Daily Tech News show is made possible by its listeners, thanks to all of you including Justin Zellers, Pepper Geesey, and Eric Holm. Coming up on DTNS, Netflix has a change of heart. Open AI gets more open about Dolly too, and Quinn Nelson from Snazzy Labs explains why the M1 Mac might, might be the best gaming laptop out there. This is the Daily Tech News for Wednesday, July 20th, 2022, that's a lot of 20s. In Los Angeles, I'm Tom Merritt. And from Studio Redwood, I'm Sarah Lane. And I'm the show's producer, Roger Chang. Joining us the host of Snazzy Labs, Quinn Nelson, welcome. Hello. Thanks so much for having me. I'm excited to be on. I'm not in an exotic place like you guys, but that's all right. Where are you? I'm in Salt Lake City. That's quite exotic. This is our second Salt Lake City person in a row too. Hey, you know what? Good things happen in news, they say. Well let's start. With a few tech things you should know. Oh, it seems like just yesterday we were talking about how younger folks prefer using TikTok and Instagram over Google search to find places nearby. That's because we actually talked about it yesterday. On Thursday, which was also yesterday, which was Tuesday, that's what I meant to say. Meta CEO Mark Zuckerberg announced a new searchable and dynamic map feature on Instagram. So it's all kind of getting, it's happening, letting users explore popular tagged locations around them, filter location results by categories like restaurants and cafes, beauty salons. Instagram's map experience has up until now only included posts. You might have said, well, they already had maps, right? Yes. But the update now lets users search the map itself for not only posts, but stories, guides and hashtags. But they don't use maps. Wait, that wasn't the point of that story. I think I misinterpreted it. According to data from Sensor Tower, BeReal has hit number one among free apps in Apple's US app store. Sensor Tower also reports that about 35 percent of BeReal users are in the US, followed by the UK with 17 percent and 10 percent in the home market of France. The app has been installed more than 20 million times. If you're unaware, BeReal is the one that just lets you post once a day. And the idea is it will tell you when to post and give you two minutes to be real about what's happening in your life. Microsoft launched a little social app in its productivity chat and a little voice chat in its gaming app. Viva Engage is an app inside Teams that encourages work-based social networking for all Microsoft 365 commercial customers. It includes a news feed-like storyline section and stories for ephemeral content. Viva Engage will replace Yammer communities for teams, but the Yammer platform will continue to be available. Content created on Viva Exchange will be viewable on Yammer and vice versa. Meanwhile, Discord voice calls are coming to the Xbox mobile app. Once those are linked, the calls can be connected to your Xbox One or series console available now for Xbox insiders. At the Alexa Live Developer Conference, no, I triggered mine. Sorry, apologies for that. Amazon announced several new changes to how its voice assistant works. Developers will now be able to create routines for users. You'll still be able to create your own routines, but as an example, Jaguar Land Rover is offering a good night routine pre-made for you. If you execute it, it will lock your car, check the charge and fuel levels, and turn on Guardian mode. Universal Commands is another feature Amazon announced. It will pass along a command if Amazon can handle it, but the voice assistant you're talking to can't. This is if you've got more than one voice assistant on a device. For example, the Verge pointed out Skullcandy headphones. You could say, hey Skullcandy, set a timer, and it would pass that on to Amazon because the headphones themselves don't have timer functionality, but Amazon does. Ooh, this is getting interesting. Google began publicly testing smart glasses based on the project it announced at this year's Google I.O. conference. The glasses use built-in cameras to recognize objects in an environment using local AI, but don't take photos or videos. Initial tests will focus on use cases around translation, transcription, visual search, and navigation. Google began testing with dozens of pairs in the field, but will ramp up to several hundred throughout 2022. It's only been a couple months since I.O. though. Usually they're supposed to wait a few years, you know, before they take these wild concepts and put them in the actual field. That's interesting. All right, let's talk about Netflix. Big news out of Netflix's earnings report on Tuesday was that it lost 970,000 subscribers and that that was good news. It's Netflix's second quarterly drop. It's largest yet, but it was also half of what either Netflix or analysts had expected and most of that drop was domestic. Netflix fell by 1.3 million in the US and Canada. They also fell about 770,000 in Europe and West Asia, but they gained around a million in Asia Pacific while staying essentially flat everywhere else. Even with the subscriber decline, companies revenue was up 8.6% over last year and as for this current quarter, Netflix is back to projecting again. They think they're gonna get a million new subscribers in the current three month period. But as always, the part that matters most to us is those little details. On Tuesday's show, we talked about the tests that are going on in Latin America to combat account sharing. Netflix plans to launch one of its plans worldwide sometime next year. Netflix also said it will launch its advertising supported tiers in markets where ad spending is significant, starting in early 2023. The company previously to hope to launch new plans by the end of the year, it seemed like they were going pretty quickly, a little bit delayed. Netflix keeps insisting that its way of doing ads will be fundamentally different than linear TV. Yeah, so they're probably not just gonna roll interstitials is what that means, but who knows what else it means. One new bit of info on those ad supported tiers is that not all the content you can get on ad free Netflix will be available because of licensing. Netflix can play ads along with anything it owns itself and anything where the license doesn't stop it from doing that, but not all the licenses allow that. And even some of the shows that they call Netflix originals may have restrictions if they're made by somebody else. For instance, TechCrunch points out that Warner Brothers owns the licensing to you. Universal owns the licensing of Russian doll and Sony produces the crown and Cobra Kai and coincidentally the Wall Street Journal reports that Netflix is in talks with each of those three companies about new deals. Co-CEO Ted Sarando said that Netflix quote will clear some content, but certainly not all of it by the time the new tiers launch, but also added the vast majority of what people watch on Netflix we can include in the ad supported tier, but it won't be one to one. Another change is Netflix talking about radiance because it did not used to. The company cited a Nielsen's estimate that Netflix made up 7.7% of all US TV viewing in June. That's 1.334 trillion minutes making Netflix the most viewed TV service in the US well ahead of second place CBS, which has 753 billion minutes. CBS is the number one broadcast network in the US right now. So interesting comparison. Co-CEO Reed Hastings said, it's definitely the end of linear TV over the next five to 10 years. Ooh. Hastings likes to say stuff like that, doesn't he? I don't know if he's gonna be right about five to 10 years, but he's probably right. He's probably closer, right? Anyway, the company also addressed suggestions that it might do better if it released episodes on a weekly schedule like most other outlets do. Netflix said it likes the freedom of not having to do things like theatrical releases and it likes letting customers choose how to watch a series. So it's gonna keep flaunting those norms because it considers those to be quote a significant long-term business advantage. Interesting to see Netflix, who has been the leader in streaming from the get-go? Face the headwinds first. Quinn, as a consumer of television content, how do any of this strike you? It surprises me and it doesn't. I've been a Netflix subscriber for years. While I have not yet canceled, it's more tempting because the price continues to go up and up and up. And the content that I'm personally attracted to from Netflix is decreasing. I feel like most of their good series, at least the ones that I'm interested in, tend to get pretty short runs or they get canceled at kind of last notice. And now that bigger companies are getting, I shouldn't say bigger companies, but more historically successful companies at this, like HBO are getting into the game. It just, it makes it that much trickier for them to compete. Well, yeah, the ad supported model is intriguing to me because I keep, you know, this is one of these situations where it's like, I don't see it until someone shows it to me and then I go, oh, that's a good idea. I mean, besides like horrible product placement in every new original series or movie that Netflix puts out, I don't totally understand how varying away from the linear TV model of ads, I just don't see it yet. I'm sure Netflix has thought about it a lot and that's probably why that's not something that they're ready to roll up by the end of this year. But yeah, that's something that people aren't used to. Netflix is, like you said, Quinn, I mean, a lot of people have been Netflix subscribers for many, many years. I mean, even if you weren't in the DVD days, I wasn't, it was streaming only really. Still, that was a long time ago. Netflix has seen a lot of changes since then. And at one point, Netflix was like the only player in the game and there are quite a few now. So sure there's competition. Sure Netflix had some subscriber bleed out, but not as bad as the company thought it would be. So that was good news. I mean, it was, you kind of look at it on its face, you go, ooh, they lost a lot of subscribers, but well, you know, if they thought they were going to lose more than twice that and they didn't, that actually is good news. But I, you know, the weekly schedule thing, I go back and forth. Some shows, I think it's really show by show, person by person, of course. There are certain shows where I like the suspense of waiting, you know, throughout a season. I like being like, okay, it's Sunday night. I haven't waited since last Sunday because I really like the show.  