So, welcome. Smile for the grenade. Camera, go bang. I'm Josh Marpet, otherwise known as Quadling and this is Vlad Gostum, otherwise known as Recompiler. We're whack job security guys. We do everything you can imagine and then some. We are literally the people that do everything. Vlad has held every pen testing job in the business and created some of the pen testing jobs in the business. I've done everything in physical security you can imagine and some you probably can't. I'm an ex cop and you know, ex everything is the way it goes. We both work in physical and information security and we have a good time doing it and we love doing things that cross that over. This is our stuff. Not our employers, not our friends, not our families. Don't blame them, please God. Don't call our employers and go, hey, I saw that cool grenade presentation. Won't go over very well at all. This is our stuff. Lawyer time over. Okay, let's talk about cameras. There have been wireless cameras for decades. Some of them wireless in only a very metaphorical sense. Okay, it is wireless. The camera that caught the Kennedy assassination, notice it's getting smaller. Smaller yet, not exactly tiny but it's getting smaller. Okay, this is a camera that fits in a briefcase now. That's not bad. We're getting there. Hey, it's still a wireless camera. It actually still records on to physical media. Anybody have a Mavica here? Well, not now. I mean, did you have one at any time in the past? A few of you did. Okay. Nowadays, it's scary. For 70 bucks from what site? You can get a Wi-Fi or wireless network surveillance pan tilt zoom camera with 10 led IRNOT vision and a microphone. So not only can you spy on your neighbors, you can hear them. Anyway, so these things, they started militarizing surveillance cameras a while ago. This thing makes me sound like I'm hissing like crazy. They started militarizing surveillance cameras a while back. This is one that's currently being used in the sandbox or litter box, depending on what you call it. It's in mind clearing mode right now. That's literally a mind clearing line of explosives. They have this, which is a million round a minute 40 millimeter grenade launcher. No, we didn't bring one. God, you guys are sick. They also make unmanned flying vehicles, which are of course wireless as well. It's kind of tough to have a three mile wire. Okay. And there's all these different kinds. Some of these are armed. Some of these are unarmed. These are all different kinds. And this is a great website if you're interested in how they're looking and watching down on you on your nude sunbathing in the backyard now. Not that anybody would want to watch most of you sunbathing nude. Just saying. And I'm including myself in that, by the way. So they even have cool things. Let's see if this runs. Nope, didn't run. But that's a video of a RC helicopter outfitted with a Glock 19, which fires on command from the, I think it's an Xbox controller. Yeah, yeah, yeah. That's not a good idea. Okay. Just FYI. They even have unmanned marine vehicles, some of which by the way are armed. So this idea of wireless cameras going in different places is starting to percolate through a lot of different battlefields, if that makes sense. And that's why we did this. Because we decided to see, by the way, that that's why they're armed. I mean, because we all know about this, right? Am I that ugly? That's a bet. And I take bets. Don't do that. Okay. Inspection cameras. They have cameras that will just two feet for an inspection camera. They've been doing this for 20 years. Fiber optic cameras. This is interesting. This will go a thousand feet down a pipe and you can see on the other end of the pipe. That's what they use it for. Could they put it through an air duct in your wall and see at the other end of your air duct? They could.  You're already paranoid. But it happens. Okay. So they even have throwable ones. That one's about the size of a, you know, like the little mini pizza boxes you get when you have two slices because we all know how pizza comes. And the iRobots, which we were just showing you, the throwable ones, are very hackable. Vlad, we'll tell you about that. Yes. So people actually play Frogger with these on highways. They program the robot to try and avoid cars and cross food 80 in Pennsylvania. Tons of fun to watch. Don't run mine over. So I would actually like to command iRobot. They're very hackable and they release all sorts of information for them, for developers to mess with them. There's also throwable cameras for use by SWAT teams for throwing into buildings for a hostage rescue situations. And of course, to get into the really fun stuff, which is pertinent to our talk, the ballistic cameras. All right. Ballistic cameras are from an Israeli company named Rafia Land Systems. It's called the Firefly Camera System. There's a couple of articles on it. Basically, it's a camera that comes out of a 40-millimeter grenade launcher. It goes 600 meters that way, or, you know, figure direction. And it has fins. Is it the next slide? Yep. It has fins that come out that stabilize it, so the camera's always watching down. And it shows the Marines or the Army or whatever, what's going there in the next 600 meters of their walking or whatever. Is there an IED? Is there a sniper? Is there a whatever? That's a great idea, except at the end of it, it hits the ground and goes boom, ballistic decommissioning. It doesn't explode, but you hit a nice computer from 600 meters up and drop it to the ground. Is it going to work afterwards? Not normally. And by the way, it ain't cheap. We'll talk about that in a bit. But you can't have the Firefly System. That's what the man says. Why? 40-millimeter grenade launcher, destructive device. Very expensive, very, very, very, very, very restricted. Okay? So we decided to do it ourselves. We wanted to make it something that civilians could have that was cheaper. Make it in the do-it-yourself make spirit, okay, and all kudos to make. For search and rescue, airsoft, SWAT teams, just because. We wanted to try. What the heck, right? You never get anywhere unless you try. So we tried. So before we get burned to the system, you want to cover safety. I'm sure all of you have heard somebody say or know somebody who said, hey, y'all, watch this. If your like, if your puke response is low, close your eyes now. That's actually a 37-millimeter launch gone wrong for somebody. So safety. You can buy commercial 37-millimeter and 40-millimeter rounds. You should never ever disassemble them. It is very dangerous. Primers have been known to cook off. Don't attempt to build a system yourself unless you know exactly what you're doing. Email us. We'll be happy to share information with you. We're encouraging people to make their own stuff and to build their own stuff. We have literally years of experience with firearms, reloading, launchers. We just don't do this. Call us, email us, smoke signal, something. Don't do this. I can't say that enough times. Okay, this is what happens. This is what happened to the guy whose hand you saw a minute ago. All right, talk about this. So somebody was actually building fireworks for 4th of July and shooting them off in front of their family. That launcher in the picture is actually a similar launcher to the one you see on the table after an explosion. The guy lost most of his hand, three and a half fingers. We believe that he double charged it. If you double charge it, which means you use too much propellant, you actually inadvertently create a destructive device, a non-registered destructive device, which is highly legal as in jail time and huge fines. Safety.  