing beams from a couple of flashlights. It was working out this hour. Why? Eddie. Eddie, the elevator boy. What are you doing here at this weird hour? Two o'clock in the morning in an empty building with no passengers. The lead footed I moved down one flight and waited there on the sixth floor straining every cell to hear the whistling again, but I didn't. Instead I heard something from the fifth floor and got my way down toward it. Because I know Marie, and I know that when she talks like this, she's not even half what she's saying. And then we've been drinking. I haven't been drinking that much. We've seen him out in the airport. All he's got to do is walk in. Even before the cell is out of the plane, he's ready to cheat us out of what he promised. Marie. Wait a minute. I'll tell you something, Mr. Dupreeze. You don't know who you're fooling with. You start calling names and you're going to get hurt worse.  You figure you don't get enough for what you do. You figure you're being cheated out of your cock, but I'll tell you something now.  No way, Joe. You think we're so scared of you, we'll stand for anything, don't you? Well, if I get nothing, you get nothing, too. Open my mouth! I'll open my mouth over! Look down on the platform from 7, it would have been tipped off by the flashlights. Sir Kissian would never have had his workmen stumbling around in that sketchy light that he was in on a two, handing something a little hotter now than sandwiches. But after the doors opened, I noticed that the whistling stopped like being caught in two by a knife. The longer I stood there, the more unhealthy my position became, and I wanted about Albert. The night watchman. Well, was he? He had a gun, but suddenly I wanted to get out of there more than anything in the world. Just get out. My dash for the stairs to the fourth floor was like a slow motion dream, and the wheels clumped to the floor just out of ranges and nearing light as the elevator dropped toward me. Suddenly there it was. A nakedly lighted little room suspended in space. I was staring through its fourth wall at Eddie. His face looked sick and green as he hugged the corner there at the controls. If nothing could make him look behind to the rear of the car where a big man I hadn't seen before held something in his arms. Something like an oversized doll whose legs dangled lently. When the panic was fighting my fogginess, I stood there trying to think. Then I remembered the telephone. There was a perfectly good telephone in my office, but that was five flights up, five long flights. It had been a phone on one of the other floors of the station. I thought my way upward again and found it. I found it on the third. I remembered not to close the door of the booth completely because if I did that, the little light would flash on and light was the last thing I wanted.  Fumsly, I dialed the operator. Number please. It wouldn't come out. Number please. I thought I heard a slight scuffling from the street north. There's a robbery going on here. A robbery. A girl's been killed. I worked in this building. The address is all right, Mr. Hang up that phone. I decided. It wasn't Joe. It wasn't any of the men I'd expected. It was the guy had almost written off as gagged and tied or dead. Albert! Albert, the night watchman. I was looking for you a while ago. It's all right. I'm McLean. Mark McLean, remember? Stillman and company up on the seventh floor. Hang up that phone, Mr. McLean. I understand. You don't know what's going on. I know what's going on. Well, you take that gun out of my face.  Never should have been here the night, Mr. Come on. We're going to see him. Him? A hundred times for combat use, but I never tried it before when it really counted. I came to the head of the stairs, I whirled and chopped down hard in my right hand. I heard the gun go clattering, but my timing was way off. My lover and luck never got into the play. Albert had me by the throat and two of his pitchers down the steps towards the landing. Locked together at the brunt of the fall. We just knocked out before. How long?  The gun fall too. The gun. Albert would come too and start talking. Stumble over to my desk and put the gun down and started to reach for the phone. Move out where we can see. Even before I thought the desk door was open, I put the gun into it and moved in front of the door. Okay, Mac.  Yeah, sure, Joe. You're late, Mac. I went down the hall for a minute. I had some work to do. I guess I fell asleep. When I woke up, I just want to find out what time it was. Hey, mister, I think you pick a bad night to work late. P.D. You wait in the hall and keep your eyes open. In five minutes, we'll be out of this dump. You stay here with this guy, Feds. You know good to work downstairs and I'm tired of looking at you. P.D. I'll tell you what. We better make it easy for Feds. Oh, no, look, Joe. What are you talking about? What are you talking about? I mean, suppose we take this guy's time, make a nice hand car for him. Then these two businessmen can sit down and talk business until we're ready to go. In a few seconds, not only were my wrists locked together, but I was sitting in the chair facing the door while Ferris slumped in the seat I wanted to have. A chair at the desk. Inches away from the gun in the drawer. Ferris didn't look at me at all. Silence ticked by an agent. I shifted a little in my seat, testing Ferris, seeing if he'd noticed and I got no reaction. There's that clear look on his face. The elevator would be bringing bad news in a matter of heartbeats. Hey. Hey, who's that? Huh? What? The phone, the telephone. Oh, the phone? Well, it's my wife, I guess. You see, I told her I'd be working