Tactility v. Darwin.

Design at Your Fingertips: Researchers Struggle to Model Sense of Touch
Evolution News | @DiscoveryCSC

The late pianist Victor Borge (1909-2000) was beloved not only for his comedy shtick but also for the sensitivity of his keyboard touch. He maintained the ability to interpret the most subtle pieces such as Claire de Lune (click on the image above to go there) with extreme delicacy all the way to age 90, when he was still giving 60 performances a year. It would be hard to design a robot with that level of durability, reliability, or sensitivity. Scientists know, because they’re having a hard time understanding it, let alone imitating it.

Four researchers from the University of Chicago and the University of Sheffield (UK) have made major progress over previous attempts to model the sense of touch. In a paper in the Proceedings of the National Academy of Sciences, “Simulating tactile signals from the whole hand with millisecond precision,” they announce their new mathematical model of a single hand’s neural responses under a variety of fingertip-touch experiments, hoping to assist robotics engineers wishing to imitate human touch response. Note the words code and information:

When we grasp an object, thousands of tactile nerve fibers become activated and inform us about its physical properties (e.g., shape, size, and texture). Although the properties of individual fibers have been described, our understanding of how object information is encoded in populations of fibers remains primitive. To fill this gap, we have developed a simulation of tactile fibers that incorporates much of what is known about skin mechanics and tactile nerve fibers. We show that simulated fibers match biological ones across a wide range of conditions sampled from the literature. We then show how this simulation can reveal previously unknown ways in which populations of nerve fibers cooperate to convey sensory information and discuss the implications for bionic hands. [Emphasis added.]

Unlike previous experiments that attempted to measure neural spikes from individual sensors in the skin of monkeys or humans, this new model simulates the responses of thousands of sensors based on knowledge of their classifications and distributions in the skin of the human hand. The team incorporated three classes of nerve fibers into the model:

Slowly adapting (SA) sensors: these respond primarily to spatial information from the stimulus.

• Rapidly adapting (RA) sensors: twice as densely packed as SA sensors, these provide a mix of spatial and vibration responses.
• Pacinian sensors: less densely packed than the other types, these neurons are sensitive to vibrations and waves generated by movement across the skin.

Each of these fibers produces spike trains that encode different aspects of the stimulus, such as edges, compression, and vibration. One type alone might not convey much about the source, but together, they give the brain a rich array of data. Interpreted correctly, this information allows the brain to draw conclusions about size, shape, and texture of an object by touch alone. A blind person can thus “see” Braille letters with the fingertips where these neurons are most densely packed: “each fingertip contains just under 1,000 fibers,” the paper states, providing fine resolution, especially from the high-resolution SA1 fibers.

The spike trains become more complex as the fingertip is moved into or across the source, activating more of the RA and PC fibers. Simply pressing a key on a computer keyboard is a complex act, with surrounding neurons becoming involved as pressure is applied or released. Moving a finger across a surface sets up waves that propagate throughout the hand, activating more sensors along the length of the finger and into the palm. This all happens within milliseconds (thousandths of a second), as it must when you consider the fast action of typing or playing a rapid piano piece. Even though PC fibers are less densely populated, their activity “dwarfs that of active SA1 or RA fibers,” the authors say, since they almost all become activated during a grasping operation or when feeling vibrations.

The authors describe their efforts to “tune” or “fit” their model to known facts about neurons in the hand. Eventually, they achieved a good match for things like edge detection, edge orientation, and direction of motion for simple actions. Nevertheless, they omitted important capabilities such as temperature or pain — two important inputs that can generate reflex actions that activate arm muscles to jerk the hand away before the brain is aware of danger. Needless to say, their model completely overlooks things like sweat glands, blood vessels, immune cells, and all the other equipment packed into a fingertip.

While the new model reflects admirable progress in understanding the sense of touch, and while it will undoubtedly help engineers seeking to improve prosthetic devices and robotic capabilities, the authors admit in the last section a number of limitations to their model. For instance, they tuned their model to information from rhesus macaques, knowing that humans have an additional type of tactile sensor called the SA2 fiber. They also fit their model to compression actions but not to sliding actions. In addition, they didn’t take fingerprints into account. Here’s why that could be a serious shortcoming of the model:

Third, the skin mechanics model treats the skin as a flat surface, when in reality, it is not. The 3D shape of the skin matters during large deformations of the fingertip. For example, pressing the fingerpad on a flat surface causes the skin on the side of the fingertip to bulge out, which in turn, causes receptors located there to respond. Such complicated mechanical effects can be replicated using finite element mechanical models but not using the continuum mechanics (CM) model adopted here. To the extent that friction is a critical feature of a stimulus — for example, when sliding a finger across a smooth, sticky surface — or that the finger geometry plays a critical role in the interaction between skin and stimulus — as in the example of high-force loading described above — the accuracy is compromised. Under most circumstances, the model will capture the essential elements of the nerves’ response.

