So, Jonathan H. H. starts with a metaphor which I like, it's a metaphor that's going to get me off on a tangent, but it's probably a metaphor that is worth talking about because it both shows you the quality of the Jewish Christian God and also serves as a metaphor for life today. So, the metaphor Jonathan H. uses is that of the Tower of Babel, and I don't know how many of you remember the story of the Tower of Babel, how many of you remember it from when you read the book of Genesis? I'm curious, how many of you have read the book of Genesis? How many of you have read the Old Testament? I'd be curious to know. Anyway, in the book of Genesis, we are told that the descendants of Noah, the descendants of Noah actually achieve great things, and they are successful, and they are confident, and they are excited, and what they do is they go out there and they actually build a skyscraper. They build a tower that is reaching the heavens, and wow, I mean, this is like, I don't know, 5,000 years ago, and they're building this amazing, really, really, really high tower feet of human engineering and coordination and work together, and it's quite spectacular, and God looks at this, God looks at this, and he's offended, he's offended. And I'm quoting here, this is Jonathan Haid quoting the Bible as humans. This is a good translation, I don't know. He says, look, they are one people, and they have all one language, and this is only the beginning of what they will do. This species, these human beings, this is me now, they have one language, they're one people, they're united, they can communicate, they can trade, they can build stuff, they're making stuff, they're achieving things. And this is only the beginning of what they will do, God says to himself, I guess he's talking to himself. Nothing that they propose to do will now be impossible for them, wow, what a great statement about human beings, isn't that terrific?  He doesn't like the hubris of these human beings who think they can do anything they want, who think they can build skyscrapers into the heavens, who think they can achieve all the things and do the impossible on earth.  She goes down and he basically gives them, it makes it impossible for them to understand each other. They all have their own language now. You know, this is anti-globalization, right? This is, you can't trade, you can't communicate, you can't build anything together, and as a consequence of the inability to communicate, inability to work together, inability now to cooperate because they're all talking different languages, they can't understand anything. They falter in the building of the tower, the tower collapses, it goes into decay, it's not clear from the book, but that you get this complete another fragmentation of society, complete fragmentation at the individual level. So Jonathan Haid is drawing the comparison to the world in which we live today. What we've got is more than tribalism, he tells us. It is complete fragmentation, it's person against person. It's we live in a society that is hateful and there's a society where people are going after each other and a society where there is very little unity, coordination, trust, common language, the things that are necessary to build, create, make. Now I think this analogy of a Tau Babel is a really powerful one. It goes to the idea of disintegration and it goes to the idea of the importance of concept and language and the ability to communicate and the ability to trade and the ability to cooperate. It goes to the importance of all of that and one of the, I'd say one of the great beauties of the modern world has been our ability to, in spite of the fact that we speak different languages, in spite of the fact that we come from different cultures, in spite of all that, the amount of cooperation, the amount of communication, the amount of wealth creation that has resulted from the cooperation and communication is astounding and a lot of that is due to what somebody called in the chat, the universal translator. But yeah, in a sense, a kind of ability to translate quickly, an ability to maybe have a uniform language, which is English, which is a globalized language, a global language. But more importantly, the ability to translate quickly and the ability therefore for us to travel all over the world and communicate with people that not that long ago we would have had a really, really hard time communicating.  They speak English, they speak Spanish into the app and the app translated and the app speaks English to me and then I speak English into the app and translate it in Spanish and it's pretty amazing, you can do that in pretty much every language on the planet, pretty cool. So it's interesting that on one hand, we today have the technology to solve the problem created by the envious God who wanted to put human beings down and created a fragmentation of languages. By the way, that is I think the origin of different nations and different, the origin of different languages. It's kind of a weird, weird story to explain all that and presents God in a pretty awful, pretty, pretty awful perspective. But we have today the technology to unify us, to allow us to understand each other, to allow us to cooperate with each other, to allow us to benefit from trade across 8 billion people around the globe and to make us all richer as a consequence, to make our lives all better as a consequence, to enhance the lives of almost everybody on the planet through mutual trade and mutual communication and mutual cooperation and by, in a sense, going back, going back to building the Tower of Abel. And I think that's the path humanity has been on. It's been on a path of greater and greater cooperation, integration, cooperation, building wealth creation on this upward path. But Jonathan Haig tells a slightly different story. He says, certainly since the early 2010s, the United States has started completely fragmenting, losing trust in one another. And he blames, for all of this, he blames social media. So I'm going to disagree with him because I don't think, I don't think, oh God, Adam Campbell says, Iran Bible study is the best. I've always wondered why God never told mankind the earth was round. It never occurred to me it was out of jealousy. Most Christians haven't read the Bible, ignorance is bliss. Thanks Adam. And yes, maybe you should do, that's an idea. We should start a Sunday school. And maybe the episode of the Iran book show every Sunday, we should take a passage from the Bible and study it together. I think that would be hilarious. If we could get a good translation, that would be a lot of fun. And I know many of you, I have indeed read almost the entire Old Testament. I think the five books, the five original books, first books of the Bible, I think I've read more than once because, and we studied them at school. So when you read them, we studied them, we studied commentary on them for pretty much every grade in school. In Israel, you study the Bible. There is a Bible class as part of, and you matriculate in your matriculation, you take a matriculation exam in math and in physics and in history and in English and in Bible studies. Everybody does. It's a required course. So Israelis are pretty well versed in the Old Testament. Maybe that's why Israel is a relatively, other than kind of the nutty religious guys, Israel is a pretty secular society because they've actually read the book. They can't take it too seriously once you read it. So Jonathan, he says, you know, we basically are disintegrating and I have a lot of sympathy for this because I think there's a lot of truth in it. We have over the last 10, 15 years, we have as a culture, we have lost trust in all of our institutions. We've lost trust in our government, you know, and both sides. Left hates the government, right hates the government for completely different reasons, but they hate it. We've lost trust in our institutions of our universities, our schools, our teachers and in each other. We don't like each other. We don't trust each other.  I mean, the vitriol that the left expresses towards the right, I remember this came, this really came, the first time I really realized this was in an episode of Curb Your Enthusiasm, episode of Curb Your Enthusiasm. I don't know if you've seen that show, hysterical show, and you know, really, can be really, really funny and really, really embarrassing sometimes. Anyway, this is a show, this is a show, Larry David, Larry David at the time, I can't remember what season this was, but this is a season where he's still married, he's married to his wife. And I think for their 10th wedding anniversary, his wife says, you can sleep with anybody. You know, once you can go out there, find out what you can sleep with, you can have sex with another woman, I'm fine with it, go do it.  Anyway, he finds somebody, right, an actress, I think, and this is in her, in her trailer.  And from the corner of his eye, he sees a picture, I think it's of like George Bush, right?  And she says, yeah.   He can't have a relationship with a Republican woman. And you know, she's like incredibly good looking and everything. And she's just, you know, this is the, this is the thing. This is the one time he's going to have to sleep with another woman with his wife's permission and he can't go through it. Not because of anything, not because of morality, not because of, he doesn't like her, not because he doesn't love her, not because he's not attracted to her, not because of any of those things, but because she's a Republican. And that's when it kind of hit me. Yeah, there are people who are like that. They, it's not just they disagree. It's not just that they think the other party is wrong. It's that they literally despise the other party. They literally hate the other side. I think that's the first time it really hit me in, in, as an expression of the popular culture, a question of the popular culture of that. So, you know, society is broken apart. And of course, there's a sense in which American society has always had this.  And it was always a kind of a spiteful and angry and there were long debates and arguments and people called each other names going back to the founders. And, and they didn't agree and they, but it seems like it's much worse today. And it seems in a sense that it's infected more than just the intellectuals, more than just the politicians, it's all of us. And again, Jonathan Haidt, as we'll get to blame social media for this. And that it's a distrust of all the institutions that, you know, one of the things he mentions is the news, obviously. Newspapers, TV, TV stations, and so on. But again, universities and government and all these institutions, we now resent, we hate, we don't believe, we don't trust. And of course, he doesn't say this in the article, I will say it. I think what we've grown most distrustful of is, or put it this way, what used to unite us in spite of all the divisions, which used to be common among Americans in spite of all the divisions, was a particular sense of life, was a particular view of the world that don't tread on me, I can achieve anything, leave me alone, let me be free. So a sudden, and this was true, I think, in the past of left, right, you know, maybe not the progressives, but the thrust of the American people had a deep respect and love for the founding fathers, a deep respect of love for America, but a deep respect and love for America not because of the beauty of the countryside, not because of its religiosity, that's Tucker Carlson's view of America. But they had a deep respect and love for America because it was the land of the free, because if there was the land of opportunity, because it was the land of, leave me alone, let me do my thing, let me go and achieve. And maybe I'm giving you my explanation for all this before we even get to Jonathan Hayes, that has gone away, that we no longer trust. We don't no longer believe that this is the land of opportunity. We no longer believe that this is the land of freedom and liberty, and with good reason, right, we don't share that view. We're now divided and entitled into looters, moochers, producers. We divide ourselves and have been divided by the mixed economy into fighting over who, how to divide the so-called pie. Rather than going out there and pursuing our dream and achieving and succeeding, we're now squabbling off of the crumbs that are left to us by the welfare state. So if I had to pinpoint the origin of the division in America, the origin of the fragmentation in America, it is