as well. So welcome everybody. Really delighted to be here, to share with you our work on AI in classroom. And I'm here with my colleague Neha Gupta. Neha, do you want to briefly introduce yourself before I make a start? Yes, so hi everyone and absolutely delighted to be present on today's webinar. So I'm Dr. Neha Gupta. I'm assistant professor in information system management and analytics groups here at Warwick Business School. Prior to my academic career, I have been a practitioner in IT industry, you know, as a data scientist and software developer, but I have been in academia for the last three years and very aggressively we have been working on this AI task force project, the findings of which we want to uncover as part of this presentation. So I was very fortunate to support my senior colleague Dr. Isabel on this project, so that's me. So you will hear from me in the second part of the presentation, but over to you, Isabel. So yeah, similar to Neha, my background is in industry where I worked in technology projects. So I led a technology project mainly in digital payments. So hence my interest in AI and I start off with giving a contextual overview. Then we discuss the opportunities we established from our task and finish group and how it's linked to pedagogy. And then I give some basic examples and Neha takes this on and with more advanced examples. And then depending on time, I might just be online and some of you will have heard it before, but I developed an AI based formative feedback tool. And for those of you who has not been aware of this tool, I'm very happy to discuss it at the end of our meeting subject to us still having time for it. So as an outset, I would like to outline that there is no single AI and we are aware of this and we are using AI as an umbrella term. So it's really because I'm aware that some people find it important to point out that there's not a single AI and that I do agree with it. And we're looking at different variations here, but we are using the word AI as an umbrella term for you as you can see this onion graph for various aspects. And please do put questions in the chat if you have any or raise your hand if you have any questions. So we believe that AI has the opportunity to democratize education and we call it the pedagogic paradigm 4.0. So here AI education students work together as partners on having a clear vision and the vision and one challenge being really about fairness. In our view, it's about trying to use AI ethically and to the greater good of actually trying to improve current perceived weaknesses in education. And one of those perceived weaknesses as we know from NSS data across the sector is formative feedback and fairness of assessments. And that's the reason why we focused on formative feedback from the start because we believe there is something we can actually improve. And using AI is not standalone. It has to be embedded within processes and pedagogy. And you see here an extract of an article we recently published. We say that actually AI has to be embedded in such a way that students actually want to listen to you. So from the start, students might have the perception because they're younger and they see themselves as more digitally able. So we want to acknowledge that actually students use technology on a daily basis. Perhaps not in the way though that actually we want them to use technology as part of this teaching or as part of the preparation for their future work. So we acknowledge students' knowledge of technologies while actually acknowledging as well that there are different aspects that they might not be aware of. So there's something they can learn. You then might want to position yourself as an expert because we find ever so often that students actually want to be taught by an expert. So it's about you demonstrating your knowledge and discussing hot topics from industry and how they apply to the technology that you want to convey. So students are inspired and it's only then that you can then move on to facilitate a role where actually you encourage students to explore AI. And here we come to an article that Neha and I are about to submit for a special issue on Genitive AI is that we actually find that at the moment students do not use the full potential of AI because they don't really explore it for themselves. They want to be told but we have to tell them actually that they can explore and develop their own understanding first. But this role as facilitator and conveying what AI is for them to explore needs to happen only after you've established first what students expect you to do is showing your expertise and acknowledging that they are actually quite good in using technologies generally. And by following this sequence you might get more buy-in with students conceptualizing, visualizing and shaping the unknown future. With a group of over 50 members from across Warwick and also other institutions within Europe and worldwide. And we have, would you mind sharing the article? Okay, thank you for your interest. I'm going to send the links but to not cut my task. So I'm going to do this when Neha talks. I'm going to post some of the links to the articles and let me just, and this is the slide I'm showing here, the link, the link I have to hunt. I know it's the link I didn't have to hunt because I just, this is the link I shared with the other. But the link I shared before, so somebody here and you can see it. And so thanks Neha for doing it. And here we are looking at the moment at the slide of our resources because I really, really encourage you to spend time on our resources and share them going forward and making sure that actually students use the site as well. And so we have colleagues of mine for big modules of over 500 students. They use our resources to get students to start to think about AI and how to use it in an academic way. And you can find our resources. There's a full report on the top left. And in the right column, you have, for example, a student presentation for students, a student report for students, a student report for academics, academic advice for academics. So you have a mixture of resources that you might want to get your students to engage with, or you might want to engage yourself with. But it's six months work of many of us. So do spend time on the resources. They are up to date and hopefully you make use of it. But you see broadly that we have split the report into six chapters. We have first the AI Enhanced Learning Environment, where we try to explain why we believe that using AI in education is a good thing rather than moving back, let's say, just to unseen exams. We have a chapter, and this is headed by Neha, on the teaching of the future. We have a chapter on ethics and academic integrity. So our decision at Warwick was to leave lecturers the choice of whether they want students to embrace AI, to use AI if they want to, or if they want to forbid AI. And you can see our rationale in chapter three. And we have a chapter on assessments and principles of using AI for assessments. We have a chapter on feedback and dialogues with AI. In that one, you also have the discussion of formative feedback provided by AI. And then we have a chapter which looks forward thinking about possible changes in higher education, to environment more agile, and developing a supporting infrastructure. And so we have six broad chapters, but each of them is in three different parts. One part is produced by students only, one part is produced by our working groups, and one part is produced by external contributors. So in total, you'll find there are 18 subsections. So, absolute amazing nighttime read, so don't miss out on it. And at this point, maybe we can have some feedback from everybody here, is because I'm going through easy examples. And the reason I mentioned easy examples is that we find actually, and I was just recently at the same with doing an internal presentation, and we asked who of colleagues had actually themselves used generative AI in different forms. And actually it's a very small proportion of colleagues who have actually tried out different forms of AI themselves. So my question here to the audience is, I give you the example of Dali and Heijen. Who of you has used Dali for hard work, and who else you have tried Heijen? Anybody? Just write in the chat or raise your hand. Nobody? Okay, so if you haven't, so Michael, perfect. And so Michael, you, and Andrea, you both have used it, and Mike as well. And so for those who have used Dali, and I think it's easy to use, and I assume you agree with it. The other thing is Heijen. Heijen is a new way, so you upload your picture. Thanks. Most have used Dali, good. And so if you haven't used Heijen, you find here three examples of me producing a birthday message. Actually for my godson. So you see my first recording, and then you see the translation into French, all done by AI. And it's all a question of me typing in my birthday wishes, and then it's then translated and it's produced as a talking picture. So you see the picture I have on the first page here of this presentation, and that picture starts to talk in a, I think I chose British accent rather than American accent. For those of you, and I see nobody has used here Claude yet, it seems. And I think you should really start looking at Claude. I'm really, really impressed with Claude. I also like Bart, and none of you mentioned Bart. Bart is so easy to use. So, and do sign up for them.  But, you know, and each about, okay. And so Gamma, do you want to tell more about Gamma? Because I haven't tried Gamma yet. Do you want to unmute yourself to share the insides of it for slides? Okay. So, yeah, Nia, are you going to talk about Gamma afterwards with your PowerPoint presentation? Yes, I think there is a link I have provided to Gamma saying that, you know, and I will show the chativity way of creating presentation also, but Gamma is a tool or, you know, a app which can help you generate that. There are multiple ways to do the same thing, and Gamma is one of it for PowerPoint. Yeah. Thank you, MSC. I'm quite happy to see that a lot of people are actively using AI here. Yeah. So, Nia, your turn. So, you can now continue with the more the advanced versions. Yes, yes, that's correct, Andrea. I'm just reading the chat messages, you know, and I think Andrea is quite right in saying, but saying that, I think, yes, it produces non-reliable information, but for me it's about, you know, how we can productively manage our time, and even if, you know, we get some courses slides to begin working with, it saves, you know, some amount of your time. So, the more we start getting used to generating stuff automatically, and then refining it later on what we want to put our experts in that, then I think, you know, that should be the way forward in my view. So, yeah. So, thank you, Isabel. I think, yeah, so, for your introduction and, you know, for sharing your knowledge. So, from my side of things, I would like to share four use cases, prompt engineering. I'm sure if, you know, you are using Dali and CharGPT extensively, you might have come across prompt engineering, but I will touch on that. And then there are other two or three tools which I have used myself personally, which I think they are good interest in use cases and teaching context, which I would like to share with some fellow colleagues here. So, that's the overall broad agenda, you know, for my doc here. Yes, I think you're right, Andrea, you know, Adobe and Microsoft, they are already, I think, embedding plugins in their applications, which can allow you to do that. And I don't see that in your future, it could be months, we will get the latest update, who knows. So, that's absolutely right. Next slide, Isabel. Yeah, so, prompt engineering. So, how many of you know about prompt engineering? I think most of the people we have got five, yeah, co-pilot is good, MSc for coding kind of things. I've seen it a bit, Andrea says, okay, so let me just, okay, Mike has done micros about prompt engineering. So, this is a very useful resource for prompt engineering.  