Xavier Gallo Hope Foundation has charged stakeholders in the education sector to create a cultural environment where every child would feel valued, loved, respected, and included in decision-making processes. Chinanghe Onora, project manager of the Foundation, made the call at the sixth edition of the RHF 2024 Edit-Stake Conference held in commemoration of the International Day for Education in Lagos. She said the call became necessary as inclusion education would go a long way to reduce inequality, social exclusion, discrimination, but empower the people to recognize and value their unique abilities. Joining us live in our studios is Aurelio Lua Abiodun, who is a dedicated partnership and communications strategist at RHF. Thank you for joining us, Aurelio. Thank you for having me, Aurelio. During the International Day of Education, your foundation's theme on that day was learning for lasting peace. Can you expand on that? Okay. Thank you so much. One second for having me here. So during the International Day of Education, which we called the Edit-Stake Conference, coined from two words, educational stakeholders. So the theme was actually uniting for inclusive education, but the global theme for the International Day of Education was learning for lasting peace. So uniting for inclusive education to us, what it means is that we're pushing for inclusion because inclusion is a bedrock for diversity, inclusion, you know, sponsors diversity. So we're looking for an environment or we're pushing for an environment where every child can feel included despite their abilities or disabilities or special needs. We're looking for a classroom where every child can feel loved, they can feel accepted and there is that communication. There is no bridge in that communication between the special needs children and the in quote, normal children. So at the Edit-Stake Conference, we brought together educational stakeholders. This conference was partnered, we had partners such as Rights Foods Limited, Patent Avroco Limited, Byview Technologies. So we brought together educational stakeholders to drive this inclusive, in this inclusive text, right, to drive this inclusive word to them. We created this conference to be able to empower them with the right insight. So when I talk about educational stakeholders, I'm talking about from students that secondary tertiary institution students to educators, school proprietors, community leaders and even the government, because we believe that these four key stakeholders play a vital role in education. So we hope to drive this message so that they are able to pick it and then they are able to run with it. Did you not teach us in that conference as well? Yes, we did have. Okay, so your focus this year is on education, you know, so the Foundation said inclusive education will go a long way to reduce inequality. Can you explain shade of that? Okay, so we believe that education is not just a privilege. Education should be a fundamental right to everyone and anyone. And then, like I mentioned, inclusive education is bringing together everyone together so that everyone can have a partner in education. So what we have these days is like a segregation. So we have maybe people with special abilities learning in one place and then normal children in quotes learning in another center. But we want to bring all of these people in one classroom so that the people with the special needs or illnesses in quotes can also feel loved. They can feel accepted. They can have that eye-surface thing. They can be able to interact with people, yes. And then Regalo Foundation, in terms of education, we also run programs such as digital training, youth development workshop. And this year one of the major goals that we have is to take the youth development workshop that we run to tertiary institutions because we've always had this program run in our resource and learning center. But because of the sitting capacity and the number of impact we want to reach this year, we're taking it to campuses. So the youth development workshop is actually designed to equip young people with the right skill sets, interpersonal communication skills, leadership skills, soft skills so that they can stay relevant, so that they can be able to compete, so that they can be able to thrive in the market place. Okay, so you're saying that basically the special needs children do not need special schools? Yes. Is that what you're saying? Yes. So if they don't need special schools, do you think that they're going to have the same impactful education as the kids without special needs when they're in the same environment? Yes, so one of the solutions we prefer to this during the conference was that policies should be signed first off by the government to be to encourage this for institutions, for secondary schools and tertiary institutions. And then also coming down to the schools and communities, they should be open minded to accept these children. They should be able to take extra hands, extra professional hands. They should be able to employ therapists to be able to help this course so that these children are able to learn at the same pace with every other child. Okay, so what can you say about uniting for inclusive education and how it can be implemented? Okay, so like I mentioned, one of the implementation is government policies. Yes. Secondly, schools being open minded to this because we started off with an educator seminar that we had where we just had only educators and teachers. But then we had to bring up this conference to be able to reach the media, to reach the press so that they can help us carry this information. We also had government representatives in this conference. So we believe that every institution should come together to unite. Because most times when we talk about inclusion, it's just a thing of the mouth. Like there's no real action coming into play. So we want actions, we want actions this year, starting from this year, right? We want action so that everyone can come together to foster inclusion in education. So have you thought about having teachers who have special education for the special kids because you know special kids need more attention, more care, and a certain syllabus that will suit them at their pace. Yes, so one of the many things that we also mentioned at the conference was a review of the curriculum, a review of the curriculum by the government. And one of the panellists is a diversity, inclusion and belonging strategist. So she pushed for that, right? She has three inclusive centers that she runs. So she also pushed for that, that we should have, we should look at the curriculum so that it can accommodate those special needs children so that they can also learn at the same pace. Thank you very much. Good work you're doing. Best of luck. See you and then the Ministry of Education because I know that they'll be in charge of this. Thank you very much.


A was good everyone it is behavior here again for another trailer reaction This one is from Sony Pictures Entertainment the equalizer to the official trailer Directed by Anton Foucault core store in Denzel Washington as far as the first film is concerned. I liked it But overall I kind of felt that it was just like a little over the top You know the way I don't want to spoil it for anyone that hasn't seen it But Denzel basically just goes around kicking people's butts rightfully so but I just kind of felt some of the kills He did and the way he took out some things. I just kind of felt it was just kind of unrealistic to a little too over the top Especially in the ending. I was like really bro You're gonna go across the world and do all of that just like that, you know, I was entertained But I just didn't love it. I think I was disappointed because my expectations were so high Denzel is great But let me go ahead and put on these headphones and y'all can stop listening me talk and watch me watch Equalizer to the trailer. Here we go We're gonna have a good soundtrack first time to Turkey Turkey Now you come back. Yes, I'm looking for something you can find whatever you wish in Turkey How about a man who kidnapped a little girl from an American mother Would not be looking for such a man who'd be dangerous for you Men like him would think that It's great you're helping all these random people and everything Stay off the radar something happy to one of ours So I'm obligated to look into it. Don't you ever type? Oh, I am just like you're dead She's powerful. I guess not maybe yeah, what's the matter? They killed her They even look floor. She was on their tango police hands. Exactly Seven years back it's a mistake to go to war with him. They're going to war with me I can't wait to see that camera shot. He said I was spinning around chart skill sets They're with the agency family It was he started very good Oh They killed my friend so I'm gonna kill each and every one of them and only disappoint me is that I only get to do it once Senior carol those books around I figured you for some kind of teacher my level paid for That's what's up. July birthday month Yeah, man, I like that Actually watching that trailer kind of made me remember that I like the first one a little bit Better than what I just said before I saw the shirt, but this looks really really good I really do want to see Denzel meet his match in this movie I mean, I want to see him as the true assassin, you know going around kicking butt, you know And all types of crazy different techniques combinations and things like that However, I do want I mean, I don't want to have I'm not gonna go on this thing with high expectations other than I hope it's good But it would be nice if he met somebody that was as good as he was Or slightly better and it was just like a nice showdown and maybe they kind of had like a mutual respect for each other But they're going at each other, you know because they work for different people to submit some stuff I don't know. This does look pretty good and the reason why I said that I'm liking it a lot better than the then what I initially thought The very beginning of this trailer you see him in Turkey in that scene right there I remember that was a scene similar to that in the first films where he timed himself and beat up everybody It was cold as hell. I liked it. Also in the first equalizer I remember I think it was at one scenes where it was like a construction site or something like that and Like I think it was two on two of like two opposing forces, you know One group thought there was better than the other one and this one dude got his face Stomped in repeatedly. I was like, I don't mind gore, but it was just too much for me I felt like God don't stop it.   Is that was is that am I getting Yeah, glory came out in 89 If you ain't seen glory you need to see it, you know Anton Foucault So did the book of Eli love that movie. He did fences Like the movie didn't love it. My dad liked a lot the great debaters You know so you know Anton Foucault Anton Foucault does this thing my brother But guys, you know, that is just my opinion for this trailer. Okay, you know, you may feel different So what was your opinion?  Do you agree with me or do you disagree with me? Let me know down in the comment section below Let's get this conversation going and keep it flowing If you liked this video go ahead and give me the thumbs up and if you don't that's fine But guess what you can still subscribe to my channel You can also go to my website check me out that bookmark and also look me up on social media Especially Facebook if you like that page it would help me out a lot The links and all that is down in the description box below It's right there at the bottom of the screen while you listen to this nice little melody Going out for guys again Thank you so much for tuning in to my opinion slash review slash reaction for Sony's the equalizer to trailer story Denzel Washington directed by Anton Fukua and before you go Don't forget that my name is Brandon Keith.  They read and that's just my opinion peace


So, I'm here today with co-founder of the Green Team, Carlos Sanchez. Carlos, lovely to meet you. Tell me a little bit about the Green Team. So, we are a cannabis delivery service. Am I right in saying you're essentially an Uber for weed? Um, no. This would all be illegal if it was in England. And smoking weed is still... Well, it's not illegal here. Do you want to smoke or should we do this after? I'm a non-smoker, but maybe later. How do you feel about feeding into the moral degeneracy of this nation's youth? Can you repeat that? I'm sorry. If everyone smoked weed in this world, there would be no wars, there would be no fights. So, if anything, I'm helping America. That is very noble. Thank you for that, Carlos. Um, what? It's 420. Oh my God, please. It's lit, Carl TV. Have you had any advice for students setting up their own company while they're at university? What would it be? Smoke weed. Well, that's the word from Carlos. Get into the drug business. We've gone right above Carlos' head and I've found the founder of Green Team, Carl Bolster. I heard that the Daily Cow recently named Green Team the best weed company in Berkeley. That's right, baby. Yep, just happened a couple of days ago. They called us up. We're just running the shift, normal shift, and they're like, Hey, are you guys available for a photo shoot right now? We're like, uh, what do you mean? They're like, are you in here? You guys just want Best in Berkeley?  Hell yeah. This must be a busy day. Today was quite the busy day. Yesterday was out of control. What makes your weed better than other people's weed? Is it laced with harder drugs? I mean, sometimes, you know, if you're going to get the green crack. Green crack. Are you by any chance Irish? You guessed it. Yeah, yeah. Shouldn't you be more in the alcohol business as an Irishman? It's ironic. You know, I actually don't really drink too much. Not a fan again. It's definitely not. This is the only Irish guy in the world who would ever say that. St. Patty's Day or St. Patty's Day? St. Patty's Day. With a D, right? Yeah, Patty's, dude. Yeah, get it right. Fuck. Any advice for people setting up a business at a young age like yourself? Hire a lawyer. You might not eat for a couple of days or sleep and then be like, oh shit, but just keep at it.  All right. Thanks so much for talking to me, Carl. Best of luck with the green team. Most definitely. The pleasure was all mine. And have a good 420. Hell yeah. Happy 420. Hell yeah. Green crack. Yeah, yeah.