the welfare state. And it is the mixed economy. It is the complete disintegration of any kind of ideology. And the horrific way in which we mean taught history, the founding fathers, and the principles in which this country was created. And this is true, again, left and right. This is not, I blame them all. So yes, on the right, you get love of country, you get patriotism, but you don't get love of America. You get love of country. Fill in the blank. But a love of America requires an understanding of American principles. It requires the understanding of what America stands for. It requires an understanding of what liberty and freedom are. And that's what we don't have, and that's what's causing this fragmentation. But, see, Jonathan Haidt can't see that. And most of the intellectuals today can't see that. Because they occupy the mixed economy. They are the mixed economy. They, you know, these what I consider better intellectuals, certainly better intellectuals of the left, they occupy this mishmash, this convoluted middle, this no principled stand, this, they can't quite articulate what made America great and what changed. Well, what changed was, what changed was less liberty, less freedom, greater dependence on the one institution we should not depend on, which is government, beyond our safety, your government. So you see this from all these intellectuals in the middle. They are intellectually a mixed economy. And that cannot hold. And the culture of a mixed economy cannot hold. A culture that says, we want freedom, but not too much. We want people to be responsible for themselves, but we also want to give them a robust safety net at other people's expense. We want entrepreneurs, but we want to control them. All of that is completely untenable. And then, of course, there is a reason why we came to distrust our institutions. There is a reason why we distrust government. It has failed over and over and over again. It has failed in every promise it has made from suddenly over the last 50 years from the war in Vietnam, massive failure to the war on poverty, massive failure to the war on drugs, massive failure to the war in Iraq and Afghanistan, massive failures to the promise of economic growth, unstoppable economic growth and prosperity, massive failure. Our government has failed over and over and over again. The promise of an uncorrupt government. Well, we saw Watergate. And of course, we saw the way politicians have behaved and the way they've gotten rich since then, massive failure. So there's a reason why we lose trust in an institution which has nothing to do with social media. It has everything to do with those institutions deserving, deserving that we stop trusting them. There's a reason we've lost trust in the universities. Universities have become and not just over the last 10 years as Jonathan Haid would argue. No, systematically over the last 100 years they become more and more and more captured by one philosophical attitude, view, political attitude, view has become less and less open debate, new ideas, less and less respected, less and less treated well. I mean, just as an objectivist, how many times do I hear the story of students telling me that they're afraid, afraid to bring up Inran's name in class? Not afraid because the professor would challenge them, ask them difficult questions. Afraid because the professor would dismiss it out of hand and not even wanna relate to it, not even wanna comment on it, dismiss them and Inran completely out of, completely dismissing the student themselves. And of course, that has become the scope of what is acceptable in universities has shrunk and got narrower and narrower over time. There's a reason we stop trusting the universities. And what they do teach is often so ridiculous and silly and unintelligible and ludicrous and impractical that yeah, we've lost trust. And then of course, there's the media, which is another major source of lack of trust. It used to be that people trusted the media. There was their local newspaper, there was some national newspapers, there were three networks and that was it. And even back then, I questioned how much people should have trusted the media. Even back then, you could get out and out communists, what was the guy's name who in the 1930s wrote for the Washington, for the New York Times, I forget. But even the media has become more and more and more biased, more and more and more, even before social media, more and more and more partisan. Social media has made that much worse but I doubt that started with social media seems to be started with cable news but of course cable news, one of the reasons cable news was successful was because it started before that. It just that the only thing that you had was left-leaning media, that's all there was. There was very little anything else. Then you had talk radio, which provided some right-leaning, and then only you got Fox and everything kind of splendid from there. So our institutions in a sense deserved the lack of trust. Deserve, yeah, Walter Durante, thank you. Thank you, Alex, Walter Durante. Our institutions deserve, you can look him up, he's interesting, interesting how much influence he had on FDR, on I think many American intellectuals, his portrayal of Stalin's Soviet Union, or Russia, communist Russia at the time, was super influential, you should read about him. Yes, so all these institutions justifiably, I think, started losing our trust slowly a long time ago, but you see, some of us knew this all the time. Yeah, I can't really, you have to be careful when you read the New York Times, you have to adjust, I in fact, of course would read the New York Times and comment on it and comment on the biases and comment on the way they spun stories. And we all knew this, but it didn't become popular, it didn't become something everybody knew until more recently. So the lack of trust was something that evolved, it's something that has grown and that social media has allowed to explode, allowed to come out and dominate the scene, because what in these does social media do? And it's interesting that I hate, and I've seen others do this, they point to a particular moment in time as the beginning of the deterioration of social media, because if you think about early social media, if you think about Facebook and Twitter, particularly Facebook in the early days, or as though in some other social media platforms before Facebook, the primary thing that they were good for, it was you put up pictures, you chat with friends, you discuss common interest, it was all free, you connected with people you hadn't connected with in a long time, this is my space, right? My space, this is a wonderful thing, this is just communication, creating new spaces, new groups of people who share particular values. But what happened is, I think, you know, people would share information about themselves, but it was limited to the people in your group and your universe, right? And that in a sense changed. And it's changed, I think in 2011, right, is the year where everything in social media changed, because it changed when social media introduced first the like button, so people could come and like your stuff, then Twitter introduced the retreat button, which basically not only expressed liking the stuff, but now sent whatever it is that you tweeted to everybody that I know and affirmed my support of what you were tweeting, so now I'm voting in support of you. Facebook immediately followed with the share button. And as soon as you got the retreat and the share, things just exploded. Now again, these things exploded into a world where we're already losing trust in institutions. But suddenly you had millions, tens of millions, hundreds of millions of people suddenly participated in what they thought was good, what they thought was bad, what they think was okay, what they think should grow, what they think should shrink, suddenly everybody got a vote. And suddenly it became a game. Who could get more likes? Who could get more retweets? Who could build the biggest audience? Who could get the most subscribers? And how do you do that? We do that through outrage, offense. It turned out that the things that got the most positive response on Facebook and Twitter were the things that were angriest, the things that were most offensive, the things that took the most craziest stands possible. And suddenly the world started fragmenting into little groups of people angry at other groups and achieving fame, success and fame on the particular social media through attacking other groups.  And again, people didn't know what to trust. They didn't know who to trust. They didn't trust anybody maybe. Which left them open to conspiracy theories. It left them open to wacky, crazy, silly ideologies. It made them open to anger and hatred. So what dominated, both on the left and on the right were a few small groups who took their ideology super seriously, whose ideology became more and more and more detached from actual reality. But who agitated and who dominated started to dominate this public sphere. And that made people who already distrustful, disagreeable, it made them even more so. It made them more suspicious. Now this whole phenomenon of social media and people using social media to get the news and people using social media to discover the truth and people using social media, in a sense as a forum that replaces all those institutions they used to trust. So social media became the place where you went to find out about stuff instead of going to the newspaper, instead of going to TV or instead of going to a book or instead of going to anything else. You went on Twitter and asked a question or you went on Twitter and made a comment. Now there's something here that's interesting and I'm gonna just talk about this for a second. This is me thinking aloud if you will because I find this interesting. There's a sense in which, particularly when it comes to media, maybe even when it comes to universities, the 20th century, 19th century and the 20th century build institutions that were focused around expertise. They were focused around the idea of division of labor in this way. Most of us are too busy making a living, getting to work, raising families. I mean, particularly in the 19th century, early 20th century, we spent a lot of time at work, a lot of time scraping by, a lot of time getting by. We didn't have time to investigate, to figure out exactly what's going on, to have vast opinions about lots of different things. Most people, most people accepted a certain division of labor where the media were experts. They did their job, they got the news and we consumed it and we did our thing and who has time to question what they're saying. Who has time to figure out biases or anything like that which is accepting. And there's a sudden sense, the same at universities. People didn't read that much, people didn't know that much, people weren't exposed to that much information. There were experts, those experts told us and I think most people just accepted what the experts had in pretty much every field, from the intellectual fields to the sciences, to medicine, to pretty much everything. But what has happened, what has happened because of a combination of two things. What has happened because the combination of more free time, wealth, more time to read, more time to investigate, more time to think, even for the average person. The work week is for shorter, we have a lot more time on our hands. We have fewer kids, we have a lot more time on our hands. So the combination of having more time on our hands, with the internet, has suddenly brought vast quantities of information, to our fingertips, to our eyeballs, to right there, right in front of us, it's right here. Why do I need the New York Times, when I can, I'm getting the information directly from the sources, supposedly at least. Why can't I come to conclusions, why can't I decide, what do I need, what do I need the experts for? And what we've seen is this idea that, well, I have enough time, I can figure out what's true and what's not, I don't need to listen to experts, everybody can figure this out, we have access to the data, it's right here, it's right in front of us, the internet gives us all this, and it's massive amounts of information. It's unbelievable amounts of information. So information went from being scarce, only the experts could access it, to information being massive. And now institutions fell apart as a consequence, not a consequence primarily of this, consequence of ideas, but also this, suddenly, we were looking at stuff and looking at what they were teaching at university, or looking at stuff and reading what they were saying in the newspaper and saying, this doesn't match. And people were doing this across the political and philosophical spectrum, these things don't match, what's going on here? And suddenly everybody thought they could become an expert. You saw this more than anything, you saw this with COVID. Everybody suddenly became an expert in statistics. Everybody was reading studies, everybody could tell you what a good study was, what a bad study was, everybody could tell you what drugs worked and what drugs didn't work, everybody had an opinion about the vaccines, everybody had an opinion about masks, everybody had an opinion about everything. How many people are actually qualified to have an opinion about any of these things? Very few, very few. But the internet, social media and our time, particularly during COVID, we had lots of time, gave everybody the impression that this isn't hard, of course it is hard, it's super hard. And of course what really happened wasn't that people were looking at studies, people just picked and chose based on who knows what, emotion mostly, which experts they wanted to listen to, but now they had access to a million experts. Yeah, many experts got it wrong and almost all the amateurs got it wrong because epidemiology is a difficult field. It requires years of study. Statistics is a difficult science. It requires real study. And it's statistics in particularly probability theory is super counterintuitive. Your intuition doesn't work when it comes to probability. So everybody becomes an expert suddenly.  