We'll start up again. Are the three of you going to stand the whole time?  So we're doing these one at a time. The two people who aren't speaking don't have to stand. But we are now going to shift gears a bit. Panel three is on disruptive technology and the data deluge. And the first speaker from our extramural division is Jeff Schloss, and talking about challenges of the changing genomic landscape, advances in sequencing technology. Thank you, Eric. So many of you have walked around in laboratories, and you know that laboratories have benches and lots of bottles and a few instruments scattered around. In contrast, the laboratories that sequence the human genome for the human genome project tend to look more like factories like this, where you'll see rows and rows of sequencing machines. And this is what it looked like, what the sequencing centers looked like, when the human genome project was being completed. During the time the genome project was done, the cost of sequencing came down quite considerably. This was as a result of several things, technology improvements and automation and economies of scale like that lab that you saw. We noticed that it was taking about 10 years to reduce cost by 100 fold. And so at the time that the human genome project, so it was at Eric or Francis said it cost about $400 or so million to sequence the first human genome. If you had done it again at that last moment, it would have cost about $50 million, that's the estimate to sequence a human genome in about 2003. As you know, what we're here to talk, part of what we're talking about today is the subsequent technology developments that have brought those costs down considerably, which now seems like second nature, but during the planning process, so actually I guess no one's mentioned the planning process really. So Eric will talk about the planning process that we're embarked on right now, but the last time we did that was in 2002 and people asked whether there might be technological leaps that seemed so far off, this was 2002, as to be almost fictional, but which if they could be achieved would revolutionize biomedical research and clinical practice. And one of the several ideas that were listed there was do you think it might be possible to develop technologies with which to sequence a human genome for $1,000 or less? So fast forward to today and we're not there yet, but we're actually getting reasonably close and this slide just represents one of the reasons that we got there and that's that the whole workflow to doing sequencing has changed dramatically. So that the process by which we sequenced human genomes involved not only the sequencing machines, but lots of robots, large machines, upstream of the sequencing machines and handling every single sample in a separate test tube. In contrast, today we can make a library of a whole genome in one test tube and then apply that to the sequencing device. And second, what we call a run, that is running the machine once on a bunch of samples, you could only analyze 100 samples at a time when we were doing the human genome sequence the first time, now the machines analyze something on the order of 100 million samples per run. Now the runs are longer, they take about a week or two, whereas they used to take an hour or two, but even so the multiplication, the multiplier there is very dramatic. So the process is simplified and so what does it look like? This is what today's sequencing machines look at, there's a microscope in there essentially and they see these little dots and each dot is a feature that originated from a small fragment of the human genome and what the machine is watching is as time progresses over minutes or hours, it's looking at the change in color of each of those features. And just to give you an idea of the scale here, 100 microns in the lower left is about the size, whoops, about the size of a human hair. So that's how many features you're looking at and of course the slide extends vertically, considerable distance, that's the number of different sequencing reactions that you're looking at in the size of a human hair, right there. So the NHGRI launched a program to advance this technology development, I won't really talk about that program in this talk, but you have summaries of that in the materials that Larry sent you links to. And so some of the outcome of that is this series of commercial machines that have been released starting in 2005, one a year, and then many of these machines have been upgraded now, updated and new machines keep coming out every year to produce these torrents of sequence data that you've been seeing. This, I'm not gonna go through this slide in detail, the point here is to say that there are quite a number of machines that are out there now doing sequencing, they have considerably different features, that's good for any number of reasons, that is the qualities of data that you get from different machines are different, different machines are good for different experiments, perhaps most importantly, there's competition, which is really valuable for making this, for accelerating the pace of technology improvement and accelerating the pace of cost decrease is this competition. And another advantage of these machines over the machines that were used for sequencing the human genome is they can do a number of different kinds of experiments and they do the experiments in different ways. Again, I don't have time to go through that in detail today, but a number of the kinds of experiments that were used for that were done by micro arrays, today are done on sequencing machines and they give us more quantitative data than we could get on the micro arrays. So these are very versatile machines. To give you an idea of the kind of technology advances, I've prepared these slides, so if in 2003 you calculated what it took to sequence the human genome, you can run through the details are here, but the bottom line is with the technology we had that sequenced the human genome, it would take about three months with 100 machines to sequence the human genome. Did that calculation in 2007, so that is shortly after the first few next generation machines came out and it would take about the same time, three to four months, but instead of with 100 machines with one machine. Late last year, same calculation, now down to less than one month with one machine and projected by the end of this year, one week with one machine, but you saw all those little features, you can now pack so many of those individual genomic fragments on one machine, that in one week on one machine you'll sequence two genomes. So the numbers are just stunning. So I had to comply with Francis' prediction that somebody was gonna show this slide again, so there it is. This is updated for another year and the cost has come down to roughly another order of magnitude. So where are we going? We're not done. The technologies that we have in place now are producing vast quantities of reasonably high quality data, but there are a number of challenges to using them, several of which you'll hear about in the next couple of talks, but we're not done with technology development, there are new machines and new approaches on the horizon. Some of them eliminate all the microscopy and eliminate many of the custom reagents. So this is a company that announced earlier this year, the availability of its machines, and this uses instead of using fluorescence, which most of the other machines are using, this simply detects a chemical change, change in pH, the ion concentration. So the early chips have one and a half million pH meters on the little chip, the chip is this big, it's a little computer chip. And will be a purely electronic readout. Other approaches, there's an approach that uses what some people call free running polymerase that's being developed by two different companies. Both of them use nanotechnology as a key feature in enabling the visualization of the sequencing process. And again, because of time I don't have, I can't go through how the technology works. The bottom line is that this technology can not only read the readoff, the sequence data, as the trace on the bottom shows, but very importantly, considering what you just heard in this last session about methylation, it can directly read from genomic DNA molecules without doing all the chemical conversions that Andy talked about that they have to do today that are quite expensive. It can directly read from genomic sequence, where's the pointer, there it is, methylated bases. So this is a big advantage if this technology ends up working extremely cost effectively and at very high accuracy. The other thing that these free running polymerase technologies will probably offer is very long read lengths. So the technology was used for sequencing the human genome. The first time gave approximately 800 base read lengths. Most of the technologies in use today give somewhere between 100 and 300 or 400 base read lengths. And this is a big deal when you're looking for some of these other kinds of variations in the genome, such as structural variation and copy number variation. It's very hard to get with short reads, but if you can get long reads, you can put the genomes together much more easily. It should reduce some aspects of the bioinformatics burden that you're about to hear about. And then finally, quite a number of people are working on nanopore sequencing technology. The idea here is that you'll put a DNA molecule through a small hole that's the diameter of the DNA molecule. And that if it works that the ion, if you flow ions through the channel, and the DNA is going through the channel, that the different bases in the DNA will disrupt the ion flow differentially. So you'll be able to read A versus G versus C versus T with a purely electronic signal. And again, advantages here, let me just show you this and I'll talk about advantages. And once again, people have shown that you can not only read off individual AC, G and T, but also methylated bases using this technology. So once again, no conversions. You could do this, you could do these measurements directly on genomic DNA isolated from cells and potentially on very long molecules. So there's a long list here of advantages if this technology works. Long reads working directly from genomic DNA. It's non-destructive, so you could actually go back and read the sample over and over again. This would simplify a number of kinds of experiments, for example, microbiome sequencing. If this works for genomic DNA, it should also work for RNA. So we could directly read RNA molecules and their modifications, methylations and so forth. It would be a fully electronic method, which means that you should be able to develop small handheld devices so they should be deployable in other than large sequencing centers or large clinical labs. This is of tremendous interest, not only to the genome sequencing community, but to several other communities like Homeland Security, for example, for monitoring emerging infectious diseases or potentially bioengineered disease agents. And so I'll wrap up with this last slide to say if this nanopore approach works, how long would it take to sequence genomes? And with a small array, that is an array of just 1,000 nanopores, if you can do this, you should be able to make arrays of many more than 1,000 nanopores. It would take much less than a day, larger arrays, perhaps just a couple of hours to sequence a genome. So that's what I want to say. Okay. Our second speaker, is this next one down? Yeah. It is Vivian Benazi, who's going to talk about the Bioinformatics Challenge, data deluge and analysis. So my talk's a nice sequitur to Jeff's lot because I want to talk to you a little bit about the data volumes. And I think Jeff gave you some numbers, but I want to give you some context here. Firstly, the old sequencing methods, the ABI 3730s, which are really not that old, generate about 30 megabytes per run, and I'm just using averages here. But when you look at the new data volumes, Jeff also had a slide that covered these numbers. Depending on how you do these experiments, you can see just from these numbers that the volume of data is 10 fold, 100, even greater. The other issue is that as these newer technologies, the third generation technologies are coming out, I'm not entirely sure what their data volumes are going to be, but we know they're in their terabyte ranges. That creates a huge problem on the other end for informatics. So let me give you an example here. Most of what we've talked about, and I assume we talked about it this morning, describes base pairs, but we in the informatics piece deal with the bytes, and it's not a one to one ratio here. The ratio is one to 20, and these numbers change depending on what you actually store, and the numbers are also changing based on the technology we have. But I'd like to point out that a base is not a byte. So you have to think about what it is you want to store, and if it is a one to 20 or greater ratio, the problem here is when you store it, you've got a lot more to store. So what you want to think about here is what is the cost to store this data. This has all sorts of implications for data centers and how you store it and what you store. So for the computational challenges, remember informatics is a combination of biology and computing. So you've got to mesh those two fields, and so clearly with all these data volumes, you're going to have an infrastructure which needs to be able to handle this type of data. In the past, as I showed you before, when you've just got stuff coming out of a tap, most of us biologists who had computing training like myself can usually write programs, but as you start generating a lot more data, you really do need to bring folks in with IT backgrounds and software engineers. So just to summarize some of the computational challenges on the infrastructure side to support this kind of work is the data storage. As I've just mentioned, with that volume of data, it's going to impact the storage. Secondly, when you've got that much data and you want to do something with it, it's going to impact the type of analysis you want, so CPUs are going to have an impact here as well.  