So, there are a lot of clues and guides and examples there on this website, which tells about how you can go on getting better at writing prompts when you are interacting with these generative AI models. So, one of the well known method, which is called short prompting, this is to do with, you know, when you write your prompt to a generative AI tool and you provide specific number of examples in which you want your output to be seen as that's called short prompting. So, you write a basic prompt and then you refine it that this is the kind of tone I'm looking in the text or this is the kind of example I want. I want you to bullet points the stuff, you know, in the generative AI or large language model is written in the output. So, that's called short prompting. So, you can continue from a very coarse looking shallow prompt and going on to the fine key prompt, you know, so that's that's something which is that bit is something which comes with practice. The more you start writing prompt in this way, the more you will get better in writing prompts and get better output from these large language model. You can use it for structuring the data, as I said, you know, writing emails, different writing style bullet points, paragraphs, giving example, and for image prompting, as Isabelle mentioned, open AI, a Dali tool is also the one which kind of works on this prompting kind of mechanism and you can refine the prompts to get a better output. So, on this particular website, I have seen, you know, there is list of some example products. I think one of the links you will find on this website that which all products, which all language models or generative AI tools take prompting as an input to generate an output, there's a big list. And that becomes, I think, a window to many other tools, which we don't know generally, you know, because I think part, chagivity, drama, being, they are the common ones, but then behind the doors, there are many other which are available out there and how do you know which are these ones, so this can also kind of an encyclopedia of it. Do you think users need to understand how AI is actually creating output, as otherwise they think of it as magic? I think that's right. I would say, Mike, but I think there are two audiences here. As educators, we ourselves need to have some sort of basic understanding of it, how the output is generated, but it gets into really machine learning and deep learning side of things and how these large language models are written and how the data is actually processed, you know, internally. So, I don't think all educators coming from different discipline backgrounds are, for myself, I understand up to fair bit because my background or my training is in computer science, but I think it is hard, but I would say there is a very useful resource. I can share the link later on with Edith. I had seen a slide from Dominic from Oxford University. I think he is the technology support person and he has got some very interesting slide share. I think I'll be sharing that link in one of his presentation, in one of the upcoming slides. There, he kind of talked about in at least five, six slides how exactly the output is being generated. So, from a layman point of view, you can get some sense of, you know, how the data is being combined. So, can I just come in here, just briefly. So, first of all, the University of Oxford. So, Dominic's page is linked on our page. So, you can see it in additional resources at the bottom right. So, if you go to our page, you can see the input from the University of Oxford. Second, we're coming here to do students need to understand it. We also need to think who is going to teach this. So, because if you think about our various modules, if you're doing, let's say, a third year undergraduate student, you as the lecturer on a topic, let's say, accounting, wouldn't want to then start to explain how AI works. Because actually, students might have heard it before, they might not, but it's not really the topic you want to cover. So, on the one hand, I don't think it is the lecturers role to now start to all explain AI. It's more like a university decision. That's why we have our AI resources there, is that students actually have to take some ownership of engaging with AI if they want to understand the detail of machine learning. So, I don't think, coming back, I don't think the lecturers role to start to now talk about it. I do think students need to know how the basic understanding, and I've written two teaching cases, for example, to explain the basics of AI. So, do look at my, you know, on the page, there are two teaching cases, and you can use them if you wanted to discuss the basics of AI with students. Yeah, I think Mike is saying we need to understand the limitations. Yes. I think the way we can share that knowledge with our students, in my point, we can give them examples, you know, examples where an AI goes right, examples where AI goes wrong, you know, and that's how we kind of communicate, you know, the barriers, as Isabelle said, or the limitations of it. But opening up the black box, I think that that cannot be possible because people are coming from different disciplines, isn't it? And then coming back to the limitations, exactly those two teaching cases I wrote are talking about ethics and the ethics of AI. So, do look at those teaching cases and possibly use them in your teaching or share them with colleagues. Yeah. Yeah. Yeah. So, I would say I agree with it. So, I think continuing on our slide, Isabelle, can I move to the next slide, please? Yeah. So, as I mentioned, you know, example, prompt engineering. So, in this one, you know, as part of writing the report, you know, I was like, you know, let me play the chat GBT because I wanted to reword a certain paragraph or kind of rephrase it using chat GBT. And you can see, you know, in this particular example, the rephrase output which was generated by a chat GBT for a particular paragraph, which I inputted to chat GBT is kind of very ambiguous in its meaning because in the paragraph, if I can read it out, you know, the one which I wanted to rephrase it, I wanted to rephrase the discussion of this is trying to focus on the potential use of AI and some of the commonly available AI tools that can be employed by educators and students alike in a collaborative manner. Okay. So, the question here was educators and students collaborating with AI. And I wanted AI to rephrase it. AI completely changed the context of it in this case, chat GBT. And it's a, you know, the discussions entered around AI potential use of readily available tools for collaboration among educators and students. So, it's not about AI collaborating with students and educators. So, AI on one side and educators and students on other side, however, the meaning completely is changed. So, the more you play with the prompts, you will see such examples coming in your own teaching practice. And what I tend to do, I tend to take the screenshots of this and save them in my learning material. And on the beaks, when I'm going to teach a little bit about AI, how they are going to use AI, I do share these examples as limitations of, you know, of AI.  So, here, you are doing short prompting, you are providing specific instruction to chat GBT. You can also use 80-20 rule in prompting, meaning you want to say, insert a topic, I want you to learn about the topic. Can you identify most important 20% learning from this topic that will help me understand 80% of it? So, that's like, you know, extracting the gist of maybe a big page or two pages of an article and generating new ideas is something which I've heard, you know, most of our students have started using it as prompting to ask, you know, new kind of ideas, what can I write about, you know, particular topic, what are the some of the initial ideas and that can be a starting point. You know, removing some barriers and thinking. So, that's prompt engineering. Next slide, please, Isabel. The second one, I think most of you are already going the way to interact, change to be more conversational rather than prompting. It can, yes, but I think I already see prompting as the interaction in my new mic, you know, because when I specify examples, when I define my language, the way I talk to the AI tool, I feel I'm interacting with my AI assistant. So, I think we're already moving in that direction. So, you just in response to Mike's, Mike's question point here on the conversational tone, and I looked at how others prompt and surprised how many people still use words like please, could you please outline, so they really develop this conversational tone and make it very human-like. And then I keep saying, why bother to write please? But anyway, so yeah, I can see Mike where you were coming from. Yeah. No, what I'm really getting at is, I mean, this prompting, it's based on the fact that the way the human, the humans have built, you know, the question answering and on top of the large language models.  People are, it's an art, not a science. And surely, you know, in the future, you know, a more conversational model which will say, could you make some slides about this? And the AI will come back and say, okay, how do you want me to make the slides? And rather than this weird way of writing prompts, trying to work out how to interact with a black box. And I'm imagining that prompt engineering, people are teaching it at the moment, perhaps by next year, I'll be out the window. But then Mike, it's a bit like what I think a lot of no code, no code environment, isn't it? And it still takes some time, doesn't it? You know, we talked about no code, low code or low code, no code for some time. And we're still, I think, using code. So it's not as when you said, you know, it's, I think progress isn't as fast as we imagine. I think it's only the prompting that will change the shape of it. I think we are already using part of conversational element in prompting already.  As Isabelle mentioned, I think two years ago, there was this hype, you know, no code, no code is meant to replace everything, but people are still coding. So I think it will take a while before it becomes completely conversational. Okay.  