late. She's probably calling to say that she's on her way down to pick me up. Why is she coming down here? Look, you don't want nobody else buttoning in down here. I don't want her down here any more than you do, but the phone ringing and ringing like that, she'll just figure I've dozed off and come on down. Yeah, but you're my... Hey, Ferris, don't you think this guy had better hands? Come over here, Mr. Talk to her. Tell her, uh... Tell her, she... she don't have to pick you up. Hello, Bar. Hello, darling. I figured it was time you were calling. Oh, it is you, but, darling, I've been worried out of my mind at three o'clock in the morning. Hermie and I have tried to... Oh, yes, dear, yes. I've just about wound up the work for the, uh, Warren people. I figured you were just having a little trouble putting Junior to bed. Listen, tell her not to come down. What is it? What are you talking about? Who is Junior and the Warren people? What's the car back in the garage? Hermie... Hermie is here. He's gonna drive me home. But Hermie's here with me, don't you... You're absolutely right, dear. Absolutely. Oh, God, darling. All right, don't talk all night. Goodbye, darling. Who is this Hermie? He's a friend of mine. Sometimes we both work late and he drives me home.  I didn't want to call him back. Yeah, well, we'll fix that. So he pointed me back to my chair and took the receiver off the hook. All the time we might have been pictures on the wall for all the interest that Ferris took in us. Did he went back out in the hall and when Ferris did speak it was all I could do to keep from jumping out of my skin. And he spaced. I suddenly remembered when and where I'd seen a look like the one on Ferris's face. Funny thing. All this time I never knew you were married. Oh, yes, yes. We were married three years. Marie and me, we... That's what we had in mind all the time. We figured we could make it work. We were going to get married. But you know how it is sometimes. You run up all you make on clothes and cars there. Still said no ruster. Marie just got excited. He didn't have to. He didn't have to. Right in front of you, in a desk drawer, open it. Get the gun. You went out to find what time it was. Smart boy can't keep his nose out of people's business. We're going to fix all that up.  We found the watchman, wise guy. But one little thing we didn't find. We don't find the gun you took off of him. Now you're going to tell me where that gun is. I got something figured out for that little guy. Okay, well... Come on. In the darkness I groped for the outer office. He was through stopping anybody. When I got to the window there at the first landing, I didn't waste motion this time in the lock, especially with my hands done up the way they were. I kicked quite. Pain was out of the window and then I was out. My escape was rickety, but it had a railing and the cold, wonderful night air was smacking me in the face. I realized that the throbbing in my left arm meant that not all the stray lead had missed me. I heard the music that I had been waiting for. Bless me. It was my captain. Without putting too great a strain on my blood pressure. A situation which not only pleases Mrs. McClain, a girl named Captain, but also meets with the unreserved approval of her husband. Aside from an aversion to a little white pills and a tendency to sneak frequent looks at his watch long about closing time each day, that husband is reputed to have only the normal number of faults. His greatest strong point remains his memory, which is convenient equipment for him. It will also ever be cherished for reminding him that the look on the face of a fellow named Ferris was a carbon copy of the expression he had last seen that say one of the men in his outfit disobeyed orders and wiped out half a dozen belligerent niponies. Ferris got only two, while a man who was protesting what had happened to his brother got twice that many. But it is possible that he died just as happy. Personally, I... Oh, good Lord. Look what time it is. Where's my briefcase? When the barker sings out, ladies and gentlemen... AutoLite makes more than 400 products for cars, trucks, planes, and boats in 28 plants coast to coast. These include complete or perfectly work together perfectly because they're a perfect team. So friends, don't accept electrical parts opposed to be as good. Ask for and insist on AutoLite, original factory parts. At your neighborhood service station, car dealer, garage, or repair shop. Remember, you're always right with AutoLite. Thursday for Suspense, our star will be Mr. Edward G. Robinson. The play is called A Case of Nerves. And it is, as we say, a tale well calculated to keep you in... ...a spend. Tonight's Suspense play was produced and edited by William Spear, directed by Norman MacDonald. Music for Suspense is composed by Lucian Morrowek and conducted by Lud Bluskin. Very much like a nightmare is an original play written for radio by M. Carl Holman and John Michael Hayes. Dennis O'Keefe may soon be seen co-starred with Ann Sheridan in the Howard Welch production, Woman on the Run. In the coming weeks, you will hear such stars as Charles Boyer, Broderick Crawford, and Jack Carton. Don't forget, next Thursday, same time, AutoLite will present Suspense, starring Edward G. Robinson. You can buy AutoLite's faithful batteries, AutoLite resistor or regular spark plugs, AutoLite electrical parts at your neighborhood and AutoLite dealers. Switch to AutoLite. Good night. This week marks the 16th annual observance of World Trade Week. Every one of us has a stake in the commerce our country carries on with the rest of the world. This is CBS, the Columbia Broadcasting System.