Measles

This page looks at several interesting papers on the epidemiology of measles and influenza and tries to make sense of them. Cases dramatically decreased before the vaccine rollout. Modern measles is not a childhood disease and outbreaks are correlated with weather conditions.


Travelling waves and spatial hierarchies in measles epidemics – Grenfell, Bjornstad
https://www.researchgate.net/publication/11615103_Travelling_waves_and_spatial_hierarchies_in_measles_epidemics

This paper from epidemiologist Bryan Grenfell shows some very interesting features in the epidemiology of measles, A ‘wavelet’ model is created that characterises the UK data as a series of wave-packets that originate from the large cities and spread out over the rest of the country,

Further computer modelling shows how various features of the wave model can be explained by human-to-human transmission driven by population dynamics and seasonal forcing.

The wave patterns are quite surprising, are not obviously connected to the seasons and are not consistent with the idea that measles is the immediate result of a poisoning event.

This paper is a good example of why mathematical modelling is sometimes necessary and how it can give insights into the underlying structure of noisy data.


London measles cases 1944 – 2000

The chart shows the “Wavelet time series analysis for the log-transformed weekly London measles time series” – so the data has already been manipulated somehow.

Several interesting and surprising features are immediately apparent.

The chart starts with an apparent seasonal variation which, by 1950, has transformed into a biennial pattern with strong peaks every two years and a ‘mini’ peak in between the main peaks. The peaks are very well defined.

After 1970 the absolute number of cases declines and the biennial pattern degenerates into just ‘noise’.

Varying time period

Somewhat surprisingly for such a sharply defined pattern, the period is not actually tied to the seasons and is not precisely biennial. Instead, mathematical analysis suggests a period of slightly larger than two years that, even so, varies as time progresses; see the blue line below.

The red line shows the onset of vaccination programs and this is assumed to somehow affect the biennial rhythm.


Phase differences between cities

The modified data from three cities, London, Norwich and Lincoln are plotted on the same chart and we immediately see that measles in the three cities peaks in different years and at different times of the year.

In addition to this, the blue peaks (Lincoln), at first out of step with the other cities, are perfectly synchronised by 1990.


Modelling the data as a flexible wave function

Data such as the above are not expressible as a single mathematical equation and so are not amenable to statistical analysis. What is needed is a further abstraction of the data in order to somehow obtain a quantitative analysis of these phenomena.

The illustration below shows (top chart) the data modelled as a set of ‘flexible’ waves and we can now clearly see a striking pattern, that of three wave functions having no initial phase relation gradually and smoothly attaining a perfect synchrony.

Note that in 1951 the black and red have perfect phase-alignment with each other but are completely in opposition to the blue line of Norwich.


The bottom chart shows the calculated phase difference between Cambridge and Norwich and between Cambridge and London. The averages are non-zero and differ from each other but eventually achieve synchrony in 1962 before starting to diverge again.


Spreading from major cities

The illustration shows the phase difference of disease incidence relative to London.

Measles outbreaks that start in London will radiate outwards from the capital city at a rate of about 5km per week, with the rate of travel depending upon local population densities. The pattern is clear for a radius of about 30km around London with ‘randomness’ in rural areas eventually dominating.

Similar ‘spreading’ patterns exist with all the major cities with things being less clear in the North-East where the proximity of several large cities leads to interference patterns in the spreading waves.


A transmission model

Grenfell does not make the claim that these data prove contagion, rather contagion is assumed and the task of the paper is to try to explain the characteristics of the data in terms of transmission parameters.

  • Measles epidemics are self-limiting and will subside when all ‘susceptibles’ (children) gain immunity
  • ‘Extinction’ events occur in rural areas when the disease dies out for lack of new victims
  • Disease remains ‘endemic’ in larger cities and replenishes the surroundings with virus on an approximately biennial basis when there are enough new children
  • What is effectively random transmission between individuals will form stable attractor patterns at the population level and it is these that are manifest in the data
  • Phase-locking between attractors along with seasonal forcing gives rise to synchrony between cities and an apparent underlying rhythm
  • The decline of measles after the introduction of vaccine programs is assumed to be because of those vaccination programs

Concerns and questions

Seasonal forcing

The stated importance of seasonal forcing seems at odds with the model which at no time shows a precise biennial pattern, which varies across time and is different for each city.

Because epidemics do not suffer local extinction, and because all the cities experience the same seasonal forcing, no lags are generated.” – Grenfell

The task is aided by epidemiological models, which capture both the nonlinear dynamics of childhood epidemics as a function of local population size and the impact of significant environmental forcing. This forcing mainly comprises seasonality in transmission, due to schooling patterns, and longer-term variations in susceptible recruitment, due to birth-rate variations and the onset of vaccination” – Grenfell et al

The saw-tooth shape of an epidemic

The paper concentrates on modelling epidemics as waves and therefore does not address the issue of the characteristic ‘saw-tooth’ shape of the epidemics.

Other authors have commented upon this with respect to influenza. We expect from an epidemic that the initial increase in cases is rapid and follows an exponential curve and that thereafter a rounded peak will be reached and a long decline will ensue. The tail end of the curve is expected to stretch out as the disease finds fewer and fewer people to infect.

What we see instead is a very sharp peak that is followed by a decline that is much faster than the initial rise in cases.

