Showing posts with label bio. Show all posts
Showing posts with label bio. Show all posts

22 September 2016

gene and race

So there was this discussion on non-racialism at this book launch last night ...
and a member of the audience asked that typical question about the obvious genetic basis to race.

I was working up to try and answer there, but found it a bit hard to get my thoughts together in time. So below is working toward how I'd like to answer that in future. I'll correct/refine it with time, hopefully, and corrections and comments from you, the anonymous or not anonymous public, are as welcome as always.


 Attempt One:

Once our very early, singular human population separated geographically (after what appears to be a series of survival bottlenecks), they evolved separate and particular traits according to the now well understood processes of natural selection and genetic drift. 

Even while populations were largely separated, there was still sufficient movement and interaction to allow for genetic information to pass between populations. This, most importantly, would have included the successful spread of immune response adaptations and anti-parasite counter-measures vital to early population survival. [which is why all so called races have within their populations a spread of proteomic pathways for immune function that are common between races] 

As populations interacted and genetic information spread, there are a few mechanisms by which certain traits persisted within local populations, despite the relatively thorough statistical mixing of other genetic traits. The  mechanisms that ‘preserved’ local ‘race type traits’ include environment specific adaptations (like skin colour) that were continuously selected for within separated populations by survival pressure, and culture-specific sexual selection criteria - that proceeded along with cultural evolution. There are more mechanisms i think, but these two come to mind for now.

Via these mechanisms, an aggregation of certain traits (the sexual selection model accounts for why these are most often just externally visible) would accumulate within populations, while allowing the fortunate mixing of other vital genetic survival strategies without which local populations would most likely have fallen from parasite/pathogen load/stress.

This explains why, with the exception of this small percentage of ‘race type traits’, when looking at particular sequences, we often see more genetic diversity within races than between them — for example, there will be a particular immune system function that is expressed in different ways by several different protein pathways (lets call them A,B,C,D) and each race will have individuals carrying sequences for A,B,C,D. For the larger pathways, two individuals from different ‘races’ carrying the sequences necessary for A will often have more in common genetically with each other than with members of the same race carrying sequences for B, say.

[Immune function has been a big driver for evolution throughout the entire animal line, and so its not for nothing that it gets emphasised when discussing genetic variation.]

Even as our populations experienced civilisational shifts that allowed for more interaction between populations and more and more geographical displacement, patterns of mechanisms like the culture-specific sexual selection mentioned above - now intimately coupled with power, violence, etc. - still worked to keep certain traits prominent within local populations. 

Anyway, I think that's the type of traits the dude from yesterday mentioned. That small percentage of genetic traits, often highly visible, that we based the myth of race on.


 added later:

Of course, the other reason that immune system is so important when discussing us, is that in the very brief evolutionary time since we split up, nothing much else changed.

Sure some of us lightened our skins and straightened our hair, but these were almost insignificant changes when seen against our vast evolutionary history.

The most significant changes to humankind since our ‘forking’ is that the big brains that we evolved before we split led to us being super successful wherever we went - and that success was met in turn by a multitude of parasites rushing in to live off a newly found ecosystem - us.

So, most of our real evolution as humans, neglecting the very very minor surface tweaks, have been in complex immune system responses to those parasites since we became successful. That is why we are closer to members of other races that share our immune pathways than those of our race that don’t - immune system complexity makes up most of our evolution since we got here.

07 May 2016

principles of hierarchical-temporal-memory

HTMemory and Sparse Distributed Networks. Lots of progress in understanding the neocortex - how intelligence works in the brain - and modelling that for a cortical approach to machine intelligence

UPDATE: broken link: Video moved to http://numenta.com/learn/htm-videos-from-jeff-hawkins.html.

Direct link on youtube: https://youtu.be/6ufPpZDmPKA

24 June 2013

speaking evolution

I still really like the idea that ..

if I say out loud, my mother's mother's mother's mother's.... and keep going non-stop for about a 1000 years - I'll eventually be speaking about amoebae

----------------------
at 3 mother's per second
--------------------------
it's a 1000 years to amoebae, and a 100 000 years to bacteria
--------------------------
Btw, if you too are doubting the scale of that 1000 year estimate, its not just an exaggeration. I'm using a conservative estimate of 10^11 (100000000000) generations between me and my amoebae ancestors - based on very small times between generations for the first 400 million years of animal life. A number so mind-bending in size, that it will take 1000 years to count to at 3 times a second

20 November 2012

explaining epigenetics .. adding more nurture to the nature-nurture mix

A log-worthy RadioLab episode - Inheritance - Which includes a lovely explanation of epigenetics, explained through some beautifully told stories, as usual.
Listen, to find out how, for instance, mom's licking turns on a behaviour, via protein, via turned-on-gene ..

