Memomics And Longevity

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Project Infobox
Self researcher(s) Stuart Calimport
Related tool(s) pen and paper, Fitbit
Related topic(s) Mood and emotion, Genome and microbiome, life extension
Builds on project(s)
Show and Tell Talk Infobox
Featured image Memomics-and-longevity.jpg
Date 2013/05/11
Event name 2013 QS Europe Conference
Slides Memomics-and-longevity.pdf
UI icon information.png This content was automatically imported. See here how to improve it if any information is missing or out outdated.

Memomics And Longevity is a Show & Tell talk by Stuart Calimport that has been imported from the Quantified Self Show & Tell library.The talk was given on 2013/05/11 and is about Mood and emotion, and Genome and microbiome.

Description[edit | edit source]

A description of this project as introduced by Quantified Self follows:

Stuart set out to sequence a memome to find memes ("ideas" and "concepts" rather than viral pictures) associated with longevity and find factors that affect how his memetics change. He recorded most of his ideas that they had over time. For over two years, he logged over 25,000 ideas and categorized them whether he thought those ideas would increase his lifespan or decrease he lifespan. He shares what he's learned, and some inspiration about large scale memome tracking.

Video and transcript[edit | edit source]

A transcript of this talk is below:

Hi there. I’m Stuart Calimport and I’m going to be talking about memomics and the longevity interaction, so thank you for having me.

So, memes. A lot of people are using this word for different things and different meanings. I’m using it so we can think about language data as a science, so you can use bioinformatics data science for it. What I’m really talking about in memomics is sequence data and I’m interested in sequence data longevity actions; how the sequences of words affect how long we live. Genes are sequence data, you know, we sequence them, we’ve got genomes and lots of species in the same way that language is sequence data and we are collecting that quite passively in some cases online. So genes are powerful, right? They exist in an organism like this jellyfish and it allows it to become biologically immortal; that’s the power of sequence data and I think that is something to reflect on. Genes can also regenerate tissue, but in the same way memes and language data has led to regenerative medicine, so we can repair our own tissues as well, this is power of sequences of different kinds. So when you think meme, you think virality, and those are very powerful properties, rapid evolution, rapid spread, incorporation into host, so they are really powerful, so which memes should be spreading and which memes should be sequencing. You know, if ideas in a language are that important, we should be recording it and seeing what it does and seeing how it interacts with health, and seeing how language and ideas evolve over time and how that also interacts with our health and survival. Those are some properties that are powerful, which come with evolutionary processes. So, sequence-based therapeutics, is the idea that you can use DNA to create a therapeutic, well maybe you can do that with words and ideas. We do with books and advice already, but how far can we take it and how far can our memes take us? What are your ideas doing to you right now? Where will they take you? Where will they take you into space in time? How long will you live because of the ideas that you hold? How far could they take you? So it’s more actionable specifics; some Quantified Self. So can we track them and can we change the ideas that we hold, and can we do that to increase our health, our well-being, and our lifespan? So, what did I set out to do? Like a genome, I set out to sequence a memome to hopefully find memes associated with longevity, find factors that affect how my memetics change. So I recorded most of my ideas I had over time. I changed ideas in my perspectives, how quickly my ideas changed, and what sort of memetrics I have on behavior and personality. These are some of the things that I tracked. Biochemical, genetic, activity trackers, a really wide range; social metrics, mood, location, anything that could be related to longevity. Over two years, I’ve logged over 25,000 ideas and categorized them whether I thought those ideas would increase my lifespan or decrease my lifespan. Here is a word net with a word frequency. You can see some top ones. "Optimal", "consciousness", "infinite" were some of the words that I used the most frequently. Here, you can see the distribution of my memes on a log scale, and looking at the parable distribution of them, which looks quite regular for natural language, so that’s interesting. But, the top memes in my databases that were "optimal", or "sub-optimal" for lifespan were very different to the natural language from the Oxford database, and also from each other. So I think you could presume to get memetic profiles of people. Here I have logged distance travelled in Fitbit versus memes that I’ve logged, and there is a negative correlation that is somewhat insignificant between not doing very much activity and becoming more intelligent. What did I learn? Activities that have increased the probability of reaching an average lifespan may interfere with taking on ideas to increase maximum lifespan. I learned to think about differentiating between markers in metrics, the average lifespan, and those increasing maximum lifespan; I really want you to think about that. So, I didn’t just record some ideas just for me and health data for me. I did a citizen's science project that I started called "The Human Memome Project," where I tried to collect health and longevity data and memes from people around the world. Six continents, 25 countries and we have got over 150 participants so far. I’m making the data open if you want it. And what did I learn from my citizen science experiment? Well first of all, that some people are quite interested in how their ideas and attitudes affected their health and lifespan. And they wanted more information on it and they were interested in it in thinking about it for themselves. An example, of the frequency of the word "exercise," was higher in the lists of word frequency for those that consider themselves healthy, and there is some correlation between word frequency and health states. I think that is a very powerful statement. So, should sequence data longevity research for lifespan extension be something of a really core human pursuit and something Quantified Self could help with? In general, where next? Big open data analytics, the hashtags people use for different health states and correlate that stuff with Quantified Self apps and posted on Twitter. Machine learning, apps to help people empower actions to increase lifespan and gather more data. And also, maybe getting more ambient data from video and things.

Thank you for listening, and I know it’s been very quick. Any requests, feel free to contact. Thank you very much.

About the presenter[edit | edit source]

Stuart Calimport gave this talk.

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We talked about this project in the following meetings