Copernicus redux?

  paper demonstrates superiority of the design heuristic

Cornelius G Hunter

Did you know Mars is going backwards? For the past few weeks, and for several weeks to come, Mars is in its retrograde motion phase. If you chart its position each night against the background stars, you will see it pause, reverse direction, pause again, and then get going again in its normal direction. And did you further know that retrograde motion helped to cause a revolution? Two millennia ago, Aristotelian physics dictated that the Earth was at the center of the universe. Aristarchus’ heliocentric model, which put the Sun at the center, fell out of favor. But what Aristotle’s geocentrism failed to explain was retrograde motion. If the planets are revolving about the Earth, then why do they sometimes pause, and reverse direction? That problem fell to Ptolemy, and the lessons learned are still important today.

Ptolemy explained anomalies such as retrograde motion with additional mechanisms, such as epicycles, while maintaining the circular motion that, as everyone knew, must be the basis of all motion in the cosmos. With less than a hundred epicycles, he was able to model, and predict accurately the motions of the cosmos. But that accuracy came at a cost—a highly complicated model.

In the Middle Ages William of Occam pointed out that scientific theories ought to strive for simplicity, or parsimony. This may have been one of the factors that drove Copernicus to resurrect Aristarchus’ heliocentric model. Copernicus preserved the required circular motion, but by switching to a sun-centered model, he was able to reduce greatly the number of additional mechanisms, such as epicycles.

Both Ptolemy’s and Copernicus’ models accurately forecast celestial motion. But Copernicus was more parsimonious. A better model had been found.

Kepler proposed ellipses, and showed that the heliocentric model could become even simpler. It was not well accepted though because, as everyone knew, celestial bodies travel in circles. How foolish to think they would travel along elliptical paths. That next step toward greater parsimony would have to wait for the likes of Newton, who showed that Kepler’s ellipses were dictated by his new, highly parsimonious, physics. Newton described a simple, universal, gravitational law. Newton’s gravitational force would produce an acceleration, which could maintain orbital motion in the cosmos.

But was there really a gravitational force? It was proportional to the mass of the object which was then cancelled out to compute the acceleration. Why not have gravity cause an acceleration straightaway?

Centuries later Einstein reported on a man in Berlin who fell out of a window. The man didn’t feel anything until he hit the ground! Einstein removed the gravitational force and made the physics even simpler yet.

The point here is that the accuracy of a scientific theory, by itself, means very little. It must be considered along with parsimony. This lesson is important today in this age of Big Data. Analysts know that a model can always be made more accurate by adding more terms. But are those additional terms meaningful, or are they merely epicycles? It looks good to drive the modeling error down to zero by adding terms, but when used to make future forecasts, such models perform worse. 

There is a very real penalty for adding terms and violating Occam’s Razor, and today advanced algorithms are available for weighing the tradeoff between model accuracy and model parsimony.

This brings us to common descent, a popular theory for modeling relationships between the species. As we have discussed many times here, common descent fails to model the species, and a great many additional mechanisms—biological epicycles—are required to fit the data.

And just as cosmology has seen a stream of ever improving models, the biological models can also improve. This week a very important model has been proposed in a new paper, authored by Winston Ewert, in the Bio-Complexity journal.

Inspired by computer software, Ewert’s approach models the species as sharing modules which are related by a dependency graph. This useful model in computer science also works well in modeling the species. To evaluate this hypothesis, Ewert uses three types of data, and evaluates how probable they are (accounting for parsimony as well as fit accuracy) using three models.

Ewert’s three types of data are: (i) Sample computer software, (ii) simulated species data generated from evolutionary / common descent computer algorithms, and (iii) actual, real species data.

Ewert’s three models are: (i) A null model in which entails no relationships between

any species, (ii) an evolutionary / common descent model, and (iii) a dependency graph model.

Ewert’s results are a Copernican Revolution moment. First, for the sample computer software data, not surprisingly the null model performed poorly. Computer software is highly organized, and there are relationships between different computer programs, and how they draw from foundational software libraries. But comparing the common descent and dependency graph models, the latter performs far better at modeling the software “species.” In other words, the design and development of computer software is far better described and modeled by a dependency graph than by a common descent tree.

Second, for the simulated species data generated with a common descent algorithm, it is not surprising that the common descent model was far superior to the dependency graph. That would be true by definition, and serves to validate Ewert’s approach. Common descent is the best model for the data generated by a common descent process.

Third, for the actual, real species data, the dependency graph model is astronomically superior compared to the common descent model.

Let me repeat that in case the point did not sink in. Where it counted, common descent failed compared to the dependency graph model. The other data types served as useful checks, but for the data that mattered—the actual, real, biological species data—the results were unambiguous.

Ewert amassed a total of nine massive genetic databases. In every single one, without exception, the dependency graph model surpassed common descent.

Darwin could never have even dreamt of a test on such a massive scale.

Darwin also could never have dreamt of the sheer magnitude of the failure of his theory. Because you see, Ewert’s results do not reveal two competitive models with one model edging out the other.

We are not talking about a few decimal points difference. For one of the data sets (HomoloGene), the dependency graph model was superior to common descent by a factor of 10,064. The comparison of the two models yielded a preference for the dependency graph model of greater than ten thousand.

Ten thousand is a big number.

But it gets worse, much worse.

Ewert used Bayesian model selection which compares the probability of the data set given the hypothetical models. In other words, given the model (dependency graph or common descent), what is the probability of this particular data set? Bayesian model selection compares the two models by dividing these two conditional probabilities. The so-called Bayes factor is the quotient yielded by this division.

The problem is that the common descent model is so incredibly inferior to the dependency graph model that the Bayes factor cannot be typed out. In other words, the probability of the data set given the dependency graph model, is so much greater than the probability of the data set given the common descent model, that we cannot type the quotient of their division.

Instead, Ewert reports the logarithm of the number. Remember logarithms? Remember how 2 really means 100, 3 means 1,000, and so forth?

Unbelievably, the 10,064 value is the logarithm (base value of 2) of the quotient! In other words, the probability of the data on the dependency graph model is so much greater than that given the common descent model, we need logarithms even to type it out. If you tried to type out the plain number, you would have to type a 1 followed by more than 3,000 zeros!

That’s the ratio of how probable the data are on these two models!

By using a base value of 2 in the logarithm we express the Bayes factor in bits. So the conditional probability for the dependency graph model has a 10,064 advantage of that of common descent.

10,064 bits is far, far from the range in which one might actually consider the lesser model. See, for example, the Bayes factor Wikipedia page, which explains that a Bayes factor of 3.3 bits provides “substantial” evidence for a model, 5.0 bits provides “strong” evidence, and 6.6 bits provides “decisive” evidence.

This is ridiculous. 6.6 bits is considered to provide “decisive” evidence, and when the dependency graph model case is compared to comment descent case, we get 10,064 bits.

But it gets worse.

The problem with all of this is that the Bayes factor of 10,064 bits for the HomoloGene data set is the very best case for common descent. For the other eight data sets, the Bayes factors range from 40,967 to 515,450.

In other words, while 6.6 bits would be considered to provide “decisive” evidence for the dependency graph model, the actual, real, biological data provide Bayes factors of 10,064 on up to 515,450.

We have known for a long time that common descent has failed hard. In Ewert’s new paper, we now have detailed, quantitative results demonstrating this. And Ewert provides a new model, with a far superior fit to the data.

