Modern Biology
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Modern Biology
New Paradigms. This collection spans from (8 / 2012) - (present). 9 pages. Completed by 4/ 3/ 2017. Correction, by 9 / 2017 (date changed 5/ 18/ 2016), New version on paper in 2018.  All pages are updated weekly with related articles grouped, so new stuff may be on any page. This is a catalog of my journey to creating irreversible chain theory. --Colbert
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A theory of biological relativity: no privileged level of causation (The Royal Society, Denis Noble)

A theory of biological relativity: no privileged level of causation (The Royal Society,   Denis Noble) | Modern Biology | Scoop.it
Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of ‘biological relativity’ and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined.
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Highlight:

" This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes.." 

 

Downward causation is an interesting concept. 

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Bacteria replicate close to the physical limit of efficiency (Nature)

Bacteria replicate close to the physical limit of efficiency (Nature) | Modern Biology | Scoop.it
The common gut bacterium Escherichia coli typically takes about 20 minutes to duplicate itself in good conditions. Could it do it any faster? A little, but not much, says biological physicist Jeremy England at the Massachusetts Institute of Technology in Cambridge. In a preprint1, he estimates that bacteria are impressively close — within a factor of two or three — to the limiting efficiency of replication set by the laws of physics.
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Paper by Jeremy England

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The Histone Code: Judd Rice Laboratory at the USC/Norris Comprehensive Cancer Center

The Histone Code: Judd Rice Laboratory at the USC/Norris Comprehensive Cancer Center | Modern Biology | Scoop.it

"Increasing evidence indicates that the post-translationally modified histones serve as extremely selective binding platforms for specific regulatory proteins that drive distinct nuclear processes.

It has been known for over 45 years now that histones can be post-translationally modified by specific enzymes that ‘write?a histone code by adding or removing a number of different chemical modifications, including acetyl, phosphoryl and methyl groups (Figure 2). Since these modifications occur only on specific amino acid residues on specific histones in various eukaryotic organisms, these observations strongly linked the modifications?involvement in nuclear processes. For example, the acetylation of key lysine residues of histone H3 and H4 by enzymes known as histone acetyltransferases (HATs) was known to play a pivotal role in transcriptional activation." 

 

Colbert Sesanker's insight:

Someone should use an algorithmic information theory style  complexity measure here to get bounds on how compressible this process is. Would make an interesting paper. 

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Science: Nutritional Control of Reproductive Status in Honeybees via DNA Methylation

Science: Nutritional Control of Reproductive Status in Honeybees via DNA Methylation | Modern Biology | Scoop.it

"Fertile queens and sterile workers are alternative forms of the adult female honeybee that develop from genetically identical larvae following differential feeding with royal jelly. We show that silencing the expression of DNA methyltransferase Dnmt3, a key driver of epigenetic global reprogramming, in newly hatched larvae led to a royal jelly–like effect on the larval developmental trajectory; the majority of Dnmt3 small interfering RNA–treated individuals emerged as queens with fully developed ovaries. Our results suggest that DNA methylation in Apis is used for storing epigenetic information, that the use of that information can be differentially altered by nutritional input, and that the flexibility of epigenetic modifications underpins, profound shifts in developmental fates, with massive implications for reproductive and behavioral status."

Colbert Sesanker's insight:

 The bee colony is genetically identical, but Queens are made when workers feed larva 'Royal Jelly' for an extended period of time. Queens exhibit vastly different gene expression patterns from workers. This is caused by epigenetic modifications. 


The queen differentiation program was replicated by blocking (using small siRNA to silence methyltransferase Dnmt3) CpG methylation in larva.

 

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Paper archive from Stanford CS class (cs379c) that includes: Von Neumann's "Theory of Self-Reproducing Autonoma".

Paper archive from Stanford CS class (cs379c) that includes:  Von Neumann's "Theory of Self-Reproducing Autonoma". | Modern Biology | Scoop.it
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Search 'Neumann1966.pdf'  to get a a copy of the pdf

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Neural Networks and Deep Learning

Neural Networks and Deep Learning | Modern Biology | Scoop.it
Neural networks are one of the most beautiful programming paradigms ever invented. In the conventional approach to programming, we tell the computer what to do, breaking big problems up into many small, precisely defined tasks that the computer can easily perform. By contrast, in a neural network we don't tell the computer how to solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at hand.

Automatically learning from data sounds promising. However, until 2006 we didn't know how to train neural networks to surpass more traditional approaches, except for a few specialized problems. What changed in 2006 was the discovery of techniques for learning in so-called deep neural networks. These techniques are now known as deep learning. They've been developed further, and today deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They're being deployed on a large scale by companies such as Google, Microsoft, and Chinese search giant Baidu.