So thank you for that. And moving on, Gama, I think, as many of you mentioned, you have used it. So Gama is an app which can be a web app, you know, you can use to generate presentation slides automatically. But you can also do it in chat. So if you know, VBA is visual basic code, which is available in most of the Microsoft suite of products. It's a code, it's a coding language, visual basic. And if you ask chat to give you a code that can generate a PowerPoint presentation about a particular topic. So for example, in this screenshot, as you can see, you know, if you give charge, you can say, give me a VBA code that generate a PowerPoint presentation on a topic of a lesson plan to teach AI challenges and opportunities. I need, and I need four slides again, you know, short prompting just to specify the stuff. Then you will see charge, you will actually generate the code for that. And it'll even tell the steps you need to do in your PowerPoint. So if you mind, Isabelle, I can I share my screen because I want to give the live presentation of this how I have done it. Let me share my screen, give me a sec. So I hope you are able to see the PowerPoint slide. I'm opening the PowerPoint slide here. Yeah, we can. Yeah. So what, so the, so charge it gives me this instruction here, it gives me the code here. I, I just, can you still see my screen? No, I think you know, we only see the PowerPoint. No, no, no, I think I want you to see my entire screen. Just a sec. Because I'll be entire the screen. Yeah, I think I'm on. Can you see my screen? Yeah, I think you can now. So if I go here, and if I, so you go to your PowerPoint, which is like this, and you just say alt F11 here, and that's what I'm doing. Alt F11. So this, this is the, you know, the visual basic window, which gets open, you just simply follow the instructions, click insert. And you're inserting the code here. So you just open here, you copy paste the code. And what does it say in the module you created a new module copy paste it, and then you close the BBA editor. So this is the window wherein you have, so you just close this visual basic editor. Now you have copy pasted the code there and then you run alt F8. So these are all shortcuts. So you say alt F8. And then you can say, you know, this is automatically this module was created when I copy pasted the code. And if I say run, the four slides are generated. So this is really course, you know, but then you can refine your prompt, but you have got at least five or 10 or 12, whatever you specify in the prompt, and it will give you some basic slides. So I can start, go into each slide and start editing and, you know, do more stuff. So I think chat, if it is able to give that code, and you can use that in your, you know, if you want to create a presentation about a basic presentation about any topic. So Isabel, can we go back to the slides, please? I'll stop sharing. So there is a question. Do you need to provide additional information prompt for the slide to be accessible? No, no, no, no, no. It's just that you just asked chat GPT about this code, which is a visual basic code. It gives you the code, follow the instruction, and that's it. You are done really. So you don't really need a lot of coding experience, you know, and you can still play the PowerPoint on that. Is that okay? I did. Has that answered your question? Thank you. I was thinking of accessibility from the user point of view. If you had a screen reader, for example, screen reader, I didn't get that. So the PowerPoint, yeah, sorry. I was just going to answer that. If you're using the standard PowerPoint template, it'll be accessible. Thank you. Thank you. Next slide, please. So yeah, so these were the steps I, as I, you know, just showed in the demo. So next slide, Isabel. Next one, please. Yeah. And the next one, which I have used. Yeah. What about one? Responding to emails, marking. Responding to emails, I think there is this example, you know, paraphrasing. So I don't think I have tried with any AI, automatic AI respond to my emails yet, but it's really good in terms of paraphrasing and, you know, in terms of writing nice emails, I've tried that, but not automatically I have tried marking. So then it depends on your institution. And at Warrick, we have a clear instruction that we do not use AI for marking. We also, lecturers are not allowed to upload anybody else's assignments onto any form of generative AI. So we have a clear no for marking policy, no for AI for marking. And of course, your institution might handle this differently. But I would go with your institutional guidance. And I generally think at the moment, and from my work on using AI for formative purposes, it is, it is, you know, we always discussed about automating versus augmenting. I do think in the long run, we will be able to augment the marking process. But I do think it has to be a collaboration between the human marker and AI that actually work together. And AI can make the marking process quicker. But I don't think AI is that they're yet to actually and accurately provide a mark that students believe they get based on the teaching you have provided. So and we are very happy to discuss it. And one of the teaching cases I wrote was actually using AI for marking. And if you look at it, it's even, even if the percentage difference is small, and the stakeholders, the students, the greater public would not approve of an institution to move marking to AI. And last point on this, I do think as a first step, we have to differentiate between low stakes environment, let's say, assessments that are worth less than 20% or 20% less of the module mark and high stakes environments, which is like the main over 80% of the mark. I do think if you want to start using AI for marking, we have to be upfront with students about it and develop something in a low stakes environment, rather than just lectures, trying to upload essays that are worth 80% of the students mark and then coming up with the feedback and marking at the moment in a non structured way. Just on that, there is nothing stopping a student putting the marking rubric into AI and uploading their essay and seeing how it marks it according to the rubric. Absolutely. And then that comes back to using AI to actually improve, because then you students use it to engage further with their assignment. So absolutely, students can do it, but for us lecturers to do it is next step. So I think I responded in the response was for marking, I do think students should use AI to get forwarded feedback. Thank you, Isabel. I think, yeah, so the next one, another tool which I have used and I found it, I think just, yeah, sorry, Isabel. So there is a, you know, a YouTube link if you want. I think in the interest of time, I would not like to give a demo, but let me just very quickly share my screen. So this is a speech texture actually, yeah, so this is a speech texture. And if you see, if I press, So we, at the moment, we cannot hear the sound, but normally you would hear the sound and then you can see how the video gets straight away transcribed. It's a bit like when we use teams and we allow transcription, so that's similar speech texture. So yeah, so there are online tools. Thank you, Isabel. There are online tools available, you know, which can help you write some lecture notes, you know, if you are hearing a podcast or, you know, and there are important points, which you think you might need to include it in your lecture to share as an example, or as an industry use case with your students. And if you're not using teams, for example, or so there are things like speech textures, which can help you generate text to do it. Okay. Oh, that's good to know. Thank you, Mike. I didn't knew that Google Chrome has a free extension. So that's an excellent share of knowledge, you know, which can allow to do the similar stuff. Isabel, back to slides, please. Thank you. Yeah. Next one, please. Yeah. And then it's part, it's coming. This is then excerpt from our report, you know, we have shared a lot of public tools as on, you know, a month ago, or as of today, which we are available out there in the market, which can be used, you know, to enhance your education practice. There are some people to follow on LinkedIn, which I personally follow, John Betty, where I is one group, super human, ethylmolic Wharton professor, Wharton school professor in United States, he's quite active in sharing his voice with regards to whatever, you know, new progress is happening in January day where I may be following his LinkedIn page and AI French triad is another one. So these are the four ones where I see, you know, a lot of top voices on LinkedIn and what is being conversed on the generating AI piece.  So, you know, that can be a good idea to follow similar kind of influencers on LinkedIn pages. Next one, please, work, business school, the research cohort from ISMA information system and management analytics group to which myself and Isabel belong, you know, our PhD students came up with this cheat sheet AI for research. And here we share some of the links, which are there, you know, or some of the tools which can be used for literature generation, analyzing the literature, you know, literally guiding your research forward if you are in process of writing a paper and how you can utilize this one. So all that knowledge is captured as part of this cheat sheet. Please don't go with the word cheat sheet is, you know, a vocabulary which is used a lot of time in the science and in technological domain, you can find Python cheat sheets, Python is a programming language or our cheat sheet, see, people just use it, that it's a summary of concepts in one page, you know, and someone few years ago came up with the term cheat sheet and you know, just following on from then, but it's basically a summary of important concept on one of the pages, how you can get it done. What is what policies on using AI tools? Isabel, would you like to take that question, please? Would you know? So actually, so the policy for students, they have to sign, you know, their documents and the thing is, it's a bit like before you could have somebody to proofread your documents. So I do believe that at the moment we cannot, we know that there's no way that we can actually check and students writing that, you know, all attempts by the sector and not just all attempts by the sector to detect whether students have used AI, F and failed. So what we say is, you have to make sure that your assessment is authentic enough and provides them, is measured by things that do not depend on rephrasing using any form of AI. And so the policy is that actually we don't know if students use it and they have to sign documents where they might say that they haven't used it, because you remember I said that Warwick has a three prompt approach and lectures can actively encourage students to use AI and lectures can accept the use of AI or lectures can forbid and if lectures forbid the use of AI, it's up to them then to try to devise assignments where they can detect the use of AI. But generally the policy is so students, students, you know, we cannot deter students from doing so because it's just non-detectable. But one thing I find is that actually we worked on the same research because in May I had encouraged students to use AI for their assignments and we found that most students just use it exactly for the rephrasing and for forgetting the