Everybody for a steer rip in 2023 poem chrome baseball choice 12 box case for 26 random teams We will random now and then we will rip. Good luck All right, six and five eleven one two three four five six seven eight nine ten and Eleven L chew down to Jason oh and eleven times on the teams one two three four five six seven eight nine ten and eleven Brewers down to Cardinals save a Jake save it Get of a get tell me in a minute here All right, I'll shoot the brew crew Michael a giant adp reds Christopher E. Phillies Brandon S twins Shanichi Rangers Crispy White Sox Nationals Duke F Dodgers James E angels Shanichi a's Matt SL with the Yankees Darius with the Marlins Mark L Mets Jonathan L Orioles Robert B Diamondbacks Tim L Cubs Jays adp guardians Daniel L Tigers Matthew S H Red Sox Giovanni with the Mariners Josh H Padres Choy H Astros Alexandros Royals Christian L Rockies eddy P raise Jim G pirates Brandon S Braves and Jason over the Cardinals Let me get those posted. We're gonna be ripping Clio Mac has sacked him four times on his own so far like What? Maybe they should probably try to protect the rookie quarterback starting for his first game from Khalil freaking Mac okay Just three to go on Cosme next everybody one second we're ripping here I'm probably not gonna recap this one guys because you are behind a little bit already Pretty quick rip. All right. Just mark some teams down again. Let's do it. Let's do it So the remaining three were just marked down No, same teams are marked down in the second half of the case to the ones that were available in both It was a Mets Marlins and Rangers Mets down 20 bucks, and they're all down Mom's on 10 bucks Rangers and Mets down 20 bucks each.  All right First auto Ricardo Perez Phillies and refractor first auto to 499 is these Tories for the Guardians victories Tories Nemu emu first auto is junior Franco for the Diamondbacks base auto Astros Kennedy Corona First Bowman Another refractor auto here first Bowman is one Benjamin Guardians the 499 Cleveland with the solid break so far two numbered autos. I wish we got a different football game on rather than pets Cowboys Could be good, but little NFL you gotta have like 30 apps to watch football They're like almost as bad as baseball now First auto is blades hit well for the Mets Next up base first woman auto blue jays is y'all stress to Loeta and a speckle first moment autograph of yo, Andrew Vargas 56 of 299 Dodgers Duke Duke with the Dodgers Duke like Duke Snyder updates Keto's last game Right dude Tito miggy Wayne right Feeling my childhood is officially over by the age of 31 base auto Jacob Melton Astros Choice parallel the 150 first auto is Jesus Galees G's Galees speckle First woman auto White Sox Christian Maynard at 299. Okay, let's do it guys There's a cosmic next cosmic Tito the legends probably tough day for him Retiring and and and and the wake news first auto is Omari Daniel For the twins first base auto for the Diamondbacks. Nice one here Davis and de los Santos So it's a sign over there for some reason and a Speckle I think atomic. Oh atomic atomic first auto Abner Yuri Bay 62 of 100 for the brew crew He's gonna be important for them if they're gonna go for the postseason, I think They need him to be nails. Man. I wish Tito could have retired as a red Sox He's been with Cleveland longer than he was with Boston then right first auto is Daniel Mateo for the Rangers Base first woman auto is a Ruben Santana for the Diamondbacks Go Diamondbacks and a true gold True gold is Luis Perales red Sox auto with 19 out of 50 Red Sox Perales He might be leaving now We'll ask him when he says bye first auto Cade hunter for the Reds Clue Mac with five stacks now. Good Lord Dimebacks again here do dimebacks are killing it. They must know a Santos base auto here. I'm gonna send a boxer rapper. There's like some Smudging that I don't think is gonna come off super easily We'll try with a microfiber, but we don't do much more than a microfiber. We want we don't want to like mess up the car or anything Not coming off yeah, it might come up with some little extra something, but That is up to you to decide first speckle out of the two 99 pirates oxy all plaza So you can try to get that one replaced with And if you got Clue Mac and like an individual defensive player fantasy league Got so many points Five sacks was still first half to right First base auto is in Adrian Pinto for the Blue Jays or they just start the second half Gotcha either way it's like just after Dan what up dude first auto Gorsepo for the Cardinals In a refractor first moment auto Luis Perales Red Sox to 499 or the only time Tiger Stadium or whatever they call us been sold out for like a full Homestand in a long time Hello, Joe Throw Revis in the house Revis Island. Let's go Taylor Swift first auto Arielle Castro twins Should put Taylor Swift in the thumbnail Very interesting first auto is Kristen Maynard for the White Sox You may not be wrong Yellow refractor first moment auto axial plaza the 75 pirates With that color match the last person All right, Jake, just me and you Yeah, trade night starts in 30 minutes, you just set up for it Get all the cards out Sounded like there was a threat from Joe right there First auto is Tories guardians things don't go well should be Yeah Yeah, make sense Green love at a 99 first auto Jacob Reimer Mets Yeah, Steve. It's trade night.  Yeah, we're trading first purple auto axial plaza 250 pirates Yeah, dude, it's the worst You had a brain cancer He was trying to keep it from getting out but Kurt Schilling like two days ago was like Yeah, Tim Wayfield has brain cancer That's the most this one of the most Kurt Schilling things you could ever do right there Go against someone's dying wishes That first auto Alfredo do know for the reds Yeah, really sad and and Tito retired in the same day Just sad Alfredo do know Yeah, leave it open. Thank you The first auto fountene for the reds Is the third plaza a lot of plaza refractor first moment out of the 499 Brent wisely for the rays So, yeah, oh boy, all the salutes are going on chat now Joshua, what up Joshua? Oh man.  I'm not uh, I'm not military. Why am I saluting? Hmm You got first auto Javier revost pirates with a solid break Really good break actually Choice first woman auto. Yeah, you're Casanova brave is number two 150 The last 10 of the break is blue ray wave First woman auto Hendry Chevely seven of 150 twins And that is going to do it. Thanks again everybody and no recap due to time We were starting the next break very very shortly. Thank you. Thank you


Hey friends, my name is Arpata Karwa and you are on your favorite channel to prepare for UGC Net examination. In this short video I am going to quickly take you through 8 sins that you should never commit before your UGC Net exams. These points might look very simple and generic but mind you guys we sometimes make very simple silly mistakes that cost us a lot. So even if these things that I am going to share sound ridiculously simple, please keep a note of it before your exams. Before I start, if you are new to this channel, please hit the subscribe button and never miss a video because if you are a UGC Net aspirant, this channel is a mecca for you. So to begin with, the first tip is to stay calm. It's very common for students to pick up their notes a day before the exam and feel lost and this induces panic. While revising, in case you face difficulty remembering the points, take a deep breath and don't put pressure on your brain. This is not the time to panic or to cram. Focus on revising the topics that you have learned thoroughly and are comfortable with. This will give you the confidence that a major chunk of your syllabus is done. Friends, don't forget that a human brain can only retain a fraction of what you feed into it.  Hence, it is very important to stay calm. Also, don't forget that no one can remember it all. All the competitive exams are about scoring more than the other aspirants, more than your own competitors. That's why we never talk in percentages, but we talk in percentile. Friends, the next thing that you should avoid is being around negative people during the last days of your net exam.  Not just that, their comments may hurt your sentiments in one way or the other, which might overthrow your entire preparation. One of my friends had this habit of calling students and asking them about how much they have studied. When she saw that a lot of people are better prepared than her, she was so overwhelmed that she decided not to sit for the exam the next day. This might happen in your case too. So I would suggest you to stop doing such things. This is the time when nothing should play havoc with your confidence. The third thing that you should note is that you should avoid junking before the day of the exam. I might sound like your fifth grade school teacher, but please make a note of it. Eating junk will make you sleepy and lazy, and your mind will be less active on the day of the exam. In fact, I would recommend you that even when you're preparing for your exam, keep a strict tab on your diet. Consume salads and other healthy foods. Once in a while, junk is okay, but strictly no junking before the day of the exam. You don't want all your efforts of past five, six months to get wasted because you were sleepy on the exam day. The next important no-no before exam is leaving from your house empty stomach on the day of the exam. Again, this will create headache and you might feel brain fog. Plus, you might feel hungry throughout the exam, which will not let you focus on questions in hand. Have a healthy meal before leaving your house. The next sin is wearing fancy or uncomfortable clothes to the exam center. Friends, make sure you wear only comfortable, breathable, loose clothes. We don't realize it, but our attire plays a role in how we perform on the exam day. If you are not comfortable in your clothes, you will not be able to give your best because of that discomfort. Friends, the sixth tip is not to carry anything unnecessary to the exam center. Make sure you carry only your admit card and required stationery. Please avoid carrying any expensive jewelry, wallet full of debit cards or money. If possible, I would even advise you to keep your phone at the home. I have seen a lot of students carrying unnecessary things and running here and there at the exam center to keep that stuff safe.  The next no-no is reaching late to the exam center. Whenever we are late, you know guys, a lot of physiological changes happen in our body, which takes a lot of time to get normalized.  It is extremely crucial that you reach at least one hour before the exam time.  Friends, the next important tip is not to forget to carry your water bottle to the exam center. Again a tip that generally your mom would give and you would brush it away saying that, I am not a kid, but on a serious note, it's a very important thing that you remember to keep yourself hydrated. Though exam centers generally have water bottles in every room, but there might be a case that your exam center doesn't supply water. This will then create a huge problem because if you are not well hydrated, you will not be able to focus on the exam paper.  This is when water comes to the rescue and helps you stay calm. So make sure that you always have your water bottle handy. And with this we have reached the end of this lecture. I would love if you guys can comment down below, exam do's and don'ts that you follow or you intend to follow. Also, also, also, if you have any other topics that you want me to make videos on, put that in the comment section below. If you have any doubts, you can contact me on WhatsApp and I shall be happy to answer your exam related questions. If you are looking for regular exam updates, exam prep tips, important videos, you can follow me on Instagram. We put quick revision reels on Instagram and Facebook every week to help you revise topics. That's it from my side for this video lecture. I'll meet you very soon with another insightful videos. Till then, keep studying hard, loving literature and keep following arpitaakarwa.com.