The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
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concise and clear introduction to neural networks including deep learning

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Should We Fear or Welcome the Singularity? Nobel Week Dialogue 2015 - The Future of Intelligence

Should science and society welcome ‘the singularity’ – the idea of the hypothetical moment in time when artificial intelligence surpasses huma
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Nobel Prize panel discussion. Either Kurzweil is wearing a wig or he paid for some novel hair replacement procedure.
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Are Brains Analogue or Digital? | Prof Freeman Dyson | University College Dublin

Abstract: We know that creatures like us have two separate systems for processing information, the genome and the brain. We know that the genome is digital, ...
Colbert Sesanker's insight:

The question Dyson is addressing here means a little bit too much to Walter Pitts

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Rhythm and the Brain Project - Gazzaley Lab

Rhythm and the Brain Project - Gazzaley Lab | Modern Biology | Scoop.it
Rhythm and the Brain Project: Unlocking the power of rhythm to understand and enhance brain function
Colbert Sesanker's insight:
Like Anirban, this lab approaches things from the resonance paradigm
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Why Explore Cosmos and Consciousness? (Anirban Bandyopadhyay) | Closer to Truth)

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This one is actually good. Especially as an X believer that  strong AI can emerge from substrate independent computation.

 

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Schumann resonances - Wikipedia, the free encyclopedia

Schumann resonances

The Schumann resonances ( SR) are a set of spectrum peaks in the extremely low frequency (ELF) portion of the Earth's electromagnetic field spectrum. Schumann resonances are global electromagnetic resonances, generated and excited by lightning discharges in the cavity formed by the Earth's surface and the ionosphere.

Colbert Sesanker's insight:
These seem like a more plausible candidate than light pulse entrainment to the sun. These change with the solar cycle via its effect on the ionosphere.
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The Man Who Tried to Redeem the World with Logic

The Man Who Tried to Redeem the World with Logic | Modern Biology | Scoop.it

Though they started at opposite ends of the socioeconomic spectrum, McCulloch and Pitts were destined to live, work, and die together. Along the way, they would create the first mechanistic theory of the mind, the first computational approach to neuroscience, the logical design of modern computers, and the pillars of artificial intelligence. But this is more than a story about a fruitful research collaboration. It is also about the bonds of friendship, the fragility of the mind, and the limits of logic’s ability to redeem a messy and imperfect world.


Via Complexity Digest
Colbert Sesanker's insight:

Nice story.  On the other hand, it exemplifies finding what you're looking for: 

 

"Which got McCulloch thinking about neurons. He knew that each of the brain’s nerve cells only fires after a minimum threshold has been reached: Enough of its neighboring nerve cells must send signals across the neuron’s synapses before it will fire off its own electrical spike. It occurred to McCulloch that this set-up was binary—either the neuron fires or it doesn’t. A neuron’s signal, he realized, is a proposition, and neurons seemed to work like logic gates, taking in multiple inputs and producing a single output. By varying a neuron’s firing threshold, it could be made to perform “and,” “or,” and “not” functions."

 

Oh yes. After days of mulling over binary operations one begins to see strange things. Hallucinations, flashes of bits saturating everything. All of a sudden, one no longer inhabits a world of things, but a world of bits. The bits, deeply burned into the cornea like a cataract, never seem to get out the way. Is that a brain, or is it bit soup? Ahh, I never thought about it that way. What a *nice* way to think about it.

 

Interesting Section:

 

"There was a catch, though: This symbolic abstraction made the world transparent but the brain opaque. Once everything had been reduced to information governed by logic, the actual mechanics ceased to matter—the tradeoff for universal computation was ontology. Von Neumann was the first to see the problem. He expressed his concern to Wiener in a letter that anticipated the coming split between artificial intelligence on one side and neuroscience on the other. “After the great positive contribution of Turing-cum-Pitts-and-McCulloch is assimilated,” he wrote, “the situation is rather worse than better than before. Indeed these authors have demonstrated in absolute and hopeless generality that anything and everything … can be done by an appropriate mechanism, and specifically by a neural mechanism—and that even one, definite mechanism can be ‘universal.’ Inverting the argument: Nothing that we may know or learn about the functioning of the organism can give, without ‘microscopic,’ cytological work any clues regarding the further details of the neural mechanism."

 

The idea here is that the map from behavior to neural mechanism is one to many. They are many turing complete circuit topologies, so the idea of finding THE circuit for behavior or action X breaks down. 

