Hi, everyone. I'm Ben. I'm going to do a live demo for you. So a couple things just to start off. You should, in order to do this live demo, as we said repeatedly, you have to have an account set up to do this. I'm going to run it through as if I am you. I'm going to log in to a brand new account that's never been used before and do it that way. The steps of a demo are outlined in the handouts which you can download. So if you ever get lost, you can refer back to that. I am going to first kind of zip through the example that we're going to do, which is going to be a little tiny analysis of a half a chromosome of RNA-seq data, which we are going to run to completion through the entire ENCODE pipeline. And it's going to take about 50 minutes to run all the way through. So that's why I'm going to sort of whip through how to set up a job and start it. And then we'll move back a little bit, explain a little bit more about how the interface works. And if anyone gets stuck while they're trying to run it, please raise your hand and someone will come and help you. So here we are, logged in. So this is the login panel. All right. So when you log in, you start. You'll see it's my free trial. I have nothing in here. This is sort of the project panel. So you organize all your things and projects however you want. They're like a folder, effectively. So to start here, we're going to go to the ENCODE Uniform Processing Pipelines project. So what we're going to do is wait for this to load. And we're going to copy the long RNA-seq and the reference files folders to a new project in your account. So this little icon here is to create a new project, because you don't have any projects. So just call it demo or something. All right. So you actually have to click Copy into this folder. Done. OK. So now note that I'm still in the ENCODE Uniform Processing Pipeline folder. So I'm going to click this back arrow here. Have a new project. Click that. Oh. The whole setup to run takes about three minutes. So I can go first. I'm in my demo project in this long RNA-seq folder. We use the word long to substitute for getting transcripts as opposed to other microRNAs. So inside this folder, there's a couple folders here and a couple little boxes which I'll go into. Again, I'm going to explain most of this in detail once we get some stuff running. This little symbol is a workflow. A workflow is what DNN-Xs calls what we might call a pipeline.  So if you click on that and open it up, you'll get this image that you shown before, hopefully. And specifically, the one-replicant paired end is our example that we're going to use. So there's four in there. Some of them are for multiple replicates. Some of them are for single-ended. So the way the workflow is organized here is that this is a step. This is a step. This is a step. This is a step. This is a step. These are the inputs on the left. These are the outputs on the right. And the outputs are connected to the inputs. So, and again, we're going to come back and do this again if people have trouble. In order for this to run, to click the Run button, we need to turn all these little guys in the middle, which we're calling applets green. So let's just start. This should be set up. So this is a paired end experiment. So we need two fast queues, one for each pair. So I'm just going to go back to the beginning for people who are still having, haven't created a project with the files in it, okay? So first thing I'm going to do is click on the featured projects, end code, uniform processing pipelines. So this is our sort of master copy of the pipelines and the reference files. So I'm going to select the long RNA-seq and the reference files by clicking the checkboxes. I'm going to go over to the left side and click the Copy button. So that's going to open a interface in order to copy to another project, which for our purposes is a, your personal project that you are maintaining. So we're just going to copy the software and the data onto there. So this little funny-looking suitcase plus link here is create a new project, which if you haven't created a project yet, you will need to create one project. So if you click on that suitcase link, you'll have to enter a name for that project. So I'm going to just type in this case end code demo into the box, hit Enter, right? It's churning a little bit. So now it should say something like no data available here. On the right hand of this, it says copy into this folder. So if you click on that, it should copy the data that I selected in the first place. You might have to click it twice. Okay, so let's spin it off. There it goes. So you get a little 100%. It's done. People still working on that? It's fine if you are. So now this leaves me at this project, which is a project that you guys can't really edit because it's our DCC project. And this bird beak over in the corner that points to the left will take you up a level or back to your personal account and the list of all the projects that you have. So because I did this twice, I have two and some of you may have two too. They're just little files on the cloud, so feel free to delete them if you don't need them later. So you can see, actually, both of these have some data in them, 348 gigs. So I'm just going to click on this one to enter that project. I'll wait and give it a second. Spin it a little bit, and I'll hit Helix. So is anyone not who's trying to work through this demo not at this page yet? One, two, that's pretty good. So from this, your project that you've created screen, there'll be a little folder. It looks like a little folder, a guy there. One of them is long RNA-seq. That's where the workflows reside. A workflow is DNNX. This is a way of explaining which processing steps to run in what order. So I'm going to make this a little wider so you guys can see it, but should be able to see it on your screen. So we have four here. One is for single-ended RNA-seq data and one is for paired-ended, one pair of them. And then there are two separate ones, depending on whether or not you have one input replicate for your experiment or two. We're just going to do the one replicate one. So you can click on that. It should open up a window like this. Is anyone who was... Is this where we are now? We're good? Okay. So just to repeat here, the way these are organized is that the outputs of the previous step in a pipeline are connected to the inputs of another step. So for example, this star underscore NO underscore BAM, when I highlight it, it highlights it there. That means that this BAM file, which is one of the alignment files that we're going to create, gets passed on to this applet here, which is the quantification applet. The applets in the middle here represent software that's going to be run. So in order to get this started, and I think we have to pretty much go through this once and do it so that we will finish, is you start clicking on these items here and filling in the boxes. So if you click on that, it should show you a little menu, which shows you the valid GZIP FASCUE files that you could select for there. And it's going to take a second to find them. But we're going to pick the one that is called like HEMI, if maybe yours is faster than mine. There we go. So there's two here that are described as HEMI, chromosome 21 HEMI. So this is the middle of chromosome 21, which is just to be a small dataset that we can finish in the workflow time. So the one, and this is the NC file that these are extracted from. So we are going to take the one with a one HEMI version, click on that, puts it there. This also inputs it to the next step here, which we'll go over. So the, oops, don't right click. We'll get that menu. So now we do the same thing with the other pair of the reads. Same menu. Just make sure you pick the one with the two. Does everyone get how to put files into the system? Anyone confused? So notice how they're both different, one and two. Now, we have to actually, when you're doing the mapping, you have to actually pick the correct genome. So these are reads that I happen to know or from a mixed human sample. So I need to, and then we have the genomes pre-indexed for the various pieces of software. So this is the star alignment. So we're going to click on that, and it's going to give us a bunch of choices of indexes to choose from. So there's a mouse one. There's several human ones. One that star works is that it takes not just a genome, but also a transcriptome, and we use spikens for some quantifications. So that's what all this spinach is. But if you just pick hd19mailv19erccstarindex.tgz, click on that, it fills that in. So we're going to go to the next step now. So scroll down. Here, this is another alignment program, which is you may be more familiar with this top hat. The reads are automatically set up to be the same reads that you put in in the first place.  