One of the most common questions I get asked is, you know, how do I start day trading? So what me and my mentor about it for our viewers on YouTube is create a free mentorship course that reveals our 12 secrets that every single brand new day trader should know before they get started. But please take note that there is limited seating every single week. So please reserve your spot at myinvestingclub.co. So loud. Wow. Not used to that yet. All right. July 20th. First day of real earnings. First real earnings sticker was Netflix. So closed rather unchanged. On the day and after hours and everything else. So it had a pretty wild dip. But this is normal for earnings releases. And usually that happens. You'll get some kind of crazy move like this. And up or down. And then it finally settles out as the news gets digested. So what was, what was the earnings per share was 297. So they missed. Oh, so they missed, but it's still everything looks. So I guess they had some, I haven't read the earnings report. I have no, I'm all I do with earnings reports. So I don't know if you can really even look. At anything else. Sounds backwards. Beat earnings. How do they beat earnings? If it's less than the actual. Expectations. The S ones were three 16 to share the actuals were 297. So they would they beat on revenue or something. Membership growth beats. Okay. So there's where they'd be in membership growth, which in turn is directly revenue. May find no business like show business. What a catch. Yeah. Membership growth seems. Seems like that. Yeah. Okay. Yeah. So beat new subscriber expectations. Gotcha. CMG did well. Stock is never something I ever touched. Look at how illiquid that is. Look at the liquidity on this thing. My on the right stock. Is that, is that the right thing? Yeah. I mean, look at this. Look at the volume. Just horrendous. Just. Not something I would ever want to trade. What I mean, Jesus. That's a lot of money for some burritos. But okay. So Netflix. Earnings came out earnings were rather flat. Which is a positive thing. Slightly gaping up, but. We had a nice rally in the market today. This is that pattern I told you guys about, I was telling, I was telling you guys during, during the market open that this is, this is that, this is that pattern. Apple was doing it. So, you know, Apple's probably one of the biggest. Impacting tickers in the spy, but they trade almost identical. So it's hard to say, you know, which is kind of the one to go with, but this is what I really liked about the spy. And why I took the trade today. I don't know what my phone is. That's my computer ringing. I don't know. Oh, I think it's charging. No, it's charging. Nevermind. Nevermind. All right. So. Sorry. Anyway, what I really liked about, about spy touching the 50 period moving average, average is we've done it three times in the past. So fourth time around was, was something that was going to be very closely watched by pretty much everybody. Sorry. I'm looking at you, buddy. You know, for the team. No. That was good. That was good. Could you share that for the team? I can hear the sarcasm and the dots. It's just like, for the team. All right. So anyway. Yeah. Sorry. I don't share this little bullish thing. Anyway, so. Okay. We address that address that. Yeah. All right. So here we are. The purpose of the trade was to basically exploit. The shorts wanting to continue to God, I just can't believe I still can't. God, this day wouldn't went so differently. Okay, whatever. Forget it. All right. Yeah. I use the pivot points for sell or cover points rather than the resistance. I use them as both. They are all of the above. They are all of the, but yeah, now I won't make that freaking mistake again. Absolutely. And you know the reality of it though. You know the reality of it. Is it probably will never happen again. Right. It's probably never going to happen again. It was just this one time.  I don't know what to do for that one too. Absolutely. Absolutely. All right. So I go, I use them for everything. Sell points, cover points, resistance lines, support lines, all of it. All of it. Same with VWAP. Okay. Same with VWAP. VWAP is the same way. Except I don't use VWAP as a resistance or support. I don't really like that. VWAP is the same way. It works in small caps for me. It's not really a thing in big caps as nearly as much. Because in my opinion, there's more algorithmic volume that happens in large caps than there does in small cap. Most of volume in small caps is like retail people, right? Retail day traders. So retail day traders are going to watch VWAP a whole lot more closely. Now VWAP is still important in large caps, but it's not something where, can you explain the misprint? No, if I could, I would have fixed it and would have known, right? Steve? Yeah. Can't explain the misprint. I don't know. I don't know why it happened. Call TD Ameritrade, ask them. Ask them. Yeah. So, yeah, how do you, how do you get the new pivot calculation? I also can't explain it. I know, right? Who? What? Somebody just texted me on WhatsApp and was like, is this Sam? Who's Sam? I don't even know who Sam is. Anyway, all right. So, can we answer that? Do you include pre-market and after hours in your pivot calculations? MDDG? No. No, we do not. So if you are in DOS, go to study config. And then the check box that says include pre-market and post, uncheck that, and now you will have the right calculations. Or if you're in trading view or somewhere else, but just make sure that all it's doing, because there are pivot points that do this, okay? But the reality is, is that after hours and pre-market range does not really matter because what does the herd do, right? What does the herd do? They look at the daily candle. They don't know that, you know, maybe a stock was 140, low, and got as high as 145 that day. But during pre-market, it got as high as 150, right? And then in after hours, it got as low as 110. Thank you.


Dear students, in this topic we shall discuss the mechanism of lung ventilation in birds. The birds respiratory system consists of lungs and air sacs. The number of air sacs in birds may be 7 or 9. These air sacs include two posterior or abdominal air sacs, two anterior, thoracic or cranial air sacs, two posterior, thoracic or cordal air sacs, and two cervical air sacs, which may not be present in some birds. And they include one intra-cleavicular air sac. Dear students, now we shall discuss the respiratory cycle of a bird. In birds, it takes two ventilation cycles to move air to the entire respiratory system. In this way, the inhaled air remains in the respiratory system for two complete inspiratory and expiratory cycles before it is fully used and then exhaled. During the first inspiration, the inhaled air enters through the nostrils and passing through the larynx, it enters the trachea. From the trachea, air does not directly enter to the lungs. Air rather travels to the posterior abdominal air sacs. Simultaneously, a small amount of air passes through the posterior air sac to the lungs through the ventro bronchi and dorsal bronchi. So, this is a difference in the bird's inspiration, that the first air that is inspired goes into the abdominal air sacs and during that inspiration, through the abdominal air sacs, through the ventro and dorsal bronchi, a portion of the air is sent to the lungs. In the lungs, the ventro and dorsal bronchi divide to form small diameter parabronchi and air capillaries. These parabronchi are supplied in large quantities by blood capillaries. These air capillaries are the sites which are the sites of gas exchange where oxygen is absorbed and carbon dioxide is added to the blood for the outgoing air. Now, the next cycle is the first expirience. The spent air in the lungs flows out of the body through trachea, which is used by the lungs to go out directly through the first expirience. And this air from the lungs which is out is replaced by fresh air which is present in the posterior air sacs. The first air entered in the first expirience in the abdominal air sacs enters the lungs through the first expirience. In this way, even during the expirience, the lungs get fresh air. Here, students, after this, when there is a second expirience, the fresh air again enters the posterior air sacs. Lungs also receive fresh air from the abdominal sacs replacing the used air. That which displaced air air lungs say that does not go directly out from the trachea because it cannot exit through the trachea because fresh air is flowing inward. This means the spent air moves from the lungs and enters the anterior thoracic air sacs. Now, second expirience is that the air moves out of the cranial or anterior thoracic sacs and it moves out and goes out and the air in the lungs also goes out. This flows out through the trachea. Fresh air which is present in the posterior abdominal sacs again enters the lungs for gas exchange. If there is a second expirience, the fresh air also supplies the lungs. Dear students, now we shall see how the air flow is maintained during these inspiratory and expiratory cycles. Air is moved in and out of the respiratory system through pressure changes in the air sacs. The mechanism of movement of air sacs is the pressure changes in the air sacs. The air sacs do not have any diaphragm to create any negative pumping pressure in the lungs. So, the pressure changes in and out of the air sacs are caused by air in and out movement. The thoracic muscles which cause the sternum to be pushed outward, this creates a negative pressure in the air sacs causing the air to enter the respiratory system. Similarly, expirience is also not passive. It requires certain muscles to contract to increase the pressure on air sacs and push the air out. Dear students, bird's car, your respiratory system head that is more efficient than mammals. It transfers more oxygen with each breath. Second directional air flow of fresh air ensures that our fresh air passes over the respiratory epithelium during both inhalation as well as exhalation. It enables near continuous state of gas exchange within the lungs and this is the reason that the lungs absorb about 90% of the inhaled air from this fresh air flow.


