So I want to talk to you about what we know about optimal pre-processing methods and how we know it. Talk very quickly about the cases where microarrays really matter a lot and highlight some of the key issues in each of those, and then quickly provide some code overview and comments for you guys moving forward. So a core question that we addressed that we talked about yesterday and didn't really address in detail is what's the right way to process your data set? And this really clearly shows that that's an important challenge, but we still don't have an answer of how do you know what is the right way. So the Expresso function allows you to take very, very large sets of normalization methods. These are all the things that are enabled by default in Expresso, so three, seven, three, and five.  You find out that there are 315 ways of processing your data using the AFI package alone. The AFI package is not the only way of doing it, and I do not recommend people process their data 315 different ways. That's a lot of work. But on the other hand, how do you even evaluate that? How do you identify this technique is better than another? There are two ways that are prevalent in the literature. One uses what we call defined data sets. A defined data set is a data set where it is actually entirely synthetic, entirely constructed. So somebody will create a tube, and in that tube, they will put X proportion of molecule A, and two times that of molecule B, and one third of that of molecule C. And of course, they'll do this using robotics. So they will create an entirely synthetic sample of RNA. That, of course, means that you know exactly what is in that synthetic sample, and you'll be able to move forward readily. Alternatively, you could use something like real-time PCR nanostring as the gold standard, as something that provides some absolute universal truth. And that's a great technique too, because at least that's likely to represent what people would do downstream of a microwave study. But any of these other technologies have weaknesses. And so both are in the literature. Both have been looked at a number of times. And we'll focus on one particular method. This is a wholly defined spike in data set. So in other words, it's a data set where they created the entire thing synthetically from all the nucleotides that they synthesized themselves. So preferred analysis methods for afro-metrics gene ships, two. Wait, why is this part two? Well, good question. It's part two because way back in 2005, the same group developed preferred analysis methods for afro-metrics gene ships, revealed by a wholly defined control data set. So they varied it from wholly defined to wholly spiked in and so forth. But they basically tried to do this five years earlier. This initial paper was controversial. I was a PhD student when this paper came out. And everybody who did micro-analysis looked at the paper and found a different thing wrong with it. And there were a lot of challenges with it. Just to give you an idea of what was said in public, don't ever mind what's been said in private about this. There are some comments here that you will rarely get. So for example, first, the spike in concentrations are unrealistically high. We demonstrate that background noise makes it harder to identify differentially expressed genes at low concentrations. We point out that the concentrations for spiked in features result in artificially high intensities. Second, a large percentage of genes are differentially expressed. This design makes the spike in very different from that used in many experiments. And people writing this, they're wondering, are Raphael Irizari, who created RMA, Leslie Cope, who wrote the AFI package, and Xi Jinping, who created GCRMA. So people who really, really know what they're talking about, and we're being fairly robustly rude about it. But a better one is the rebuttal from John Story. John Story created the false discovery rate adjustment. So if there was a Nobel Prize for statistics, you would win it. Unfortunately, serious errors are evident in the charadelle data, disproving their conclusions and implying that the dataset cannot be used to validly evaluate statistical inference methods. That's not a gentle thing to say in a public rebuttal to a paper or anything like that. And at conferences, much more strident things were said about it. So there's a couple of things that they did wrong in the first round. And five years later, interestingly enough, none of the authors on this paper, on the first paper are present on the second paper, except for the senior author. So all of the people who were involved in the first paper said, ah, I'm not going to try this again. But the PI was like, no, we can do better. I'm confident that we can do better. And so they took five years and they came up with what they called round two. They came up with a couple of different ways of designing their study to try to make it robust and encapsulate some of the problems. And I'm not going to go through the problems with the first study because there are a lot. It's actually almost easier to say what they did right than what they did wrong. They had real challenges with having an asymmetrical sample, with having biases brought between samples so they didn't have proper independent replicates, by using concentrations that were a physiological, by mishybridizing their arrays so there was saturation of signal intensities where there shouldn't be, and like a billion other problems. So their second attempt was much, much more rigorous. And so the first thing that they did is they created a block design. And so by that, what I mean is that they created their experiment into two groups, one of which they said would have technical replicates where each individual sample they would have done in triplicates. So sample E1 would be the three times half a baby in gamma. So these are closer to biological replicates. This is a technical replica where you take the same aliquot from a tube and you repeat it on the microarray. And the only source of noise should be the microarray-dependent noise that is hybridization differences or manufactured in biological replicates. Difference between me taking a sample and making it today and making the same sample tomorrow. Cell lines might drift over the course of a couple of days so there would be differences. Two separate groups, the angel also made some, these are Drosophila, I believe that they were using Presenter Absinthe. And they know exactly if a gene is Presenter Absinthe because they put it there. So there's a gold standard of whether or not this gene is there. And you'll see the analysis methods ranged from one that was basically random chance, which in other words, the methods were all getting accuracies of about 75 or 80% at even telling you if it's right up here at one, so the analysis, the first thing that they showed is that background correct deviation. So if you take a background correction methods tested and these are different replicates and different points within that caused by choices in background correction. It's fascinating to see that RMA, which is the most widely used method looking at differential gene expression, and they produce these plots. I think the way I would phrase it after reading this paper four or five times is a little bit of a hot, very intuitive ways of expressing what they need. And in action there's been a lot of controversy about the second paper about how you ought to have been displaying and it's asking, what is the percentile accuracy and what fraction of methods and combinations are reaching that accuracy of other normalizations. The purple, red, and blue lines are interesting. The purple is the lowest