The Nigerian Medical Association, NMA, has called on the federal government to renew its drive towards reviving the federal vaccine centre Yaba as the centre is capable of reducing the country's over-dependence on vaccines from other countries. The national president of the NMA, Professor Inosent Uja, while addressing newsmen in Abuja, commanded government efforts in addressing unpaid hazard allowances of health workers and appealed for expedient action against paining issues, as failure might lead to disruption of services in the public health sector. Uja also says the worsening security situation in the country has affected the activities of health workers and called on the government to quickly address the situation. I think cognizance of the fact that Nigeria had demonstrated capacity in vaccine production against yellow fever in the past through the federal vaccine production laboratory Yaba but stopped in 1991 for several reasons, strongly appealed to the federal government to spare no efforts at ensuring the success of the renewed initiative with the inauguration of the Bav vaccine in Nigeria Limited, a joint venture between the federal government of Nigeria and Mayor Mbeka, Nigeria Limited. The Nigeria Medical Association will be very pleased if the initiative sees the light of the day so as to reduce the over-dependence on other countries and achieve some efficiency in vaccine production.


I do agree. I do agree. I do agree.  Because I feel like that's the scene. All girls have gone through heartbreaks, like have you ever went through a heartbreak where you've been ill? Oh yeah. I literally lost two stone in like a month, didn't eat for three months, then it put me on under depression. It's like I was awful.  Did it affect you at anything else? Yeah, it set me up to fail for ever a relationship I've had. And I didn't even know. It was mentally abusing in the way that I would not even realise at all when my friends from the outside were saying, Liv, you have changed. Where are you? And I could not even tell. And then it affected two years and then I got back with him after 18 or 20 years. You got back after all that? I was just a broken person and I had no self-respect and then Love Island literally saved me completely. It was a no look at you. Yeah. But at the beginning it was hard because you sort of heartbreak then it made me think that everyone else was like that. And I just hope that no other girl thinks like that because there is people out there that are decent but it genuinely makes you scared to trust anyone ever again. Yeah. I remember the first, well yeah the first ever heartbreak I had. It was when I came out to the Valleys. I was with this guy for five years. Like we were like best friends. Like he lived with my parents, lived with us and we were really close. And I come out and I remember we lived together and he just said I don't want to be with you anymore. And I remember we lived together and had the keys to my house. And I went into my house one day to get some of my stuff and he was sat on the sofa eating a dominoes, having a tool for Tuesday with this girl. And I remember just breaking. Oh my gosh. Yeah I was literally sitting on my sofa that I bought as well. That was just probably the worst feeling ever. I didn't eat, couldn't sleep. Like he was, it's weird because you're so used to being with someone and then they're just completely cut out of your life and the way he done it was just like awful. And I think it's probably that as well. Like when they finish you or they do something, it makes you want them more. You know it's so wrong to go back. It's like you want, because they've done something with another girl. You're like want me? Yeah. A lot of girls do that and that's not a good way to go. I think once a boy breaks your heart and does it. It's so strong. Yeah. And leave it. Yeah. What do you think makes like a happy relationship? I don't know. When I look at you, like I think a lot of people as well, like you see you and Alex on social media and people look at you and they're like oh my god we want to be happy like that. But about at the same time there's probably close stars and you probably have a round one with the bloody, the dinner getting over. Like we bicker about the serious things but we don't stay mad at each other. And I won't lie. I think I'm one of the luckiest people to end up with someone like him because you think we've got a relationship but in this day and age is so rare because so many people believe the grass is greener. Yeah. And that's the issue. Like we've got Instagram, Facebook, we've got so many ways of people connecting you. And you can get in touch with a girl just by messaging and then that's awful and ruined it. That's what frustrates me about the relationships nowadays. I think to have a happy relationship you need to learn that not every opportunity is worth taking and you do have enough at home and you do have something that's more worthwhile than going to sleep and nicking on the other roads for one night. And I think me and Alex both realise that and the reason why our relationship is happy is because we both know we've got something good. Because it's a happy, people want it. Yeah, people don't see happy relationships on TV. You see, for example on The Anywhere's Essex, you see relationships flash. You see all of that because people don't want to see something that's working because it's not interesting. But that's where it works and we're not bothered about that. But again, you can have a good relationship but it has to be based on trust.  Oh, because I feel like that's the same.  Oh, it means like that. But I said I'm improved. I don't like...  I wouldn't even fart in front of a door. I literally pop a fart out 25 times constantly.   No way. He never farted. You're lying. Can you believe it? In six years. Jordan's sure, girl.  I can't believe that. I always wanted that. I envied other people. No, even now. I'd be like, no, you cannot fart in front of... I think it's me though. I'll have a poo and I'll chat to him. I'll have a wee. I'll do a wee. But wait a second. What happened when you give birth? Was there anyone there? Oh, yeah. What do you mean? When you give birth, was a boy there looking? Well, yeah, he was there at like... Yeah, of course.  So do you think because they're seeing that? Do you think because they're seeing everything?  I feel like that's a point in a relationship where, like...  What's it like being a single man, then, this year? Like, is it harder? Like, when I was a single man. Yeah, when you were a single man, like, relationship-wise. Oh, yeah. When I was single, it was hard. Like, obviously, I'd speak to guys, but then you'd have to think, like... Yeah, bring them home. Yeah, would I bring them home too? How did you do that? It was hard.  Yeah, my mum had boyfriends and stuff. They weren't nice people and I would never want anyone like that around her. I always think, if I meet a boy, like, I don't want them to have a kid. Really? But it's not shallow. It's not shallow. I think in my head, like, I don't want them to have a child because if I have a child, I want it to just be our bond. Yeah. But I know it's so nice for other people to probably take on other people's children and stuff, but for me personally, I just think it's... It depends on the person. I think I'd rather someone not just not have a child. If they had a child, I think I'd just one date them.  Because we would be on the same level. But I get why you think like that. That's why I used to be... I think until I have a probably child of me own, I'd never understand. Yeah, exactly. Do you find that you think in a completely different way? Oh, a completely different way. Yeah, before I had winter, definitely. Everything changed. Changes in life, doesn't it? Yeah. I know what I'm going wrong.