And I applaud Netflix for letting me do that. I don't know, Netflix has always tried a lot of stuff. I mean, remember Quikster? That didn't work out well, but they still had the gumption to try it briefly. Yeah, I think the bottom line is that Netflix and CBS, being the two most watched in the U.S. is very apropos because what's happening to Netflix happened to CBS. It also happened to ABC and NBC, which is they were the dominant way to watch television until cable television rose up. And suddenly all these niche cable networks, right? Music television, arts and entertainment started to eat away because they were brands that you could understand. Oh, if I want music, I go to MTV. And the broadcast networks had to reorganize themselves. They couldn't be the home of all the sports you could watch because ESPN existed. So they had to come up with a new way to adapt to be mainstream while fending off these niche attacks. And they did. That's why CBS is the most watched network outside of Netflix because they figured that out. Netflix has to figure that out. Netflix has to address Quinn's feeling of, I just don't know if there's anything for me on Netflix right now. The way that broadcast networks had to address that feeling to cable subscribers back in the 1980s. Finally, Netflix announced it had acquired Australia's Animal Logic Animation Studio, which has made films like Happy Feet, The Lego Movie and more. So expect some animated movies coming out more of them. They already partnered with Animal Logic. Now they own them. So you're gonna get even more of them. All right, let's talk about art. Specifically AI art. Open AI announced in a blog post, it's making its image generating AI system, Dolly 2, more widely available. Expediting access for wait-listed customers. I am one of them with the goal of reaching around 1 million people within the next few weeks. Previously free to use, Dolly 2 will move to a credits model that gives you 200 images for your first month and then 60 images every month thereafter. Now, if you're saying I need more than 60, additional packs of 460 images would cost $15. Keep in mind, each generation uses four images and edits and variations use three. So it does add up. Open AI says the artist will also be able to apply for subsidized access. And here's another big change. Starting today, Open AI will give users full usage rights to commercialize the images that they create with Dolly 2. So that would be reprinting or selling or making merchandise out of your creative artwork. And that goes back to when Dolly 2 was announced in April. Back then it had just passed the 100,000 user threshold. Open AI says mitigating bias and toxicity in Dolly 2's evolving generations and policies, governing images created by the system is what made the wider availability possible. They weren't willing to just unleash it on everybody until they felt like they had that fairly well under control. For example, Dolly now uses a technique to generate images of people that more accurately reflects the diversity of the world's population if it gets a prompt describing a person with an unspecified race or gender. Open AI says it's also now rejecting image uploads because remember you can upload to edit an image with text. It's rejecting uploads that contain realistic faces or attempts to create the likeness of a public figure. Dolly 2 already tries to avoid creating images that aren't G rated or have anything like self harm or hateful symbols or legal activity in them. And Open AI researchers note in an academic paper called Risks and Limitations that an open source implementation of Dolly could be trained to do stuff like make stereotypical assumptions based on terms like CEO or lawyer or something like that. So they're trying to guard against that. Quinn, I don't know how much you've played with the mini version of Dolly. We had some fun with this on DTNS a few weeks back. I find the story just incredibly fascinating because as the world starts to understand what these systems are capable of and some of the Dolly 2 imagery is so pro and beautiful that it's just, it's like a mind melt for me to be like, I just can't believe it did that. Based on just maybe some keywords or a small bit of training from the user themselves. Have you, what are your thoughts on this? Yeah, I've played with Dolly mini as well as a couple of the other alternatives that existed. There's been several that have been around for a few years. Obviously the images that come from Dolly 2 are kind of a step above. And they really are pretty spectacular. I do think that there is a bit of skill that's involved in creating the images because of the way that you have to structure the prompts because it's kind of a garbage in garbage out scenario or if you don't know what to kind of queue for, then you end up getting images that are not really that workable. And so I think it is kind of beautiful that it's got this kind of connection between human and AI. As for its potential kind of futures as an entity and as a business, I think this is, I mean, these are reasonable, obvious steps to take and I think they're doing them in good faith. I do kind of wonder beyond, oh, look at this kind of neat little image I made, what impact it will have on society or how it will become a sustainable platform that sticks around for more than a couple of years, but it's certainly interesting and a lot of fun to play with. Yeah, yeah, it's funny. I imagine if this had come out in 2012 instead of 2022, they would have just posted it with limits, right? They would have posted it with the credit limits and said, go at it. But over time, companies have learned that that is a way to have unforeseen consequences bring you down. So I think it's fascinating to watch the evolution of rollouts of these sort of things where open AI, it's open as right in the name, is being very cautious about opening it up for everyone to use and I'm sure they won't get it perfect. But I think what they're gambling is that if they're upfront about saying we're being careful, we're going slow, this is what we're doing, that hopefully the missteps that do show up will allow them to have a little bit of credit with people and say, we're gonna now fix that because we wouldn't have found it until we had this many people using it. The commercializing of all this is another, I'll be interested to see how this goes. I'm sure that there'll be plenty of t-shirts that are made with Dolly Artwork that end up being popular because they are for whatever reason, maybe it's the artist or maybe it's just the image or some combination of the two. I wonder if we start getting into that territory and a lot of people will take advantage of this is okay, well, let's say an image starts to become problematic. Is it problematic before somebody puts it on t-shirt or on a mug or otherwise asks people to buy it and it comes back to like, well, it's that Dolly too program that never should have been unleashed. The bad actors have too much of this. I know that OpenAI is doing, I think, a really good job of saying, okay, we're gonna roll this out slowly and take a lot of notes and intervene as much as possible so that this doesn't turn into a tool that just ends up being a toxic wasteland but you can't get rid of all of that stuff. So yeah, there's a lot of questions yet to be answered. Yeah, I mean, it's brave of them to go first. It is. In today's blog post, OpenAI said, expanding access is an important part of our deploying AI systems responsibly because it allows us to learn more about real-world use and continue to iterate on our safety systems. So I think they're doing the right thing here but as for similar limitations in the space, you might recall Google recently announced it wouldn't release Imogen to the public, that's its own AI generating model because of the current risks of misuse and Metas recently announced Make a Scene which is an art-focused image-generating system has limited access to what the company deems prominent AI artists, so not me. Well folks, it's special guest week here on DTNS. Quinn is one of them. If you like what you're hearing on the show, spread the word, thank Quinn on Twitter or TikTok or YouTube or wherever you do your thanking and tell friends and family to watch or listen to Daily Tech News show because we've got great folks on all this week. We've reported on all the praise heaped on Apple's new M1-powered MacBook line, well-deserved by the benchmarks. It's even good when it's translating x86 to ARM instructions. It's faster than a lot of x86 machines it replaced. In fact, Quinn, you made a video saying that Apple might have accidentally made the best gaming notebook on the market. I thought it was fun because in the video you go through what you would expect which is I'm gonna try to run some Windows games and you tell us that, well, running in parallels isn't perfect, it isn't better than a Windows laptop and running in wine through crossover, well, it's not really an acceptable replacement for a Windows laptop. So what gives, what are you saying about M1 being a gaming laptop here? Yeah, I mean, if you look at how Macs have traditionally been able to run games, how most people would choose to run them was just through boot camp, right? But that's no longer an option. And so you're really, it comes down to virtualization. And the performance in those sections, there are a lot of hiccups. When you can get a game to work, it's shocking how good it is because everything would indicate that it's going to be a disaster. You look at, for example, we played the Witcher 3 and that was something that, this is an x86 Windows binary that's running, it's being virtualized by Windows ARM, which is being virtualized by parallels inside of the Mac, which DirectX is being virtualized into metal. And so it's just, there's so many different layers to this onion that by the time you get to the end, you're like, this has gotta be abysmal. And it, in some cases, is actually playable. And so the question becomes, well, what could you do with such a machine if it didn't have all of this crap that it had to dredge through to get to a workable play state? And there's really three categories, if you ask me, where Numax can excel at gaming. The first and most obvious one is by playing Apple Arcade and iOS titles, right? Because you've got the app shop that's available to you that they're available. And as the iPhone and the iPad get more advanced, more sophisticated games are coming to the platform. And Apple Arcade is great because one of the requirements to be on Apple Arcade is that all games support all platforms. And so you'll find that you can actually play some fairly