normalization, which is absolutely standard for two color, one color. Quantile normalization, which is what RMA uses, and BSN, which is again for one color arrays. So there's a lot of evidence from this that there may be methods that are invented for microarrays, but not broadly applied to aphometrics arrays that would be abused. Inclusions from the study are kind of interesting. They claim that most commonly used methods perform strongly and that there is no single best way to analyze aphometrics microarray data. Their performance is not great. The best methods reach an 85%-ish sensitivity with a 5% false positive. So in other words, their best methods are missing 15% of the hits, 15% of differentally expressed genes. We had a couple of questions about merging multiple methods together and only taking the common ones. This data actually goes in the other direction. It says that we have a much bigger problem about missing things than we do about having false positives and that at any particular cutoff or method that we choose, we are going to miss more hits than we are going to have false positives. So these conclusions are a bit interesting because in the best study in the field, clearly a lot more work is needed, but their basic conclusion is that it doesn't really matter what you use, that all of the methods are equally good and that doesn't quite concord with what we've seen. We've seen that there are big differences. Their argument would be you are missing a different 15% and you're making a different 5% false positive and that encompasses 20% difference in the hits that you're going to have. That's basically the source of all your problems and that's reasonable, but if you give me two gene lists which differ by 20%, I'm going to be a little bit worried about the results. And if you start to do downstream analyses like pathway analyses, 20% is enough to completely change it. So their study has some clear merit to it and has some truth to it, but it kind of describes the need that we have as a community to do better work in this field. And as microarrays are starting to be seen as clinical diagnostic tools, there's an increasing need for studies like this that will allow us to kind of essentially say, this is the way in which we should be analyzing these data and that's something that's going to need to be firmly decided and established, especially for a diagnostic where it needs to be locked in and not changed from patient to patient. Optimal pre-processing methods may not exist. There is a huge amount of work in bioinformatics now to ask if we can merge multiple methods together. Basically the question is, if we analyze our data using 10 different techniques, can we get a better result if we merge or integrate them? The basic strategy is to use machine learning methods to integrate the result for different pre-processing methods. So there was a paper from my group last year and there's a paper that I've reviewed that's in press at a good journal right now that does the same thing for different kinds of applications. And there's a lot of reason to believe that if we do it cleverly, we can integrate multiple techniques beneficially and then instead of having to choose, we just use all of them. The right way to do that remains completely up in the air but that's something that's likely to change in the next three or four years in the way we think about processing arrays. Any questions about pre-processing? The last couple of things I want to point out is that microarrays of course are a big in the area of expression analysis and that's something that is diminishing fairly rapidly and that's something that I think we're going to see people doing less of in three or four years. But there's a number of areas where I actually expect microarray usage to grow. The first one is QAQC for sequencing studies. So I'll give you a good example of this. Every time my lab does a sequencing study, we run a microarray to make sure that our results are reliable. It's cheaper to do that as a quality control metric than anything else. It also provides very affordable copy number profiling. There was a paper in sale about two months ago which did whole genome sequencing of about 60 prostate cancers. Whole genome sequencing, you can call copy number variations off of the whole genome sequencing. Very, very top bioinformatics group at the Broad and yet they did not use their whole genome sequencing data to call copy numbers. They believed that the cheap SNP6 mathematics array that they used was more reliable. So in that sense, for their perspective, spend a few hundred dollars on array, get more reliable data, not have to worry about it, not have to think about how do I do the analysis? How do I run it through the pipelines? Well, it's a lot more established. So there's a lot of reasons why groups are continuing to use it for copy number profiling. Similarly, for one of my large projects where we're doing 500 RNA-seq experiments, we're doing a matched aphymetrics expression array with every single one. Thinking that's a good QAQC requires less RNA and is very affordable next to the RNA-seq. Another application that's growing very quickly are these custom SNV chips. So especially Illumina has this ability to create, I think it's a 40 or 50,000-featured chip for about $100, $150. You can put whatever SNPs you want on that chip. You can then interrogate a set of samples with it. And because it's $100, you can now start using that in routine clinical diagnostics. So PMH has a hospital probably sees about 10,000 patients a year. $100 times 10,000 patients, I can't do my math is, what, a million dollars. So if you were able to come up with a set of 20, 30,000 SNPs that you wanted to profile on every patient, now you can do it for an entire hospital for a million dollars a year.  So there's a big advantage to being able to do these massively parallel experiments looking at lots and lots of patients. And with 10,000 patients, now you can start doing some really interesting downstream statistics. And lastly, for methylation studies, microarrays remain the standard. They are dramatically cheaper, dramatically cheaper than sequencing methods. And here I mean by an order of 15, 20 fold still. They provide the same quality of data at least today. And it's likely that advances in sequencing will eventually eliminate that. But I imagine that's one of the technologies that will remain array based for a long, long time. Did anybody have any questions about the integration of arrays and sequencing or how those two things might fit together? Yeah, so two things. One, that comes down to validation. And two, I would trust the array over the RNA-seq for most things. So for example, if a gene was going up by array and down by RNA-seq, I would immediately think to myself, the anthropometrics RNA array experiments in my lab validate 95 plus percent of our hits. The RNA-seq experiments at my institute validate 80 percent. So if there's an error or discordance, I think it's much more likely to be the RNA-seq. Now there are things you find by RNA-seq that you can't find by array. Fusion proteins, weird splice variants, alternative start sites, point mutations, all those things you can't get. But for absolute abundances, especially for low intensity genes, the arrays are much, much more accurate. So that would be the general approach, but if I really cared about that hit, I would start off by validating using real-time or nanostring or something. I'll give another example of what we use arrays for. In standard whole genome sequencing studies, you can use the array to genotype a million locations for an individual. Use that to update the genome, give an improved estimate of what that patient's genome looks like, and then do your whole genome sequencing relative to that improved estimate of the genome and improve the accuracy of your alignments in your whole genome sequencing pipeline. That's one good step. Another one is to identify sample mixups using the array before you bother spending $10,000 on sequencing. And the third one is giving you an estimate of the true positive, true negative rate of your array, of your sequencing relative to the array. So there's a lot of things you can do with that. Any questions about any of these applications?  