The study examined the link between vaccine hesitancy and vaccine-preventable disease outbreaks, and found that cognitive processes such as conjunction fallacy, sunk cost bias, information avoidance, and present bias may contribute to parental vaccine hesitancy and vaccine-related behavior. This article was authored by Tiffany DiPamaris, Allison M. Buttenheim, Avnica B. Armin and others.


Thank you. So basically, most of the work has already been done, so that's nice for me. That's cool. But I will still introduce you for misunderstanding that we often have when we think about big data. So these are the four misunderstandings. Four plus one is actually four, because we don't have the time for the plus one. Let's keep that one. The first one is that when we are talking about big data, in fact, the first wrong thing is that most of the time we are not actually talking about data. We are talking about something else. We are talking about traces. Basically what we do, and it has been said already, is that we use the little traces that basically us and the object that we use live in digital media to extract information or sense from them. A little bit like the little birds following and so on and eating little breadcrumbs, like exploiting a little breadcrumbs, as a greater left for themself to find the way back home. That distinction between traces and data is actually very important, because data and traces have a completely different nature. And the first important thing that we tend to forget when we don't make this distinction is that the data that we are talking about are not produced for academia, not for the social sciences, not for the national sciences. For the most, they are produced for various reasons, relative to surveillance or technical efficiency or economic exchange, et cetera, et cetera. So when we say, and it's often said, big data is making, or digital technology are making data less expensive for the academia, this is not actually true. What they are doing is that, in fact, they are shifting the cost of collecting and producing those traces and those data to someone else. The people or the companies that manage and handles these digital networks. And that is important. This, for example, it shows you the capital industry investment, who actually paid for a good share of data revolution, and is actually an interesting controversy about this image. Because if you, which also shows you you can easily sort of light with data and the way you present them to connect with the last presentation, it's actually very important. This is the community of unadjusted data. But if you represent them the right way, which means you de-inflate the investment and you show the investment here per year and not the community, you see that it's not actually growing through time, it's actually declining a little bit. Just to give you an example of how the way you present data can make an important difference. But it's more than that. When we work with digital traces, we have to always keep in mind that they have been created by someone else with objective that might be different from ours. And so the condition of creation of these traces are very important. I'll give you an example. A few years ago we were working at the Media Lab, we were working in Paris, we Google what is called Google Trends or Google Insights for Search, which is a very interesting set of data that Google generously give out for free. But in a strange way. About the researches, the queries that are made to research engine. Right, so you can take whatever what you want to Google Trends, like I don't know, soccer, and they will tell you how many people have searched for that word in the last days, in the last weeks, in the last years. And you can break it down to different parts of the world. So we were working with an economist, as you probably know it has been used to build a pretty controversial but still pretty good indicator of flu spread around the world because people look for their symptoms. So it can be used in real time to detect the spreading of influenza pandemic in the world. So we were thinking about trying to do the same thing for economic indicators. So matching search queries in Google with economic indicators. And while we were doing some literature analysis, we found this very interesting discussion paper by Kitter and Zimmerman, where they show that there's actually a very, very good correlation between the unemployment rate, which is the black line here in the US, and the search for antidepressors side effects, side effects, side effects of medicine side effects. So there's a lot of people that you should take, how many people typing, I don't know, Zoloft side effect, for example, and you match it with unemployment rate, it sort of correlates very well. And so they make the hypothesis, which kind of makes sense that this of course is the start of the crisis, the economic crisis in 2008. So they made the hypothesis that this could be used, the search for antidepressor side effects could be used as an indicator of unemployment because people lose their jobs under the press, so they take under the press so they look for the side effects. But then we try to reproduce those results, and it works, but we also find out that there were a lot of other words that sort of add the same curve, for example, templates, this is actually Google tries. For example templates, it also spikes up starting from the end of 2008. So we thought, what, and recipe as well, a lot of these sort of side words, and in fact then what we discovered is that what happened is that in August 25, 2008, Google introduced its suggest feature. So now when you type Zoloft, Google proposes you side effect, when you type Apple Pie, it proposes you recipe, right? And so all these sort of side words spike up starting at more or less at the same time of the economic crisis. But so the correlation is very, but if you forget that those querying are made through Google, and so the condition of the production of those data depends on the technical infrastructure that collect those traces. You can easily miss this little, but important distinction. And this is another example we are working on, but then we go very fast, but we are working on a project called Contropedia where we use data on Wikipedia, Wikipedia edits to detect a controversial topic. So basically when we see there are pages that are very much reverted by different people, we take that as an indicator of controversy or debate. But we are very, very aware that all the time there's a problem and what are we mapping here? What are we seeing? Are we seeing the media or are we seeing the content? Are we actually seeing some societal debate or are we seeing something about Wikipedia? Are we learning something about society or are we learning something about Wikipedia? And often we are learning more about Wikipedia and about society. There's a lot of discussion that's going on about Wikipedia that are very, very specific to the bureaucratic format of this encyclopedia, but it does not really relate to what happened in society. So here's a few articles that I've wrote on this because you're interested. So the second misunderstanding is probably more important is that quantity is less interesting than varieties, not that variety. Do you know what happened to Google in September, 25 September 2015? Can you spot the difference? This is September, this is 24 September 2005 and this is September, two days later. Something is different, apart from the cake, which is the birthday of what Google is up, I guess. Sorry? No, that's not that. 2005, yeah? It's September 2005. It's September 2005? Yeah, it becomes the same. Okay. Oh yeah, yeah, yeah, that's the Google. I don't think it doesn't mean that they have been funded for seven years, so that's better. But apart from that, there's another thing, and it's this one. They remove