From the site: http://www.radiolab.org/2012/nov/19/:
"Once a kid is born, their genetic fate is pretty much sealed. Or is it? This hour, we put nature and nurture on a collision course and discover how outside forces can find a way inside us, shaping not just our hearts and minds, but the basic biological blueprint that we pass on to future generations."
the file is here ...
http://www.podtrac.com/pts/redirect.mp3/audio4.wnyc.org/radiolab/radiolab111912.mp3

and streamed here
http://www.radiolab.org/audio/m3u/251876/

Another brilliant RadioLab's episode. Check out their podcast - it's some of the best produced radio, ever! .. methinks

07 October 2012

sugars, glucose, fructose, etc. etc.

possibly worthwhile 38 min audio doccie in this science and the city podcast
the mp3 for the episode is here http://ne.edgecastcdn.net/000210/podcasts/AThoughtForFood_SugarInTheMorning.mp3

Don't get too annoyed when you hear the (possibly) pro corn-syrup scientists talking their science in this interview - it still comes out that even though fructose and sucrose are roughly the same in organic chemistry, we've evolved to getting the majority of our sugars via glucose/carbohydrates. So we hear how the ratio fructose:glucose is significant, that we drink and eat way too much fructose via corn-syrup's extreme place in our intake, and how that's not a good idea.

The following gets affirmed and boosted too. the carb/sugar content in a can of soda is about equivalent to a full meal, so unless you replace a meal with that soda, or do enough exercise to burn off an extra meal, drinking cokes and such are going to be pretty bad as a long term behaviour.

That being said, one of the points being made is that the science is contested - despite the smoking guns, we don't yet have a complete mapping of the complex pathways involved.

grokking htm for ai

or .. Notes on Understanding Hierarchical Temporal Memory and its use in Artificial Intelligence and Knowledge Representation.


A few years ago I blogged about a TedTalk by Jeff Hawkins on how brain science will change computing. To summarize, the idea was that intelligence was more about prediction than behaviour, that the neocortex evolved to to basically be a mechanism to predict the future, and that it could be simply modeled as vast networks of hierarchical elements that predict their future input sequences - a hierarchical temporal memory (HTM) system.
Importantly, rather than the much more difficult task of modeling the entire brain, including the ancient and incredibly complex areas below the neocortex that deal with things like emotions and behaviours, one could approximate intelligent behaviour by modeling the much simpler cortex as a HTM - with simple repeated structure and algorithm.

Here's an update with more from Jeff Hawkins, and HTM ..

The following links are for a 2008 talk given by him on AI.. give it a watch
Jeff Hawkins on Artificial Intelligence - Part 1/5
Jeff Hawkins on Artificial Intelligence - Part 2/5
Jeff Hawkins on Artificial Intelligence - Part 3/5
Jeff Hawkins on Artificial Intelligence - Part 4/5
..some notes from the above:
- Work started by looking at what the structure of the brain could tell us about memory/knowledge storage.
- Memory - the bottom is close to the sensory system - retina for visual system, skin for touch, ears .. etc.
- Top nodes in the hierarchy get assigned to specific concepts/objects - like the individual neurons that fire every time you see or imagine Tupac and only Tupac (true story) 
- All nodes in the hierarchy are basically the same, and they all ..
    - look for temporal and spatial patterns/sequence
    - and pass the name of the recognized sequence up
    - pass the predictions they make down the hierarchy
- You get fast changing patterns at the bottom, slower changing as you move up the hierarchy.
- After training an HTM system (in silicon or neurons), you get something that learns hierarchical models of causes (statistical regularity) in the world - using bayesian techniques to build a belief propagation network.
- HTM's make the assumption that the world is hierarchical
- Predicting what can come from htm:
   - we cant, but ..
   - it could be much faster - neurons are slow
   - it could have other architectures - bigger bottom layers, fueled by big-data for example, or from large sensory arrays, etc. 

The latest thing I've come across from Hawkins' company Numenta, is their new Grok system (love the Heinlein reference). Grok is a cloud-based prediction engine that finds complex patterns in data streams and generates actionable predictions in real time. Check it out on Numenta's site, and their tech page
...
(more to follow.. soonish)

02 March 2012

the long road to a cell

Check out this truly amazing representation of the molecular machinery at work within each of our cells.
..
One thing that helps understanding these processes is to remember that at these scales, molecules are knocking around at much more than a million times per second - so it isn't that the right molecules miraculously find and fix themselves exactly where they belong in order to perform these very complex tasks (as these depictions sort of suggest), but rather that they are perpetually colliding at incredible speeds, and that it is only the (relatively) rare collision that is successful enough to allow the process to move forward.

All these random accidents accumulate, with the basic rule that what can persist, does, and on larger timescales seemingly miraculous complexity emerges.

Still, judging by the glimpse we get from this video, it's no wonder that evolution (a process that works with that same basic rule from above) took longer with these inner workings of a cell (2.8 billion years) than all the further developments on the tree of life combined (1 billion years)

.. give or take.