Because they read it, they saw a video on YouTube. I saw a video on YouTube and it says XYZ and everything's right. So you really get this perfect storm in the 2010s. You get a situation where the institutions are crumbling because of the mixed economy. And again, mixed economy here also, this is around ideas, a lack of any kind of principled ideas and to the extent that there are any ideas, they're kind of wacky left ideas. A mixed economy of the soul and a mixed economy of the economy and a welfare state. So the institutions are crumbling, we're losing trust in them anyway. And then you get the internet in around 2000 really, mid 90s, early 2000s, just flooding us with information. And then on top of that, you get social media that now takes that information and puts it in bite sizes and allows us to consume only the information from people who agree with us already. So it's reinforcing the ideas that we have, reinforcing our supposed expertise. So when we go and look up COVID, we only see the experts that agree with us. We don't see anybody else. And if we see anybody else that presented as, oh, those wackos, those crazies. So we get these echo chambers, these bubbles, however you want to call it. So I think social media has been over the last 10 years super destructive in that sense. But it's only super destructive because it came when it came as all these institutions are crumbling as we were getting flooded with information and as we're wealthy enough to have time, to have time to supposedly analyze all this. Thank you for listening or watching the Iran book show. If you'd like to support the show, we make it as easy as possible for you to trade with me. You get value from listening, you get value from watching, show your appreciation. You can do that by going to iranbookshow.com slash support by going to Patreon, subscribe star locals and just making a appropriate contribution on any one of those, any one of those channels. Also, if you'd like to see the Iran book show grow, please consider sharing our content and of course subscribe, press that little bell button right down there on YouTube so that you get an announcement when we go live. And for those of you who are already subscribers and those of you who are already supporters of the show, thank you. I very much appreciate it.


Liane did her PhD with Kristal van Sen in Liège and she'll tell us about the outcome of her work, the floor is yours. Thank you. Good morning and thank you Karsten for the presentation. I'm very happy to be here. This title may seem a bit specific but with simpler word it means that I'm going to talk about learning from data. And I'll take the specific example of systems in biomedicine. When you are doing data science there are some generic steps that you will apply in most cases. And here I will take a use case, a question to show the importance of these steps and to show how we can deal with them. So the question that I'm going to take here is how could we understand better the genetic architecture of complex diseases? Complex diseases are caused by the interaction between environmental genetic and lifestyle factors. And it's important to understand them because while understanding the problem is often the first step towards solving it. So for example in the medical context it can be the first step to translating biological findings into real life improvements such as drug recommendation or tools for diagnosis. And in any data science project the heart of the project, the foundation is the data. What data do we need? What data do we have? And how should we represent the data? And this last question is actually very important because the way you represent the data will have a direct impact on the model that you use and then on the result that you get. So it's not only important at the output level it's also important at the input level. In life science and actually in many domains such as computer science and sociology we have more and more proofs that often elements are not independent they are interconnected. And a good way to see that is to see the elements as a system with interconnecting features. And this is why networks come in because networks is a good visualization of connected features. Networks are a data structure with nodes and edges modeling the relations between the nodes. There are different flavors of networks, different families of networks. For example, networks can be binary. In that case the nodes and the edges will either be present or absent but it can also be weighted networks where the edge weights can represent the strengths of the association or the confidence we have in the association. So graphs are not only useful to see a global visualization of the data. It also shows the different substructures so you can zoom in and see important details about the substructures. In the meantime we have increasingly complex data and this gives rise to heterogeneous networks. In other words we have different points of view to look at the data. We also have a different methods to analyze the data and this will give differences in networks. This is what I call network heterogeneity and this complicates the replication and the interpretation of findings. And the last concept that I need to introduce to reply to the main question which was how could we understand the genetic architecture of complex diseases better is the concept of epistasis. Epistasis occur when the effect of a combination of genes is not due to their independent effects. So in epistasis networks, the nodes will be genetic units of analysis for example genes and there will be an edge between two nodes if there is an effect of the combination of the nodes on the phenotype. Epistasis detection is a quite complex problem, especially because it involves a lot of statistical tests. So there are many methods developed for that but they all give rise to different epistasis networks. And today there is no real grip on what aspects of the methodology is producing the differences and so similarity in the network or in the networks or dissimilarity in the networks. Okay so now we have all the concepts and this figure is summarizing the main steps we are proposing to solve the problem. So first we will see how detecting and understanding the sources of heterogeneity will help to see the differences we would like to reduce in the networks. Then we will specifically see the reduction of the heterogeneity between the networks and finally we will have a look at how it can enhance the interpretability. So in this first project we are interested in graph comparison, we would like to create groups of graphs. In one group the graphs are similar but there are several specificities in our context. First we would like to derive an algorithm that is as I said specific for graphs that mean that the distance that we are going to use for example should be derived for graph. And then we would like to do clustering, so unsupervised classification because most of the time very often and especially in the biomedical context. We don't know the group label for example in this typing, sometimes the group labels are not known. And actually here we don't even know the number of groups we would like to derive so the algorithm will need to derive it itself. And finally we would like to incorporate some notion of significance in the algorithm because we would like to make sure that if we say that two groups are different then they are statistically significantly different. So we developed the pipeline presented here that we called Netanoga. Starting from a list of networks we compute the pairwise distance between the pairs of networks. There are many existing metrics for that. It will be dependent on the context for example in epistasis. We don't only want to compare the structure of the networks but also the name of the nodes is important genes. So we will need a distance that requires no not correspondence. We apply an unsupervised clustering algorithm to get some groups and to really see where we should put the truncation point in the dendrogram. So how we could obtain the final groups we apply a recursive algorithm. So we go from the top to the bottom of the dendrogram and we apply a customized ANOVA test to see whether the within group variation is smaller than the between group variation. And we do that for the two first group. If the groups are statistically different then we do it in each of the two subgroups and so on. So we are kind of building a tree where there are two sub conditions. Either the groups are not statistically different or the groups are too small. And to evaluate the model, the algorithm, we did some simulations. We created an original network. We disturbed it to create group networks and then we disturbed them again to create individual networks. And the goal is from the individual networks to trace back to which group they belong. To do that we evaluate the performance with the jacar index. So it ranges from 0 to 1 and 1 is the best value. This is not shown here but we evaluated the type 1 error and it's under control. This means that when we don't have any group then we don't detect any group which is good. Here is the figure for the power. So when there are groups do we detect the groups? We can see that some factors influence the results and some not. For example, the number of networks and the density of the networks does not seem to have a big influence on the detection of the group. But for example, the number of groups or the multiple testing correction algorithm that we apply will have an effect. And regarding the multiple testing correction, it's the thing that we need a not too stringent multiple testing correction because the algorithm in itself is already a bit conservative. Now we can detect groups of similar graphs so we can understand the sources of heterogeneity. And now we would like to reduce this heterogeneity. So in this project we'll talk about the importance of choosing the variables wisely and cleaning the data. Yeah, the importance of cleaning the data. So here we started from two observations. First, we have a growing biological knowledge and there is a need to study the combination of this biological knowledge with epistasis to see if we can get better results. And the second thing is that genes are often the natural units of analysis in biology because they are easily interpretable, more easily interpretable and they can be linked for example to pathways. Biological pathways, but in epistasis the unit of analysis is very often the SNPs. So a smaller unit of analysis. So we would like to study the impact of going from SNPs to genes at the bottom of the slide. This is a simplified version of the workflow. We test the SNP, the epistasis at the SNP level and we define some function to map SNPs to genes. For example, it can simply based on physical distance, but