All right. Hello. My name is Natalie and I'm with New Mexico EPSCORE and today we're going to talk about conference posters and I'll give you some general presentation tips as well as some tips on how to design your poster. So posters are a really important part of science communication whether it's to the public to students or faculty or people who are in your field of expertise who have been around for a while and also potential funders at certain conferences. Posters should be considered a snapshot of your research and it's communicated clearly while encouraging that you read it to want to learn more. You always want to include funding numbers and logos as well as any images and if you have images you should always include captions. Even if you have to make the text really small, captions are really important and so are credits if you have any pictures on your poster that aren't yours. And a lot of people ask me this all the time. The typical size for a poster is 48 inches by 36 inches and a horizontal horizontally. A lot of people actually have done vertical posters and I'll show you an example of one here in just a minute. But like I said, typically they are horizontal and about that size. Okay, so when we're talking about poster design, a lot of people tend to put not only tables on their poster but also kind of separate the text into boxes. Tables make this sort of especially problematic. It just kind of looks like you're looking at a bunch of boxes and I'll show you an example here in just a second. Graphs and images and figures communicate more effectively than tables do. So the general design rule is if you can try and stay away from tables and represent your data a little bit differently if you can. Tables tend to belong in papers and journals. And then the two rules of two is you should have about two fonts. You know, either a serif and a sans serif kind of like you're seeing on the slide right now.   So that's just another rule of two, black and white plus two other colors. All right, and here's some general poster tips. I'll explain them as we go along here. You want to make the purpose of your poster clear. You want readers to, do you want your readers to engage in a discussion or maybe express interest in beginning a collaboration? It needs to be really clear right from the get-go. The title is super important. If not the most visible part of your poster, it certainly should be one of them. And it should be akin to a newspaper headline, concise and attention grabbing. You don't want to use all caps and you don't want to use title case for your title. You want to use sentence case. You definitely want to identify and cater to your audience since not all conferences are the same.  They may not know anything about your field. It could be educators who are trying to find some tips on how to translate your research to younger people, K through 12 educators and stuff. So you want to definitely avoid jargon and make sure that everybody can understand your research. And obviously in order to do that, you have to understand your research. If you want, you can provide abstracts to create simplified content. And that's why we ask for abstracts when you register for events for EPSCORE. It kind of helps you get an idea of what your poster should be communicating in a concise way. All right. And for clarity, you definitely want to make your poster easy to read and not just with regards to the font. But layout and format are almost as important as content. Your poster should guide the eye easily from one step to another. And you'll see what I mean here in just a minute. This includes margins and grids and lots of white space. White space is your friend. You don't want to cram a whole bunch of stuff in there as much information as you can.  There's a good trick you can do to see if your type is big enough to see from far away without actually printing out your poster first. You can print it out on just a eight and a half by 11 sheet of paper and hold it out at arm's length. And if you can read the text on your poster pretty well, it's probably a good size. And that same goes for pictures and graphs. Keep your content concise. Everything on your poster should contribute to your message. Don't overuse text and edit, edit, edit down. Always edit. You know, when you first put your poster together, have somebody take a look at it and read it for you. See if you can bring down the text a little more, replace some text with graphs or, you know, other visual representations. If you're, if it's the first time presenting a poster, you definitely want to be especially ruthless for this.  You want to attract people from far away, and you also don't want to bore them when they look at it. You want to be able to kind of keep them around and get interested in your research kind of through your poster. All right, so we're going to do some examples. The first step is, this is a template that we made that you're welcome to use. You can find it, I'll give you the website here in just a minute, but you can find it on our website. It's kind of more of a guideline than a fill-in-the-blank sort of thing, but this kind of gives you an idea of what I was talking about. There's no tables on here. There's a few bullets. You don't want to go overboard with the bullets. And there's some advice in these sections too. You can, you know, obviously change the titles and rearrange things. But it's just kind of a general example for what your poster really should look like. And if you see the title, it's in sentence case, not in title case, which is really important. And we have a couple of different fonts and a couple of different colors here. The graph really stands out with the blue on the white background. And then again, as I said, for the pictures, you want to make sure that you have captions even if the captions have to be really small. So here's the website for where you can find actually both templates for both a poster and for PowerPoint presentations, if you'd like. And there's also our logo on the same page. And you can actually, you know, read all the rules for using our logo and any advice that you might need for that. All right, here's our next example. This was created in a program called Latex, which I'll talk about in a little bit. It's actually free. But this is a mathematics poster. So as is typical for this kind of research, there aren't a lot of options for images or to tone down the text. So it's kind of a difficult poster to create that, you know, make it interesting. And the person who did this actually did a really good job. His name is Nathaniel Johnson. And he's using two well-placed focal points, which are the two boxes in blue. And one of them that one of them spans two columns. So you have a four column set up and the one in the middle there spans two columns. And the eye can flow to the different important parts of the poster, which include the two figures that he has there as well. He put some small logos in the corner. It's still pretty hard to read. You know, it's not a great font choice. But, you know, for what he has to work with, I think he did a pretty good job. Four column posters in general tend to be a bit tedious to look at just because they kind of show that you have a lot of text. But again, sometimes can't be avoided with certain research. Here's kind of a typical example. I see a lot of posters like this. It's not a terrible poster. But again, here we have what I was talking about earlier, which is the problem of boxes. You know, you just you have a colored background. And then, you know, in order to be able to read the text, you have to put it on a white background. So you make these boxes and it's just not super interesting. It just doesn't stand out as much as it could. Another problem with this is if you look in especially the middle box, actually the middle box and the ones on the left hand side, the margins are really close to the edge there. And you don't ever want to do that. It makes it really difficult to read. There's a lot of text on this poster. I have a feeling that they probably could have pared it down a little bit, made the font a little bit larger and made it a little bit easier to read as well as fixing the margins. But he's got a nice references box there in the corner, which is fine. One more thing, if you look at the table at the bottom, you have a really interesting example of bad resizing. It looks like the table was probably an image and he stuck it on there and didn't resize it properly. You always want to keep the resolution and the size of your tables and your images to what they were before. Okay. All right, so here's going to be a couple before and afters. This is a before poster. And so we have a problem, we have a few problems with this poster. The margins are really tight. You have this table at the bottom, which is pretty problematic and it's kind of distracting as well as the background gradient is also fairly distracting. And it's hard to read the title. The title's pretty small and they've got it crammed in next to that logo there up at the top. So the person who redid this poster, oops, sorry, here's the after. And what they did was make the logo smaller in the corner, get rid of that background gradient and fix the tables. And it makes a pretty big difference, as you can see. You know, it's still the same amount of information. It's still the same research, obviously, but just giving it a little bit more white space and getting rid of the distracting colors on there has made a big, big difference. All right, so here's a vertical poster. It's got a good use of color, good choice of fonts. It's really different and it will attract attention from far away, even at a big conference. It uses lots of imagery and diagrams, which is great and it has a pretty good title. So what's the problem? Well, let me show you the after here and you can see. So the first poster kind of is really crowded. You don't really know if this is divided into columns or if it's divided into rows. And so by just adding a little bit of white space between the area that says the problem and the area that says the data, and then closing in the margins a little bit more, it kind of makes a big difference and makes it a lot easier to read. Even though, again, this is still the same information. It's the same images.  