The basic process of micro-a data is to load it, pre-process it, do QAQC, and do statistical analysis. And if you kind of keep that in mind and go from step to step, you'll be able to get through almost any of the analyses that we would be looking at. There are little tricks and tips in how different things will work, but that kind of core will take you most of the way. And I want you to remember three things. First, pre-processing is very hard. Nobody knows this is the right way to do it.  I used X. Oh, that can't be right. Then the person probably doesn't know what they're talking about. It's much more likely that they've always used a method because it worked for them a few times and blah, blah, blah. But actually, we have data sets in my lab where one pre-processing method will generate a high validation rate and another one will generate a low validation rate. And with experience and looking at the characteristics of the data, you can start to get an idea of what that'll be. And that just means seeing lots of data sets, analyzing it, starting to come up with some familiarity of what are the characteristics of good and bad data. The second thing is nothing else really matters if you've designed your experiment incorrectly. Find a statistician and talk to them over a beer for your statistical design. That is incredibly worthwhile. And one of the numbers that's on Steve's dial on my phone is a statistician I worked with during my PhD. And every once in a while, it'll just be like, speak down.  If I have X and Y, can I do Z?  But every once in a while, the answer would be like, huh, I have no idea. And then she goes and calls somebody who's on our speed dial and eventually an answer comes back to me that's like, well, four or five statisticians talked about it and we think this is an unresolved problem.  And so that's really interesting because now there's an opportunity to improve statistical analysis and it creates that communication between the two disciplines. And so finding a statistician who can be kind of a consultant or an advisor, even if they're not gonna be deeply involved with your studies is extremely valuable. Also, when you decide to do a statistical test, think really carefully about what you're asking. What is the biological question? If you can't precisely design the biological question, you'll never pick the statistical test to match it. And once you have that, you have to ask yourself, okay, I have the statistical test, but what are its assumptions and are they met? And that time thinking up front about these issues is extraordinarily worthwhile in improving the quality of your analysis and your results.  You can't skip or you can't go backwards and forwards. You have to say, I'm over here and I'm going to hear next and get one step finished properly before moving on. It's the single biggest mistake I see people make is skipping a step or saying, oh, I've got something. I really wanna know what the genes are. And then we'll start seeing a gene and go, oh, this gene is really interesting. And then they'll tell the supervisor and the supervisor will get excited. And the next thing you know, there's a follow-up experiment when the QA, QC wasn't done properly and they're gonna have to exclude three of the arrays that were causing this entire outlier. I've seen that happen over and over again. And it's important that you always go through one step at a time and no matter how much your supervisor or a collaborator is saying, can we see the results? Can we see the results? You say, no, we have no results yet. I'm busy trying to make sure that the data is the right quality. I often will use lines like, I can show you the results, but they're gonna be wrong.  And you know, that's a little bit mean, but it's not incorrect. You wanna give people results that are accurate. And so it's worth taking the time to do those upfront steps of the pipeline properly.


When you work in agricultural businesses such as farms, forests, nurseries and greenhouses, you need to know as much as you can about pesticides and how to protect yourself, your family and your customers from pesticide injury. Pesticides are chemicals that control pests. They include insecticides for insects, herbicides for weeds, fungicides for plant diseases and chemicals for other pests such as brodants and birds. Unfortunately, many pesticides can also be harmful to people, pets and other animals and the environment if they are not used carefully and according to label directions. The worker protection standard is a law passed by Congress and enforced by the United States Environmental Protection Agency which requires that people who handle pesticides receive information and training in the safe use of pesticides. You are handling pesticides if you apply pesticides, assist with pesticide applications, clean, repair or maintain pesticide application equipment, mix, load or transfer pesticides into application equipment, dispose of pesticides or materials with pesticides on them such as containers or if you work as a flagger during a pesticide application. In any of these activities you could come in contact with a pesticide or pesticide residues.   Symptoms may include tiredness or dizziness, headache or blurred vision. Certain pesticides may cause stomach cramps, vomiting or severe sweating. Exposure to other pesticides may cause eye irritation, trouble breathing or chest pains. Some people are allergic to certain pesticides. They may get a severe skin rash when the pesticide touches their skin. If these symptoms reoccur each time you are near that pesticide you may have to stay away from that particular pesticide. Some harmful effects from pesticide exposure may not show up for a long time. Studies with laboratory animals show that certain pesticides may cause cancer and other problems such as damage to the lungs, liver, kidney or nervous system. Exposure to some pesticides may also cause reproductive problems in both men and women or could harm their unborn children. You can protect yourself from pesticide exposure in many ways. The most important thing to do before working with a pesticide is to read the label. If you have a question about the label or are unable to read it, ask someone to help you. When you first look at a pesticide label, notice the brand name. And directly below the brand name is the list of chemicals or ingredients in the pesticide. Also on the front of the label is the type of pesticide, for example insecticide, herbicide, fungicide, rodenticide, etc. You will also notice the word caution, warning or danger on the label. These are the signal words. They tell you how poisonous the pesticide is if swallowed, inhaled or absorbed into your skin. The word danger tells you that the pesticide is very hazardous. Just a little bit of a danger pesticide less than a teaspoon can make you very sick if you swallow it. It can also cause serious burns if spilled on your skin or in your eyes.  