we also tried mappings based on QTL information or chromatin information. Then we convert SNP tests into gene tests using the adaptive truncated product methodology to aggregate test results. So basically it aggregates the p-value that are below some pre-specified threshold. Then we used biological knowledge to reduce the search space. So we focus on gene interaction that are known and importantly it's not gene interaction that are known for the disease and their investigation. It's gene interaction that are known in general for any biological process. And finally we combine the analysis with the pathway analysis to see the broader context in which epistasis may occur. This is an extract of the results. We applied different variations of the pipeline I just presented to an IBD dataset which contains approximately 70,000 individuals. Half of them are cases. They either have transities or cerative polices and half of them are controls. What we see here is that when we apply different mappings, for example here is standard physical mapping or QTL and chromatin which is more related to functionality. And also for QTL and chromatin we filter for biological knowledge. We keep the gene interaction that are known to be interacting. Then we see that we get very different results. For example on the left with the standard pipeline, even though we test a lot of SNPs and a lot of genes. So the multiple testing correction is quite strong but still this is a pipeline which gives rise to the bigger network. And for the two networks on the right, yes we have less interactions but these interactions are more interpretable and they are more robust and also they lead to relevant pathways. So we get to have different points of view when trying all these different backlines. Then another way to reduce the heterogeneity between networks is to aggregate the networks. For example if we use the first project I presented with group graphs into groups of similar networks and then we can apply an aggregation in each of these groups to get only one representative graph per group. It means that we are creating an aggregated graph which is kind of a summary of the group. So it's also based on simulations. We created a network that we call the true network and some partial networks that only partially represent the true network. And the goal is from the partial networks, from the edges of the partial networks to predict the value of the edges in the true network. So the rule of one, the edge is absent or it's present. We tried three models to see if it's possible, we tried K-means, Latin class analysis and similarity network fusion. And we evaluate the performance with the F1 score because we have data in balance. We have a lot more edges that are absent than edges that are present. We tried multiple settings. For example in graph C we increased the number of nodes in the partial networks and in graph T we increased the number of partial networks. And what we saw is that the Latin class analysis methods is outperforming the other in all scenarios. So this is an example of a method that we could use if we want to summarize multiple networks into only one to get a representative network that is easier to compare to other representative networks. Results are comparing many networks from many groups. Okay, so now that we have all the methods, let's solve the main question we asked at the beginning. Let's gather them to see what we can do with them. We have seen that when we apply different pipeline for epistatic detection, then we obtain very different results. So for example here when we applied 10 different epistatic detection tools to the IBD data set, the same as in the second project. And they already differ in the input they require. For example, some of them will require imputed data or some of them will only deal with binary phenotypes. And they also differ in their output. Some of them will output statistics or P values or some of them will just output the ranking of the, the snippers. So for each of these tools we applied four different variants. Either we corrected for population structure or not, and either we included some biological knowledge or not. So here is an extract of the results at the SNP level so no network information is used here. What we've done is that we selected the top thousand pairs for each workflow for each analysis. And then we computed the number of pairs in common in each different pairs of methods. And what we see is that epiblaster line of regression and boost are quite are very similar to each other. And this is this means that at the SNP level, the comparison is indicative of the tool that we use. It's differentiating the tool that we use and actually is differentiating between the modeling framework because epiblaster line of regression and boost are all based on statistics. On the right is a comparison at the SNP level as well, but based on the rank and we see approximately the same thing. So I will not go into the details. And now we go from SNP to genes. So for that we do the same thing as in the second project. So we map the SNP to the gene using a map function. Here we used the QTL one and we either post filter for biological knowledge or not. We applied the algorithm of the first project of the one where we were building the groups of similar networks. And here we see that we have a big purple group. And in this group, we find all the methods where we used biology so all the biologically driven methods. So that means that when we include biology in the methods, then we reduce the heterogeneity and it's kind of entreated because we target we are reducing the search space here. And on the contrary, when we don't use any biology in the pipeline, we see that it gives rise to many different clusters. So for example, all the analysis based on the neural network weights are in a different cluster and the one with HPM as well. And finally, we applied the network aggregation method that I showed in the third project for all the purple network. So all the networks in the purple group. And what we have seen here is that it gives us to this network where I couldn't write the genomes because there are too many. But many genes are related to the HLA region and the HLA region is known to be very important in IBD. So this overlap with a non biology of IBD. The message of this project is multifold. First, it gives insight into the differences of the methods that results in differences in epistasis networks. Also, it shows the importance of applying multiple epistasis methods on the same data set because we obtained different results. So we obtained different points of view of the disease and also it shows the importance of including biological knowledge. So to summarize, I showed three different methods and I gave them to show their impact on a specific use case. The first thing that we can say is about the versatility of the methods because here I used them for networks. Which was my main PhD topic, but actually most of the methods that we developed are applicable to a lot of types of data. It also shows the important steps that we always apply in any data science project. So choosing the data and the variable wisely here it was focusing on genes instead of SNPs to get more interpretability. Also representing the data, the representation of the data is important. Here we decided to go with networks because we were looking at interactions. Then it's important to look at the sources of variations between the observation. In this case, we did that by grouping similar observations together and by aggregating then this observation, these similar observations. And finally it shows the importance of cleaning the data. Here it was related to the way we filtered the ages in the networks while including biological information. So I would like to thank my collaborator and I also would like to thank you for your attention and let me know if you have any question. Thank you for the questions from the audience. Maybe I start by asking at the end when you compare these different epistasis detection methods to each other you introduce another level of multiple testing, not only in the space of SNP peers or combinations but also in the realm of methods. Have you thought about this and how this affects your results? Since we compare the networks obtained with the different tools or methods, we didn't correct for multiple testing here. We are not doing any prediction and we didn't go a level further the epistasis networks. We started there so we, I mean to my opinion, there was no need to correct for multiple testing. So, but have you looked at questions like whether the consensus between these different methods, for example, has particular properties. So are the interactions, so you show coincidentally showed that the results differ in general that you can detect differences. You also looked at the question whether the common parts, the intersection between the different methods has any special. When we don't use any biological information, sometimes they don't overlap at all. So there is no intersection and that was also one of the reasons we wanted to do this comparison because sometimes there was no intersection, nothing in the intersection at all. But when we do integrate biology then there is indeed something in the intersection and in that case it could be the common biological processes that is probably the most relevant for the disease. And then things that are not in detected in most of the epistasis networks but only in one or the other. It's probably either less important. I would say it's probably not the correct biological word but it's not, at least it's not, it doesn't seem as essential as the gene pairs for example that we find in most of the methods. Thank you. Another question for the end. I would have one more. When you saw these different methods to not necessarily compare exactly or search exactly the same space. So, making up an example someone search pairs of snips other search higher order combinations of snips. So with three or more snips, and then when the methods are a parameter sensitive in the sense that you may choose like a significant threshold for each of these methods but this may mean something different. A quadratically growing search space or a cubically growing search space. And so it's I think it's very hard to find the best way how to compare these different approaches to each other such that the parameters mean exactly the same work. And this and the parameters have an impact on the results that you obtain. So have you considered this point how to maximize the comparability between the different methods. This is a very valid concern. And so what we've done is that we didn't set the exact same threshold for each method first because sometimes it was not possible because as you said sometimes it's just a ranking and sometimes it's a p value so we cannot deal with significance exactly the same way. And what we've done is that for one tool, we took the recommended way of doing significance assessments in the paper where the tool was published. So it's true that it's different ways of looking at significance for each method but since they are so different from each other then there was no way to apply the same threshold. And even if we were applying the same threshold as you were saying then it could compare different things because while the methods were differently so they are not looking at the same search space. Could you look at something like a ranking of the edges that either you try to find one common way of representing the results and you rank all the edges in the network as ranked by the different methods. I mean you're looking at subgraphs sometimes or like like combinations, but maybe, maybe the way to do it is to bring it down to the simplest form, the simplest element or a simplest unit edges, and then to get a ranking of edges for each method and to compare these rankings to each other. Of course you lose some parts of the interaction then if you boil it down to two edges so somewhere you will probably lose some information. I understand correctly. It's a bit similar to what we've done on the right here. So we've ranked the sneak pairs. Yes, yes, and then we compared the ranking, but we needed to again set a threshold because some methods output very few sneak pairs some method output a lot of sneak pairs. So we we went for the minimum. Okay, okay, that's how you how you made it comparable good. Thanks. Okay, so with this we thank the DM for her presentations and her answers.