the number of pages in which they were searching. That had been, I don't know if you remember, it was, you know, now everyone has forgotten about it, but for a long time there was this big competition between Google and Yahoo on about who were, which search engine were indexing more pages. Like in every week that we publish, we are indexing five trillion pages, we are indexing five, blah, blah, blah. And on September 2005, sorry, no, 2015, Google said, in fact we don't care, right? What is important is not how many pages we're indexing. Because we're not indexing, we're only indexing a small fraction of the web anyway. And that's good, because you know, our users are not interested in the old web anyway. So what is important is that the first five results are relevant, which remind us that we have to take the metaphor of data mining seriously. When we say data mining, and we refer to mining, if you think of what mining is, it's like throwing away 99.9% of dirt to keep the only 0.1% of gold or whatever. That's interesting. And when we're talking about digital data or digital traces to be more precise, they look more like, it's often said information is new oil, exactly an misunderstanding. Because it gives you the idea that you just drill somewhere and there's data flowing up easily and sort of almost for free. This is not the case. Digital data resemble much more to unconventional data, like oil sand and all this. They're dirty. They require huge infrastructure. They're complicated. They have to be clean and they have to be extraction. Without this work of extraction, there's actually no very little value in digital traces. An example is this map of the web, which is to my knowledge, the most exhaustive map of the web that exists by now. It contains a few millions website of this point here. The circle is a website. And it's also useless because there are only two things that you can, it's very nice to look at. But the only two things that you can learn from this map which are not very surprising. The first is that website that share the same language tend to cite each other. That's why you have the yellow, which is like China, cluster here and you have the blue, light blue cluster here, which is English speaking website. Then this, I think, is Brazilian, Portuguese, this is French, et cetera, et cetera. But that has been known for a long time. And the other thing is that it's the power love of the web, the fact that there are some websites that receive very few websites receive most of the attention. And there you see there are very huge websites size is the number of link that each website receives. And there are a few ones that are very, very big and a lot of that are very, very tiny. But that has been known for a lot of time. So if you want a good map of the web, that is a good map of the one. This is produced for Le Monde, the French newspaper, by a company called Linklurens. And it's only, it's specifically, it's not a web. So it doesn't even try to map the web. It concentrates focus on a very specific part of the web, which is the French political blogosphere. So it's a tiny map, it's only 1,400 websites, almost 5,400 websites. And there are only political blogs. But since they have done the job to manually select those few websites, then this map can show us interesting things. And to give you an example of the sort of findings that we can see in this map is that the extreme right and extreme left have a very different profile in the French blogosphere. The extreme right is spiked, it's very, there are a lot of blogs, but it's also marginal, see, it's here. They tend to be very connected among themselves, but it's connected from the rest of the blogosphere. The extreme left, on the contrary, is composed by fewer websites, but they are spiked all around and they play a very important role, which is some way unexpected, instead of bridging the discussion between left and right. So when you think about which type of data you're looking for, sometimes it's not, the question of how much a big the data set is, is less important than how interesting it is, how diverse it is, how it opens up observation that could have not been done anywhere else. So for example, to talk about climate change and a project that we have done in the Media Lab, we went to map the climate negotiation and we hesitated a lot if we were going to go for Twitter or for the Earth Negotiation Bulletin and eventually we decided the Earth Negotiation Bulletin because it's much more specific. Like, Twitter is much, of course, it contains much, much, much more data, but the data is less specific, so possibly less interesting. And so this is what we end up producing and that is what is the UN is producing on Twitter, which is also very interesting, of course. I will come back to that image in a minute. So the third misunderstanding is that digital is not automatic. Doing digital research is not just pushing back, right? Sometimes we hope in the academia, at least in the social sciences, so when people come to the Media Lab and ask us how we can help them, most of the time they'd expect that we would have sort of solution that would make their life easier. It is not the case, right? Digital traces would make your life even more complicated because precisely this work of extraction that is necessary to produce the map that I showed you before, this one, this little image and this little page, we had to go through this super complex protocol which I will explain very quickly. So we extracted, we took all the PDF of the Earth Negotiation Bulletin, then we selected the part that only the one that were about cops, conference of party of UNFCCC, then we cleaned out only the section that were about cops because sometimes it's mixed up and you get also other things. Then we identified terms, for example, names of countries or negotiation groups or noun phrases that refers to notion mobilizing the negotiation. For example, climate ray disaster, financial resources, et cetera, et cetera. Then we extracted those terms from the bulletin, from part of the bulletin that we kept, trying to find all the different sort of variants, linguistic variants for that. Then we went back to the bulletin, we read all of them and we claimed up the term that we extracted so we sort of look at which term actually meant something and which other were two ambiguous for bringing sense. Then we merged the term that meant the same thing, for example, technology transfer can be said in many, many different ways. So we merged all of them and then we built this other map that shows each little point here is one of the terms that we kept after the cleaning and they're connected if they appear in the same paragraph of the UNB. And so we could detect this sort of thematic distribution of these terms in the climate negotiation. And so we sort of detect these different clusters of noun phrases, one about the modus and the TC, one about greenhouse gas emission or body counter protocol, et cetera, et cetera. And then we use, so the fact that these words appear often together is not decided by us. We found it in the Earth Negotiation Bulletin. So then we could use each of these clusters of words as a sort of dictionary to identify a specific topic in the negotiation. And then what we did was a network sort of time analysis of the appearance, the occurrence of each of these themes in the negotiation so that we can then trace how much in each cup, each of the theme or what's discussed. And this is the map that I showed you before just to give you an example. This is the pinkish stream here is the post-keoto agreement. And of course, it becomes irrelevant when it's starting from 2005 to 2006. And then it gets very important. This one is climate change, the light blue year. This is a climate change impact. They're very visible at the end. Not discussed at all from the beginning, et cetera, et