When you are handling these pesticides, you must have someone check on you every two hours during the day and every hour at night to be sure that you are all right. Pesticides with the signal word warning can also be harmful. Between one and three teaspoons swallowed by an adult can be fatal. The signal word caution is used for pesticides that are the least poisonous. These pesticides can still harm you if you are not careful. The section called statements of practical treatment tells you what you should do if you swallow or inhale the pesticide or get it on your skin or in your eyes. An emergency phone number to call in case of spills or exposure accidents is also listed. All labels have a directions for use section that tells you how to mix and apply the pesticide safely. It also contains information on storage and disposal of the pesticide. Some pesticides are extremely dangerous when they are first applied to the crop and a certain amount of time must pass before it is safe to go into the field. This waiting time is called the restricted entry interval or REI. This period is usually from 12 hours to 3 days, but it may extend for several weeks for some pesticides. If you must enter a field before the restricted entry interval is over, you must wear the protective clothing and equipment listed on the pesticide label. The label also lets you know which parts of your body need special protection and if you need to wear personal protective equipment when you handle the pesticide. By law you must wear the personal protective clothing listed on the label when handling the pesticide. This may include waterproof gloves, boots with shoes or socks, coveralls, hood or wide brim chemical resistant hat for overhead exposure, waterproof apron if you are mixing and loading pesticides, protective eyewear, goggles, face shield or safety glasses with side and brow guards, and a respirator. This must be stored in a locked, ventilated building. Containers need to be closed tightly and stored upright so they will not tip over and spill. Container should be regularly inspected for leak breaks or weak spots. The pesticide storage area should always be locked when not in use to prevent people and animals from entering. If you have to transport pesticides make sure the pesticides are in the back of the truck and tied down securely. They should be placed in a tamper proof container or locked compartment if the vehicle is left unattended. Do not allow people, pets or livestock to ride in the same compartment as the pesticides. Also, keep food, feed or clothing away from pesticides. Wear extra protection when mixing and loading pesticides. Use an apron over your protective equipment. You need to wear protective eyewear too. Carefully open paper and cardboard pesticide containers.  Instead, put the pesticide container on a low, flat surface. Use a sharp knife or scissors. Then wash the knife or scissors with soap and water before using them again and use them only for opening pesticide containers. Build the label directions to find out how much pesticide you need and then measure it carefully. Pour liquids carefully to avoid splashes. Be sure your face and eyes are well above the container while you are pouring. Before you apply any pesticide, read the label information about the required pre-harvest interval. The PHI is the period of time during which application of a pesticide must not be made prior to the harvest of the crop. The pre-harvest interval varies for each pesticide and the crop it is used on and can range from a few days to several weeks. So check the label to see when is the earliest safe date for harvest following the last application of the pesticide. Before you apply pesticides, put on all personal protective equipment that the pesticide label requires. When a respirator is required, the pesticide label will give the MSHA-NIOSH approval code number. This is important because different types of respirators are used for different pesticides. Make sure that the MSHA-NIOSH number on the respirator matches the number given on the pesticide label. Follow the manufacturer's instructions on when to replace filters, cartridges, and canisters, even if you don't notice a problem. If there are no instructions, then filters, cartridges, and canisters should be replaced at the end of each eight hours of use.  Do an application equipment check. Make sure that there are no leaks and that the hoses and nozzles are in good condition. Make sure that the sprayer is calibrated properly to ensure that the right amount of pesticide is applied. And when you are ready to start, check the application area in which you will be applying. Make sure that there are no people, pets, or livestock in or near the area. Also check to see if there are ponds, streams, or wells in or near the area to be treated. Never apply or mix or load pesticides or clean equipment where there is a significant risk that the spray will drift or rinse water containing pesticides could contaminate these water sources. When applying pesticides outdoors, check the weather conditions before you begin spraying. Don't apply pesticides if there is more than a five-mile-per-hour wind blowing. Wind carries the spray out of the treated area. Stay alert while spraying the pesticide. Watch for clogged nozzles. If you need to clean a nozzle or screen, use a non-metal cleaning tool, especially designed for that purpose. A toothpick or toothbrush will also work well. Electric metal can ruin the nozzle.  The name, address, and telephone number of the nearest place to get emergency medical help must be posted at the place where you store pesticides and should be readily available in case of an emergency. All pesticide labels have an emergency first aid section found in the Statements of Practical Treatment. Read it or have someone explain it to you before you handle the pesticide. If you do the wrong thing in an emergency, it could do more harm to you or others. If pesticide gets in your eyes, rinse them right away with a gentle stream of clean water. Hold your eyelids open and keep rinsing your eyes for about 15 minutes. If pesticide gets on your clothes or skin, remove the contaminated clothes right away and wash your skin with lots of soap and water. The faster you act, the less likely you are to be injured. By law, soap, a clean change of clothing, towels, and enough water to wash pesticides off your body must be kept near the area where you are working. If someone swallows a pesticide, it is necessary to follow the first aid directions on the pesticide label. The label may provide important information on whether or not to induce vomiting. Some pesticides are corrosive and could cause further damage if vomiting is induced. Whether induce vomiting if the person is unconscious or having convulsions. Take the victim and a copy of the pesticide label with you to the doctor. If you breathe in a pesticide, get to fresh air immediately. If you are having difficulty breathing, call for help. Then sit down and try to breathe normally. It is not good to walk around if you are having difficulty breathing. Before you rescue someone who has inhaled pesticides and may be unconscious in an enclosed area, make sure you do not expose yourself to the same danger. Wear the appropriate respiratory protection and move the victim to fresh air. Loosen the victim's clothing and call for emergency medical assistance. If you wear personal protective equipment in hot weather, you could suffer from heat stress. Many symptoms of heat stress are like the symptoms of pesticide poisoning. If you are not sure what is making you ill, get help right away. Get out of direct sunlight and away from pesticides if possible. If you suspect that someone has heat stress, it is very important to cool that person down as quickly as possible. To cool a person down, first take off their outer clothing. Use a cool wet towel on the face and neck and fan them