That tube right there is actually what contains the round. You saw the hand, you don't want to have your hand there. Proper safety equipment, fire retardant clothing, eye and ear protection. It does get very loud. Even a flare can create about 120 decibels, which is enough to damage your hearing. That's the right way. So yes, even when shooting flares, I know I may look retarded or paranoid. That's what you want to be doing. Another right and wrong slide. That's actually the same launcher mounted on AR15. Let me do this. So you got a guy stacked up waiting to go into house, right? Swat team. Hey, what's that around the side? What's going on? I can't see because we're all stacked up and we look like sardines and fishing a barrel and you know what happens to fishing a barrel, right? Okay, cool. Well, what are you going to do? You're going to have this guy shoot one of those, shooting this to put a camera up above everybody to find this one. The dangerous kitten sniper. Well, you probably wouldn't. But what you might find is one of these. And if you find one of these, your guys are in trouble, right? Okay, what do you do?   But the receiver is this big. This is a DVR. It records, it shows battery powered.  And so the camera, our green flare logo, is sending information to the guys in the stack. And it's also sending information back to the long range receiver on the short range receiver. And it's also sending information on the long range receiver back to the mobile command center. Hook that up to an 80 inch plasma. You can get some detail out of it. You can find your kitten sniper. So it works really well. And we said, wow, this is actually a good idea. So what do we want? This is what we want. Now what features do we want? We want it in 37 millimeters so civilians can own it easily. That's not even a firearm. That's not even a title one firearm. It is a flare launcher. 5.8 gigahertz cameras. Pretty good range. Not a very cluttered spectrum. Not hugely cluttered. The shells are hand loaded at the moment. We wanted a parachute so we could hang. Remember the firefly system, 600 meter arc, right? We wanted to see over one house. So we wanted to go up and hang. All right. About 400 meter range. Urban search and rescue, disaster, SWAT teams, airsoft. He's done ground search and rescue. There's places you can't reach. Now I can. That's pretty cool. Okay. You get the idea. We've seen this. So some capabilities based on millimetre and MATLAB. We are projecting you should be able to hit 400 to 600 meters vertically and about 400 meters horizontally with the system. Keep in mind this is a very low pressure system. Only 25,000 psi. Meaning it's low pressure compared to let's say a howitzer. This is just a graphic of the shell. So why is our system not a destructive device? One of the very important things that makes our system not a destructive device is the fact it's a life safety device. It's not meant to be anti-personnel. We're not shooting this at people. We're not shooting at buildings. We're shooting it straight up to try and find people and try to help people. Also our system has signal capabilities. We're working on an IR beacon. Another very important factor is the amount of charge we're using. We're using less than six grains of powder, which is more than 20 percent below what would make it a destructive device. Here are a few variants of the launchers. Going back to World War II, we have the 26 and a half millimetre variants and the 40 millimetre variants as well as the 12 gauge variants. Recently there's been a lot more that have been developed thanks to the future warrior combat system as well as the six and eight shot variants. And of course the lovely 20 shot system you see. Okay so now we're in the design phase. We know what we want it to do. We know what we want to mimic. What are we how do we build the freaking thing? Okay well we had a lot of trial and error. We're still in the trial and error phase. I just want to warn you. We've got to find cameras. We've got to find recorders. We've got to find field recorder, command center recorder, power source, payload recovery system, launcher shelf.  We tried it all. We're still in the process of trying it all. There might be better things out there. Okay this is the capabilities we wanted for it. We wanted a 600 meter vertical shot. We'll aim for 600. If we get 300 we're happy. Okay we want to fire over a five-story building for urban search and rescue. 400 meter horizontal range. These are pretty cool things. This is pretty ambitious right? This is what we wanted. We might not get it. Probably won't some of it but we're going to try. This is the killer. Take a look. The firefly system I told you about $2,000 a shot plus a destructive device launcher 200 bucks for the launcher 200 bucks for each shell. Non-military sale yeah that's nice. Our stuff $200 per shell full cost. Non-destructive device, non-destructive launcher. There's a huge DIY community. The launchers let 400 to 700 bucks. Okay so now our challenges. So uh I'm sure you all saw the hand uh first consideration is always safety and followed very very closely behind being legality. None of us want to go to federal prison. Camera arming. Once you stick the battery pack in there the camera goes live. You want to have a decent amount of actual shell fly from the camera. Six months, eight months, however long it takes. Temperature. When the round is actually fired out of the launcher the low pressure chamber gets between 900 and 1100 degrees centigrade. Things tend to melt, crack. G-forces. If you're hitting anywhere between 3 and 9g uh when the system is fired uh things tend to disintegrate including many are doing their controllers. Space. This is the amount of space we have to work with including the lift charge, parachute, safety-widing, camera, battery pack, and of course camera orientation. So it's not fun. Basically try to take everything you want and then squash it down. It ain't easy. So irrespective of the arming which Vlad talked about a minute ago uh there's all sorts of ways to do it. There's all sorts of challenges for this but talk about this for just a second. So uh if you look at the firefly system and it seems like fins are definitely a great way to go. Uh trying to get fins to pop up reliably out of 37 millimeter launch is a challenge we're still working on. Uh we've bent a lot of sheet metal. Uh helicopter recovery, very end conducive for stable video. Basically helicopter recovery is one of the things you get a parachute skewy and it starts going like this which makes it really really tough to get usable video. And of course you aren't even going to mess with uh spin stabilization. Uh basically if you're trying to stabilize either something the size of this film canister or this baby soda bottle uh long enough to capture 480 by 680 uh video. Okay this was another challenge. You get cheap cameras. Where are you going to get them? Where are you going to get them? Really cheap cameras. Where is that? China. Okay call China. That's fun. That's so much fun. I spent a lot of sleepless nights calling China because of the time difference and the language difference. I had a mandarin to English dictionary and Google translate and I still couldn't get past hello.  But we managed. We got cameras. I have cameras right here. This is a full camera system with a 5.8 gigahertz analog wireless system in the camera. That's it. That's pretty cool, isn't it? Okay then wire it. Because you see this connector? The connector for getting the battery or the or the wall wart hooked up to it took up too much space. So I cut the cord because you know it's DC. Two wires right? No four. By the way if you polarize them wrong get the wrong polarity you blow the camera. That's an expensive trial and error. So we had some problems. So okay so we get through this piece by piece by piece. I've actually got emails into the factory for the next round of problems. Testing issues. Safety. Talk to the hand. By the way where do you test grenade launchers? Does anybody here have a grenade launcher testing range? Oh I actually saw a hand go up. Jesus okay. I did not expect that. I swear to God I really didn't. I'm impressed. All right so then there's the shell loading. So this is actually constructing the whole system okay. Vlad is going to talk about the shell loading. You might have noticed I've been handling electronics. Vlad's been handling the things that go bang. So the way we load this is actually very simple and very innovative. We got somebody who does custom CNC machining and does a lot of 37 and 40 millimeter loading. You start out with the base of the shell that fully disassembles and we insert the small pistol primer and then we fill this little area with the actual