wording. But actually what students are assessed on is critical thinking for example. But critical thinking didn't come across you know if you just get things to rephrase you don't get the high marks because it's not going to show and demonstrate critical thinking. So what we have to do is to say to students look you can use AI to rephrase but make sure that actually you bring in your own IP and your own ideas to actually be able to demonstrate critical thinking. And so let's see the thanks Nia for sharing the link. So that's it from my side. I think you know I just wanted to share some examples which where I have started getting engaged with some of these AI tools and obviously there's more to it and fellow colleagues might be using it in a different way or similar ways. But it was just you to share what we have been doing here at Warwick and share it with our colleagues based on the practices we have adopted in our teaching curve. So over to you Mr. Ben, that was it. So the last part was on the tool and we can briefly discuss it afterwards but first to the respect to the question on the effect of the use of AI tools. So when I developed this AI formative feedback tool the students who used the tool and based on you know some analysis did six percent higher mark. So if students using the tool beforehand gave them a higher mark however we do have to differentiate between correlation and causation. So it's maybe not causation maybe it's not the students who use the tool that get a higher mark but it's mainly the students who are engaged in their learning who would have got a higher mark happen to use the tools and thus got a higher mark than other students. So let's be careful between correlation and causation. Absolutely Mike and equity issues yeah and but interestingly and I am you know those who have the paid version on charge gpt and actually and if you if you use a combination of let's say Claude and Bard and the free version you can actually receive actually have quite good support without paying. And yeah we as a school actually look into those things that we do look at it but at the moment we don't provide everybody with funding for getting charge gpt forward but I think they would be not right either because as I said Claude and Bard do offer alternatives so why would we go with charge gpt just because it's at the moment the paid version is better than the free version there. Any other questions and then I'm conscious that it is that before that we want to finish on time let me just briefly show you some slides and so this is and we have done this formative feedback tool and and the idea here is to bring different different parts together processes people and data and it's an additional report and it uses various form of technology it doesn't use gpt it uses bird as a transformer and the reaction of staff was mixed and AI ethics is important for us and we have the advantage that we actually know that the data we collect is not kept anywhere but we delete everything so we really really proud of this tool and it works as a formative tool so it's not involved in high stakes assessments as I mentioned before I believe that actually we have to try things out in a formative or low stakes environment first here you get the presentation that the type of features their students get and it's well perceived by students students appreciate it and the environment the IT environment might not be necessarily there to actually use AI tools in full yet but this is maybe to come so that was in a nutshell the part of formative feedback tool I did previously a presentation as part of an edith group on the formative feedback tool so you might have heard me talk about it but it's just to say genitive AI is not all and there are other forms of AI that are important and that helped us to democratize education and especially by providing a tool such as this which is free for all students we are bypassing the equity issues we've discussed before so um that's really the end so any last so any last questions otherwise we finish five two on on time any questions can you guys if I'm so I didn't try I didn't try exercise yet because I teach topics only with a word and I finish using mainly um qualitative research you know I haven't tried it either but I think I'm tempted you know when Michael says you're converting excel to csv enough to plot I think I'll give it a go Michael getting some ideas from here you know I haven't tried it as yet the big issue generally and this is last my last point is and then it's always very difficult to pitch AI talks at the right level it's it last time we did you know and I did something internally and we I felt we pitched at a too high level this time we and now I think actually with you as an audience we should have actually made it you know pitched it higher it's very difficult to predict and thank you for staying with us thank you to Isabel and Niha for this really engaging talk I think lots of conversations in the chat so thank you so much and everybody have a really good afternoon thank you bye bye thank you thank you very much thank you all thanks for listening thanks for having us


Well, I'm Jim Garvin. I'm the chief scientist here at NASA's Goddard Space Flight Center. Great job. My job is strategic. We have a large workforce of scientists who study the cosmos, the earth, here at Goddard, and my job is to help them form strategies for new science questions to ask, new missions to fly, new experiments to develop to help NASA do its job to explore really this universe we live in.


Okay, so this next agenda item was actually only scheduled on late Thursday afternoon. It was originally going to be the introduction to the closed session discussion of the review of the sequencing programs, but when Adam ran through it of course to practice, it seemed to me that what he was saying in terms of the history and current status of the large scale sequencing program was actually be pretty useful to talk about in open session. So what the presentation here is going to be, where we've come from with the large scale sequencing program and where we're at right now, and that will be basically the introduction and then when we go into closed session, we can start right away with the discussion of the applications. Adam? Yeah, thanks Mark. I'm just going to talk a little bit about the, I want to qualify this as recent history because there's obviously a lot of history to the program and it's a fairly high level history mostly to provide context, some background and context for all of you leading up to the discussion in the closed session. So you all probably have seen this. This is the recent history of decreasing cost, especially in the last few years. It's gone down quite considerably and because of the new platforms, but it was going down before that. This is a log scale. This is, I just want to point dating all the way to 2001. So we all know about the decreasing cost. That has allowed a reduction of funding over time and the NHGRI large scale sequencing program has tried to reduce its total annual funding over time and this graph only goes back to 2004. If you went back to 2001 or 1999, it would be closer to $200 million a year and this isn't even inflation corrected. So it's gone down over time. It's been fairly steady in the last funding period, but of course there's still increasing capacity. Again, everyone knows why this is and we expect roughly the NHGRI program output to be in terms of terabases, 350 terabases for the year of 2011 and this projection is holding true and it's based on only actual data for quarter one and quarter two of this year. I should say that looking at this hides some things. It hides the sort of kind of sequencing that's being done over this period and the kinds of changes that have happened in the last four or five years. Of course the human genome project provided and the related model organism projects provided a single focus for a number of large centers to do and as capacity grew, those centers were still working cooperatively on some large projects but at the same time were diversifying the kinds of projects they were doing and taking on multiple projects per center and that had a lot of consequences for the science, great consequences for the science but also consequences for the program was managed especially in terms of finding the good projects to do and finding good samples to fulfill those projects. So there have been many accomplishments. I can't talk about any of them in detail but just to summarize at a very high level over the last four or five years. The first three highlight kind of medical sequencing that's being done that wasn't really being done five years ago at large scale. So cancer sequencing with glioblastoma ovarian and another 20 in the pipeline, at least complex disease studies for diabetes, autism, cardiovascular, these were unapproachable five years ago pretty much because they require thousands of samples and during this time these kinds of projects have gone from, they've constantly had to change as capacities increase. So first these all started with the idea of doing 100 loci in thousands of people and then that was then whole exome sequencing came along and then it's whole exome and now people are talking about whole genome sequencing. Mendelian diseases first started as sort of mapped but you didn't know the genetic lesion to now completely unbiased or at least whole genome unbiased, I mean whole exome unbiased. Variation resources have really taken off a thousand genomes and all along still many, many organisms are being done in the last five years although I would say there was a little bit of a hesitation until recently because the new short re-technologies aren't so great at assembling de novo but there's a lot of population genetic models. Insect genomes and fungal genomes certainly have been amenable for a while with the new technologies, et cetera and also increasing addition of other modalities of sequencing and then just genomic sequencing as additions to some of these model organism projects and of course metagenomics is beginning to take off in the last four or five years. The current breakdown as of the second quarter of what is a year eight of this year, fiscal year of the grant funding is roughly this of the different kinds of projects with the bulk being 57% being cancer, about 20% being medical sequencing and 7% being organism sequencing and thousand genomes coming down from a much more substantial proportion is now a bit less. In addition to the achievements of the program, scientific achievements, we think about a lot of the benefits of the program. Benefits is probably not exactly the right word but there are other things than the production of papers and scientific results that come out of the program. There's the most obvious which is building community resources of extremely high value but also disseminating tools and technical know-how, promoting a data sharing ethic for genomics. This is something that's often cited as maybe the most lasting contribution. I've heard it cited as that. Leading standards for formats and quality, at least debating them, I think probably the first, the largest center of mass for discussions about what these