Margins are really important. Here's another example. This one's kind of interesting. It's creative. I'm not thrilled with the dark red color in the background but it'll definitely make it stand out. So I understand the choice behind it. It's a good use of flow. It's got kind of an interesting shape in the middle. The font I think is a little small but she in the center section where it's the most important information, she does a great job of using bullets and keeping the text small and using lots of images to kind of help. She's even got a little table there on the side which isn't bad, especially since she has a bunch of other data displayed in different ways. She doesn't have just a whole bunch of tables and she has kind of a call to action there at the bottom which is neat and different. It's risky but I think it pays off and like I said, the dark color can be a problem but she avoids the boxes issue with the way that she lays this out and the way she had kind of a large header background up there. This is one of the more interesting posters I've come across. This is a very, very risky poster. You really, really have to know your material well and not only well but you have to be comfortable speaking about it. This is a great poster for someone who's not in your field who really doesn't understand a lot of the science that goes into your research and they just kind of wanna know what you've been doing and if you have someone who is in your field you're gonna have to basically stand there and explain your research in a much more detailed way because it's just not on your poster. It likely depends on your audience. Like I said, this is a great poster for a conference outside your research field and it encourages the reader to actually interact with the presenter, which is a big deal at a poster conference. It really just comes down to personal preference. This is, like I said, really unusual. It's probably the most unusual poster I've come across but it gets across its message pretty well. All right, so here's some more little tips. There's a few programs you can use. The most popular one, I think, is there at the bottom MS Office. You can either use Publisher or PowerPoint. Pretty much everybody has access to PowerPoint so that's a very, very common program to use. Latex is that one up at the top and was one of the examples I made. It's free and they have templates available. Scribis is a little bit more like Publisher. It's also free to use and I think they have a couple of templates as well. Poster Genius is actually a paid program but they do have a free trial and it's one of those where it really walks you through it so if you really need help, Poster Genius is a good one to try, give their free trial a try. And then the rest of the links there are just some of the sources that I use for this presentation and for other presentations about posters. There's some great examples. The bottom one, perfectly putrid posters is actually from the American Geophysical Union and it's just a funny little article about what to do if you want people to completely ignore your poster at a conference and it's worth taking a look at to know what not to do. All right, here's some general presentation tips. This is for any presentation, whether it's for a poster or for a PowerPoint presentation. You don't wanna chew gum, go easy on the cologne and the perfume, dress nicely. Think, write, design, optimize, present is, are the five tips that I give when I talk about giving presentations and basically it's just a step-by-step way to make sure you're getting your message across in the best way. You think about what you're gonna do and what you want your message to be.  Always face your audience and speak clearly. You can tell a story and you wanna obviously make it clear that you enjoy your research so that your visitor also wants to enjoy your research. Tell a story but don't get off topic. It's really easy for somebody like me to get off topic. And for poster presentation tips, the biggest thing is you definitely wanna practice your elevator speech and or same as a two-sentence rule. Poster visitors are pretty busy people especially if it's a large conference. They have a lot to see and a lot to learn and it can get pretty exhausting. The two-sentence rule is come up with a two-sentence overview of your research before explaining anything else. And if those two sentences are interesting enough, the poster visitor will wanna learn more. And you always wanna thank your visitors and that goes for any presentation anyway. Avoid vagueness and jargon which I've said before and you wanna include some personal interactions so don't turn and read your poster and talk to your poster. You wanna face the people who are there to learn from you and you wanna make sure that you talk to them. All right, and that's it. And I am happy to answer any questions you might have. You can email me and thanks for watching.


So, welcome everybody, I'm really happy to see the number of participants today. Our speaker today is Fabrice Cordelier, who will present an introduction to collocalization. Fabrice is working at the Baldo Imagic Center, it's a microscopy facility in France. We as panelists are here to answer all your questions from the Q&A and moderate the session. So don't be shy, ask your question, we will also interrupt Fabrice if needed to ask some questions to him and he will make some breaks. So we are here together for one hour 30 webinar and the floor is yours, Fabrice. Thank you, thank you, thank you very much for giving me the opportunity to give you this really short introduction about collocalization. I won't go into fancy advanced topics, I will just try to see, to show you the basic tools that you can use and the basic elements that you can use to build your own workflow of collocalization. Of course there are a lot of methods, depending on the topic you're working on, but my idea here with this first introductory seminar is really to revisit the generic methods and to try to make you understand what is behind, how to use them, how to combine them and how to play and get significant numbers out of your images. So let's dive into this black box that is collocalization and everything is always starting with an image and I really like to use this image that is from the Fiji website. This is a nice image with two channels, one red and one green and everyone knows that when you're looking at collocalization what you're expecting is to see some yellow dots, some yellow areas and if your monitor, if your screen is not that good and if the compression about the network is not that good, you may see that we've got indeed some areas, some yellowish areas here, so why should we bother on and why should we try to quantify this collocalization? Because we see it, yeah, but once more, depending on the means to display the image and so on, you may be tricked into thinking that you've got indeed collocalization, so you may think this is an obvious problem, but when zooming a bit, you will realize that the two signals are indeed separated. So this is why it's important to look at the image, this is a starting point, especially to identify the areas where you may have collocalization, but then it will be important to find a way to measure and to assess that indeed your diagnosis of collocalization is true. But the problem is, this is a problem that I come across quite a lot working on the facility, the problem is that collocalization compasses quite a lot of different things, different topics, and in fact collocalization is some kind of big word that encompasses several typical experimental situations. When the user crosses the door here on the facility, sometimes here she is expecting collocalization, but in fact here she is expecting some kind of core expression, which means that you see in this example, with the single cell with two laborings, one in the nucleus and the other one in the cytoplasm, you will never see these yellowish dots, and the user will ask for collocalization when the thing that he's looking for is core expression, so two proteins located in the same structure in the same cell. So already at this step, you see that there is one thing that matters, the resolution and the topology of where the event is taking place in here, what you are expecting is two signals not to be at the same location, as you would expect when talking about collocalization, but you're expecting the two proteins to be in the same, in the same structure, or in the same cell. Then what you may expect is some kind of core occurrence, meaning that the two proteins of interest are on the same spot, on the same structure, on the same location, but without having a specific stoichiometry of association, you may have one green for two reds and another structure, you may have two reds for four greens and so on. So in this case, you're indeed expecting core occurrence in the same structure, but no real relationship in term of intensity. If you are expecting a certain stoichiometry of association to the structure, you will rather be looking for correlation, correlation of intensity of the two labelings on structures of interest. And finally, sometimes what you want to see, you want to see the patterns of distribution of the proteins are somehow related. And in this case, you will rather talk about core distribution. So of course, depending on what you want to do, you may use certain tools. Core occurrence will use some tools that are not the one that you will use for correlations. This is why you've got a huge panel of tools that you will take benefit of. And then you will try, you will start your journey into the co-localization analysis world. And for the purpose of this talk, I've been separating this journey into five big steps. Checking data integrity, then pre-processing, then choosing a reporter or metric, then comparing and interpreting and finally assembling the workflow as you've been establishing which tools, which elements you will assemble. First, let's have a look at how we check for the data integrity, because already when preparing the sample, you can totally screw your co-localization analysis. Already when you're acquiring the data, you can screw your co-localization analysis. So there are three main points that you should take care of, and we will see which important steps we should go through. First of course, you will have to choose dyes that you can image separately. If you're looking for co-localization and you're really not choosing the dyes you're using well to, if you're not choosing the appropriate dyes that will be easy to separate when doing the imaging, then you may come across some troubles like having a bit of one channel going into bleeding through the other channel. And this is quite easy to assess simply by using this representation. I hope that we see the pointer on the screen. By doing this kind of plot where you will use the two intensity for the same pixel, the green intensity and the red intensity as coordinates, x, y coordinates, and you will plot it on this kind of diagram, which is called the cycle diagram. So if you're looking for co-localization, what you will see is a big, and if the intensities are linked between the two channels, you will see a big cloud more or less compact on the graph. If you have some lead through from one channel to another, you will see some kind of relationship between the two sets of intensities quite close from the axis. And this should be a sign that maybe your acquisition parameters or the choice in the dyes are not really appropriate and you may go back to the bench or to the microscope to do better acquisition or better prep. The other thing, of course, is your imaging.  Of course, even if you're using the best objectives and so on, you may have images where the channels you're imaging are not well registered. And in this case, you should absolutely before starting to measure anything to do the co-localization, you must assess that your setup is really appropriate and that co-registration occurs. If it's not the case, then you may try to analyze this non-core registration and try to correct for that. And one way to do it is to use some reference objects that you know that are multi-labeled. You acquire them in the same situation as your sample and you check if everything is well aligned, well co-registered. And then maybe go back to the microscope and try to fix the things or simply characterize and correct, which is something that I will talk about in the preprocessing step. Finally, resolution is really important. You should know at which resolution you're working.  