The atom: an origin story

 https://www.youtube.com/embed/Ml1bk9wDXVo

A heavenly witness against design deniers?

 Animals Tune Behavior by Lunar Cycle; but How?

David Coppedge

Tonight’s moon will be full, so here is a timely question. Many unrelated animals tune their behavior by the lunar cycle. How do they do it, given that sunlight overpowers moonlight?

Researchers in Austria think they have found a clue: a cryptochrome protein that appears to respond to the lunar cycle. Cryptochrome proteins are also implicated in the geomagnetic sense in birds. Whatever they found, it surely must represent only a piece of a biological puzzle. Let them explain in this from the University of Wien:

Many marine organisms, including brown algae, fish, corals, turtles and bristle worms, synchronize their behavior and reproduction with the lunar cycle. For some species, such as the bristle worm Platynereiis dumerilii, lab experiments have shown that moonlight exerts its timing function by entraining an inner monthly calendar, also called circalunar clock. Under these laboratory conditions, mimicking the duration of the full moon is sufficient to entrain these circalunar clocks. However, in natural habitats light conditions can vary considerably. Even the regular interplay of sun- and moon creates highly complex patterns. Organisms using the lunar light for their timing thus need to discriminate between specific moon phases and between sun and moonlight. This ability is not well understood. 

The first statement should alarm evolutionists. Circalunar clocks are found in very unrelated animals (evolutionarily speaking): vertebrates like fish and turtles and invertebrates like worms and corals. Each of these must have hit upon lunar tuning independently.

 The researcher’s paper in Nature Communications points out that we humans have connections to the lunar cycle, too:

In addition, lunar timing effects have also been documented outside the marine environment, and recently uncovered correlations of human sleep and menstrual cycle properties with moon phases have re-initiated the discussion of an impact of the moon even on human biology. As recently documented for corals, desynchronization of these reproductively critical rhythms by anthropogenic impacts poses a threat to species survival.

The Bristle Worm as a Test Case

P. dumerilii, the so-called bristle worm is a polychaete (“much-haired) worm in phylum Annelida. Smithsonian Magazine lists 14 Fun Facts about these polychaetes, an “amazingly diverse family” of marine organisms:

Unbeknownst to most landlubbers, polychaetes rule the seas. There are at least 10,000 species of these swimming bristly worms, some of which pop with brilliant colors or light up with a bioluminescent glow. They’ve adapted to every imaginable marine habitat, from deep hydrothermal vents to crowded coral reefs to the open ocean—and many have found ways to survive that are definitely bizarre.

Interested readers can browse through Smithsonian’s list of facts and look at the pictures. A short horror movie shows a lionfish learning too late not to mess with a bobbit worm (Eunice aphroditois), a different species of bristle worm in the Atlantic. It’s a creature of nightmares, so be forewarned. The poisonous lionfish can’t use its defensive weapons against this lightning-fast monster: a worm! It’s a terrifying creature right out of the movie Tremors. More about bobbit worms can be found at ARCReef.com. Do not read this: some bobbit worms grow up to ten feet long! Fortunately, attacks on humans are rare, limited to “nasty bites” (Daily Mail).

Evolutionary Challenges 

Back to P. dumerilii, a much more benign polychaete only 2-4 cm long. A type of ragworm, this species is found worldwide. Wikipedia calls it a living fossil. Although it’s an invertebrate, “it has an axochord, a paired longitudinal muscle that displays striking similarities to the notochord regarding position, developmental origin, and expression profile.” It swims with a coordinated system of cilia on its surface. “Whole-body coordination of ciliary locomotion is performed by a ‘stop-and-go pacemaker system’,” the article says. That’s not the only pacemaker in this amazing little worm. Despite having “a pair of the simplest eyes in the animal kingdom,” it can “see” the phases of the moon. Those little eyes do not help the evolutionary story:

The ciliary photoreceptor cells are located in the deep brain of the larva. They are not shaded by pigment and thus perceive non-directional light. The ciliary photoreceptor cells resemble molecularly and morphologically the rods and cones of the human eye. Additional [sic], they express an ciliary opsin that is more similar to the visual ciliary opsins of vertebrate rods and cones than to the visual rhabdomeric opsins of invertebrates.

The bristle worm’s genome also challenges Darwinism:

The genome of Platynereis dumerilii … contains approximately 1 Gbp (giga base pairs) or 109 base pairs. This genome size is close to the average observed for other animals. However, compared to many classical invertebrate molecular model organisms, this genome size is rather large and therefore it is a challenge to identify gene regulatory elements that can be far away from the corresponding promoter. But it is intron rich unlike those of Drosophila melanogaster and Caenorhabditis elegans and thus closer to vertebrate genomes including the human genome.

Wikipedia prudently abstains from speculating on how these worms evolved.

Possible Lunar Oscillator Found in P. dumerilii 

In the introduction to the paper, the authors say, “Despite the importance and widespread occurrence of lunar rhythms, functional mechanistic insight is lacking.” They found a cryptochome protein they call L-Cry that appears to keep time to the full moon. Its asymmetric dimer appears to have two monomers with very different light sensitivities, which “provides the molecular basis to sense and interpret light intensities across five orders of magnitude.” 

This is important because full sunlight swamps moonlight, so the worm brain must be able to discriminate the smaller peaks of illumination from larger ones. Additionally, L-Cry must be able to avoid being tricked by artificial light that can also outshine full moonlight. It must also be robust against darkness on cloudy full-moon nights and by “natural acute light disturbances, such as lightning.” 

Experiments in the “worm room” under controlled simulations of sun and moon illumination cycles demonstrated this ability. “L-Cry’s major role could be that of a gatekeeper controlling which ambient light is interpreted as full-moonlight stimulus for circalunar clock entrainment,” they say. If an organism can set its lunar clock to a full moon, it can also discriminate other lunar phases.

The full moon is unique in having the longest duration of light at night, followed by sunrise. A circalunar clock presupposes, therefore, the ability to measure the duration as well as intensity of light. L-Cry may do this with a ratchet mechanism: as the protein accumulates photons, it reaches higher quantum levels that photoreduce parts of the low-sensitivity monomer. The authors also observed L-Cry accumulating in the nucleus and diminishing in the cytoplasm during the simulated moonlight exposure time. “This suggests that different cellular compartments convey the different light messages to different downstream pathways.”

Even so, this cryptochrome discovery only delivers “the first molecular entry point into the mechanisms underlying a moonlight-entrained monthly oscillator.” The photoreceptor for L-Cry is unknown. Additionally, L-Cry must cooperate with the circadian clock genes, adding to the regulatory complexity. How these proteins signal a cascade of physiological behaviors when it’s time to spawn remains curious. “Certainly, more extensive mechanistic studies are required to further verify our models.”

Convergent Functionality

Finally, an evolutionary consideration: Monthly synchronization by the moon has been documented for a wide range of organisms– including brown and green algae, corals, crustaceans, worms, but also vertebrates… Furthermore, recent reports also provide increasing evidence that the lunar cycle influences human behavior… Are the lunar effects mediated by conserved or different mechanisms?

Since L-Cry is not known in these other species, the authors speculate that either conservation of other proteins will be discovered, or that other proteins with analogous functions will be found. 

Last, but not least the molecular mechanisms underlying the circalunar oscillator also await identification, and it is possible that conservation exists on this level. Examples are known from circadian biology and it will now require further work to reach a similar level of understanding for moon-controlled monthly rhythms and clocks.