You can try, but it'll die. So the next thing here is we need to develop new hardware for this kind of work and also software, and I'll explain a little bit about those, but we need different kinds of architecture to support this kind of data analysis and storage. Of course, when you talk about volumes of data, again, I think Jeff was talking about volumes of data, and I know you've spoken about this in a previous talk. A lot of these next-generation sequencing machines are going to be covered, they're going to be placed in labs that generally didn't do this in the past, which means you've got large sequencing centers from the first picture of Jeff's slides. But you've got to think about that smaller labs are going to be doing this as well. So you've got now the large, ginormous sequencing centers, and then you've got these smaller groups who are now going to have one or two of the aluminum or whatever machines in place. And I think one of the numbers that Jeff said, and he can correct me later, I think is about 70% of, I think the aluminum machines you were saying, go into the smaller labs, is that right? Think about that. That's now going to impact the amount of data. Now, when you want to do something with that data and you want to, say, move it to NCBI, EBI, any of the large genome repositories, you've got to move that data around, which means it's going to impact the way that you can transfer data. And if you have a lot of data, most of you who've used the internet have realized at times it can be very slow, especially when you try to upload stuff through your ISP and how slow that can be. But at the same time, I'm sure you've had, as I've had the example of this very recently, where Verizon comes in and actually digs up my yard to put in files. Why? So they're actually increasing the pipes, but it takes time to do that. So we have to think about what is the impact of doing this on this kind of work? Lastly, but not least, is data security. Obviously, as we move data into, and we move data around, particularly when we're dealing with human subjects data, how do we protect that? And it's really strong about making sure that as we generate this kind of data, that we're very careful about not releasing it under certain, without certain policies. So, here's our challenge. We've got all this data, and we're assuming we've looked at some of the infrastructure pieces. In the past, as I mentioned, in the top slide here, you can drink from a water tap, but if you try doing it from a fire hose, as this gentleman has tried here in the bottom of this picture, you're not gonna have a lot of success. So, if we look at the analysis challenges, assuming that we're working at our infrastructure challenges, we have to look at developing new tools. Because remember, we're gonna have not just more data, but the ability to compare complete genomes against each other is just something we're gonna be able to do. There's new types of analyses that we can do, and we need tools to be able to do that. We also need to be able to refactor old tools that were important for doing analysis. For example, common tools in informatics or assemblers for genomes. Also for gene finding tools. Those kind of things need to be refactored, because we still need to do it from the next gen sequencing technologies, but they don't necessarily run on the systems in terms of the infrastructure. Or they don't work because the sequences are very short, as we've heard a lot, those base pairs, they're very short reads. We also need to optimize a lot of these tools to work on those new computing architecture platforms. The other thing is, I think we need to have improved visualization methods, because as we generate all this data, if we see a lot of numbers on a screen, computers are good at that, but we visually want to see things in graphs and some sort of visual display. And we also have to think, and I'll bring this point home again too, is as we generate all this data, think about all those folks in the smaller labs that don't have high-end informaticians. They need to be able to crunch this data, be able to see it efficiently. So we got to realize that we're no longer catering to those geeks, which I include myself, in terms of who can actually use this. So I just made the same point again. Robust tools, making those tools robust, often when we write these tools, they're scripts or some sort of program that we write, that doesn't transport well to different systems. So we need to make them robust, particularly for those non-informatic specialists that can actually use these in the laboratory. Data integration is also really important. We have all this volume of data, we want to analyze it and find interesting biological themes in this kind of information, but we need to think about integrating it with other data types. For example, we've talked a lot about genomic data, but clearly functional information from the genome is extremely important. So integrating that with proteomics data will be really key to look at function alongside. Included in that, obviously, is a lot of image data is coming out. Gene expression data is a good example of this. And as we add this, first of all, image data is quite large. So you have to think about the compression issue. So there's the compute aspect, but you also need to think about that kind of data is very valuable to you in terms of visualizing what you can see from genomes. So how do you integrate those two pieces? Another piece here, and I won't go into a lot of detail, is the metadata. When you're generating sequence information and you generate base pairs, often it's related to some sort of experiment that you're actually doing. So you want to be able to catch you the information about that particular experiment. For example, the run, the run length, anything that you did for your PCR, any of the experimental information, and you also want to link it to potentially clinical information. For example, we've had the microbiome project that I'm working on, but TCGA also fills into this. You take sequence base pairs, but it's all related to a human, and you want to link that information back. So you've got your sequence information and you want to take it back to the actual patient itself, and you need to be able to think about what that data is. That brings up issues of data volume, how you store it, the fact that you've got clinical information data that you need to protect security, that you link these two things, data integration. So you need to think about these pretty carefully. Last but not least, as we generate this data, when you have a lot of data, you need to think about standards. If you don't, we'll end up having the tower of Babel, and we've certainly had examples of that through history, and this is just going to be multiplied enormously with the volume of data that we're actually going to have. So what do we look at in terms of solutions? There are a number. I'm not claiming that we've solved it, but we're thinking about it. First is data reduction. From these raw base pairs that we generate, they're bits of base pairs, right? They're just bits of data. So there is discussions abounding which relate to do we keep the derived data? For example, do we keep the assemblies? We don't keep the raw sequences that were there. Do we keep enough information about those raw sequences that tell us stuff so we don't have to keep all those terabytes of data? An example of this, do we keep the SNPs that are stored? Do we keep the genes just a little listed? Do we believe that the assembly is correct that we don't need to go back to the base pairs? The other issue why this is important is as these new technologies come about and they become cheaper, you have to remember that you can then resequence something rather than storing it. So here it may be good to generate all your base pairs and then just keep the actual assembly itself and throw away the rest. If you make a mistake, you can resequence and it's cheaper. Another point that I forgot to mention before, everyone's had these lovely slides which talk about the reduction cost of base pairs. Like to remind you that the cost of storing something is actually now more expensive than it is to actually sequence it. That's part of the problem. So the two-fold issue here is one, it costs more to store it and two, you have a lot more of it. So Informatics is becoming much more of a bottleneck than it did in the past. And so for the reasons I just described before, we need to really address those. Couple of additional things, which is we really need to actively involve the community in our discussions and we have done that. And I mean in this case, both the biological community as well as the computing community. We've had two workshops recently and there's some folks in here that I talked to recently about these workshops and you can talk to me later if you want more information. One was the Informatics Analysis and Planning Workshop which was to really talk to the community about what were the analysis needs, what were the kinds of things we had to solve and I just touched on those. It fits, that particular workshop fits into the overall NHGRI planning process, which I believe Eric Green was talking about and Jeff mentioned as well. So to help us figure out what those strategies are in terms of the Institute and also the Cloud Computing Workshop which I'll explain a little bit in a minute. So here and the last piece here is education. That is we need to be able to train people in the use of these tools, train more people to actually develop these tools and to be able to integrate those pieces. And the last slide. So in terms of a solution, Informatics is both biology and computing and so the top of the slide is really that integration piece. I didn't speak much about it today but one of the possible solutions we could look at for the computing is potentially using Cloud Computing as a way of storing and managing and doing the analysis of this data. I haven't gone into it today because we just don't have that kind of time but those of you who've used the Cloud, example, Twitter, Gmail, most of you have heard of it, or the Amazon Cloud, a lot of people are using it because of the ability to store large amounts of data and you can actually do computes within the Cloud which speeds a lot of things up. So the key here is we really need to integrate and think about how we wanna do it. And I'm gonna finish with this last slide. I was debating whether I'd do it but this is how I feel a lot of the time. And it really highlights the problems that I see in terms of