So, word of advice, if you want to prove that there is co-localization, just use the magnifying glass. It will be easier. But, okay, when you're going for a diagnosis of co-localization, this diagnosis is for a specific resolution. In fact, and this is a bit hard for biologists to understand it, it doesn't mean that because you find co-localization, the two proteins are actually at the same place. In fact, the true conclusion of the co-localization test will be that knowing the current resolution, I can't exclude that indeed the two proteins are at the same location in the same surrounding. So, this is why it's important to using some reference slides, some reference beads to measure this resolution, and when publishing always say, okay, I've been doing co-localization tests at that resolution. Of course, with the expansion of the new method, the new super-resolutive methods, you now see that the two proteins that were co-localizing are not co-localizing anymore. This is not that the previous paper was wrong. It's just that the technology has evolved and the resolution has improved, and now you can separate your proteins of interest. So this is, it's really important to always do the three steps, choose the dice well, check for core registration, and finally assess your resolution so that you can state it when publishing. Sometimes I see users coming to the facility with images that are not really, really good for co-localization, and unfortunately, in most of the cases, there is not that much we can do. Image processing is not a magic one. You can't really improve the images afterwards, or you can do it, but in the limited thing. And it's always best to have the best samples, the best acquisitions before jumping to the next step, which will deal with image processing, the pre-processing, and choosing and applying a metric. So these are some of the commandments that we should apply. You should choose your dice well, know your microscopes and its limitation, use appropriate sampling, of course. Take benefit of the full dynamic range, especially if you want to work on coincidence or correlation between intensities, avoid saturation, or, of course, you shall go back to your bench and talk to your facility people. Now, questions so far? Yes, Fabrice, at least, I think one is really interesting right now is how to check properly about substance through and can it be done with controls samples? That's what you mean? Yeah, yeah, yeah, this is this is one way, one way to do it. Of course, this is something that people tend to skip, but having mono-labeled samples is something that is really important. This way it will allow you first to check if you've got it through and potentially do some like in flow cytometry where you do some some kind of gating compensations and so on. But well, if you already on the sample, you already have the evident bleed through, maybe, maybe try to separate a bit more, choose dice that are spectrally more separated. But sometimes you don't have the choice, so you will try to compensate. No, the question right now, one we need to clarify anyway, but regarding the bleed through, would you recommend to always work on, let's say, sequential acquisition mode or always then I see you nodding. Yeah, it's it once more. The main answer that I will give is it depends, for instance, if you're working on fixed samples, yeah, you've got the time to take the time and do some sequential acquisitions. If you're in live mode and you've got something that is moving fast within a cell, a vesicle or whatever. Of course, this will be a trade-off, so yeah. Okay, thanks, maybe you can continue. Okay. Okay, about preprocessing. So as I told you, we will do, we have at least four phases of four ways, four meanings for a single word, colorization. And of course, we may want to work on objects and coincidence of objects or coincidence of intensities. And this is the nature of the image. An image is a support carrying some information, and you've got several types of information within a single image. An image is, of course, a collection of pixels, and you may want to work on the intensities to see there is some kind of coincidence of intensities, some kind of correlation of intensities. It's also a collection of frequencies. I won't go into details about collection of frequency, but you've got some, this is one way to perceive an image. An image is also a collection of objects, and we can work on the coincidence of objects, but we must first define what is an object. And sometimes the object that the image is carrying, the objects, they are small enough so that you can summarize them, extract one point of interest. This could be the center of the structure. The structure is close to the resolution. So this is another way. And this opens some additional methods about how to assess collocalization. And nowadays, now that we have access to super resolution, an image is not an image anymore. You will extract a list of detection, a list of events, from which you can go back to a collection of objects. And we will see with some examples later about what I'm talking about right now. Let's go into the preprocessing. I've been talking about bleed through and crosstalk, and if you don't have any other means, if you can't go back to the bench, if you can't go back to the microscope, or if you optimize the full process and you can't improve anymore, there are ways to try to correct for bleed through and crosstalk for one channel going into the other, or for a single light source, exciting the two dies at the same time. This is called spectral convolution, but in fact I would rather use spectral and mixing. There are some nice plugins that do the job, but of course you will have to have some references to know how much one channel is bleeding into the other and the reverse way around. About the chromatic shift, if you have the miss registration between the two channels, of course the simple translation can correct, but once more, when correcting over the image, keep in mind that you're correcting at the pixel level, not, you won't be able to correct at the sub-pixel level, unless you're doing some kind of approximation about the distribution of intensities and you will have to do some interpolation, which may impair later on the processing. About corrections, sometimes you've got a low signal, you've got a high background, a lot of noise on the image, and in that case, using some kind of filtering or denoising could help, but be careful when using filtering, linear or rain filtering, of course you will impair, you will modify the intensities that you're working on. So if you want to assess the correlation between intensities, then this will be a problem. And about denoising, once more, don't use it as a black box, try to understand what is behind so that you don't create additional artifacts. I've been talking about resolution and sometimes you're missing a bit of resolution and in this kind of situation, of course the convolution could be a pre-processing method of choice. As long as you're sure that the PSF is not changing too much about the field of the acquisition and the depth of the acquisition. Be careful about the algorithm that you're choosing. It should be conservative to have exactly the same intensities but distributed in a different way. So be careful, once more, the convolution is not to be used as a black box but it can restore a bit of your resolution. And nowadays machine learning is also a way to do a bit of restoration of the image. But keep in mind this question, is the processing changing the intensity? The intensities? If so, be careful when using correlation between intensities. If you're missing a bit of resolution, of course you may ask help from a friend and this friend could be called super resolution. If you're missing resolution just try to push it. You've got expansion microscopes. This could be also a different method to try to improve your resolution. Finally, if you're planning to work on the objects, you must, from the image, define what is an object, what is a background, and group the pixels that are part of the object into the actual objects. But before, the first step, so differentiating the objects from the background pixels, how do you do that? Do you do a simple threshold? Do you do some adaptive or local threshold? Well, is it good to work on the signal you're trying to quantify and try to isolate the structures from this signal, running the risk to get rid of part of the signal? These are questions that you should ask yourself. And I will invite you to go to the YouTube channel and to see Kota Murat's talk about where he's talking about this kind of topics, not only about this kind of topics. Then, of course, you will have to isolate and delineate the objects. You will have to group the objects, the pixels into objects. This is the Connectivity Analysis. Maybe you would like to work rather on contours, so you will have to work on snakes to try to isolate the contours. Once you have the objects, maybe the thing you would like to do is, especially if you've got some really small objects relative to the resolution, you may want to work not on the objects directly, but summarize the objects as points of interest, for instance, the centers. And I will show you some method that works on that. And the opposite way around is true. When you've got points, you may want to work directly on the detections like in super resolution. Do you want to work directly on the detections or maybe do first the desolation to have these ties that then you can try to work on? This is a question that should be addressed. So, pre-processing, you've got corrections to try to revert the problem to the data. You've got restoration to try to improve, to push a bit further on the resolution to get a grasp at the resolution that is in the image but that requires some processing to get onto your image. And finally, if you're planning to work on the images, you've got to do some segmentation. But how? These are the questions to ask. Questions so far? Should I jump to... There is a couple of questions that maybe you can answer. Romain, I can't hear you. Sorry to mute. I'm in an open office. Sorry, so there is a question regarding the unmixing, spectral unmixing. Can it be used to help to solve the bleed-through problem or is that another problem? It can be used to try to revert the bleed-through but you've got to make sure that you've got references that were done in the same situation. And once more, this shouldn't be something that you use first. First, you should try to improve the sample prep and to improve the way you acquire the images and if you're still stuck there, you should try to make sure that you have the information, the help from the facility, staff and so on, then maybe try this. But once more, it's better to have... I do... This is personal preference, but I do prefer to start colloquialization analysis with really the data as raw as possible. If you're starting to process and to over process, into the workflow is not adding additional stuff that will lead to over estimation of the colloquialization. Another interesting question. Actually, it's quite frequent within the question. It's about how to use control, some people drop thresholds and so on. I guess that's what you will cover in the next minutes. So maybe it's not necessary to talk much about it, but there is one question that I think is really interesting is what do you do with your control images? Do you have to show them, for example, in your manuscript? Well, if you want my opinion, this should be part of the dataset that you released together with the manuscript and you link it and so on. But in practice, how many P.I. do care about having all the controls? How many