here is Brian Jones and he's a research associate professor at G. If you think that was cool, wait until you see this slide. I'm really raising the bar high for him here. But he's going to talk about some retinal remodeling and some of the degenerative diseases of the retina. We don't do a lot of translational work per se. So I'm not going to pull up with you any active translational work that's going on in the lab. But the work that is going on has massive implications for a lot of translational work that's actively going on right now around the world. So thank you, buddy. So we study the retina, the back of the eye, or you guys are sort of used to this view. We're interested in what happens in two diseases. A retina is pyrotosa and age-related macular degeneration. This is sort of the classic view of R.P. We're going to sort of look at the histology a bit more. If you sort of zoom in, this is a backlit view from behind the globe sort of illuminating the retina in these different espicules. If you look carefully, you can see a couple of ganglion cells there. This is sort of another view up close. Another patient of R.P. I have a pigmentation. But again, a lot of these pigmented bone espicules. And if you look at these pigmented bone espicules, you have to sort of start wondering about the histology. So there are some tools that we can use clinically. OCT in this case, and it sort of can give you a view, this is an early case, of R.P. of what's going on in the retina. But really the gold standard is histology. So the important thing with histology, we're going to look at photos that are sticking up in these samples. It looks obvious now, but in a minute, it'll get a little more complicated. So the important thing to remember is a million has about 70 kinds of cells. So if you start making changes to the numbers of cells and their connections, it turns out it has a lot of implications into how information is actually processed. So light comes through, it's photoreceptors go back, photoreceptors do detection, and then transduce those signals through a set of circuitries that do ruins detection, contrast detection, movement detection, a lot of the sort of visual primitives before sending those data out to higher structures, cortex, and sub-portex for further analysis. The problem is, in disease like R.P. and AMD, these photoreceptors become compromised. And a lot of the community for a long time presumed that when these died, this structure or these structures romantically hang out in the way to be rescued. And so we've been beating this drum for about a decade and using animal models. And so what we can do with animal models that we can't do very well with humans is identify the gene, give them the disease gene, and then track the disease as it progresses through animal models. This is at an outstreadment, over 700 days, as it's remodeling. So the photoreceptors get stressed, the photoreceptor outer segments shrink, photoreceptor cell bodies start to die. Bipolar cells and amryprin cells start to die and move and do wire ganglion cells, migrate into the amryprin cell layer, and the whole topology of the retina changes. So this happens in every single animal model that we have looked at. Rats, mice, pigs. There's a very expensive transgenic pig that's available from Louisville.  We have a rabid model now that's much more affordable, smaller, and still a large-time model. It's about 230 size of human eye. And it turns out it's a perfect gimmick for our somatomic hearty humans. But regardless, all of those animal models took this. And we've been hammering on this for years and are distressed by the number of human clinical trials that are going forward in biologics and biological and gene therapy and optogenetics that aren't taking this real biology into account. So we started collecting human tissues. This is a normal human retina. Actually, it's probably early AMD. It did not have a guide, most of us, when we got it. But the reason I say it's normal, probably early AMD, is this is one of the first things that we see in AMD. So this is the rebelpigment epithelium up here. And in normal, healthy, rebelpigment epithelium, you shouldn't see any color variability in this particular label. So this particular label is actually three separate labels that's taurine, the small molecule taurine, small molecule glutamine, and a small molecule glutathione that are assigned to red, green, and blue color channels. And that gives us this sort of view where we can visualize human cells in gold and the photoreceptors and some of the other cells. But it also allows us to see nicely the RPE here. And I'll show you in a minute, in normal, healthy tissue, you don't see any variability there. And we think that this is actually the first indication of disease and locations in dry AMD. Here's another view where we've changed the labels. This is GABA in the red color channel, for lysine in the green color channel, blue color channel. This allows us to see excitatory and inhibitory neurons in here. So the important thing with this normal human rep notice, even though there are some indications of disease, the overall apology is intact. Photoreceptors out here, you can see cone photoreceptors standing up here. There's got a nuclear layer, interplexiform layer, some inhibitory neurons and cells, and on-cone bipolar cells here and the ganglion cells down there with some vascular organs. Here's a normal, happy primate retina. We can, it turns out, we can get primate tissues, non-human primate tissues, faster than we can get human tissues. This was collected within about 20 minutes post-mortem and a lot of the signals are a little fresher, but the important thing here is to look at the RPE. So this signature, this fingerprint, it's what normal RPE looks like. And you can profile all the RPE cells in your normal human and you'll get absolutely this. So again, so if you keep this in mind, normal healthy retina, ganglion cells down here, here's the optic fiber layer down there. Again, we can switch it. This is the view that allows us to see excitatory inhibitory neurons. But again, normal lamination, everything looks really, really, really happy to healthy. This is central retina from a person with RT, about 74 years old, NLP, no visual process. The interesting thing is the IPL in this particular guy was largely intact. There are almost no, there's no rod photoreceptors, there are a few cone photoreceptors and this is actually important. We'll come back to one minute and this is Galaglice glutamate labeling. We, if we can get the tissue freshener off, it's still a lot. And we can do some experiments, we have a molecule called 1-UFO, or LARGO-UTENANT. It's a non-selective blue-leach channel marker. So we basically put the retina in each of you and stimulate the retina with pharmacologic succonic acid and NDA or with light, in this case, light wouldn't do much. And blue-leach channels open up, HV flows into these channels. And so we can get a visual picture for which neurons have been activated. So we basically recorded from every single neuron in this tissue, the more green in particular cell, the more HV that's flowed in. So we can do this HV labeling in addition to our other small molecular markers. And what we can do is we can generate a classification mask. And so what we can do is we can find all the horizontal cells, we can find all the ganglion cells, and then in an amortority, an amortority and clustering, you can leverage ganglion cells in different bipolar cell classes here. And so the interesting thing here is in a normal amortority and ganglion retina, the bipolar cell class, your on-combed bipolar, your off-by-polar, and your rod-by-polar, you should have about 33% of each. And so there should be equal numbers of, equal ratios of those bipolar cells. The first thing that starts happening in RP, we've seen this in animal models, and now we've confirmed it in humans, is that these ratios change. And what you see is you see the on-combed bipolar cells dramatically reduce the number, and the off-by-polar cells dramatically increase the number. So in this particular case, they've effectively doubled. And that's interesting. Even though the retina, even though the topology of the retina is intact, you can still see some comforters that are up here. And it turns out as long as cones are present, the overall topology of the retina is maintained, this is what's seen in the retina, cones disappear, that sort of restraint disappears. So here's another region of retina, and this is the YGP, or the TQE, just to slide that down. So again, torian glutamate, glutamate, glut-data, glycine glutamate, glutamate. So these are the same cells that you're seeing in both. We're just visualizing them differently so that we can see different things. And the first thing to look at is we're starting to get, so here's a retinopathylium, so it's died in this particular case. And then we can look down here. This is actually very interesting. Brad's doing the lab now. She's chasing this problem aggressively. So normally Mueller cells should all be the same color too. They all have the same small molecular. Interestingly, this is the case