Surely, though, conservation of function using entirely different molecular mechanisms poses a severe challenge to Darwinism. It would seem to require entirely different sets of mutations to be selected for a common function. In design theory, intelligence starts with the concept and can use different instruments to play the same tune. 

The Palolo Worm

We end with a spectacular case of circalunar time tuning. Another polychaete, the Palolo Worm of the South Pacific, undergoes a remarkable reproductive cycle timed to both lunar and annual cycles. Britannica explains its life cycle:

The palolo worm of the South Pacific (Palolo siciliensis [P. viridis or Eunice viridis]) inhabits crevices and cavities in coral reefs. As the breeding season approaches, the tail end of the body undergoes a radical change.The muscles and most of the organs degenerate, and the reproductive organs rapidly increase in size. The limbs on the posterior segment become more paddlelike. After the animal backs part way out of its tubelike burrow, the posterior section breaks free and swims to the surface as a separate animal, complete with eyes. The anterior end, still attached to its tube, regenerates a new posterior end. 

The free-swimming half-worms contains sperm and eggs. Tens of thousands of these half-worms swim to the surface as if on cue, and release their reproductive cells always at the same time of year and at a particular phase of the moon.

The free-swimming section always makes its appearance in the early morning for two days during the last quarter of the Moon in October. Twenty-eight days later, it appears in even greater numbers in the final quarter of the November Moon. At the surface of the sea the sperm and eggs are discharged, and fertilization occurs. Palolo tails, considered a delicacy by the Polynesians, are gathered in vast numbers during swarming.

Worms. Such simple, lowly creatures. But what wonders await the biologists who delve into their mechanisms. Like everything else in biology, design-inspired awe explodes in the details.

The fossil record continues to be a bomb thrower for Darwinism

Fossil Friday: Fossil Golden Moles and the Abrupt Origin of Afrosoricida

Günter Bechly  

Last week we looked into the fossil history of elephant shrews. This first Fossil Friday in the new year we will move on in our series on placental mammal origins to another group of mainly insectivorous afrotherians: the order Afrosoricida, which comprises golden moles (Chrysochloridae), otter shrews (Potamogalidae), and the iconic tenrecs (Tenrecidae) from Madagascar. As in other small mammals their fossil record mostly consists of isolated jaw fragments and teeth, just like the featured fossil of the golden mole Diamantochloris inconsessus from the Eocene of Namibia (Pickford 2018).

Mainly based on molecular data (Springer et al. 2003) it has been suggested that the Afrosoricida originated 65 million years ago, right after the K/Pg impact event or even before (Tabuce et al. 2007: fig. 5, Poux et al. 2008: fig. 3, Everson et al. 2016: fig. 4). Of course, the fossil record does not at all support such a view (Sargis & Dagosto 2008: fig. 5.17, Asher 2010: fig. 9.1), so that some authors decided within a year to simply place the assumed origin 10 million years later (Tabuce et al. 2008: fig. 1). Isn’t evolutionary biology a wonderful science? Here is a brief list of the oldest known fossil genera in each group of Afrosoricida with their estimated stratigraphic range (based on PaleoDB and Seiffert 2010):

Afrosoricida (48.60–0 mya)

            Chrysochloridae (48.6–0 mya)

                        Damarachloris Pickford, 2019b (48.6–40.4 mya)

                        Diamantochloris Pickford, 2015a (48.6–40.4 mya, primitive Chrysochloridae acc. to Pickford 2018)

                        Namachloris Pickford, 2015c (40.4–37.2 mya)

                        Prochrysochloris Butler & Hopwood, 1957 (20.43–15.97 mya)

            Tenrecoidea (= Tenrecomorpha) (48.6–0 mya)

                        Dilambdogale Seiffert, 2010 (37.2–33.9 mya, rather about 37)

                        Eochrysochloris Seiffert et al. 2007 (33.9–28.4 mya, rather about 33)

                        Jawharia Seiffert et al. 2007 (33.9–28.4 mya, rather about 33)

                        Nanogale Pickford, 2019a (48.6-40.4 mya)

                        Plesiorycteropus Filhol, 1895 (0.012–0.0 mya)

                        Qatranilestes Seiffert, 2010 (33.9–28.4 mya, rather about 30)

                        Widanelfarasia Seiffert & Simons, 2000 (33.9–28.4 mya, rather 33.9)          

                        Potamogalidae (40.4–0 mya)

                                    Namagale Pickford, 2015b (40.4–37.2 mya)

Tenrecidae (40.4–0 mya)

                                    Arenagale Pickford, 2015b (40.4–37.2 mya)

                                    Erythrozootes Butler & Hopwood, 1957 (24-16 mya)

                                    Parageogale Butler, 1984 (= Butleriella) (24-16 mya)

                                    Protenrec Butler & Hopwood, 1957 (23.03–11.608 mya)

                                    Sperrgale Pickford, 2015b (40.4–37.2 mya)

Golden Moles (Chrysochloridae)

Golden moles are small, burrowing mammals endemic to sub-Saharan Africa with 21 living species (Asher et al. 2010). The oldest and most primitive fossil golden moles, Diamantochloris and Damarachloris, were discovered in Middle Eocene (Lutetian) sediments from Black Crow in Namibia, which are maximally 48.6 million years old (Pickford 2015a, 2018, 2019b). Together with the tenrecomorph Nanogale (see below) they also represent the earliest fossil record of Afrosoricida known so far. The best known but slightly younger genus is Namachloris, of which about 120 remains from almost the complete skeleton have been found (Pickford 2015c). They show that even these early representatives already had the burrowing adaptations of their living descendants. Another very old alleged chrysochlorid is Eochrysochloris from Fayum in Egypt (Seiffert et al. 2007, Seiffert 2010). However, Pickford (2015a, 2015c, 2018) suggested that Eochrysochloris “is probably not a chysrochlorid” but rather a tenrecid.

Tenrecoidea

The oldest representative of Tenrecoidea or Tenrecomorpha is Nanogale fragilis from the Eocene freshwater limestone of Black Crow in Namibia that can be dated to maximally 48.6 million years (Pickford 2019a). There is only a single mandible fragment known, which represents the smallest mammal from the fossil record in Africa. It has some characteristics resembling tenrecs and others rather resembling otter shrews, so that it may belong to their common ancestral lineage. Other very old tenrecoids were found in Eocene/Oligocene (37-30 mya) outcrops of the Jebel Qatrani Formation in the Fayum region of northern Egypt (Seiffert 2006), and include the genera Dilambdogale, Eochrysochloris, Jawharia, Widanelfarasia, and Qatranilestes (Seiffert & Simons 2000, Seiffert et al. 2007, Seiffert 2010).

Otter Shrews

Otter shrews (Potamogalidae) only include two living genera with three species of nocturnal and amphibious mammals, feeding off crustaceans and fish. They are believed to be closely related to the Malagasy tenrecs but only occur in Western and Central Africa. According to molecular clock studies their lineage should at least date to the Lower Eocene (Everson et al. 2016), but the possible fossil record is very sparse and controversial. Van Valen (1967) thought that the genera Erythrozootes and Protenrec might be fossil otter shrews, but most other and later workers rather attributed them to the genuine tenrecs. Seiffert (2007) again found the Miocene Protenrec as sister group of Potamogale instead on tenrecs, but subsequent studies did not accept this position. The only unequivocal fossil record of otter shrews is Namagale described by Pickford (2015b) from the Late Eocene (Bartonian) Eocliff in Namibia, which is 40.4-37.3 million years old.