vigorously. In severe cases of heat stress, take the victim to the doctor right away. To avoid heat stress, on hot days, try to do jobs that require protective equipment in the early morning or early evening when it is cooler. Drink lots of water before, during, and after work. Drink at least a cup of water every half hour, more if you are sweating a lot. Take a rest break in the shade if possible. Pesticide spills can occur at any time. Here are the steps you should take if a spill occurs. Think first of protecting yourself, other people nearby, and the surrounding area, especially water sources. Rope off the area or set up barricades to keep everyone from the contaminated site. Wear protective clothing required by the label. And check the label for additional precautions. But when uncertain what has been spilled, wear the maximum protection. If the spill is on soil, shovel out contaminated soil into a disposable container. Containers for holding contaminant materials must be sealable and suitable for transporting. The container must be labeled with contents, including pesticide name and toxicity category. Check with your county agricultural commissioner on how to dispose of the holding container and its contents. When you finish the application, put your equipment away. Don't leave the field unattended. Pesticide containers should be triple rinsed immediately after you empty them. Fill the empty container with clean water until it is one quarter full. Put the cap on. Carefully shake or roll the container so that the water rinses the inside completely. Pour the rinse water from the container into the spray tank. Repeat the rinsing at least two more times. Never dump the rinse water down a sewer drain or onto a field site. Spray the rinse water that's now in the tank on the crop or weeds. Puncture rinsed containers so they cannot be used for any other purpose. Even well rinsed containers may still contain small amounts of pesticide. Therefore, never leave empty containers lying around. Don't use them for any other purpose and do not take them home. You will occasionally have leftover pesticides that you can no longer use, as well as empty containers that need to be disposed of properly. Never dispose of partially filled or empty pesticide containers in trash bins. Store them carefully in a locked storage area. Even though the pesticide label may have general directions for disposal of pesticides and their containers, laws regarding disposal may be different in your county. You should check with your agricultural commissioner's office regarding disposal of pesticide containers in your area. Once you have finished cleaning and putting away your application equipment, it is then safe to remove your personal protective equipment. Keep your gloves on while taking off and washing your other protective equipment. Respirator filters, cartridges, and canisters and some kinds of disposable coveralls, gloves, shoe coverings, and aprons cannot be cleaned and they should be thrown away after a day of use. Put all of your used protective clothing and equipment in a place by itself until it can be cleaned or disposed of. Do not wear home or take home contaminated clothing or equipment. Wash the outside of your gloves before removing them. Wash your hands, face, and any other exposed skin. Use lots of soap and water. Put your used work clothes into a separate container away from family clothing and wash them separately. When washing contaminated work clothes wash only a few items at a time to allow plenty of agitation and water for dilution. Use a heavy duty liquid detergent. Choose the highest water level and the hot water setting for the wash cycle. If possible, hang your work clothes outside on a line to dry. And always shower at the end of your work day. For your health and safety, remember to read the pesticide label. Follow the proper procedures for transporting, storing, mixing, applying, and cleaning up. Be prepared for any emergency and know how to get to the nearest hospital or medical facility. Remember, the label has additional instructions for emergencies. When you handle pesticides or when you work in areas where pesticides have been applied, do everything you can to protect yourself, your family, and consumers from pesticide exposure.


Egas Friyantek vs Coco Goff is the fourth quarter final for the ladies event at the French Open for 2023. And both players have had different rows to get to this stage. Goff started the tournament as the number 6 seed and will play Massa Rover in the first round. And after losing the first set, would fight back to win 3-6-6-1-6-2. In the second round she'd take on Grabber. This was by far Goff's best win of the week, getting through in straights 6-2-6-3. In the third round she'd take on the qualifier on Draver, the 16 year old sensation in a much hyped match. And after losing the first set, her experience would show getting through in 3-6-7-6-1-6-1. In the fourth round she'd take on Smedlova, who had taken out Kudamatova in the first round. And after a close first set, Goff would get through in straights 7-5-6-2, to get to the quarter finals. Egas Friyantek started the tournament off as the number 1 seed and defending champion and would take on Booksa in the first round. And after a close first set, would get through in straights 6-4-6-love. In the second round she'd take on Lu. And again, after a shaky first set, Friyantek got it together in the second. 6-4-6-love. In the third round she'd take on Wong, we're taking out Buskova, the 31 seed along the way. And this was by Farsh Friyantek's best performance, getting a double bagel, 6-love-6-love. And showing everybody why she's the number 1 in the best player on the planet. In the fourth round she'd take on Sharenko, who had taken out Kredjikova and Andrescu in straights sets along the way. But unfortunately Sharenko was sick and couldn't finish the match, which Friyantek winning 5-1, retirement, to make to the quarter finals. These all played 6 times before, which Friyantek winning all their matches in straights sets. And also on the clay courts of last year, in the French open final, that was an easy 6-1-6-3. So confidence is definitely with Friyantek in this one. Goff's gonna win this one, she needs to forget about all the other matches they've ever played. Pretend like this is the first time they've ever played. And also being in a quarter final won't actually help Goff a little bit as well. Because in the final of the French open last year, she did look a little shocked by the moment. But she's in the quarter final, she's been here before, she needs to serve well and hit big. If she's gonna be any chance of getting any sets of Friyantek. If Friyantek's gonna win this one, she just needs to keep riding the wave. She knows exactly what she's been doing, she's been baggling everybody so far this week. And she knows she can beat Goff, she's beaten her so convincingly, so many times. It hasn't even been a close set in the last 4 or 5 matches they've played. So she just needs to keep doing that. She has the game plan to beat Goff, and she has that psychological edge as well. It's gonna be an interesting matchup because we've seen this match so many times over the last 12 months. And of course it's been the same story every single time. And I think it's gonna be the same story again. Friyantek in straights sets, it's so hard to pick against her when she plays Goff. Maybe if she was playing someone else like a Pagola or a Sabalenka, you could say Friyantek might be a little closer. But against Goff she knows how to beat her, so I'm gonna go with Friyantek in straights sets. But let me know down in the comments below, who do you think's gonna win this one?