across species. So burns, turtles, fish, mammals. This sort of signature of this mammalian Mueller cell signature, the job that Mueller cells are doing in metabolism is very robustly made across evolution. But what starts happening when retinas get stressed is we start seeing this variability in the Mueller cells. So the metabolism in the Mueller cells starts stretching that metabolic envelope, and they start doing some very different things. We can start visualizing that here. So in these particular Mueller cell problems, there's more tooring in them, and so they're going to more red than regular sort of Mueller cells here. This is also true on the ultra-structural level. If you look at the ultra-structural data, but it's not gonna show here, there's lots of protein changes that start happening as well. And we can see that with electron microscopy. If you look at these boxes, so this one is just to show you that there are some cone photoreceptors, even though there's sort of nubbins, the nubbin in the outer segment is still left, but it's helping to maintain the overall topology of the retina. This box we're gonna zoom in on here. And the important thing here is to look at sort of these finding processes that are coming up from glycinergy-gamma cells. So normally glycinergy-gamma cells are here, and the processes come down, to connect in the center plexiform layer down here. In this case, they're starting to project up in the bone direction. We actually saw this in the animal almost first, but what they start doing is when you effectively de-affirmed the retina and you remove photoreceptor input, all the cells downstream have two choices. They can either die, which a lot of them do, or they start sprouting, and they start sprouting these processes, and they start talking to other neurons. And they do this likely to maintain calcium regulation in gene regulation. And there may be some other mechanisms for that, but this is a very common point in this sort of scrubbing. Turns out if you look at the ultrastructure, there are synapses in these spreads, so these are not sort of quiet processes. They're active processes where these cells are funding other partners that they shouldn't be talking to. Things can get really weird. These are amicron cells in the subredimal space. So these are cells that they migrate up from where they normally live, and they're now taking up residence right here. So in addition to sort of the sprouts, the cell bodies can migrate and go in weird places. And then we've taken a punch here, so this is a three millimeter punch from human RT, and so look at the red strip here. We basically took a histologic slice right out of there, and we wanted to look at far periphery retina. So the idea is that the data that I showed you before was sort of more central retina, and because RT is sort of the periphery of the central, the disease, we wanted to see how bad it could get. So we looked at OCT, not a whole lot of OCT signal, not a whole lot to see, but if we look at the YGE, there's basically a lot of vascular components, the vascular components are sort of in hypertrophic. There's almost nothing left of the normal retina. There is nothing to see, which is why you couldn't see anything in OCT. There are some mule glia elements here. Turns out there's some astrocytic elements for some other work that I could play for the grad student to learn about is what we're currently chasing. But almost all the ganglion cells are gone. A lot of the gavagy inhibitory neurons are gone. Collisionage ganglion cells are still left. Most of the bipolar cell population is gone, and all of the bipolar cells are effectively gone. This is, for all intents and purposes, a dead retina. There's no rescue in this one. These are other mappings. This is toward gliobrimary. I'll show you sort of the glial components. They're still there. There's some proteins, G-FAT, C-R-L-V-P, and arginine. We're sort of trying to look at some protein. Which proteins are still left? So small molecules change much more rapidly than the proteins do. And this is a probe for adopsins. So there are no, so normally you should be able to see nice, pretty photoreceptors up here. The only oxins that we can see are sort of trapped in the retina's vascular elements. They're probably sucked down and failed metabolize. So this is another sort of stretch of peripheral retina. Again, just to show you how bad things can get. So this is a little distance away from that red stripe that we showed you earlier. And again, this is what a normal retina and normal retina thing should look like. So if you sort of take this bottom edge, this vitriol edge down here, and sort of go up, this is where the disease retina stops, and that's how much further it should go. So the idea is in IP at least, this is completely true. And this is sort of a, we took a horizontal section through the retina, through the mule or cell. So we sectioned into the screen just a little bit more. We'd be into the vitreous. This is just before we get to the vitreous. So these are all of the mule or cell goofy. In normal, happy, healthy tissue, you should see this nice sort of mosaic tiles, which all be the same signal. This is clearly disrupted. There's a lot of stretching going on. There's some vascular elements that are sort of pushing in. And the signatures are all different. So this is glutamine sympathase toward the glutamine and red-green blue. And again, this is showing that there's effectively metabolic chaos in the mule or cells. And this is sort of another question at what point. So sort of mule or cells are really important for maintaining a normal homeostasis in the retina. They help extremism respond more to the components and recycle a lot of components for their translators. So the question arises, how far can you sort of push the metabolism in the mule or cells and expect retinas to be able to function normally? This is another mapping of chlorine that just to show you again, the diversity gets pretty crazy. So the other piece of paper that we just published was AMD. So this is sort of the classic view that a lot of you guys look at. And I sort of read the introduction on what happened. From this copy, we can sort of zoom in again and we can see these nice sort of pre-indruising form and some sort of pigment structures here. Here's some histology. There's some nice cuts to here, I apologize. But again, it's sort of directing the RPE. Again, it's sort of a tiled appearance, which would be absolutely smooth and influence signatures. There's a small groove right there. This is very early. This is Tori and Lacy, we were finally mapping. And this was just looking at Tori. So this is just looking at the RPE here. So here's the vascular chloride. And here's some putters that are outer segments. This was a cruel image because it shows you the tiling that goes on. So each one of these is separate, retinal pigment that would be in themselves. And normally, they're coupled. So they're gap junctions between them. And that allows the small molecule signals to flow in between them. And that's why the signature is absolutely reforming the normal level of tissue. What's happened is the gap junctions have become uncoupled. And each one of these cells is doing their own thing. And we suspect that leads to dysfunction. And so what we're starting to see, we don't quite know what they are, but these are Tori rich deposits, sub-retinal, sub-fruzine components. Who knows, actually. There's some argument when we published this paper, there was actually hardly a black and port with three of the reviewers, exactly what these were. We don't quite know, but what we think is that it's fail outer segment metabolism. So the RPV is no longer phagocentrism, effectively the outer segments of photoreceptors. We're starting to get debris that builds up underneath the RPV, which is an exciting stress. And I'll list all that. But it's just the arrow that goes across. Here's some other early AMDs. This was OCT, not great. But here's the histology with Tori. Again, we've got some nice bruising deposits. And so the question is, what's going on underneath the sub-fruzine, at least sub-retinal underneath the bruising deposits? Again, you can start to see the RPV. It's becoming uncoupled in and around the cells. And then we can start to see changes in metabolism. So this comb photoreceptor here, did I highlight that? Yes, this comb photoreceptor here has a very different signature than the comb photoreceptors on the other side, the same in this box as well. So we're starting to see the earliest signs of metabolic dysfunction that comes underneath these bruising deposits. So something is happening to the RPV, and it's causing downstream cell stresses in the photoreceptors. Here's another really AMD, again, variations in the RPV coupling. This is looking at oxen, a common oxen. So when the rules start happening is you get a build-up of debris underneath the RPV and an oxen de-localization. So normally, the oxen should be up in the color of the segments. It's de-localizing down around the inner segments and down even to the synaptic input, where it synapses with bipolar cells. This is sort of an