Extinction events

The virus is said to disappear from rural areas in between epidemics but to be replenished from the big cities in time for a new outbreak, meaning the survival of the virus depends upon the specific population densities and behaviours. The question then arises: “How did measles survive before modern population densities, primary schools and contemporary commuter habits?”

Measles is ‘endemic’ in large cities

“In the large town, measles is endemic throughout the interepidemic trough, so that a new epidemic occurs as soon as the effective reproductive ratio of infection exceeds unity; this threshold is determined by the accumulation of susceptible children, modified by seasonally varying transmission rates associated with the school year.

By contrast, in the small town, infection goes extinct locally after an epidemic; therefore, another epidemic cannot happen until an infective `spark’ is received, generally originating in a larger (endemic) community.” – Grenfell

What does it mean to say that measles is ‘endemic’?


Standard transmission models

The graph below shows the outcome of a basic epidemiological model.

Cases (red) initially show a rapid (exponential) increase in numbers as the infection spreads to more and more susceptible individuals. As the number of susceptible individuals reduces so the increase diminishes but still infections remain high as there are still plenty of ‘spreaders’ around.

Source: Benji Tigg

As the number of spreaders starts to wane and the number of susceptibles continues to diminish, the curve takes a steeper downturn and infections decline rapidly.

In computer models such as this a long ‘tail’ is produced as, although new infections are declining rapidly, there is a large pool of infected individuals remaining.

This hides what is really happening which is that contact with an affected individual is becoming increasingly and rapidly unlikely. Take a look at the number of susceptibles; it declines rapidly as soon as the epidemic starts and reaches almost zero even whilst cases are still near their peak.

We have, at peak number of infections, only 0.2 infected people per 1000 which means 2 cases per 10,000 – and the disease is still being passed on somehow!

Even so, the model is assuming a perfect mixing of the population and within this model there is always a non zero probability of a sick person making a transmission to a healthy. In practice I think this would not be the case and that instead there would be a very sharp decline in new cases once the proportion of infected individuals reached some threshold, below which transmission simply did not occur.


Non-epidemic’ activity

This then is a glaring weakness in the transmission theory, that the number of susceptible individuals decreases to almost zero during an epidemic and yet must somehow remain above zero for another two years to spark off the next epidemic.

A spreading virus is only able to stay alive by actually spreading and once the effective reproduction rate is below one, it is declining rapidly.

To make any sense of this, modellers must somehow keep the virus alive and yet not spreading during interim periods and so will add some other mode of survival to allow for this:

We then fit a seasonal regression model to the truncated series to estimate the expected baseline number of deaths in the absence of epidemic activity. A nonepidemic threshold was defined by the upper limit of the 95% confidence interval derived from the seasonal regression model. Only influenza activities that remained above the threshold for >2 consecutive weeks were included in the analysis”Viboud et al

So there is now something called ‘non-epidemic’ activity for influenza which keeps the virus alive somehow even though there is no measurable spread. In the case of measles, lifetime immunity is claimed which further reduces the possibility of spread in between epidemics.

Without this ‘fix’ to the models there would surely be very many extinction events even in population dense areas.


The decline in measles

The chart shows measles deaths from 1900 to 1960. A strong rhythmic pattern with a period of about 3 years is seen, along with a marked decline, almost to the point of extinction, before vaccines were introduced after 1960.

The vaccines therefore cannot be the cause of the decline in deaths.

Note that these data are averaged over a whole nation so we don’t have the geographical refinement of the Grenfell paper but if we take all these results at face value we have a disease showing an approximate three year cycle that, as global incidence declines, diminishes to a two year cycle, followed by a one year cycle and eventual disintegration of structure into mere ‘noise’.

What produces this? Do Bruce Grenfell’s attractor patterns extend to the whole of the United States as well?


Modern measles age distribution

The chart below from Muscat et al suggests that measles can no longer be considered a disease of childhood.


Seasonality

Measles is seasonal in many countries particularly in the spring:

Modelling seasonal measles transmission in China – Bai, Liu


Measles and the weather

The effects of weather conditions on measles incidence in Guangzhou, Southern China – Yang et al

The morbidity of measles shows a seasonal variation. In temperate climates, measles outbreaks typically occur in the late winter and early spring every year, whereas in the tropics, measles outbreaks have irregular associations with rainy seasons, which suggests that climatic factors partly underlie the seasonality of measles virus infections.” – Yang et al

Compare with the epidemiology of influenza:

Influenza seasonality indicates that New Delhi would likely benefit from springtime vaccination (May–June), whereas vaccination in the fall (October–November) would be better for Srinagar. We recently illustrated that India and most other tropical countries in Asia exhibit influenza seasonality that coincides with the monsoon season, June–October” – Koul et al

The charts from Yang et. al. show an increase in measles cases correlated with:

  • Low humidity
  • High sunshine
  • Moderate temperatures



Other researchers have found correlations with both season and specific local weather events:

Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation” – Jia et al

The team discovered a strong and consistent annual pattern of measles outbreaks that was associated with rainfall. Specifically, they found that the rainy season was associated with a lower risk of measles case reporting, whereas measles cases were higher during the dry season.” – Blake et al

The analysis revealed that there is a statistically significant relationship between weather parameters (Temperature and Rainfall) and the occurrence of measles in the study area.” – Alhaji et al


Cosmic influences on humans, animals and plants – JT Burns This book is an annotated list of studies on the correlations between planetary movements and biological events on Earth. Several hundred papers and books are summarised. Measles is not mentioned.