And we're going to get the top hat index. Now it's important that you pick the same index, right? Like I don't want to have one aligned to mouse and one aligned to female. And you should know which version of the genome, in this case hd19 you're mapping to, because when you do a visualization, you don't want to have it drawn on the wrong genome, right? So that's that one. Now the next step is the quantifications. So this is a program called RSEN from Colin Dewey's lab. So that uses a slightly different flavor of the index. And if you'll notice when it comes up that there's no female version of the genome, and that has to do with the fact that we want to write out zeros in our quantification file for stuff on the Y chromosome. So it doesn't really matter if we use the nail or not. Click that link. That's there. The last input is that these two steps here are going to take our alignment BAM files and make wiggle files out of them, big wig files that we're going to visualize. And this requires a chromosome length file. So let's just take this one here. Let's write one. So now this one's runnable. It's green. This one's runnable. It's green. This one's not runnable. What that means is if you put the input, you figure parameters, params, I guess. So if you click on this black box in the middle, this option isn't checked. So it needs to be checked that the library's parrot ended. So we'll save that. Now it's green. Okay. Oh, here, what you click? Sorry, I could... Oh, uh... maleHG19chrome.sizes. Sorry, I didn't read it out, did I? This one? So these other two have just a little text you need to put in. You can just put in here. This is a required field. It's just to save some files on the name. So just type in demo in this box here. Save it. And the top one, the star line, it's the same thing. So look, I'm all runnable. And I can run my analysis. So, yep. Also, this whole process should be on the handout if you want to refer back to it, that you can download from the agenda page. But I will be happy to go through it again. All right, so I'll just... Okay, so starting from the pipeline, let's see if this should be blank. I don't think I'd save my stuff. Okay. So what we're doing is... Sorry for the... What is this? Sorry? No, that's totally a fair question. I just want to say that our plan was that because it takes a while for it to run, that we would try to get people started and then we would describe the RNA-seq pipeline. So I wonder if we should... So what you're going to do is put in the inputs to the various pieces of software which are in black about the RNA-seq pipeline. Okay, so I'll try to... Oh, okay. Sure, sure. I guess I can do that. Let me just... Okay. So people who are at this step, I want to go back for some of them who haven't done the original thing. So I'm going to go way back to the beginning. But you should be able to, like, go download the handout from the encode2015.org website. And that can help you follow the details while I help the people who are even behind you, okay? Sorry for that, but that's just how we do it. Okay, so you should be at a page, something like this that says projects, right? You should have nothing here. So what you do is go to the encode uniform processing pipelines. What? No, that's why I didn't tell them to run it yet. Oh, well, then that'll be fine. Okay, so those of you who clicked run, that's perfectly fine. There is one little subset. That's why I didn't say actually run it, which is that you can specify where the output files go, like in another folder. So the people who are running it, you'll just get a pile of files in a folder, but it will work fine. It won't make any difference.  All right, so for these people who just got internet, so you got a processing pipelines here. This is the master copy of the thing, right? You copy the long RNA-seq in the reference files. So you hit this copy in the left-hand corner. So you're not going to have these two, so you need to create a new project, which is this crazy thing. See this? Just name it whatever you want. I just named it demo type in demo two in the box there. So click copy, done. Okay, so now you're still where you were when you started the copy. So you have to get to where your new project you created. So you have to go back on this arrow here. You should see your project you created there, right?  In here there's a folder called long RNA-seq. Click on that. Then if you scroll down a bit, there's these guys, which is the universal symbol for workflow. How do you get files? So I don't think we're going to have time, but what you can do is if you go to, like, if you want an encode file, yeah. You can... Basically there's an add data button if you ever want to put fascues in your system. And add data will let you copy a file from a URL or a file from your computer. Please don't upload any files in this room now. No idea what it will do to the internet. But that's how you get files in. It's pretty easy if you just poke around a little bit. All right, so... Oh, this is the wrong one. Don't click on that one. See, this is the two-replicant one. Don't do that. Missed it. So this is the one... Here, I'll make it wider again. One-replicant paired end for you guys, and you should get to where we are inputting stuff. Okay? Press... It's got it. Okay, I'm going to go through the inputs again. That's... Yeah. I... You're welcome to try to design the UI. It's not my system, so... Interesting that it's not... Yeah, I mean, some people like using the command line and just write your script, so you can do that, too, if you want, but not in the demo. All right, so... You get the read one. So this is a list of possible fascues you can input. I probably should have deleted some of these, so take the one that says... We're going to look at the two that say chromosome 21 hemi. So for pair one, just pick one. All right, I'll do it again. This one... ENCFF646CCF underscore 1-CHR21 hemi. Okay? And now the other box, we're going to do the same thing, but get pair number two, and now we need an index genome for star to align to. So we click on that box. We don't want a mouse, one from MM10, so we want a human. We're going to use the male, and we're going to use the version that is ERCC, which is just a code name for the spikens, which... So I think I'm going to try to this slightly perpendicularly for people who are following along, so we're just going to finish this top step here. So we're going to just click configure params, and this star here means that this is a required parameter, and that's why it's orange and not green. So we're going to just click configure params, orange and not green, so you can put in any sort of text string in here as an identifier. So we use this for a while for like ENC, BS, 111, but it doesn't matter, you can put in any string in there, so demo is fine. Save that. That step is done, right? So this alignment step, just because I think we're not going to have time to go over the whole pipeline very much, is