biology and computing. Not only do we have data volume issues but we have the issue that we can't talk to each other very easily, right? So quite frequently I'm asked about the database and yes, I tell them what color do you want. And we need to think about how we communicate with each other. So even if we solve these problems in terms of analysis and infrastructure, we also need to solve the problem of communication and I guess that's part of the reason for being here today. Thank you. And our last speaker in this panel is Jim Mulligan who will give us a view from a sequencing center director. No slides. Yeah, very good. I have no slides. I was asked to give this talk at an accelerated pace just like the sequencing world is running right now since we are just before lunch. And so I'll try and get through what I was going to talk about today in a little less time than I was given. Previous speakers have introduced already both what is coming in the technology world and maybe what the solutions are. But I am running a sequencing center now that Eric ran up until just December of last year which he initiated back in 1997. And when he started up the sequencing center back then through cooperative agreements through all of the institutes because he really felt that having a sequencing center at the NIH was important. He established the NIH Intramural Sequencing Center or NISC back then in 1997 and was able to hire six people and purchase six of that time, the latest sequencing machines which could generate in total all six machines about a half a megabase of sequence per day. Things have come a long ways since then. We now have a budget of $7 million a year. We have 42 people on our staff and it's a wide range of expertise and a lot of robotic automation. The laboratory that we have is about 5,000 square feet in size for all of the sequencing and laboratory operations and then the rest of the space and other 5,000 square feet or so is set up for doing all the analysis and storage of data. Prior to the introduction of these new sequencing technologies, we were operating at a level about 7 million sequencing reads per year which is quite a nice pace for a medium scale sequencing center which we have. And we were working on projects like you've heard today, the comparative vertebrate sequencing program, the ENCODE project, the mammalian gene collection and medical sequencing. So how has NISC dealt with the latest round of disruptive sequencing technologies that have been introduced and the associated data deluge that we're talking about in this session? Well, first of all, we do keep our eyes open. We know what is coming in the technology field and try to be prepared for it. We also work closely with other sequencing centers, larger centers like the Broad Institute and the Washington University Genome Sequencing Center and they have enough capacity at their centers to really try out the latest technologies at a very early stage. So we do take a somewhat cautious approach. We listen from them and then once we have an idea of what will be the best type of technology to bring in, we'll bring it in in an R&D type environment. Fortunately, Dr. Elliot Margulies did that with one of the machines, the what was then called the Selexa sequencing machine, now is owned by Illumina and called the Genome Analyzer 2X. About a year ago, after it had been in R&D for about a year, we realized it was time to move it into production. So it was transitioned out of the R&D environment and into production very quickly. We now have two kinds of next gen sequencing machines. One is the 454 and the other is the Genome Analyzer 2X. With eight of those Genome Analyzer 2X machines in production, we can now generate 50 gigabases of sequence. That's five orders of magnitude more than in 1997. So we've come a long ways and it's caused some stress. I must say. Like it's caused a lot of strains and but we're all also really quite excited about having this new technology. Just to give you an idea, the lab staff had to learn new protocols, technicians learned how to operate new machines. Our sample tracking system had to be reprogrammed to associate new types of data and information with each sample. The number and diversity of kinds of projects expanded. Data transfer rates changed dramatically, putting new strains on the network data storage and computational needs. The software development team was stretched to its limits at times trying to keep up with not only processing the data that was relentlessly pouring through because these machines were online, but we also had to adapt every month or every two months to a new software update from these sequencing technologies, sequencing companies that release new data with those machines. So it's caused us great stress, but I must say it's great potential as well. The NIH, NHGRI's intramural program has invested in a lot of the expansion of the next gen sequencing machines and related infrastructure. And now we can support various types of data in our processing. So we're working with chip-seq type experiments from investigator-driven projects like chip-seq experiments, which are chromatin immunoprecipitation sequencing, which investigates interaction between the proteins and DNA in a cell. RNA sequencing, which looks at genes within the nucleus of a cell that are being expressed, and to medical sequencing, which looks at changes in the DNA that are associated with an individual's cause of disease. Let's, if that's what we're looking for in that type of experiment. So we have a mix of projects at the sequencing center. We have the large-scale projects like the Human Microbiome Project, the Undiagnosed Disease Program, and ClinSeq, which you'll hear more about later today. And it's great to have in a production environment these larger projects, because that gives us kind of a buffer of a whole bunch of samples to keep processing as we move forward. And also accept into the sequencing center dozens of smaller projects that will range in size from one to two samples up to a few tens of samples. And over the last two years, we've been bringing in these kinds of projects, the small-scale projects, at about one or two per week. So it's an incredible rate of induction of these projects. And we talk to each of the investigators, figure out what they want to do, and implement an optimal sequencing type approach for their research. To give you an idea, we've already had 20 of these smaller-scale projects completed, and we have another 33 of them active in our pipeline, and another 14 are waiting on delivery of samples. One of the issues with receiving samples, especially human samples, is that they have to have been consented properly before we can apply some of the sequencing technologies that we're using, for example, whole exome or whole genome sequencing. So they need to go back and review their IRB and their protocols to see if it's okay to have their samples sequenced. If not, they may need to go back and get those re-consented. But once we do have them, then we run them through the sequencing pipeline. We can give them variation information very quickly within just a few months of delivery of the samples. And now, having had the next gen sequencing in production and applying it to various projects, some of the first publications are starting to come through. One is titled Massively Parallel Sequencing of Exons and the X chromosome identifies RBM-10 as the gene that causes a syndromic form of cleft palate. And Dr. Les Beeseker here today is senior author on that paper. And that's just a tip of the iceberg. There are many more projects that are coming through that are transitioning us now into the clinical, well, we've been in the clinical focus of part of our operation already for about four years, but now with the next gen sequencing, we can really push that forward even faster. And at a scale that the individual investigator can come to us with a few samples and with their own budget can have them sequenced in our lab and get data back fairly quickly. So it's opened up a whole new world. And even though it's been pretty tumultuous transition, it's been well worth the efforts. And I think a lot of good science is underway and we'll continue to be underway. Well, I just see people haven't stopped coming to us with more samples and more projects. So it's an exciting time. We love what we're doing. It's been stressful at times. And Jeff, if you're right in getting these nanopore sequencers running, it'll just be even more exciting, I guess. Thank you very much. Okay, this panel is now open for questions. Everybody getting hungry? One of the issues I would raise, I'm not sure actually it's a question, but maybe anybody can comment on this. And I think it might be of interest to the audience is that major technology advances are a blessing and a curse. You sort of heard about that. You hear about it, Jim, saying that sort of is very stressful to change. It's not just true for big centers. You can imagine that what's going on now with the sequencing technologies is that it's like every six months, another instrument becomes available. And so for individual investigators, that's a huge commitment for them to purchase one of these instruments. And they're constantly, the big centers are constantly wondering should we make a major investment in buying 10 of these machines? But there's also just hundreds of individual investigators who are trying to decide, should I buy this instrument or should I wait six months and buy the next one? And it's not just the price of buying the instrument. It's the personnel investment and getting that instrument to work well in the laboratory. It's, of course, the computational challenges that Vivian spoke of. Every instrument, the output's a little different. The software needs to be a little bit different. It has different nuances. So it's a blessing and a curse. These technologies are revolutionary in their nature, but they come with it some hard decisions, especially when money is tight. I mean, these instruments cost half a million dollars often. Individual investigators, that's a lot of money to raise to buy it. And if it's gonna be an outdated machine in a year, they'll be kicking themselves. So it's, there's a lot of nuances associated with that that shouldn't be underestimated. I don't know if anybody wants to comment.  I guess it's a question of which problem you wanna have. Right, right. And the other thing about it is so different than what the situation was for so long.  We couldn't believe we were basically dealing with one company, maybe two. And, but at least everything was very standard and very stable. And yeah, we got the price down incrementally. But then you gotta be careful what you wish for, because now we have the other extreme where there's so many different things that you sort of can't figure out where to put your money down. Question, yeah. On a sort of related question, I'm wondering if the NIH and in fact, the federal government is making plans for resources to be able to analyze all of this data. I mean, we're getting into ecobytes of data here pretty soon. And, you know, having myself been tested and having thousands of genomic traits annotated, I have no idea what to do with that because analytical tools don't really exist. You know, is there another phase here that's being anticipated and planned for? Absolutely. I was gonna make some comments about, I mean, Vivian set it up a little and she could extend anything now. I mean, I was gonna make some comments about that at the very end. But without a question, data production is not limiting now in biomedical, at least certainly in genomics. Computational biology is becoming, and just computational challenges are becoming the bottleneck. And it's not, it's being discussed extensively at NIH and it's getting considerable amount of attention. I'll have some things to say about that at the end. I don't know if you wanna add anything or Jeff. It's being discussed not only at NIH, but there's a whole industry growing up around this. Just at the Consumer Genomics Conference in Boston this past week. And there's, the venture capitalists are interested a lot of different kinds of companies with different models for how, so, well, actually it's gonna go hand in hand. The collection of the data with human genome sequences with phenotype data that are in medical records will drive the research