Tenrecs

Living tenrecs are hedgehog-like mammals endemic to Madagascar with 31 living species classified in 8 genera and 3 subfamilies. Until recently the oldest fossil tenrecs were the three genera Erythrozootes, Parageogale, and Protenrec from the Miocene of Kenya and Namibia (Butler & Hopwood 1957, Butler 1984, Poduschka & Poduschka 1985, McKenna & Bell 1997, Mein & Pickford 2003, 2008, Asher & Hofreiter 2006, Seiffert et al. 2007, Pickford et al. 2008, Poux et al. 2008, Asher & Seiffert 2010). Strangely, these genera seem to be most closely related to the Malagasy tenrec genus Geogale (Asher & Hofreiter 2006). Poduschka & Poduschka (1985) disputed the relationship of Parageogale, which they had invalidly named Butleriella, with the living genus Geogale, but this very relationship was vindicated by new material and further studies (see Asher & Seiffert 2010). This relationship arguably would imply a back dispersal event from Madagascar to Eastern Africa more than 267 miles across the Mozambique Channel of the Indian Ocean (Douady et al. 2002, Poux et al. 2008, Everson et al. 2016). This is a quite daring hypothesis to say the least (see Bechly 2018). Apart from this anomaly the general colonization of Madagascar by tenrecs has been dated with molecular evidence to have happened between 55 mya and 37 mya by Douady et al. (2002) or between 56-30 mya by Everson et al. (2016), which falls well within the range of the oldest African fossil stem tenrecs and well after the separation of Madagascar from mainland Africa about 165-120 mya. 

These oldest putative stem tenrecs are the two species Arenagale calcareus and Sperrgale minutus described by Pickford (2015b) from the Late Eocene (Bartonian) Eocliff in Namibia, dated to about 40 million years ago.

We should also briefly mention the Malagasy Aardvark Plesiorycteropus, which is known from two subfossil species. Apparently, these animals went extinct just a few hundred years ago, likely due to anthropogenic causes like overhunting and deforestation. They were long believed to be related to Xenarthra or Tubulidentata, and even assigned to its own distinct mammal order Bibymalagasia. However, a molecular study of ancient collagen revealed that they represent another major branch of Tenrecoidea (Buckley 2013), though no older fossils are known yet.

But, we have not yet exhausted the potential candidates for the oldest Afrosoricida. There are two obscure fossil mammals from the Late Paleocene of Morocco that may qualify: Todralestes variabilis was originally described as an insectivoran of the polyphyletic waste basket taxon “Proteutheria” (Gheerbrant 1991, 1994, Gheerbrant et al. 1998), while Afrodon chleuhi was described from the same outcrops as an insectivoran of the extinct family Adapisoriculidae (Gheerbrant 1988, Gheerbrant & Russell 1989, Gheerbrant et al. 1998).

Both Todralestes and Afrodon share dental similarities with supposed early afrosoricids like Widanelfarasia, Protenrec, and Dilambdogale (Seiffert & Simons 2000, Seiffert et al. 2007). Asher & Seiffert (2010) and Seiffert (2010) even recovered these two genera as most basal stem Afrosoricida in their cladogram. If this should be correct, it would place the origin of the afrosoricid lineage into the Late Paleocene, right in the brief window of time when most of the placental mammal orders first appear in the fossil record. Of course, as always there is considerable disagreement about the phylogenetic affinities of these taxa: Tabuce et al. (2008: fig. 1) put a question mark at the alleged Late Paleocene occurrence of stem afrosoricids suggested by Seiffert et al. (2007), without explicitly listing the concerning genera. Pickford et al. (2008) considered Todralestes as a member of the unrelated extinct mammal order Cimolesta, and Afrodon still as an adapisoriculid insectivoran of the living mammal order Lipotyphla. The consensus tree of Halliday et al. (2015) did neither support a relationship of Todralestes nor of Widanelfarasia and Dilambdogale with Afrosoricida or even Afrotheria.

Like in all the other groups of afrotherian mammals, the modern consensus of attributing Afrosoricida to the African mammal clade Afrotheria is almost exclusively based on molecular data (Nishihara et al. 2005, Seiffert 2003, 2007, Tabuce et al. 2007, 2008, Asher & Seiffert 2010, Heritage et al. 2020), while anatomical similarities were universally interpreted as evidence for a relationship with insectivorans (e.g., Van Valen 1967, Butler 1984, Novacek et al. 1985, McKenna & Bell 1997) and even explicitly rejected an afrotherian clade (Asher 1999). Pickford (2019a) mentioned that “many recent phylogenetic analyses of Afrotheria seem to be incompatible with each other.”

A Repeating Pattern

Again and again we find the same pattern:

Darwinism predicts gradual accumulation of small changes over long periods of time but the empirical data of the fossil record point to rapid bursts of biological novelty.

Darwinism predicts that anatomical similarities should align with genetic similarities but the actual trees and/or classifications generated from these data conflict with each other.

Darwinism predicts that molecular clock estimates should agree with the stratigraphic appearance of taxa in the fossil record but they don’t.

Should we draw any conclusions from such consistent empirical failures of a theory? Maybe we should instead modify a popular dictum by the famous evolutionary biologist Theodosius Dobzhansky into “Nothing makes sense in biology in the light of (Darwinian) evolution” to align it better with reality.

Next Fossil Friday we will have a look at the early fossil record of hyraxes, another afrotherian group with an interesting history.

A hill to die on?

Astrophysicist Bijan Nemati: Why Intelligent Design Matters

Evolution news 

On a new episode of ID the Future, astrophysicist and intelligent design proponent Bijan Nemati shares the first part of his story of science and faith. Those who follow Discovery Institute’s Center for Science & Culture may know Nemati from his appearance in the popular ID documentary The Privileged Planet. Born and raised in Iran, he moved to the United States shortly before the Iranian revolution, became an atheist in college, but eventually found his way to a strong religious faith, in part through his exposure to the scientific evidence for intelligent design, first in biology and then in cosmology. Along the way he landed a high-level job with NASA’s Jet Propulsion Laboratory (JPL) and became a leading expert in space interferometer telescopes and the science and technology of detecting earth-like planets. Here he shares with host Eric Anderson his journey of discovery. Download or listen to the podcast here

Darwinism's simple beginning problem 2.0?

 Minimal Complexity Problem in Prey Detection by the Sand Scorpion

Bijan Nemati

In the introduction to the “Waves” chapter of Halliday, Resnick, and Walker’s Fundamentals of Physics (6th edition), the authors mention a remarkable ability of the sand scorpion. Living in the highly arid and hot region of the Mojave Desert, a sand scorpion must hunt its prey at night. Its visual, olfactory, and auditory abilities are minimal, and not sufficient in the nighttime desert to catch prey. Yet, catch they can, with remarkable efficiency. When a beetle comes within a couple of feet, the disturbance that it creates on the sand is detected by the scorpion first to determine 

 direction, then to determine distance. 

         The sand scorpion, like other arachnids, has eight legs. The terminal (“tarsal”) segments of the eight legs form a rough circle. It is at these eight points that the scorpion can detect tiny vibrations, of order 1 Angstrom (the size of a hydrogen atom) in amplitude, that emanate from the prey which is passing by. Detailed studies by Philip H. Brownell of Oregon State University in the 1980s demonstrated that the scorpion detects direction by comparative timing of the disturbance as it passes and is sensed by the legs. The legs that are closer to the prey sense the signal first, by as little as a few microseconds. 