indication of the cell stress. And another image of early AMD photoreceptors are starting to get shorter in this particular line. But down here, we're starting to see the same signals in the Mueller cells. So Mueller cells, again, like RP, the Mueller cell signals are starting to diverge, and they're no longer uniform. As you know, we'll look here to sort of show you the signals that are starting to change. We don't know if this is a dynamic process. These are just individual snapshots in time. And we don't know if metabolic cells are ringing, if metabolism is toring or ruining these cells. It's coming and going in waves, and we're just capturing into this particular cell with up-regulation of toring, while in this particular Mueller cell, right next to it, as normal toring. We don't know that yet. This is important because it shows, again, in early AMD, early to mid-AMD, in this stretch, we don't have any drusen, but there are some drusen. Other areas, the glycogenome consuls are starting to spread again. And so in this cell, this patient still had vision, but the visual performance was likely declining. So anal consuls and porzonal cells do a lot of sort of spectral tuning of signals, help you sort of refine the visual performance. And this may be some of the earliest sort of cellular histologic evidence that the circuitry in red is breaking down. Oh, yeah, we can sort of zoom out to show you some of this spreading a little bit better here. This is GABA here. So these are GABA-regulated cells. So GABA-regulated processes are coming from evidence cells going along the road. Here's a case of late AMD. We're starting to get some vascular involvement here. It's going to be a sort of healthy drusen. There's the GABA-blessed glutamate signal. And we're going to zoom in on it. Very here, we can start to see photoreceptors are dying off and being lost. The IPL is getting a little more loose. We're starting to see changes to the retina underneath the same thing in new cell cells. You can't really see with this projector, but the signals are starting to change in the nutrients as well. So the model that sort of holds in AMD, RP, and any other disease that causes photoreceptor cell death is that there's sort of happens in phases. Photoreceptors get stressed. The other segments sort of shrink, and rods are lost. As long as the cones are present, the overall topology of lamination is good. Cell populations seem to be intact. There may be some spreading that's starting to occur, but everything looks pretty good at this point. By phase three, when cones start to die, we get early neurotic modeling from GABA-regulated glycerinic amicron cells. Horizontal cells actually also aggressively spreading this as do ganglion cells. And then by latent phase, there's local remodeling. There's massive cell loss, massive sort of topological destructuring of the retina. We've done a lot of molecular work in these phases. So in phase one, we get a variety of genesis. We produce photoreceptor cell markers in phase two, which is the O and O elation. We get changes in the glutamate channel expression. I'm sure these are some of those data, but we also get changes in retinalic acid signaling. And once the cones start going, a lot of these signals continue. We get changes in glutamate channel expression, or two receptors go up, or five receptors go down, or more than six receptors go down. And then we start getting changes in proteins as well. This is an area that Becca's chasing for her PhD dissertation right now. It turns out there are some massive changes in the glutamate synthetase, and once GS disappears, it never comes back. And so there's a real question in late-stage disease if you can even rescue a retina once the glutamate synthetase, which normally turns over to a receptor, or neurotransmitter molecules, goes away. So the translational component comes because a lot of people are looking in ways that sort of remain. And bionics is one of the big sort of approaches. So there's a couple of ways to do bionics. One, you can sort of put a bionic implant underneath the retina, and you can surgically detach the retina, slip the bionic implant in with the idea that you stimulate the surviving retina. The more diverse, the more successful, in terms of sort of the market, penetration seems to be the under-retinal implants, but they're putting down the ideas to stimulate the ganglion cells. The problem is there's a field effect. And so you have to sort of ask yourself, which cell populations are you stimulating? And you can say, OK, well, we're only verbally stimulating the ganglion cells. It's still a problem. There are also 12 to 20 channels that outflip the ganglion cells to, and it just doesn't go to a visual cortex as opposed to a subcortillary as opposed to a super-collecular cell gene in a lot of different places. So you're sort of stimulating these ganglion cells and discriminating them in a lot of cases. Well, in all cases, we're using bionic implants. There are some other approaches to do optogenetics. You've done some of these studies where you can take a virus and engineer it in an opsin. Basically, if photoreceptors disappear, you can put the opsin in surviving cells. And the idea is the light comes into the retina. It's these cells that have been genetically transduced so that you can express the opsin. And then the thinking is that these cells then stimulate surviving retina. The massive assumption here is that once you do this, that the retina will stop degenerating. And it turns out we did some early experiments and retinal remodeling in this process that we call this sort of plasticity. It's a freight train. And because you put a little plastic one from the freight train, it doesn't stop the freight train. The freight train is still going down the track. So we intervened with optogenetics. And we waited and tracked the animals for a couple more years. And the retinas just wanted to help, even with the optogenetic varieties. But the assumption is that you can stimulate bipolar cells, you can stimulate amycones cells. This can say two amycones cells. Or you can put in optogenetics and ganglion cells. And there's a lot of very cool evidence that this works. In fact, the first guy that did it really got kind of drunk around this guy and went to State University. It was the first to actually show that you can do optogenetic therapy in retinas. And he largely got the award. He presented that at Argo in 2005. And it was the single coolest thing I saw that year at Argo. And Stanford is a little better at promoting their people. There's some very talented people. Actually, Ed Boyden is wonderful. He's now at MIT. And Carl Fisseroth really got most of the fame for actually doing this optogenetically. And there are a lot of reasons why that happened. But Pan was the first guy to actually do this.  The cells rewire. And this circuitry sets great changes. So then the question is, how bad does it change? And the other main mission of our lab, this is sort of Robert Marks, maybe, was that we studied how more wet than it is required. So these are two A2M cells. And it turns out there is a very specific structure. There are very specific synapses and gap junctions that all of these cells make with other populations of cells in the retina. And it's very tightly constrained, the signal, the cell partners that they normally partner with. And so it turns out we can identify these actually, we can go in and pick in our connectable volume, and pick synapses and gap junctions and actually visualize them in the electron microscopy. And we can start this and make these beautiful sort of figures. And then we can sort of make these connection diagrams. So here's one of those A2M cells here. And we can start mapping out broad bipolar so that when we put onto it, there's another gatherage of the energy that we put. And then we can sort of start mapping this out. The trick is it gets really complicated. So this is just two synaptic hops away from that A2M cell. And so if you start asking yourself the retinal degeneration, which one of these connections gets broken, and how many of them can you break and still expect normal retina signal processes to occur, that's kind of what we're at now. We're trying to figure out what goes wrong, where. And then there's a lot of modeling that can be done to try and figure out how bad can we get at what point can we no longer intervene with biologic approaches. Just wrap this up real quick. So we've done some biologic work with people. We've done some optogenetics work. And we've done some cell-based work. Turns out underneath biotics, the modeling is accelerated in a lot of these cases. And this is distressing. Optogenetics, as I told you, the modeling progress just precedes, just keeps going down the track. We've done some cell transplants. Turns out the modeling is accelerated in transplants. So we haven't done a lot of genetics yet. There's some other experiments that we'll be doing both in. And the question's open there. The basic take-home message is that there's a lot of rules that are happening that we need to understand what the rules are. This is the lab, these are some of our collaborators.