should be and these are becoming increasingly important especially as lots and lots of groups want to contribute data to public resources and pioneering new project types. For example, whole exome capture, approaches to whole genome sequencing, analysis approaches. This entails a lot of feedback between developing the new methods or adapting the new platforms to new analysis methods to new capture methods to new upstream and downstream. Some other benefits of the program that are sometimes undervalued so improving and maintaining the reference sequence and I think the notion of the reference sequence is getting more complex as we go forward and are going to try to interpret clinical sequence data. This is going to become, come to the fore again, I think, and generally finishing refinement which is not done in a lot of places and it doesn't get much attention when we often when we talk about the program but it still is an important capacity that we have and maintaining the commitment to for high quality especially as new platforms come online with an eye towards those platforms should be able to reach high quality as well. And again, this is increasingly important as we start to understand the role of structural variation in disease. So we're next setting up for the next four years, two pieces of context, one is the strategic plan for genomics that you all know about and I think you saw the slide in the previous presentation and the other bit of context with the results of the large scale sequencing planning workshop that happened in 2009 that led up to the new set of RFAs. This is the diagram and just across the top is this direction from basic to more applied for medicine and where we want to be and where we think the field is going in general. In the next 20 years or so, 10 to 20, 10 years, we don't have so much time as I thought. So this is what we did in response to that. So here's this axis across the top again of where we want to be and tend to be on 10 years. Here's the sort of single mode large scale sequencing centers. We're proposing to shrink it and add these elements. So this is going to shrink down to about from $110 million a year down to $90 million a year and make room for these three additional components, Mendelian disease sequencing centers, clinical sequencing exploration centers, and sequencing informatics tools development. And these are the different RFAs that you'll hear discussed in turn during the closed session, actually in the order of this one first than this one and this one and that one. So just in words, large scale centers at 90 million a year, continuing benefits of large scale. They should be flexible, involve resource development, undertake major projects that require scale or have other unique features that requires this kind of an organization. Mendelian centers, the RFA was written for $10 million a year to focus to organize identification of all variants underlying Mendelian disease. The clinical sequencing exploration centers, the RFA is for $5.5 million a year to identify requirements for routine clinical use, essentially by pushing trying to do it and learning and figuring out what those lessons are, that's the exploration part. And then finally sequencing informatics tools because during our rounds of advice, we got lots of advice that if sequencing is ever to be democratized that people need ready to use robust tools that we don't have. So this is the new funding picture. The program as a whole will stay at about $110 million a year. The large scale centers will go down to about $90 million a year. And that's the whirlwind history and intro. I want to thank all these people. The sequencing team has grown with the number of RFA's that we're trying to, programs we're trying to put together. And with that, does anyone have any questions? Yes, Howard. Do you have any metrics? There's always those beautiful slides on the cost per genome, cost per variant, that sort of thing. Are there any, is the same thing happening on the analysis of the genomes? No, we have a really, I think, kind of poor handle on what it should cost. And even what it does cost. We know for certain projects, we know what the analysis costs are. But what we haven't done is, especially for different kinds of projects, identified consensus endpoints so we can start to understand exactly what costs are. But I think that's something, a very high priority for the next phase is to know what the informatics costs are. We know what data storage costs are, roughly. But the analysis, again, there's not a, there's a lot of churn about how to do it right. There's a lot of funds that haven't going to go capitalize the development of it, because people are still arguing about what the best way to do it is or what mix and match of the current programs are. And again, we haven't defined some of these endpoints. Bill? So, Adam, this slide with the increase in size of team sequence. With the increased capacity, with lots of collaborations between different institutes, from your point of view, what are the challenges for staff in terms of overseeing all of these projects? Yeah, so the biggest, the biggest is, I think, knowing enough. So it's just like Howard mentioned, keeping track of everything. I think what we have a pretty good handle on are what the best opportunities are for collaborations around sequencing with other groups. There's what Terry talked about. Mark has undertaken some prior efforts to understand what the sequencing universe was. So I think for the large projects we know about, the difficulties are going to be, as the program democratizes or as it shrinks in funding, being enough of a center of gravity to set precedence is going to be harder and harder. So I already noticed, for example, in the area of setting, trying to set standards for clinical sequencing, that there are many, many groups independently organizing to try to set standards for clinical sequencing, just technical standards for how good the machine output has to be. And those are kind of the things that we think are probably pretty important for us to have a real say in, but we're only one of a number of players now. So I think that's very hard. And again, as the program shrinks farther, it's going to be harder for us to get up and say, well, these are the standards we've developed and everybody else following. So as the NHGRI commitment shrinks or holds, is disseminated into different areas, what about the rest of NIH? How does the total sequencing budget from NIH look? Yeah, it actually looks reasonably good, but it is directed at specific disease areas, as you might expect. So there's a lot of work in cardiovascular disease from NHLBI. NCI has put in additional funds for cancer sequencing. NIAID has some. And there are others. So in terms of dollars, it's pretty good. And it's certainly at least maintaining the current, or the previous is probably another 60 or $70 million a year or something like that. But again, their focus is on, is not on capacity building, it's on finding the answer.  It's probably, it's under $35, it's around $32 to $35 million. I have some more figures in the closed session. But it's very hard for me to know what happens in out years. It's, I just don't know. And I think we can come back to these kinds of issues in closed sessions, because once we've figured out, once we've discussed the individual applications, the individual RFAs, we're going to look for counsel's advice on the structure and shape of the program going forward. Okay, thank you, Adam.


So we can start now. Hi, this talk is about how to create beautiful cloud-native landscapes So my name is Kristen Berndt. I'm working with OpenStack for over 10 years and We are 23 technologies. We were founded in 2021 by myself We are cloud-native experts located here in Germany and we partnered About a half year ago with Klojera previously known as city network to build and run their managed Kubernetes Service that should be available soon. We hope so and We are also preparing managed Kubernetes for Some other CSPs here in Europe and we are also working together with Audi and Bosch and Trump and some other bigger companies in the Stuttgart area on software-defined Manufacturing to be able to bring cloud-native technology to the to the OT to shop floors and so on This talk about is about where are we today about cloud-native workload and That is normally running today on And Kubernetes layer that is running on an infrastructure as a service layer Sometimes OpenStack, sometimes other solutions Sometimes based on OpenStack, but normally on top of an infrastructure as a service layer and There's a problem. So the problem is that we want to deploy a lot of Different cloud-native workloads and there are a lot of ways to deploy the cloud-native workloads on top of an Kubernetes layer and there are also many ways to deploy and maintain and to operate on Kubernetes which runs on infrastructures that are all different and The problem is now that it's really really hard for an an user of an application to provide their software on top of an Kubernetes that is different everywhere and We we we tackle this this issue sometimes and then we decided to fund the company to make this easier and We we try to make Happy users in the end so that an operator can deploy their workload their applications on Top of the same Kubernetes everywhere So our goal is that we provide an Kubernetes cluster that looks the same on any infrastructure out there and to be able to do this we choose to Use and Kubernetes as a service layer as a layer between the Infrastructure and the Kubernetes cluster so that we do not deploy directly the Kubernetes cluster with some tooling like Cops or QBADM or something like this on top of the infrastructure. So we have an Control plane between and Kubernetes as a service that manage the infrastructure in a universal way So that the infrastructure is still different, but that's fine now because the now happy user does not see the underlying infrastructure and To be able to do this We use an universal Kubernetes at scale Developed by SAP as an open source project. It's called Gardner The target of the of Gardner is to establish and control plane that is multicloud aware so that you can Deploy Kubernetes clusters on a lot of different infrastructure as a service on-premise and off-premise on Multiple clouds on Ali cloud AWS on Azure Google heads now equinix open stack bare metal VM where it doesn't matter in the end so that the Kubernetes clusters look the same everywhere so that the workload does not know the underlying infrastructure and it's based on infrastructure resources provided by the Infrastructure providers so that you have to use standard resources. We like do not rely on AKS or EKS or something like this We simply deploy our own the mware resources on Network resources or on storage resources on you put to or on garden Linux or on chorus and then we run an Control plane on top of this and this way we are able to manage thousands of clusters with one Solution so that we have an fleet management with a minimal TCO So it's possible to operate thousands of clusters in the end with one FTE and This makes it possible that an small CSP can offer and high-quality managed Kubernetes