cetera. But that was not enough. That was not finished. From that, we also have to build a narration. So we have to tell a story about this because otherwise it's visually by itself. Doesn't really say a lot. And so then we write this whole thing that you can find online on the here, cloudmaps.eu, which is a platform that we produce at the end of this project. And that contains this story about climate negotiation and five others. And this is a couple of paper that you can read about it. And I'm almost done. But the last misunderstanding, which is possibly more relevant for those of you that are in social sciences, which is definitely more relevant for them, is that more quantification demands more qualification. That's rooted in the methods that we use in social sciences, this distinction between qualitative methods and quantitative methods that you probably have heard of. Qualitative methods, anthropology, direct observation, in-depth questions, that allows you to collect intensive data, meaning rich data, but about very little population. Quantitative data, statistics, pools, allows you to collect extensive data, meaning poor data, but on large population. And for a long time, there has been this, when you enter the social sciences, even when I enter the social sciences, when I enter my PhD, I sort of had to choose my supervisor were, you wanna do qualitative or quantitative? And the interesting thing about digital methods that allows you to completely overcome this distinction. I have an example here. Would I go there if I click? Yeah, nice. Okay, oh, but it doesn't show up. Maybe I have to move it, yeah. Yeah, okay, so this is a project that we didn't meet a lot, but unfortunately it's in French but I will drive you through very quickly. So it's about, it's not about how much change our environmental data, it's about law. It's called la fabrique de la loi, the fabric of law, the industry of law, and it tries to answer the question that you see there, does members of the parliament do the law or not? Because you probably know in political science there's a big discussion about whether laws are actually made in the parliaments or they're just passed through the parliaments but in fact imposed by governments. And so we look at this in the French parliaments and we built this database that contains more than, that contains about 900, sorry, 290 low text. You can see them here. You can filter them or see all of them. This is only 2013. Oh, we can see all of them. Okay, so here are the 300 laws discussed in the last four years in the French parliaments and you can see them in different ways. For example, this book, interesting news, the quantitative way. Here, where you would see how much time each law spent in each part of the parliaments. So the Assembly, the National, the Senate and the different commission. So that's a very aggregate view. So that's a very quantitative view of these laws and you can compare very easily in one screen of your computer, 300 laws. But then we can start drilling down and we can click on this one. For example, this one is the homosexual wedding. Big controversy in France. And here we have again, very aggregated information on these laws. We know that there have been 9,000 amendments that have been submitted. That only 0.47% have been accepted. That the size of the text has decreased, which is strange, of 37% and other information. But we can click on it, explore the articles. And now we will see all the articles of the law at different stages of the discussion in the different parts, branches of the parliament. And when you move your mouse on each of them, they will tell you how much it has been changed. So this one has been added at this part. But if you go to this one, for example, article one B, it has been modified by 80% at this stage. And if we click on it, here I will see the modification. So I have the versioning of the article, say, which part has been added, which part has been removed. A little bit as you would have in Wikipedia. And you have that for every article of every law at every stages of the discussion. If I click on another one, it will say here. But we can do better. We can click on explore the amendments. And here we will see all the amendments that have been submitted. Well, this is on this specific article. Let me click on here so we see more. There are all the amendments that have been submitted to the law, but I can do article by article, at this specific stage. And I can see by which party they've been submitted and which are their final destiny. They'll be accepted or rejected. Most of them are rejected, of course. And if I click on each of these things, for example, this one, I can actually read the amendments and the people who sign it and when it has been submitted, et cetera, et cetera. But I can do better. Again, I can click on this little thing here. And now I can see data on the discussion about these amendments. So I can see how many words has been spoken by different parliamentary groups at each stage on each article, on each amendment, on each article, at each stage of the discussion. So if I click on this one, I see the UMP, for example, has spoken those many words on this amendment, this stage, blah, blah, blah. And I see the people. So I can see who actually spoke those words. And I can do better. I can click on this intervention, read the intervention, and I can actually read the verb team of what they have said. So the point is, I'll come back to the presentation. The point is, in a few clicks, like four clicks, five clicks, I can go from a super aggregated view on this data to the verb team of the word spoken in the parliament. And it's this possibility to navigate through that escape that is sort of dissolving the distinction between qualitative and quantitative data. In a sense, it has always been possible, right, to desegregate the data, I come back to the original data, but it took a lot of time. It took to go back to the archive, to find out the original collection of the blah, blah, blah. Now you can do that quickly enough, and that changes everything. Okay, so this thing you can read. So why does it, what is that, and then I will confirm this one. So why is this important, this sort of overcoming this qualitative, quantitative distinction? The reason why it is important is that this qualitative, quantitative distinction, as for a long time in the social sciences, be connected to another distinction that is about what's called the micro level and the macro level. So the micro level would be the level of individual interaction, or individual choices of consumption, for example. And the macro level would be the level of social structures, like big institution, like the markets, the market economy. And sociologists have, for a long time, and see most of them, thinks that these two things exist on two separate levels, that these are two different level of existence, right? And what we are trying to push in the media lab is the idea that it might not be the case, that it might be only a sort of an artifact of this divided vision of this strabismus that we have so far. And that is particularly important, I think, in the case of environmental debate, because when we talk about, for example, climate change, we see it very quick, very well, working in this EMAPS project. You could either, probably some of the most important discussion do not take place neither at the level of the individual citizens, nor at the level of the big global structure. But what is happening is what the debate is all about, it takes place in between, in all the multiple sort of intermediary bodies and institution and groups and occasion and meetings that exist between this micro and macro, which were very difficult to observe with the traditional conventional method of social sciences, which are becoming open to observation. Thank you to Richard Lometis. Thank you.