Hi everyone. I hope you're having a good day. Today is Wednesday and this is Monica Wahee. I'm just starting the chat a little early so you can get in. I'm actually kind of not ready, so I'm kind of opening some files here. So today I wanted to talk to you about box plots and percentiles because I've been doing some work on my YouTube channel and obviously I'm a data scientist and YouTube has really cool analytics. So one of the things I was noticing is people all over the place seem to want to know about box plots and percentiles. And they look really simple and cute, but they're actually not that easy to make. And they weren't that easy to teach back when I taught them. So I thought I'd do a little livestream where I could kind of show you different ways to not only make a box plot, but to teach how to make them. And if anybody has any questions about interpretation, I'm happy to talk about interpretation. They're very... I wouldn't want to say easy to interpret. They're useful and it takes you a while. You have to see them often and then you get used to knowing what they mean. So I'm happy to talk about interpretation. But the main thing that I'm here to do is sort of show you different ways you can make box plots depending on what you're doing, whether you're actually just analyzing somebody's data or your data, or you're trying to teach somebody how to analyze data, or you're trying to teach yourself, or you're trying to maybe make a blog post explaining what you did. And so I'm going to actually demonstrate a few different things from my experience in box plot land. I'm going to show you a lot in R. I love R and I'm always using R. But I'm also going to show you other things besides just R. But R is cool. So of course I'm going to show you R. So let me give you one more minute, maybe a few more people join and then I'll start my little shtick. And this live stream is going to have files associated with it. I'll post them on GitHub. I always like to go back and update the description for my live streams because I'm always going to all these websites and stuff. That way, if you're watching it now and you want the code I'm going to show you and stuff, just come back and there'll be a link to my GitHub. All right. Well, now it's time to start the live stream. My name is Monica Wahee and I'm a data scientist. But I also teach statistics. I kind of teach a lot of different things I say I teach. I mean, I'm a consultant so I just do data science. But it always seems like I'm teaching and even when I'm doing consulting, it seems because I'm always working with people because I'm an epidemiologist. I'm always working with physicians and nurses. So it seems like I'm always teaching because they don't know how to do it because I'm an epidemiologist. That's our job. So I'm supposed to know how to do it. I guess they like it working with me because I always teach them stuff. But anyway, so the purpose of this live stream was to just show you how to make box plots. Now, if you wonder what one is or all that, you can watch my video on how to make them and what percentiles mean everything. But first, let me just start by sharing my screen and talking to you about the data set that I'm going to use here. And I actually stole it from my last live stream. This is the data set where you're going to use, we're actually just going to use the staffed beds here. And let me show you where I got it. This is Massachusetts data set. And just to remind you where I got it, actually I should have done there first. Let me show you this other web page. Okay, this website here is called ahd.com, American Hospital Directory. And if you go on here under free national stats, and I explain these are statistics, they're just on the web. And why? Because they're public. Here I went to Massachusetts. I live in Massachusetts, so I'm kind of used to the hospitals here. And the city is here in the geography. So I went and I basically just turned this page into data. And then now I'm going to show you that data. I probably shouldn't have explained that first. And here's the data. It's data about hospitals. And so you have the hospital name, and then the city like I'm in Boston. And the staff beds, that's how many beds are actually staffed that they actually have staffed on that are open. Because you'll see some of these hospitals, if you come to Boston, they're really big, but they may have a lot of outpatient going on and may not have staff beds. So, and then this is total discharges, patient days, and gross revenue. We're going to be working with the staffed beds. That's what I'm going to use for the box plot. But you're welcome to download this data set and try the box plot with different things. All right, let's see here. So, all right, so why don't I start by, why don't we go to R? So I put that data set in R. So let's go to R. Okay, so let me see if this looks good. Okay, so that data set is a CSV that I read into R. And you can see here, here's my read CSV mass chooses hospital data demo set. And again, I'll put this in GitHub. I'm calling hot at Haas A. Well, you know, I'm calling it hot. I always, if you take my LinkedIn learning courses, you know, I always do this a when I, when I start with a data set, because I like to increment the suffix as I transform it. So let's look at what's in Haas A. Let's see how it comes. Yeah, we know what it looks like, right? Here's a name and all right. Okay. And again, here's the column names, the fields of the data set here. Okay, so let's just remember what a box plot is. It's actually, I probably should have shown you one here. I'll bring up one of these, one of the ones that I'm going to show here basically. Let's bring up one of these. We're going to make this one later. This is just one of them that we're doing. Let me see. So I'm going to, let's see. Okay. So here's one of the box plots. Actually, let me see if I can figure. Okay. See this box plot? Now it's a box and this top bar, this top, I guess it's called the bar, is at the 75th percentile of a quantitative variable like staff beds, right? So this would be at the 75th percentile. The bottom of the box part is at the 25th percentile. And this middle part is at the median, the 50th percentile. And then you've got the minimum and then the maximum at the top. And you've got the tail. So it's actually a box of whisker plot or I guess those are whiskers. These are tails and these are whiskers. They're not animals. I don't know why I'm going into this, but anyway, that's what we're trying to make as a box plot. Okay. And then I'm going to show you now how, so the first thing you would probably realize is that if you were going to draw one, you could draw that box. You could just draw it. But the problem is you'd have to know those things I was telling you, the 75th percentile, the 25th percentile, the median, the minimum and the maximum. And if you want to do something fancy with outliers or do all that, you can do that extra, but you would at least need to know those things. All right.  And actually you're right. There is a command in R and it's called quantile. Right. But I'll cut you the chase. If you read in that data set and you try to do quantile on it, it's not going to work. And I'll tell you why. It's because for whatever reason, see this class command, the staff beds variable reads in like a character. Maybe there's something in there. I mean, I looked at it and I couldn't figure out why I read in like a character, but you know, I didn't see any characters in it, but this is not unusual. If you do data science, this is like so normal. So I was like, well, I just want to do this demonstration. Maybe I can just use this as numeric wrapper and, you know, get out of having to do anything about it. Because if you do as numeric, you'll see they'll just list them. And these are the staff beds. And you see there's this NA. Well, one of them must have been blank. Now remember where this data comes from is I think it's quarterly data. The hospitals have to report to Medicare. That's our public insurance for elders. What's up with them? How many staff beds they have? And this American Hospital Directory makes some of that data available. Like they clean it up and make some of it available online. So who knows what happened. Maybe they didn't submit it or whatever. They probably have staff beds. Like you see all these zeros here. They probably have staff beds too. And it's just bad data. But I just was trying to do a demonstration. But anyway, so we get these NA's. And that's what our users, if you're a SAS user, you see a period here. That's what they use for null. So you can't just do quantile on as numeric staff. And then NA remove equals true. I tried that. But I think I am having an order of operation problem. SAS users will know what this means. Because if you do things in the wrong order in SAS, you get things like this where you thought you removed something, but you didn't remove it. Because you've got to put it in the right order because of the way SAS processes. So I thought, well, if it's acting like SAS, I'm going to have an R answer.  