to add meaning to the individuals, to the interpretation of the individual's information, but also how to represent that to consumer independent of the medical profession and how to represent it to the medical profession so that they can not have to be absolute experts in every condition, in all the ins and outs of human genetics, but can treat these data much the same way they treat many of the other biochemical tests that you have done when you go into a clinic and be able to sort out what do I do with this information for this patient? It's a big industry growing up around this. So your question, if I heard you right, was what is NIH doing? Is that right? Well, yeah, I mean, are there resources? I mean, in other words, as they're planning for, when this deluge sort of, we do get to the thousand dollar genome. So let me talk to you a little bit about that and to lead on from what Eric was saying. Firstly, a lot of institutes, ours included, tend to think about this institutionally, but we've seen this issue across so many different institutes. In fact, when I did my cloud computing meeting, we had over 15 people from different institutes coming in because they knew they all had the same problem. So one of the things that I'm taking hold of is looking at what does the cloud provide for us as NIH to do this kind of analysis? And in conjunction with that, the Center for Information Technology at NIH, which is basically the nuts and bolts of the computing, the wiring, all that kind of stuff. I've been in discussions with them as well because they don't do the scientific research, but they provide the nuts and bolts for us to do that. So one, working with different institutes, coming together to a cohesive fashion about how we can marshal our forces is one of the discussions. And the second one is working with, for example, CIT, to figure out how we do it. For example, in their case, they recognize that there may be a need for having a cloud within the context of NIH. The reason for that is the volume of data and the security issue as well. And the third piece to that puzzle is we've just started exploring some work with third parties, both commercial and academic, in terms of how we would do that because they're very interested to work with us. And so those are the three areas that cover what NIH is doing. And just to extend what Jeff said, Vivian, and if I can set you up when you had your cloud computing meeting, Jeff was saying the private industry is getting interested in this problem. Who were some of the participants in your cloud computing meeting? So Google was very interested, and Amazon and Microsoft. And they're all... They all showed up, right? Yeah. I mean, 10 years ago, we would invite groups like that to come to a genomics meeting. They'd yawn and say, not interesting. Now they're coming. The other thing is we had the Argonne National Labs. They do a lot of high-performance computing. They're very interested in this as well. And I think we've heard this before where we're now becoming interesting to these communities because our data wasn't sufficiently large or complicated enough. And now we're very complicated and very interesting. So I guess that, by human nature, makes us interesting. This is a short comment, too. I was happy to see on your slide, you were mentioning environmental, scanning other areas that need to be integrated with the genomics. Absolutely, I think we need to not be myopic here. Okay, one more question before lunch. I'm Louise Rosenbaum, I'm at NIAMS. And so just to step back to your comment about the individual sequencing centers facing these challenges. What sort of efforts are there so that perhaps there is more consolidation? I know that the Broad offers sequencing services for people from other institutions, but it's a challenge of scheduling. So how that might be able to be arranged so that it can be more productive and less effort for individual sites. Does anybody wanna take that? Is Adam? CTSAs, I think, are organizing that. But again, is that limiting and what options are there in the future so this could be done more collectively? Is Adam still here? He would be the best, I guess he's not here. Jeff, do you wanna? Well, so we've, there's a lot of debate whether consolidation into a few large centers is the best way to go versus trying to disseminate. We're supporting both approaches. A lot of people don't want to have to wait for large centers to be able to do the production. I should say that actually you can also, most of the, many of the companies are many of the sequencing companies, but also many other companies run sequencing services.  So all the models are being supported and we don't want to sort of converge on a single model because we think that would be too constraining. Part of the value of these instruments is that at some level you don't have to have as large an infrastructure to do a significant sequencing projects as you had to even three or four years ago, but there are some costs. So another thing to add there is, if I heard your question about in terms of how do we work with these other institutes, many projects, the human microbiome, TCGA, a lot of these, all the sequencing centers, the usual suspects, you know, JCVI, Washu, Baylor, and Broad, they're all working on these projects and our experiences of that are that they have to work together more closely because when you're generating base pairs from a particular project, it's not related to a chromosome location like many years ago you do a sequencing of a chromosome. They're just generating base pairs, which means they have to, all the base pairs are going into a pot. We have to analyze it so they all have to figure out standards, how they work and how they operate. That's one thing. The other thing is I've got an example with Washu, which is Washington University from St. Louis. They're getting a lot of people coming to them saying, I want to work on smaller projects and because they don't have the computes to do that and Washu's generating the sequence data, they're working with these guys. So they're sharing their knowledge and telling us a little bit about it and I think as Jeff is pointing out, each one of the different institutes is doing this as well. So it happens on a couple of different levels. Okay, I want to thank this panel for their talk. Thank you. So we, it is lunchtime, but we need to keep moving on the schedule. So right outside of these doors are some box lunches.  We're going to try to start off in about 10 minutes. And while you are eating, our luncheon speaker will be Sharon Terry. So if we could please.


Securete, Securete, Securete. This is US Navy warship 7-2 conducting live fire gunnery exercises. All vessels are requested to steer clear of my position eight nautical miles. This is US Navy warship 7-2 standing by for all concerned traffic. Out.


Thank you very much. I just want to look here then at the some of the labor market issues that are coming into play in the context of these new transport innovations. Now there's certainly a sense that there's a digital allure, the idea that you bring in the digital, the driverless car, the digital taxi, et cetera, that you are somehow making your transport system more efficient, but I think we need to look a little harder on what these digital systems are doing and decide whether they actually are making a useful contribution and where they can make a useful contribution. So the issue of digital taxis arises, they've spread across a number of African countries and I want to think about what they do to labor markets and to the formality, informality nexus between transport systems. So digital taxi systems in Nigeria, I've done some work this summer on digital taxis in Lagos and Abuja in Nigeria, including a survey of 200 digital taxi drivers across those two cities. Now what they promise to do is to turn this guy into this guy and that looks good. One has a sense that this is really formalizing and modernizing the transport system and making it more efficient, but I think we need to think a little bit about what formalizing means. I think informality is getting a bit of a bad rap. Informality is a technical term. It means income generating activities or at least within the economic sphere, income generating activities that operate outside the regulatory framework of the state. That is the stock definition. That means they operate outside labor laws. That means they don't pay key taxes. That means they're outside statistics. So I'd like to look quickly at the ways in which the fellow at the top and the fellow at the bottom relate to issues of formality. The reality is that digital taxis bypass labor laws and bypass taxi registration. They also bypass commercial transport taxis. There are about five different commercial transport taxes that are applicable to taxis that are not paid by digital taxis. Even the issue of modernization, digital taxis because of the race to the bottom in the competition among different taxi companies, 70% of the digital taxis in our survey were over 12 years old, as much as 18 years old. That's not all that current and modern in terms of vehicles. A second issue is if we look at the transformation of actual employment. The promise of rapid high quality job creation isn't quite being met. I looked at three different major digital taxi companies, Uber, Taxify, and the big Nigerian one, OgaTaxi. Together they claim to create Taxify, about 10,000 jobs. Uber, about 9,000 at the time. Now they revised it down to 8,000. And OgaTaxi, about 10% of the market, about 2,000. So that adds up to 21,000 jobs. But in reality, 60% of those digital taxi drivers drive from more than one company. About 40% of them do it only part time. And it actually boils down to about 7,330 jobs, new jobs, over four years across three companies in a country that puts 1.8 million new job entrants onto the labor market every year. So it's not creating a lot of employment. It also undermines both car hire and regular taxis. So it's not clear that the net creation of jobs is positive. The incomes in digital taxis promise these, they promise very high incomes, but there are a lot of hidden costs. As you can see by that protest banner, one minute, wow. Incomes are relatively low. Many car drivers, many digital taxi drivers don't own their own cars. And so they are getting incomes that are basically about the minimum wage. They work extremely long hours. And the turnover is extremely high, which suggests that the activity is both temporary and not very sustainable. A much bigger problem is the whole problem of the dignity of work. The idea of creating jobs is about creating meaningful work. Now, 59% of digital taxi drivers in Nigeria are university graduates and commercial driving is considered a lowly activity. University graduates don't want to be this guy. They want to be these guys. So they find digital taxi driving something they'll do temporarily and they feel it is quite demeaning. But commercial drivers also find it quite demeaning. They feel it devalues their occupational skills of road knowledge, mechanical knowledge, and long-term customer relationships. So they feel it de-skills them. Now, if the people who want to be driving taxis don't think digital taxis is a good job, there's a real problem here. And the people who are driving them don't want to be driving taxis. So 41% don't see this activity as a job, 25% don't see, only 25% see it as a formal job. And the reality is that it also exacerbates a number of social tensions. Northern Nigerians who have the biggest problem of unemployment, 60% higher than in the south of the country, don't like this job. Only 3% of them were doing digital taxi driving. So it's not creating employment where employment is needed. And there are similar social tensions in South Africa. So my time is up, but I just want to suggest that the importance of paying attention to local transport strategies, focusing on upgrading local taxis with some of these digital developments is better than displacing them. Supporting national digital taxi firms to create higher value jobs for graduates is probably better than bringing in external firms where the higher value jobs are outsourced to other countries and insisting that they must pay commercial taxes because they are taxis. And that's how you finance the mass transport that makes these big cities actually tractable. Thank you.