Another limitation may be even more significant. They didn’t take into account the networking of responses in adjacent nerves. Their model treats an affected area as an isotropic “hotspot” wherein all the fibers react the same way, but nerve fibers are known to branch out and affect neighboring fibers. This can produce complex interactions between neurons, adding to the encoded tactile information the brain receives.

Let’s dive one level deeper into the details to consider what goes on at the cellular level. A neuron embedded in the skin does not see anything. It “feels” the outer skin deforming slightly because it contains mechanosensitive portals in its membranes. These portals let some ions in, and others out, creating a wave train of signals down the cell’s length. That’s the electrical “spike” the authors talk about, but it doesn’t just happen without each neural cell first being equipped with molecular machines able to respond to pressure, and able to quickly reset and re-fire as the source changes. As the signals propagate toward the brain, the neurons must cross synapses that convert the electrical signals to chemical signals and back again, preserving the information and the timing of the signals as we saw in the case of 3-D hearing.

Once again, the simplest, ordinary action of touching a fingertip on a surface is vastly more complex than we could conceive, challenging scientists to come up with simplified models to understand it. With this in mind, try an experiment: with your eyes closed, touch your index finger to a variety of surfaces around you: a table top, clothing, bread, liquid, the skin of your arm, a puff of air from your lips. Try to discern by touch alone information about each object’s friction, temperature, smoothness, shape, and hardness. Think of all those thousands of sensors providing that information to the brain with millisecond precision! Imagine what the brain has to deal with you when you plunge your whole body into a cold pool on a hot summer day.

The authors say nothing about evolution in their paper. Design is so abundantly obvious in the human body, as Steve Laufmann discussed in his recent ID the Future podcasts about Howard Glicksman’s series on physiology, our best engineers cannot even conceive of approximating that level of functional coherence, performance and integration. Not even close.

The undead in review.

Jonathan Wells and Zombie Science — Reviewing the Reviewers

Evolution News | @DiscoveryCSC

On a new episode of ID the Future, Ray Bohlin gets biologist Jonathan Wells’s reaction to early responses to Wells’s new book, Zombie Science: More Icons of Evolution.

Dr. Wells shares his favorite endorsement, discusses evolutionist Jerry Coyne’s “review” (Coyne admittedly didn’t read the book), and describes a spoof review that … well, listen and decide for yourself what you think the reviewer’s real message was.  Listen to it here, or download it here.

Two billion year old tech Vs. Darwinism

How Evolutionists Stole the Histones;
Cornelius Hunter

The recent finding that the DNA packaging technology and structure, known as chromatin, is not limited to eukaryotes but is also present in archaea, and so from an evolutionary perspective must have “evolved before archaea and eukaryotes split apart—more than 2 billion years ago,” is merely the latest in a string of misadventures evolutionists have incurred ever since they stole the histones.

Histones are the hub-like proteins which (usually) serve as the hubs about which DNA is wrapped in the chromatin structure. Like a thread wrapped around a spool this design packs DNA away for storage with an incredible packing factor. Interestingly, the histone proteins are highly similar across vastly different species. Again, from an evolutionary perspective, this means they must have evolved early in evolutionary history to a very specific design. As one textbook explains:

The amino acid sequences of four histones (H2A, H2B, H3, and H4) are remarkably similar among distantly related species. For example, the sequences of histone H3 from sea urchin tissue and of H3 from calf thymus are identical except for a single amino acid, and only four amino acids are different in H3 from the garden pea and that from calf thymus. … The similarity in sequence among histones from all eukaryotes indicates that they fold into very similar three-dimensional conformations, which were optimized for histone function early in evolution in a common ancestor of all modern eukaryotes. [1]

But the new finding pushes back this evolutionary “optimization” far earlier in time. Once again, evolution’s heroics are moved to the distant past where no one can see. Early life was not simple.