Okay, we're back here live at HP Discover in Germany. We're at the CUBE's Silicon Angles flagship program. We're out to the advanced extract the city from the noise. John Furrier, the founder of Silicon Angle, and I'm joined by my co-host. I'm Dave Vellante of wikibon.org, and we're here with CUBE alum, Stephen DeWitt, who's the senior vice president of the enterprise marketing operation. Formerly, you were doing enterprise group, told us off camera. It's now the whole enterprise. So that comprises enterprise group, the software services as well. That's correct. Oh, fantastic. Well, first of all, congratulations. And second of all, smart move by HP. We were just talking in our editorial segment about the messaging, you know, and I think that's going to help a lot. So anyway, welcome back. It's great to be here, and I agree with you. You know, at the end of the day, we're very uniquely positioned. We are a portfolio company. We operate from the client to the data center. We provide services from the client environment to the data center environment. We provide rich services around, you know, application transformation around big data. It's critical for us to be able to take the story of HP across that continuum and apply it to our customer's problem set. And we're doing a much better job of that. Stephen, one of the things that we've been covering on silkenangle.com is obviously the consumer web and that is kind of going through with people talking about all the bubbles bursting and not a lot of more Twitter's out there and VC funding and kind of all that entrepreneurship activity. And so that's being discussed, but the enterprise is hot. Okay, so right now, all the build out and real economic boom is happening on the enterprise side. Big data, obviously you mentioned, obviously on the infrastructure side, a lot of transformation with hardware and software. So share with us and share with the audience HP's enterprise business, because as you guys are kind of putting this together over the past 12 months and then going forward, that's taking a different shape. So could you just share with the folks out there how HP's organized and how you view the enterprise? Because HP has a lot of moving parts. So just share kind of how, what is the enterprise group at HP and what does that entail? Well, you know, I think we're entering perhaps the most intense decade that we've ever experienced. And look, we've gone through some pretty extraordinary things over the last couple of decades, but in the 10 years that follow us, there are going to be massive inflections. First, let's start with the cloud. You know, our industry historically has been designed by, or defined by siloed technologies, storage, networking, compute, and those have been delivered historically through denominations and sold to end customers on a price-proport metrics or a compute, number of cores, number of processors, or from a storage perspective, this is how you deliver storage arrays. All of that is evolving dramatically. And by the end of the decade, all of those numerators and denominators will be wiped from the face of the earth. That's one major change that's happening. And by the way, that has huge customer ramifications because CapEx is turning into OPEX. And for various businesses, you know, we have Jeffrey Katzenberg here from DreamWorks. You know, Jeffrey's going to talk a lot about the role of Hewlett-Packard in terms of our technology across their enterprise. You know, the simple reality is it takes five years for them to build a movie. And inside of that five years, it's very difficult for them to go to their stakeholder community and say, I'm going to sink tens of millions of dollars of CapEx on something I don't know what the outcome's going to be. So they want a more variable-ized model. So do the banks, so do the airlines, so does every transportation company. So there is a huge sea change in the way technology is delivered and consumed. That's one big block. There's another major block happening in terms of what's happening underneath the covers of technology. A lot of inventions are coming. We're pioneering things like memrists or technology, photonics, which fundamentally transform the definition of a system. So there are significant technology, milestones that are coming. There are business operating models that are changing. I think this plays very well into HP's strategy of being a continuum provider of IT solutions. I think it differentiates us materially from the rest of the industry. So we have organized our company along that continuum in order to deliver that integrated value to our customer. You're seeing here at the show 1HP. We are driving that integration between the technology so the end customers don't have to deal with that complexity. So we'd say to say that consumerization of IT has been kind of the punchline is where it kind of kicks that term around. Really is happening. Look, I think the human experience and information technology have melded. You know, by the end of the decade, you're going to have more than six billion people online. You're going to have 30 billion devices that are held in the hands of the human population. You know, we're moving to a world where public sector services, educational services, and everything in the business environment is going to be delivered as an app. It's going to be delivered in a way that's context sensitive. There's no more complexity around operating systems and Moore's law type evolutionary trends. All of that is gone. Technology, content, and the human experience have all melded. So for the customers that are out there, I'll say everyone's looking at the transformation of their business model. So we're seeing a lot of that activity. The business models of the customers are changing. You're seeing big data with instrumentation of their businesses, analytics. It's driving a whole another level of modernization at the business model level. And then at the infrastructure level, you have software defined blank, you know, is happening at a large scale. So a lot of customers are trying to evaluate these seismic changes you mentioned. So how do you view and how do you plan to communicate to those customers about HP? You know, because it's complicated, but they want trust, they want to cross that bridge with HP that either been with HP or they're evaluating HP given all the context in the marketplace around HP today. You guys have a good portfolio. Donna Telly was talking about it, but you have to package it. So how do you think about that? And as you put that high level messaging around that story, what is that story? Yeah, that's an excellent question. And actually I want to nail one of the things that you put in because I missed it and that was software defined networks or software defined storage or software defined servers. You know, the simple reality here is existing technology business models, like one of our major competitors in the networking space that begins with a C, I'll leave it at that. One could argue that inside of the decade as I was just sharing, their entire operating model is wiped off the face of the planet. Because if you think of the value proposition that's delivered today in the lower levels of infrastructure, all of that is going to be orchestrated through a new control plane that's emerging in the industry. We're driving that. You know, a handful of years ago we birth converged infrastructure and the industry followed. Software defined networking, you know, together with partners like VMware and others we are driving the heck out of this, it means material change for the end customer. So your question about how you package it all together, I think the best aspect to where we're going versus where we've been is that the packaging is around what people are trying to accomplish.  Well, inside of that universe, every single day of the week, there'll be more than half a trillion e-commerce transactions that happen. And if you're a business, no matter what your business is, no matter where you are on the globe, you want to be able to drive traffic, drive conversion, bring your goods and services, differentiate, and you have to move at the pace of business, not the historical pace of IT. So what you'll see in the stories that we tell around HP is matching the business requirements or matching IT to the business requirements of today, both the economic reality of today, the fact that we're in a borderless world and moving to a borderless world. You got to compete across every geography and do that in a way that's simple and aligned to the human experience. Steven, you've talked about differentiation before and you gave a lot of points of differentiation that related to your portfolio, your technology, your global presence. I wonder if you can help summarize that because I look at companies like IBM, companies like Oracle, they, I put them in a class obviously of HP.  