Cosmic events include solar flares, lunar tides, eclipses, strength of Earth’s magnetic field, planetary orbits, planetary alignments and oppositions.

Biological events range from measured chemical reactions to behaviours of individuals include epidemics, admissions to mental hospitals, car accidents, metabolite levels, birth defects, the shape of leaf buds, rate of water uptake in seedlings, blood clotting parameters, blot tests etc.

The brain, nervous system and embryo seem to particularly sensitive to such influences with personalities seemingly affected by the month of conception (more likely than birth date surely?).

A lot of the correlations seem crazy (the thyroid activity of cats is related to the orbit of Mercury for example) and some have been ‘debunked’.

Many researchers tried experiments in Faraday cages or in deep underground caverns. Often some reduction of effect was observed but rarely was it eliminated. Both electric eddy currents and magnetic potential currents seem implicated then with a Faraday cage providing some protection from the former but not the latter.

So what are the causes?

Viral transmission?

Unlikely:

  • Attempts to transmit any disease in a clinical trial invariably fail
  • Isolation techniques are highly contested
  • Computer models need ‘tweaking’ to get plausible results
  • The need to add a seasonal factor to models suggests a seasonal influence
  • The possibility of extinction events seems too high for virus survival
  • The characteristics of the epidemiology seem too structured for random transmission

Poisoning?

Again unlikely: How to explain the epidemiology?

Annual crop spraying or vaccination schedules might just explain how toxin administration is coordinated over a whole country but it isn’t strictly seasonal and ‘drifts’ from year to year. The epidemiology is complex and has patterns that are both local and global.

Cosmic influences?

To most people this will seem the most unlikely of all, but what else is left?

The epidemiology needs explaining and here we at least have a chance of correlating disease with ‘something’ although at the moment it isn’t even clear what that ‘something’ is.

I doesn’t seem credible that the planet Saturn can have a direct influence on biological processes but more likely that various electrical events in the cosmos can and do have an influence and that these phenomena may well correlate with planetary alignments and solar activity.

These patterns, with seasonal variation and local coincidences with weather events are similar to those seen in the epidemiology of influenza. See here: Influenza and weather

Hypothesis

Population wide biological events are triggered by electromagnetic activity as opposed to gravity and that filaments of such energy pervade the solar system, emanate largely from the sun, connect the sun, planets and moons and will move, interact and intertwine as the planets orbit the sun.

If this is true then certain events and patterns are explained that are not expected from gravitational influence alone. Filament interaction will be roughly rhythmic but with various deviations.

We could expect:

  • Roughly seasonal effects but with various ‘harmonics’.
  • The observed ‘effect’ on Earth may precede the supposed ’cause’ (eg solar flare) because both of these are caused by a third and unsuspected phenomenon.
  • ‘Influences’ of two or more planetary orbits may interact in a complicated way.
  • Odd phase shifting phenomena may be seen
  • Correlations may appear consistent for decades and then disappear, either suddenly or gradually.
  • Random and sudden events seemingly unrelated to planetary motion.
  • Absent or inverted dose-response relationship (weak stimulus seems to give strong response etc.)
  • Relationships which seem outstanding to the eye but disappear upon statistical analysis.

The last above is because it is the wrong things that they are trying to correlate and because the ’causes’ themselves may be only quasi-periodic. The Earth’s rotation speed is not quite constant and solar cycles also vary in length in an unpredictable fashion. Many periodic influences from the solar system are in any case filtered through our ionosphere and weather system which have local rhythms of their own.

All these patterns above are described in the book by J.T. Burns and many are seen in the epidemiology of measles and flu. Many cannot be explained by conventional means so the idea of electromagnetic filaments stands as the most likely explanation for now.

It sounds like almost any pattern of disease outbreak may be possible and that the hypothesis is therefore unfalsifiable. This may well be true at the moment but the hope is that a more detailed understanding of the electromagnetic nature of biology and the electromagnetic activity in the cosmos will some day give something concrete to test against.



References:

Travelling waves and spatial hierarchies in measles epidemics – Grenfell, Bjornstad
https://www.researchgate.net/publication/11615103_Travelling_waves_and_spatial_hierarchies_in_measles_epidemics

Modelling a modern day pandemic — Developing the SIR model – Benji Tigg
https://medium.com/geekculture/modelling-a-modern-day-pandemic-developing-the-sir-model-8d77599050ce

Influenza Epidemics in the United States, France, and Australia, 1972–1997 – Viboud et. al.
https://wwwnc.cdc.gov/eid/article/10/1/02-0705_article

The State of Measles and Rubella in the WHO European region – Muscat et al
https://pubmed.ncbi.nlm.nih.gov/26580789/

The effects of weather conditions on measles incidence in Guangzhou, Southern China – Yang et al
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896574/

Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China – Jia et al
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-023-16350-y

Measles outbreaks in Niger linked to rainfall and temperature, study finds – Blake et al
https://www.sciencedaily.com/releases/2020/08/200825110805.htm

Impact of Climatic Variables on the Prevalence of Measles in Wudil Local Government, Kano State, Nigeria – Aljhaji, Nasir
https://www.irejournals.com/formatedpaper/1701750.pdf

Differences in Influenza Seasonality by Latitude, Northern India – Parvaiz A. Koul et. al.
https://wwwnc.cdc.gov/eid/article/20/10/pdfs/14-0431-combined.pdf

Cosmic influences on humans – JT Burns
https://www.amazon.com/Cosmic-Influences-Humans-Animals-Plants/dp/0810833131

Modelling seasonal measles transmission in China – Bai, Liu
https://www.sciencedirect.com/science/article/abs/pii/S1007570415000088


Measles again, this time from the WHO – peaking in spring
https://vaxopedia.org/2019/07/08/when-is-measles-season/

The HART group

The HART group gives some interesting arguments as to why they are rejecting the idea that viruses do not exist. One is a flawed statistical argument and others are observations of genome sequences. None of this explains the epidemiology. This page shows why they are wrong and suggests an alternative model for influenza outbreaks.