And of course DNA needs to be accessed so this histone packaging is quite dynamic. It can roll or it can be removed and moved. The histones themselves have tails that stick out and are tagged with small chemical groups that influence whether the packaging is tight or unrolled. Again, early life was not simple.

But the fact that histones are so similar across a wide range of species leads to an entirely different dilemma for evolution. For from an evolutionary perspective, it means that the histones must not tolerate change very well. Here is how a leading 1994 textbook described it:

When the number of amino acid differences in a particular protein is plotted for several pairs of species against the time since the species diverged, the result is a reasonably straight line. That is, the longer the period since divergence, the larger the number of differences. … When various proteins are compared, each shows a different but characteristic rate of evolution. Since all DNA base pairs are thought to be subject to roughly the same rate of random mutation, these different rates must reflect differences in the probability that an organism with a random mutation over the given protein will survive and propagate. Changes in amino acid sequence are evidently much more harmful for some proteins than for others. From Table 6-2 we can estimate that about 6 of every 7 random amino acid changes are harmful over the long term in hemoglobin, about 29 of every 30 amino acid changes are harmful in cytochrome c, and virtually all amino acid changes are harmful in histone H4. We assume that individuals who carried such harmful mutations have been eliminated from the population by natural selection. [2]

So the reason the histone proteins are so similar, again from an evolutionary perspective, is because mutations changing those proteins could not be tolerated. This is the evolutionary prediction and here is how the next edition of that same textbook, eight years later in the year 2002, added to the discussion of the high similarity of the histone proteins:

As might be expected from their fundamental role in DNA packaging, the histones are among the most highly conserved eucaryotic proteins. For example, the amino acid sequence of histone H4 from a pea and a cow differ at only at 2 of the 102 positions. This strong evolutionary conservation suggests that the functions of histones involve nearly all of their amino acids, so that a change in any position is deleterious to the cell. This suggestion has been tested directly in yeast cells, in which it is possible to mutate a given histone gene in vitro and introduce it into the yeast genome in place of the normal gene. As might be expected, most changes in histone sequences are lethal; the few that are not lethal cause changes in the normal pattern of gene expression, as well as other abnormalities.

There was only one problem. That is false. In fact, even at the time studies had already shown that histone H4 could well tolerate many changes. It was not merely an example of evolution pointing in the wrong direction and producing yet another failed prediction. It was an all too frequent example of evolution abusing science, force-fitting results into its framework. And of course all of this became doctrine for wider consumption. As a 2001 PBS documentary stated:

Histones interact with DNA in the chromosomes, providing structural support and regulating DNA activities such as replication and RNA synthesis. Their ability to bind to DNA depends upon a particular structure and shape. Virtually all mutations impair histone's function, so almost none get through the filter of natural selection. The 103 amino acids in this protein are identical for nearly all plants and animals.

But it is not, and was not, true that “virtually all mutations impair histone’s function.” That was not science, it was dogma disguised as science. And since then the dogma has become even more obvious. As one recent paper summarized:

Furthermore, recent systematic mutagenesis studies demonstrate that, despite the extremely well conserved nature of histone residues throughout different organisms, only a few mutations on the individual residues (including nonmodifiable sites) bring about prominent phenotypic defects.

Similarly another paper bemoaned the confusing results:

It is remarkable how many residues in these highly conserved proteins can be mutated and retain basic nucleosomal function. … The high level of sequence conservation of histone proteins across phyla suggests a fitness advantage of these particular amino acid sequences during evolution. Yet comprehensive analysis indicates that many histone mutations have no recognized phenotype.

In fact, even more surprising for evolutionists, many mutations actually raised the fitness level:

Surprisingly, a subset of 27 histone mutants show a higher intensity after growth (log2 ratio >+1.5) suggesting they are collectively fitter and maintain a selective advantage under glucose limitation.

It was yet another falsified evolutionary prediction, and yet another example of evolution abusing science.

Now evolutionists propose a redundancy hypothesis. Those histone mutations are well tolerated because evolution constructed a backup mechanism. Both mechanisms would have to mutate and fail before any lethal effects could be felt.

As usual, contradictory results are accommodated by patching the theory with yet more epicycles. The epicycles make the theory far more complex, and far more unlikely, if that were so possible. In this case, evolution not only struck on incredible complexity, and did so early in history (before there were eukaryotes and nucleus’s in which to pack the DNA), but the whole design now must have incorporated layers of redundancy which we haven’t even been able to figure out yet.