propellant. In this case about six grains of unique. We then install a burst disk right up there and what that burst disk does is it actually lets the high pressure chamber build up 25,000 psi during ignition. What's a burst disk made of? Pennies. That is actually a penny squished by 20 ton press. I just find that amusing. I don't know why. Once the burst disk pops it will add the gases will actually expel through these holes and drop the pressure down to 25,000 psi to 900 psi. Which is what's actually going to push the the wadding out through the shell. Prevent everything from catching fire and cracking and boost the parachute and the payload out through the rest of the shell. So it worked! No. That picture on the lower left is me and Vlad decked in proper protective gear firing our test shell. You notice the camera is actually sticking out the end of the launcher. That's the problem with space. You see the camera the picture on the lower right that's one of the camera systems in a baby soda bottle. Now this is a baby soda bottle for those who don't know that is a two liter soda bottle before they blow it out in the mold with hot air. Okay they're great. They cost like 50 cents to a dollar depending on how many you get and where you get them. They are vaguely temper evident. Dear god I'm not going to say temper evident here somebody will laugh at me. Because you can put the cap on it has the ring that separates and they're really really waterproof and good. So we cut holes in them and put the cameras in there used tin foil to wad it and then since I had the problems with the connectors my cr123 battery packs were basically fitting in there but not fitting really well so they were longer than we expected. We dealt with it. We managed but failure is always an option. Okay you can always have problems. You can always have issues. You can always have you know details. Trees and buildings jump in the way. I love that. Okay I'm not going to bother showing this video. We actually did get about 150 meter range out of these little tiny cameras with an antenna about an eighth of an inch long. It's pretty amazing. Okay but here's what I'm going to show you. It's about 20 seconds. This is us on a farm in an unnamed location which shall not be named.  Why is it not working? We actually had some issues with primers. Notice the incredibly safe distance I'm standing. Yeah aren't you glad he was wearing his helmet? We actually got about 30 feet. Now one of our problems was literally not us. The sealed powder that we used to hand load the shell with were like why is there powder spilling out of the shell afterwards? It literally didn't burn so we took a lump of it and put it on the ground on a rock and lit a match to it and it's like poke it with a stick. Come on. Didn't work. Powder was bad. So the 30 feet we got is just a primer alone. A small pistol primer pushing the charge out. Which for a tiny little like literally smaller than the tip of a you know small in a pencil eraser isn't bad. Okay when we get the powder to work right we literally did this by the way the Saturday before DEFCON. We started this whole project three months ago just so you know this is version 0.01 you can see it on the right. We do expect to honestly get that far about 400 meters up. Considering a pistol primer got 30 feet it's not bad. Now the only other problem is that by the way this is from the camera and that's about the best video you get. Oh no I'm being loaded. Okay the video feed from the small flight didn't work. We found out these cameras are susceptible to motion. They're designed for surveillance where they're going to be standing mounted or on somebody's person so they're designed to be moved at human speed not bang speed. Minor issue. But what we figure is when they actually get up in the air so we're figuring on deployment we're not going to get any video but once they hang from the parachute they're going to be descending in a much slower rate we should be getting usable video from them. So we're not actually worried about that. Okay so yeah we failed.  It's not working what do we do.  I know that happens during most of your assembly required projects but it wasn't something we were happy about. We burned the fireproof parachute or fire resistant parachute. Yeah that wasn't fun we're like oh let's just try it again. We look at the parachute we're like crinkle crinkle crinkle never mind. So basically we failed. We failed our first prototype but we got a lot of information a lot of experience a lot of knowledge we're gonna do better with our next one. So if you want to keep all your fingers ask some questions. Good question. Swat teams and airsoft teams carry 37 millimeter launchers now. We wanted a system that they already owned for cost so they didn't have to buy a new launcher system as they didn't have to retrain on a new system it was like basically modding what you already own is a lot easier than buying new crap because you know is your significant other going to let you buy new stuff? We already know you're on a budget dude.  So yeah when the system actually fires during the boost phase you're not going to be getting good video during the descent phase you're only coming down about 25 feet per second which is more than usable and since the receiver module records everything to SD card you can review it and play it back as slow as you want to. More questions yes? Yes we actually got a note from that from the building manager something about something crawling from the 14th floor. Yeah we got in trouble yeah okay respect but remember that we're talking about systems that they already own we don't want to make them buy a new launcher. Now the one question we did get by the way was that we talked to three or four SWAT teams and they asked us can you duct tape the camera module without a shell to a rope? Yeah of course why because when they do a high angle rescue they want to drop it down with the rope in a waterproof airtight package and see where the rope is going. So we actually have interest in the camera module alone without the shell because it's kind of interesting. Yes dude we're talking about a camera that's so tiny you know you could put it up your nose I mean okay that's going to be version five can I work on version one? So yes I actually started out in high power model rocketry years and years ago the problem is most SWAT team guys don't do high power model rocketry they don't have their equipment there's also failure to ignite and all sorts of engine issues also with high power model rockets you need to call and launch into the FAA you need to actually have valid permits for high power model rocketry versus they only own a 37 millimeter or 40 millimeter launcher they pop the shell in they're good to go. Absolutely no problem we intend on open sourcing the design again in the spirit of the make community we intend on open sourcing the design with the picture of the hand on the front cover those are I believe solid copper pennies but again just under a 20 ton press any any copper it's just they're they're nicely neatly provided and a little disc is great are you a treasure agent? Fed! Yes sir I don't have $2,000 to spend on one of their projectiles so no. One other issue was their system is actually 40 millimeter launcher so they're not constrained by it not being a destructive device they're using significantly more than six grains of powder and if there's incidental ignition I can share the slide with the hand again. We're actually we haven't had any problems with the camera that we shot still worked afterwards no mind you is it a much lower velocity and flight than we wanted but it still worked afterwards just fine. What you're actually thinking about is filling it with epoxy one thing you played with is actually using the glue sticks for the hot glue gun and using a hot air gun to melt it in there once all the packages are. By the way if you want to see this stuff come on up or come to the Q&A room please come on up take a look. What's that? Richard Deem is in the Q&A room. We'll probably stick here because sometimes people they'll still be in there. Do not touch the launcher is the only restriction this stuff feel free to touch.