beautiful indie games on the Mac, but I wouldn't go so far as to say that they're difficult to run. I mean, they're running on phones that are years and years old, right? So the next obvious step would be if you can't virtualize Windows, maybe you can emulate something less advanced. And so emulation on the Numax is actually fantastic. Now, that enters into a legal gray area very quickly. But there are, there's XEMU, which is an Xbox emulator. There's Dolphin, which has long been known as one of the best Wii and GameCube emulators. And those will play back games fantastically. Even the base model MacBook Pro played, for example, an unnamed Nintendo game with a little man in a red hat flying around a galaxy. Oh, it's him. Yeah, right. It was able to play that at 4K in 60 frames per second. Fantastically. I mean, it looks incredible. And so the question becomes, well, what if you could run titles that were designed and built for the Mac on the Mac? And the problem is that, well, those don't really exist because the Mac has never been a place where gaming has been much of a thing. And so even though many games are designed in Unity and Unreal and have export functions to create macOS binaries that generally work okay, developers just don't do it because they have to debug and they have to provide support for platforms that they're not familiar with and or a platform that has, frankly, very, very little market share. And so it's never been something that's happened because it hasn't been worthwhile. We did a video back in 2017, I believe, running Steam VR on the Mac when Apple and Valve partnered up on that to bring it to Final Cut Pro. And there were games that theoretically could run, but just none of them were available. So we reached out to a bunch of game devs and said, hey, you're building these in Unity, you're building these in Unreal. Can you just export though? Can you just make a Mac binary, one click, don't debug anything, just let's see how workable it is. And we got three or four games that played pretty well. But that was back in an era when the hardware was effectively the same, right? So now that it's much different, there's really only a few games that have been, that we've been able to test this on. There's Dirt Rally, Tomb Raider, and Planet Coaster. Those are three games that we've, that we kind of benchmarked in our testing. And what's interesting about them is they are all x86 binaries. They're running through Rosetta, right? So these are not compiled for ARM. They're not made to the M1, yeah, yeah. Right, but they are compiled to run on the Metal Graphics API. And in Dirt Rally, we were playing that game at 4K Ultra on the M1 Max MacBooks at more than 100 frames per second, which was almost double that of a competing NVIDIA 2070 Super Max-Q SPECT laptop. The 30 series laptops have gotten quite a bit better on the NVIDIA side. And so we tried Tomb Raider in 4K at Ultra, and we were getting about 80-ish frames per second. And that was pretty comparable to an NVIDIA 3070 in a laptop, which is a much hotter, much larger chip. And as we moved to Planet Coaster, a game that was fairly CPU heavy, Apple Silicon really flexed its muscles. And the performance was frankly better than I've seen on almost any PC, certainly laptop, but even desktop. So that's where things get potentially exciting is this hardware is capable. And there is one title, literally one, that you can go out and buy today. It's in the App Store. And not only is it running on metal, but it's compiled specifically for Apple Silicon. And that is the remake of Mist, the classic kind of Mac game. And the performance is excellent. It's as good as a laptop that, I say this in my video, that only costs several hundred dollars less, which sounds like a jab, but it's not. Because Apple has historically been not even in the same price bracket when it comes to gaming, their hardware that they had released, historically did not have very good video performance. And when you compare it dollar per dollar, it was an abysmal value. And now the fact that it's even close is impressive. But I would go so far as to say that even if the performance doesn't quite get to where many of the best gaming laptops are on the PC side, it doesn't matter because the Mac has things that are not available literally anywhere else. Yeah, it's just a matter of getting the games. I get what you're saying now, which is they made the best gaming laptop. They just didn't create all the on-ramps to make it easy for developers to transport it over there. Yeah, I mean, if you think about it, the 30, 70 laptops that have been coming out, the total package TDP, and those are a lot more efficient than the prior generation, these are PC laptops that are drawing in excess of 250 watts TDP of energy. They're incredibly hot. They're very loud and noisy. And the MacBook Pro is not, I mean, it's dead silent and its power draw is so low that it can run for literally hours longer than the closest competing PC laptop. It's got excellent speakers, which are never found on PC gaming laptops. And the display is excellent. Not only is it higher resolution than most of the other competing PC displays, but it's 120 frames per second refresh. So very, very quick, thanks to ProMotion. And it's mini-LED, it's the best laptop display period. And so to see all of that available in a price point that's only marginally more expensive than the best PC gaming laptops is really exciting. The downside is that we come back to Apple and software. And I don't know that that's going to change. I mean, if you look at the most recent keynote with the M2 release, they talked about how Resident Evil Village and No Man's Sky were coming to Apple Silicon this year. But that's been a joint effort that they've made with individual game studios. And the reality is that there's just not the market share to make those ports worth it for almost anyone. Certainly not large game companies, but even indie devs. I mean, you're kind of pushing the buttons. And it really makes me ask the question, does Apple need to start acquiring gaming companies to make this happen on the Mac if they want to? Because the people don't realize, and are unwilling to admit, that Apple has an enormous hold on the gaming market, just not the console kind of PC gaming market. In fact, in 2018, the Wall Street Journal reported that Apple had recorded an $8.8 billion net profit in that fiscal year, which was more than Sony, Microsoft, Activision, and Nintendo combined, right? So Apple is just making an extraordinary amount of money on the App Store. And I think because of that, haven't really cared to push into a much more competitive kind of AAA gaming market. But they now have the hardware to do that where previously they didn't. And so... It's just a matter of whether they'll ever get the desire to do it. When they've got that gravy train of mobile, that's way easier to compete in. Oh, totally. That's a very good point. I think they'll have to acquire companies because you look at Microsoft, Microsoft recently purchased Activision Blizzard. They purchased Zenimax, which owned Bethesda a couple of years ago. Sony purchased Bungie of Halo fame, which was previously on a competing... So for good or bad, there is consolidation in the gaming market going all the way up to the top. And Apple's kind of just sitting on the sidelines saying, hey, you guys, we've got these really good graphics APIs and our hardware's nice. It's not going to compel anyone to come to the platform. Well, folks, if you've got ideas for what you would like to see Apple do, email us feedback at dailytechnewshow.com. Before we get out of here, let's check a couple of those emails from the mailbag, Sarah. Yeah, Scott wrote in about our Monday show with Jack and we were talking about the term hacker and what that means. Is it good? Is it bad? Scott says, we shouldn't let inertia lead to a default win for the misuse of terms. I stand by hacker as not being intrinsically a bad term and those who feel the same way need to dig in and resist. By continuing to use the term and its proper positive context as well, we vote for the language future we desire. Yeah, I don't know if I feel like it's as important as some other things that you might need to dig in and resist for, but I get where Scott's coming from. And I think Jack helped, you know, fortify me for the good fight there. Tony in Albany, Oregon said, I just finished Friday's episode as for drone delivery, I think more likely than accident from a faulty drone is less likely than someone taking one down from the ground. As an FAA part 107 licensed drone operator for part time real estate photography, I've had people throw objects at my drone on at least two occasions. A delivery drone carrying heavier objects has even more potential for causing harm or property damage if it falls out of the sky. I know this has come up before, but just thought I'd mentioned it again.  Packaged theft isn't the only reason people could attack these. Two times is not bad. I'll be honest, that's a low incidence, but I'm sure it didn't feel low when it happened to your drone. So I totally get where you're coming from, Tony. Well, thanks for the emails, everybody. Keep them coming. Feedback at dailytechnewshow.com. We love to know what's on your mind. Something that we talked about, peak your interest or spark a conversation you'd like to keep going, do send it our way. Also thanks to Quinn Nelson for being with us today. Quinn, such a pleasure to have you. Let folks know where they can find you on the Nets. Well, thanks for having me on. You can find me at youtube.com slash snazzy and on all the socials at snazzyq. That's a good handle, snazzyq. Oh, thank you, thank you. Yeah, good. Also good are brand new bosses. Boy, do they make our day. Today, Simon and Hardwired spelled very leady. Lead speaking. They just started backing us on Patreon. So thank you to Simon and thank you to Hardwired. Yeah, big thanks to both of them and a special thanks with a four for Hardwired. And a seven for the T. Yeah, exactly. Yeah, because we're lead. There's a longer version of the show called Good Day Internet, aka GDI, available at patreon.com slash DTNS and starts right after we wrap up here. But just a reminder, we're live Monday through Friday at 4 p.m. Eastern, 20 hundred UTC. You can find out more at dailytechnewshow.com slash live and we're back doing it all again tomorrow talking about how to get started in 3D printing with Joel Telling joining us. Talk to you then. This show is part of the Frog Pants Network. Get more at frogpants.com. The Diamond Club hopes you have enjoyed this program.