runs an alignment of your paired fascues against the genome that you selected. It creates this is a essentially some QC numbers that you may see at the end if we get there. It creates a regular alignment file to the whole genome. It creates an alignment file to the transcriptome, which is included in this genome index. It's gencode version 19, and it has a log file which has other numbers in it, right? So we're just going to repeat that partially for the other four steps. This is the alignment of two top hat, which is just creating an alignment BAM file. The inputs are already set for you, so we just need to set the index, and again we're going to take HG19 male V19 ERCC top hat index. Put that in there. We're going to click configure, params. We're going to put in demo into the identifier for biosample library box. Now one's green. Anyone having trouble turning these two guys green? Great. Yeah. So the next step is the RSAM quantification which takes the annotation BAM. See if I can show you this here, because I want to do it anyway. So see how this output on the right is marked to that input on the left. So that file is going to go when this file is created, it's going to go directly into this step. So we don't have to put that input at all. That's part of the pipeline is that it's all plumbed together. We're going to take the index for RSAM. So here we just need the HG19 male V19 ERCC RSAM index. That's good. Still not green yet. Configure the parameters. Now this one we need to tell RSAM how we're going to use this for RSAM quantification. We use this piece of software for both the paired end and the single-ended, so it's got to know which one you're inputting in. The output of that file are what we are just calling these results files. They're just tabbed limited files with genes as rows and like FPKM, TPM and other numbers there. The last two steps are going to take the output files one from Star and one from Top Hat. They're going to convert it to BigWig files and the BigWig conversion script needs a chromosome name-length file. So we click on that. It's going to open another window. Get to pick again our genome which is going to be male HG19 chrome sizes. I should point out that you can arrange these in such a way that they're coordinated but I wanted people to go through the steps of actually doing it. So our things are all green now. Is everyone there at this point? All right. You still need help? Nope. Okay, we're good. So set the output folder. This is not a critical step but it will keep your files managed if you put the output in the right there.  So you click on that arrow down in examples there's input and output. So you just click output for example or you can give it any folder. It selects. There's nothing in it. That's why it says no data available. It's an empty folder. That's what you want. So now I've got a folder in there. I can click run. Trumpets. Okay, so this when you run it it takes you to something called the monitor tab. So that before we were working sort of the manage tab and the monitor tab will show you all the jobs that you have running at any given time as well as all the jobs that were finished all the jobs that may have crashed all the jobs that you turned off. It can go as far as you want. So this is the master job. If you notice this plus sign here it will show you all the sub jobs that this job is going to create. Hopefully. The second. Right? So these little lines here correspond to those steps in the workflow that we started. So there's two alignment steps. A quantitation step. It's a little bit hard to see. And two BAM to bigwigs stranded steps which is the correct one. So it requires the quantity. Let's start with these. The BAM to bigwigs, I'm sorry I'm a little thrown off. The BAM to bigwig steps require the BAM to be created otherwise it can't create a bigwig, right? So these will run automatically when their respective alignment steps are completed which will take on the order of 25, 30 minutes with this little example. You run a bigger example it can take longer. Similarly, to quantitate the genes and the transcripts we require the output of star which is what we're going to quantitate. The encode consortium decided that we only needed one set of quantifications but we wanted to do two sets of alignments both to compare to previous experiments that had to be done and also because Topbat is sort of the industry standard RNA-seq alignment file but we found in our hands that star actually performed a lot better so we wanted to use that as well. Okay, it is now. So I think people have gotten the hang of raising their hand if they need help but I'm going to check here anyway to see where people are. If, because I, especially in the time straight, what will happen here is that if this, if one of these jobs actually dies or something, like for example, and this can easily happen, if you gave it the wrong input, right, like if you accidentally, it was hard, but if you, if you put the Topbat, if you really go out of your way, you can do this. You put the Topbat index into the star BAM which you shouldn't really do but it will run for a minute or two and then throw an error and it will turn red and you can actually, from this interface, right, look here, so I clicked on the job that's one of the jobs that's actually running. It's coming up a little slow here but I can actually view the log file that it's creating so you can actually, you can actually effectively peek into the virtual machine that's running this job and it shows you all the output of what's going on. I'm not going to do that because no one wants to see a bunch of text stream but if you want to do that there. I think, yeah, the R symbol, the star alignments which, it's a great question. I can show you also like, I think I was trying to demonstrate it but I'll trick it in here. Actually, I think, you have a question? Okay. Well, you can go back here. So I'm skipping some assets because he's busy. Skip that. So here, here's sort of a view, like a on one page view of what you guys just ran. So, I mean, there were good reasons to do this first but I did want to try to get it started. The code that's available that, so each of these, those applets or those steps here are just a little shell script that runs a program that you can download like Top Hat or Star, sorry, or Star or R SEM. Or here, there's a step that runs, actually these are sort of the steps here. Maps, this maps with Star, this maps with Top Hat, this maps with Star. This converts the bams to big wigs. There are a whole forest of big wigs that get produced. So for a stranded, a stranded data set, it will give you the plus strand and the minus strand. It also gives you all the reads, including the non-uniquely mapping reads and there's a file that's just the uniquely mapping reads. So that's four per alignment program. The Star BAM outputs are also shoved off through this quantification here. So at ENCODE, we have a rule that we always run everything twice. We always run everything twice. So what I've drawn here is this is that, so for every experiment we have some form of replication, either a biological replicate or a technical replicate, ideally a biological replicate. We talked about how we had a multiple replicate pipeline or a workflow you could use. So that effectively, for the same experiment, sorry I'm losing my mouse pointer here, we can take the quantifications that are output from, there's one more step in that double pipeline, the double replicate pipeline, which takes the RSAM quantification file based on genomes from one replicate and the one from the other one and runs some QC calculations based on it. We are currently working on the IDR calculation for RNA-seq, but we don't have it implemented yet. But we do do something from Rafa Izrahi, which is like a, it measures the dispersion of the log twos of the sec ratios. Tell you more about that. We get, from