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Gregory Chaitin - Complexity from simplicity

Gregory Chaitin - Complexity from simplicity Fonte: closertotruth.com
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Understanding the Omega number sums up the meaning of Godel's incompleteness theorems and the core of NKS

Read METAMATH here:
http://arxiv.org/pdf/math/0404335.pdf

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Statistical physics of self-replication (The Journal of Chemical Physics)

Self-replication is a capacity common to every species of living thing, and simple physical intuition dictates that such a process must invariably be fueled by the production of entropy. Here, we undertake to make this intuition rigorous and quantitative by deriving a lower bound for the amount of heat that is produced during a process of self-replication in a system coupled to a thermal bath. We find that the minimum value for the physically allowed rate of heat production is determined by the growth rate, internal entropy, and durability of the replicator, and we discuss the implications of this finding for bacterial cell division, as well as for the pre-biotic emergence of self-replicating nucleic acids.
Colbert Sesanker's insight:
Irreversibility. Irreversibility. Irreversibility. The defining feature of life and you know what else, I won't use the word here.
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A New Physics Theory of Life (Quanta Magazine)

A New Physics Theory of Life (Quanta Magazine) | Modern Biology | Scoop.it
An MIT physicist has proposed the provocative idea that life exists because the law of increasing entropy drives matter to acquire lifelike physical properties.
Colbert Sesanker's insight:

Getting back to the outline in "What is Life?". 
More of an * announcement * that the Schrodinger perspective on "What is Life" is reviving.  

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Whatever happened to the ‘microtrabecular concept’? - Heuser - 2012 - Biology of the Cell - Wiley Online Library

Keith Porter culminated his stellar career as the founding father of biological electron microscopy by acquiring, in the late 1970s, a high-voltage electron microscope (HVEM). With this magnificent instrument he examined whole-mounts of cultured cells, and perceived within them a structured cytoplasmic matrix he named the “microtrabecular lattice”. Over the next decade Porter published a series of studies, together with a team of outstanding young colleagues, which elaborated his broader “microtrabecular concept.” This concept posited that microtrabeculae were real physical entities that represented the fundamental organization the cytoplasm, and that they were the physical basis of cytoplasmic motility and of cell-shape determination. The present review presents Porter's original images of microtrabeculae, after conversion to a more interpretable “digital-anaglyph” form, and discusses the rise and fall of the microtrabecular concept. Further, it explains how the HVEM images of microtrabeculae finally came to be considered as an artifact of the preparative methods Porter used to prepare whole cells for HVEM. Still, Keith's “microtrabecular concept” foretold of our current appreciation of the complexity and pervasiveness of the cytoskeleton, which has now been found by more modern methods of EM to actually be the fundamental organizing principle of the cytoplasmic matrix. During the impending eclipse of Porter's microtrabecular concept in the late 1980s, many of Keith's colleagues fondly described the cell as being filled, not with protoplasm, but with “Porterplasm.” Despite the fact that Keith's view was clouded by the methods of his time, it would be fitting and apt to retain this name, still today, for the ordered matrix of cytoskeletal macromolecules that exists in the living cell. In the end, the story of what happened to Porter's microtrabecular concept should be an object lesson in scientific hubris that should humble and inform all of us in cell biology, even today — particularly when we begin to think that our most recent methods and observations are achieving “the last word”.
Colbert Sesanker's insight:

frohlich proposed this lattice as the substrate of energy transfer for coherent excitations in the early 80s. Interestingly, the microtrabecular lattice idea WAS an artifact of early HVEM during this time. Originally proposed for the medium of excitations before the solid state conception solidified. 

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Life as Evolving Software, Greg Chaitin at PPGC UFRGS

"Few people remember Turing's work on pattern formation in biology (morphogenesis), but Turing's famous 1936 paper On Computable Numbers exerted an immense influence on the birth of molecular biology indirectly, through the work of John von Neumann on self-reproducing automata, which influenced Sydney Brenner who in turn influenced Francis Crick, the Crick of Watson and Crick, the discoverers of the molecular structure of DNA. Furthermore, von Neumann's application of Turing's ideas to biology is beautifully supported by recent work on evo-devo (evolutionary developmental biology). The crucial idea: DNA is multi-billion year old software, but we could not recognize it as such before Turing's 1936 paper, which according to von Neumann creates the idea of computer hardware and software."

 

Colbert Sesanker's insight:

The most interesting part is the relationship between innovation and incompleteness. There is too much deification of the static DNA sequence. Regardless, the only thing this abstraction requires is that the heritable information can be represented as a binary sequence.

 

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IEEE Spectrum: Machine-Learning Maestro Michael Jordan (Berkley) on the Delusions of Big Data and Other Huge Engineering Efforts

IEEE Spectrum: Machine-Learning Maestro Michael Jordan (Berkley) on the Delusions of Big Data and Other Huge Engineering Efforts | Modern Biology | Scoop.it

"Why We Should Stop Using Brain Metaphors When We Talk About Computing.