Coming up on D T N S dating apps want your health info, but can you trust them? Plus foldable start to get better and a webcam you stick in the middle of your screen. This is the Daily Tech news for Wednesday, December 15th, 2021 in Los Angeles. I'm Tom Merritt and from studio Redwood. I'm Sarah Lane in Salt Lake City. I'm Scott Johnson and I'm the show's producer, Roger Chang. There is a longer version of this show full of almond butter and ethical dilemmas called Good Day Internet. You can get that at Patreon.com slash D T N S. By the way, a big thanks to our top patrons, including Bjorn Andre, Jeff Wilkes and Paul Rhys. Let's start with a few tech things you should know. Google announced that its lightweight Android Go OS reached over 200 million daily active users. The company also announced that the Go edition of Android 12 will roll out to some devices in 2022 opening apps up to 30% faster and providing smoother animations. The Financial Times sources say the US Treasury will add eight Chinese companies, including DJI, to the Chinese military industrial complex companies list. This is different from the entity list. This list blocks US citizens from investing in the companies. US Commerce Department already named DJI to the entity list. That's the one that bars US firms from exporting to a company without a license. Disney Plus added support for Apple's SharePlay feature that lets people co view videos over FaceTime. SharePlay already worked with Apple services as well as NBA, TikTok, Twitch, Paramount Plus and Showtime. A SharePlay session can share a Disney Plus video with up to 32 people. You have to start the FaceTime call first, then open up the Disney Plus app, which will then have a notice about SharePlay. Then you tap that you want to start a video and everybody else can see it and control its playback. You can even choose to keep the FaceTime call on one device like an iPhone and the video playing on another like an iPad or an Apple TV, if you're in that universe. All participants will need a Disney Plus subscription for this to work and the latest version of their Apple devices OS. Hey Android fans, you want a tablet? We got a new tablet. Samsung announced the new tablet, the Tab A8. Coming to the US in January has a 10.5 inch screen, 1920 by 1200 resolution. You can choose three or four gigabytes of RAM between 32 to 128 gigabytes of storage. Also has a micro SD card slot so you can put an SD card in there and get it up to a terabyte, a 7,040 milliamp hour battery and supports 15 watt fast charging available in gray, silver and pink gold. No price on it yet, but it's expected to be around the $230 of the previous model or maybe even less. A little bit more Apple news. The company removed mentions of its announced program to scan iCloud photos for child sexual abuse materials or CSAM from its child safety webpage. Apple announced it would indefinitely delay the feature back in September following concerns from privacy and security researchers. Given that they launched the modified text message notification and they removed this, I feel like this one's done that we're not going to be seeing that. All right, let's talk of more concepts. Do you know, Dell's doing that Dell thing, showing off some concepts in advance of CES. Three prototype products on display this week designed for a seamless work experience. There's the concept Flow, the concept Stanza and the concept Pari, not P-A-R-I-S, but P-A-R-I. Flow is a Wi-Fi 6 dock that you can hook up to a monitor keyboard, mouse and charger, and then you don't need to click your laptop into a physical thing. You just set it down near the dock and it collects over Wi-Fi. Bluetooth is used to determine if the laptop is near or not. Second is the concept Stanza, that's an 11 inch companion device tablet, specializing in converting handwriting to text. If you're really into that, I guess it's really cool, but it doesn't do much else. And finally, concept Pari is a magnetic webcam that can stream 1080p footage over Wi-Fi, which means no more nose cam. You can put this wherever you want it. It can go anywhere a magnet can go. So it's a wireless, it's like a little cylinder, and if you get a monitor with magnets behind the display, presumably Dell would make those too, then you can put that little tiny cylindrical camera right in the middle of your screen, right at what you're looking at when you talk to somebody on Zoom or if you're reading something. That way you can look right at the camera while also looking at your screen instead of looking right below your camera. Yes, the company that put a webcam underneath a laptop screen spawning endless instances of nose cam has mended its ways and has a solution for you. Of course, you could also keep it in the charging dock, which you could mount in the normal place on top of your monitor, but you could also hold it in your hand so it has some other flexible options like showing off stuff like sketches or prototypes on your desk during a meeting. You could even put it in its dock backwards because it's wireless and make sure it doesn't turn on accidentally. Anyway, these are all just concepts. None of them are necessarily going to become shipping products, but I'm kind of into concept curry. I think that's, that's clever. It is, but I don't trust, I don't trust a camera without a battery, Sarah. I don't, I don't. It's got a battery. Or excuse me, with the battery is what I meant to say. I don't trust one with one because here's what happens. Imagine those times when you're, uh, I don't know, you're professionally gaming and your mouse, which happens to have batteries. They die in the middle of the most important match of your career. But I don't want that happening to my camera. Like in the middle of the DTNS imagine if my camera died. What would we do? Oh no, we'd all freak out and everyone would be mad at Scott for his camera going dead. So that's the only caveat. Otherwise that's a really cool idea. I just don't know if I trust batteries. It's a cool idea.  Um, I understand what it means. Uh, and it's something that I think a lot of us deal with, you know, my, my, uh, my webcam is above my screen. And so you'd probably get a little nose cam going on there. Uh, putting a camera in the middle of your screen while your eye level would be nice because of that. It doesn't seem like it makes a lot of logical sense for the person with the webcam. What if you, what if that web cam is right over a really important word? That's kind of the idea. This thing is like smaller than a finger, right? You just, just pop it in the middle. You can work around it. You can move your window. I suppose you could. Yeah. And maybe, maybe it's a non-issue. That was just, that was my first feeling about it is like putting something in the middle of the screen. And then, you know, my screen is set up very specifically for a reason. I can't have anything in the way of that. Yeah. I think the device looks cool though. I'll give them that. That little cylinder's rad. Yeah. I like it. I do wonder if this all solves all nose cams though, because a lot of nose cam, some of the nose cam comes from having it too low, like in that Dell laptop.  Well, and it's at the top of your laptop. So you could, if you put that thing even lower, you might be risking nose ear cam. Even worse. Well, and to be clear, and I'm new to this concept, but nose cam can be the camera's too high. So you're looking up, right? And you kind of look funny, or the camera's too low and it's actually looking up your nose. Yeah. That's the, the more common is, is your, it's below you and it's shooting right up your nose. I do think it's not lost on me. This is small enough to put inside your nose. Oh, hey, really want to do now. Now we're talking. Now we're talking. Forget about Google Blaster or Spectacles. It's all to put inside your nose, but where is it going to go? You're looking the other way at this point. That's a way to, that's a privacy feature. If you want to make sure no one could see your video, stick it in your, no. Yeah. It's Justin. Sorry, guys. Nose cam. Super nose cam. Well, we talked about Oppo's Find and Phone the other day, and we have a few more details the company announced. The Verge's, Sam Beiford has been using it for a week and called it impressive and refined. That's pretty good, right? The Find N is a 7.1 inch, 120 Hertz OLED display, when unfolded with a nine by eight by four aspect ratio. So it's slightly rectangular. Also uses a foldable panel made by Samsung with some Oppo patented technology included like the hinge. The screen wraps around a tear drop shaped hinge, which makes the crease wider, but also less visible. Might be good, might be bad, depending on who you are. The hinge lets the phone shut without a gap, probably something that a lot of people would like. The screen is protected by ultra thin glass and rated for two 20,000 folds, which is the same as the Galaxy Z Fold. That's a lot. You know, is it for the life of your device? Depends on who you are. When folded, the Find N has a 5.49 inch 60 Hertz screen on the outside, with a two by one aspect ratio, making it look a little bit like a regular Android phone, a little thicker with a slightly larger bezel on the left side. Maybe more than you would normally expect, but this is a flagship phone. Otherwise, pretty normal specs. Snapdragon 888 up to 12 gigs of ram, 50 megapixel camera, etc. The fingerprint sensor is in the power button and it supports reverse charging with the Qi standard. So if you're in China, you can get one starting December 23rd starting at 7,699 yuan, about 1200 US dollars for comparison if you'd like to do that sort of thing. The iPhone starts at 1300 