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So glycan binding as in fact, I realized yesterday that I spoke about mass spectrometry as if all of you had training in mass spectrometry, and maybe you have not. So I apologize. Somehow I was imagining that people who are interested in in glycans and glycobiology would probably know, I mean about proteins and then if they were into proteins in all likelihood they would know something about mass spectrometry. So if I went very fast on that, I apologize. It might have been an oversight but then maybe you can catch up on that side, because we are talking x ray crystallography NMR screening. This is a completely different world. And when I talk about screening, for instance, there are such things as glycan array. I mentioned that yesterday. They, they are printed arrays with glycan molecules that can be tested. And so there's all sorts of possibilities that are listed that you can you can assess with with this sort of technique and this is actually quite a challenge in glycobiology, because this is what we've been preaching through glycoinformatics informatics for the past decade that people have a different view on glycans, depending on the experimental technique that they use. And in a way, those people who are completely into glycan binding screening are not necessarily interested in the fact that a protein is glycosylated. It's not in their immediate concern. So the field of glycobiology is very partitioned between those who look at the glyco conjugates and what decorates the glyco conjugates, and those who are actually looking at those glycans as ligands, and that's all they are. So it is arbitrary in the sense that functionally, it doesn't make any sense, not to consider the three types of molecules, the, the, the glycoprotein, the glycolipid, maybe RNA we don't know, the glycans and the glycan binding molecules. They are all one and the same, I mean part of the same system. And it's only because of technology that they are separated. But otherwise than that, biologically speaking, it makes no sense to separate them. And so I'm opening the door to all sorts of other problems, apparently, yet, biologically, they are absolutely on the same line. So I wanted to make that point, because it's important and it's a challenge. We are really promoting glycoinformatics as a means to reunite those different communities that are apparently not talking to one another that much. They are, but not enough. So regarding the screening and the, the development of the microarray technology for glycans, there was in the US, a strong consortium that was the consortium for glyco, glycomics, functional glycomics. I mentioned that in the portal yesterday, when I was talking about the new website of glycomics at ExpoCy, there was one portal pointing to CFG, so I'll just show you and maybe explain why it's not active anymore. And there's another source of glycan array. So there is this lady at Imperial College, Prof. Ten Feizi, who has collected so she has an approach which is to customize glycan array. So the functional glycomics consortium were providing glycan arrays, kits of glycan arrays, so already ready to use glycan arrays with the same glycans that, I mean, it evolved a little bit, like about 600 glycans on a plate, and anybody could actually test these, these arrays. Ten Feizi is really, each time she has a sample, she's composing the array accordingly. So that's a very different approach. And she had software that was developed in-house and now she's trying to support software that could be used by other people. And as far as I know, the paper is not out, but I'm not quite sure. I checked and didn't see it. But that's the name of the software for related to this library. And then there's recently a tool which is called GLAD, part of the glyco tool kit, and I'll demo because it's a very easy interface that you can check also with glycan array data. I still haven't quite gotten what format they are processing, but this is detail that can be addressed if you're interested. So there's also another category of molecules of interest when we talk about glycan binding, which are the carbohydrate binding modules that you find in a special section of the carbohydrate active enzyme data. And then there's another base, CASI, which is much more known for its classification of glycosyl transferases and glycosidases. But this is an important part. And this is called glycan binding, it's called adhesion to carbohydrate. And these are different molecules because they are usually just a domain part of an enzyme that is binding the carbohydrate. And it's not as if it's actually reading the glyco code like lectins really trying to to get the function out of that. Here it looks more technical for the enzyme to work it has to hook on to something and this is how it works. This is also a classification and there you can find that on the CASI website, which is a bit old, but has a lot of content. I would like to go back to viruses because this is actually quite a good example in terms of a glycan binding and especially those glycan arrays. There's a lot of results that were obtained by testing viruses on those glycan arrays. So the CFG data, there's a lot of flu virus testing, same with 10 phasey glycan array customized, she has produced quite a number of interesting comments on glycan arrays for the flu virus and on the behavior of the flu virus. And actually, I think if I'm not mistaken and maybe Catherine can correct me, but the the famous difference between an alpha three or alpha six linkage for the salic acid at the tip of the hemagglutinin is and that explained differences between the human flu and the avian flu was detected with a glycan array. So this is why I'm back to viruses, not because of the pandemic. Okay, so in, sorry, I'm here on on a page on the viral zone page, where I mentioned before viral zone is this encyclopedia of viruses, and we have worked with them so that we would have at the level of interaction. And it says that the interaction is via salic acid. And so there's a link to a database which is called sugar bind, and that we actually develop. Whoops. This is a record you can't read so that's much better of sugar bind so we have sugar bind I have to be give you a little bit of history of that sugar bind was a database developed in the US. And they, the purpose of this database was to collect information on ligands, ligands only so we are talking about looking at the cell surface of the host, having receptors, glycostalated receptors at the surface of the host, and having the virus, recognizing the binding to the sugar the glycans of that receptor. So this is, if you remember just an hour ago I showed you the animation of the virus entry via sugars so it's really those sugars we're talking about and the ligands at the tip of those sugars. It's a whole different story to consider the sugars that are on the viral protein, and that also play a role, but they are, they would be recognized by antibodies or human lectins. And at the moment, I'm not in that scenario. I mean the other scenario of the virus recognizing host glycans. And so sugar bind was developed to gather this information on human ligands is in all pathogens. So that means that at this stage, the hemagglutinin is considered as a lectin because it's carbohydrate binding. And we have now you familiar with the color code, the protein, the tissue, the structure, the ligand and the disease that is in that database. Unfortunately, sugar bind is not very alive. We've improved it. It was basically a super duper Excel spreadsheet when we got it. And we made it into a decent database cross referenced with other databases and and being also cross referenced for instance from viral zone. And we are a bit short of cash to to put more data into it. What is important is that each ligand. So we have the SNFG cartoon, you can actually have some results of the SFG CFG sorry CFG like array data so you can see here with the stars. You can choose the affinity of each of the ligand on H3N6 in that particular case. So this is the swine flu. And you can see that there is a preference for some glycans some ligands, and we are chasing that. If you click on the record, you have some information on the properties, of course the disease. And what we have glide to can identifiers as well. And what is interesting and possibly connecting is the structures. So this is a substructure of a fuller structure, the full structures are in glycans. So the idea is to be able to look at what full structures contain that substructures in glycans. So you can see here we have agents are this is the taxonomy. So these are the passenger agents. And here the corresponding lectins. So if you click on those. You get to the full structures that contain the motif that was actually the substructure that was isolated. So that was the ligand for these influenza virus. And when you actually consider one particular structure here, which is an o-linked, you can see the glycanet entry and you can actually see that this is in the mucin and in the respiratory system. So this is the sort of thing we wish we could do that with far more pathogens, but we are sort of not able to do more at present. So all of that is also based on a substructure search. So each time I give a glycoinformatics training, there's something that has broken in the meanwhile, and that needs to be updated. So last year the octopus was not working, composited did not exist. And so I had to justify the fact that octopus had a previous version that needed to be updated. It was in the process so I couldn't demo the octopus. This year, we have reached the limits of the substructure search. Our substructure search needed a real rethink so that we can actually also map substructures in compositions, for instance. And it would be interesting to see if you have a composition and if you have a substructure, what are the potential correlations and so on. So this is in the making and I can't demo it and it will be for next year. Anyway, this is just to explain why maybe if you try sugarbind and you click on substructure search. I'm not absolutely sure it works. It's pre calculated so it should work but there are issues. Now, I will focus on lectins. So these are my favorite carbohydrate binding molecules at the moment, and they are really part of that picture I keep on showing you at the cell surface. Classification of these protein is not easy. And people have tried so, for instance, to look at the specificity of the lectins as a criterion for classifying them. So you can think of different types of functions, you can think of sub-cellular locations. So these are the mammal lectins, for instance, you're probably familiar with galectins and selectins and calnexin, etc. Yet, until recently, it was not clear which were the