Hi guys, how are you doing? So what is Kusac? Kusac is basically talking about a server Since you guys are... Kusac is if you do not like it So this is the basic Once you send it, this Contact the GP API So this is the server side You just get a request I mentioned that this is an exit server Our htmlab server is basically set by the html Stores some information to the local with acingstory.setter Set some key as to hit some public You cannot hit it So for people that is a 10 Which you can expose your Expose your local So what I did was, the implementation is that It generates some wild content So the next thing you should try out That is If you run a database, it's about container I will also show something about containers So I have a dog file here So container is anything that It pulls it It says that it doesn't work But it works for me So container solved this problem That now instead of pushing it To a get repo, I will push it To a Docker registry So container is a big piece of software Which contains a proper environment And all the dependencies And including a source code Everything just works out of the box If the Docker image works with me It will work with me But containers were basically To move software around So this is a Docker file What this does is It creates a Any VM running inside Inside something It pulls from A small operating system Which has a node in Word That creates a directory And she does it Then you copy the source code Then you copy the source code And expose code Because the server is going to be running on 3000 And then just CMD node server.td And that's how it will work It finished all the steps So what do you have to do? This image once pushed It can be pulled by anybody from a Docker registry And it did work Now it's not running for me So it won't run for anybody But if it does for me It will run for me That's basically the thing about Docker container You just have to read it Read about Kubernetes Which is a tool to deploy Docker images Sorry that I lost it Any questions?


Okay, here we go, a little unlicensed music there. Welcome to Dashcam Confessional, episode five. We are trekking right along here.  I am amazed and I haven't been teaching that long. I've only been teaching six years, but in the past six years, I noticed on the first day of class, let's say my first time teaching six years ago, walked into the classroom, there'd be some people talking to each other because smartphones weren't that big back then. Fast forward now to six years later, on the first day of class, nobody is talking to anybody because they are all on their phones and it is crazy how linked to our phones they've become.  I'm really, really bad at, you know, on my phone all the time, but I'm trying to break the habit because I think it's way more important that we keep interacting with each other and it's great that we have all these social media friends and these social networks that are online. And I don't take away from that. I mean, there's some really good stuff that happens in relationships that can be formed online, but honestly, when you are having a chance to have an interaction with a person in real life, let me turn this down, in real life, you've got to take that. And like I said in this morning's little picture, if you want a status update, ask them. It's not that hard to say, hey, what's up? What's happening? It's so easy to get caught up in all that social media. It's so easy to get up on the Twitter and Pinterest and this thing called the Facebook. I just think that we need to kind of take a step back and learn how to interact with one another once again. That's all I have to say about that. We'll see you guys tomorrow. And that's hashtag ChazzLifeLesson, episode five. Oh, this is a recap. Again, I'm looking for other suggestions. I love, this has been fun for me.  I will answer them. Mike, as long as it's PG-13, maybe a light 14A, this is to Mike who asked me, does it have to be a question about anything? Yes, it can be a question about anything. As long as I don't have to get naked or drop an F-bomb on it or be inappropriate to the point where I could never let Bodey ever or my kids see this. So there's that. So aside from that, fair game. Let's talk about it. All right, see you guys tomorrow. Episode six coming up.