yuan more. Yeah. So the iPhone is more expensive than this because when you do those conversions, it's like, well, that's $1,200, which sounds expensive, but that's cheaper than the $1,400 equivalent of the Fold from the Galaxy Fold. It's also cheaper than an iPhone because prices are sometimes different, right? You can't just do this straight comparison. That's a good comparison to make there. This thing is expensive, but it's not as expensive as a lot of the foldables have been. I think that's the takeaway there. I think this shows that there's something to the foldable market. The fact that Oppo went to the trouble of saying, well, let's get the display from Samsung, which makes the display. If you're like, why is Samsung selling a display to a competitor Oppo? It's because it's a big company and part of its business is displays and selling displays to everybody kind of the way Samsung sells chips to everybody, even though it also makes phones, two different parts of the company. Anyway, this is Oppo saying, let's take that display and let's figure out how to improve on it. Let's make that crease a little less visible. And Sam Biford said that it kind of disappeared, which it never really does with the Galaxy Fold. The fact that it lays flat, like you said, Sarah, when it's folded up, that's a big deal. Whereas the Galaxy Fold never quite lays entirely flat. And that screen, even with that little bit of bezel that you're talking about, sure looks like a phone. It's a lot thicker, but it looks like a phone. Whereas, again, the Galaxy Fold doesn't really look like kind of a tall, weird phone when it's folded up. So this is all incremental progress in making foldables work a little better. And I think that is telling the fact that we're seeing that kind of progress means maybe there is a market here. But boy, is it incremental. We've been talking about this for a couple of years now. Still getting to that point. I remember hearing about the first one and going, the first foldable I'd seen and went, I don't know about this, this seems ridiculous to me and kind of, you know, prototypy and not going to be a thing that we'll see do very well. And I think I was wrong. I think this might be the future if they can figure it out. And the thing that is most impressive is I'm looking at thing laid flat. I don't see a seam. That just looks like a tiny tablet to me. And I think that's the idea. So the closer we get to that, the better we get in that range, the better off we're going to be, you know, when, when will it become a mainstream draw where people are like, I gotta have a foldable. I don't know when that is, but I'm starting to think they're probably here to stay. Yeah. And that's, that's where I think I'm going with this is, sir, you're, you're entirely right. Like this isn't like, oh, here's the one we fixed it all. Right. But the fact that they made even that incremental improvement means it was worth doing. They didn't look at it like, well, this is all we could do and the market's not that big. So let's not bother.  Those are, those are things that we'll be able to get people to, to hop on board for. So for sure. I mean, when the, when the day comes where it's like, I've got a phone or, you know, a small mobile device. And I also have a tablet and it's all the same thing. I'm in. I think we're, we're still figuring that out. Yeah. It's, this is still for early adopters. It's, but it's getting better every time. So that time when you might be like, Hey, I think that's, that's the one. I mean, obviously everybody's waiting for Apple to come out with theirs that, that often is the harbinger of a product category being accepted. But, but yeah, everybody's waiting, waiting for that one. We get some of our best ideas on what to talk about on the show from you. Man, we do a lot of work. We look at feedlies. We look at Google news. We look at tech meme. We look at subreddit. In fact, your subreddit is one of the best ways to let us know, Hey, this is important to us. So get on in there and vote at dailytechnewshow.reddit.com. All right. You want to go to Norway? Let's take a trip. Let's talk about data protection. Norway's data protection authority has fined dating app Grindr 65 million Norwegian Chrome, which is about equivalent in US dollars, 7.1 million. The authority determined Grindr's violated or Grindr violated the GDPR by sharing some data, including sexual orientation with advertisers without proper consent procedures. Basically until it had until it changed the practice in April of 2020 Grindr required you to agree to sharing this information in order to use the app at all. That is a big GDPR. No, no. This kind of thing is notable given the increase of the amount of data individuals are sharing on dating apps themselves. Much more people are manually adding things like vaccination status to their profiles and apps like hinge and bumble are considering features to let users add other kinds of sexual health information. It's a little bit different though than just taking that information in general. The conversation, the conversation meaning the entity, the conversation notes that profiles in apps like Grindr and Adam for Adam can include HIV status. For example, seems like a good thing to do. However, some dating apps like Tinder's sparse structure don't lend themselves to sharing such info and may need to adapt. What this comes down to is you all goofed. You have too much information that you're sharing and you didn't go through the right channels to do it.  Well, sure. Grindr stopped doing it in April 2020. So that's good. But there's a whole question here about data sharing beyond that. That's the news hook of like, hey, they got busted for this. They say they've mended their ways. They even try to argue that this shouldn't have been and they might appeal it. But there's more sensitive data coming, not just sexual orientation, but also things like sexually transmitted diseases where people are wanting to share that. They want a good way to handle that kind of sensitive information so that people are informed when they're when they're making dating decisions. Now, you may sit there and say, like, who's going to admit that? And yeah, a lot of people won't. And that's a whole problem of itself. But there are situations where, like you said with HIV, for example, people are willing to say, like, yes, I've got I've been tested. This is my status. And it won't necessarily prevent them from getting a date. It's just good practice.  So vaccination status, COVID, what I'm what I'm comfortable doing in a world with COVID, that's a huge part of this as well. And so with all of that together, like, these apps need to accommodate that, not only make it easy for people to share, but make it easy for people to share privately, people to be in control of that. No, it's not going to be shared with anyone else, but the people they want to share it with when they want to share it. And some apps, it's like, like you said, like Grindr, Adam for Adam and others like that are doing okay with this. They're sort of leading the way. But there's a lot of apps out there that are dragging their feet. Yeah, I think, I think that's, that's the big thing. If you're on a dating app, if you're interested in meeting somebody, and I'm not even talking about any app in particular, but an app where people are, you know, potentially going to meet a romantic match, there are all sorts of things that you would potentially want that other person to know about you or want to know about them. And that feels kind of safe, right? Well, but then when you get into a situation where it's like, well, this app is sharing this information with advertisers, advertisers that, you know, you're not in control of, you don't even really know what it is.  That's where it just, it gets bad. And it sounds like the GDPR issues that Grindr went through in 2020, you know, they're trying to rectify. But, but this, this is something, you know, I think about this all the time, you know, even when I'm sharing information with my, the company that pays my healthcare, you know, it's like, this is all stuff that is, we should have as much information about as possible. Yeah, I don't think we're trying to imply that, that if you share your vaccination status or your HIV status or some other sexual transmitted disease on a dating app, that it's going to go to advertisers. In fact, I think that Grindr story shows that it's not, they're going to be careful about that. The next step is, but are you going to handle it responsibly? Are you going to make sure it doesn't get shared with other members accidentally? Are you going to be HIPAA compliant or wherever the equivalent is in the country I live in? Are you going to make sure that it's secure, that it doesn't get leaked out in a data breach? I mean, that's where I'm going with this. I'm not worried that Grindr's going to share it with an advertiser. Forget that anymore. It's like, yeah, that ship is sailed. They've been punished for that. But there's even more sensitive data coming down the pike that they need to be even more responsible with than just not sharing with third-party data or third-party advertisers. Back in July, we told you about the 11-inch glass lantern Bluetooth speaker from Sony called the LSPX S3. It was the third version of the speaker and one of the least expensive, although it's still fairly pricey, at $350. It's taken a minute, but the Verge finally has a full-on review of this very weird but beautiful speaker. On the downside, the candlelight-looking speaker has no player pause controls on the device itself, only volume, a call answering button, and power. Seems like an odd choice to put the call