P.I. will agree to have an additional, let's say two or three pages in the manuscript with controls because who cares about the controls after all? That's a good question. So this is why making all the dataset for one study available, publicly available, applying the fair principle and so on. Making all this available would be the best. This way, if it's not in the paper, at least you can find it somewhere. You can review the controls, you can review everything and make sure that what you agree with what has been published. Are we good? I think we're good for the moment. There is more controversial questions that we may cover at the end when you will have, cover your content, I think. Okay. Okay. Now, you've got your images down. They have been acquired the best way as possible. Maybe you've been processing them just a bit, just a tiny bit to correct few things. And now comes the moment where you've got to choose a reporter, a metric. And so, we, with Susan Bolt and others have been trying to separate to categorize the reporters and the metric. And here is one way we present the thing. And this might be a bit controversial, but okay. Let's assume that we've got a set of reporters and we want to split them into two groups. The thing that we do is by looking at what exists, what has been published, I would say that you've got two big families of reporters or metrics. You've got the indicators on one side and on the other side, you've got quantifiers. So indicators is a number and a scale. So you put this number over the scale with minimum colorization, maximum colorization and in between values. This is just an indication and it works really well when you've got several experimental conditions because you will be able to compare them. But having only one value, it will be complicated unless you're on one side of the scale. So you will need to find tricks and we will see in the next part the tricks when you've got only one single experimental condition. So these are the indicators on one side. About quantifiers. Quantifiers are a bit easier to understand because they will rely on physical parameters. For instance, you will try with quantifiers to define the level of overlap between two structures. So the percentage let's say of the area that is involved in the colorization process or the percentage of the volume that is involved in the colorization process. Or maybe the distances between sets of coordinates and try to see the percentage of those sets of coordinates that from one channel to another lie behind the resolution limit. So of course, quantifiers will rely more on physical parameters, physical descriptors. So you will have a direct readout for the indicators you have to have the scale and you have to have several values to compare. So but enough categories let's jump into the generic indicator that everyone is using. Let's talk a bit about Pearson correlation coefficient. This might be scary and you can skip it but you just have to keep in mind that the Pearson coefficient is simply an indicator on how well the site of the hologram where you've got intensities from channel one and channel two serving as coordinates. It will define how well the cloud of dots here looks like a line. If you take twice the same image and try to compute the Pearson coefficient, you will have something that is close to one. If you have two signals that are separated, first on the site of the hologram you will see one cloud here another cloud there and no real relationship between the two. You will have an exclusion and a Pearson coefficient close to minus one. If you don't have any correlation, then the dot cloud will be spread everywhere and the Pearson coefficient will be zero. So and I think this has raised some kind of controversy but I personally find it hard to interpret when you've got a value alone that is far from one minus one or zero. I find it quite hard to interpret alone. Of course, if I've got several experimental situations drug, no drug and I compare the two situations of course I will position the two values and be able to say, yeah, I've got more collocalization here than there. But if I've got only one value and this value is far away from the well identified three values, I find it hard to interpret. Of course when using the Pearson correlation coefficients, you're comparing your dot cloud to a single line which makes it hard to use when for instance, you've got let's say let's imagine a receptor and a ligand for the receptor. Of course, there is a moment where you will have enough ligand going into the receptor and then you will have the receptor saturated. So you will have collocalization up to a certain level where you will reach a plateau. So when the shape of the curve won't be aligned and in this kind of situation, the Pearson correlation coefficient won't be good the appropriate one to use. And this is where the Spearman's coefficient comes into play. Basically the thing you will do when you've got something where you've got saturation that happens saturation at the biological level not over the image you will have this kind of curve. So comparing it to a linear distribution is not something that is relevant. This is where the Spearman's coefficient comes into play where instead of having the single intensity values you will replace each single value by its rank. And by doing that you will linearize the curve and you will be able to apply the Pearson coefficient that we've seen earlier. Of course, the condition to be able to apply this coefficient is that you don't have a curve that does something some kind of wavy pattern. It should go up, it should go down, but it should be it should go in only one direction to be applied. So many other indicators exist. Some have been engineered so that instead of having a range between minus one to plus one you've got a range between zero and one which makes it a bit confusing because sometimes people will use some of the coefficients that are between zero and one and relate them to a percentage of codocalization which is not exactly the case and not always the case. So quantifiers now and of course you all know about the Mander's coefficient. Mander's coefficient is I've been trying to summarize the principle here. You've got a structure that is labeled in red, another one in green, and you've got an overlap here. You will collect the intensities within the overlap region and those intensities you will divide them by the total intensity of the red channel. This will give you the M1 coefficient. In fact the M1 coefficient is just the quantity of material that is engaged into the percentage of the material that is engaged in the process of codocalization. M2 is exactly the same that the reverse rate round for the other channel. Oops. And so already here we've got this kind of measurement and you've got some kind of value that you can really interpret alone. But of course if you've got and I hope that you have several experimental conditions this is even better because you can position them one relative to the other. You've got a derivative from the Mander's coefficient where instead of looking at the percentage of signal that is engaged in the codocalization process you will have to look at the surface or the volume that is engaged into this process. And once more having one value is okay because you will be able to have an absolute value but having multiple experimental conditions is even better. I've been talking earlier about small objects really small objects that have size and overall size that is close to the optical resolution. If you've got two tiny dots close to the optical resolution and you're trying to measure an overlap between the two using the Mander's coefficient of course this won't make sense you won't have enough pixels to define a precise percentage of overlap and you will have extreme values if you've got nine pixels on one side and nine pixels on the other side the overlap can take only few of the possible values one-third one-sixth etc etc so it will be difficult to use this kind of method the Mander's coefficient on really small objects this is why it's okay to try to summarize the problem by isolating the small objects and maybe summarizing them with one set of coordinates with the center of mass or the geometrical center of the objects and once we have this summarized version of the objects we can do several things we can define a rule we can define our own metric about what is co-localization let's assume that we've got these two objects and we just check for the center of the red object we could set up a rule saying I consider that there is co-localization when the center of the red channel the object on the red channel is falling onto the green object so in that case of course we don't have co-localization here in this situation where we have the plain red circle which is perfectly centered over the green object then of course its center is falling onto the green object and I consider that there is co-localization and now comes a specific situation where I've got a ring that is perfectly well centered over the green object the center of the red ring will fall onto the green object and I will conclude about co-localization and this is at this step that you realize that maybe maybe in the workflow even if it's not appropriate to measure the overlap maybe having a look at how the overlap behaves in combination with this method will allow me to go even further in the conclusion I don't see much overlap between the ring and the circle which means that they might be separated but when using this strategy here the center is falling onto the green object which surely means that the red signal is outside of the green signal so you see that and this is quite important by going into the co-localization black box understanding all the methods and so on you may start to think about additional ways to analyze your data and find several methods that will give you one focus that will allow you to conclude about what's going on in your sample here on the previous slide I've been only summarizing one object by its center here I will use the center for both channels and now I will set up a simple rule if the two crosses are separated by a distance that is less than the resolution then I can't exclude that the two objects and the two centers are co-localized so you see it makes some weird things because if you've been applying the right something rate you may have two crosses that are separated one from the other but if the distance is below the optical resolution the two crosses will actually co-localize and it's weird it goes against the principle that to have co-localization you should have yellow when merging red and green so in this kind of situation we are above the resolution so we don't have co-localization even if we have a bit of overlap here we still have co-localization and once more with the ring and the circle we have co-localization do we have questions at this step? we certainly have questions no but it's really interesting some of them are very very specific and since I don't know exactly what you will cover it's a bit difficult to decide but for example I will take one about the Mender's coefficient do you recommend on choosing a Roy yes no and how to subtract background and what to use as control Mender seems highly sensitive to the choices and the thing is on each of the methods you presented briefly I think we can make an OBS Academy event of at least one or two hours yeah so the problem is this kind of question and I'm not skipping the question but it all depends on the biological problem you're working on it all depends on the nature of the noise you're working on and I don't have a precise answer except