Boys and girls, we have it. My review of the Discovery set of... My review of the Stevens line, the Discovery set, where have you pronounced this? Navitas?  And I will tell you that the first one, it is, what is this, Laotus? To be rather than to seem. And the note breakdown is Neroli Oil, Rabina Lavender and Uzu Juniper Berries, Frisier Oris, and then the bass, Ambergris, but truly Rosewood, Sunderwood, Musk, Vanilla Absolute, let me just say this. This one, on the initial spray, it kind of reminded me of, gave me of a vibe of a, why the Portugal from Creed, or the pre-reformulation of Dior or Sauvage, but eminently more wearable. This stuff is nice. And I don't tend to lean heavily towards fragrances like that. You guys know I'm a Rose, Amber, Oud, and Oriental, but I could see Rocky and wearing this as a signature fragrance in kudos to you, Steven, for incredible packaging and presentation. I am very much on the bougie side and I like great presentation. Shout out to my dog, big beer business, but I am not an eff the box kind of guy. Nope, nope, the box matters to me. So next we have what looks like it says Arcanum, which initially I just said this is gonna be me. The top notes are Cinnamon, Coconut, Frankincense, Tears, their heart, Lobdenum, Osmanthus, and Oud, Lausian Oud, and the bass, Dark Amber Woods, Vanilla, and Tonka Bean. Oh yeah. And guys, I've already smelled these many times before I warned them, I just wanna get a refresh. This is definitely one that's right up my alley. When I wore this out and about, I had people stopping asking me, what are you wearing? I like the bottle, the color, the glass, good stuff, Steven, this one. Yeah, I like this one a lot. I'm not usually a big fan of cinnamon in a fragrance, but as it starts to settle down and gets into that mid, anything, ooh, you got my attention. Oud and Tonka, hmm, very nice. Now let's get into the final one, Opulentus. Opulentus, luxurious, complex, and refined.  It's one of the notes that I find to be most challenging. It's one of the reasons I'm not a big fan of Promise from Frederick Mall, but once apple tends to leave a fragrance, what's left behind is incredible. Let's see, in the top we have apple, pink pepper, and lemon. In the mid, we have a rose, cepernol, natural oud, and patchouli, and in the base we have amber, woods, and oak moss. Of all three of these, this would be the most difficult for me to wear, but I love the blend. And I will say that when I was out, this actually started up a lot of conversation, and this was actually a lot of compliments on this too. Maybe it's the time of the year. This is the first one though, that's kingly. That's a distinguished gentleman, Frager. I'm gonna say that. That's a winner for me. How do you pronounce it? L-A-U-T-U-S, however you pronounce that. Yeah, I could see going to Wall Street wearing this daily. Yep. This is class and a bottle. And then right here, Arcana, this is definitely gonna be, you know, that, you know, date night, special occasion, you know, something where you need a little bit sweeter, something that you really can't wear to the office because it'll distract the females too much. Yeah, yeah. And like I said, when the apple starts to kind of simmer down and things like this, see when a fragrance is complex and well blended, you must, must give it multiple wearings over different times of day, different temperatures, different moods. A lot goes into giving a fragrance an accurate review. And you know, opulentus, luxurious is aptly, aptly described as luxurious. So if I had to rate these, they would rank in order. Loutus, Arcana, and opulentus. Yeah, nice job. You know, I know when fragrance reviewers release fragrances, the community can sometimes be mixed on things. I will say that I expected nothing other than something of this caliber from Stephen.  I expected, and I expected the juice, the scents, to be commiserate with his skill level, his level of dedication to the community, to the art and the craft. So well done, my friend, well done. Yes, good stuff. I am, I cannot wait to try more from the house. I like that. That's so unusual because I'm, why I keep coming back to this? Because why the Portugal and the Ocevaj or fragrances I wished that I could love? But they were just not something I really would go to, to wear. I would test and spray, but I wore this and I was captivated. I thought the Oriental, Arcana, would be the one I would go to the most. But surprisingly, it was that one. So who was the perfumer on the first one? Oh, I'm not going to try to pronounce that name. Jesus Christ, why don't you have some simple names like Keith Jackson or John Thompson? Or like, I mean, come on, really? My bad. I'll write it down in the description. Do me a favor, guys. I don't review anything on my channel that I don't believe in, and I get sent stuff often. I would never put anything in front of you that I wouldn't risk my reputation and stand behind. So you can't buy that. You can't buy me.  So thank you, Stephen. I think it's a great job, great presentation, great fragrances.  Till the next time, talk to you later.


Lil Peep, Mac Miller, how many young rappers have to die before we do something about it? What is up everybody? This is Chris from the Rewired Soul where we talk about the problem, but focus on the solution. Make sure you subscribe and ring that notification bell, and in case you are new, in case you didn't know, I have a daily blog that I've been doing, and I just talk about different mental health subjects to try to give you something to think about during the day, take with it what you will, hopefully you learn some lessons that might help improve your mental health a little bit. But yeah, so anyways, I wanted to talk about Lil Pump. How did this come up? How did this video topic come up? Because I'm not a fan of Lil Pump. It's not that, you know, his music's bad. Like, I think that, you know, there's somebody for everybody's type of music, right? And Lil Pump's music is just not my music. But obviously, the dude blew up, so I know of him. I know who he is. So anyways, a while back, I was actually thinking about doing a reaction video to his video, Drug Addicts, because those of you who don't know me, I am a recovering drug addict and alcoholic, and I was thinking about doing a reaction video to it. Just never ended up doing it. But knowing Lil Pump and his breakout song, Gucci Gang, and things like that, like I looked at this, and like I mentioned in the intro, I've seen so many people pass away from drugs, from depression, from suicide, and things like that. And Lil Pete, Mac Miller, those are two people who are in the same kind of genre who died from drug addiction. So I want to talk about this. I want to talk about being worried as well as maybe some things that might be able to help. And I don't know Lil Pump, all right? I am not, you know, his therapist. I'm not his friend. I'm nothing like that. But I want to just toss around some ideas of what people might be able to do as a community, maybe somebody close to him could do. But also, if you have somebody in your life who might be struggling with the same thing, maybe there's something that you could do about it as well. And I hope to share my experience as a recovering drug addict who almost died six and a half years ago. Maybe some things that might be able to help. So anyways, the other day I was thinking about, I'm like, whatever happened to Lil Pump, right? And I looked up, I looked up like Lil Pump and I saw some recent videos about him and I'm just like, okay, cool. And I checked him. And a video, a video came up by Domus Live News. I don't know if I'm saying it, right?  I'm like, okay, what's that about? Anyways, here's a clip from Domus Live News talking about a recent, I think it was Instagram story that Lil Pump put up there. So I go to a story after people telling me to go through it because it was getting pretty worrisome. Now, the first thing he posted, he goes, not feeling good right now. I don't know if I want to keep doing any of this. He then goes on to say, I feel like I'm going to die soon and all this will be over peace. I'm looking at this situation because like, I've seen a few times Lil Pump, he's like, I don't want to be here anymore. So this obviously gets people concerned, right? Like, I don't know if you remember a few months ago, Pete Davidson made a pretty cryptic post and everybody was, you know, trying to call wellness checks. I think Machine Gun Kelly even flew out to, you know, go see him, go help him, things like that. A lot of people were really worried about Pete Davidson. And now Lil Pump is doing the same thing. Now again, I don't know Lil Pump. So all I can do is share my experience. And I look at this situation and we see Lil Pump is a fan of, you know, the scissor. I don't know if that's what the kids call it, or they call it lean or whatever it is. And those of you who don't know what that is, or purple drink, right? It's liquid codeine mixed with, I don't know, purple drink, something I never had that. But I was a huge fan of codeine. I was a fan of opioids, liquid, pale, whatever you got, I was going to take. The opioid epidemic in the United States is absolutely out of control. Tens of thousands of people are dying every single year and it's not slowing down, right? And just my opinion on the rap community and the rap culture, there's a lot of promotion of things like lean, right? And this is, this is an issue because it's killing so many people, but it's part of the culture, right? So it's not really like looked at as a bad thing. But again, we have to look at how many people are dying of this. But when I was watching, you know, Dominus Live News and I saw those posts, like I could relate. I could relate. And I don't know what Lil Pump is going through. But I can tell you from my own personal experience, I could relate to that as somebody's whose life was completely controlled by drugs and knowing that there was something more out there that I knew I could be a better person. Like, and the times I tried to get clean, but I didn't, like, I knew, you know, I didn't know, but I felt like I was hopeless. You know, I felt like it. And again, we don't know the full context around his post, but I just want to bring some awareness to this very serious subject because in Lil Pump's case, he's living life in the, in the public's eye, right?  