answering and not the play pause, but okay. It is also a little fragile. It's made of glass. It's got a very weighty bottom, so it's not easy to tip over, but for a Bluetooth speaker that you can move around, something you want to move around carefully, has no voice control options, so it's not that kind of speaker and comes in any color you want as long as it's silver, so just the one color. The battery lasts eight hours on a charge, but again, it's kind of heavy and kind of fragile, so I'm not sure how much moving around you're going to do, but maybe taking it outside for a barbecue or something like that. The light has three modes. One is pretty steady and you can adjust the brightness on that. One that flickers and you can adjust the rate of the flickering. They say it's pretty subtle, though, and then there's one that pulses in time with the music, which is also apparently very subtle. The candlelight effect is apparently pretty convincing. The sound quality is clear, and it even supports the high-bit rate LDAC codec on Android, which Apple and Tidal and a bunch of others support as well. But no voice control options. No voice, yeah. Oh, is that it? That's the killer for you? Really? Oh, for sure. Like, you're mentioning, like, it's fragile, made of glass, kind of beautiful, don't want to move it around too much. Fine. But no voice control? I mean, what am I getting there? It seems weird in 2021. Also, they missed an opportunity. They should have only sold this. It should have only been available on April 20th at 4.20 p.m. for $420. You may be right. I'm telling you, missed opportunity. It does look like the kind of thing that would be used at such a time. I see what you're saying. But think, do neither of you have a Bluetooth speaker that's not voice enabled that you just used as like a portable speaker for your phone? I do have one over here that's currently sitting here, but when it's warm outside, I take it out in the back yard. So I do have that. That's what this is being. That's why it doesn't have voice, because it's like, well, it's not for that. It's not a fixture in your house. I could see that. I just think, I just think Sarah fragile candle. Exactly. So it's kind of living in both worlds. It's like a purse. It's like a permanent piece of furniture in a way. And in 2021, if that thing is a speaker and it doesn't have voice control, it feels like an odd man out, given its stationary status. Yeah. Otherwise, you're just not going to move it around. It's like, no, you don't don't touch your way to bed at night. You'll carry it with you at the soothing. On if it's fragile, I won't.  Like my whole household and studio where I am now is all voice based. Everything is, you know, I tell to do something at this point or that point, or, you know, there's certain routines that I've set up ahead of time. I'm very voice oriented. So to have this sounds great. Sounds like a smart bulb. Smart bulbs do candlelight stuff too, but I can talk to them. Yep. There you have it. All right. Well, let's, let's, um, let's move to the sea. Shall we? If you're not familiar with a small jellyfish found in the Mediterranean sea, specifically called the Clidia hemispherica. Well, let's tell you about it. Brady Waspard, a postdoctoral scholar at Biology and Biological Engineering at Caltech, lead author on a recent paper in Cell, highlighting this very jellyfish that's been genetically modified so that neurons grow, glow rather when they fire, which could offer new insight into the workings of brain activity and non-human species. The researchers introduced a snippet of DNA called G-C-A-M-P, which creates a green fluorescent protein. It's been used in mice, zebrafish flies in the past. So to insert the glowing genes, the researchers took account of Clidia's reproductive system, which is triggered by light. This is where it gets cool. Weisberg says two hours after the lights gone, the jellyfish released eggs and sperm into the water. They're like, okay, we're ready to go. Then the researchers collected the eggs, in this case, injected them with the snippet of code for the green fluorescent trait that they wanted to insert, along with a protein that helped to splice it into the jellyfish's DNA. Now they have a creature to observe under our microscope as it eats. Well, it's neurons glow. Weisberg says you can do really high resolution experiments looking at every neuron's activity over time while the animal is behaving. Essentially, they are understanding the brain activity of the jellyfish. And the weird thing about this particular jellyfish, not all jellyfish are like this, but this one has a modular brain, not a centralized brain. So they want to figure out, like, how does that work? How does it know? Because it does the same behavior of, like, grab food with tentacle, move food towards mouth. But this weird fish can keep eating just as a mouth.  And they're like, we need to see how this works. And this is the way to do it, like, have the thing light up as it's doing stuff so we could see how it's communicating. Also, anybody reading the expanse, I will say no more, but there's a connection here to a protomolecule you may or may not know. I thought about that as well. I also, I just, I don't like to make giant leaps, but I'll make one in this case. I could see a future where we, I mean, we already do this with some dyes when we're getting CT scans and other things where we can kind of see where stuff is and where it's moving to. And, you know, we're getting pretty good at scanning the brain and stuff. There could become a day where we could put a little light in our brain, we could follow it around and have a better idea of how things are functioning. Why not? Yeah. I mean, I've been injected with dye quite recently to figure out what my brain activity is doing but it's not the same as this. No, it's not quite. This is real cool. Like, oh, Sarah's thinking, fluorescent lights, fluorescent lights. And we want voice control. Yes.  It's COVID. Well, if you haven't urged to travel, speaking of COVID, but you're not able to go very far, don't worry. Chris Christensen has a few sites you might want to check out. This is Chris Christensen from Amateur Traveller with another Tech in Travel Minute. As the COVID numbers rise again, I know some people are getting a little concerned about traveling again, but you may have some opportunities for traveling locally as a tourist in your own city. I had a chance to do this in San Francisco just last weekend and went on a big cruise, which I haven't done in many years. You can find a number of those opportunities at various tour companies like Viator.com, GetYourGuide.com or Airbnb Experiences and check out your local city as you would see it as a tourist. I'm Chris Christensen from Amateur Traveller. That's cool. It's cool to have those resources. I did that once when I lived in San Francisco. I went to Fisherman's Wharf and stayed with some friends at a hotel and we pretended to be tourists. It was really fun. That does sound fun. Yeah. Yeah. All right. Let's check out the mailbag. Rick in Kenwood, Texas wrote in and said, after watching the news story about the LG, and this is on our show yesterday, the rolling TV from LG Battery Powered made me think about my version. The photo, which anybody watching the video version of DTNS will see, shows my 43-inch high-sense TV on a rolling stand. All I need to make it wireless power is a jackery power box. I think it'd be a lot less money and would have a longer battery life. This is what I usually watch DTNS on while I eat dinner. I love that. I want that. We don't know what the price or battery life of the LG is. True. I'm going to guess Rick will probably come in right that he's going to be doing it for cheaper. May not be as stylish as LG. So may not it be for everybody. But that's pretty cool. I love the idea of plugging a jackery into that thing. Look at this. People back in the old days of 60s or whatever. They'd roll a TV in there at dinner and put it up there. This looks great. Well, Nathaniel had another thought piggybacking off of Rick's thought. Nathaniel said, I think the mobile LG is a DIY machine. If I can take it from my workbench to under the sink for a how-to video to the engine compartment for my car for assembly instructions to the utility room for furnace wiring diagrams, what a wonderful tool. It's a giant helper tablet. This is a person DIYing. And once the doesn't want to DIY the TV, but once the LG TV to help with the DIY. I love the different perspectives on this. It's super cool. And then we also got Desmond in Richmond, Virginia with a business model idea based on that Dell recycling program we talked about yesterday. Hey, DTNS. I was just listening to your latest episode, episode 471, where you talked about the new Dell laptops that are designed to have the parts all swapped between them. My mind went to an awesome business program they could design where they can serve the same laptops and reuse the parts for one business. Some of their customers at the business or some of the employees might need very high-end machines to do certain kinds of tasks, while others don't need as high-end machines and they can move the parts and machines between all the different ones and Dell would provide the service. That might be what they're going for as an option. Love the show, but I'm listening since 2005 to Tom every day. Thanks. Hey, thanks, Desmond. And that is the kind of thing that I think Dell is playing around with, whether they've thought of your idea or not. Maybe you should, you know, get on board as a consultant because I think that's a cool idea to tell an enterprise. We will supply you the parts your folks