each replicate we get two BAM files, we get, if it's paired we get four big-wig files, times two is eight, and then we also get two quantification files, for which the genome quantifications are much more reliable. The transcriptome ones are more for reference, but it's hard to actually judge isoforms from these methods. So you had a question, what a great question. There is indeed such a way. I was waiting for someone to have a failed job, so I could show them. Anyone have a, did anyone job fail? Yours did? Okay, so then this is for you. So go to monitor. I can't really do it because my job's not failed, but it will sort of told you, here, go to monitor and let it spin for a little bit. So when you click on the plus symbol here, it will show you all the jobs. So the whole thing will fail if any one of its sub-job fails, but you probably want to check to see which sub-job fails and look at that. But for the whole thing, what you can do is you can... I don't have an example, do I? I can maybe switch to one, but what it will show you is that on a failed job, when you click on this... Actually, let me do it anyway. There should be a button that will show up here. This shows up, which basically just says rerun. So rerun with the same input. Now, obviously, if you rerun with the exact same input, then it will probably give you the same error, not necessarily. But it will rerun with what you loaded and then you can look through it and change what you need to change. There are actually the files that are specified as outputs so that they're on the right-hand side of that screen. Those are not cached. They're permanently saved until you delete them. So the DNNX system keeps track of what job used to create them and what parameters were used to create them. So if, for example, this can happen, certainly when developing your own pipelines in the system, let's say my alignment step works really well, but I have some bug in my quantification script. So I run my whole pipeline, the alignment works perfectly and the quantification fails. So now it's not because I had bad input, it's because there's actually a bug in my applet code, which is we developed all the applet code with help of the encode data analysis center. And so we had to effectively not really compile but ship that code to the DNNX's platform. And so if there was a bug in that code, which there were many, which we have fixed them all, there are no more bugs. What you can do is remake that applet and then rerun that step depending on the same previous input. So that's one of the advantages of having, and I think when that type of technology is very difficult to implement is one of the reasons why we use this system is because it's not that it's impossible. There's a, you have another question? Okay, there's, sure. Yes, it depends on how you've implemented the applets, right? So for our purposes, we do want to keep them as efficient as possible, but we gain a lot of throughput by basically running a thousand, like literally a thousand, we can submit a thousand experiments at once, and if the whole thing takes five hours, they will be done in five hours because we have effectively an infinite supply of cores at Amazon. But you can also change the number of threads. You can optimize your applet so that it speeds things up. And last question, at some point, can you show us how to upload FastQ files and download other kinds of files? Yes, that's the second time. Can you do that around, I don't know when we should, we'll definitely try to squeeze it in or find me if I don't get to it. So another question in the back. Yes, so the question is that, is your top hat and star parallel or serial? So one after another with the unaligned reads from one step taking to the other. That's my first question. And the second question is the star index is very sensitive to the length of your reads.  So the first question I think is pretty simple. If I got it right is that, what is the dependency of the star and the top hat? Alignment steps? So they're completely independent. They use the same input and they have completely different outputs and they don't interact. Okay, so what do you use with the top hat output then? Because in the flow chart you're saying that you're using RSCM only with the star and you're not merging them together or... No. So what are you doing with the top hat output? Well, the idea is just to have a comparison for previous data that was done. I mean, what it has to do with the fact is that the consortium or the RNA working group as a whole really liked how the star program performed and to be fair it was written by Alex Dobin in Tom DeGeris' lab, but other groups who use top hat mostly wanted to compare the results directly at the BAM level but they weren't worried about producing the quantifications for it. So your second question is a technical star detail about the length of the reads, right? So I don't think we take that into account but since Alex helped us write the pipelines, also almost all of the current RNA-seq data we're running through it is pretty much the same length because it's done by the same people, but that's a good point. We might have to remake the indexes for that. Yeah, for the users, you know, because for you it's the same length for the users. So like you think it's different at 36 and 100? Yes. Okay. Yes, anything above 100 it should be okay, but anything below 100 if you have 50 or 60 bases, you know, around that range the star index really. Thanks. That's really useful to know. I did not know that. So there's a question. There's a question that's been asked by a couple people so I wanted to bring it up for everyone because you may have the same question. The question basically is do we have a pipeline for paired and unstranded libraries and as a defined encode uniform processing pipeline, we do not because all the encode RNA seek data that's been generated by the consortium is either single and unstranded or paired and stranded. And so that's why the pipelines that you see in the encode uniform processing folder, pipeline folder are those. However, you can take a look at the parameters for the single and unstranded. Take a look at those. It is essentially the same software components. You can look at the parameters. You can modify the apps, the applets. We actually did have a few test examples that were those, the other two. So paired, unstranded, paired, unstranded and unpaired, stranded. Sorry. But we didn't actually... It was already four pipelines. It was already too many. And actually, I don't even think you need to do any coding. I think you can just rearrange the steps in those pipelines and make a new workflow and it would work if you needed that. If someone has a specific use case, I'll show you how to do it. I guess we could publish them too if we could organize it in such a way. Yeah, but I think that the point that Ben addressed was also the one that I wanted to bring up is part of the beauty of the DNA Nexus platform is that you can mix and match applets that are public to make the workflow that you want to run your data through. So approximately 40 minutes ago we were supposed to have Seth talk about Gypsyq. It's okay. Actually, we have this built-in. So I think you want to go back and do that or you want to skip out. Let's see how long my job has been running. Let's do that. Because I think we can analyze it. So it's been running 10 minutes. So I believe that the earliest we can visualize is about 30 minutes, which is probably going to finish us up. So there's a couple of options. One, I can go to... I have an example of this already completed in a different account. So I can show you how to take the BigWig file and draw it to... to draw it on the UC Santa Cruz browser, which is kind of cool. Or we can have Seth come up and we can talk more about the pipelines in general without in sort of a less workshoppy way. But... Okay, sounds good to me.