Good morning everyone. Myself Dr. Surajit Chan. I am a third-year resident from Department of Radio Diagnosis, University College of Medical Science and Gurudwagadur Hospital, New Delhi. Title of my paper presentation is Spectrum of Sarcoidosis, a rare case report of Neurosurpretysis. Introduction. Sarcoidosis is a systemic disease with multiple non-casiating grandmas. Most commonly affected organs are lungs, followed by lymph nodes, oculosarcoidosis in. It is most commonly seen in the middle-aged females. Neurologic manifestation is rare and appears in within two years. Cranial neuropathies are the most common presentation. Seventh nerve being the most common nerve involved, followed by eighth and second nerve. Meningeal involvement like aseptic meningitis and neural braceletion, neuropathy and encephalopathy are the other common presentation of Neurosurpretysis. Aim and methodology. Aim of the study is to evaluate the role of MDCT in suspected case of sarcoidosis. Patient underwent CCT head, orbit and chest in the Department of Radio Diagnosis in USMSGTB Hospital. Taste presentation. It is a case of a 30-year-old male patient with no previous medical history. Patient presented to the outpatient clinic with complaint of redness, pain, blurring of vision in the red eye for five to seven days. There was associated fever, headache, chest pain and involuntary weight loss over the last three months. No, there was no history of facial deviation and hemoptasis. On examination, we can see in this picture that there is an iris nodule with hemorrhage seen in the right eye. Patient underwent lumbar puncture. However, there is only a slight increase in the CSF pressure. The sugar and the protein levels were normal. On neurological examination, there were no cranial nerve pulse noted. Plasma and CSF ACA levels were normal. Patient underwent sputum cibinate and serum ADA. Both of them were not positive for TB. Onto-antibody testers were done. ANA and ankle levels were normal. Cryptococcal antigen levels were normal. These all the tests were done to rule out other granulomatous infections. Here in these images, we can see this is a CCT image of axial and coronal section. There are multiple homogeneously enhancing limb nodes seen in the medial stenum, mainly in the right paratracheal region, pre-vascular region, pre-carinal region, and bilateral hyaluridin. There is similar limb node also seen in the periportal region. The same patient underwent CCT head also and we can see there are a lobilated multiple homogeneously enhancing lesion. It is durol based lesion with broad base towards tibra which can be seen as orange arrow in axial and coronal, axial and sagittal section. There is surrounding vasogenic edema involving the white matter which is marked as green arrow. And in this fourth image, we can see there is no underlying hypostasis of the bone. We did histopathology from the endobonchial biopsy and bronchial vela laser. In this image, we can see there are multiple well-defined granulomers with surrounding naked limb nodes which is marked as green arrows, size-tip of non-necrotizing granulomers. Discussion. Neurologic involvement in the circuitry is uncommon. However, it can serve wide variety of presentation. Both clinically and radiologically, it can be difficult to diagnose. Histology confirmation of non-casiating granulomers is needed to support the diagnosis. It is important to rule out other etiologies of granuloma information, like tuberculosis, fungal infection and autoimmune diseases. A major challenge in the neurocircuitosis diagnosis is the difficulty to obtain nerve tissue for histology and microbiologic analysis. Neurocircuitosis can be assumed in patients with confirmed circuit involvement of the other organs and neurologic symptoms. In our case, we can confirm the diagnosis by finding non-necrotizing granulomers in endobonchial biopsy and bronchial vela laser. In our case, the patient was administered pedicillin 40 mg BD for two months and her symptoms improved significantly. The significant improvement seen after therapy highlights the importance of the awareness towards the clinical entity in order to start the treatment as early as possible. Here are my references. Thank you.


Cool. Hi everybody. I'm Kelsey Braceman and I'm from the Environmental Data and Governance Initiative. And today I'm here to talk to you about our Project Environmental Enforcement Watch, which is an approach to environmental data justice through participatory data science. So that's already a mouthful and you can look us up at these Twitter handles down here. But basically my organization, the Environmental Data and Governance Initiative, which we call edgy, is a group of people who got together mostly in the wake of the Trump election over concerns about environmental data and how it was going to be handled by an open climate denier having control over a lot of this information. I actually wasn't there at the beginning. What edgy has grown to over time is more of an interdisciplinary collaboration between people who are volunteers and people who are paid through foundation grants up to half time. Nobody's a full-timer. And we have people kind of filter in and out kind of as suits their schedule and interests. And we organize sort of based around what people find worthwhile to do. So there's no real management or bosses. It's much more of a, like, does this seem like a valuable thing to do. And if not, we just don't do it.  And one of the things that I really like about edgy and our approach is that it's very interdisciplinary. So we do work on data and it's right there in the name. But we also have this fully contextualized concept of how to critique the federal government and its accountability to environmental governance. And that includes sometimes anonymous interviews of people inside the EPA to get their take on how, like, how does this administration treat you or treat this kind of science or policy? It also includes web monitoring, where we've done a lot of web scraping and then like hand accounting for changes, like including just typos in federal agency websites over time and whether that's important or not. And then over time, as we've been working on this, what we've seen is much longer term patterns than just a single administration, which is that as many of you may know from these previous conversations in these presentations, there's a lot that's just kind of not done well in general with respect to data. And part of that's just a failure modernized, but also a lot of it is rooted in racism and much bigger problems. And so what we work on here, and especially through the Environmental Enforcement Watch Project, is we primarily use one data set. And that's the EPA's enforcement and compliance history online. And I'll talk more about that later. And we look at it through this lens of environmental data justice and environmental data justice combines environmental justice and data justice. So you might have heard about data justice is about, you know, whose data is this, whose data matters, in what way does this data need to be formulated in order to be understood as meaningful or legitimate. And I'll talk more about that later as well. And then the combination of that with environmental justice, which says, okay, well, who are the people who are bearing the worst brunt of environmental problems, such as climate change? And of course, you'll see a lot of overlap with historically marginalized societies, you know, in general, none of this is terribly surprising. But our lens is to say, okay, well, how does data intersect with that existing injustice? How does the collection of data, the process of the collection of data, the legibility of the data, like who is it legible to and why and for what purpose? And does that actually protect the people on the ground who most need protection? So that's our basic challenge. Taking this database and saying is the APA protecting me? And