A Spectacular Ability

While this sensitivity is amazing, the most spectacular ability of the scorpion is to detect distance. When the scorpion has established the direction, it will hold completely still. At the next movement of the prey (often beneath the sand at a shallow depth), the scorpion rapidly moves to the location of the origin of the disturbance, plunges its pincers to its estimated location of the prey, and catches it. Once caught, the prey is immobilized by the neurotoxin delivered by the scorpion’s stinger, and its is slowly consumed. 

         Brownell showed that two type of disturbances — longitudinal compression waves, and transverse “Rayleigh” waves — with different propagation speeds, propagate effectively over these types of distances from prey to predator, and that the scorpion uses the different arrival times of the pulses to estimate distance. Brownell’s data indicated that at 15 cm or less, the accuracy of its distance estimates was excellent.

            Irreducible Complexity

But how does the scorpion “know” the propagation speeds of the longitudinal and transverse waves? And how does it know how to calculate the distance? This is a simple freshman physics problem if someone gives you the calibrated speeds. But for a Darwinian theory of the origin of species, it presents an incredible minimal-complexity problem. The minimal ingredients are 1) the sensors (atomic level sensitivity of amplitude, sub micro-second timing, the ability to distinguish transverse and longitudinal pulses), 2) the distance-to-velocity equation with the assumption that the two disturbance types were simultaneously originated, and 3) the calibrated propagation speeds for the two types of disturbances (Rayleigh and Compression waves). 

              Without all three of these innovations in place, the scorpion cannot survive. 

Tito: a brief history.

Josip Broz Tito

Wikepedia 

Josip Broz (Serbo-Croatian Cyrillic: Јосип Броз, pronounced [jǒsip brôːz]; 7 May 1892 – 4 May 1980), commonly known as Tito (/ˈtiːtoʊ/;[2] Serbo-Croatian Cyrillic: Тито, pronounced [tîto]), was a Yugoslav communist revolutionary and statesman, serving in various positions of national leadership from 1943 until his death in 1980.[3] During World War II, he was the leader of the Yugoslav Partisans, often regarded as the most effective resistance movement in German-occupied Europe.[4] He also served as the president of the Socialist Federal Republic of Yugoslavia from 14 January 1953[1] until his death on 4 May 1980.

        He was born to a Croat father and Slovene mother in the village of Kumrovec, Austria-Hungary (now in Croatia). Drafted into military service, he distinguished himself, becoming the youngest sergeant major in the Austro-Hungarian Army of that time. After being seriously wounded and captured by the Russians during World War I, he was sent to a work camp in the Ural Mountains. He participated in some events of the Russian Revolution in 1917 and the subsequent Civil War. Upon his return to the Balkans in 1918, he entered the newly established Kingdom of Yugoslavia, where he joined the Communist Party of Yugoslavia (KPJ). Having assumed de facto control over the party by 1937, he was formally elected its general secretary in 1939 and later its president, the title he held until his death. During World War II, after the Nazi invasion of the area, he led the Yugoslav guerrilla movement, the Partisans (1941–1945).[5] By the end of the war, the Partisans—with the backing of the invading Soviet Union—took power over Yugoslavia.

            After the war, Tito was the chief architect of the Socialist Federal Republic of Yugoslavia (SFRY), serving as the prime minister (1944–1963), president (since 1974 president for life) (1953–1980), and marshal of Yugoslavia, the highest rank of the Yugoslav People's Army (JNA). Despite being one of the founders of Cominform, he became the first Cominform member to defy Soviet hegemony in 1948. He was the only leader in Joseph Stalin's time to leave Cominform and begin with his country's own socialist program, which contained elements of market socialism. Economists active in the former Yugoslavia, including Czech-born Jaroslav Vaněk and Yugoslav-born Branko Horvat, promoted a model of market socialism that was dubbed the Illyrian model. Firms were socially owned by their employees and structured on workers' self-management; they competed in open and free markets. Tito managed to keep ethnic tensions under control by delegating as much power as possible to each republic. The 1974 Yugoslav Constitution defined SFR Yugoslavia as a "federal republic of equal nations and nationalities, freely united on the principle of brotherhood and unity in achieving specific and common interest." Each republic was also given the right to self-determination and secession if done through legal channels. Lastly, Tito gave Kosovo and Vojvodina, the two constituent provinces of Serbia, substantially increased autonomy, including de facto veto power in the Yugoslav parliament. Tito built a very powerful cult of personality around himself, which was maintained by the League of Communists of Yugoslavia even after his death. Twelve years after his death, as communism collapsed in Eastern Europe, Yugoslavia dissolved and descended into a series of interethnic wars.

        Some historians criticize Tito's presidency as authoritarian,[6][7] while others see him as a benevolent dictator.[8] He was a popular public figure both in Yugoslavia and abroad.[9] Viewed as a unifying symbol,[10] his internal policies maintained the peaceful coexistence of the nations of the Yugoslav federation. He gained further international attention as the chief leader of the Non-Aligned Movement, alongside Jawaharlal Nehru of India, Gamal Abdel Nasser of Egypt, Kwame Nkrumah of Ghana, and Sukarno of Indonesia.[11] With a highly favourable reputation abroad in both Cold War blocs, he received a total of 98 foreign decorations, including the Legion of Honour and the Order of the Bath. 

Ann Guager on becoming Ann Guager

 The Evolution of Dr. Ann Gauger

Stephen Dilley

Editor’s note: We are delighted to present a new, occasional series on the “evolution” of top scientists who have helped advance the case for intelligent design.

“It was like the cast of characters from an Illustra Media film.”

That was biologist Ann Gauger’s droll comment on her first visit to Discovery Institute’s offices in Seattle. The year was 2004. Dr. Gauger’s scientific credentials had caught the eye of Stephen Meyer and he had invited her to come talk with him. On the day of the meeting, Gauger arrived and settled into a conference room. In walked Meyer, Jay Richards, and Jonathan Wells — the usual suspects from Illustra films such as Unlocking the Mystery of Life.

The occasion of the meeting went back to two weeks earlier. A friend had recommended to Gauger an article in DI’s newsletter, Nota Bene. The article summarized Steve Meyer’s controversial piece on the Cambrian explosion in the peer-reviewed journal Proceedings of the Biological Society of Washington.1

Gauger had been reading ID literature for some time. She was interested and decided to subscribe to Nota Bene. When she signed up, she included “PhD” after her name. “I wonder what will happen?” she mused.

Twenty minutes later, she received a phone call from Logan Gage, an administrative liaison. Logan went through a checklist. 

“You have a PhD, right?”

“Yes.”

“You’re aware of the Dissent from Darwin list?”

“Yes. In fact, I’ve already signed it.”

A pregnant silence. Then a reply, “Can you send me your CV?” 

Gauger promptly did so. “I wonder what will happen?” she thought again.

Twenty minutes later, Logan was on the phone again. “Can you come in to DI to talk with Steve Meyer?” Nothing was the same after that. 

Evolution as Default

Yet by the time Gauger watched the Illustra movie cast walk into the room at Discovery Institute in 2004, her concerns about evolution had grown. Why? There were many reasons, yet chief among them was the Cambrian explosion.