can just swap them out as you need them, cascade them down from the power users to the folks who don't need them. That's a great program. I could totally see that. Oh, and he said he's been listening to me since 2005, so we should mention if you want to pretend like you're still listening to me in 2005, there's the BuzzTown archive that Amos and folks have put together of all the original Buzz Outlaw episodes from CNET, circa 2005 through 2013. Go check it out, buzztownarchive.com. Excellent. Well, we love your feedback, whether it's audio, whether it's written, anything that you have photos to share, we'll take it. We love your feedback. Feedback at DailyTechNewShow.com is where to send that email with your thoughts. Also, we have a celebration today because we have a brand new boss, Bernardo Teyes, who just started backing us on Patreon. Thank you, Bernardo. Yeah, Bernardo gets all of that to themselves. Nicely done, Bernardo. You could be Bernardo tomorrow, though. Patreon.com. Nothing's stopping you. Scott Johnson, we'd love to have you also on the show. Let folks know where they can keep up with your work. Well, just the usual old places. You can check out frogpants.com for everything I'm up to. And I'll say this, just a little bit of a hint drop. If you are a fan of retro video games, meaning old arcade classics and games from your NES era and that sort of stuff, if that's interesting to you, you're going to want to keep an eye on frogpants.com in the coming month, the start of January. We have something really special coming. Watch for that. I'll say no more for now. And if you want to find me on Twitter, you can do that real easy. I'm at Scott Johnson as well. Well, we're live on this show Monday through Friday. If you didn't know, now you know, 4.30 p.m. Eastern 2130 UTC. Find out more at dailytechnewshow.com slash live. We will be back doing it all tomorrow, because again, Monday through Friday.


We are in front of the Senkata plant where the gas for people's cooking as well as for the cars is stored and there has been for two weeks now a blockade that the people who are against the coup and have been pro-Evo Morellas have been setting up to try to put pressure to stop gasoline from going into the city of La Paz and force the government to resign and it's been an incredible mass movement of people all over here who have set up barricades. Now this street that you're looking at now this is the main street that connects the whole country that connects La Paz to Cochabamba to Santa Cruz and obviously to Potasi to Sucre. There's not one car going along here because they have shut this down just like they've shut down the trucks that would be carrying a lot of the merchandise the food that would go to the city so there are shortages of chicken and shortages of eggs and vegetables all kinds of things and it's part of an incredibly amazing grassroots movement of most the indigenous people to say no to the coup. Now you see these military people are the ones who came in yesterday and massacred people. We don't know yet how many people have died. We know there's at least eight and probably more that haven't been confirmed yet and dozens of people that are in the hospital.  And that's the main demand of these people right now is to say get the army back in their barracks. And one thing that has happened since this coup took place is that the woman who declared herself president Janini Agnes had passed a law which she should have no right to do because she's only interim to convoke new elections and she passed a law saying that the military could shoot down people without any consequences. And so right after she did that we saw the massacre near Cochabamba and then we saw the massacre here yesterday. And so people have been gathering all day. There's a big town hall meeting that's happening right down there with thousands of people outside where they're talking about what they're going to do to the And I have a massive march that's going to the city and they're dead down into La Paz to say this won't be allowed. Can I ask you a couple questions? They said that this building was dynamited and I don't see any trace of it dynamite. I want to say that the press in Bolivia is totally propaganda. All it reports is what this coup government wants people to hear. And so it pretended that the violence was caused by the people and that they dynamited this, which is totally wrong. The violence was the military. In fact, we saw the dead bodies with the shots in their back and to their heads. Now the only way to get out here is by walking out here, right? You can't come out here. You can't drive here. Tens of kilometers to get here. The only way we got in your lap just recently was with the inside the Hertz.  And the only way we got in today was because we were with the Hertz that was bringing in one of the dead bodies. And so the people kept opening up the spaces and removing the stones and the barricades to let the Hertz inside. So if you want to see, I have people right here and here are some of the students. What does their sign say? There won't be peace as long as there is a community. And these people think they're one of the dead people from yesterday's massacre that are inside here that the military is keeping. We are the dead bodies. We are the dead bodies. We are the dead bodies. We are asking for international aid to the world. This president is asking for help. He's asking for help from the United States. This man is saying they want international help because they are unarmed people and look what they're up against. And so they want the international community to come in and help. We were the dead bodies. We were the dead bodies. We say that this president is an assassin. That the filmmaker is a dictator and we want her gone. We're not going to stop this mobilization until she's gone. We're not going to stop our mobilizations until she's gone. This is a dictator's government. This is a dictator's government. This is a dictator's government. If anyone hears these people and this guy... So let's just stand over here and help the people pay.  So they're saying that if you are murdered, you should be back. Don't listen to what this new government is saying. How can you murder your own people? Don't listen to what this new government is saying.  As far as I can tell. They're killing the people.  Thanks to the people above, we have money. We have laws. Even animals have laws. But here, yesterday, they didn't kill us, they didn't admit us. And no information on our channels is being informed. I'm going to go ahead and reocid. So she said that you can't even just murder dogs here. But look how they murdered us. And if you turn on any of the guests here, you won't see anything of what happened yesterday. So that's why we need the international community to come here and do something to help us. This government has given us a white card.  This government has done that in 460 years. And this government has made it a great thing that says they didn't know what they were going to do, whatever they want, and they won't be held accountable. And of course, they're going to give them carte blanche to kill whether they're going to do. They're going to kill, which they're doing. And that's what he was saying. So we residents who have been protecting our community here and doing our peaceful vigil, we're being called terrorists. And they're saying that there wasn't even one bullet that came from them when we saw all kinds of bullets. In fact, we have bullets.  But that's what the press is saying. But we've got a picture of a man with a bullet that's holding his back. Cold blood. From our side. The only thing that happened was from there. They shot at us. They shot at us. With coke and alcohol. Our national debt is being sold. It was the mayor's debt. The mayor is going to make a move. Listen to me. Yeah. A way to report this president is ready. It's over. Alright, we've got to make a move. We've got to leave because they're coming up close. Yeah. Where's the head? We have a feeling you're going to come after us. No, don't turn it off. Alright. Keep it. Go that way. I don't think so. Get in the middle. I'm telling you. I was on the phone. Watch the whole thing. Alright, so we're getting out of here because they're coming closer to us. And they put out a call that the foreign press that cover these kinds of things will not be tolerated. So anyway, we'll take off right now and be back later.


Now the coroner's inquest into the death of the late Nigerian singer Mubad has commenced sitting on Friday at the magistrate court sitting in the Kuru De Lagos. Journalists were banned from gaining access into the courtroom and no reason was given for this development. Now the coroner, magistrate Tau Fikat at the Dio, showed to be the third parties in the inquest that their day's proceedings was to discuss the modalities for airing of evidence. The airing of the evidence is easy to commence on Friday, the 13th of October, as Mrs Fumi Falano, who represented Mubad's family, alongside all the lawyers, at the inquest to journalists after deep proceedings that the legal team would bring witnesses to the inquest to ensure that the cause of justice is served. It is an equilateral sitting, so what we just had to do is to decide on the modalities of how and when and the witnesses to bring before the courts. In other words, for us to get to the cause of justice, we need to know the procedures to take. Our position is to show solidarity and identify with the current happenings and the current concerns of Nigerians and also to show our position as a record label that stands for social justice. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.