once more go to your facility people and try to get the devices from your facility people because well if you've got noise you can always try to filter to the noise and so on if you don't have a way to improve the sample and the acquisition about the region of interest about region of interest yeah on the Mender's it all depends on the on the distribution of your structures so I couldn't I couldn't say on this region of interest I can comment with one project we had where you have cells that have different phenotypes and basically if you look at the overall image you might not see something but if you do the analysis cell per cell then you see that you have a difference of phenotypes between cells and you end up with a difference in your results but it's more tedious because now you have to find the region of interest and at the same time if you're if you're thinking that maybe the Mender's coefficient may depend from one region of your cell to another the thing I would recommend is maybe try to measure object per object and then try to make a map where you call you make a color map of the overlap between your objects and maybe you will see some regions that are homogeneous with a certain percentage of overlap other with another one etc etc and if you want to learn about that if you want to because this will require a bit of automation but if you want to learn about that we've got an excellent set of videos about an introduction to the macros with Fiji on our youtube channel all the questions you might answer them later it's about 3d vs 2d and about the software that exists for localization okay so 2d vs 3d of course if you can go for 3d go for 3d because the biology is not 2d anymore it's 3d so gone just try to imagine if you've got a localization that is taking place along the z axis okay red green okay and if I slice just here what will I conclude this object is alone so you can't unless you're forced to and but please go 3d about about softwares well I'm the author of this plugin which is called and I insist on the because it was written with a german my german colleague and ja is ja in german so it's just another localization plugin but well it's not that I don't want to recommend it it's just that it depends on what you want to do and how much you want to go into automation and so on so depending on if the method is there go for for it if you're using IC you've got additional plugins and if you're writing your full workflow in IC have a look at the tools I refuse to give you advices on the tools because there are many out there and many that are really good it all depends on your full workflow and how you can integrate and jump from one tool to another and maybe you want to to get stuck into image fj or maybe you want to go to IC in that case explore what is there and have fun I think we'll have to let's say to complete this kind of questions on the image forum on the thread we'll have a complete list of software we are often using or recommending and don't despair if you see that we are not answering your questions it's also that sometimes I mean in the Q&A window sometimes it's really difficult to make something that will be less than 1000 words about the software when you come to the software I will give I will do auto promotion for one chapter one review chapter at the end where we have the table with depending on what you want to do the kind of software that exists okay great okay about comparing and interpreting now well you will have to do a lot of statistics you will have to first plan your experiments to know how many cells, how many objects etc etc and to be honest I was trained as a biologist I am now a bio-image analyst and statistics is not something that I really master so the advice once more would be to get help about statistics get help from people to know how to do them in France we have had a really nice training which is called image stats statistics for images and where we have been able thanks to the organizers to have a look at what is an experimental plan on the statistical point of view and I can tell you that it's better to ask them before you start your full experiment because these are the questions that we always get how many cells, how many objects which condition, which test etc etc this is not the kind of thing that personally I feel confident to tell you to explain to you but I can show you all the tools before or after this discussion to give them material that they can use I've been talking about comparing and the problem that I personally found with indicators if I've got only one experimental condition so how from one experimental condition can I put some kind of diagnosis of collocalization when I've got only one value well I need to generate additional values additional metrics and one way is simply simply to take one data set my only data set well I hope that in this kind of situation I had several images of the same experimental condition where I will establish the person coefficient between channel A and channel B I will have a certain value and you see that this is typically the kind of mid-range value that I find personally hard to interpret how do I do to get something to compare it to maybe I take exactly the same data set at least for one channel and I use a second channel that's rotated by 90 degrees and try to see what is the person person coefficient and I see a huge drop which means that yeah I surely had something there so this is one way to do it of course if the image is crowded with the signal just flipping one image it won't be enough because you will always have some kind of random correlation between the two signals so that if you've got some sparse objects this is one way to generate the second data set from the original one then there are methods that have been engineered simple methods where simply you will take the original data set but you will displace one channel you will translate one channel relative to the other and I don't know if this will be visible on the webcam but the thing I like to do when I'm explaining this Van Stinsel approach is taking my two hands like that channel A, channel D when they are overlapping I've got maximum of correlation maximum expectable correlation and now if I translate one relative to the other I will progressively lose the correlation I will do it in one way in the other way and each time each time I'm translating one image relative to the other I'm computing the Pearson coefficient and then I'm plotting this series of Pearson coefficient relative to this transmission if I had first co-localization then by shifting I will lose the correlation if to the opposite I started with exclusion when I'm shifting I'm running the chance to have an overlap when translating so depending on the shape of the curve that you have at the end you may say yeah I started with co-localization and then I've lost it so this is indeed co-localization one additional thing with the Van Stinsel approach which is nice with the chromatic aberration the maximum won't be at zero it will be slightly shifted the width of the curve of course depends on the size of the structure if I've got two fingers it's easy to lose the correlation if I've got a finger like that and another one like that I should move quite a lot in order to lose the correlation so this will make the bigger del shape finally a third strategy to try to generate something to compare your value to is to randomize you will take one of the two images you will cut this image into small blocks taking into consideration that due to the way the image is formed you've got a local correlation between intensities this is due to the point spread function of your optical system so the bricks that you will make in your image are a bit bigger than one pixel in general if you've been applying the right something it will be three by three pixels so you cut this image into small pieces you put all the pieces in a bag the bag and then you just get pieces and reassemble them into a new image this gives you something that on a biological sense doesn't have any sense but at least this is exactly the same information but located differently and now the thing you will do is compare this randomized green image to the red channel you will compute the Pearson coefficient you will do it once, twice a lot of times and then you will end up with a big distribution of Pearson coefficient to which this corresponds to a situation where co-localization may have happened but on a random basis and now the thing you will do is take back the two original images and compare the Pearson coefficient that you got to this distribution and if it's far away from this distribution, this will mean that you've got a chance to have actual co-localization so this is another another way to work with the images when you've got only one experimental situation but be careful because getting numbers out of the images is fine but it's fine also it's like with the ring circle paradigm that I've been talking about it's important to have several informations and to combine the informations before stating about co-localization because if you're looking at this slide you will see that the percentage of co-localization in terms of surface is the same but it doesn't correspond exactly to the same experimental condition here you've got an overlap here you've got only part of the green signal that is engaged in the co-localization process here you've got a bigger co-localization area but a bigger object and though if you're measuring the Mender's coefficient here you will find exactly the same and finally you've got the reverse situation so it's important not only to focus on the indicator on the matrix from co-localization but maybe to widen a bit the analysis so that you're sure but that you're comparing or describing exactly the same phenomenon so what question should you address knowing the matrix that I've been presenting and the comparison and interpretation method that I've been presenting what type of co-localization method is the most appropriate for your problematic are you working on the intensities are you working on physical coincidence are the published methods adapted to your problematic yes because this is not because you find Pearson coefficient, Mender's coefficient in all comfortable softwares in all image processing softwares that are linked to the microscope in all the publication that these are the method that you should use maybe maybe a different metric will be more adapted to your specific problematic so depending on the interest that you will give if the answer is no you've got to be creative to build your own metric to characterize it and to use it and publish it well if you're happy if your problematic works on your specific problematic if the method that have been published work on your specific problematic and if if this is okay then just go and find the tool that is the most appropriate for your work through especially if co-utilization is only one part of the work through you've got to be able to change all the steps so that you can automatize and remove the bias the human bias in the analysis about the tools we've been writing a review with Patrice Mascalchi and at the end of this review you will find the table where you have some kind of list of all the a lot of tools that exist and when to which what kind of co-localization that allow you to do and where to find them do we have questions here again I would say yes we do have questions for example one that I like is we have someone wanted to do a co-localization between three channels or three objects do you have recommendations well most of the tools that exist are done for only two channels but once more if it's trying to find