Here we go What is up guys and welcome back to the Johnny Q channel? Thanks so much for watching I'm actually really excited about today's video because for the longest time I've wanted to collab with photographers in the area now when I say collab there's so many different things that we could do But I decided to kind of start out small and see where this goes and potentially even do more of these But what I did was I said guys I'm gonna give you one photo that I took and I want you to edit how you would edit that photo And basically I chose that route because there's so many things that we could have done But I like the idea of that every photographer a videographer out there every creative has a different angle a unique angle to Their story and how they do things creatively that simple edit can do more than just put up really good colors and make it look good It actually can give people emotions. It can give people something to think about and it can move someone to do something That's why I love photography So that's why we're doing this segment and in edit the same photos so pretty much six photographers including myself are Editing the same photo that I took and with every edit that comes up I'll show you who edited the photographer and their Instagram so you can go follow them So let's get started and just a quick reminder guys I also have a soundtrack giveaway right now going through June 20th Which is next Thursday and that's three months of free music through soundstripe All you have to do is subscribe to the channel number two comment below in one of my videos hashtag JQ and you'll be entered to win one of the free trials that I'm partnering with soundstripe So first before we even get started into the edit I'm gonna show you the original picture and that's this right here. It's an okay photo. It's shot in a raw and Let's go right into it. The first photographer that I'm collabing with is Brighton Dressel. He's a really good friend of mine. This is his Instagram follow him awesome photographer an amazing videographer and This is his edit. Okay, let's break it down. I Like it. Yeah, I mean, there's there's like no wrong way of of doing this edit I there really isn't and I like it because of two things it shows his His character and his edit style if you go to his Instagram page You'll see that his page is full of kind of this like really saturated yellow tone kind of almost in the field Golden hour, you know sunset kind of feel and I love that I dig that about his edit Obviously, he didn't like what was going on on the right hand side here along with the little, you know US Bank ATM or garbage Whatever that is but he crop that in so that you can only be focused on those arches kind of making you think What are those things and then obviously the skyline again with his edit of the golden hour saturated? I don't know what that's called dude, Brighton, but I love that edit. You should name it something like Name it something yet. You got it. You got it. Okay next photographer Jeremy Lee a Jeremy Lee is another photographer in Minneapolis amazing dude awesome hang and good photographer This is his edit. Okay, let's look at it. Let's analyze it. I Like it and I'm not just saying this because they're my friends or because I'm trying to be nice I genuinely like these photos and these edits So if I didn't like it, I tell you guys but I like that he went black and white only because Everybody else did color and that's fine, but the black and white in this photo. I think really does work only because There's so many things happening in this photo that I think you can actually pick up on like features Like if I can zoom in. Oh, that's not do man in right here Like all this feature all the features in the background like kind of makes you wonder the people walking like on the railroad Wow, what is going on? Apparently I can't use Lightroom today. It almost I mean it makes me feel like it was shot like a Film camera almost. I don't know if the grain was intentional, but there's some grain in the edit I like it. I really do Jeremy. You killed it and moving on Alex Mel Dosa his Instagram handles right here. Go follow that guy that guy is a workhorse I think is an LA somewhere in California and he works to the bunch of different artists and he just goes hand with photography every single day Okay, let's look at the edit Yeah, I mean it's I like it close to the original which I like but it's not like over saturated or it's not over Processed it's kind of like true to the picture true to the actual setting But so you can see where the sun's coming and hitting on the arches and then the shadows in the back where it's getting darker Obviously, I don't think this this was not cropped which is fine So it leaves you wondering like what's this stuff and on the right hand side like are those bars was I like on a staircase? Which I was but it makes you wonder like okay, there's more to you know There's more to this picture than what's set on screen. I like it. It's very simple but yet effective very effective Alex You killed it. Good job, dude Next on the list is Isaiah Instagram handle right here Go follow that guy because that guy is literally crazy He just got done with a 45-day challenge and that I think he was taking a photo every single day for 45 days Trying to improve himself trying to make himself get out of his comfort zone and shoot something for 45 days That's that's so dope. That's awesome. I'm encouraged by that. Okay. Let's look at the photo. Let's analyze it. I Like it because it's close again to the photo that was taken like I'm saying that golden hour You can tell it's it's somewhat processed a little bit But what pictures aren't processed again? This makes me feel like it was shot like a film camera the way it was edited I don't know just something about it. Is it just me like let me know I feel I Feel it looks like you shot in film, but it was not it was shot in a canon 5d mark, too Yeah, I mean it just to me looks old-school that this edit. Yeah, it's great. I like it Again kind of that golden hour saturated look in some parts like the arches down here. I Mean we've been down here in the right hand corner where the trees and the light in the poles I mean the buildings are really crisp. You can see the Sun kind of just Kissing the buildings this building over here looks like it was Photoshop. It wasn't but it just does it looks that way Yeah, I say you killed this dude. This is awesome. These are all so good right now Next on the list is Miroslav. Okay, this guy took some of my photos on our second anniversary with my wife and that was Purely amazing. This is his Instagram handle. Go follow that guy. He does weddings.  