service without Yeah, the hassle to implement it itself with Terraform or Ansible or something like this How does it look like? We have a central gardener control plane that is running somewhere It can be on the same infrastructure. It can be on in other infrastructure So it's just in Kubernetes cluster which has the API server for the gardener itself the dashboard and so on and then we have Infrastructure providers and for example AWS and for each region you deploy and so-called Seat plane and the seed plane has the control plane for the so-called shoot clusters So a shoot cluster is in the end in Kubernetes cluster for a customer. They can access the shoot clusters via the Cube config and can do what they want to do there and the seed plane has the API server and the Every required service by the Kubernetes and and this way it's possible to have an and shared a control plane for a lot of Clusters so you normally have around 200 clusters for each seed plane and This way the regions are independent from each other So when the control plane dies or one of the region dies It does not affect the other running seed planes or when a shoot cluster dies It does not affect other clusters. So in the end you can spawn a lot of Shoot clusters that can be consumed by your customers and then you they can run their workload on top of it And this is also possible to make an hybrid Deployment so the seed plane for example We have an public demonstrator of on the gardener and their deceit plane is running on the Azure cloud and the shoot notes Are running on the Hatsner cloud so you can combine different Cloud environments to to have different SLAs or to have an on-premise control plane and off-premise Clusters to allow a bursting that when you have a large open stack environment on your side And then you want to spawn additional resources in the AWS cloud then you can do this by simply adding a new cloud profile to the to the control plane And the inside is that you have on the control plane running all of the required Kubernetes services like the API server or the scheduler or the controller or the manager inside Kubernetes cluster itself, and this is the so-called seed cluster and on top of the seed cluster you add then Additional services required to spawn the shoot clusters so that you can control the underlying infrastructure via Kubernetes itself or the machine controller via the outer scalar and and so on and This way This slide is broken Yeah, and then you can attach to the the seed cluster now contains the control plane for Shoot cluster and the shoot cluster is the workers for a customer and you can simply Attach then the worker clusters to the shoot to the seed clusters And then you have an so-called shoot cluster and the shoot cluster is then for the for the customer and the The shoot cluster cannot access the seed cluster So this way it's safe that the customers can only control these things that are running on the shoot cluster and on the workers and they cannot Join other customers or something like this via the seed cluster and then you can control everything with This why the Gardner cluster API on top of this via an API or via in UI or you can simply write in Qt manifest and define your Gardner cluster there and Condense on your Kubernetes clusters and this is the central entry point for the users or For the CI processes or stuff like this Okay, so this is the end of the presentation. We have them. I think a few minutes left so we can have a short Demonstration when my internet connection is working Hopefully not like the last time so I have to check Okay Yeah, so this is the The login so I have no So you can simply look in there via and GitHub So there is an dex proxy so you can integrate whatever you want and then you can access the UI of the Gardner and that's all in the end what you can see there So you have them clusters running somewhere and here you can see where this running This is the so-called clothe profile and here you see the versions and the querying readiness and the heels of the cluster And there is one dialogue to create new clusters and and here you can see the different Cloud profiles for example for AWS or Azure or Google or Some open stack based clouds so you can add simply you'll say I here's the keystone endpoint I have this and this image in those flavors available and then you can just click together your cluster You can also do this via Cube config file so you just have this definition and then you can give this to the Kubernetes API server of the Gardner and then you can spawn the cluster and that's all in the end So we can also I have to to move a little bit to see it. That's not so easy Yeah, something's Yeah, it does not work this way so I cannot see anything here but but you just have to to you can just create this cluster then and in the back and then there's running and reconciliation process so it creates the required control services in an namespace in the seed plane it spawns the Kubernetes API service and and so on and so on and then it will start to create the required infrastructure resources on the specified cloud profile and it will then attach the infrastructure resources to the seed plane and then the Kubernetes is ready So it takes around five minutes at the moment to to spawn a new cluster So it's not optimized and it heavily depends on the underlying infrastructure So when your infrastructure is slow, it will take a long time to spawn the cluster then you have an fast Infrastructure it will take only a few seconds or minutes. So we try to to optimize it But yeah, you have a little overhead To to spawn the first the network and then the router and then maybe you add the load balancer to it And and so on and so on. Yeah, but I think this way it's possible to Yeah Spawn a lot of Kubernetes cluster on an infrastructure without Caring about the underlying infrastructure in the end because everything is Consolidated in extensions. You can add new infrastructures when they're not yet supported. For example, we added the Hatsner cloud extension so that it's not possible to run in Kubernetes cluster on Hatsner cloud as well and it's not a big deal to to add further extensions there. Yeah So, thank you for being here and for your time


The first regional transportation council meeting meant to discuss transportation challenges in Beltrami County. The council aims to advance transportation opportunities in the region and to improve mobility for the area. They want to work with public transit, non-profit organizations and veterans. This region has a pretty good system already, but there's gaps. You know, there are people who can't use the bus because they work after hours or the bus doesn't go to where they work. Currently they're working with transportation providers to make it easier for people who are unable to get rides to get to their destination. So is this kind of a group or a little council agency to help kind of determine how we can get that those issues solved? Three other outreach meetings are being held across the region to garner transportation feedback from the public. Anyone is welcome to attend the meetings. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


I want to cover another topic on blogs that we get asked quite a bit, which is how often do I blog? Okay. Now, I will say as a general rule, the more content the better. Google rewards multiple blog posts. They want more content because they don't really care which website they show on Google when someone Googles a keyword. They just want to show the best website that answers their user's questions. And you can a lot of times cover a lot of the keywords and build authority on the keywords that you want by growing that blog feed. So that still doesn't answer the question how often do you blog. So I would recommend blogging four to six times a month at a minimum up to two to three times a week if you can blog that often. The main thing is also is you don't want to be posting your blogs like a robot. Google will catch up on that. You don't want to kind of every Tuesday and Friday post a blog. You want to kind of keep it organic. Keep it moving. You can post one, sometimes you can post three in a day. Just make sure you're around that four to six a month if you're trying to aggressively blog.


A pilonytal sinus is a type of infected tract located in the skin, usually containing a tuft of hair. It is often found in areas of the body where there is friction between the skin and hair, such as the buttocks or axillary region. This condition is more common among men who have excessive amounts of body hair. When left untreated, this condition can lead to severe pain, swelling and infection. In some cases, it may even require surgical intervention. Our patient presented with a recurring infected sinus on his anterior chest wall, which had been previously treated by other physicians. After careful examination and exploration, we were able to identify the source of the problem of pilonytal sinus. This condition is not uncommon and should always be considered when evaluating patients with long-standing sinuses over hairy areas. This article was authored by Naya Sumro and Hira Purbas.


You can 3D-print your own case for the Adafruit Micro-Lipo Charger. This features Fival input for charging single lithium-ion or lithium polymer batteries, and it ships with a free JST cable. The new V2 features a built-in slide switch so you can easily switch between the two charging rates. The PCB has pads for USB, so you can plug it directly into anything with USB type A. It features charging status LEDs so you know when your battery is fully charged. This supports all of the Adafruit Lipo batteries so you can safely swap between the different types. For all of the technical details, be sure to check out the product learn guide. 3D-print the two halves in your favorite filament. You can use translucent PLA to make it look like those clear enclosures from the 90s. The PCB sits over the bottom half, and the top snap fits over the sides. The case features a cutout for the built-in slide switch so you can still access the charging rate. The JST connectors are covered to help keep out any dust to provide a bit of protection. We hope you enjoyed the new Adafruit Micro-Lipo Charger and 3D-print the case for your next project.


Hi. You want to carry it? Here we go.  These animals came from the evacuation areas that were put in place by the Sheriff's Department in conjunction with the Fire Department. Everybody's just been amazing. The only reason I evacuated was because of them. They're that important to me. I wouldn't leave them. Come here, sweetheart.  This is little man's full brother. We had topped out at 580 animals between all the different livestock from goats, donkeys, chickens, geese, pigs. It's a great feeling when they get to leave the property. When you reunite the owner with their animal, people have asked me, why are you doing this? Is it worth not getting any sleep? Is it worth not getting any sleep? Absolutely. When you see these people reunited with their animal, nothing can beat that.  Horses are animals. They're not people. I understand that, but they're my babies. I don't have children. I have horses and dogs. And they love me so much.