the overlap between the three then one of the workflows that I will present after will help it will just have to be generalized to the third channel then well if you're working on the intensities I think that the easiest way will be to go two by two with the tools that exist but if you're willing to implement for instance the Pearson coefficient I'm for sure you can extend it to three dimensions but I'm not sure that we've got an implementation that already exists about that my answer would also be that it will be difficult to make it understandable by viewers because 2D scatter plots is already challenging sometimes but then if we make 3D graph yeah it will be difficult yeah you know for the 3D graphs the thing I would do is use there was a plugin I can't remember the name to do these scatter plots on RGB images there is an old plugin about that so this would be a start point we'll find it for the post on the forum maybe you can continue for the moment thanks Fabrice okay so this is the final section and in fact it's just few examples really basic examples just to show you that collocalization study has not to be complex and that by thinking about the elements the individual tools that you may put together all the tools that exist within the image you may already have your workflow set to do what you want to do this is a true story with a user who came to the facility with this kind of labeling with cells cells that are labeled in red and green and the idea is to identify the the ones that are carrying the marker A only B only and both A and B so the question is how to isolate each single cell how to count each type of cell and how to estimate the percentage of co-expressing cell you see this is not the collocalization the sub-cellular collocalization as a lot of us think about this is this is still collocalization that collocalization is not the right word to characterize the situation this is called co-expression so first how to isolate the individual cells of course on this kind of image the thing I would like to do is to set a threshold to identify the background and differentiate it from the object but how would I do it especially on the yellow cells there are methods you just open image and you will see that there are plenty of methods that exist to set a threshold are they appropriate to these problematics I don't know but this was a good opportunity to give you this an explanation about a method that exists that is implemented in YACOP which is cost automatic threshold what cost automatic threshold will do is first do a scatterplot of all the intensities and in fact it will put a threshold as high as possible to start with and it will look at the Pearson coefficient below here and if this Pearson coefficient is still non-zero then it means that down there here there is still a bit of correlation of correlation then it will move the threshold down a bit have a look at the Pearson coefficient if there is still a correlation it will move the threshold down until it reaches this step where you see this big cloud is uncorrelated so now we can set up the threshold here which maximizes the number of pixels having a correlation of the intensities and lowering as much as possible the uncorrelated population so this is one way to set automatically a threshold where you twist already the problem by trying to maximize here the number of pixels that you take into account and pixels that have intensities that are correlated I'm not sure this applies really well to this problematic where you've got co-expression it's not said that you've got a correlation of intensity between the two but I thought this was a good moment to tell you about possible ways to set thresholds on this image the thing I would do is try to get the two masks the mask with red cells the mask with green cells simply by using some binary combination between red and green I will have the double expressors the cells that are the yellow cells the one that are carrying red and green intensity then the double expressors I will I will use them and I will remove them in a way from the red mask to have the red only cells of course I will have small crappy little things that I will have to figure out later on I will do exactly the same for the green mask to have the green only cells and finally I will recombine the three I will be able to use for instance analyze particles in image it count the double expressors the red only cells and the green only cells and you see no minus coefficient no fancy Pearson no fancy randomization and so on so I will use my regular tools that you can find in image you can do the color utilization workflow and have the numbers at the end another workflow which will be the last one more related to super resolution of course with super resolution microscopy you end up with a lot of detections so small crosses and you see that there is something happening here it seems that there is you don't have crosses overlapping so but you seem to have a distribution that is related between the green and the red channel so how can I do to prove that here I've got some relationship between the two signals how to do color utilization on that well you've got three choices the first one is to take a good old Gaussian blur or to take the pss the point spread function to convolve the image to have this blurry image where you can do all the measurements that we've seen previously but what is a point to do super resolution if to quantify colorization you should go back so for sure you should adapt the method that you're using maybe you need to define the zone of influence of each single detection and to do some kind of desolation and then work on the ties on how they overlap and so on this is one way to proceed the other way would be to work on special statistics and to evaluate the relationship between the two distribution and I must say that I'm not an expert on that field but we know two experts on that field so Florian and Thibault who will give you the seminar next week so if you want to know precisely how they would process this type of images so join us on the next seminar finally word of advice colorization is dangerous because well if you're not using the appropriate method in the the application field it was made for you may end up with with numbers that won't mean anything or will screw the full story of your paper but you might be invented you might know the tools you might know the tools for colorization the tools that you may find in image and try to think sit down and try to invent your really own way to do colorization of course you will have to to test it to make sure that the method that you came up with is is behaving the way you're expecting so well that's advice when doing colorization think be creative test get help and repeat until you you end up with a good a good process and that's the end of the talk so I'm pretty sure that we still have a few questions thank you Fabrice I think we do have some questions I found the color inspector was it the one you were thinking about that's it there is all the questions regarding a creative way of doing analysis so I will try to say that loud can you assign objects to groups based on the degree of colorization e.g. object A is colorized to object 1 and 2 but higher volume of A so you see it's going to be crazy so I think you mention it you can do whatever you want as long as you document it explain it explain your choices and explain why you're doing it sure sure and you see with the development of networks like Nubi as well we are training a lot of Nubi in image processing and we are training people to do their own macros and so on for sure these things so creating new methods and also assessing that they are working really well testing with generating some static datasets on which you may test the method it's something that is becoming easy and I'm quite surprised to see that we are all going back into the same things so Pearson, Mander I don't know which one to use so I will open the Jacob tick of the boxes and tick the one method that works for me which is just stupid just have a look at your images and try to describe what you try to see what you want to describe on the biological point of view and then try to build the metric that will reflect this and try to see if this is responding the way you are expecting and so on and work on synthetic images to be able to vary all parameters the metric that you've built is actually responding and I would say maybe if you feel alone and lost in this call localization world maybe do not hesitate to post on the image forum maybe look if there is a similar question first and then post on the forum maybe one of us will help you with that I don't know Marion or Anna or Thibaud did you see a question that I forgot to ask but that would be very interesting to ask right now so I would like to do a question I'm sorry just report question from a user so you exposed how from the facility point of view you would do analysis of call localization and question from user was how do you convince users that maybe they are not aware of these different tools that effectively the risk call localization so what is the suggestion the trick you usually use you present them with other paper I think they can find in literature or you know opinion leader in the field or what do you do thank you what do I do when I've got a user coming to the facility and asking for call localization I would say first I will I will listen to the user about his the problematics the biological problematics then I will ask for images to know the kind of structure he is working on depending on that can I summarize the problem as dots or is it is it what is the shape and and so on and then I will assess the quality of the images sometimes even if we are an image facility that takes into the full process into into account sometimes when the user becomes longer in front of the microscope then it degrades the quality of the image and then then once I've got everything okay then we start to work on the on the metric and if the user is not convinced then this is this is a problem in general this is not the user that is not convinced because the user will have read all the papers about call localization before coming to me this is a PI in general that is that might say okay you should do Pearson you don't have any correlation between your intensities so this is a bit stupid so sometimes yeah making a bit I've got a set of papers that I sent to the user to be sent to the PI but I didn't have to directly send to the PI yet maybe in my former lab does it is it the kind of answer you are looking for? Yeah I think it's enough thank you Ana, Marie-Anne, Thibault do you see any questions to cover right now? or good so with that we'll do our best to fill the gaps of all the questions we didn't answer in the Q&A and prepare the post on the forum and with that I would like to thanks Fabrice again. Thanks to you who have been typing answers to the questions and thank you also to the CREC team who solved the technical issue in no time so we could start with only a few minutes delay that was impressive also so thank you guys see you soon


Iroh ochi na chipi, ino fichili fi de esa. Iroh kwa tiparwa ni saa na nangunga grupi e sudi saa riti para saa. Shikamu ni kama. We feel that leadership, we are no longer future leaders, we are the current leaders. So when you want to become a leader, you need to face challenges. So this is a challenge. You don't want to live zombie in a bad speech whereby you find that our own chubi rupti. This fight is not for all of us who are here but even those who are fond of fighting others is a lesson to them because they have gotten one zombie and one nation.