So, welcome to Grand Rounds today. We have the unbelievable pleasure of learning about the slave labor that Shamima has performed this last year. I believe she's the most prolific cornea fellow ever, at least in my long tenure here, in terms of putting out research papers. But also, she's someone who I think each resident would vouch as someone who's an excellent teacher who really cares about people's education, despite only being here one year, she's really become, I don't know, kind of a member of the resident family in a sense, and that doesn't always happen with fellows. She was recently married after a long search, and she's still happy. They are living apart, and I think that's one of the big reasons why they're so happy, because my wife is happier when I'm away, so without further ado, Dr. Sikter. Thank you for that memorable introduction. It is a pleasure for me to present this morning, I really do appreciate all of you coming in. It's nice to present in June, you kind of have the whole year to look back, and it's been an amazing year, and I hope that over the course of the next hour, you all are able to appreciate some of the wonderful opportunities that I've been able to participate in, and how I feel very, very lucky to have been a fellow here at the Moran over the past year. So basically, we all know that this fellowship is awesome, and so I just wanted to take a moment to step back and sort of quantify how amazing it is. This is a little sample of some of the surgeries that I've been able to do this past year. And so I think Dr. Wilson picked up on the highlight of the slide. There were eight globes, and seven of them were with Dr. Slade. So it's been an honor and a privilege to close globes with you, Dr. Slade. I think you'll be on call at the beginning of the year because you are, like, the resident expert, and hopefully you can share some of the pearls that we've shared listening to Lady Gaga on Pandora while closing globes. So it's been very, very entertaining. And then, actually, the eighth globe was with Bryce, and so that was an honor to help close Bryce's first globe. But it's been a great year, as you can see, lots and lots of surgery. And this is definitely one of the reasons why this was such an attractive fellowship. But there's another component that I didn't quite realize was so enticing about this fellowship, and that's the opportunity to engage in research. So as of today, I've submitted 16 papers and nine have been accepted. And technically, if Dr. Oberg and Dr. Holt kind of finish up the last round, we'll submit two more papers this week. And I had the opportunity to be involved in two Arvo posters. I had two invited papers, and I gave two talks at Ascrist, which was a first for me. So that was a delight. And so what I'd like to do is sort of go through some of the papers that have been accepted and share with you a little bit about that experience. So the first paper was actually accepted into JRS. And this was a prospective study looking at artisan, iris-supported phakic IOLs in patients with keratoconus. And so the interesting thing about this paper is it looked at 13 patients with 16 eyes, and these patients were defined as having stable keratoconus, defined not only by keratometry, but also refraction. And there were a number of reasons why these patients were considered a good candidate for these phakic IOLs, which are, as most of you know, inserted through the anterior chamber, clipped to the iris. Here in the US, it's also marketed as verisice, although abroad, it's marketed as artisan, simply reflecting the difference in manufacturers. And so these patients were found to be contact lens intolerant or simply did not like wearing the strong prescription glasses that they needed. And so in these 16 eyes, 14 received just the standard phakic IOL and two actually received Toric IOLs. And it's important to note that Toric, artisan, phakic IOLs are not available yet in the US, and the study was conducted in Iran, which was nice to sort of start off with an international collaboration. And so here, looking at our data, you can sort of see the range that these patients experience. I mean, the vision, visual acuity was on the order of correctable to 2020, usually 2022 about 2040, but the range of power ranged from minus nine all the way up to a minus 22 and a half. And so clearly these are patients who really benefited from insertion of these lenses, and you can see postoperatively, their uncorrected visual acuity ranged from the 2020 to 2060 range. Although even the 2060 patient could be correctable with relatively low residual refraction postoperatively. So all in all, all the patients had a final visual acuity of 2040 or better, and about 85% had a final corrected visual acuity of 2032 or better. So in this paper, we concluded that phakic IOL implantation is effective for treating these patients with stable keratoconus who are usually contact lens intolerant. All right, moving on to paper number two. So what we had the opportunity to do was review a prospective contralateral study, which was done here at the University of Utah, looking at patients who received PRK, photorefractive care tectomy, versus patients who received thin flap lasik. And in this study, thin flap lasik was defined as creating a flap with the intralase laser of approximately 90 microns. And so in this paper, we were able to compare and contrast the visual outcomes. And so overall, there were 52 eyes that we looked at. 26 had PRK and 26 had thin flap lasik. In looking at the postoperative data, as to be expected, there was a visually, there was a statistically significant outcome in the visual acuity at the postoperative month one, namely with the lasik patients doing better compared to the PRK patients. That in and of itself is hardly a surprise. However, as we continue looking at postoperative months three and six, the difference no longer statistically significant. And we see that the outcomes were great between the two groups. As far as predictability and safety, the outcomes were similar. We looked at contrast sensitivity as one of the factors as far as outcomes. And you can see here graphically that the contrast sensitivity was similar between the two groups. An interesting thing that we looked at were higher order aberrations. Most people consider higher order aberrations to increase after laser treatment. And we can see that that generally was the case. It's consistently with PRK and lasik. And most notably, the total higher order aberration in the spheres increased postoperatively, whereas threefold is pretty much consistent, both pre and postoperatively as well. Any questions so far? Okay. So moving on to project number three, I think I'm going to finish very quickly because I seem to be speaking in dictation mode. So the next project we looked at was a series of patients who had iris-fixated faking intraocular lenses. And it was interesting because these patients presented with decomposition of their cornea. And so taking a step back, Dr. Moschfar has a lot of experience with these faking IOLs that are clipped to the iris. And in general, in his experience, which reflects sort of the FDA experience, corneal decomposition is not common. However, these were cases that were done by outside ophthalmologists that presented to us. And as far as the management was concerned, as far as the cases that we looked at, there were two cases at that time of patients who had had iris-fixated intraocular lenses. And they were noted to have corneal decomposition at 22 and 36 months, which are pretty far out from initial implantation. At presentation, their visual acuity was approximately count fingers or 2400 in each patient. And one patient underwent faking IOL explantation with concurrent DSEC at the time of surgery. And another patient underwent explantation with faking IOL of their lens because of cataractus changes that were noted with a concurrent DSEC surgery. And post-operative, both patients were actually noted to have vision of 2050. And so this just sort of highlights how we're able to efficiently treat endothelial disease when we sort of bring it on with our surgical treatment. Okay, yes, please. You know, Dr. Moslin? No, it wasn't dislocated. They're actually on Dr. Mosfar's service. We did see a patient on whom Dr. Mosfar had operated who was lost to follow-up and he had to change in insurance.  And that was more when the lens was being placed sort of clipped inferior or superiorly. And so it wasn't dislocated as far as removal from the actual iris. But you could see that there was perhaps some contact that was happening. And then he did later admit that he did rub his eyes a bit. And it didn't require any surgical intervention. We just monitored him and he was able to sort of overcome that localized decompensation. Okay, so we'll slow down a little bit and talk about this project, which was sort of one of the highlights of my fellowship. Ultra-thin decimates stripping endothelial care toplasty donor tissue preparation and profile analysis. So basically the principle here is everyone is sort of seeking to make a donor tissue for endothelial care toplasty. And of course the endpoint marker for that is decimates membrane endothelial care toplasty where you're simply transplanting decimates membrane and the endothelium. This of course in principle sounds wonderful and practice is quite challenging. And so in order to sort of approach the benefits of DMEX surgery which are believed to be transplanting thinner tissue which allows for less interface issues as far as stromal-stromal interaction. And theoretically just transplanting the tissue that needs to be transplanted. We looked at trying to create ultra-thin desect donor tissue whereby we minimize the amount of overlying stroma to sort of approach the visual outcomes associated with DMEX surgery but still have the sort of ease of desect surgery. And so in this project basically what we did is modify our standard approach which is to use a microkeratome in preparation of donor tissue. And usually using the microkeratome we take the full thickness cornea which is mounted in an artificial anterior chamber past the microkeratome over and basically leave behind stromal bed with underlying decimates membrane endothelium. Typically our stromal bed approaches the thickness of approximately 150 microns. But with the double pass technique our idea is to basically shave down a second time in order to permit minimal stromal tissue overlying decimates membrane. And so this was a fun project to do mainly because we were able to collect the data in one relatively long afternoon but it was very satisfying to sort of see this project be completed from start to finish. And what we were able to do is look at 11 human corneas glow rims donated by our eye bank and mounted in the artificial anterior chamber I mentioned and use a microkeratome in order to produce this ultra-thin tissue. So the way that our study was designed we had access to several different depths of microkeratome hence. Typically in the ORM practice we have access to a 300 or a 350 micron cutting head. But in collaborating with Moria we were able to get access to different cutting heads and so for the superficial free cap that we created as demonstrated here we were able to use four different cutting heads and then we basically created a second pass which resulted in this in Drillameller stroma lenticule and in order for that pass we were able to use either 110 or 130 micron cutting head and basically we would be left with the donor residual stromal bed very thin layer of stroma with decimates membrane and endothelium. So in doing this you can see the histology that beautifully demonstrates epithelium with stroma alone and then finally a thin bit of stroma with decimates membrane and endothelium. When we tried to compare the depth of the microcaratome cutting head with the thickness we saw that generally as you can imagine the thicker the cutting head the thicker the cut but what's interesting to note is there was a little bit of variability between the cut that was anticipated in the depth of the cutting head namely when it was a thinner cutting head you were a little bit closer although here so the numbers were varied but as you generally increase you