Oracle very clearly has its differentiation strategy, Oracle database and software. IBM has jettisoned a lot of its consumer-oriented business. How do you simply sum up HP's differentiation in terms that are meaningful for the customer? I think we're one of the few companies on the planet that can look a customer in the eye very directly and say the following. First, we invest an extraordinary billions of dollars in R&D across the IT continuum. If you're an end customer, you have to look across the IT continuum in order to accomplish whatever it is that you do. So the fact that we invest across that curve gives customers the confidence that they can leverage our R&D better. That's one side of it. I think there are a couple other axes that we also are uniquely positioned. First, and let's never lose sight of the fact that the axi of being best in class matters. You know, being a pioneer, being committed in each of the core aspects of IT, being committed to IP generation, IP innovation, and IP leadership on a global scale is a huge benefit to the end customer. And we operate very heavily on that axis. We do it through both our product teams as well as through our labs infrastructure. Another axis that very much differentiates us against the peer group that you just talked about is that you've got the best in class axes, but you also have the converged axes. Technology operates with other pieces of technology. And if you're a company that just talks about, hey, I got my best in class widget, you're losing the reality of that widget and the widget that's next to it have to operate well, have to shelter the end user from operational cost and complexity. We do that exceptionally well. That's where convergence is all about. That's why we stress that over and over. Customer benefits, best in class, and across the continuum, we do all of that. How about openness? I think generally people give you higher marks than some of those other two that I mentioned and maybe some of the others in the industry. Is that, can that be a differentiator? Is that a differentiator with customers? I can't even imagine a customer that in 2012, 2013, or at any point in the future that would sucker themselves into a proprietary architecture. You just wouldn't do it. And that's because not one company can't do it all and every business has their own cadence, has their own set of economics, has their own need to invest in innovation at their own pace. Again, go back to DreamWorks. Jeffrey needed investments into color, into rendering, into cloud. And in order for him to go out to the industry as a whole and get all that together, he needed partners that encourage partners to come in and build on top of the platform, take cloud. You know, everybody in the world is talking about cloud right now. Our approach to cloud is to have a consistent architecture from traditional private cloud build out to hybrid environments or managed environments to our public cloud, which we've made some big announcements about over the last handful of months. A unified architecture that's open, built on open source, open standards, and an open philosophy of partnering. You know, HP's been about this for a long time. It's part of the reason why customers love doing business with HP. We do a tremendous amount of research with our customers we hear over and over again. That is one of the defining characteristics that separates HP from the IBMs and the Oracles. Steven, I want to ask you something about, I talked with Donna Telly about, Dave and I just recently, we talked about hardware and I wrote a blog post yesterday about, it's not about the hardware, it's about the software, but hardware's not going away. No question. Because you need more cores to process all that big data. You need more cores to deliver mobile apps, et cetera, et cetera, he said, quote, hardware's just a delivery software's the key. So I want to build on that. I wrote a post yesterday talking about software-defined infrastructure. That's really kind of how I see this market. So we just released a research report called Software-Led Infrastructure, which talks about the modernization of converged infrastructure. As you guys said, you built that out, everyone followed. So I want to read you what I wrote here and I want you to comment on it. I wrote yesterday in my blog post, this is in context of the storage announcement. The combination of software-defined infrastructure and big data will drive a mega shift to a modern data center and by result, the new modern business. My prediction is that we will be, this will be the biggest shift in the enterprise market since the computing and networking explosion in the late 80s and early 90s. So you've been there. So one, comment on that quote and tell me how this next explosion that's happening right now and what is it going to mean for the market? And the reason why I ask the question is I don't want to talk about HP per se. There's a lot of people in the marketplace on Wall Street and in business who don't understand the magnitude of the wealth creation that's about to happen from startups to HP and ultimately to the customers. So one, do you agree with the quote? And two, share your color on as an industry participant over those years and now. Yeah, it's depressing that I've been an industry participant since the early 80s, but anyway. Do you remember the TCPIP? No, no question about it. And I've also been very fortunate to be with a lot of companies at interesting inflection points and have built a number of companies through the years. I agree completely with your quote. And in fact, I think probably the biggest or the seminal change is it's a world about apps and the life cycle of apps and the speed of apps and the human consumption of apps versus I'm following an IT evolutionary curve and I'm hoping that I can build things on. The pyramid's been inverted. It is all about the apps now. It is all about the experience. Now we've seen a lot of great things over the last handful of years. We've certainly seen the massive transformation that came from the internet. We've seen e-commerce explode. We've seen the massive consumption of mobile devices. We've seen the elimination of binary compatibility which slowed down the process to things. Companies now can literally innovate at the speed of light and IT has to be able to follow that path. So talk about that modern data center because the modernization of the data center really is a huge, there's a lot of legacy there. Obviously we're seeing a lot of different approaches. Companies like Facebook are building their own stuff and obviously Google is Google and Amazon is now kind of following Google's path but that's just kind of examples of some unique straight data points. But the big bank, you mentioned financial transactions, what's that data center going to look like? Because that really seems to be the next explosion. Well, it's interesting, the data center doesn't have to look like it did in the past. And you're so right, think about it. Think when you look on TV, businesses have become marketing businesses.  All of these industries are changing. Their economics are changing. Their profitability construct is changing. And when you think about all of that, what IT has to do is IT has to become less and less visible, less friction, less cost associated with the orchestration of IT.  When you turn your tap on to get water, you don't worry about all that infrastructure that sits there that delivers that. Business now is about, I want to deliver this app, I want to deliver this capability, and I want to deliver it now. And so when I think in the back end, well, what kind of computing infrastructure do I need? Do I need a high density environment? Do I need more of a robust, big memory, non-blocking architecture? You shouldn't have to worry about it. All of that should be delivered as a capability. An app has to have this reliability characteristics, this performance, this usability, and IT just needs to deliver. So you're basically saying it's the same game, just different world, apps and compute and infrastructure. Yeah. So final question, I know we're getting the hook here, next year, the next 12 months, what does it look like for you, for your current role as an HP? What's your objective, and what do you hope to accomplish over the next 12 months? Well, I think, you know, certainly from a marketing perspective, you know, wipe away the drama because fortunately most of that's in the rear view mirror and that really doesn't get to the heart of HP. I think it's important for us to, you know, much like IT has to simplify, we have to simplify the HP message. I think we need to provide a better platform for articulating and driving the value proposition of the continuum of solutions that we deliver. I think another key goal for us over the next handful of months is alignment with our partner base. You know, we've got great technology partners here that help complete the solutions that we build. We have great channel partners and bars and integrators that add that level of value, elegance, simplicity, and a compelling storyteller, a story told in a compelling fashion. You'll see that in this coming year. Okay, so if we do it at HP, heading up to marketing for HP Enterprise, the enterprise is hot, booming, a lot of value there, a lot of change, transformation, opportunities across the board from startups to companies changing business models. This is theCUBE. We'll be right back with our next guest after this short break.