The statistical argument is listed at number 1 and is tackled first.
The screenshot below is from their website



  • Note that it is number 1 on their list.
  • Not everybody got sick as part of an outbreak and that number of people is considered “significant
  • People who shared the same environment got sick at the same time – consider then an environmental cause or trigger
  • The majority of sufferers were part of an “outbreak” i.e. an event where many people got sick.

If an outbreak is defined as a large number of people getting sick at the same time then it isn’t surprising, given the population distribution in a city, that the majority of sick people got sick as part of an outbreak.


Shown here is a city grid of equal sized squares where some squares are densely populated and others not so. This sort of situation is ideal for studying epidemiology if only decent data were available. We can look at how many people in each cell got ill and try to correlate it with population density.

First imagine that some non-infectious pathogen is introduced into some of the cells on a completely random basis. Think of toxic gas being released, 5G death rays maybe or some sort of bio-energy beamed down from space.

We would see then that the chance of a cell showing disease in any of its residents is unrelated to the population of the cell so that a sparsely populated cell is as likely to show disease as one containing many occupants.

In this case then, disease would be correlated to location and since the release of a toxin into a densely populated cell would result in many people being sick we would see in the overall population that the majority of sick people would necessarily come from cells where there were many other sick people.

This is really just saying that the majority of people in a city come from population-dense areas.

This much is obvious. It is also precisely what is described in point 1 of the HART group’s statement above. They even attribute the illness to “having shared the same environment” as opposed to “having been with other sick people“.

Now let us fantasise about infection. If this were possible then we would expect that infection would spread more effectively in densely populated areas. We would also expect an increased likelihood of seeing disease in these areas as there are many more people to introduce the pathogen into the grid square from elsewhere.

Statistical analysis should then show a correlation between cell population and the occurrence of disease in that cell. Recall that in the first case, cells get affected at random and there was no correlation with population density; a 5G death tower does not know how many people there are living nearby.

There is the possibility then that an infection model can be proved or disproved merely by looking at some statistics.

If only somebody had thought to collect some data..


Fred Hoyle (1915-2001) was an astronomer and statistician who looked at the epidemiology of flu by studying incidence of the disease in English public (boarding) schools. Some pupils will board at the school and be in contact with each other 24-7 whilst others will go home at weekends or evenings. [paper]

All these schools have children of the same age groups, eat similar food, are subjected to the same harsh exercise regimen and engage in stereotypical social contact. Schools themselves are organised into ‘houses’ and dormitories, giving a controlled structure to the possible transmission routes.

These are ideal conditions to study incidence of influenza. Hoyle looked at epidemiological patterns as described above and found that, for example, a dormitory full of boys was as likely to demonstrate influenza as a solitary boy sleeping at home with his parents.

Hoyle believed in viruses but still concluded:

  • Person to person transmission is ruled out as a significant cause of the disease.
  • The overwhelming cause of the disease comes from ‘elsewhere’.
  • Low level outbreaks occur completely at random and unconnected to each other.
  • Larger outbreaks occur in geographical clusters which vary in size from a whole school and its environs to a single dormitory or part thereof.
  • A virus is ruled out as the actual cause.
  • Viruses are manufactured within the body in response to an external trigger.
  • The external trigger is some kind of ‘virion’ that comes from outer space.
  • [The results do not indicate food poisoning or collective detox.]

The chart below shows that population influenza is best modelled by a simple gaussian distribution with a mean around winter solstice and a 90% interval of only a few weeks.


Observations must be explained and the ‘viral model’ does not explain the epidemiology.

The members of the HART group know this and know that transmission studies have failed, but are still sticking to their model because: “The virus model explains all of the above in a way that no other proposed model can (yet).

Other individuals have complained of a lack of a better alternative and that viruses are ‘still the best explanation‘ for what they are seeing.

Again, from the HART group: “Scientists form a model that best explains the majority of the evidence. ” So we need to explain the epidemiology.


A statistical model:

  • Seasonal incidence: Outside of the tropics, populations will succumb to influenza in the two weeks either side of winter solstice
  • Latitudinal patterns: Finer grained structure is seen along lines of similar latitude (Hoyle and others)
  • Local outbreaks are delineated by location and are distributed at random

No mechanism is suggested here but we have achieved:

  • Prediction of timing: over 95% of cases will be in midwinter although how many seems to vary a bit.
  • Characterisation of outbreaks as being somehow related to location (we can’t even say environment).
  • Better interpretation of epidemiology: The assumption that clustering implies contagion is incorrect and has been dangerously misleading.
  • Scope for further research: What is there that is special about certain latitudes and locations?
  • A model that actually fits the observed data: Other models based upon the flawed assumption of transmission have failed spectacularly.

This seems like a good basis for a model as being grounded in observed reality. We can refine it later and look for biological mechanisms to explain these patterns but the foundation should be as described above.