can see here using a 350 micron cutting head our average cut was actually 467 whereas using a 200 micron cutting head our average cut was 210 keeping in mind the numbers were very low and so clearly you'd need more data in order to have a stronger claim as to the consistency of the depth of the microcaratome cutting head. The other important factor to notice is that when you pass the microcaratome the pressure of the artificial anterior chamber is very important because any changes of fluctuation in the pressure definitely affect the thickness of cut that we are able to produce and we see that clinically when preparing our donor tissue and so in a series of OCT imaging we can see here that this is the virgin cornea with its relatively uniform profile that we expect as it grows thicker as we move to the periphery and here is the residual stromal bed after the first microcaratome cutting head pass and you can see that it's largely maintaining the same sort of contour and consistency namely that the center is the thinnest and sort of grows thicker and outwards and that trend is maintained as we perform the second microcaratome cutting pass and so looking at residual stromal bed thickness as far as actual quantitation you can see that as the various residual stromal thicknesses arise there's uniformity across and there's more variation that thicker the residual stromal bed thickness you might also notice here that we're pretty high up in the numbers and this is the reflection of the fact that the donor tissue that we used wasn't quite necessarily the freshest and so it became a demodus with time but I still think that there's value in learning that because certainly our colleagues who use this tissue internationally or may not have access to the freshest tissue can still work with tissue using two microcaratome passes in order to produce a relatively thin end product so in the data that we collected we developed an algorithm that basically looks at the thickness of the tissue and so what we propose is if the initial picimetry measures between 500 and 600 microns you're probably best to use a 250 micron cutting head whereas if it's thicker you're better off using a deeper microcaratome cutting head and then subsequently regardless of which you use in this range it's best to be conservative with 110 micron cutting head but the 130 micron cutting head might be most appropriate when the residual stromal bed after the first pass is still microns. Any questions? Yes. We didn't. We didn't but that definitely would be the next step. Obtaining fresher tissue and being able to do not only vital staining but I think it's specular microscopy would be advantageous as well. So more or less we basically tried to use a tonal pen between passes to make sure that we were sort of in the same range although when we did the experiment it seemed like a good idea to make it nice and firm so we were actually aiming for pressures of 560 but one could make the argument that maybe you should be a little bit more physiologic when making your passes just to maintain the health of endothelium. We actually were we did get one perforation and in that case it was because the residual stromal bed was still so thick that we were a little bit on the greedy side and still wanted to pass one more time and it was the third pass that resulted in perforation. So we're going to switch gears a little bit and talk about some case report of CTK or central toxic care top of the after LASIK and so this was an interesting case of a 58 year old woman who presented with not too unusual of a myopic correction and so she underwent LASIK which by all means was considered pretty standard and post-operative week one she was noted to be hyper-opic with a plus 1 and 25 on the right and a plus 250 on the left and clinically she presented with this sort of haze across the linear pattern on the front of the eye where the underneath the LASIK flap and so as we followed her over time she had the diagnosis of central toxic care top of the and we noted that pre-operatively she had pichimetry that sort of dipped down and then slowly gradually made its way back up both in the left and the right eye and as far as her refraction was concerned she definitely had a hyper-opic shift that slowly came back and you can see that her vision was correctable sort of after the first month and so gathering data with this patient we tried to clean more information about understanding what is really going on in this principle of central toxic care top of the and so here looking at some of the pentakam data we can see that if we look at the posterior curvature as to be expected it's pretty constant and that makes sense because certainly we're doing refractive surgery on the anterior portion of the cornea we wouldn't expect changes on the posterior curvature but as we looked at the anterior tangential curvature we can see that there was this swift change where at week one it definitely became more flat and then gradually increased in curvature this we tried to correlate with looking at the pichimetry and so we can see that the thickness dropped down immediately and then slowly had this increase in thickness of cornea over time looking at OCT imaging we can see here this was sort of in the initial first week period you can see there's this hyper-reflective area underneath the flap that results with time this is sort of at month three where you can see that not only is this hyper-reflective area gone but this sort of delineation between where the underlying stroma in the flap is sort of decreased as well and so our hypothesis is that this may have reflected localized inflammation associated yes question yes so here you can see here yep yeah that was supposed to stop particularly no problem so it was that haze that was underneath the laser flap and so we see these images that changed unfortunately we weren't able to obtain very good specular microscopy images because it would have been very interesting to see if we could actually see the inflammatory cells located within the stroma since publishing this paper there has been another paper that's come out in the French literature that does talk about this case of CTK where they are able to do specular imaging so if you look at the image it's black splotch with like these teeny white punctate spots which they propose are inflammatory cells like macrophages that are in that area and so from looking at this patient we concluded that the majority of the corneal tissue loss occurred mainly in the first post-operative week however in order to fully understand the disease mechanism it's important that we had important to have more post-operative data especially early on and it would have been helpful had we obtained an OCT image of this patient before the onset of this disease process of course con focal microscopy would have been helpful as well and ultra very high frequency ultrasound would be something that would be interesting as well to sort of delineate where that information is and how it changes over time yes please I personally think it's within a spectrum of DLK and this sort of represents the endpoint I think we don't have enough information as far as on a cellular level to be able to differentiate why is this not just an extreme form of DLK which I kind of think it is and in order to get that information I think if we had more data we'd probably have a better understanding as far as what the inciting factor is I think once upon a time when we were using blades to make flaps it certainly made sense that perhaps the mechanical chatter of the blade itself was causing a lot of local information but now that we're using interlays I'm not really clear as to why the mechanical separation would be pro-inflammatory do you have any comments? we looked at the OCT imaging to us it seemed like well first it seemed like most people thought that this was just an issue that was happening underneath the flap but our feeling was that looking at the imaging it actually involved the flap itself and that there was stromal changes going on that the thickening wasn't just at the epithelial level but in the stroma itself I think that's a valid concern actually you can do hopefully spectacular microscopy to sort of see what the state of the endothelium is I think that circumstance is sort of unusual because most standard tissue that you receive from the iBank at the time of transportation is not going to be that thick however there was a case the first six months of my fellowship where the tissue that we received from the iBank actually had de-epithelialized and so there was market thickening certainly not to the extent of 800 microns but that is the circumstance where perhaps the tissue that you receive is a bit thicker than you expected it's good to know how to handle that tissue as well yes that was documented but I'm not sure that it was necessarily reflective of what the cell account may have been at the time of using the tissue so we're going to switch gears a little bit more clinically instead of experimental and this paper looked at the use of desec for treating endothelial patient endothelial failure in patients who had penetrating keratoplasty and if you were to do a literature search you would see that there was a flurry of papers that were published at the same time regarding this topic so definitely a lot of institutions were thinking along same lines and so in our study we were able to evaluate 22 consecutive eyes in 19 patients who had undergone previous penetrating keratoplasty and then had desec following that mean preoperative vision visual acuity was 2180 and there were actually eight cases that received best corrected visual acuity of better than 2040 and I think it's important to take a moment just to step back and look at that and say well are we really doing a service to these patients if only 36 about a third of these patients are having vision that's better than 2040 and I think it's important to note that these patients are not your standard like oh they had a little bit of flukes and now they're having desec and they're expecting vision to be 2020 or 2025 these are patients who've had likely several surgeries with different amounts of scarring may have retinal diseases as well and so in some cases the reason for us to do the desec was more therapeutic simply for comfort to treat bolus changes that were causing patients a lot of pain and so this was not a study that was meant to try and highlight great visual outcomes it was meant to sort of reflect our experience which was dealing with very complicated patients who had failed PKPs and of the patients that we evaluated only one was noted to have a partial dislocation which is a dislocation rate of 4.5% which is excellent when compared to similar results in the literature of note and if you were to review sort of the papers that were published at the same time noteworthy changes or differences in our paper were the fact that the graphs were designed as to be the same size as the failed PKP whereas some papers proposed that may be having a larger desec graft would be advantageous because if you're avoiding that sort of graft host junction then you might limit the amount or the possibility of that graft dislocating because of an uneven edge against which it's placed in our patients we did not remove or scrape desec membrane and there's definitely some controversy in the literature as to the benefit of scraping there are some people who really swear that scraping the base stroma really helps as far as making adherence of the donor tissue improved however in our experience we don't do that and didn't do that with these cases however all of our patients had paracentral venting incisions and this is something that is part of our standard clinical practice and we believe allows us to ensure that the graft is in good position and while other techniques involve using forceps to introduce the tissue we used a glide pulling technique using a 4.5 millimeter incision at the limb this and so here is an example of one of our patients who had successful desec after penetrating care to plasticine here you can see on the OCT how beautiful the images where you can see the actual graft host interface of the desec tissue as it nicely opposes to where the PKP is and