Let's analyze this this edit here I like it because it's so much more different than the other ones It's it's got this really just yin yang dark and white kind of thing going on for it Like I don't know if it's really in golden hour I suppose. Oh, I totally love the dark Like like the dark and brown gold down here in the right hand corner The Sun's kissing the buildings right here right down there and then you have this like Stark blue cold area and in the skyline and then you come down here You come down to the south side Then you have this weird architecture with these like really weird art artwork on the buildings and arches And it just gets super light and then it just gets really dark kind of it kind of makes you move into You start here you move you go here and it brings you all the way up. Like he says how that's how I saw it That's a good. It's a good s-curve. I like it.   Good job. Love it. I think that was all and this is my edit Okay, that's my edit Obviously, I kind of cropped it in as well. So you're not seeing the bars in the right hand corner I wish that stupid US Bank ATM or trash can wasn't there. I wish this pole wasn't there I probably could have photoshopped it out, but I didn't want to go to that length Maybe that's just being lazy on my part But again, it's part of the picture like why fake it. It's all there the the pole was there as well So let me just look at this photo again But when I tried editing this photo I specifically tried making it true to what I was saying when I took that photo and that's what I like about You know photography and how I edit my pictures and I take a photo of something I really tried to make it not over processed But in a way that's processed where yeah, I took that photo and that's what you saw Like if you were standing right in that little spot with the staircase in front of the US Bank Stadium in that time of day You would totally get this exact same picture Without camera you'd just be seeing that so that's why you know, that's that's the way I edit things doesn't you know You don't have to edit that way, but that's just the way I edit I mean, I like it just because it's just true to what you could be looking at at this exact same moment The highlights are good. There's dynamic range in this photo. I love I love the shadows within the arches and then over here you have the sun kissing this building right in the right hand corner And I like it all I really do I like it that much I would frame all of these edits They're just that good and just so you guys know this photo was shot on a Canon 5D Mark II an Old old camera which really goes to show you you don't need the latest gear to take good photos You don't need the latest camera equipment the latest lens to get good edits You just have to kind of know what you're doing with the camera point and shoot and make sure you're really exposed properly not over exposed and You can edit somewhat well in Photoshop Lightroom or whatever you use just make sure it's not over processed So there you guys have it that is six photographers editing the exact same photo and I'm gonna tell you it turned out super awesome Thanks so much for everyone who collaborated on this edit with me. Thank you so much again Please follow them on Instagram Give them a like give a follow and let them know how cool and how awesome they are taking photos So with that being said guys, thanks so much for watching and I'll catch you next time in the Johnny Q channel. Thanks. Peace


Girls get flowers, boys get tomahawks. I'm mad at my heart. That is definitely my favourite colour. Blood. Smells like Betsy. Oh, Betsy. My first time flipping. That one went into the slips that one. Good night, sweet prince.


them coming home and breaking the big door, we were at the door of the church. And we heard them fighting in the house. And Yossi says, I'm from behind, I'm from behind. I'm holding the door, and you see, there's no room for them to open the door. The door opens like this. And then, with the help of a handkerchief, it opens the door, and says, there's nothing to do. The door opens one by one, and it's really like a scene from a movie. We enter the right corner of the room, like this, and the door opens. The door opens like this. And they were in the room. They're still looking at us. Yossi, I'm from behind. And we say, don't hurt us and ask. Don't hurt us, don't... the door is open, and then we enter another two. And then they start to open, and they open a big door, like this, with a handkerchief, with a knife in the back. And then they start to pull on me, like, on the walls. And I say, don't hurt me, like, don't hurt them. Like, my daughters, we're still looking at them, and that's what they're doing to us, with the knives. We're all in the dark room. No one is angry, no one is talking to us. They're all in the room. And they're looking at us like this, with a handkerchief. They're looking at us like that. They're looking at us like this, with a handkerchief, like this, like this, they're looking at us like this, with a handkerchief, like right now, with a handkerchief, half Egyptian academia, half Palestinian stuff, half Egyptian culture, they're still looking at us like this, with a handkerchief, with a handkerchief, with a handkerchief. And they're looking at us like this, with a handkerchief, We're all here together, and there's a small chichor garden. Both of our guests enter the kitchen door, and the door was open. It's like a door. They enter the kitchen door, and the door opens from this side, and the door opens to enter the kitchen door, and then they enter the kitchen door. And then they get out, and they call the doctor, who's always here, and he tells her, we're going to open the door, and they try to get in this way. This is the last point I've seen. We're staying here, standing here. We're standing here with two people, who just wanted to get out of the kitchen door, and to get out of the kitchen door, we were staying here. And then I told them, OK, let's go. We have to get to the door, so we can get out of here, and then we got out of here. I left the kitchen door, and it's a house that the guests were already in, because all the guests were there all over. We got out of the kitchen door, and we're standing in the kitchen window, and I went to see where the door was. The door's door was open. So we just got out. And then we got out of there at home, with my hands on the head, and then suddenly we realized that there were other people inside, and that's the first thing that everyone's in the house trying to figure out who's in the door, who's not. Everything is in one big order. So we got a message, that he's in the kitchen, and he's a chef, and after a while we realized that he's in the kitchen. Look, I'm in my room on the balcony, and I know what's going on there. A few other people can currently be identified more with the word kibbutz than Dr. Alon Pauker.