The fossils of the Cambrian era raised the puzzle that Gauger had pondered while studying invertebrates: how did all of these different body plans emerge? Of the 27 phyla recorded in the fossil record, an astonishing 20 of them emerged during the Cambrian explosion. Only 3 phyla appear before the Cambrian, and only 4 others appear after that era.2 It is the major event within organic history.

Gauger also realized that the neo-Darwinian mechanism lacked the creative power to generate so many new body plans in the time available.3 And even the promise of evo-devo had fallen short. In particular, Gauger was impressed with the Nobel Prize-winning work of Christiane Nüsslein-Volhard and Eric Wieschaus. These geneticists had studied the fruit fly Drosophila melanogaster, mapping its genome and analyzing is early development. They discovered that mutating or perturbing early-acting body plan molecules invariably kills the fruit fly.4 In order to generate a genuinely new body plan, early embryonic changes must take place. Yet for evolution to occur, these changes must be viable rather than lethal. By contrast, Nüsslein-Volhard and Wieschaus observed that early developmental mutants never even hatched as larvae.5 Other problems plagued evo-devo, too.6

Moreover, Gauger’s own research after 2004 helped illuminate key problems for evolutionary theory. Among others, she articulated the causal circularity problem,7 the waiting times problem,8 and the implausibility of human evolution.9 Gauger has also helped to show that a first couple is possible in the context of human origins.10 And more on the way: a volume she has edited on the positive case for intelligent design, by contributors arguing from a Catholic perspective, is just around the corner.11

Full Circle

Gauger recalls with a chuckle her initial meeting with the Illustra cast in 2004. “Steve Meyer walked me through his PowerPoint presentation on the Cambrian explosion. He had the right argument. But I spotted a typo and said so.”

The “typo,” as it turns out, was a technical point about invertebrates. Only someone well-versed in the field would have had that kind of knowledge. Dr. Gauger’s years of research and study had prepared her perfectly for the road ahead.12

Evolution by design v. Design by Evolution?

 Brain Scientist: Consciousness Didn’t Evolve; It Creates Evolution

Denyse o' Leary

In a recent episode of Closer to Truth, Robert Lawrence Kuhn interviewed University of California cognitive scientist Donald Hoffman on a challenging topic, “Why did consciousness emerge?”:

There was a time when there was no consciousness in our universe. Now there is. What caused consciousness to emerge? Did consciousness develop in the same way that, say, the liver or the eye developed, by random mutation and fitness selection during evolution? Inner experience seems to be radically different from anything else. Are we fooling ourselves?

Donald Hoffman is the author of Visual Intelligence: How We Create What We See and coauthor of Observer Mechanics: A Formal Theory Of Perception (Norton, 2000).

A partial transcript and some notes and questions follow:

Robert Lawrence Kuhn: Don, you make the extraordinary claim, backed up by some sophisticated computer simulations that evolution, by favoring fitness, drives truth to extinction. Yeah, how then can we deal with reality and what are the implications of that? (0:19)

Donald Hoffman: It’s such an extraordinary result. It is at first a little bit surprising and you would wonder how could true perceptions be useful? How could it possibly be that true perceptions could guide useful behavior? And fortunately we have a nice metaphor with the advent of computers and laptops and user interfaces that I think can help us to see what’s going on here. (0.41)

If you look at your laptop interface … you might have a blue rectangular icon for a file that you’re working with and that icon might be in the lower right hand corner of your of your screen. Does that mean that the file itself, that you’re working on, is blue or rectangular or in the lower right hand corner of the computer? Well, obviously not. (1.06)…

The whole point of the desktop interface is to hide the truth and to guide your behavior. You don’t want to know about the diodes and the resistors and all the electronics inside there and all the magnetic fields and voltages and all the software. If you had to know all of that stuff you could never paint a picture, you know, edit your photograph or write a paper. So what you want is an interface that hides the complexity that you don’t need to know so that you can do the things you need to do. (2:02)… It’s not lying to you; it’s actually helping you. But it’s helping you by hiding the truth. (2:16)

So evolution has done the same thing for us. It has given us perceptions that are like a user interface (3:06)…

Note: Let’s leave “evolution” out of this for a moment. Here is what we know, irrespective of how we came to know it: All sources of information, however derived, are specific and partial. Through an open window, I see a herd of deer trotting across the parking lot. A sharp-eared neighbor, not near the window, hears their hooves striking the pavement. A small dog in a pen under the window senses the deer by their smell and may even guess their size and sex in some cases. Not one of us sees the whole picture. In the same way, humans, using abstractions, develop symbol systems on computers to represent functions. All information systems necessarily represent information filtered in some way. But — absent any reason to believe that the information provided is erroneous — what does that prove about truth or consciousness? Kuhn picks up on one aspect of this:

Robert Lawrence Kuhn: Now is your metaphor a strong metaphor or have you thought deeply about it? Because that metaphor is enormously powerful in terms of reflecting our lack of capacity of understanding what reality is. I mean, it would be hopeless, it’s impossible to tell from the user interface on a computer, just what the source code is. But all the electronics and the voltages and the capacity and the structure of the CPU… I mean that’s just so far beyond anything that you would even know existed (3:29)

Donald Hoffman: I agree. I mean if someone were to say, I want you to use only what you see on the desktop the pixels and tell me what’s going on and from that figure out a theory about what’s going on inside the computer that’s going to be a really, really tough time… (4:00)

Note: But we don’t need to know everything about how a computer works to use the desktop icons to get us where we need to go, any more than observers need to know much about deer in order to determine whether they are present. Again, all information is necessarily partial and focused and our consciousness enables us to determine the information that we need. To get more information, we formulate a specific question, to which the answer will likewise be partial and focused. To see everything as a whole we would need an unlimited consciousness. That is what many people call God.

Donald Hoffman: Right. So you have to make assumptions, right? So you’re free to make assumptions and I’ll just jump to the assumption I make here to solve the problem. So I don’t take our perceptions of space and time as literally true. I take them as a desktop. (4:19)

To solve the mind–body problem I’ve tried to say, let’s take consciousness as fundamental. So what’s behind the interface is consciousness, right, just like in the example of the computer. what’s behind the screen are all those diodes and resistors and so forth. Yeah, I’m saying what’s behind space and time and physical objects for us is a world of what I call conscious agents or consciousness. (4:41)

The nice thing about that theory is, I’m conscious, you’re conscious. I’m proposing that the objective reality behind this interface is not utterly alien to who I am. There is a chance for me to begin to understand that objective reality behind the interface because I’m not utterly separated from it so it’s a different situation than what’s behind the computer screen so anyway. (5:07)

But what happens when you then ask the, question where your consciousness came from?, because it came through an evolutionary process right? So, when you take this point of view now, if space and time were not fundamental, right, then we have to rethink evolution from the get-go. (5:26)

Note: At this point, we surely do need to rethink evolution from the get-go. Hoffman sounds like an philosophical idealist. He calls his position conscious realism. But according to current evolution theory, consciousness is a randomly evolved illusion created by the brain to help the human animal hunt better. To grant any primacy to consciousness is to imply that the human mind is not simply the user illusion that evolution theory dictates that it must be. How does Hoffman get around that?