The HART group, by contrast seem to want to plunge straight in with assumed bio-molecular causes and to worry about the facts of disease later on. This is the wrong way round to do science: the ‘majority of the evidence‘ needs explaining.

The group is asking the virus sceptics to explain various molecular and genomic phenomena. These all sound very interesting but they do not of themselves constitute disease and have not been shown to cause any disease.


Towards a mechanism

The model described above is purely statistical in nature and may well make useful predictions but it gives us no ‘understanding’ and describes no biological mechanism whereby disease might be caused by seasonal change.

  • Seasonal incidence: This is so precise that the only possible way that this can be achieved by resonant entrainment to some seasonal influence, either daylight hours or maybe the Earth’s magnetic field.
  • Latitudinal coincidence: This again suggests the Earth’s magnetic field is involved.
  • Local outbreaks: Tricky. Hoyle suggests virions from outer space, I will suggest cosmic ray showers or eddies (vortices) in the Earth’s magnetic field, but I am certainly open to alternatives.

What else is it that we need to explain?

The HART group is asking to explain things like a unique RNA sequence found in people who appeared to have similar symptoms, Now nobody goes to their doctor complaining about a unique RNA sequence. We don’t need to explain this, we need to explain the symptoms.

The symptoms, even by mainstream accounts, are caused by an altered bio-regulatory state. This state (erroneously referred to as the ‘immune response’) consists of an an orchestrated sequence of events leading to symptoms that include sweating, muscle aches, elevated temperature and lasts usually five days before returning to normal.

I don’t say ‘returning to homeostasis’ because this state is managed by the body itself and is perfectly stable although not sustainable.

It is this state that causes distress and constitutes what we call ‘disease’.

For this process to fit within our model then, we are looking for some way that it is produced as a direct result of the seasonal rhythms and without the intermediary of a viral particle.

This is the research to be done. It sits firmly within the purview of bio-regulatory medicine and not so much virology or genetics.


Top-down causality is common in biology and is implemented via means of attractor systems which interpret external stimuli to effect change at the cellular and even molecular level. Attractors can be highly sensitive to rhythmic input. It is quite conceivable that people in similar physiological states can produce RNA with similar sequences.

Now since most disease is just assumed to be viral in nature it follows that most disease research is performed by virologists who are really geneticists and think almost exclusively in terms of bottom-up causality, that is to say, that a small piece of RNA has the ability to destabilise a system that demonstrates organisation and robustness to perturbation at all levels.

The idea of an attractor is not something that would readily spring to mind to one trained in biology but it is essential for the understanding of living systems. Attractors are the key to top-down causality, providing an interpretive interface between the organism and its environment.

Influenza is just a sudden phase change in an attractor state triggered by some external input. Genetic events are the end point of attractor activity, not the primal cause.

Why do symptoms differ between individuals?

This is behaviour typical of chaotic attractors. Paths will converge to the attractor but diverge on the attractor so no two people will demonstrate identical disease progression. Attractor phase states are general patterns which are not precisely definable or predictable. Attempts to refine diagnosis by increasing accuracy or number of measurements will just cause confusion as there is no meaning in these details.

Since each individual is on their own specific attractor path and in their own ‘state’ at midwinter, it is now expected that not everybody will get ill at solstice. This is natural behaviour for attractors. We would expect that there is a component of disease risk that is actually independent of other health factors; an element of ‘randomness’.

Attractor states are highly stable, making routine treatment somewhere between difficult and impossible. Phase changes can be sudden and apparently non-causal, resulting in what are usually described as ‘miracle’ cures. So miracles do happen and we now have a scientific explanation for them.

From Mae-Wan Ho

Attractors present a problem from the point of view of determinism . Their behaviour is stable and predictable insofar as they will reliably produce meaningful biological patterns that result in a robustly functioning organism. However, this behaviour is not predictable from examination of their parts and not predictable from any finite history of that behaviour. This is a hammer blow for traditional reductionist science; there will always be something incalculable and unknowable where biological systems are concerned.


Summary:

  • The data comes first, the explanation comes later
  • The mechanism of genetics is interesting but does not cause flu
  • Influenza is a disturbance of organisation – not cellular damage
  • Top down causation is provided for by attractor patterns
  • The presence of attractors implies a ‘cloud of unknowing’
  • Observations (seasonality) require explanations

References:

Why HART uses the virus modelArguments against “the virus doesn’t exist”
https://www.hartgroup.org/virus-model/

Viruses from space – Fred Hoyle
https://www.hoyle.org.uk/resources/virusesfromspaceCompressed.pdf

Surveillance of influenza and other seasonal respiratory viruses – UKHSA
https://www.gov.uk/government/statistics/annual-flu-reports/surveillance-of-influenza-and-other-seasonal-respiratory-viruses-in-winter-2021-to-2022

The gene: An appraisal – Keith Baverstock
https://pubmed.ncbi.nlm.nih.gov/33979646/

Epigenetic Regulation of the Mammalian Cell – Keith Baverstock, Mauno Rönkkö
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0002290&type=printable

A theory of biological relativity: no privileged level of causation – Denis Noble
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3262309/

“Meaning of Life & the Universe: Transforming” – Mae-Wan Ho
 ISBN-10. 981310886X ; ISBN-13. 978-9813108868

The phantom leaf effect

A portion of a leaf can be cut off and will still be apparent when photographed using Kirlian photography techniques. The image is produced by electrical discharge when the leaf is subjected to a strong energy field. A persistent bio-field comprising electro-magnetic field vortices has preserved the details of the organic form (Konstantin Meyl).