Hi, I'm going to show you how to easily password protect the Google form so you can keep people that you don't want populating your data out of your Google form. So first thing you need to do is create a new form and your very first question should be the password. So for the question title, I'll just title it password and please make sure that this is a text question and then go down here where it says data validation. Click on that. Check the box. You want to make this regular expression contains matches and here's where you have to put your password in.  So right in here, it is case sensitive. So make sure you type exactly like you want the password to be on the custom error text. This is where I would just put in. This is what someone will see if they put in the wrong word. Most importantly, make this a required question that way they can't skip by it. Everything will be set up. This will be the first question they see. But in order to get past it, you really want to have all of your quiz on another page. So right down at the bottom where it says add item, if you were to click the drop box, go over to a page break and then from here on out, this would be your questions. So here's your quiz and then you can add your items. Maybe it's a text question, not doing so well typing today. And then as you go, you would make the rest of your quiz. Now I'll show you what this looks like once you go to that live form. The first question that's going to be asked was that first password that we had set up. Now you have to remember what your password is and we put your password here. So when I go to that live form, the first question is enter your password. I'm going to put cat in, spell it wrong, continue incorrect password. Your password here and then that will let you go on to that quiz. A pretty easy way to make sure that your quizzes are only given to students that you want to take them, not somebody else. If you want to put a link on a website, now you can. In order to take the quiz and populate your data, they'll have to have the password. Hope that helps.


End user license agreements. The bastard brainchild of corporate lawyers and the makers of impossible to open plastic packaging. We've all seen these things called end user license agreements, or ULAS, or rather we haven't seen them. They're the things we click to get rid of so we can get to the really important business of puppy burrito photos on Instagram. Just listen to this presentation at Google about ULAS. How many people read license agreements when you install software? This really surprised me, so no one here reads license agreements ever. Great. These are some of the smartest software engineers, and even they don't read before clicking agree. The last time someone agreed without reading the text first, Harrison Ford ended up in Indiana Jones and the Kingdom of the Crystal Spell. Bad choice my friend. Next time, read the script. It's not just that people don't read ULAS, they can't read them. ULAS are lengthy, boring, and tedious. They're basically season 14 of Grey's Anatomy.   You're done. According to one study, it would take 244 hours a year just to read all the privacy policies you accept. And that's only one type of agreement you encounter every day. In 244 hours, you could binge watch the entirety of friends three times in a row. Ross and Rachel would have broken up and gotten back together 12 times before you finished reading those policies. And yes, they were on a break. Privacy policies are also relatively short, usually just a page or two. Compare that to Apple's iTunes ULAS. Consumers guide to iTunes, its new 56th page, Terms of Use. Who is going to read through all of that, and if you did, would you even understand it? Exactly. No one is going to understand the 56 page ULAS. The instructions for Monopoly are only eight pages long, and no one's read the whole thing.  Take that hotel off Fenton Avenue.  It's not your turn. In fact, the British company Game Station ran an April Fool's Day joke of inserting into its ULAS a term requiring customers to hand over their immortal souls to the company. Seventy-five hundred people agree. Seventy-five hundred immortal souls, sold to a company that sells sci-fi games about aliens. Take that, Scientology. Now, because no one reads ULAS, companies feel free to put all sorts of bizarre and ridiculous terms in them. Apple, for example, states that you are not allowed to use iTunes for making nuclear missiles or chemical or biological weapons. And RemixOS, a type of Android operating system once included terms in its ULAS, warning users against spreading rumor, disturbing social order, or undermining social stability. Those are terms that belong less in a software contract and more in Mao's little red book. But ULAS aren't just harmless jokes about immortal souls. In large part, they're about controlling you for the consumers. Ordinarily, when you buy something, you can do what you want with it. If I buy a USP flash drive, I can give it to my friends. I can throw it away. I can fix it if it breaks. I can even slap on a mini-statue of Justin Bieber, and I'm not even sorry. That's what we mean by ownership. But companies don't care about those rights. In fact, they're perfectly happy to get rid of them. Why should I get to make my own custom flash drive when I could pay good money for the authorized license to limited edition Bieber drive? I'll bet it seems to. In fact, cutting off your consumer rights is the very reason that ULAS exists. Here's an interview with Seymour Rubenstein, who claims to be the inventor of the ULAS. Well, the way I thought of it is, I thought of it just like phonograph records. Right. You're on the record, but you're not on the song. Right. And so, you're not on the software, you're on the meaner. Copy of the software. Now putting aside that the creator of the bane of everyone's existence is basically John Lithgow's creepy third uncle. He's got a point. If a company can use a ULAS to cut off your ordinary consumer rights, then the company has all sorts of power to control things. So the question is, does this trickery actually work? Can a company use a ULAS to say, you don't own that product you bought, and you have to use it the way we want? It turns out in just a few days, the Supreme Court will be hearing a case about this. The case is called Impression Products versus Lexmark International. Lexmark makes printers, and it wants to make sure that when you buy a printer, you have to come back to buy ink and supplies. But when you buy an ink cartridge, you'd think you own it. You could do things like, say, refill it with ink. So Lexmark sells its ink cartridges with an agreement. A ULA prohibiting you from refilling or even reselling their ink cartridges. And if you violate those terms, Lexmark says you're actually violating its patents, which could put you on the hook for massive damages. And who is this Impression Products company that Lexmark decided to suit? Some giant corporate behemoth? Impression Products is a family-owned business headquartered out of Charleston, West Virginia. My father started the company, Walter Smith, in 1978. The small family company is in the fight of its life for refilling ink cartridges that it legally purchased. Imagine if you owned a car and could be sued for refilling the gas tank with the wrong brand of gasoline. Or if you own one of these highly collectible Duck Dynasty chia pets. If I want to grow alfalfa on Willie Robertson's beard, it's my God-given right to do so, and no one can stop me. Indeed, if Lexmark and its ULA conditions prevail, it could have widespread effects for repair shops, resellers, and all consumers. As Impression Products attorney explains here. The question is whether or not there can be resale markets for patented goods. And that includes virtually anything. That includes cars, cell phones, any equipment that companies buy and use in their production. If Lexmark's rule is correct, the seller of those goods can say, once you purchase it, you don't have the right to resell it on the open market or you don't have the right to repair it. And the point is, those are rights that are really important to everyone. They seem especially important to this one video blogger. The moment you sell me something, I should be able to do whatever the f*** I want with it. And user license agreements are the devil. They should be banned. Maybe that sounds a bit harsh, but comparing ULAs to the devil isn't that far off when ULAs sometimes claim your immortal soul. It's not just patent law that companies use to strip your ownership rights. For years, they've used a part of the copyright law, Section 1201 of the Digital Millennium Copyright Act, to make it a felony to repair or use your stuff in ways that they don't want. John Deere of the Tractor Company has actually threatened farmers who dare to repair their own tractors.  