best criteria to actually organize the knowledge around lectins. Often lectins are multivalent. So that's why the specificity is challenged. They are also making a lot of, I mean, they need many subunits to be functional. So this was dropped. And finally, the idea of choosing the falls as the first. So having a hierarchical classification that would start with what are the different falls that are available to lectins for binding carbohydrates. So this is all the inspiration of Anne and Bertie, with whom I work very efficiently and have for the past four years in putting this project together. And so she suggested the hierarchy. And so the idea, and we also looked at what is done once you have falls, you have a lot of structural classifications that are based on fall like CAF, which is developed in the UK. Scope, and so on and so all of these are starting from general falls and seeing then if you can find subgroups in each of these falls that are similar. The challenge for bioinformatics is that at the level of the amino acid sequence, the level of similarity is very poor. So the similarity is at the level of the 3D structure, more so than the sequence of amino acid. The basic approach of CAF is to say that if you consider a particular fault, you can actually have some subclasses of the fault, depending on a certain percentage of identity which is above, around 20%. So it's up to 20%, I mean 20% similarity. And so here you can see that this is a sort of reference beta propeller and you have variation on the theme with these propellers, for instance, containing a number of different blades, repeated blades. This is why they really look like a propeller. And we'll see in the demo how this applies in the case of lectins. And then once you have those subsets more or less well defined, the idea is to again go down in the hierarchy and look at homologues. So those that have really a high percentage of similarity to group them together. And usually they are grouped by species, because within the same species or same film, they tend to be very similar. So, unilectin was launched in 2019. So that's relatively recent. It was based on lectin work that Anne had actually done before and that was part of another portal which is called Glico 3D so really focused on 3D. And so we started building unilectin as a platform with one module, which is called unilectin 3D. That now includes over 2000 3D structures of about 500 different lectins. So this is the current contents. Anne keeps on chasing PDB and adding some information as soon as she sees something that is of her interest. And as I mentioned yesterday, this was developed by François Bonardelle, who is now not in the group since yesterday. So, here, the numbers for what I explained just before is that she listed 35 folds and each of these folds are hierarchically connected to 109 classes. I didn't put the 109 classes there, but you can see that if you have this alpha beta OB fold, this is the class for the cholera toxin for the pertussis toxin. So you see that it's really the toxin area. So you have families here. So this is when you have 70% or more sequence similarity. So this is the hierarchy, 35 fold, 109 classes and 350 families. So, you see that here, if I look at the beta sandwich, and I have the garg-tose binding domain like, I have F-type lectin, H-type lectin, C-anamon lectin, and if I look at F-type lectin, I have also variation on the scene. So this is really the basis upon which we're working. So we are, of course, not, but we are prepared to have more folds being included. This is part of the game, creating more classes and possibly more families. This is certainly going to increase. So if I, I think there's a bug in my presentation in, I had something about bacterial folds in particular to illustrate for instance what's happening here. And for some reason this morning when I was repeating, it disappeared. So never mind, I'll show it to you live. In newly lectin, we have this, we're part of this big program which is called glycoalps. We have over the past three years come up with a number of modules. So as I said, we started with unilectin. Then we focused on the better propellers lectins that with the way we were organizing knowledge, were making sense with a classification based on the number of blades and we're going to have a look at that. And we have also only recently, a few, a few likes at the end of 2020, released Lectom Explorer, which is really gathering all the possible lectins that we could find according to our prediction, which of course I will explain in databases. So we are in a bit of a vicious circle. And this is what we're trying to break with Lectom Explorer. Lectins were poorly classified. So lectins were poorly recognized. So lectins are poorly annotated and lectins are absent of genome annotation. This is a sad observation of the situation. So we thought okay, if we are able to actually get the classification, if we're able to tidy the classification, make the prediction at the level of each class, which is precisely defined, then we'll be able to annotate lectins in complete genomes in a much more efficient way. And I hope I will convince you that's what we are in the process of achieving. So before I go to the prediction part of the talk, I would like to go to the site so that you have an idea, but I also wanted to show you a bit of the CFG before. So I go back in time a little bit. So you see that the websites, I think some pages were updated a bit more recent than 2010, but hardly. So it's still alive, but it's in the coma. So you have the data, so glycone array, glycone profiling gene microarray, because the idea was to have a combination of transcriptomic of the glycogenes, so all the glycosyl transferases and their expression could be possibly connected to glycone array. So this is the interface as it was offered for searching the arrays. So this is really if you have no knowledge of glycans and lectins, this is somehow cryptic. You could, for instance, decide to look at galactons and look at data and submit. And then the results are like this. So you have a big Excel table basically, and the only way to actually access the data is to download an Excel table. So I think it's relatively basic. And so if you look here and click here, you have some details of the galactin itself. This is a lucky situation where you have an accession number, but most of the time are very often often enough they were not. And then you can have a look at. No, that's what I just did. Sorry. Where do I see? I thought I had the results somewhere. So this is weird. Oh, yes, it should happen. No, I had another here. What happened to this one? Or maybe it's another one. Then you see this is the result of a glycan array when you see the intensity of binding and each time you can actually find out which sugar is binding and so on. There's some biology was done with that, definitely, but bifuramatically speaking, it was very, very difficult to exploit and to find anything, unless you had a dump, but the lack of cross references to sequence database was always an issue. So this is why I mean this explains partially why it was not made popular outside glycobiology because already for glycobiologists, it was limited so outside was another problem. So the glad system here, so this is the local tool kit is a is a JavaScript so really web based development made in Rick Cummings who is one of the top person in glycan array technology. In his lab and he has a dedicated person so you have a website, a web interface. My internet today is depressing. I have this dashboard so I actually selected the example to load, and you can see the data as bar chart so here they have this control set where they have a number of lectins. They expect these lectins to recognize sugars of of some types so if we look, for instance, at this one you have the sugar that is named up there but you have this fantastic little tool here that actually helps you see which sugar is there instead of reading the IUPAC. So you can see that are different lectins have different specificities and I assume that they are using this control to make sure that when they're using a lectin on a particular glycan array, they will find these structures because they are binders as specific to that lectin. So you have different. You can see that as a heat map, and you don't use information you can actually yes extend your bar chart if you focus in particular area. So RFU is the measurement, the units for measuring the intensity of binding to glycans. And you have also a force graph. So here again, you have this graph that shows you the lectin so it's the same color code here as in the bar chart and in the middle. It actually tells you the common binders between the different lectin so here is this one. And if I click on this one it's another one if I click on this one it's another one so you can see that this is infinitely more pleasant to use. And if you are so you can choose to load your data screen them. I mean, you can have some normalization so basic statistical tests on them. And you can have these variant variable various tools to visualize the data and and see what is important or not binding or binding etc etc. So this as far as I know is the only tool online that does that job and for for passing glycan array data. Just a note on Kazi Kazi is here. You have the carbohydrate binding modules that are there. And you can actually so this is the interface of Kazi. Again, unless you know what you're looking for. It's a little bit cryptic. So the information is easier to find from Uniprot entry linking to Kazi than than from the Kazi site. Personal opinion. Now, unilectin and unilectin 3d to begin with, and I will talk about the other modules. So there are many ways of actually entering the database, you can actually decide to look at the different class, you can look at the species, you can look at the specificity, and here is our famous So, the toxins that are there. So all these toxins are really binding sugars. You have the cholera toxin here. So you have different ones. And if we want to focus on these, we can actually find them here with as soon as the ligand is known of course it's registered but this is not always the same so this is all derived. Everything here is taken from the PDB so you always have a PDB accession number and different specificities for each of these that are there. So we have different species and we can decide to look at a particular one here. So we are really relying on light mold as you have seen in glyconect in sugarbind. We did sugarbind before Uniprot actually used it. And we are very keen on the plugin that was added to light mold by Oliver Grant, who is from the glycam team who are specialists of 3D modeling of glycoproteins. And I'm not talking about that really I should, but again this is