In 1975 V. Adamenko in Russia performed the following experiment. After a part of a living leaf was cut and the remaining part was placed into a high frequency electromagnetic field, a visual image of the whole leave appeared. In other words, a phantom image of the cut part appeared which lived for 10-15 seconds and could be recorded on film. The experiment was reproduced by the Gariaev group and many other laboratories in the world.” – Gariaev et al

In the photographs below from the Garaiev paper, parts of a leaf have been cut out and the remainder has been photographed under an electromagnetic field. The portion of the leaf remove from the top right of the picture was the most recently removed and its ghost image is still visible in the photograph.

Ghosts of the other pieces have faded away.


Konstantin Meyl observed a similar effect: “Students of electrical engineering were able to produce photos of the leaf, using their self built high voltage device in the dark-room, even after the original had been removed( 1991). The potential vortices still present underneath the acrylic remained detectable by their storage effect.


What is going on? The image shows a typical corona discharge which happens at the boundary of an electrically charged object and the surrounding air. When the voltage gradient from object to air is high, an electrical discharge from object to air and a visible corona can be seen. Textbooks say that what is happening s that the air around the leaf is breaking down, the gas is ionising and electric currents are the result.

The problem with this explanation with respect to the phantom leaf phenomenon is that the same corona effect is visible when part of the leaf is missing so that the corona effect cannot have anything to do with the material substance of the leaf and therefore nothing to do with any sort of gradient from leaf to air or solid to gas.

The corona effect is not ‘fringe’ science and can be observed on power lines where it leads to significant power losses. Meyl is claiming, moreover, that the observed spikes in the discharge (the corona itself) should not be present according to classical physics, but instead a uniform shining should be produced, being the result of a smooth field gradient from high to low potential.

The corona spikes, according to Meyl, are consistent with the idea of vortices forming in the electromagnetic field itself. This sort of formation is not allowed for by the standard (Maxwell) equations and so a new physics is needed.

In accordance with the text books, the gradient field increases towards the surface of the conductor too, but a consistent shining would be expected and not a
crackling. Without potential vortices the observable structure of the corona would remain an unsolved phenomenon of physics.

But even without knowing the structure forming property of the potential vortices, which acts as an additional support as we must conclude, it can be well observed that especially roughness on the surface of the conductor stimulates the formation of vortices and actually produces vortices. If one is looking for a reason why, with high frequency, the very short impulses of discharge always emerge from surface roughness, one will probably find that potential vortices are responsible for it
.” – Meyl


Electromagnetic vortices are theoretically self-sustaining to a degree and so the solution to our problem is that living organisms possess a bio-field consisting of interlinked electromagnetic vortices that retain their integrity for a while even when the physical matter supporting them is removed.

The field reflects the shape of the original leaf and when under a high voltage will discharge from any spikes or sharp discontinuities in the outline, giving the characteristic aura that is observed.

Note that a bio-field comprised of light (photons) is not indicated here as photons can only travel at the speed of light and will not stick around for the few seconds needed to take these photo graphs.

We are not ‘beings of light’ but ‘beings of toroidal scalar waves’.



Interestingly and maybe surprisingly, vegetables will retain something of their aura even after a bit of cooking. (Cooked tomato pictured). These vortices contain energy and possibly some useful information so we are eating both electromagnetic energy and biological information.

Several authors have called this a ‘phantom leaf effectand it has often been misinterpreted as a paranormal phenomenon. In reality this is due to the storage capacity of the potential vortex having been made visible, which has only ended up in the field of parascience, because Maxwell’s field theory did not include a potential vortex.” – Meyl


Of course human beings also have an aura – but what does this mean? We are all sharing fields with each other and with the rest of the ecosystem and we are all eating food which contains living bio-fields. We are all therefore sharing some sort of biological information and presumably responding to it in some way.

What effect do these fields have on our immediate health and what effect have they had on evolution as a whole? When Darwin’s finches flew to a new island and faced new challenges, the rate of evolution seemed to speed up to meet the new challenges, resulting in a collection of very suitable beak shapes very quickly.

Meyl seems to regard the human species as sort of waste product of atmospheric discharge: “All results of the evolution in the biosphere that have arisen between the ‘capacitor plates’ of the earth itself and its ionosphere can be regarded as structured capacitor losses, which also apply to humans“.

These vortex fields are surely worth considering as a mechanism for Distant cellular interaction.



References:

About Vortex Physics and Vortex Losses – Konstantin Meyl
https://www.k-meyl.de/go/Primaerliteratur/About_Vortex_Physics_and_Vortex_Losses.pdf

DNA and Cell Resonance: Magnetic Waves Enable Cell Communication – Meyl
https://www.researchgate.net/publication/51730085_DNA_and_Cell_Resonance_Magnetic_Waves_Enable_Cell_Communication

Principles of Linguistic-Wave Genetics – Gariaev et al
https://www.researchgate.net/publication/228926241_Principles_of_Linguistic-Wave_Genetics

The phantom leaf effect: a replication, part 1 – John Hubacher
A normally undetected phantom ‘structure’, possibly evidence of the biological field, can persist in the area of an amputated leaf section, and corona discharge can occur from this invisible structure.
https://pubmed.ncbi.nlm.nih.gov/25603488/

Dr Lorne Brown interviews John Hubacher, M.A.
https://youtu.be/qd9WQ1C8yBs

Evolution and entropy

One of the mysteries of life is the question of how organic forms seem to absorb energy and information from the environment and sequester it permanently in an orderly fashion. This is seemingly in contravention of the Second Law of Thermodynamics which is often interpreted as predicting a general decrease of order in the Universe.