you can see the margin of sutures here a little bit over here yes okay so now we are going to change gears a little bit moving from the FAKIC IOLs which of course we know are FDA approved to these new color iris implants in FAKIC guys which are actual plastic discs which are inserted into the anterior chamber in order to produce a cosmetic change this is not FDA approved as you can imagine actually has been proposed by an ophthalmologist in Panama and so we had a little bit of experience in management of patients who had this implant placed and then unfortunately experienced decompensation in particular we had a 19 year old who presented with a place of subjective vision decrease and he had had bilateral blue color iris implants done in Panama the week before this vision was 20 30 at the time presentation as pressures were normal however on gonioscopy there were evidence there was evidence of early PAS and several quadrants and the implants basically looked like they were lodged in the angle and given the fact that flair and pigment itself were present we had a long discussion with the patient and proposed taking out the implant so this is the schematic of the surgical technique that we use the plastic is basically like a flimsy floppy plastic but there is still some rigidity to it and so what we proposed and what we did was basically make clear corneal wound and then basically cut through the implant approximately 180 degrees away and then after doing so continue rotating the graft within the anterior chamber in order to create third sections which could then be removed through the clear corneal incision and the idea of course doing this under viscoelastic is to try and minimize trauma to the anterior segment and the cornea while removing this implant and this is a sample of what it looked like and you can see here it's pretty flimsy looking and more importantly you can see that maybe this is where we cut it and we can be responsible for some jaggedness but this is the actual edge of the implant and unfortunately in reviewing sort of experiences in what's published and that's more published in the late literature apparently the surgeons who do this sometimes pardon exactly customize yes oh the new color iris implant okay what is the consistency of this compared to that silicone I thought this one is silicone too the one that you showed us before sort of has like a rubbery silicone feel to it and have you used the other one because my impression is that this one is a little bit more plasticky is that incorrect no this does not look like a tree fine at all unfortunately yeah I actually had a patient when I was in residency she came and she had this form and she asked that I fill it out and basically said that she was going to have this implant done and I said did they tell you to come here to Wilmer she's like no they told me to come go to Walmart to get an eye exam but she ended up at Wilmer and so we had a long discussion about how it was just not a good idea to have this done and she was upset because she came and she wanted her form filled out so she could have this done and it was just I mean you try rationalizing and the response you'll get my friend did it and that's sort of the end of the discussion because you're dealing with someone who's like 19 and really is interested in having their eye color changed so it's definitely frustrating yes yes right yes yes okay okay yeah yes right good no I think it's several thousand yeah like five thousand con doctor con yes okay so that being said this is just a little sort of pearl for the residents so we wrote this beautiful paper and we included what was pretty scanned but what was the review in the literature and when we submitted it the feedback we received this is great but you have too much literature review so basically we just took the paper and reformatted it into two and so that's how I was able to get another paper out of this year and so here's the picture actually doctor candle of the patient yeah right and you can tell how stunningly handsome he looks with those implants wow I would totally understand why someone would want to pay five thousand dollars that's crazy absolutely crazy it looks ghoulish and not only that you can see that the placement here the light reflex is centered in the people and you can see where it is here so I mean it's just it's atrocious to think that these things are being implanted and it's very sad to think that people think that this is attractive enough to risk their vision for so basically if you do look at the literature looking at patients who had different implants put in available in blue and green the onset of symptoms range from anywhere from a week to six months and you can see at the time endothelial cell density was measured in some patients I mean some people dropped down 19 and 21 year olds dropping down to less than a thousand it's just it's really really sad this is going on this is a pentakam image demonstrating basically that this is a shoved into the angle you can see that it's just sitting on the iris shoved there yeah right so this is not paper number nine this was a little review article on soft tech lens which I think is worthwhile sharing because we do actually have access to it now in the operating room and basically this is designed as a aspheric lens that is acrylic the idea is that there would be less dysphotopsia and the biocompatibility and clarity is good and that hopefully it's resistant to damage during insertion actually I had a chance to implant one of these couple weeks ago and it is a very very very gummy lens I mean it almost doesn't want to stay rolled for that short time that you're inserting it and it just flops and unfolds immediately and so the theory behind zero aberration is that an average the mean corneal spherical of aberration is about a quarter micron and so most patients have positive corneal spherical aberration and so if you implant a zero aberration lens you don't necessarily have to worry about alignment errors which can be a factor in a placement of other lenses and the other interesting thing about this lens is it comes in quarter diopter increments in this 18 to 25 range and then half diopter increments in that broader range in one diopter even in the higher ranges extremes and so that allows a little bit more custom tuning as far as providing optimized visual outcomes to our patients and so okay ooh that was nine papers and there are a few more that are in review so I didn't want to jinx it and put it up here because you never know but I think it's been out on the market probably about two years so not that long I don't no sorry just wanted to take a moment I mean this has been a phenomenal year so you can see I've had a chance to do amazing surgery work with amazing people and participate in a lot of really nice research projects and I think what I want to share is obviously it's been a busy year and as all of the faculty know balancing academics definitely is a challenge it requires time management and what I found most wonderful is the opportunity to collaborate and to delegate because it really does make a tremendous help in trying to get these projects done I think the joke among the fellows has been if only we could get a fellow to help us with some of our work we'd be we'd be in good shape and so what I wanted to share with the residents as far as my experience over the past year I think every project sort of has a different scope and the easiest way or I think the most efficient way is to sort of frame a question what is it that you want this paper to share with your scientific colleagues but more importantly I think it's important to have a modest goal I definitely know that we all are aware of certain projects that you may get involved in and they become sort of bigger and bigger and more ominous as time goes by but I think the projects that sort of have modest goals to begin with are definitely the ones that sort of have the best chance of being successful that's something that you can be proud of because you feel like you've worked on it from start to finish and I think sometimes what gets overlooked is actually taking the time to review what's in the literature because there is so much out there and I think keeping up with scientific proceedings is not what it used to be I mean there are so many ophthalmology journals and there's so many papers and now there's online publishing and it's hard to keep up but when you set your mind to a certain task it's important to know what's out there, know the audience and that way you can sort of focus your efforts so I'd like to take a moment to share some acknowledgments I mean clearly this year could not have been possible without the support of Dr. Mifflin and Dr. Moschvar and Dr. Newfer was a tremendous co-fellow that I really do appreciate having the Moran faculty staff have been amazing I wish I could take all of you back with me in particular Monette if you know Monette in the laser suite in the OR she's phenomenal the Moran residents have been wonderful to work with I really have not dreaded being on call because it's been delightful working with all of you in particular Chris, Tom, Derek and Lloyd have participated in some research projects with me and I really do appreciate their contributions Jeff Petty is about to be mentioned because he's working on something now but I'll see what his turnaround time is and then as far as medical students are concerned I've been really lucky that I've had a chance to work with some really intelligent medical students Lisa I'm sorry I forgot to put your name up there but Lisa Leishman is one of the incoming path fellows and so you'll have a chance to get to know her over the year and she's phenomenal as well my female fellows that's what we kind of call ourselves and Mark it's been great to have such a wonderful group to sort of survive the year with and also my family has been tremendously supportive so I'd be happy to answer any questions and this would be a chance for anyone to make an escape because I'm going to show some wedding pictures I've already seen them 10 times minimum 10 times okay so you had your chance Dr. Mifflin here you go round 17 so as some of you may know I got married on April 9th this year and it was wonderful in particular because Dr. Mifflin let me have some time off and that was much appreciated and so here I am with my family, my parents and my sister and we actually had as you can imagine several events and this was the one the day before the wedding which actually took place at our house and so this is me being escorted out by my cousins everyone's matching, here we are up on stage that's my groom and you can see he's pretty happy which is good, a good sign and then this is actually the day of the wedding the next day, the wedding ceremony where we're exchanging rings and then there we are for the reception and here is a photo we had this stage set up and so everyone as is custom in the evening comes up


Hello, this is Laptop Media, and today, we will show you how to open the Lenovo Idea Pad 5 Pro 16 2022 and what's inside of it. To access this laptop's internals, you need to undo a total of 9 Torx head screws. Then, pry the bottom panel with a plastic tool and lift it away from the chassis. So far, nothing unorthodox. Please remember, it would be of great help to us if you just hit the like button and subscribe to our channel. That would motivate us to make even more and better videos for you. Inside, you will find a 75Wh battery pack. It lasts for nearly 11 hours of web browsing, or more than 10 hours of video playback. If you want to take it out, unplug the battery connector, and remove the 7 Phillips head screws that hold it in place. Unfortunately, the memory is soldered to the motherboard. The maximum supported configuration is 16GB of quad-channel LPDDR5 RAM, working at 6400MHz. As for the storage, there is one PCIe X4 slot. It supports Gen4 SSDs. The cooling setup features two heat pipes shared between the processor and the graphics card. Furthermore, you get two fans and a pair of heat spreaders for the VRMs and the graphics memory. If you'd like to see more disassembly videos of the latest notebooks we would highly appreciate if you hit the like button and subscribe to the channel.