So this is me pulling out hospital A staff beds as a vector. I called it bed-vec. Bed-vec, of course, underscore A. So when I did that, I still got NAs. But I got, this is now a vector. This is in the data set anymore at the vector. And it's a vector. How many things does it have in it? I put a length at 72. But we still need to get rid of that NA. So here's the command to get rid of the NA. I made bed-vec B. So you see how I do this? That's how I don't run out of names. And then the length of B is C71. I got rid of it. Now I can do, what was I even doing, right? I was doing the quantile. This is the quantile command. Now we can do it on that vector. And you can see, here's the, here's the minimum. Here's a 25th percentile. Here's the median. And here's the 75th. And here's 100, right? So now is a good time for me to show you what would happen if you were trying to just demonstrate to a class like drawing or whatever, you know, how to do this. Like, they got the quantile. So let's draw a box. But I just find that a lot of times I'm having to demonstrate how to draw one by hand, even though I'm teaching R, you know what I mean? So I'm just going to show you how I do that. And I'm going to show you in paint, because it's like everybody seems to have paint except for people who have max. I don't know why this opens so wide, but let me, let me then share my screen with paint up. Okay, so I can just draw on this, and I'm not a very good drawer.   So we know if we're going to make a box, but we're going to need like a Y and X axis. So I'm just making this as like, this is going to be the Y axis right here. And I already know that this is going to be zero, right? Okay. But what am I going to put at the top? Well, I'm going to sneak back here and look at my maximum. My maximum is 852. So I'm going to make the top, I'm going to cheat, make the top 900. That's how you do it on the fly here. Okay, so then what do I do? Before I do the rest of the box, I'm going to make a few lines here like, so this one's halfway, right? So I'll go and I'll say 450. But it's hard to graph like that. So I'm going to make a few more. And remember, you're just demonstrating. And you just want it to be right. And you don't want to take all day. So here I could probably do like, maybe 400 here and 500. And six, seven, eight, four, 50, 400, 300, 200, oops, 100. You can put your hand on control Z a lot. And you can put also just some notes in here. Like this was 400. Here we have 300, 800. Just to give them an orientation. All right.  But usually what I start drawing to make sure it doesn't turn into a hot mess. So I start with the 75th percentile, which was 270 from our quantiles. So let me go back and so where where was what did I just say was 270? That just seems like really down there. Okay, so 270 be down here somewhere. So you're going to need to make this kind of out here. I'm making just a line. So I start by making this one. If you're clever, you could make the whole box, you know, maybe we could do that. Well, that's the top of the box. The bottom of the box is going to be at the 25th percentile, which is 67.5. So that's really down here. So if you go with the box, you could have just done this. Like, well, maybe I'll just do it now. Have the box. Oops. Did I do that wrong? Yeah, I just grabbed the box. Here we go. Okay. Yeah, I guess it's a little easier. Okay. So now we've got the box part. We need to put in the median across. Just look over there, 145. And that's really close to down here. So you can already see it's skewed, right? Those of you who are used to looking at box plots, you know, that this is skewed. Where was the minimum maximum? Well, max was 852, and the minimum was zero. Remember, because we had some probably bad values. So here's zero. Let's make another one. Go from zero to 852, which is up here. It's too bad. I could put a, you know, color this over there, but I should have probably put that in there first. Or I could put them separate. Maybe it makes it a little clearer. And then up to two. You can draw the whiskers on. And of course, I wouldn't keep this. I'll keep it for you as an example of this demonstration, but I wouldn't keep it this way. Let me say that as a demonstration. I wouldn't keep it this way for the class, but I would just show them that this is how you'll roughly how you draw it. Because if you've got a class, they're going to have to draw it on something if they're trying to draw it. Like if you're not, it depends on how you make the assignment. If you're trying, if your assignment is do it in R or do it in some software, then you don't have to draw it. Okay. And I will show you an example of some online software you can use, which I actually use in my class where, you know, because if you're making them draw it and you're making them calculate all these things by hand, you can't really give them an N of 100, right? And so, so I had that, I gave them small ends to calculate by hand and make box box by hand, but I'll show you another thing that I use to, so that they could use more numbers and see bigger box lots of bigger numbers. But okay, but for now, let's just go back to R. Let's assume that you don't need to do it by hand. You can, you can just, you know, teach it on here. So where we left off is we had done our quantile. And one of the things, since we're doing quantile here, in case you wanted other percentiles or other probabilities, these are the default ones, because they know I'm making a box plot, I guess. If you wanted other ones, here's a great example. So this is it without any arguments. If you want other probabilities, like I pretended I wanted 9.95 and .05, like the 5 percentile and 95th percentile, you just make the C here, this combined, .05. And you can put as many in here as you want, and it'll tell you the probabilities right. So watch this, I'll do this, and it'll tell me. Here they are. So even the 95th percentile is pretty high. Okay, so what have we done so far? Well, first we looked at our data, which was staff beds, and then we ran the quantiles of it because we wanted to see where these things were in case we wanted to make a box plot by hand or demonstrated. All right, but now we don't want to do that by hand because obviously it's very messy. Let's do it in R. Okay, so we've got our bed vac. So base R has a box plot function, which I'll just run. Okay, there it is. So this is a box plot. So you can see it kind of looks like ours, but you know, because we had to go up here, but like the zero is here, I don't know, it's a little misleading. But you can edit this box plot, you can put titles and stuff on it. But I don't, because I'm an R snob.  You should be using a ggplot too. So that's what I'm saying, you should be using ggplot too. And there's 101 reasons why among them is that it's just make sexier plots. But I will make an argument for base R. And I'll tell you why, because I often want a quick and dirty box plot like I just showed you, because I'm just trying to figure out the distribution. I'm just in a hurry. You know, what I'm going to show you now, it's like your box plot, it's like your box plot is the sewing machine that's always out.  But it makes it fancy. So where I'm going to show you just, we could just spend the whole time in ggplot too. But here's me running the library and getting it into, okay. So I find it easier, it's up to you, but I find it easier to use ggplot too with data sets than vectors. So what I did was I sort of remade our vector into our data set. So I started with hosp a, and I just converted staff beds to a variable called staff beds underscore n. Okay, so now that, and it's still mad about this. And how many rows does this have? Well, that's 72, just like the vector had 72. Okay, now I'm going to get rid of that whole record with the na in it. So this is me creating hospital B and dropping that record with the na in it. And how do you know I dropped it? Well, let's look at the n row. See, I dropped it my way. And also you can just look, you can just look at the field by here and you can see. All right. So if you want to just get a box plot out of ggplot too, you need to, you know, if you're using data, you need to declare the data. And you need to declare the aes, the x under the aes command, I guess it's a set of commands. And you've got to add a plus and call the geome underscore box plot. That's how you get the box. So let's run this ggplot. Actually, I gotta run the whole thing, right? Yeah, here we go. Okay, so you see, and you saw me drawing the box spots, like in a vertical orientation. So I don't feel good when I see him horizontally. And I don't know why. I think in healthcare, in healthcare, if you read journals, most of the time, you're going to see them in the vertical orientation, but there's nothing that's right. Just that the first thing is this freaked me out. And I was like, Oh, I don't like it. But that's the most basic. Now you see make it pretty. See, I can go crazy with it. Here, I'm making it pink, pretty, putting some labels on it. And I do the coordinate flip, right? That way, I've got my night. And you'll notice my labels are mixed up, right? The X label of staff beds, but then when you flip it, the coordinates flip. And then I put theme classics. So now we're going to get something beautiful. Look at that. So much more gorgeous. So this is way better than the thing I was drawing, right? And I don't know about this command. I'm going to have to look to see how they decide on the outliers and stuff, but they're putting the outliers in. It's nicer. Now definitely, when I publish, I definitely just totally deck out these GG plot twos. I just make them gorgeous. You know, I'm a snob. I just, I just spend my whole day on that figure. But you can't do that. If you're just looking at distributions, you've got to get your work done. So just don't get stuck in GG plot two with this. All right? However, it is easy to do. So I'm going to stick on GG plot two just for a second, because it is so sexy. Like, what if I wanted to compare box plots? Because that's what you're always doing. Well, the problem is there weren't any certain natural groups in that data set.  