Welcome back to Every Way Woman, so we're talking social media, and I want to have a discussion on what Facebook can do on your body image. I see you working out on Facebook all the time, Felicia. Yeah, gotta work that sweat off. But I do find it motivating.  Really? Are you kidding? Why? Why do you feel bad about seeing her work out on Facebook? Well, no, just in general, I think a lot of times it's easy to compare yourself to this idealized version of your friends or the people you're following, and it can really have a negative start to my day. I stopped even looking at it before I got out of bed, because I thought, you know what, I'm gonna start my day me and not compare myself to anybody else. But I think a lot of women are dealing with the same thing. Am I wrong? I mean, what I will say is that I think it's, I like being on Facebook because I like seeing what all my friends are up to, all of the great things they're doing, traveling the world, and even if someone looks better than I do, it's like motivation for me to get my butt in the gym tomorrow or that morning, you know? And then if they're doing like new beauty, you know, they have a new hairdo or makeup or something. Like Mariana? Yes!  Well, the first thing that pops into my head is regarding body image. Sometimes I don't even believe what I see, even on Facebook. A lot of things are Photoshop guys, like, you know? It's true. Everybody can Photoshop and filter and take away a little mark here. For a transfer, XX Pro, I look a lot better with my hands. I'm a lot thinner. But I can relate to you a little bit. I mean, I think, depending, like when I was younger, I probably would have looked at that, like, threatening, you know, looking at pictures of models of, you know? Well, and what's interesting, it's not even Vogue anymore or any of these other high fashion magazines. It's like looking at these hot friends from high school, you know, what happened to their bodies and what they've got going on that you might not have. I totally have this deer in the headlight look.  Because I'm struggling with, I don't do a lot with Facebook. I know I should. I do for the show, but for the body image for my daughters, I just think that it's... Well, truth be told, my daughters don't do Facebook because they say that's so passé. It's Instagram, but the pictures are still the same thing. Okay, sure. And they say most of the time that those pictures are not real. People have Photoshopped them. They'll put somebody else up that is not filmed, kind of like that catfish thing. So you feel like the next generation is very aware of, you know, how fell these things can be? Yes, exactly. And I think that they just put it up for the moment. So for someone to feel insecure about that, I don't know, because I don't think it's really true half the time. Not to be wrong, but I don't think it's really true. I mean, I have met a lot of girls that I follow on Instagram, and they're beautiful. This one girl has like 200K followers because all she takes is selfies. Everybody, huh? Right? You know what's interesting now? On that note is that I've seen a lot of girls with eating disorders on Facebook, on Instagram. They're hashtagging ProAnna...Anna, they call it. ProAnnaRexic website. Hashtag BikiniBridge, where it's literally, you know, the bridge over your hip bones on a bikini. That's dangerous. Okay, so let's follow with that. Are they posting actual pictures of what they look like? Actual pictures of what they look like to inspire these other girls. And instead it's feeding into literally eating disorders. It's feeding a lot of followers. Absolutely. Millions and millions of girls. Young kids think it's cool. I mean, think it's hot. In college, one of my college best friends was Anna Rexic because of who she was following on Facebook. She's like, look at this model.  And then she started doing the same thing.  It's the society we live in, I think. I mean, we have to be careful. I think with our kids, I don't have kids, but I know if I had a kid, I would be very hesitant in getting them on Facebook or social media because of that. But you know, the body image places all over the place. We're talking about Facebook, but it's everywhere. But don't you think social media just kind of takes the next step? Well, yeah, because that's what's in front of the young people now, is that Facebook, Instagram, Twitter, Snapchat, Vine, all these things. I do. I have to. I got teenage daughters. But at the end of the day, the body image conversation, I think, comes from at home. And I don't necessarily want to stay because we're talking about Facebook, but it starts at home, and I think if you take care of that, it will cover all of those social media outlets that's portraying where the body image should look like. Right, because me right now, I wouldn't even be endeared into anorexia on media. I want to see, you know, the most popular fitness program, the most popular trainer right now and follow them in their work ethics and what they're eating. That's where that comes in handy, what they're eating. What they're eating. You know what they say?  Yeah, because there's a lot of nutrition ways of eating. Like, people fight. I didn't even know the whole thing of juicing. My sister started juicing. And she juiced on her Facebook? No. I'm not juicing yet, but she is. And she found it through Instagram. Okay, well, find some of Anna's juicing tips online. We'll be right back with more Every Way Women. Stay tuned.


I'd like to first thank the organizers for the opportunity to present the latest or recent and ongoing work from our lab on the integration of multiple data types for virus discovery. About 10 to 15% of cancers worldwide are caused by viruses and are several virus types which we know which are associated with cancer. The role of others are controversial. One of the things that viruses do is integrate into the host genome and whereas others remain epizomal and do not integrate. One of the questions that we wish to address in this study is what is the role of integration in gene expression and also how is integration associated with gene initiation and progression? It's a broader question. Then more broadly yet the questions we wish to address are even though many types of viruses are ubiquitous in the human population cancer is not. So a large question is why do some people get cancer and others do not? The TCGA data provides a rich source by which we can start to address some of these questions. This is a graph of samples which are available for various diseases with various data types. For this particular work I will be focusing on four different types of cancer. All of these have viruses known to be associated with viruses to one degree or another and we will be looking at bladder, cervical, head and neck as well as stomach cancer. So a brief just overview of the particular pipeline that we have developed for this work, we start off with RNA-seq data which is aligned to a human reference and we extract the unmapped reads which are then aligned to a using blast to a large virus database. This then allows us to, this is the virus discovery part of the analysis and it allows us to form a small select virus reference which includes just the viruses of interest. We then have, we start with whole genome data which is initially aligned to the human reference and we realign it to this select virus reference which includes the virus and the human reference and also for the cervical work in particular we use cervical sequences which are aligned with, created with HPV probes and are aligned to the HPV reference to start with and we use these data to query viral integration. So just to give a broad overview of the viral patterns that we observe in different cancer types, in bladder cancer only about 5%, only about 5% of the cancers are associated with virus and these are the human papilloma viruses, the various herpes viruses and also BK polyoma whereas in cervical cancer nearly all cancers are associated with some form of HPV. For head and neck about 85% have no virus with HPV 60 and 33 being dominant and then stomach cancer has about 10% EBV. So to analyze the integration we use a reference that has human genome as well as the select virus and typically reed pairs will align to adjacent locations in the genome near one another whether it's human or virus. On occasion though we'll find a discordant reed pair where one of the reeds maps to the human and one of the reeds maps to the virus and it's these discordant reed pairs that we then analyze in order to look at for evidence of integration and in particular we take the coordinates of the the genomic position of the human reed as well as of the virus reed and we construct a coordinate of one point which corresponds to one discordant reed pair. We then look at a number of such of such reed pairs and clusters of such discordant reed pairs then correspond to evidence of viral integration and these can be identified both visually as well as using tools such as Pindell. So looking at viral integration we find that for EBV viruses we there are we haven't found any integration events. For viruses for HPV 18 in all the cases we looked at we did find integration. For the other HPV viruses about half to two-thirds are integrated. So for the next few slides I will now drill down into a specific sample and analyze that in further detail and this particular sample it's a head and neck sample which has 118 discordant pairs which map to three different clusters and these clusters map to human chromosome 14. We can now map this onto the the gene and this cluster providing evidence of integration is on the rad 51B gene which is a DNA repair gene which has been associated with virus integration in the past. Just to give you a sense of scale this is about the viral chromosome the viral genome is about roughly 8,000 base pairs this this I'm showing is a section of about 150 kilobase pairs in that dimension. This particular gene extends further out in both directions and there is one exon which is which is visible here with that location and the integration takes place in the intronic section. We can now you might have to turn your head to see this right but we can now look at the the copy number along the chromosome 14 and what we find is that outside of the integration region the copy number is is is roughly two. Within this particular section of the integration the copy number is is about 15 and then there is a smaller section here where the copy number is about four. We we can draw lines to guide the eye from the discordant read pairs to the to the chromosome and what we find is that the the discordant read pairs map onto very precisely onto boundaries in the in regions of the copy number and we find this to be pretty basically very commonly seen and we find copy number increases in integration sites in about two-thirds of the cases. For the remaining one-third of the cases we see with about equal probability copy number decreases or no copy number changes. We can also look at the copy number of of the virus and this scale here is about copy number of 30. One of the things that we observe is a is a deletion of a section of the virus. The the circular HPV virus as it integrates into the host cell DNA very frequently loses a section as it integrates and this is the last section that you see here. The the the copy number is a little bit more a little bit more noisy than we observe but for the human chromosome but once again the the discordant read pairs tend to map onto regions of differing copy number sections. We can also look at constructed histogram of the copy number and in particular I'll be looking at the blue line blue histogram here corresponding to the virus and what we find is that there are two distinct peaks in the copy number histogram and this suggests that there are two distinct viral populations which are found in this sample. We can also look at the the viral genes located here on the horizontal axis as well as the RNA expression of these genes and what we find which is commonly seen is that the E6 and the E7 viral oncogenes are expressed at a high level with the remaining genes being at a relatively low level and we can also look at the the expression of the rad 51B gene over here on the left is a is a distribution of the RSCM expression level for here for tumors a population of head and neck tumors and here on the right a population of head and neck normals and for this particular case that we're looking here the RSCM expression is the gene expression is is very high and we can actually look at more detail at at the expression patterns here is now a plot of per exon RNA expression for the 15 exons which compose the rad 51B gene and in red is the the per exon expression level of the particular sample we're looking at and then the teal color corresponds to the other head and neck tumor samples under consideration and what we find is that downstream of the integration site the the expression level per exon is very high for this particular sample whereas upstream it tends to be normal so this provides evidence that the viral integration up regulates the downstream expression or the expression of the downstream exons to look at this in a little bit more a little bit more detail here is now essentially the same data but I'm plotting the a normalized per exon expression level it's normalized so that a value of zero corresponds to an average expression level and now I'm plotting these versus position with the integration event here drawn here in