what we do is we go to communities and this will be environmental justice communities or waterkeeper groups or activist groups. And it kind of runs the gamut of folks who are like, basically well engaged with whoever is excited to work with us. And we try to help them answer that question. In this case, using this data set. A little bit more about the data itself. So all of the data that we're using for the Environmental Enforcement Watch project comes from this open data set. And if you go to echo.epa.gov, you would see this home page. And there's a lot here. There's both too much and too little, I would say. And just to kind of run you through what the user experience is of their website. If I put in the zip code where I grew up, I get a map that looks kind of like this. And I've already opened one of these, but you can see these little flag things that are different colors with stripes on them. Not totally sure what those mean. Actually, I've been using this for a while and I still don't know what those mean. And if you open one of them up, you get a particular facility. And the way that this is arranged is basically facilities are monitoring, facilities are monitoring their data and reporting it under the federal EPA and state EPAs are not included in any of this. And if they're reporting under the Clean Water Act, the Clean Air Act, or the Resource Conservation and Recovery Act, which is more or less hazardous waste, that's everything from like energy generation plants wastewater treatment to like your local CVS might have one of these or probably does. Anyway, so if you click on one of these, you can see what's being monitored here. Super helpful. The one I happened to click on is located at Unspecified Washington. Not really sure what this is. It looks like it's probably some kind of road construction thing. And under the Clean Water Act, it looks like 12 out of 12 quarters that are shown here are red, which means there's probably violations. Days since last inspection is not applicable. So that's probably never been inspected and there's been no recent enforcement. This is actually one that I just clicked on. It's not one I selected to showcase and that's just, you know, they're not all this bad, but some of them are worse and that's what you get. If you want to drill down on that a little bit more, I think this is for a different facility, just so you can get more colors on here. But basically, if you clicked on that link at the top about the project facility itself, you'd get a whole bunch more. And what you'll see is like, okay, well, no violation identified, no violation identified, violation, other violation. I don't know what that means to you.  Because I've been working on this for a while, I know that DMR means discharge monitoring report and it has to do with what actually got released into the water. And that's not actually written anywhere on this page. I did clip it, obviously, but that's all you get. And you get a bunch of other things that you probably wouldn't understand as an average member of the public. And like what we see here is that there's missing data. So did they break the permit? Did they not? Well, I mean, they didn't report so we can't know. And what that can mean is like, okay, well, if I'm trying to make a statement about whether the people in my community are getting unusually sick based on the release of certain chemical, I just won't be able to prove that because the data is just missing. So there's a lot of information. There's a lot of patchy information. Um, what can we do with it? So we started making a different interface to this that was oriented less around enforcement itself. So that dashboard is mostly what mostly used, I think, by the EPA and by facilities so that they can be basically in public conversation about, did you fill out your permit paperwork? Yes, I did. No, I didn't. Was I filling the terms out correctly? But it is our opinion at edgy that this sort of information is of interest to people who actually live there. So we decided to see what we could do by talking to local communities. And we wanted to get people kind of hands on with the data so that it's not just us saying, Hey, did you know that a bunch of bad stuff is happening around you? We wanted it to be a little bit more empowering than that. You can already tell like if you go look at this stuff, it's kind of like, Okay, well, that's bad, but aren't you the people I should be telling? Shouldn't you be doing something about it? And they're not. So they're not resourced for enforcement because as we tracked during the Trump era, a lot of the funding was cut and it was already going down. But that's, you know, sideline. Anyway, so we decided to give people a tool to say, Okay, well, if I wanted to advocate in my community, maybe I want to go talk to a facility directly, or maybe I want to make a big stink so the EPA has to come check this one out or something like that. I want to be able to say, Hey, I looked at the data personally, and this is what I found. So we built a number of notebooks. And we specifically built them so that people who don't necessarily know anything about code should be able to kind of step through it and not not feel like it's way above their heads. And we're working on different methods because as I'm aware, these are, these are still kind of scary to people who don't code, but we did put some things in here. So like beginner instructions in the markdown of every notebook, we have this how to run this notebook. If you click on a code sell a play button will appear. We use Google Colab to host these, which is where we get these little play buttons. And so it'll run the code. And then we add comments that are not just like developer comments along the lines of this is what this function does, it says, okay, this is going to build a query by appending details to a variable called SQL. And I was just going to call out to that we work with a partner at Stony Brook University. Echo actually provides you saw those 12 quarters of data. They actually have a lot more data, but they only display the last 12 quarters, which is not super great if what you're trying to do is compare across, for example, presidential administrations or if your issue happened more than three years ago, if you're looking for real trends, that data goes back at least 30 years. I think it's patch years it goes. But what we've done is, well, our partners at Stony Brook have set up a script that scrapes, I think it's on the order of every week, and compiles together and joins a whole bunch of different CSVs to make them SQL queryable, which they are not through EPA and resources. And we're actually in the process of working to make that more publicly accessible. But if you want it now hit me up, then I can, I can set you up. And then we go and talk to community members. Usually we do this sort of this two step process where the first time we meet with them and we say, hey, here's the kind of thing that we can do. And then we sit down and we listen and we say, okay, what, what is it that you need? What are you working on? And usually they have, it works best if they have some active project. So this is one in Alaska that said, we want to keep our waterways pristine, which companies should we talk to about improving their specific effluent emissions? Or an activist group we talked to said, does my legislative district enforce environmental rules? Well, because what they would like to do is host a sit-in at the legislative person's office. Or this year, we had a group say, okay, there's oil storage facilities in our community. And it's environmental justice like historically disenfranchised community. There's new permits up for review. We don't really understand what's in them, what's different about them. We don't have time to do like a huge data analysis. And so we said, okay, well, we can, we can spend some time on it. We spent, we got a whole class involved on