But it's kind of not a fair comparison because I included Springfield, and you'll see in the data set, they all have two hospitals, and you really should be making a box plot about that. But so I created this vector called Keep Cities. I thought, oh, this will be a good little data sciencey thing. So this is my vector, Keep Cities, Boston, Worcester, and Springfield. Yeah, you really pronounce that Worcester. I don't learn about it. Okay. So you know that hospital B has, what, 71 rows? We're going to use the subset command, and we're going to make it so Hop City, that's one of the fields, is in. Don't you like that? In Keep Cities. This is like my favorite trick. You can do it in SAS, SAS, and in two. You can do it in the data steps, okay? So that way you can change the vector and change the cities. Like the first I just put Boston, Worcester, then I put Springfield. I thought, oh, I like these three little boxes that it produces, but probably shouldn't have made this Springfield. Okay, so we're going to make the subset of data set, Hop C. See how I don't run out, and then we'll look at the rows. We only have 15, right? Actually, let's just look at Hop C. Here, yeah, here it is, and see there's only two from Springfield. There's only four from Worcester too. You probably shouldn't do this, so this is illegal. Don't tell anybody I did this. All right, so the reason I did it, this illegal thing, was mainly to show you the sexy ggplot2, you know, that it can get you in so much trouble. But the main thing that I changed about this data is, besides changing it to Hop C now is the data, is I added a Y, which is the Hop City. Now, these happen to be just written out here. Often cities are just like they'll have some code, and then a code would come out and be ugly, and so that's a whole other thing you have to deal with the ggplot2. And then I wanted it colorful, and I knew it would find three cities, so I put three colors out here. That's the main difference between this one and this one. So let's just run it, and is that gorgeous? See, and then there's Boston, Springfield, Lister, and staff beds. That just doesn't even seem right, right? Because I think Boston should have, well, you know, it was the pandemic, and I don't even know what's going on anymore. You know, we had field hospitals and everything. I don't even know if they're in there. Who knows what's going on? All right, the healthcare system crash, sorry, are still running, but the healthcare system crash. I thought I'd better look at hospital C. So I decided I was going to write it out as a CSB here, so I did that. And then now let's actually go look at that dataset here. I'm going to bring up hospital C from what I exported here. Okay, let me go and share it on my screen with you. Okay, it's a CSB, so it comes a little ugly here. We have our staff beds and at the end here. Now, as you can see, was it Springfield this year? 735. That was that why Springfield was way up there, but I thought Boston, I don't know, everything is different, you know, the data change, right? So I'm going to highlight this and I'm going to do data sort, and I'm going to sort it by Haas City here. That way, we're going to get all these staff beds up here. I guess I can use this one for Haas, for Boston right here, because I wanted to show you something else. And that is that I actually gave this dataset to my class who was doing things by hand, they weren't using R. And but I wanted to show them how to how to deal with the boxfile with a lot of values in it that they made themselves. So I'm going to copy these values and I'm going to paste them into this calculator, this online calculator that I wouldn't use in like a real life journal. But it's great to use with students, okay? And it's on this Alcula website and they seem to have, I'm going to stop sharing and we'll go to that, they seem to have other calculators, but let's see here. But this is the only one I found used for. So this is this Alcula and this thing has been here a long time, I don't know. But if you come here, you find this and you can choose a sample or population and you'd have to read their dock as to what they're doing exactly. And also even when I was teaching how to make these by hand, I found out there's two different ways to calculate that 75th and 25th percentile by hand. So I had to deal with that and tell the students I was just doing this certain way and I don't know. This is a lot more complicated than it looks. Okay, so I'm going to just enter this data. I just pasted it. It says enter comma separated data, but let's see if it'll handle it if it's not comma separated. Yeah, did it. Okay, see that? And it says the population is nine, this is just the Boston one. And here's the, let's see here, maximum was 804, that must have been the maximum on here. Yeah, it sure was. And so it's got, it put the commas in there. And one thing you could, if you make the class do this, what I did was I had them sample, I had them take a systematic sample and make the box plot and then everybody did it and I said, we did a simulation. Let's look at the population box plot, you know, for the whole Massachusetts and see our different simulations, your different samples compare it. It was kind of a fun little thing, but they had a little trouble saving it. You have to kind of go right click and save image as, which I did for you. So you'll get, I'll show you this image on my GitHub. But anyway, and the link to this is in, is in the description of this. So let me go back here. All right. So I just went over a whole bunch of stuff really quickly. First, I went over, you know, what do you need to make a box plot by hand? And the answer is at least the minimum maximum 25th, 75th, and 50th percentile. Then I said, okay, well, how do you demonstrate drawing one? And I showed you using paint to draw one. Then I showed you two different ways of using R to make a box plot. And then finally, I showed you this calculator that you can use online to make a box plot. And so I, and I didn't get any, I haven't got any questions yet. Does anybody have any questions about box plots? Because so the, I guess I'm talking about all this, why would you even do one? Well, the main reason is you want to see the distribution of your data. You will notice that if you think about staffed beds, okay, there's going to be, and you saw all those zeros in our data. So our data were like left skewed. I always think left, light on the left is left skewed. So they were left skewed. And, or maybe they were right skewed. I don't know. I can't remember. Let's look at the box plot. Actually, they were pretty normal now. But I've noticed that like one of the things that's always skewed is length of stay. If you ever get hospital length of stays, like every single person who gets checked in and checked out, most of the time it's like one or two days. But, you know, like along COVID, everyone's who else a long time. And so that's always skewed data. Now, if you've got normally distributed data, where the median and the mean are on top of each other, you can do certain statistics then, and you can't do them if it's skewed. And so, what are the strategies? You can use different statistics, but you could also take often a log of the skewed data. You can take a log of each of the values, and then you might get a normal distribution. But what you're doing is you're making box plots the whole time to help you see where the distribution is. So this was just kind of short. I just wanted to check in and show you a few things. And then I'll put up in the description, I'll put all the links, and I'll put up a GitHub so you can go get the code. And so, and if you're here, thank you for joining me. Everybody's very quiet today. No, he's got any questions. I really appreciate you coming to my channel. Even if you're not here to make a box plot, if you would look around and see anything you like, it'd be great. Please like it or subscribe, or both. I'm trying to put up more stuff, so let's see how I do. Well, thanks for joining me on Wednesday afternoon, and I hope you have a good rest of your week.