So while we wait for the Supreme Court to decide the Impression Products case, what can you do to protect yourself from ULAs? For starters, you can try to not be like this guy. He just goes, uh, agree, agree, agree, agree, agree. Don't do that. Don't be that guy. But more importantly, we think it's time for consumers to fight back against ULAs. So tonight, we are proud to present the Consumer's Counter License Agreement. Because somewhere out there, there has to be a software company, or printer manufacturer, or corporate lawyer watching this. If you're one of them, this is for you. By watching this video, you agree to the following terms and conditions. If you do not agree to these terms, then you should have stopped watching this video already. And now it is too late. You agree that the number and length of ULAs are too damn high. You agree that no one should be made to accept 56 pages of terms that they couldn't possibly read all of? You agree that I can grow whatever plant I want in my duct-times to achieve that? You agree that terms and conditions written in legalese are unfair to consumers who don't want to spend 244 hours a year reading that? You agree that when I buy something, I own it, and you don't get to say how I use it with your ULAs? You agree that everyone should be allowed to repair their cars, their tractors, and their Justin Bieber flash drives?  Thanks for agreeing to agree to this agreement. Now please go make your plastic packaging easier to open.


I want to do a triathlon at some point, that's my goal right now. Oh, I was just thinking about this the other day actually. I think it kind of ties into what my greatest fear is actually. It's like becoming great, which is vague and like who knows, but I just want to do something that changes the world and impacts the lives of everybody and unites us all like I was saying before. So I think I have future plans to do environmental economics or environmental and international law. I think what if my goals would be to, I already know two languages, so probably learn three or four and travel around the world and do what I love. Travel all over the world. Make a lot of money, to be honest. Just be successful and have a great family, you know, like start a great family and you know, just live the best life that I can I think. I don't know, I've been thinking, I might be a little young to say this, but I'm thinking about it, you know, like I just want to be a good dad at some point, you know, just thinking about like being at school for the first time, you know, like living away from your parents, you know, you just kind of realize how much they really do for you. So, you know, I just been thinking about that, you know, like just trying to be like raise my kids, like when the time comes in the same way that my parents did for me. Well, like career goal wise, I'm trying to be coming after. So that's something career goal wise, but something bigger than that. I would say just like in the right now, I'm like trying to get out of Fordham and like really just like, I'm a sophomore, so I want like my next few years of college to be like, I want to be somewhere where I want to be and I know it's not here and I know that I can't thrive here. So I'd say as of right now, I'm really just trying to get out of Fordham. I want to publish written works. Create something like significant artistically, I would say probably, but you know, I don't know, something on those lines, just be happy with it. Well, my goal is to go into healthcare management, so I definitely do want to make a lot of changes in the healthcare management. I also feel like the healthcare system is horrible right now. People can't even get free healthcare when they really need it, so I definitely want to work on that and along with that, I definitely want to one day hope to start my own nonprofit and help out a lot of minority groups, whether it's like minority like Hispanics, African-Americans, women or like LGBTQ community, just help everyone accomplish their dreams and don't have people question minorities or any sort of thing. I want to become a doctor when I grow up. I started majoring off in biology doing pre-med, then I went on to doing economics and business, so I can do that as a side job, but then later on I'm going to go back into pre-med, take my MCATs and graduate, become a doctor, and try to help out people, because there's a lot of people that need help and money's always an issue and I wouldn't want that to come in the way of people's lives. Just making everyone around me happy, so like the ability to, I don't know, make my mom laugh every day or make my dad laugh every day, cool, or the ability to like just hang out with every single one of my friends. The greatest thing I can achieve is to make people happy at home. That's a good question because I'm graduating, so that's something I think about a lot, but I don't know, just helping other people, like making an impact, and I don't know, I don't really care about like leaving a legacy, but I want to do something that'll help, like continue to help people pass like when they're gone. I'd like to make a little difference, you know? I'm a political science major, so if I could get involved in any way and offer an opinion that people appreciate, I think that means a lot to me. See my voice be heard. I would hope to have like an impact, like a positive impact on the environment, sort of changing how people think of like the environment and sustainability, like how can we live our lives in like a great way, but also just be sustainable. So that's something that I'm trying to work on, but I don't know. Definitely like at a young age, like coming out of school, I definitely like want to make like a substantial amount of money, but then like after that, once I'm content, I want to do something that like I'm really more interested to make myself happier. Like I'm a big sports person, so like I'm a accounting and finance major right now, because I feel like that's where like a lot of money is, and then after that, you know, I feel like doing something more like geared towards like family and helping out other people that's something I'm bigger. Man, I just want to change lives. I want to help as many people as I could. That's pretty much my thing. Some impact on the world somehow, which I know is vague, but something that actually progresses our world into a thing, into a thought process that we, that people our age can actually achieve something, and it's not just we're doing cool things.  I also live a little better than I did. I hope to travel a lot. I hope to, you know, I hope to make the places that I live more sustainable in the ways that they function, and I hope to just, I don't know, I hope to just meet a ton of really cool people.