I have placed the sample solution that we've developed here into a file task-solutions.r which I've uploaded to the repository, so if you pull again from the repository you will have that sample solution on your machine with a few extra added comments to it. Now one thing we still need to do and it's also in the task solutions is to update our list genes function. Remember our list genes function just listed the systematic gene names that weren't very informative and I've changed it here in this way to actually print out the standard genes and the systematic ID, the gene symbols and the gene names and that's what it now looks like. So we have the row number, the systematic, the standard symbol and the gene name. And let's hope if we find some interesting gene sets that there might be some clues about what these genes are in that information. I have no idea. I haven't tried that. So that's going to be new. In order to make this list genes work you'll simply have to select and define it. So let's have a look. We found some genes clustered on the PCA, this set 2 and they're kind of similar and as you know if gene expression profiles are similar one possible reason for that to be the case is that they are co-regulated and genes would be co-regulated if they need to be expressed under similar conditions in the cell because often they have some kind of related function. They collaborate. So the idea is that co-regulated genes also participate in similar functions. So our gene sets were this one and this one. So let's use our new list genes function and see what we get. Actually that's already in the code. List genes cell one. So we have a mitochondrial ribosomal protein. We have another mitochondrial ribosomal protein. Well that would make sense that these two are co-regulated. We have a nitrogen permease regulator protein that utilizes GABA and two proteins for mini-chromosome maintenance that are regulated in that way. So indeed these co-regulated genes are annotated with similar functions. So that's not something we put into the analysis in any way. That's something we got out of the analysis. We now discovered that among the genes for which we've measured profiles there are a number of genes which contribute to the same complex and to the same biological system where these genes appear to be co-regulated or change their expression patterns in comparable ways across the cell cycle. It's nice. So our set S2, oops, cell 2. In this case I see there are two genes that have something to do with methionine regulation. There are two genes that are the same gene. So we have the same gene measured twice on different spots in the microarray. That's kind of reassuring that if we discover one of these genes to be regulated in a particular way that all of the spots should be regulated in a similar way and some more biosynthesis. So once again, looking at that other set of regulations of regulated genes, number one and number six, they go in very similar ways and they are related to the same biological system. So once again, if this were our data and we would have just gotten it fresh off the microarray chip, we would have made a discovery about it from applying this kind of analysis. So being able to integrate data is fantastic for exploratory data analysis. As I said before, we are primarily concerned with looking into the structure of data sets here. Probably in the future I think we should add a module on actually how to go about integrating data in this way, but I think for that purpose it was probably very useful to basically take you through these steps. We have an idea that there is data on the web somewhere available that we should be able to integrate via matching IDs and then we set out on Google and find different ways to get that data and then we annotate our genes. The integrated data actually allows us to make new inferences about our data. Now Brandon asked this morning, what about PCA and how do we interpret these columns and what do we do with that? And that's an absolutely valid question. PCA will show us what is actually in the data. We might have some ideas about what could be interesting. So if we've designed an experiment to look at cell cycles, perhaps we already know that a cycle can be expressed as a sine wave and maybe cyclically expressed genes should look like sine waves in their expression profiles. Or we could have some other stimulus and we should believe that perhaps genes of interest to that stimulus rise first and then plateau and then decay in their expression profiles exponentially. So what we can do is we can define such ideas, such hypotheses as numerical models. For example, by simply defining a canonical sine wave and then using correlation to ask are there any genes that are highly correlated with our model? Or to what degree are particular genes correlated with a particular model? And if we measure that, we can use that as a projection. Just like we are projecting our data on the principal components, in this case we would be projecting our data on a model by measuring the correlation of an individual expression profile with one particular model. Because PCA really is a projection of the data under a high dimensional rotation and a model-based analysis can be seen as a projection along a particular model. There's no guarantee that the model will actually correlate well with a high variance in the data set. We just use it as a hypothesis that we have that might be useful. Then we can go in and explore whether what we get out of the data actually makes sense relative to that model. So it's one way of, again, exploratory data analysis. But the big advantage of using models in this way is you can interpret them. So if I use the sine wave as a model, I can interpret this as a sine wave, a cyclical function that's applied over time. Now let's define three simple models and try that out. That would be a half sine, a single sine wave, and two sine waves over the course of the experiment. And so what would that look like? We can simply use trigonometric functions. Let's define the number of values that we need, which is, of course, the same as the number of time points. We'll make some empty vectors and populate them with sine wave values or trigonometric function values. One is a cosine. So for a half wave, one is a sine. Well, I just scaled the trigonometric function appropriately. You can do that in different ways. And oh, didn't actually find it. Here we go. So these are our three models. So one is a sine wave, single sine wave. One is two sine waves. One is half a sine wave. That is simply decreasing expression over that time. So if we want to plot the correlation of each data point against our two models, we can do that by defining three vectors that store the coefficients of correlation for each row, i.e. for each expression profile. And that's very fast. We iterate over all of these values. We calculate the correlation of a measured expression profile with our model profile. So this is, again, referring back to what I said before. It's like a projection of the data onto that profile. And we can then plot it in the same way as we plotted our PC, our principal components. Now in this case, this is the correlation with a single sine wave and the correlation with a full sine wave. And again, we can vaguely see clusters of data points that correspond to more or less correlation. So these data points here are highly correlated with the double sine wave. These data points here are highly anti-correlated, so they would go in the other direction. The data points up here that are close to zero are correlated only with the full sine wave or only anti-correlated with the full sine wave. So they look differently. This is a plot of the double sine wave against the half sine wave. So again, we have data points and they're probably quite similar that are highly correlated with one. And we have other data points that are not correlated well or actually well correlated with the other profile. So these data points here would be data points that are steadily increasing over the course of the experiment because they're anti-correlated with the half cosine wave we're using there. So these data points here, these would be genes that are steadily decreasing over the course of the experiment, right? So the red line here is decreasing from the first to the last measurement. And something like gene number two appears to have that behavior. So we can again look at the plot, squint very carefully. There's other ways to actually extract the data. We'll mention that. And select some sets of data points for analysis. So I'm selecting three sets of data points. The ones in blue here, the ones in green here, and the ones in red here. So the ones in blue here, again, would be highly correlated with the double sine wave, the ones in red here, would be highly anti-correlated. And the ones in green here would not be well correlated but would be decreasing in expression. So what genes are these? So the ones that are highly correlated contain a number of known cell cycle genes. Cycline B, CDC45, polymerase, replication factor, RAT27, one of the checkpoint proteins, more polymerase, DNA polymerase B subunit 2. When do we need to make more DNA polymerase B when we're replicating the genome? And that's switched on and switched off during cell cycles. So it makes a lot of sense that these biological functions are associated with this expression behavior that we see. Firstly, again, these are CDC5 is one of the main regulators. A lot of, again, methionine genes that are highly anti-correlated. So highly anti-correlated means they're still correlated but in an inverse sense with the sine wave we have here. And cell 3 are genes that, well, they simply drop in selection over time. I see genes that are normally considered like glutamate dehydrogenase that are normally considered housekeeping genes. So maybe overall, as this experiment goes on, the cells are not very healthy. They're kind of stressed out under the experimental conditions. Now, if we plot them, that's what that looks like. So this is a correlation against the double sine wave. This is an anti-correlation against the sine wave. This is a correlation against the cosine wave. So overall, we now have groups of genes here that look similar to our model. Is this the best model we can use? Perhaps if we use different phases of the cyclical functions, that would discover more things. We could place sine waves into 17 different phases here. Perhaps if we add a decay term, that would make different models. So we could try different types of models here, guided by our biological interpretation. We could also do something else. And that is use a nonlinear least squares fit of a suitably scaled sine function, a nonlinear least squares fit, and then collect the parameters of the fit. Parameters in terms of amplitudes and phase and frequency. And we could then start comparing parameters, i.e. which functions have similar fitting parameters. So then we slowly start translating the problem of identifying features in the raw measured data into identifying and comparing features in some lower dimensional space that we derive by transforming the data that we see into some kind of model that we have implied. And that would also be a very valid approach for exploratory analysis. So especially in these cell cycles working with trigonometric functions, with sine or cosine functions, is probably a very fertile approach to discover genes of interest here. And to address Brandon's problem now, of course, these things we can immediately interpret. The blue genes here that we found and discovered are highly correlated with a cyclical varying function. OK. Now, I'd like to introduce you to a somewhat different approach. So we've basically arranged these genes in a two dimensional plot according to either the principal components or according to the degree of correlation with a model that we've defined. But that requires defining such models requires some knowledge. Is there not a completely automatic way that just pulls out similar elements from a high dimensional space? And yes, there is. It's called t-stochastic neighbor embedding. And this is a dimension reduction algorithm developed by Jeff Hinton's lab here at U of T. So in principle, what t-stochastic neighbor embedding does, it calculates statistics between individual data points and then computes an embedding from a high dimensional space into a low dimensional space such that that statistic is preserved. So whereas we can't normally say, well, we'll just take this high dimensional, five dimensional space and project it into two dimensions and then retain the important information, the t-stochastic neighbor embedding embeds it in such a way that all these dimensions are somehow contorted and worked and that original structure is maintained as well as possible. And the result is that it provides a very general way of taking high dimensional data and putting it into two dimensions so that we can then go and use our very, very sophisticated pattern recognition brains and look at this and say, ah, there's a cluster here. And we can then look what that cluster is. It says nothing about whether a cluster makes any biological sense. So in this case, it's kind of similar to principal components, but it's a heuristic algorithm. But it works quite well on a lot of data. So this is in the package TSNE for t-stochastic neighbor embedding that we should load and install the library. And the way that this works is it runs an embedding and iteratively tries to improve the embedding over time. And every now and then, it stops at a point that it calls an epoch and plots the current results. So the call to t-stochastic neighbor embedding requires us to have a function that actually does the plot, the data that we want to work with, parameter that's called perplexity. Basically, that defines the granularity of the approach and the maximum number of iterations, usually the more the better. And it starts from a random seed. So whatever the seed value is, you may get slightly different results. So it's worthwhile to experiment with different seeds and different numbers of iterations. So we need to define a plotting function. The plotting function here would be to pass in a value x, then use our crabs plot function that we've defined this morning to take the coordinates that the t-stochastic neighbor embedding has currently defined and plot the points with circles and triangles and orange and blue and so on. So that's the plot that we would use. So let's try this out, setting the seed and running this intelligent algorithm here. So this is iterating down and it's improving from the first guess over iterations and it tries to preserve that statistic.  