Scientists studying biology from this perspective almost unanimously claim that the body somehow maintains itself ‘far from thermodynamic equilibrium’, meaning that there is something special about living systems that allows them to maintain large reserves of energy and information which is not dissipated but is stored in an organised fashion in either chemical or physical processes and made available for use, as and when needed.


Keith Baverstock interprets thermodynamics and entropy in terms of their original formulation, not as an increase in disorder but rather as a movement towards a ‘least energy’ solution. This represents a de facto tendency towards an equilibrium state which Baverstock claims is now a selectable property in a Darwinian-style evolutionary process.

As an example, first consider a glass of water. The water retains its shape despite lots of Brownian motion of the molecules, owing to the fact that it is constrained within the glass. Now up-end the glass on a flat surface and lift to allow the water to flow freely. The water will fall and spread. This will be initially via gravity but will continue as Brownian motion causes the pool to spread as a statistical average of the sum of the motions of the molecules.

Now since the vibrations of the molecules are assumed to be random, there is a small theoretical chance that they all just happen to collect back together into a small pool and even draw themselves up into a glass shape. In practice though, this doesn’t happen and the water will adopt a configuration that is statistically most likely and energetically most economical; a puddle.

Now place a dry bath sponge in the middle of the puddle. The water will be drawn up against gravity and will be held suspended by capillary action. Furthermore, the shape of the water is not disordered or even tending to disorder. Instead the opposite is true; order is maintained and even increases.

No extra energy has been put into the system and energy is actually dissipated by sound waves as the water bubbles up inside the sponge. So ‘order’ here is accompanied by an energy loss as opposed to an increase, an output rather than an input. A ‘least energy’ state is achieved and maintained as an equilibrium state.

This state is not just a ‘least energy’ state but also a ‘most likely’ state from a statistical point of view.

What has this to do with evolution? Evolution is observed to proceed via a pattern of punctuated equilibrium whereby a relatively stable phenotype will occasionally be subject to a dramatic change to produce a new species before settling down again for a few hundred thousand years.

The outward form and function of animals are controlled by an internal attractor pattern and it a sudden phase change in the attractor that gives rise to new species. Once a new species has been established, minor changes in the attractor can give rise to good or bad traits for natural selection to fine tune the species to its environment.

So now let us imagine that our sponges are in a hot environment which will tend to dry them out, thus killing them and preventing reproduction and the continuation of their lineage. Sponges who manage to adopt a shape that enables the absorption and retention of the most water will have an added Darwinian advantage.

So what exactly is being selected for?

  • Fitness– the ability to not dry out. Enabled by..
  • Function – The ability to retain water. Created by ..
  • Form – an outward shape propitious for the retention of water. Created via ..
  • A physical ‘least energy’ solution for the relationship between sponge and water.

It is this last property which is the most basic, necessary and fundamental but also the one that is never mentioned by evolutionary theorists.

Organisms are not just fine-tuned to their environment but will also need to refine their own internal developmental patterns in order to achieve optimum performance.

It is no good looking at the fossil record and simply assuming that the environmental conditions were what ‘created’ a particular feature. It must be physically possible and even likely that that feature could come into being. You cannot select for something that has not yet evolved or that is unfeasibly improbable.

Therefore form must precede function and its development is thereby de-coupled from the selection of that form. The outward shape of an organism is not created by the future function or even fitness within the environment, but by the laws of physics that allowed it to happen and the laws of statistics that made it likely that it would happen.

The current theory of random mutations of DNA causing ‘traits’ in an unspecified way gives the impression that almost anything is possible and that certain features will inevitably arise if the need is great enough. Some texts will even present selection itself as a ‘driver’ of evolution, putting the cart before the horse and invoking a ‘final cause’ without regard for the mechanics of how this is achieved.

The idea that development is so incremental that it is practically parallel with selection is just nonsensical sophistry, akin to a conjuror telling you that you are seeing one thing happening when something completely different is going on right before your very eyes.

“The laws of physics must be obeyed” – Konstantin Meyl.

New species then, arise from sudden phase changes in the evolutionary attractor (see: Evolution and Inheritance) and once a species has been established, small perturbations to the attractor will tend towards a low energy solution via natural selection. The species is being ‘optimised’ to its environment. Least energy solutions are the most stable and energy efficient ways of maintaining an organism and both stability and efficiency are certainly necessary and propitious qualities as far as survival of the species is concerned.

Least energy states are probably so stable as to be irreversible. Evolutionary change has stopped and the only way to progress is for another significant phase change from the attractor.