Hi, everybody, okay. I'm gonna keep it brief.  I just call it lemonade. Your grandmother is on her deathbed now. She made it a long time ago, which is to say it was made for her, which really means she doesn't want it, but she's gonna lie down anyway. You proper pillows for her. Lace her lemonade with demoral because she is dying and dying hurts you. She says so herself. She says, dying hurts you. Then she downs her lemon and painkiller cocktail.  It looks like an onion now. A piece of old fruit, a fermenting thing. She says it tastes funny sometimes, the lemonade. She makes a face like she is turning a thumbscrew, but the thumbscrew is her face, like this. The living room is a rest haven for white noise. The air dense, tart with the copper coin smell of your grandmother and TV dinners.  Watches TV. This time it isn't mash. Tonight is courtroom drama. A police procedural. Cigarette smoke in the interrogation room, good cop, bad cop. You sit next to your grandmother, finger their lace doilies and work the remote. She coughs pieces of herself onto the floor while you explain the nuances. He's the one who did it, ma. Nights like these you dissolve pieces of black tar in a viseen bottle, sniffed deeply to keep from nose diving into carpet fibers. The detective on the show plays the hand bone card, the perp wilting under the combination of palm strikes and police jargon. Where'd you hide the body, pervo? But mostly it was mash. Honeycut, Hawkeye, witty repartee.  You don't really like that. Her hands are scary. A pair of liver spotted carcasses barely held by the skin that keeps him. The way twice boiled chicken slides groaning off the bone, only to plummet back into the soup, releasing itself from itself. Your grandmother is like that. A thing awaiting release.  Does it hurt?  Only when I'm thirsty, she says, pointing to the cup. You need to renew her prescriptions soon. No traffic on the way to the pharmacy today. Bachman wrote his all pedestrians, kids playing ball, playing house, hooky. A sheriff and a blue charger follows you for a while, riding your tail before taking the filtering toward filtering lane toward Hayward and out of the village. San Lorenzo changed the way unincorporated towns are apt to one failed mom and pop at a time. You reconcile, assign events monumental to the shifting landscape. There's the strip mall, the shoulders, the clock tower. There's the corner where bumps converge, tweakers posing as vets and vice versa to ply their trade dumpster die for bottles for cans, a bit of copper if the cards are right. You know their trade well, were part of the same union once, paid dues. Outside the pharmacy, one of them, a man with a push cart and a tiny dog, terrier maybe, offers you a paper cup and a chance to fill it. Just 50 cents, man, he says, shaking the cup for the bus, man, the bus. Judging from judging from the shallow pool of coins in the cup, the bum will still be swimming in garbage long after the bus has stopped running. Maybe even a jump through the city dump for loose metal to fill the cart, cap off the night.  He smiles, but I pushed this motherfucker, right? Point A, point B, he shakes the Dixie again. You toss him a grubby single, where are you gonna park it? The bum puts a finger over his lips like wouldn't you like to know?  He pushes the cart into the street. Point B is waiting, he says, to himself, but maybe not. Now it's the pharmacy guy with the white coat in the questions. Always asking after grandma because his is aphasia to expressive, receptive something. He's got bed sign manner, which means he smiles when he says stuff. How's our girl, he says, smiling.  Still going strong? Still going, you say, handing over the script. He reads it, demoral, arecept the works. But there's an issue with the numbers with dosage and incongruity, he says, showing his teeth. What? The pharmacist picks up the telephone cradles in his ear. Just one minute, he says. He talks into the mouthpiece, this is doses, liquids versus capsules, the possible ramifications of lockjaw. You try to keep up with the conversation, but you're whipped. Outside his voice, the pharmacy is so quiet, you can hear it. The silence. The pharmacist hangs up the phone. Everything is a okay, he says, congruent. He fills the prescription, puts bottles in a white bag, staples it. Before handing it to you, he says, my Lola, she recognized me this morning. Second time this week. Sounds like an uptick, you say. He smiles. You never know. But the thing is, you always know. It's like the point-be-thing waiting, always waiting. There are all kinds of possibilities out there. Roads bend, fork, become paths, well traveled sometimes and sometimes less and all leading to the same place. Like the five stages of grief all lead to acceptance, so what's the sense in grieving at all? Why not just accept? If two roads diverge in the woods, well, you can simply turn around, find a different set of woods. See that tree there? It doesn't grieve, just drops dead leaves in winter. Simple. Natural. Sitting in the driveway, you crack the medicine bottle open and swig it.  Grandma is still in the living room, wing back, TV dinner. Sitting forward in her chair, she jabs at the television with her cane, trying to change the channel. Each attempt, prompting dust from the cushions. Can't find the remote, she says. My mash. You get on all fours, sweep your hand under the chair and retrieve the remote. On the TV, two surgeons trade barbs back and forth, lunge, repost, repeat, while adressed with some sort of man inside, sachets around the room. You were named after him, your grandmother says, spitting flecks of food past her teeth. Hawkeye? John Kennedy, she says. That's Alan Aldama. It was right after he died, remember, and the flags flew half-mast. I was born in 73, ma. But your father didn't have a flag, so he named you after him. He never said anything. After Kennedy, grandma says, she probes the insides of her cheeks, tongues the crest of her lips, searching for crumbs. He had high hopes for you. It was true your father didn't say much. He came from a generation that spoke mostly with their hands and wore blue collars like millstones. He was buried on a Thursday. You spent the afternoon burning spoons, trying to collapse your veins. I said, your father had high hopes for you, she says. Could have been president, you say. Not that high, she says, staring miles into the television. And the two of you sit quietly for a while. She's squeezing your hand, you letting her. Even squeezing back a little.  I'm thirsty, you, she says, pointing. Demoral, lemonade, gaga. Thank you.