another whole session that should be devoted to it. So you can see here the galactose. This is of course in the PDB. But in the PDB you have this sort of representation. Actually now the plugin is, sorry if I say things that everybody knows but the PDB is now spread over the world where there's the original site in the U.S. Europeanian site which is PDB. There's the Japan PDB and all of these are sharing the same data but not offering visualization on the same ground. So as it turns out, the PDB American site has now totally integrated the plugin to show carbohydrates with the 3D SNFG symbolism. So you can't miss it. Whereas PDB E is still not so clear on that. So this is the ligand, the galactose I mentioned so you can have a close up here on the interactions at the atomic level. We have all sorts of cross references to uniprot. So the uniprot accession number, the PDB accession number, the related uniprot with greater than 50% identity. So what you find in similar proteins in uniref 50%. Of course we have a Swiss model, so uniprot is here actually. We have the paper and glytucan for the sugar when it's there. And we have a broken link to glyconect for the same reason as the one that is broken with sugarmine because we are reshaping the substructure search. So in all likelihood it will be fixed in a few months from now. So please stay tuned. This will happen again. Then there is a Swiss model, a Swiss model, a plip interaction viewer. So we collaborated with the Swiss model people to have this plip application which shows you really details and you can actually navigate in the ligands here. And see information that you can reconstitute from this model. We have the sequence viewer that is borrowed also from PDBE and also the secondary structure viewer borrowed from PDB. So each entry, lectin entry of unilectin 3D is described in that way. And you can actually, if I go back here, so you can here do it by specificity so we can look at the difference. If I look at the, I don't know, the gluconac here, we see all the possible ligands that contains this gluconac. And then we can see all the lectins that actually recognize that particular ligand that binds that particular ligand. So you will see that in that case, we will have different folds like better sandwich here, better propellers here, and so on. So this is very defined in each time if you want to explore the 3D structure that is there, you click on this and you have this information. So here, if I go back to the, of course, the search by field is, you can ask for a specific PDB, a specific fold, you can look for a specific family, you can enter IUPAC condensed, or just a monosaccharide. You can be picky on the resolution of the 3D structure. So there's nothing much, I mean, nothing extraordinary in those fields. We just hope they are practical. Otherwise, of course, you can go straight to the sunburst. So you see each time here, it says that the Proplec 7A family has the PLLL like has four items, but if we go, the better propellers have 17 families, and so on. So you can go from there in the same way you can click from here, and you can decide also instead of filling the field for taxonomy, going to the taxonomy here, looking at the different species, animal lectins. So we have like, if you want to have a look at all animal lectins here, we have, of course, human, but we have toads and rodents and so on. So this is the various options you can do, you can use to search unilect in 3D. Since the prediction is another big chunk, I think it would be better to have another break now before I explain the prediction and how to actually explore predicted lectins. Alright, so the prediction part of it now. I share my screen again. So I will start with the, what we did with the better propellers, because it was like our training exercise before we got into predicting lectins. As I said, with one of the most clear, the clearer objective was to detect lectins in genomes where we know they're hidden and often not seen. The reason for it being interesting is that they are tandem repeats, and so they are a challenge, maybe not all of you are in bioinformatics but in bioinformatics, the repeats in sequences can become a nightmare. That's why our musins, for instance, are not so well annotated often in black and black and egg, and we can't determine what uniprot entry would correspond because the repeats are in the way and people are not quite sure which one is what. So there's a lot of pathogens with long repeated segments, a lot of virulence protein that have repeats, and these toxins and these propellers are certainly representative. And the idea is that because the bioinformatics classical tools are challenged by repeats, we thought, okay, we're going to look at the repeats of the structures that we know. We're going to look at the blades, each blade composing one of these propellers, we're going to align them together and see whether we can actually improve the delineation of those domains with these alignments. And so we actually took the 3D structures of those that had only five blades, six blades, different types of six blades and seven blades. And so we had example each time. And with this alignment. So we specified it in each class, and we build models using a very classical hidden mark of model system, which is just looking at the alignment and which amino acid is substituted with, with which other and whether you have insertion and deletion and so on. And it's modeling this system. And we actually came up with a classification and the possibility to predict on that basis. And we found also confirmation that we had the assembly that we thought were there, which is actually what we had at the time was the fact that you have six blades can be three by two, two by three, or six by one. So the six by one is this type, the three, the two by three is this one. We never saw anything of the kind before. And so the prediction was an opportunity to actually determine a new assembly. So what we did is that we took the whole of uniprot trample run the hidden mark of models, put them in in boxes depending on six blades, seven blade, five blades, etc. And we found a six blade that looked like the assembly was not looking like the others in the prediction. So because Anna Bertie has a crystallography lab, they crystallized the, they got the sequence of this bacteria and crystallized and find that the assembly, in fact, whoops, was was like, like this cutting in half and this was actually not, sorry, it's a bug in the, I did this a bit quickly in Prezzie. I did not animate it as I should have.  And, and this was unknown, until we predicted that lectin so that got us very confident in thinking that we could probably do a complete database wide screening, using all the lectin classes that we had, taking all the lectin-serve motifs, using, so there's a tool which is called HMM that we can use to run the prediction from the hidden mark of models. So we took the whole of non-redundant and CBI, we took a Swiss proton trample. We took the extra genomes and proteomes to be, to be sure, and run the whole pipeline to detect lectins. So in other words, we had 109 classes, if you remember, 35 fold 109 classes and 350 families. So we did the same type of profile with the first class, the second class, until the 19th class. So each of them you can see have a different profile. Here you can see the predominance of some disulfide bridges and not here, but you have a very prevalent tryptophan. Here you have your prevalent aromatic and so on. So each one is really specific. And if you run through databases, you get a lot of results. This is what we stored in Lectome Explorer. And the first thing that we did was to look, so here you can't really read, but there will be a close up. This is a big matrix where we have the 35 folds here. And here we have the super kingdoms. So we have animal lectins, a bacterial lectins, fungal lectins, etc. And when you see a blue square, it means that this lectin, this type of fold is expected because we have a 3D structure, so it's expected in that kingdom. Whereas when you have a green one, it was, for instance, in this case, so if I go as a close up, the C-type lectin are expected in animal lectin, but in fact the C-type like are found in bacteria, in fungus, in plants, in protests, and in virus. The other way around, we had a lot of, so here you have a close up. This was really thought to be animal and in fact you find a lot of bacterial and it was actually found everywhere but bacteria and it turns out to be found also in bacteria and so on and so forth. So you see that you have expectations depending on what you have in the database in the uni-lectin 3D section and all of a sudden we can open our horizon by looking at the predictions and finding that the patterns are found in those particular lectins. So I will demo Lectom Explore after I finish with a nice story that we have on the bacterial lectins of the vaginal microbiome. We have worked with the several teams of the Imperial College in London who are involved in trying to understand how the microbiome, this biosis is creating problems with pregnancies and births, childbirths in the end. And so we collected on their advice the different strains that constitute the microbiome and some of course are labeled commensal and some were labeled pathogens. And those pathogens obviously are more prevalent when they have cases of dyspiosis. So this is a usual phylogenetic grouping. So these are mostly lactobacillus. So you have lactobacillus jejuni, you have chryspartis and so they are found in the microbiome. So in here you have the pathogenic strains where you have also some lactobacillus innards which is sort of in between sometimes on the good side and sometimes on the bad side. So you have garden and you have inshasha you have prevotella and streptococci. So you can see the color code here for commensal and pathogens. And here you can see the folds that we reported. So François actually run all the prediction on 90 genomes corresponding to these species. So we tried to map in this big chart the folds and the classes of lectins. So he run 109 classes profile on the genomes and what we found was rather interesting in the sense that we looked at the specificity of these lectins, whether they were associated with recognizing rather anacetic gcosamine or galactose or mannose and whether they were oriented in that way. And you can see that there's far more colors in the pathogen section than in the commensal. And that's what we found out in that chart that actually tells you that it's rather boring. So the lectins that you have in the commensals, they are not very various and there are not so many of them. Whereas if you consider the pathogens and their profile and lectin, there is a variety of them. In particular, they recognize more