Donald Hoffman: So I’ve used Evolutionary Game Theory to conclude that everything that we see around us in our perceptions is not vertical; it’s just a user interface, okay. and that means I have to go back and rethink what do. I mean ,what is the core of evolutionary theory that I can keep? I have to give up some physicalist assumptions that are typically made in evolution, okay? So most evolutionary biologists are also physicalists, of course. But it’s not absolutely necessary to be a physicalist to have the key principles of evolution… [6:08]

Note: Hoffman is offering a hope here, not a present reality. Darwinian evolution (the only currently respectable kind) is and always has been a physicalist theory. Physicalism is precisely what Darwinian evolution defends: mind from mud, via natural selection acting on random mutation. And, to be clear, the “mind” that the process creates is held to be a mere user illusion that enables the human 

Robert Lawrence Kuhn: Yeah, but are you saying that consciousness was there before the process of evolution began? I, you know, I say that with a tremor in my voice. (6:13)

Donald Hoffman: That’s right. Absolutely so. For me to be entirely consistent, if I’m going to actually say that consciousness is fundamental, then I’m saying that the Big Bang itself is something that has to be understood from within a framework in which consciousness is fundamental. The standard view — and I understand that this is completely non-standard, what I’m saying — the standard view is that the Big Bang happened 13.7 billion years ago. Eventually, consciousness kind of arose accidentally here on Earth and maybe other places and totally accidentally, that’s right? So my story is completely different. (6:54)

Robert Lawrence Kuhn: So when I asked the question, how did consciousness emerge through an evolutionary process, your answer is it didn’t. (6:59)

Donald Hoffman: That’s right. Consciousness didn’t emerge from a prior physical process of evolution. Consciousness is fundamental and so we have to rethink the whole history of the universe actually from this point of view, from The Big Bang up through evolution. We have to rethink it in terms of how to rewrite that story, consistent with all of our current science but understanding that it’s … consciousness is fundamental, not the physical universe (7:23)

And, you know, one thing that comes out of this as well is, no one has been able to give a reason for why consciousness would evolve. What is it for? And so my attitude is, it didn’t evolve. It’s the ground from which evolution occurs. (7:38)

Note: Look what happened here: Hoffman starts by trying to align his consciousness theory with standard evolution theory and then just chucks that and says what he thinks: Consciousness didn’t evolve. It’s the ground from which evolution occurs. That’s surely defensible but it’s not, rest assured, the fully materialist theory taught, and enforced by law, in schools. The conflict between observation and accepted theory is one reason why consciousness is, as David Chalmers has put it, a “Hard Problem.“

 organism to survive and spread selfish genes. Incidentally, dissenters from that one and only orthodox view have often been hounded from academic life.

Morphogenesis vs. Darwinism.

 Diatoms and the Mystery of Morphogenesis 

David Coppedge 

From code to art: how does a linear set of instructions result in a beautifully crafted pattern? Diatoms do it, and scientists are struggling to figure out how. So far, they can see where spots of paint are appearing on the canvas, but the system that directs the finished masterpiece eludes them. 

Morphogenesis is the construction of a functional shape from component parts. It’s a huge mystery in biology. It isn’t enough to accumulate the parts; they must be fit together according to an overall plan—in the right places, in the right order, and at the right time. Laufmann and Glicksman pointed out the mystery of bone morphogenesis in a sidebar on page 78 of Your designed body, comparing it to the construction of a house. “What does it take to build a house?” they ask; “Where are the shapes for these bones specified?”

Since bones are made by many individual (and independent) bone cells, building a bone is an inherently distributed problem. How do the individual bone cells know where to be, and where and how much calcium to deposit? How is this managed over the body’s development cycle, as the sizes and shapes of many of the bones grow and change? Surely the specifications for the shapes, their manufacturing and assembly instructions, and their growth patterns must be encoded somewhere. There must also be a three-dimensional coordinate systemfor the instructions to make sense. Is the information located in each bone cell, or centrally located and each individual bone cell receives instructions? If each bone cell contains the instructions for the whole, how does it know where it is in the overall scheme? How do all those bone cells coordinate their actions to work together rather than at odds with each other? 

On a smaller scale, diatoms face (and solve) this same challenge. Instead of organizing cells together, they fit proteins and inorganic silica together. Diatoms have a blueprint (DNA) and a parts list (proteins) for construction of their glass houses. What drives them to create geometrical shapes from the parts, and place them in accurate positions in 3D space? This seems beyond the capabilities of parts and blueprints. It would be like placing a blueprint for a house on a pile of lumber, pipe, wire, and glass and expecting the house to self-organize. Even if a complete set of tools were available nearby, nothing would happen without foremen and skilled workers.  

Diatom Houses 

There are tens of thousands of species of diatoms exhibiting a multitude of shapes. A few are five-pointed stars; others form triangles, squares, or rods. Each consists of two halves, called frustules or valves, that are made of silica, fitting together like halves of a pill box. The edges of the valves contain girdle bands that hold the top and bottom together. The faces of the valves are often adorned with pores organized into wonderfully detailed arrays that display intricate geometry under a microscope. Diatoms build their houses from the inside out.  

A species named Thalassiosira pseudonana is shown at the top of this article. Its circular lid displays “hierarchical patterns of meso- and macropores, ribs, tubes, and spines,” arranged with geometrical precision. These valves are as functional as they are beautiful. Possessing enormous strength, they protect the organism from predators and possibly from UV light. The pores may act like lenses, too, channeling the proper wavelengths of light to the photosynthetic machinery inside 

(See my article from 2021).

Valiant Effort

In a determined effort to understand how the diatom builds its glass house, a team from the Center for Molecular and Cellular Bioengineering in Dresden, Germany, attempted to identify the parts of the “silica deposition vesicle” (SDV) for T. pseudonana and determine how the protein machinery works to fit the “silica precursors” into their positions on the valve. Their results were published In PNAS by Christoph Heinz et al,“The molecular basis for pore pattern morphogenesis in diatom silica.

The magnitude of the problem can be appreciated in their introduction. The mystery of morphogenesis extends beyond diatoms, and understanding it could lead to revolutionary technologies.

Numerous organisms produce inorganic materials with amazingly complex morphologies and extraordinary properties in a process termed biomineralization. Prominent examples include the single-domain magnetite nanocrystals of bacteria that act as sensitive magnetic field sensors, the nacreous calcium carbonate layers of mollusks with exceptionally high fracture resistance, and the hierarchically porous, silica cell walls of diatomswith intriguing photonic properties. A fundamental understanding how genetically encoded machineries are capable of establishing physical and chemical forces that drive morphogenesis of such intricate mineral structures is currently lacking. Therefore, unveiling the mechanisms of biomineralization holds the promise of gaining advanced capabilities to synthesize minerals with tailored properties using environmentally benign processes.

This is all highly appetizing to consider, but reading their paper is like watching a Sherlock Holmes movie that never resolves: a plethora of clues, but no answer to the big question: who dunnit? “Come back later for the next exciting episode” is hardly satisfying. 

Not that the authors didn’t try; they isolated the SDV, a first. They identified thousands of protein molecules in the SDV and searched for matched sequences in the UniProt database to sift the data down to the most likely candidates involved in pattern formation. After pinpointing some, they ran gene knockout experiments to see what resulted. They also identified receptors in the cell membrane for these proteins. Transporter proteins, ion pumps, silica transporters and other parts were labeled in a model diagram (Figure 5). They followed up on hypotheses that silica deposition involves liquid-liquid phase separation (LLPS), causing unassembled silica in the organic matrix to organize into “droplets” ready for deposition.