this and it still took us over a month to get some pretty basic details. But we were starting to be able to answer the question of, are these new permits meaningfully better than the old ones and what needs to change? So one of the first things we do, as I mentioned, we made a number of notebooks and they analyze kind of the same things. It's mostly like, did they fill out the permit correctly? Like, is the permit being followed? Is it being enforced? If so, how, like, what kind of penalties? When's the last time that was an inspection? And we turned those questions, you know, the waterways, the legislative district, the our area, we turned them into meaningful geographies. Watershed, congressional district zip code. And then these are little clips from the notebooks. We use a lot of maps and say, okay, well, this is approximately the outline of a watershed. Now we can determine what effluence are being emitted within that space. In the congressional district, we can say, okay, in Massachusetts, congressional district four, Varney Bar the Sand and Gravel is one of the worst clean air act violators. In Buffalo, New York, here are some of the sites of Clean Water Act violations of the last, you know, however long. And we do this in the setting of talking to people, because it's not just about the data that's in here. It's also about the data that people are bringing to us. They're saying, hey, there's a lot of people getting sick around here, and we want to find out what's going on. Or, hey, like, I'm worried about my future. Who do I fight? Like, what do I do? And so it's very much a collaborative process, trying to work as closely as we can with people who are really there, really experiencing it. Here to say five minute warning. Thank you very much. And I have this last bit, slightly as an appendix, but I want to run you through it anyway. But basically, the data itself has a lot of challenges. And the last presentation we heard was excellent on this, too. This is a very complicated slide, and I'm in the process of uploading to Zanota, so you can look at this more closely. But this data, first of all, we're having facilities self report data. We know that sometimes they lie. Even if they don't, there's a lack of inspection. There's bad recording. Sometimes this is submitted on paper and then transcribed. Sometimes it's not transcribed. There's a lot that's not submitted to the EPA. And then it takes weeks to months for it to get into the database, but there's not like a guaranteed time. So when we were trying to track, like if there was changes over COVID, but we couldn't because there was no like, data just kept appearing months later and we're like, okay, so now is it final? But you can't really prove anything because you can't guarantee anything. And that makes a terrible headline if you're trying to make the news. This is an extremely skimmable slide. But one of the things I want to point out here is this is the paper that introduces the concept of environmental data justice and it comes out of edgy. And it specifically calls out that one of the things that you can do in order to make progress not happen for a community is to generate uncertainty. And that's absolutely what one of these things does. We'd like to advocate for participatory processes so that people can show up and say, Hey, I need something equitable. I need something transparent. And I made this point already. There's a lot of contributors here. If you're interested in joining, there's a lot of people who come in just for a little project and then dip back out. And I also want to thank our funders. I think I have time for questions. You absolutely do. We have about three minutes for questions. And so we've got one question in the chat. It looks like it's from Jonathan, who's our other organizer in the session. And he's wondering if you've had feedback on the commented notebooks from your intended audiences? Are these effective? Are they meeting their needs? Yeah, that's a really good question. And it's one that we're hoping to answer better and more in a more structured way, I guess. Usually when people interact with our notebooks, it's usually the first time they're seeing code at all. And they're not necessarily, we haven't seen people like spook and just say, Oh, I can't touch this. So that's a pretty good sign where I'm concerned because I've been teaching code for a long time on the side. And you see a lot of people spook. But it's also definitely not the most accessible. And we're looking into right now whether we should put an R Shiny app on our website to make it a little bit more so. We'd love someone's expertise if they have great ideas for this stuff. Oh, I bet this is the right crowd to ask that. So I have another question. I'm curious, you made this comment about the timelines that the data as it's released covers quarterly data for the last three years. But as you said, the timeline of your sickness due to exposure or other issues is so much longer than that. And so how do you have any examples of being able to push for more historical data or how are you sort of addressing that disconnect? It also seems like a problem in our political cycle, right? Like we're dealing with we're electing someone and it's just four years. But then the repercussions can like spread out year over year. So I'd love to hear you talk about that a little bit. Yeah, there's just so many problems. It's hard to choose just a couple. We do we do actually have access to longer term historical data. It's it's hard to get into like all of the details on a long way back without a bunch of data gaps. It's actually you can't even tell like how many are supposed to be being monitored if you're trying to get that kind of information. But we do actually have data dating back a long time. It's just not accessible through their UX or through their API. You have to download the CSV files and manually stitch them together. So it does exist, but it's it's very hard to make a case. All we can tell people is at minimum, these bad things happened, but probably more bad things happen. Right. Well, on that note, thanks, everyone.


This is not a sterling crisis. It's not a debt crisis. Britain is not Greece. It's not Argentina. You issue the currency in which you borrow, and that means that you are never going to have the problems that Greece had or Argentina did. But this is an interest rate crisis, because at the moment, we have inflation running at around 9, 10% in the United Kingdom. We have a current account deficit of 8%. And now we have this remarkable largesse lavished by least trust on the wealthy. The Bank of England knows that with these numbers, to stabilize the market for public debt for guilds on the one hand, and the value of the pound, it should push interest rates to 6%. This is my estimation of what would now stabilize the money markets in Britain. But that would break like a toy the housing market. It would cause untold hardship upon the middle classes that have indebted themselves hugely over the last 10 years. The British economy has been addicted to low interest rates. And now, given the fiscal largesse of this government, which is a rookie mistake, I have to make this point, I think. Nevertheless, the Bank of England now is forced. It has to push interest rates up very quickly. What they did today was to buy some time by reversing quantitative tightening and doing more QE. But now you have a situation where the Bank of England is going to raise interest rates until the housing market does break. Historically, you've been no fan of the IMF, but from the sounds of it, you're suggesting that actually what they're doing here is right. Do you agree with them that the UK government should reconsider the measures they've taken? Oh, absolutely. I'm very much fear, however, that this Chancellor is going to make a bad thing worse. That he's unwilling to reverse course. And what he will do is precisely the wrong thing.