 

Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists. You called these “cartoon models” of the brain.

 

Michael Jordan: I wouldn't want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems."

Colbert Sesanker's insight:

finally someone calls out the lost that think the brain is one giant deep neural network or (gasp) a hierarchical hidden markov model (kurzweil in 'How to Build a Mind'). 'What is thought', by Eric Baum, is another excellent book summarizing materialistic (computational and evolutionary) views of intelligence (though I think it's optimism on the feasibility of strong AI is in error).

 

Without the utopian fantasies it is my view that AI and automation can have a tremendous impact.

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Nick Bostrom: What happens when our computers get smarter than we are?

Artificial intelligence is getting smarter by leaps and bounds — within this century, research suggests, a computer AI could be as "smart" as a human being. And then, says Nick Bostrom, it will overtake us: "Machine intelligence is the last invention that humanity will ever need to make." A philosopher and technologist, Bostrom asks us to think hard about the world we're building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values — or will they have values of their own?
Colbert Sesanker's insight:

It would be more clever to make these arguments without analogies and metaphors to the human brain. An argument solely from the perspective of self-improving programs would be more interesting.  Whether or not our minds are codable isn't really that relevant. 

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Miguel Nicolelis and Ronald Cicurel: The Singularity Isn’t Near and the Brain Can’t Be Simulated

"The idea that man is a machine is an old one. And since the brain is a physical part of that machine it is often presumed that it can necessarily be simulated on a digital computer. But very little scientific scrutiny has been given on that presumption. The Relativistic Brain is a new book by Miguel Nicolelis and Ronald Cicurel where they address precisely that question: Can a digital machine simulate the human brain?

the Relativistic BrainIn their book, Nicolelis and Cicurel provide a variety of neurophysiological, evolutionary, mathematical and computational arguments to conclude that “the brain is relativistic and cannot be simulated by a Turing Machine.” And so I thought that we had all the best ingredients for a fantastic Singularity 1on1 podcast episode. I hope you enjoy it as much as I did for I only managed to ask about half of my questions and plan to do a follow up as soon as I can.

During our 90 min conversation with Miguel Nicolelis and Ronald Cicurel we cover a variety of interesting topics such as: their interesting backgrounds in neuroscience and mathematics, brain-machine-interface [BMI] and the Blue Brain Project; neuroplasticity and Miguel’s Walk Again Project; the first Brain-to-Brain communication; the Relativistic Brain Theory [RBT] and why it cannot be simulated on a Turing Machine; the distinction between mechanism and organism; computable vs non-computable [Gödelian] information; their 23 scientific predictions as a way to falsify RBT; computationalism, Plato’s Cave and the mathematical arguments against simulating the brain; Stephen Jay Gould’s “tape of evolution argument”; the collapse of the Human Brain Project; The Penrose-Hameroff Quantum Theory of Consciousness…"

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Daniel Dennett - How are Brains Conscious?

Colbert Sesanker's insight:

I can't help replay a satire in my mind where Dan Dennett and Susan Blackmore walk into a large audience and publicly declare that they are not conscious, laughing hysterically, while the audience claps enthusiastically in praise of the profound 'solution'

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Anirban Bandyopadhyay (Mind and Computation) | Closer to Truth

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Its not the circadian resonance that's important, its the 'Shuman resonances' via the ionoshpere influenced by solar cycles and the complex bath of EM radiation permeating our solar system. 

Seems a bit Vedic/Pythagorean

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Nested Rhythm Network in the Living System - Anirban Bandyopadhyay

Source: http://paloaltoprize.com/
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This one is a bit clearer and has details on the lab
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ScienceCasts: The Sun's Magnetic Field is About to Flip

Visit http://science.nasa.gov/science-news/science-at-nasa/2013/05aug_fieldflip/ for more! Something big is happening on the sun. The sun's global magneti
Colbert Sesanker's insight:
the solar cycles explained here is what interacts with the ionosphere. Note the reach of the heliosphere and how it bathes the solar system in its EM field. This heliosphere rotates slowly and forms a sheet that interacts with the planets. 
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DNA waves and water - IOPscience

Some bacterial and viral DNA sequences have been found to induce low frequency electromagnetic waves in high aqueous dilutions. This phenomenon appears to be triggered by the ambient electromagnetic background of very low frequency. We discuss this phenomenon in the framework of quantum field theory. A scheme able to account for the observations is proposed. The reported phenomenon could allow to develop highly sensitive detection systems for chronic bacterial and viral infections.
Colbert Sesanker's insight:
"This phenomenon appears to be triggered by the ambient electromagnetic background of very low frequency."

Its right in the neighborhood of the fundamental Shuman resonance of  7.83HZ
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