Proteomics analysis of the intracellular organelle for silica biosynthesis led to the identification of new biomineralization proteins. Three of these, coined dAnk1-3, contain a common protein–protein interaction domain (ankyrin repeats), indicating a role in coordinating assembly of the silica biomineralization machinery. Knocking out individual dank genes led to aberrations in silica biogenesis that are consistent with liquid–liquid phase separation as underlying mechanism for pore pattern morphogenesis.

Their model shows that dAnk1 appears to assemble the droplets, and dAnk2 and dAnk3 cooperate in stabilizing and disassembling them. But they never found the artist. The morpho genius behind morphogenesis remains unknown.

Origin of Morpho: First recorded in 1850–55; from New Latin Morphō, genus name, from Greek Morphṓ “the Shapely, the Beautiful” (an epithet of Aphrodite in Sparta), akin to morphḗ “form, shape, figure, beauty.”

Something Missing

The authors considered alternative hypotheses regarding the mechanism of pore pattern formation: is it template-driven or self-organizational? While finding that the dAnk1-3 genes “influence the morphogenesis of pore patterns,” no “morphogene” was found. This leads them to believe that “additional proteins and possibly other components are involved in the morphogenesis process.” But is this like looking for more tools lying around on a house construction site? Where is an entity that knows the master plan and understands how to carry it out?

Considerations such as this over many years led Michael Denton to reject genetic determinism and embrace structuralism: the philosophy that form precedes function, not vice versa. Biological structures arise from internal “laws of biological form,” he said in 2016, that are universal and built into the properties of matter.

On a personal note, it was my own increasing recognition that the gene-centric paradigm was failing at the cellular level and that the architecture of cells is an “epigenetic affair,” the result of the self-organization of cellular matter, which was one of the major factors influencing my own move to structuralism.

DENTON, EVOLUTION: STILL A THEORY IN CRISIS, P. 259.

Yet even structuralism seems to be missing something. There is no law of biological form that necessarily makes it self-organize into a five-pointed star or a triangle, else all diatoms would look the same. Thousands of other species within the same environment inherit very different shapes. There is no material law that could take a form like a five-pointed star and encode it into a genome that leads to consistent inheritance of that form in its progeny. Structuralism might explain snowflakes, which though profoundly unique, are nevertheless built on the same structural pattern inherent in ice crystals. Snowflakes, are also not inherited from a linear code, as in biology. The organizing principles in biological structures seem very different from any other examples of self-organization in nature.

Software in the Hardware

The only mechanism we know for sure can faithfully reproduce a form from a linear code is software. A designing intelligence that writes software does not have to be present when it is operating. A 3D printer can run automatically, producing unlimited copies of a shape, given sufficient supply of resin. If a toy shop were running software-directed morphogenesis, no amount of inspection of the properties of the resin and machinery would predict the shape of a statuette coming out. The shape was in the mind of the programmer, not in the properties of the ingredients, the laws of nature, or the environment.

More Design in Diatoms 

Two other facts about diatoms should arouse our appreciation of their intelligent design. One is that another atomic element — silicon — has found its way into the periodic table of biology. See our other articles about elements in life: phosphorus, boron, potassium, and about 20 others. The finely-tuned properties of elements have been detailed by Michael Denton in The Miracle of Man and his other “Privileged Species” books and videos. Diatoms could have taken on plain shapes without silica shells and still survive, as do other plankton. But the world is enriched by their crystalline architectures.

Another awesome fact about diatoms is their contribution to animal life. Diatoms produce about 25 percent of the air we breathe. This raises a philosophical puzzle of how necessary things can also be beautiful. Would we expect a statue in a museum to sweep its own floor? A healthy atmosphere might come from amorphous blobs of photosynthetic organisms, but the beauty of diatoms adds artistry to function.

The gold standard: an origin story.

 The birth of the gold standard in medical care see video here.

"Professor" Dave gets caught in the crossfire.

Professor Dave vs. James Tour re:the Origin of Life; ringside seat here. 

Our brains are smarter than we are?

 Mice Can’t Do Calculus but Their Brains Can. 

Evolution News 

Science writer Kevin Hartnett tells us that, based on experiments with mice, the brain sharpens control of precise maneuvers by using comparisons between control signals rather than the signals themselves:

[The research] explores a simple question: How does the brain — in mice, humans and other mammals — work quickly enough to stop us on a dime? The new work reveals that the brain is not wired to transmit a sharp “stop” command in the most direct or intuitive way. Instead, it employs a more complicated signaling system based on principles of calculus. This arrangement may sound overly complicated, but it’s a surprisingly clever way to control behaviors that need to be more precise than the commands from the brain can be. 

KEVIN HARTNETT, “THE BRAIN USES CALCULUS TO CONTROL FAST MOVEMENTS” AT QUANTA MAGAZINE (NOVEMBER 28, 2022) THE PAPER IS OPEN ACCESS.

The researchers observed, via neuroimaging and mathematics, that a simple Stop! signal in the brain would not allow the mouse to stop as quickly as it in fact did. There had to be another signalling system in the brain as well. So they decided to have a closer look at it. 

Between the cortex where goals originate and the [mesencephalic locomotor region] MLR that controls locomotion sits another region, the subthalamic nucleus (STN). It was already known that the STN connects to the MLR by two pathways: One sends excitatory signals and the other sends inhibitory signals. The researchers realized that the MLR responds to the interplay between the two signals rather than relying on the strength of either one. 

KEVIN HARTNETT, “THE BRAIN USES CALCULUS TO CONTROL FAST MOVEMENTS” AT QUANTA MAGAZINE (NOVEMBER 28, 2022).

The MLR pays attention the difference between the two signals more than the signals themselves. A bigger difference means a faster change and a quicker command to Stop! 

The researchers cast the stopping mechanism in terms of two basic functions of calculus: integration, which measures the area under a curve, and derivation, which calculates the slope at a point on a curve.

If stopping depended only on how much of a stop signal the MLR received, then it could be thought of as a form of integration; the quantity of the signal would be what mattered. But it doesn’t because integration by itself isn’t enough for rapid control. Instead, the MLR accumulates the difference between the two well-timed signals, which mirrors the way a derivative is calculated: by taking the difference between two infinitesimally close values to calculate the slope of a curve at a point. The fast dynamics of the derivative cancel out the slow dynamics of the integration and allow for a fast stop. 

KEVIN HARTNETT, “THE BRAIN USES CALCULUS TO CONTROL FAST MOVEMENTS” AT QUANTA MAGAZINE (NOVEMBER 28, 2022).

So mice can’t do calculus but their brains can. Assuming that the human brain works similarly to the mouse brain when it comes to sudden stops, then — if the researchers are correct — our brains do calculus too, even if, despite applying ourselves personally, our minds are not very successful at it.

Neuroscientist Sridevi Sarma, who was not involved with the paper, notes that “it allows you to anticipate and predict.” If we must stop suddenly, it may be more useful to know how fast we are speeding up or slowing down than to know how fast we are going. The obliging brain’s calculus gives us that tool.

Fun fact: Mice actually like to run. Even wild mice will run in wheels, given a chance:

You may also wish to read: Researchers say the human brain’s claustrum acts as a router for thoughts. Francis Crick thought the claustrum might be the “seat of consciousness,” an inherently materialist concept. The researchers think he was wrong. Of course, seeing the claustrum as a router is more consistent with the immaterial nature of consciousness than seeing it as a seat. (Denyse O’Leary)