And from an outside perspective, like drugs play a major role in this dude's life. And now you're seeing the depression come in as well. And I feel for the dude, I feel bad. And something that I always think about and try to teach others about is that we always, we always attach value to, you know, these external things, right? Money, fame, fortune, you know, whatever it is, like attention, we attach our values to that. And when we get those things, it can feel really empty. This is why you see so many celebrities who are struggling with depression and other forms of mental illness. I'm thinking about doing some lyrical breakdowns of Lincoln Park. Chester Bennington, as you know, he passed away, you know, just a couple of years ago from taking his own life. And like these, this is something that always has to come up. Like, because I know for most of us who, who aren't these like millionaires and super rich and famous, right? We don't get it. Like we look at that we're like, we don't get it. How can you be, how can you have everything and not just be happy, right? And something I realized long time ago was money does not necessarily equal happiness, but money equals less stress. All right. And less stress is great. Don't get me wrong. It'd be great if all our bills were paid, if we could take care of family, if we could take care of this and that and that, but it doesn't guarantee happiness. It just helps to reduce stress. But something that we see with a lot of celebrities, in my opinion, is that we see with that, with that money, right? With that fame, with that thing that we're chasing, we find that those come with whole new stresses. You know what I mean?  Now you got new bills. You know what I'm saying? Like you get to this point and then you start a business and everything you wanted to do was start a business, but now there's new stresses that come with that business. I can relate to this as a YouTuber. I wanted to be a YouTuber. I wanted to get this message out there. I wanted to try to help other people. I wanted my channel to get bigger so I could reach more people, the views, the subscribers and everything like that. And it's come with this whole new set of challenges that I wasn't necessarily equipped to deal with, right? So when we're seeing this, when we're seeing this from the outside looking in, somebody like Lil Pump, who's clearly going through something, in my opinion, just working in a drug and alcohol treatment center where we also specialize in mental illness. And again, I am not a licensed professional, but I worked in the industry doing groups and individual sessions just talking to people, kind of peer support one-on-one from my own experiences of addiction, depression, anxiety and all sorts of stuff. But anyways, I look at that and the first group of people who I think need to step in is like the friends and family members. Like I remember just being twisted up when I saw Lil Peep died, right? And I didn't even know of Lil Peep until he passed away. But when I started researching him, I saw that he was always talking about depression. He was always talking about doing drugs. Those two things don't mix. So when he passed away, I'm like, why did nobody step in? You know, same thing with Mac Miller. Now, Mac Miller did get clean for a while and then he ended up overdosing. But every time I looked down, I'm like, why did nobody step in? And this is one of the reasons why the people closest to him need to have some type of intervention if this is something that he's struggling with. But if this is somebody in your life, the people closest usually need to step in. And there's a ton of books. There's a ton of videos on YouTube. Hell, there's even a show called Intervention on how to do these things and how to have these conversations. And Intervention from my family saved my life, right? Now, what can people do as a community? I don't know. Like you could always send love and support and things like that. But what I've realized just from being a YouTuber, no matter how much love and support gets sent out that way, you're always going to have just as much hatred, right? And if you look at Lil Pump's videos being made about him right now, even on YouTube, like a lot of them are talking trash, right? Talking about how he's irrelevant now, talking about this, talking about that. And when you're in that dark place, like I've been to that dark place and something I can tell you is, is no matter how much love and support that I was getting in that dark place, my mind would naturally focus on all of the negative, right?   And that's just my opinion. But we have seen scenarios where some celebrities do get the love and support from their fans. And that helps, you know what I mean? But I will say this, because I don't want to be a negative Nancy.  So you know what? Screw it. Tweet a Lil Pump, leave nice comments on his things, telling that you love him, whatever it is, you know what I'm saying? But might want to also let him know that you're worried about him and hope that he gets some kind of help. And you know, I think it's safe to say looking at, you know, what he posted on his story that the dude does need some help. All right? But you know, try to help the people in your life, try to help yourself. If you are somebody who is struggling, reach out, ask for help, you know, check yourself into treatment, find yourself a therapist, talk to your doctor, talk to your friends, talk to your family, talk to a teacher, talk to somebody, talk to somebody down in the comment section because there is always hope. All right? But anyways, that's all I got for this video. If you liked this video, please give it a thumbs up. If you're new, make sure you subscribe and ring that notification bell. And a huge, huge thank you to everybody supporting the channel over on Patreon. You are all amazing. And if you would like to become a patron, help support the channel, get access to some other cool stuff, click the top right there. All right? Thanks so much for watching. I'll see you next time.


I've put a quick video together to help you to understand court positioning when you're playing singles. Now the reason I've put this video together is because in England where I live there's been a slight easing in the lockdown rules. Now players are allowed to get back onto the tennis courts but they're only allowed to play singles unless they are with three other people from their household then they can play doubles. So at our club in particular and I know lots and lots of clubs over England there are going to be loads of tennis players that are so used to playing doubles but aren't very experienced on the singles court. So this video should help those players in particular in understanding where they should be moving and recovering to after each shot. And not only would it help those players that play lots of doubles but actually if you already play singles it might jog your memory and it might give you better understanding of why you should be moving into certain positions and it will just give you better idea when you're playing yourself. So let's get into it. On this first picture you can see a tennis ball and you can see a green circle. Now the green circle is you where you're standing and the tennis ball is where your opponent will be striking the ball from. So in this first picture you can see that your opponent is in the centre of the court and so are you. Now when you are rallying up and down the middle of the court your opponent has a narrow channel that they can hit into. They don't really have the opportunity to create too many angles because they're in the middle of the court.  Now this changes depending on where you send your shot. So if for example you hit if you're a right-handed player and you hit a forehand cross court into your opponent's right hand corner you will recover slightly off-center to your right. So anytime you hit your forehand cross court you should move slightly to the right hand side if you are a right handed player. Lefties would do the opposite. Now on this picture you will see an overlay of a tennis court and I've put it at an angle. What this shows is a change in perspective from your opponent. So previously when your opponent was in the center of the court they were looking straight down the court at you. Now that you've pulled them out wide they're looking at the court from an angle. Now because they're looking at the court from an angle they have the opportunity now to hit more angle out wide. They have less of an opportunity to pull you off the court when they go down the line which is why now you're able to move slightly off-center into your forehand side. If you look at the yellow triangle now it is slightly bigger than it was before because they have more angles more room to play with but you'll also see with the green arrows that your center point has shifted so you are needing to move slightly off-center. If you look at this third and final image you'll see I've done it the other way around so if you sent your shot into your opponent's backhand side or lefty's forehand side you would have to move slightly to the left of your center point or your baseline so that you can cover equally your forehand and your backhand because your opponent's perspective of the court has changed slightly. I hope that all made sense if you've got any questions pop a comment below and I'll get back to you. When you get out there the most important thing is keep moving your feet always recover to a good position if in doubt get back to the middle but you'll start to feel when you do move your opponents from side to side if you're recovering slightly off center to the opposite side of the court to where your opponent is you'll start to find that you're covering the court a lot more effectively and efficiently and you'll actually save a lot of energy so bear that in mind give it a go and let me know how you get on. Good luck.