It simply tries to take the side dimensional space and project it down to two dimensions. And that's the result that it gets. I think for the very, very small amount of effort that we needed to put in to make this run, essentially just run T, S, and E on the data, that's a very credible result. There's a little more overlap here. But in general, we seem to be able to preserve the structure of the orange females very well and the orange males. There's no overlap in the blue crabs. Some things flip into each other and so on. It does depend on the seed values first. So if I use T, S, and E, what I usually do is I loop it through a number of different seed values and then you just watch the iteration error and pick out a seed value with a, hopefully, small iteration error. So that's for crabs. So does it work for cell cycle data? First of all, let's pick two data sets, two selection sets. In this case, with positive and negative correlations. So in my first set, I have about 10 different genes. In my second set, I have four different genes. Let's see what they are. So these are the first 10. These are the other 10. So they're respectively anti-correlated to each other. And I can write a little plotting function now that will plot all of the points of the expression data and plot my first selection in red and my second selection in turquoise. So the reason I'm doing this is so I can see once that embedding function, now trying to embed things from a 17-dimensional data space into two dimensions, once that embedding function runs, are the clusters of points that I get, are they actually cohesive? Do they stay together? So let's look at that. So that's our plotting function here. And let's go to go. So after 1,000 iterations, we come up with this result. So the negative correlations weren't well preserved, but the positive correlations are very well clustered. So we can ask, well, what do genes in the vicinity of the original selected values look like? So I have no idea which ones I've picked here. Let me check. So cell 3 is this. I'm just trying to color which points I have selected here. So the data structure that this neighbor embedding produces just has two columns. So this has the x and the y coordinates. So in order to write the text, I just need column 1 and t s and e set cell 3. Is it lower case or uppercase? It's probably lower case. Yeah, lower case. So t is in a two-cell column 1 and column 2. And the labels should be cell 3. Make it red. The x is 0.7. So these are the genes I chose. So different from the points, they are overlaying the general area. So this is where they ended up. Now what do they look like? So remember, these are our points that I didn't choose in the original selection of our points. My original selection was, which correlation is better than 0.77 to produce it? So basically saying, we have a cutoff here of 0.77. And actually, I can just add line vertical equals 0.77. So this is the 0.77 cutoff. And now the t s and e has proposed genes that it thinks are similar to these, but that don't have that cutoff versus the model correlation. So again, if this is correct, if the algorithm gives us something reasonable, we have then discovered additional genes, which should be considered similar by the criteria of the t s and e, but would not be found through this kind of a model correlation. So let's see what they look like in our matrix plot. So the lines behind it, the dark lines, are the ones we originally found. The red lines on top of it are the new lines in our cell three. So simply by inspecting this visually, I would say that the algorithm has done a very good job of finding genes that are similar by inspection to what we've seen previously, and something that we wouldn't have discovered just looking at the mathematics of the coefficient of correlation. 0.77. In other words, they are still very correlated. Yeah. But we do not have our threshold. Yeah. So I can see why the correlation coefficient against the canonical sine wave is not perfect and rather subtle. This is because the scale of these two peaks is the width of these two peaks is slightly different, and there's a decay here. So the correlation coefficient, which goes in this case to the exactly correct sine wave, basically includes a bias in this whole analysis that we should be able to remove. Now if we would be doing an actual nonlinear least square fit, it would be easy to take care of that. But right here, we're really trying to correlate against the idealized sine wave. So where are these genes on the model correlation? That's exactly your question. How good are the correlations? So we can easily plot that. So these are the ones we originally chose. These are the ones that the TSNE algorithm found to kind of overlap that whole thing. So they have lower correlation, but they're not necessarily the ones that are overall the closest in terms of coefficient of correlation. And not all of them, not all of those that actually are very close, like 161, have been included. OK, one exercise that you could do at home. And that's something that I would probably do in trying to get a better sense of what these TSNE data mean in the first place. I would take some of the models and include the synthetic data for the models with the data set that we're plotting. So I would simply append the model data to our cell-cycle expression data. And then I would follow with suitable markers here where the points for these models lie on that expression profile. So I then can see this region approximately corresponds to a sine wave. This region approximately corresponds to a shifted sine wave. This region approximately corresponds to inverted sine wave and so on. Again, this is asking, we have all this data in the 17-dimensional data space. What does a projection actually mean? How can we interpret the projection? When we find clusters in that projection, what becomes part of these clusters? So let's recapitulate a little bit what we've done with dimension reduction. The fundamental problem that we're trying to solve is that interesting data in our domain very often is quite high-dimensional data. And we find it hard to find patterns and correlations and structure in high-dimensional data because it's a problem to visualize it. So we can use dimension reduction techniques both for increasing signal to noise ratios in analyzing the data, for discovering things that act on our data, like confounding factors, and also for helping us to discover structure in this data. And one of the first dimension reduction technique that comes to mind is PCA because it has very well-defined mathematical purposes. The downside of PCA is that the projections become hard to interpret because the principal components along which we're essentially projecting the data are comprised of all of the data dimensions at once. But it works very well. It's fast to compute. And once we start plotting and exploring with it, we can get a very good sense of how our data is structured internally. An alternative to PCA are model-based correlations, like the sine waves in our expression data, with the advantage that they can be very, very easily interpreted. But it's also kind of limited because we're trying to, we need assumptions, according to first principles, how these models should be constructed in the first place. So in our case, we know we have a cyclical expression pattern. But if you are looking at gene expression profiles as a response to a certain set of stimuli like heat shock or acid challenge or whatever, or if you're looking at expression profiles that are derived from different tissue types, from different cell lines, or if you're looking at expression profiles that correspond to different cancer types or whatever, there might not be a model that you could imagine that would somehow explain biology about your data. As an alternative and an additional approach to PCA, we can use TS and E, which often is able to find very intuitive results about the data, that none of the mathematical methods would pick up in the same way. The disadvantage is now it becomes completely uninterpretable. It just says we're putting things next to each other that are similar in high dimensional space. We have no idea what that similarity actually is. But it's also maybe one of the methods that is applicable with the least effort and in the fastest possible way. No, I'm not aware of a rule of thumb. The only rule of thumb that I would use is use as many methods as you have patience to use. Each method will give you a little bit of a different perspective on your data. And the more intimate you become with the points, with the structures within your data, and you start recognizing which genes always come out, coming, lying close to each other and where particular pathways are represented and so on, the more you will learn about your data and the more secure your interpretation will become. But at TCA, it's completely uninterpretable. Are we going to go where it's like extract what you can extract from TCA or something like that? Because when we did the BIPOC report, it showed what's in the large influence or should it be. Yeah. I think that's meaningful necessarily. I hope I didn't come across the same TCA as completely uninterpretable. So basically what I said is that the individual principal components might have contributions from all of the dimensions at once. And that becomes kind of hard to interpret. Now, applying additional biological knowledge, as we did before, for example, taught us that we can interpret the first principal component for the Crab's data as related to the age. And that will remove that correlation with age. Or that we can probably interpret the first principal component of the Cho cell cycle data as kind of normalizing out variations between the microarray plates. So again, that implicitly takes care of some of the inaccuracies in measuring the data. And they're the strongest components because they affect all of the data points equally. But that certainly doesn't mean that one is kind of more important than another. The information that we look at, the strongest information, the strongest signal, indeed, is in these first and largest components. It doesn't mean that the strongest information is also the biologically most relevant information. And that's the problem that we're continuing with. Our idea of what's an optimal analysis is very different from a mathematical strongest signal. So I guess once we get to some awareness, then is it possible to go back and say, which are the most important genes affecting these particular components that are giving us good cluster? Because I know my boss's question every time I hear it. Is that, OK, which genes are driving us and we can't really go back? OK. So if you're asking which genes are driving this, you're asking a slightly different kind of questions, and that's trying to interpret these in terms of causality. And that's very, very difficult. In principle, what we're trying to do if we're looking for causalities, we hope we have timelines. We hope we have timelines with good resolution. We hope we have many different timelines under many different circumstances. And then we can hope to find patterns of genes that are preceded by similar patterns of other genes. And that would allow us to infer some level of causality. But it's fiendishly difficult, and you really need a lot of data. Now, you can, however, perhaps then imply biologically reasons. If you say which genes are driving this, perhaps you have a regulatory pathway. And you can get a cluster of genes that respond in a certain way. And then you can separate them whether they're high in the pathway or low in the pathway. That would be one thing. In terms of things like driver genes in cancer, for example, we have a different approach. And that's just looking across many cancer samples and looking for genes that are recurrent in being mutated, either with a gain or loss of function mutation. So finding such recurrent genes is not as much a question of exploratory data analysis in the data. It's just a question of tabulating and then asking, is the frequency of mutation for particular genes different from the frequency of mutation that we would expect? Of course, that's not easy either, because frequency of mutation is related to the length of the gene and to its expression status, because genes that are condensed are much less likely to be mutated than genes that are exposed and actively being transcribed. But there's ways to normalize that and then to discover that. One way to discover such correlations is working in networks, i.e. in graphs. So we've always been treating these data points as independent, because essentially they are. But we could just as well map them on a network where the nodes are the genes and the edges are functional relationships. So typical functional relationships for these kind of graphs are drawn from, say, protein and protein interaction or similar Go terms. So a good database for functional interactions is, for example, the string database, S-T-R-I-N-G. And then you can build these networks. And then you can look at, say, these expression profiles and basically paint your networks with such expression profiles, i.e. for example, correlations with a sine wave. And then you can ask, are there genes that are functionally close together and frequently mutated or correlating in gene expression and so on? And in this way discover additional related genes that you might not discover with any of the individual. So that's a very powerful way of data integration. Now if your boss asks you about what are the driver genes and what's the causality there, I think that that's probably a hierarchy of difficulties of question to answer right at the top. Almost at the top. The very top one would be why. I guess I agree with that. Specifically with the brand PCO and your gene even if it's not causing, what are the most happy quality of all of that? Yeah. Well, I mean, what I've shown you here is a number of ways to find genes that are similar to each other in very high dimensional data sets. And that's at least the starting point. If you then look at these genes and look at their properties, you might find something they have in common, which you can use as a general criterion to pick up other genes that are related there. In a sense, what we're slowly getting to is a question that's related to cluster analysis. How can we take data sets like these and split them up into clusters of things that are similar to each other? And that just happens to be the topic of our next module.