willingly sialic acids or fucose in combination with other like galactose. And you see here that you have the very, these profiles are very well known for being the profile of pathogenic strains in bacteria in general. So, again, we haven't this is a paper we just that was just accepted it's not in bio archive anymore it's actually in biofilm and bio microbiomes. But we can identify some tendencies and some obviously richness in the profile of lectins in pathogens versus commensals. So this was already a little bit of a hint in other microbiomes and we proved it yet again in that microbiome. So we are very much interested in in screening more genomes and finding out the lectins so we call them the lectins now of each species to see how potentially interesting it is. So I'm done with the slides and I would like now to go up. I have a question in the chat maybe. Where's my chat. Well, in which environment. Sorry, Carl, in the vaginal microbiome. Is that your question. Yeah. Well, it's the specificity of the lectin I don't know. Maybe there are other things to find and other specific. It's, it's a, it's a relationship between the host and the pathogens so I don't know what the pathogens are doing. And a lot of them have also some zealots at the, at their surface. So it's a mix. And unfortunately at the moment we're only looking again at the at the bacterial lectins looking at the host glycans and not the opposite way. We can discuss that in the afternoon if you want. And so I would like to go to have all these zoom things in the way to go to lectin explore. So I'm back in on the unit in the unilectin home page. So we have, as I said, we have the predicted lectin in, in some fungal genomes we have been working with some fungi specialist, and we will release very soon, or it is maybe already, I don't know. Yeah, it's not the travel, which are the same as the prop like. So it is Trefleck, and it's also repetitive. So here you see the, the different distribution of the, of the better propellers. So with the five blades with the six blades or the seven blades. So you can see the proportion of seven blades is, is, is there. And the blade distribution so sometimes you have actually, if you, if you look at a particular species. So let's look at, so we couldn't find any better propellers in a human. We thought that maybe we would create a bit of a revolution, but we did not. So, let's have a look at these particular bacteria here. And you can see that most of them so it happens that we miss. We miss a couple of propellers, because they just below threshold probably, or the, the blade is not as repeated as it seems. And, and so they are a few missing or sometimes also because we have a lot of partial sequence and fragment sequence in the database that we screen. So here this is actually, if you want not to have fragment or partial. That's the way to get rid of them and doesn't seem to have been the case for this one. And so here, you can actually consult the, the, or one thing I have. I failed to explain in the prediction and that is of importance is the score. We are actually scoring the, the, the prediction. And the score is a little bit tricky, because, as I explained, we have 109 classes and 109 models and each model is run independently. So the HMMR software, which is running the profiles for us is, of course, providing a p value.  And so we collect those p values to have the significance of the prediction, but we have 109 independent p values. So François had to think of a way of choosing, of course, the, the, the best p value or the top p values, if we have very high p values for each of the class. And then, so sometimes we do have a dual prediction. We're not actually sure whether it's one lectin or the other. So we keep two. The score is normalized with all these p values. And we have here the score normalized. So here you can actually the minimum score in all our requests is by default at two at 0.25. But you can put it up and have a really good predictions from 35. It's really good prediction. Most of the time. So we have a bit less. As a result, we'll have less entries and very high score starting here. And you can view the information here below or opening. Here, let's say I asked for it below. No, okay. So I asked for it here and the information is the prediction. Here we go. So we show what we have with the this particular hypothetical protein of free bill a Solani. So this is its sequence and how it matches the profile. This is the, the profile here. And we can see which are the amino acid that matched the profile. And this is a good score because you can see, I mean, especially when you have tryptophan matching, it's always good because tryptophan are rare. So it's likely not to happen by chance. So you have a glycine and aspartic acid here, but unfortunately the two are not matched. So this is why it's not a top score, but it's a good score. This is again in my way. Back to other things. So in Proplek, I can actually list pathogens here with propellers, propellers and we have here the detail and what is interesting. Sometimes when we have the information but we don't always have it. We have the, the binding residues that are that are mapped as well. So you can see here that gets a decent score because there's some conservation, not ideal but some conservation with the residues that are involved with carbohydrate binding. This is not ideal, but this is good enough. And here we also map some CAF information, CAF is that database of domain, and we see that in fact they map the entire protein where we map only the propellers, and we can explain exactly the structure of that particular propeller. So this is a useful list to go through and see the propellers that we have for bacterial pathogens. So as I said, this is mainly found in bacteria in archaea and in eukaryotes. We're looking at fish, we have some fish, some mollusks, and no mammal involved in this particular category. So then I'll go for the big chunk, which is lectomex floor. So lectomex floor, we can, we have put the, you can explore it with the classification here, you can explore it by taxonomy, by type. You can see that the ficalin like is very, very common, we have the distribution of the top classes that are being mapped and predicted. And then you can search by field, and by field you can do lectin class, so it's the same and we have a bit more possibilities with this expand menu here, where for instance, imagine you have found a PDB accession number in some paper. So if you'd like to know whether there's a prediction for lectin there, or you have a protein accession number, so you can actually use that window to ask unilectomex floor, whether this is it or not. So you can also search for domain architecture using PFAM in particular species, also doing some filtering on the basis of the score. So if I look at my table that I showed you, and I look for instance at animal lectins that are supposed to be like the color and toxin, I want to explore those lectins. At the end, I have two, and they are found in cenarabditis, in some species of cenarabditis, you can see that it's a repeated pattern. It's a normally normal size protein. So we can have a look. So you see that the score is just above 126. If you look at the conservation, it's not very convincing, and this is why the score is not good. So you have here a cysteine instead of a glycine, you have an aline threonine, but the important residue for binding is the tyrosine here, but we have a lysine. So we have the glutamic acid that is preserved here. We do have the serine, the glutamine, and that's it, that I conserved. So another glutamine here. Maybe, maybe not. It's difficult. It means that you have to investigate the information further. As exactly in the same way that I told you yesterday and this morning again that compositor is here to sort of parse the information and present it in a way that is going to give you some ideas on what to think and what to do next. This is the same with unilectin. We do not pretend that our prediction are approved and you take this home and say, okay, I have predicted the lectin in the center of this is Negoni. This is really up to you to check the literature to check other databases to check maybe this is why we have the genome proximity so you can actually check the neighboring genes is it a properly defined gene and so on and so forth. There's really so many things you can check if I wanted to actually go back to here. Oh, yeah, good. Again, you can you can explore why would plant lectins have cholera toxin. There's one. So, is that a true one is not not a true one. It has an incredibly high score. So, for this one. It's, but I'm not sure what the species is I'm, I'm, I'm, I'm always looking at new things in you in lectin explore I really like it banana banana moosa is banana is what I guess it's banana moosa. Okay, so anyway, it shouldn't have a cholera toxin. And we don't know whether the gene is that is there. So the high score. Yes, I expect most of the. So the genome browser can actually tell you you can click on that it's a it's a plasmid. Okay, so already you have doubts so this is this is really. And food for thoughts.  So we could, we could look at this particular the scene here. See whether it's well represented we have 24 it's all in bacteria. So it's all clean in terms of prediction. So, let's be a bit more. Take more risk by looking at this one. Another thing that is interesting and comforting in a way is that very often here we we collect the most frequent protein name, and you can see that there's quite a few that are called either hypothetical protein or uncharacterized protein. So you have sometimes carbohydrate binding. And so you have all sorts of possible combination with other domains. And really the idea is to look to see whether it makes sense to have a lectin there and browse here and choose in one species or another, or whatever. So I think on this you have question on exploration. You cannot, at the moment, feed a new sequence. I know this is going to be. We're working at it. As I said, we release like stomach, let them explore only six months ago. So, or eight months ago. So it's really a very recent addition to unilectin. And definitely, we want to provide the service for running through a sequence finding the lectin. So, at the moment, we can organize things together in a sort of collaboration, we can run the profiles for you. So if you're working on a genome, and you are keen to know what are the lectins in your genome and your genome is not annotated, and it cannot be found anywhere else in full confidence, we can parse your genome and find the lectins for you. This is what we can offer at the moment. As before, we actually provide the service online where you can do that yourself with your own sequences. This is the best we can do so.