Hey everybody Forest here ripping five boxes some football boxes here for alexandros s alexandros has two boxes of 2021 contenders optic football hobby two boxes of 2021 donner's optic football hobby and a 2021 panini one all right thanks alexandros good luck we'll start with the donner's optics there's a laurence rated rookie in the first pack Brady dominators insert jc horn rated rookie the shone right hollow rated rookie chas sarat almond raw saint brown rated rookie silver circles d on jones to 125 drivon morrig aqua rated rookie levi and lucerique number to 299 penis sewell the rookies insert almond raw saint brown with downtown john elway congrats on the downtown alexandros case hit get that mag for you of course zack wilson race rated rookie sertan the second hollow jonah smith patriots alex aqua to 299 brian dawkins palmer orange bret farv out of 199 brawny perkins red hot rookies kyle trask tylin wallace rated rookie elite series rookie autoreshad bateman the insert auto there 80 of 99 bar more rated rookie shot bateman rated rookie orange bret farv out of 199 the second orange bret farv strange yeah very strange seras marshal jr blue joey bose at a 179 chargers color match on the blue travis etion jr retro series terrell davis levy roundtree the third shane buchel hollow tyler lockett mere smith marcett rojo blue to 179 and trimmy kitty and a red rated rookie christian bar more to 99 all right pretty good first box at that uh elway downtown let's see some nice rookie color here in box two of donor soptic luck alexandros kaderious tony javante williams the rookies insert or not the rookies but good iron king's rookies insert jalen phillips jamar jefferson remandre stevens and hollow rated rookie kenneth gainwell silver circles clinton portis to 125 tommy tremble aqua steve mc michael to 299 gifted rookies kellen mons nausea harris rated rookie hollow zekiel elliott jared paterson pink velocity randall cob that's numbered 46 of 79 all aqua t higgins that's number to 299 leather wooh there's a legendary logos justin herbert jalen waddle base rated rookie let's see a big auto there there's the auto right there javante williams rated rookie base auto that one's out of 150 solid hit there smith aqua rated rookie pat frier mooth that one's numbered to 299 ritchie grant orange lemar jackson that's numbered to 199 a hubbard retro series troy akeman javon holland rated rookie kyle trass rated rookie a hollow matt ryan erick stokes almond rossain brown rated rookie orange to 199 in book and a purple 2-2 at well that one's numbered to 50 2-2 at well let's get into contenders optic now thick pack come on big rpa base cards james robinson and chiba hubbard it's a 99 on the rookie blue auto drabryl cox all-time contenders auto blackscope shannon sharp numbered to 25 nice shannon sharp hit blackscope to 25 sick looking parallel there uh green pulsar staphon digs oh a nice color coming up here the staphon digs will be the 30 on the green pulsar and a gold rookie up and coming is rashad bateman ravens 4 of 10 nice gold good box 20 of 30 on the digs as well all right big auto here come on tornado gibson javante williams elizavaretaka rookie ticket auto orange to 50 variation kyle trask red rookie ticket rps to 50 his trask that's silver tom radie and blue winning ticket kyler marie numbered to 99 one oh wow jersey number and the first one stamped one of 99 panini one last box thanks again alexandros it is a precision rpa of simi fahoko numbered 34 out of 99 there we go that'll do it we appreciate it some really nice hits in some of those boxes we'll get them right out to you


So what is pressure? Particularly gas pressure. You may be familiar with measuring pressure when pumping up your bicycle tires or a basketball or maybe even with weather systems as low and high pressure systems move across Australia. So gas pressure is the measurement of the force of the gas particles colliding with the walls of the container. So here we have a container and some gas particles. So these are moving around freely and as they hit the side of the container that's what we're measuring. So pressure equals the force over the surface area. There's a few things that pressure depends on. So let's have a look at them now. So here we have a container with no particles at zero atmospheres. If we are to increase the amount of particles in the pressure will of course increase. As there's more gas particles there are more collisions and therefore more pressure. Another factor that pressure depends on is temperature. So if we heat up these molecules of course they'll move faster and because they're moving faster they're colliding more and because of that pressure also increases. Same thing happens but in reverse when you cool it down. Here we're dropping the temperature well below room temperature and we can see that these molecules have slowed down. The third one is the volume of the container. So the smaller the surface area is the more collisions will happen as there's less free space for the particles to move around in. Because of this there are more collisions and therefore more pressure.


How are you doing Garrett. Good how are you. Doing great. Nice to see some spring baseball here in Tampa. Garrett obviously you guys you went through an unorthodox situation last year with the start the stop the go the shortened season. I know there are still a lot of protocols in place but does this spring and your ramp up and your process feel similar to a normal year getting ready for a season. Yeah absolutely. And it's I think everybody feels it you know we've we're creatures of habit and we've done this progression you know almost every year in our career and so we still have to make adjustments like you know everybody else but at least we've shown up at the same time to you know the regular field and getting our work in like we would you know in the normal year. Garrett how do you look at a light and load from a season ago and how you account for that when it comes to being prepared to do what you're going to be asked of this season. I mean I think we take it I think we take it step by step and that doesn't go for pitchers that goes for everybody. I you know there was less load in general last year so I just decided to start throwing a few weeks earlier and you know as far as you know breaking into the season right now I'm sure Aaron and Matt have all their plans on how they want us to progress through the season and get us to peak form when we need to be. Garrett Corey Kluber on the mound right now new acquisition this offseason what have you thought of him and his work so far. Yeah well I've always been a huge fan of Corey and I figured he would be pretty deliberate and stoic throughout the day just by watching him perform and very detail oriented and how he goes about his business and you know no surprise there all of it's true. I think I think he's you know doing wonderful so far and we're all you know getting to know each other and the conversations have been fun and the camaraderie is building as we continue to get through spring and you know just really fortunate to have such a great picture that we can all learn from. How good do you think that rotation can be? Pretty bullish about it you know but we got to do the work first but we're certainly excited. I think everybody has you know been feeling really good so far in spring and that's you know that's the most important box to check in and as long as you keep pitching and to keep taking the ball when you're asked to there's a good chance that you're going to get better. So from that perspective we're in a good spot and we'll just take it I think one month at a time. Yeah Garrett good to see you healthy and looking good this spring you know obviously things have changed and in the matter of a year you're settled into the northeast settled into a new home now you have a new baby you know do you feel like you know you feel all these changes certainly have given you a different perspective on your craft and your profession. I think so it's you know there's nothing quite like having a kid that's pretty life-changing and you know there's things that I learn you know from him that I can apply probably you know to my daily life and probably to performance and mainly that being patience patience is really key but I also just feel more settled I have you know done the commute now to the stadium I know a time I need to leave I know all my teammates I know everybody around the park and I've got relationships with them and so there's a lot just more clarity and less things to figure out and more time to just you know spend with your teammates and play in baseball. Well I'm sure you were happy to see Corey Kluver throw a nice tight one two three inning we could have used the longer inning with you there but thank you thank you for the time here though we appreciate it take care everybody.


What they gonna do with me now? I'm still a twerk of the town Tell me the sisters, I'm hooking them down What's going on y'all, it's Lauren Kay We here at twerk of the town And who's in the town today? Ken and money Alright Big money Period Whole lotta money Ooh, anymore Blue money Ooh I can go all day I ain't gonna let me not go It's the AKAs for me Okay, so to start off We want to get you introduced to the people So we gonna play a game of rapid fire questions Okay, so I'm gonna say the question You answer as quick as you can ASL Alright What's your favorite city to visit? Texas What's your sign? Sagittarius What's your go-to R&B song Like you just wanna sing your heart out Is it given Keisha Cole, Mary J Blonde My old school Like some maize or something Ooh, before I let go? Before I let go Alright now If you could live in any movie What movie would it be?  Blow You would live in that movie? Yeah Alright What's the nastiest food you've ever eaten? Uh Beans Like, I don't like beans That's the nastiest food you've ever eaten? This ain't I don't try all type of foods I ain't gonna lie out I like to stick to what I know Okay, it's given Picky Picky Okay, what's your favorite song by you? My favorite song by me I don't got a favorite song by me You have to have a favorite song What else song I made yesterday? I made a song yesterday It's called For the Dough Unreleased? Yeah, not now yet Exclusive Who's your celebrity crush?   Party in the USA Okay If you could pick any talent in the world to be a pro at Outside of music, what would it be? Any talent in the world So any sport Cooking Joint Cooking Okay What's your favorite phrase? Hold on to anything Okay, who's the last person you texted? My manager so we can hurry up and come here Oh, that's Shane That was Shane Okay, and the last one What's the one word you had trouble spelling as a child? A word I had trouble spelling?  Yeah Okay, so what was your early life like? I was in trouble I just was curious trying stuff Outside, you know what I'm saying My folks worked a lot So I had a lot of time with my sister So I was just wandering around trying stuff Doing stuff You was bad I was bad for sure I got to put it out of the school I went to Oh, how many schools you went to?  In South Memphis It was probably more or more I tried to go there about a couple of months I got to put out there my mother so fast I got to put it out You want to tell the people about what you did?  I always like music I like music I always be in trouble So I'm in trouble I can't You know, you're on punishment They try to put me on punishment You can't go outside I can't listen to the radio Okay And I was like your little escape Yeah, I like music So when you were younger Did you just like it? Like you just listened to it Or were you like spitting bars here and there?  And what made you pivot to decide Like, okay, I'm gonna do this music When I was a child What did I want to be? Man, I used to want to do all types of shit But if I heard you made a lot of money I wanted to try it I know, that's right You knew from young Yeah Anything So anything that make money So what made you say, alright I'm gonna be a rapper I'm gonna take this serious Man, I tried I just My one time I was like I just I had one about the little Little headset Like the McDonald's Giant with the headphones And the little thing And then she I had put the little program on Computer I was just like my uncle Just try to route See how this worked I tried I put the song on On Facebook And everybody was liking it What year is this?  