red and then this is for a million base spares on either side of the integration site and here are the various genes that are found there when we find once again is that downstream the viral downstream of the viral integration event the the exon expression is is quite high the next question we want to to see is is this a general pattern observed or is this something that we see just in this particular case and so here is a distribution of per exon expression for for samples in which which have a copy number increase in the in the integration site and we consider the expression upstream for a hundred thousand base pairs within the integration site and also downstream of the integration what we find is that upstream of the integration event we tend to find the distribution it's relatively broad but it has a significant population both below and above zero whereas within the integration event where there is a copy number increase we find that the per exon expression is tends to be upregulated and downstream it tends to be upregulated as well generally speaking so this is for a copy number increase and we can also consider expression for a copy number decreases and what we find is that within the integration site the per exon expression is downregulated upstream it's tends to be around zero and there are two different populations upstream it's around zero there are two different populations with a population around zero and then a population with a significantly increased expression so to summarize we've developed a pipeline which allows the integration of multiple data types for the analysis of viral integration we use RNA seek for virus discovery as well as for expression analysis and whole genome data as well as exon data for integration analysis and we've constructed a unified visual representation which allows us to combine visually for one sample these integration events and finally we've illustrated a close association between virus integration copy number domains and expression levels and with that I would like to thank my PI Lee Deng as well as the other lab members and others who have contributed to this project and I'd like to thank you and if there's any questions I'd be happy to answer have you looked at what distinguishes tumors which have integration versus those which don't have integrated virus in terms of whether there's a different mutational spectrum or other alterations in the non-integrated tumors that might predispose them to survive without integration and what the reason I ask is that there have been a number of reports over the years that tumors that are that are HPV positive have a portion of the tumor that becomes HPV negative and progresses or cell lines that have HPV and vitro initially lose HPV and still maintain a mortal state so you have any insight into that the question being are we observing differences between tumors where there is viral integration and whether there is not we've started looking at doing various clinical correlations between these I'd say that right now it's it's still in the early stage of the analysis and we haven't observed any any particular differences yet but that's something that actually is an ongoing work sure very nice talking really nice analysis on the details of viral integration and expression just want to point a couple papers in the literature for you one is focused on analysis of TCGA data and it's from Eric Larson's lab I think a former postdoc of Chris Sanders that was in nature communications last year another is from our group from Aquino Justina and Chandra Petamano on cervical cancer in nature at the end of last year and both of those I just noticed one of the genes that you mentioned rad 51b for example both of those papers highlight that that's right of course is this pipeline available online to the public I'm sorry is this pipeline available online to the public this is something that's under development right now I expect that it will be made available thank you in the case this rad 51 where you see the later exons being highly expressed are they part of a fusion with with some HPV gene we we haven't the short answers I'm not sure I we haven't analyzed that let's thank all the speakers one more time please so this concludes our session and now Matt Meyerson and Marco Maro will make some closing remarks


So my beautiful people I am back with another hour doing video and today guys I'm going to showcase to you how you can get a super rare bit of sorcery it's called the Explosive Ghost Flame and I haven't really seen anyone cover this thing it's quite potent it's quite deadly and for you mage's out there it may be something you're interested in. How's it going guys my name's DPJ and if you do enjoy the video leaving a like really helps out and if you like what you see more be sure to subscribe. Okay so the explosive ghost flame sorcery absolutely ridiculously powerful it really is even from my build it's to be honest I strayed away from her intelligence quite a while back but I just slid a few pieces of gear on went up to a couple of heavy-heating and tanky giants took him out quite easily not going to lie but hey quite the beast it's quite the beast so the actual item let's quickly take a look at it guys before I showcase you how you can get this thing it's probably like I said more up the street you mages and like me I don't really play mage no more but as you can see here causes ghost flame explosion that burns wide area it requires 42 in that intelligence and 30 in that faith and it costs 54 focus points to cast like I said guys it is quite decent they could obviously make this much much better obviously your mage bros out there will get this thing even more deadly than what I've got it but hey it's definitely one for that collection either way guys like I said I haven't seen anyone else cover this thing it's probably because it spawns from an enemy called a death right bird which comes only at night and it's at the top of the snow mountain so here on the map right here guys this way he needs to come to so he needs to come up here guys right up here so you need access to the actual hidden path to the hallig tree that means you need the hallig tree medallion one halves comes from commander nile within the castle soul another half comes from an mpc back down here hiding at the top of a hill inside of a basket go up to the rasky punch it guys don't hit it with a heavy weapon you might kill him and clearly his dialogue will give it a half then guys you gain access to this here don't really need to come all the way up here guys run through the dark come to I've probably come to that grace point here because the enemy spawns there but like I said only at night and let me tell you this boss isn't easy I mean I can't remember the last time I had this much trouble with their boss I ended up actually just exploiting him I just sat on a rock and took him out from afar but trying to fight him at ground level dude was absolutely destroying me but yeah guys you're seeing that on the screen now feeding this dude you get this amazing bit of sorcery like explosive ghost flame and there we have it guys the end of the video has arrived if you enjoyed it even a like really helps out if you like what you see and want to see more out of anything be sure to subscribe and hopefully my beautiful people I will see you on that next one


I mean I've always been quite good at school, I always had quite high grades so I think it was sort of naturally assumed I was going to go but I think at the end of the day one of my favourite subjects was geography and I kind of quite liked looking at other places because I grew up in Cornwall so you do meet a lot of visitors who don't go back to other parts of the world and I was always filled with the desire to go as well so I thought how am I going to best get to do that and I thought well going to university away from home is probably the best chance of doing that, not just you know to study but also the people you're going to meet and things like that so I suppose that that in part was what inspired me to sort of go on to higher education. I was told to, well I take an anthropology at college so I already knew that I was interested in carrying it on so then it was just a case of looking at which universities offered it and I had quite luckily I had a lecturer who was quite sort of clued up on where to study best and sort of bringing out your interests so I was like well what are you most interested in? Well I'm quite interested in Southern Africa, I've been on a school trip there so I'd really quite enjoyed it and it was something and I did an EPQ and extended project qualification based on what I learned while I was out there so I sort of already had the beginning of it there and then I'd never heard of Sirass which is true of a lot of people I think who come here but he just said oh well you know Sirass is a fantastic place because it specialises in Asia and Africa so if you really want the best chance of delving in deep into what you've already expressed and interested in then that is your best place to go and then I was lucky enough to do sort of a week before everyone else arrived for students who hadn't had anyone in their family go to university and I knew from that week that that was definitely the right decision for me. It was quite good, I mean it was quite good, fantastic actually. I never really realised how much I'd be able to pursue what I was interested in and my interest, from the moment I first started my interest did change simply through you know accessing what was on offer here so that was academic side and you know I'd been quite interested in Southern Africa I'd also been interested in film I didn't quite realise there was so much sort of film courses at Sirass related to anthropology so I sort of did like two or three each of my units each year was related to sort of Southern Africa or anthropology of film so by the end of the degree you know I felt like I come away I come away from social anthropology degree but I'd specialised so much I felt like you know had really really pursued my interest so that was you know what made it fantastic for me then also the other side yeah I was in the drive did a few of the societies I did a president of drama society and sort of made friends for life from that so even though some of them you know I left graduated before they did and they stayed here sort of a year or two more than me I'm still in touch with them almost five years later after graduating and that you know that's fantastic and I think that's really getting their sort of spread across the world as well now so just by coming here you know I think oh where am I gonna go on holiday this year like oh well you know so-and-so's in New York at the moment so let's go there or so-and-so's know doing a place run you know with the UN in Geneva or something like that so you sort of either keep in touch with your friends it really sort of enhances your adult life as well and that was fantastic for me I never never really expected to get that just from being here so it had quite a good impact because I think doing anthropology a lot of people then ask you well what can you do with that degree which is sort of the first thing you get asked by your parents when you're telling what you're doing so it really forces you to sort of think outside the box and think well you know what skills am I going to get from doing anthropology and where it's going to be applicable so I did a master's degree after I did anthropology here because I suddenly realized I don't I didn't really want to be academic in it so I didn't want to you know go do masters do a PhD and professor I really wanted to use what I'd learned to try and impact the world so I did a master's degree in public policy of the road to UCL with sort of the mind of working in government the UK government or you know the UN things like that and so that was my sort of initial career pursuit and after having done the master's degree and I did a few internships at different organizations I realized I was quite happy to stay in London so I really wanted to find something in London that utilize the skills of sort of making impact on on people in education particularly and so my first so when I left one of my first jobs was working for philanthropic organization so the the organization was a business consultant but they donated a small percentage of their profits to the philanthropic team within the organization so I worked for that organization mainly sort of organizing the programs calling up schools getting involved and then actually doing some of the sessions as well so doing the delivery of the programs which is quite fun and that got me into my current job which is working at the social mobility foundation which is the same principle in improving social mobility in the UK but it's working with students from year 12 and above to get them into university raising their aspirations and then also making sure that they're achieving their goals by going to the careers they want to do afterwards. First of all look on the website and find what the alumni have said and then I would even sort of look at past students you know who they are what they've gone on to do and think about whether that is you know what you want to achieve. I'd also look at the library actually because the library has some really you know unique resources that you can't find anywhere else in the world and I think having access to those kind of materials when you're at university is unparalleled.