How to Get Ready for Unforeseen Circumstances in the Classroom There can be a lot of difficulties a teacher might face in the future lesson. You might have to manage a class without any help or you might have students from different nationalities, ages, interests or so on. I believe, however, that most of the problems you might have would be solved if you planned the lessons carefully in advance. There are some reasons to support my idea. What's your lesson goal? First, you can find what you should exactly do in a lesson as a part of a whole course by planning the lesson in advance.  It is important to understand what the goal of the lesson is and what the students should learn in the lesson. Also, there are so many things teachers have to keep in mind in the lesson, so writing and organizing a plan before the lesson is necessary for every teacher. You can manage your class anyways. Second, even if you run into a something unanticipated problem in a classroom, you could control the class smoothly. This is because you may think up many alternative plans in case something happens in the classroom. For example, if the worksheet you prepare will be too difficult for the student, you can substitute other activities for the worksheet. Also, you can use your previous activities if you record your lesson plans. Know your students. Finally, as there would be many different types of students in a classroom, a teacher should deal with their students depending on the student's personalities. For example, the difference between right and wrong manners in the classroom is not easily defined because religion and culture play a significant role in these beliefs. Therefore it is not easy to teach students the right answer but, even so, I think it is very important to try. The second example is that the student's English pronunciation is sometimes completely different from their own country's pronunciation and it takes some time for learners to get used to the phonetics of real English. Let's say, comparing Japanese to English, Japanese has as few as 5 vowels and 15 consonants. On the other hand, English has as many as 12 sounds of vowels and 24 consonants. Teachers should know this difference and how to pronounce English accurately. Japanese teachers know the difficulties Japanese students often encounter in learning English and this means that they can give relevant advice to students. It is hard for Japanese people to differentiate between R and L. It is also difficult for Japanese students to understand and master the use of the tenses, the articles, and the singular and plural forms. It is difficult to teach these things to Japanese students unless teachers plan the lesson well in advance. Are you ready to teach ESL classes? As I mentioned, for the aforesaid three reasons, I think it is very important to plan a lesson in advance. I know, however, there are some people who think it's not a good idea to plan a lesson in advance because it can be a more teacher-centered lesson. Even so, I strongly believe that a well-planned lesson helps students to learn English more effectively and also it could be an asset for the teacher. Speak with an ITTT advisor today to put together your personal plan for teaching English abroad. Send us an email or call us tollfree at 1-800-490-0531 to speak with an ITTT advisor today. Thank you.


This is the example of multivariate normal, but this example is also related to the previous example. We want to find the mean vector variance covariance metric for the following density. This is the density function of the multivariate normal and f of x which is equals to this one. Now look at this, here the a11, a11 is the coefficient of x square, coefficient of x square which is equals to 2, a22 the coefficient of y square, the coefficient of y square which is equals to 1 and the a12, this is the coefficient of xy. Now you have the value of xy in the given example. Now the coefficient of xy which is equals to 2, now here the 2 a12 which is equals to 2, a12 which is equals to a21, 2 divided by 2, a12 which is equals to a21 which is equals to 1, i.e. you have a21 or a12 its value is equal to 1. Now here is the matrix a2111, also find the coefficient of x. Now the coefficient of x, this is the minus 22, coefficient of y minus 14. The coefficient of x which is equals to minus 22, the same way we have entered the value, so mu of x is the value and mu of y. Now look here, we have mu of x, mu of y is also coming to us and this is the equation. From here basically what we have done, from here we have taken 2 common, then we have divided the 2 here, then you have 2 mu of x plus mu of y which is equal to 11. This is called the equation number 1. The coefficient of y, now you know the previous coefficient of y, we have minus 14, we have entered the same values, so we have minus, minus here you have taken the common, basically minus 2, we have taken the common, minus 2, this is divided, then you have got the answer, 7. Now this is called the equation number 2. Now mu of x and mu of y, we have got the equation, their value is not zero or we have not determined the value. Subtracting equation 2 from equation 1, simultaneously we will solve or we will determine the value of one from the other. Subtracting equation 2 from equation 1, this is equation 2 and equation 1 we have this previous, after solving this, we have the result of mu of x, 4. Put the value of mu x in equation 2. Now mu x, here I have entered the value of equation 2, so the value of mu of y is 3. Now you have mu of x, then mu of y. This is the mu of x and the mu of y. Further to find the sigma square x, sigma square y and the correlation. This is the sigma square x, sigma square y and the covariance is given. Find out the correlation which is equals to covariance divided by variance square, variance square root, now the covariance which is equals to minus 1, sigma square x, we have 1 and sigma square y, 2. After multiplying, finally we have the correlation, minus 0.707. So its range is minus 1 to 1, so it exists minus 0.707. This is the value of the correlation.


Profit efficiency is a macroeconomic concept used in assessing whether an economy, industry or supply chain is expending an optimally balanced level of rent for the use of capital. Economies where too much profit is being extracted are overpaying the owners of capital at the expense of other contributors to a productive economy or industry.Citation needed economies and industries that provide insufficient returns to the owners of capital should find that capital is moved to alternate investments where the return is greater. Profit efficient economies and industries are paying a minimum profit to owners of capital required to maintain the optimal level and distribution of capital investment. This concept has importance when discussing the relative outcome efficiency of industries such as the U.S. healthcare system which has high customer costs, high government subsidy and yet has relatively poor health outcomes. This industry is said to be profit inefficient as compared to European healthcare model which have less customer and government inputs and yet better outcomes.The difference appears to be that the level of rent paid to capital investors in the U.S. system is a greater proportion of the overall. The value of the industry, similar observations are made about the U.S. financial systems impact on the U.S. economy as a whole.