And all of this, evolutionists insist, must be a fact. Anyone who would so much as doubt this truth must be blackballed.

It has been one misstep after another ever since the evolutionists stole the histones. Evolution is truly a profound theory, not for what it reveals about nature, but for what it reveals about people. Religion drives science, and it matters.

1. H Lodish, A Berk, SL Zipursky, et al., Molecular Cell Biology, 4th ed. (New York: W. H. Freeman, 2000).

2. B Alberts, D Bray, J Lewis, M Raff, K Roberts, J Watson, Molecular Biology of the Cell, 3rd ed. (New York: Garland Science, 1994), 243.

3. B Alberts, A Johnson, J Lewis, et. al., Molecular Biology of the Cell, 4th ed. (New York: Garland Science, 1994), 243.

Galapagos finches Vs. Darwin.

Darwin’s Finches Are Evidence for Evolution? Think Again:
By MICHAEL DENTON Published on February 11, 2016:


Today is Darwin Day, marking the birthday of Charles Darwin. As the world looks back on the achievements of the great man, you are likely to see many “icons of evolution” triumphantly displayed. These famous, yet often flawed, success stories of Darwinian theory are held up as reasons to believe that the neo-Darwinian synthesis and everything it entails — scientifically and philosophically — has vanquished all legitimate challenges. But that is not so.

One of the most famous such icons is a small group of birds, an inspiration for Darwin’s On the Origin of Species, that populates a remote cluster of islands in the equatorial Pacific. The Galápagos finches, with their different beak sizes, are brandished as one of the clearest examples of evolution at work.

However, that is true up to only a very limited extent. These birds are, indeed, a clear example of micro-evolution. They are closely related to each other and their beaks have obviously been adapted through natural selection to the different food sources on the various islands. However, the finches also show what is required in order to expand the mechanism of natural selection to the larger or macro scale.

The Galápagos finches put on display the two strict requirements that must be present in order for natural selection to work its magic. If these two factors are not present, natural selection is impotent to change any creature at all, much less create a new species.

First, the finches’ beaks are clearly adaptive. Each distinct variation gives the lucky individual a definitive leg-up in its specific environment. There is an obvious, practical reason why the differentiation is helpful to the species in question. This is absolutely essential in order for natural selection to pick between variations in species. Natural selection can only “see” those variations that are adaptive — causing one individual to live, and carry on its genes, and another to die and not leave offspring. If a variation is neutral or does not somehow increase fitness in the specific environment the creature lives in, Darwin’s mechanism cannot select it.

Second, there is a functional continuum among the finches’ beaks. That is, between a finch with a tiny beak and a finch with a large beak, there are tiny, step-by-step changes, and each change makes the creature slightly more fit in its environment. This is also essential for natural selection to work.

The problem for Darwinian theory comes in explaining evolutionary change where, unlike the case of Darwin’s finches, these requirements are absent. First, there may not be a continuum. That is, natural selection cannot make large jumps or drastic changes. There must be small steps. Secondly, each single step must be beneficial to the individual. It is not enough for the first and last versions of the adaptation to be helpful — all the intervening steps must increase fitness as well.

There are examples of creatures throughout the biological world that break one or both of these rules. Many creatures just don’t fit the natural selection story like the Galápagos finches do.

For example, what is the adaptive significance of the many examples of geometric or abstract forms we see in the world, such as the shapes of leaves or the concentric whorls of flowers? Or consider the case of the enucleated red blood cell in mammals, which was the subject of my postdoctoral work. Not only have we found no obvious reason that such features increase fitness, there is no plausible continuum leading from a blood cell that keeps its nucleus to one that ejects it.

There are no such intermediate forms in nature, and it is impossible to plausibly imagine intermediates that are even stable, much less adaptive. I document many more examples in my new book, Evolution: Still a Theory in Crisis.

Without workable explanations for these many anomalies, Darwinian evolution may just go the way of Newtonian physics — applicable to a small area where specific rules apply, but unable to make universal statements about the world in general.

So when you see the media promoting the Galápagos finches as evidence for Darwinian evolution this Darwin Day, take it with a grain of salt. Not every species in the world is as obliging to the requirements of Darwinism as the famous finches. And this is just the beginning of life’s richness and complexity that cannot be reduced to Darwinian biology.