Yeah I was never a fan of the whole Like oh, but y'all didn't like Something so before Like I don't matter You want them to know his music Now or not Yeah, it's straight up Man, keep streaming Keep that man independent 100% He key is still here His family is still here My new fans need to be You know what I'm saying Ain't nothing wrong with some New fans and new money To still go on this family To make sure he good Like it's over stood So what are your thoughts on Yeah, I never know how to say this word Post Thomas Post Thomas Post Thomas Okay, what are your thoughts on Post Thomas music?   It's starting to be Oh Okay They ain't gonna never be my favorite part My favorite part is the money This is what I'm here for Respect for you Yeah But touring My touring fun The Glocker Mama Tour Fun I'm ready to go back on tour I'm gonna go on this blue money tour Period Yeah I ain't gonna lie to her Glocker Mama Tour Like I don't be Just my third tour My first tour The tour with no rules Tour with Glock and Doll Mm-hmm You know what I'm saying I just went just to go And it turned for me Just going to go to Nah, bro You finna perform Then I did Every show Then it came The next year Like you finna You own the tour now And then to come to this This Glockon tour Like I was really on the tour I'm very much part of the tour Getting the bank row For the tour I spent the bank row Like any show I probably threw about $1,500 in the crowd The very next question was How much money did you actually Throw on tour? She did Studied some Studied some cities And I threw No listing $1,000 So she You good at math, right? Um, yeah Yeah Ish What's 30 times a thousand? 30 times a thousand Is 30,000 Alright This is embarrassing This is embarrassing This is about This is about what I did It's a lot of pressure Okay, so I think that Your show is super interesting Because first of all Your presence on stage Is just crazy Like But then I feel like with the money It give like Am I at a strip club Or am I at a show Like the way the fans Be reaching for it Like I've never seen No shit like that It's a giveaway It's the blue money Giveaway It's innovative Very, very innovative I just did They own Glockon tour So what you think I'm finna do on My tour With these folks For show here For blue money Is it giving 50,000 It's giving a 60, 70 Ooh You paying pills Period So what I'm saying I give To the people who deserve it Man, they streaming my music They playing my music They turning up Yeah And keeping me going Shit, I ain't got no problem Gettin' on some money I can have some You seem like When I was watching your vlogs You seem like You really love your fans Yeah, I love my fans What type of relationship Do you have with your fans Is it like a personal one Like you It's personal We know we shitin' on everybody But we just We livin' everybody Feel like they You know what I'm sayin' Period They say everything I wanna say I love that That's why I love my fans Now let's talk about Memphis a little bit I've never been there And from the vlogs From the music Not even just your music But like everybody's music That's coming from Memphis I really feel like Y'all are on Like a whole Another side of the world So what is the energy Like in Memphis right now Because I feel like This is I'm 24 And this is the mostly I've seen Memphis In all of my 24 years of livin' So what is the energy Like in the city right now Ratchet Steel Oh A fun ratchet A good ratchet Yeah, everybody have a paper But either you If you If the person That you affiliated with Or around Necessarily not there You still turn through Because they know Like you so-and-so Pondler Or you affiliated with So-and-so-and-so Like it's a lot of people With paper in there You know what I'm sayin' It's come from nothin' So she just imagine A whole bunch of people That come from shit With some money Right But everybody couldn't Have it for it It's ratchet You be outside of Memphis? I was just outside So what is outside Like over there? I don't go outside I don't I'm gonna tell you Me and Memphis We gotta Understand It's like The more I stay away From Memphis The more I can be a rapper I can't sit there Because I'm gonna get Caught up in What's going on And I'm too good At what I do there I don't need to be there I love that you understand that I need to focus on music You know what I'm sayin'? Yeah But I just was down there That somebody told me I don't be in Memphis Cause I'm scared Or something I just had to go down there And kick it real quick You been outside of New York yet? Yeah What's more Well, you gotta say Memphis Cause you from Memphis Memphis in New York You can't compare You know what I'm sayin'? And Like I will beat you In the top one In the trenches Okay Y'all gonna come back And be like Uh-uh That's too much I think New York Can say that about everywhere though I can tell you We think everything Outside of New York Is just like Uh-uh Give me a way for it What a gun is it Give me a gun Please give me a gun I'll tell you Is that what we gave? Or that's what y'all gave?   Muddy Muddy Muddy M-U-D-D-Y Muddy What y'all say? Hey Give me Give me a sound Let me think Like My partner My partner Partner My partner I might say partner If I feel like saying partner Like your friend Yeah my bro Yeah y'all want me Like what you say My dog Okay Say we play my dog Like bro Yo what?   Either T.Kief or my partner Ace Would you ever do that? Like you and producer just Yeah Do you have intentions on it a little? One day Right now I don't know the I don't know the reason I win Is cause I ain't I need to I'm telling you I like rap And I fuck rap And I'm good at this But I be needing To keep pushing myself And if I can do one whole project With one producer Like I ain't just this too easy I be needing to go listen to weird beats And other people Like all the people on my projects Are unknown people Probably got a hundred followers And shit like that Like I just be like pushing myself But I'll do it though It's interesting that you said You feel like it would be too easy Because I would think That it may be harder Given that Like you have to try hard To not replicate the same Type of song Like them two people I just told you Like Kyle Aston Kyle T.Kief Kyle Bandplay A-Red Send me Some beats right now They're gonna send me Seven eight beats And I'm gonna do all seven I ain't gonna be like I don't like this one I'm gonna do all the beats Okay My man just said I do a mixtape of Two days Why you gonna be putting it out like that Like I feel like you're very I'm gonna draw my project And then it's gonna live And then once it's done I'm gonna drop the next one Like you're not like one of those Cause I be trying to get these I be trying to let these folks Feel like they doing something Cause if I keep dropping Then people gonna realize That she like nothing That y'all putting out It ain't hard So I be honest Try and be a team player To the rap game Okay Yeah I love the confidence Yeah I be trying to be a team player I can't drop too much I drop too much It ain't gonna be fair Okay so who's your dream collab Three I already had my dream collab Don't Yeah that was on the person And then I ain't Yeah I'm gonna dream collab I can say I want to do a song With a female But I don't know who That's what you're about to ask I don't know who though Any female I just want to do a song With a female But I want to be Like one of them R&B females Like a Okay Like a Yeah Them type of people Like a damn Beyonce Or like a Rihanna Okay You and Rihanna is mad funny I didn't say that Because I just feel like You're so Well you don't consider Memphis down south do you What Uh oh right Memphis E is the self Okay cool Glad we're on the same page Personally I consider Like Delaware the self So Delaware is What the Right That's why That's why I had to ask That's why I had to ask Right That's why I had to ask Okay but we on the same page Memphis the self So I feel like Your sound is So southern Mixed with her Like pop Caribbean It would be so like I like pop I get in my bag with Rihanna What's your favorite Rihanna song Umbrella No I'm trying to Umbrella Okay so we're going to take it As Rihanna is the His dream Rihanna whenever you Pop that baby out and decide Let's do this And if this She don't work out She got going I don't know what that Yeah Oh wow We love ASAP We love ASAP So You mentioned the amount Of L's that you took Before you finally Got that big wing Yeah How did you bounce back From those L's She put them on the floor Stepped on them And got keep going Like don't dwell on the L Keep it moving That's so Sagittarius of you What's next Yeah We take L's every day That's so true But okay now Earlier you said That you just Been good at it So what type of L's Were you taking That's heavy I might take L's You know My mother might get Funny her in there Okay I might Some might happen family Like tragic events Some might happen Partners Different type of L's You know what I'm saying So not necessarily with the music But just I might have taken L's music I though I can't lose with these music Like this is This rap game is like It's so easy And so it's like If I just I have never took my time And said I'm feeling just rap Now I am Right Let me show you how easy it is I'm not gonna lie I have a friend Her name is Akia Shout out to you girl She listens to All the young doff The key glock And I'm like That's not really My lane too tough But she would post your music But like I said That's not really my lane So I ain't looking to it So when I started doing Research with you For this interview I was like Oh I'm late I'm late I'm looking at your YouTube videos You got At least half a mil On every single video Like I'm like No let me tap in And I'm very happy that I did Cause you are making Really really good music Thank you But that part come from me Like Like you just said Like I don't really be Putting out music Like I just be I be playing I'm not playing no moto I'm done playing I promise No more playing I ain't playing And we got to get the socials out Yeah Like why is your stuff not All over TikTok Cause like Like you said I like I like TikTok We got to We got to work on TikTok Yes we got to work on TikTok I like TikTok Have the people that I'm dancing to your music And stuff I got to get somebody to teach me How to do it I'm staying independent I ain't got You don't dance You get them to dance I can dance You look Yeah I can dance You want to see me dance Yeah Nope Oh yeah Not the pump fake No I gotta do the You gonna dance When the camera turn off I can't dance for real I'm just talking shit Oh Hate that for you You know my dance What's your dance I just start throwing money It's my way to That's a good dance My way to throw it out I was like yeah I ain't doing no dancing That's a good dance And the fans love it They love it Period So do you have any Last words for us Do you have any Thing coming up Like I got this blue money To it coming up I got this blue money The looks out You know what I'm saying I'm just I'm in full blue mode right now Everything new Everything blue So what was the reception Like two blue money That it looks More specifically What you mean Like What did you What was your fans saying About it What was the feedback You was getting They in love with this shit Yeah They like They hear They hear what I'm on I'm on one And I ain't playing I ain't letting up You got Do I need to let it up Not at all Can you give me a reason to let it up Never There you go I ain't letting up I ain't letting blue money I ain't letting big money Whole lot of it I ain't pepper out I ain't letting Long little doves Man we going straight up To the top Period Let the people know Where to find you Yep Everywhere All over The bank Won't find me Go to the bank Okay but can you tell them Your social media handle It's self-explanatory Kenny money I ain't got no Money They don't know that M-U-N-E How you spend it Why How you spend it M-U-N-E-Y Okay they know that Kenny money y'all M-U-N-E-Y They know how to spend it Spend it one more time M-U-N-E-Y There we go Alright y'all It's Laura K. Kenny money We out