Hi, I'm Arnoldo Serrano a graduate student in the guy lab in the Department of Chemistry at the University of Pennsylvania and an author of the recent reviews spectroscopic studies of protein folding linear and nonlinear methods along with Matase Weigli and Professor Guy. With this video, I hope to highlight for you some of the major points we touch on in that review and hopefully convince you of the growing importance of advanced laser spectroscopic methods for answering new questions in protein folding. The complexity and biological significance of corrective folding has stimulated decades of research into the topic from many different perspectives. While much progress has been made, two of the greatest element factors in providing more detailed information about folding are firstly, the need to trigger folding events, since the nature of this process generally requires population of thermodynamically unfavorable states and secondly, the lack of suitable windows through which folding can be viewed. For most protein studies, the kind of detail visible in the simulation you're currently watching is inaccessible experimentally and only a picture of considerably reduced resolution, both in time and in space, is achievable. For example, if the process is being probed by either infrared absorption or UV-CV, the experiment will primarily be sensitive to alpha helix formation. In the case where a fret pair is being used to probe the process, the experiment will primarily be sensitive to the relative distance of the two probe molecules from each other. While a spectroscopy that can provide atomistically detailed and high-time resolution movies of the protein folding process is if at all possible still a long way off, some of the techniques we discussed in our review provide what we believe to be huge steps in the right direction of achieving this goal. All of the methods discussed involve the application of optical spectroscopy. In particular, laser spectroscopy in the visible and infrared regions. Several of these are carried out in the following fashion, a short laser pulse ranging from femtoseconds to nanoseconds and tuned to the appropriate frequency interacts with the sample first. This can be through heating of solvent molecules or the photolytic cleavage of a chemical bond. All of these leave the sample in a high free energy state from which relaxation can be monitored by an appropriately tuned probe laser. Our group has successfully used this method to measure the folding dynamics of many different secondary structural elements and fast folding mini proteins. While these techniques have revealed a great deal about the motions involved in protein folding, new methods based on nonlinear spectroscopy and ultra-fast lasers are emerging as powerful new tools. An example of such a method is two-dimensional infrared spectroscopy or 2DIR, which we discussed briefly in our review. As depicted here, 2DIR involves the generation of three ultra-short laser pulses which are delayed relative to one another and interact with the sample in series. These three consecutive interactions induce the emission of a sample signal known as the photon echo. This signal, when combined with a fourth reference pulse and analyzed, provides information on the third-order nonlinear response of the sample, and ultimately results in the generation of a two-dimensional contour representation of the infrared transitions of the sample. As we explained in our review, these spectra provide a wealth of detail about the structure dynamics of the protein sample under study that is simply not accessible by more conventional methods and can be used in conjunction with the trigger methods discussed previously, and we believe it represents one of many large steps in the direction of generating high-resolution experimental protein movies.


taking advantage of Google's advanced operators. In the digital age where information is abundant, refining your online searches is crucial for obtaining precise and relevant results. Google's advanced search operators are specialized commands or symbols that can be added to your search queries to refine and enhance the search results. These operators provide you with a higher level control over what Google finds in its vast database. They can be used to specify particular websites, file types, date ranges, or even to exclude certain terms from your results. In this video, we'll explore a variety of the advanced search operators available to you and gain an understanding of how to use them. By the end of this tutorial, you'll be able to conduct more precise and effective searches, enabling you to uncover the information you need with greater accuracy and efficiency. Boolean operators or not and. Google uses three basic Boolean operators or not and and to help you refine your search queries and obtain more specific or broad results. When you use the or operator between keywords or phrases, Google will retrieve pages that contain at least one of the specified terms. It broadens your search. For example, searching for cats or dogs will return results that include either cats or dogs or both. The not operator excludes specific terms from your search results. When you use it before a term, Google will return results that do not contain that term. For example, searching for apple not fruit will show results related to the company apple but exclude results about the fruit. When you use the and operator between keywords or phrases, Google will retrieve pages that contain all of the specified terms. It narrows your search, ensuring that all the specified terms are present on the page. For example, searching for coffee and beans will return results that include both coffee and beans. These operators can be used in combination to create more complex search queries. For instance, marketing and Facebook or Instagram, not Twitter, would find results containing marketing as well as either Facebook or Instagram but exclude any that mentioned Twitter. Understanding and using these operators effectively can help you tailor your search results to better match your information needs. Quotation marks. The quotation marks are used to indicate an exact phrase. When you enclose a group of words or a phrase within quotation marks, you tell Google to search for that exact sequence of words in the specified order rather than treating each word as a separate keyword. For example, if you search for climate change effects within quotation marks, Google will return results that contain the exact phrase climate change effects. This can help you find pages that discuss the specific phrase or topic you're interested in rather than pages that contain the individual words for climate change and effects scattered throughout the text. Dash slash minus. The minus operator minus sign is used to exclude specific terms or keywords from your search results. It can be used similarly to the not operator. By adding a minus sign before a word, you tell Google to omit the pages that contain that word. This can be useful when you want to narrow your search results by excluding irrelevant information. For example, if you're searching for information about Apple the company, but you want to exclude results related to the fruit, you can use Apple minus sign fruit as your search query. Google will then return results that pertain to the company Apple while excluding any pages that mention the word Apple in the context of the fruit. Site colon. To search a specific site or domain, use the site colon command. For example, search only Canadian government websites by using site colon gov.ca or only colleges and universities by using site colon edu. If you want to find a site's content about a specific topic, follow the site command with a search term attached. This is called a site search. For example, to find more information about research on the learning portal, you could search site colon tlp.lpa.ca research. File type. The file type colon operator is used to specify a particular file type that you want to search for. By using this operator, you can restrict your search to specific types of files, such as documents, presentations, spreadsheets or PDFs. For example, if you want to find PDF documents related to climate change, you can use file type colon PDF climate change as your search query. Google will then return results that include PDF files specifically related to the topic of climate change. This operator can be handy for academic research or when you're looking for downloadable resources in a particular file format. Intext colon and allintext colon. The intext colon and allintext colon operators in Google search are used to refine search results based on the presence of specific keywords within the main text or body content of web pages rather than just the title or URL. These operators are helpful when you want to find pages that discuss a particular topic in depth or where keywords that specifically match your query are a significant part of the page's content. When you use the intext colon operator in your search query, followed by a keyword or phrase, for example, intext colon technology, Google will return results where the specified keyword or phrase appears within the main textual content or body of the web pages. The allintext colon operator allows you to search for web pages where all the specified keywords following allintext colon appear within the main text or body content of those pages. For example, allintext colon latest technology news will return results where the main content contains all three words, latest technology and news. Intitle colon and allintitle colon. The intitle colon and allintitle colon operators in Google search are used to refine search results based on the presence of specific keywords in the titles of web pages. These operators are particularly useful for narrowing down your search results when you're looking for web pages with titles that specifically match your query and sharing that the content is highly relevant to your topic of interest. When you use the intitle colon operator in your search query, followed by a keyword or phrase, for example, intitle colon technology, Google will return results where the specified keyword or phrase appears in the title of the web pages. The allintitle colon operator, on the other hand, allows you to search for web pages where all of the specified keywords following allintitle colon appear in the titles of those pages. For example, allintitle colon latest technology news will return results where the title contains all three words, latest technology and news. Inurl colon. The inurl colon operator is used to specify that the search results should include web pages where the specified keywords or phrases appear in the URL of the web page. This operator is useful when you wanna find pages where the keyword is part of the web address, indicating that the page is likely to be closely related to that topic. For example, if you're looking for government websites discussing environmental regulations, you can use inurl colon environmental regulation to find pages with the term environmental regulations in their URLs. This can help you identify web pages that are more likely to be official government documents or resources related to your search query. Now that you have a deeper understanding of how Google uses these specialized commands and symbols to refine and customize the search results, you can manually incorporate these operators into your regular searches to tailor the results according to your specific needs. If you're looking to simplify this process, you can use Google Advanced Search. It is a dedicated website page or form provided by Google and can be accessed through the URL google.ca slash advanced underscore search or by clicking on the gear icon on the Google homepage and selecting advanced search. The advanced search is a form with various fields and dropdown menus where you can input your search terms and apply specific filters. Instead of manually typing advanced operators, you can select options from the form. Both are great options that cater to users seeking more refined and tailored search results. Incorporating Google's advanced search operators into your search strategy empowers you to take control of the information retrieval process. Whether you are a researcher, a student, or simply someone looking for specific information, mastering the use of Boolean operators, quotation marks, and other advanced techniques can help you tailor your searches to meet your specific needs, ultimately saving time and enhancing the quality of the information you discover.