In summary, we propose that the life process is based not on genetic variation, but on the second law of thermodynamics .. and the principle of least action, as proposed for thermodynamically open systems by De Maupertuis (Ville et al. 2008), which at the most fundamental level say the same thing. Together they constitute a supreme law of physics..” – Baverstock and Rönkkö




References:

Keith Baverstock’s homepage https://www.kbaverstock.org/

The evolutionary origin of form and function – Keith Baverstock, Mauno Rönkkö
https://pubmed.ncbi.nlm.nih.gov/24882811/

Evolution in two parts: as seen in a new framework for biology – Keith Baverstock
https://pubmed.ncbi.nlm.nih.gov/36325932/

Natural selection for least action. Proc Roy Soc A 464, 3055–3070. (2008).
– Ville R, Kaila I & Annila A
https://royalsocietypublishing.org/doi/10.1098/rspa.2008.0178
https://www.researchgate.net/publication/243685794_Natural_selection_for_least_action/link/648c9f4e8de7ed28ba3083fc/download

Evolution and Inheritance

In order to explain the process of human evolution we need first to describe that which we are trying to explain, i.e. what it is that is evolving and which features of this need explaining.

We will find that the adult morphology develops according to very patterned laws. These have the character of an evolving attractor which serves as a template for the phenotype which itself is manifest via a physical toolkit consisting of geometric transformations, least-energy solutions and organised molecular activity. The attractor is able to assimilate exogenous information and respond accordingly, thereby assisting the organism in direct adaptation to the environment and leading to overall harmony in the ecosystem as a whole.


Karl Ernst von Baer (1792 – 1876) noticed several striking patterns in embryological development that are paralleled in evolution though not obviously explained by a random process. Indeed most modern texts will ignore these patterns as being an inconvenience to the ‘elegance and simplicity’ of the neo-Darwinian process.

  • General features common to a large group of animals appear earlier in the embryo than do specialized features.
  • The development of particular embryonic characters progresses from general to specialized during their ontogeny.
  • Each embryo of a given species, instead of passing through the adult stages of other animals, departs more and more from them as ontogeny progresses
  • Therefore, the early embryo of a higher animal is never like the adult of a lower animal, only similar to its early embryo.

These patterns noted by Von Baer are to be found in similar although not identical form in three different arenas:

  • The fossil record
  • Embryonic development
  • Morphological complexity of existing species

Compare (below) the theory of recapitulation from Ernst Haekel with those of Von Baer. In Haekel’s view, the young bird develops from an egg stage through a fish stage, an amphibian stage etc. This is discredited according to modern embryologists and Von Baer’s Law prevails where separate species are recognisable after only a few days of fertilisation yet the structure of development is very similar in each case.

By Ian Alexander – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=62670504


The continually changing form of the mature adult is assumed by the neo-Darwinists to be caused by the random mutations of DNA but the development of the embryo is certainly deterministic and based upon physical laws. The question arises then: How or why can two complex and very similar processes arise from completely different mechanisms?

Evolution and embryology must have something in common whether it be a physical mechanism or a purely informational template, perhaps, maintained somewhere and used somehow as a reference.

If we look at a developing foetus and assume that it develops according to a more or less pre-determined process and in accordance with well established biological laws, then why do we look for something different when we observe the evolutionary record?

The Whiteway Colony originated as a commune of simple dwellings but as families grew and economic conditions improved, a new bedroom was added or a kitchen extended and the result was houses like the one shown.

The homes are functional and some attractive but none are comparable to a regency villa and few have more than one storey. They have developed in a piecemeal fashion according to local pressures but with no forward planning or sound foundations. They can therefore can never progress much further than this without a complete demolition and redesign.

What we are seeing here is architectural Darwinism and whatever benefits it has, it does not conform to Von Baer’s laws, where first is built up a strong and versatile foundation of backbone, limbs, cranium etc. from which can develop specialist features such as wings, flippers or opposable thumbs.

Von Baer’s laws have produced robust adaptability for sure but they are not expected from a random process that can at most respond to current pressures and is certainly without foresight or planning capability.

Darwin proposed evolution by increment of phenotype but neo-Darwinists go one step further and want to describe that increment in terms of random mutations of a spiral molecule. This just muddies the waters however and gives us extra things to worry about. It increases the difficulties instead of reducing them.

Describing a living organism in terms of the physical properties of molecules is like explaining the structure of a house in terms of the physical properties of bricks and as Rudolph Steiner rather nicely put it: “You don’t learn much about architecture by studying the physical properties of bricks“.


Punctuated equilibrium. Darwin proposed evolution by small increments to the phenotype (see gradualism below) with a slow divergence followed by natural selection to impose some structure in the population and thereby define the separate species.

This is not what is observed in the fossil record however where we see a process of punctuated equilibrium with new species appearing suddenly out of nowhere followed by a long period of relative stability with no significant drift in phenotype.

Once a new species is established there then follows some variation around an average type. Darwinism posits that variation is the precursor to the development of new species whereas the fossil record shows it to be a ‘decoration’ on an already established species.

According to Darwinism new species arise out of random morphological drift followed by selection from these variations to form a new species. However, what is observed is that the sudden establishment of a new species comes first and only then come the minor variations on that theme.

‘Reversion to breed average’ is a phenomenon known to breeders of pigeons, dogs and cattle whereby certain features can be attained by selective breeding but will only last a couple of generations before reverting to the breed average. Interbreeding with wild species is definitely to be avoided and offending pigeons are risking swift ‘termination’.

Darwin accepted in chapter 1 of On the Origin of Species that: “our varieties certainly do occasionally revert in some of their characters to ancestral forms.”

This is inconsistent with Darwinian evolution which is assumed to arise from a process that is directionless, without purpose and lacking in either foresight or memory.

In chapter 2 he argued that species can’t really be distinguished from varieties anyway. First he proposed that “well marked varieties” are “incipient species”; then he accepted that “species are only strongly marked and permanent varieties.” Then he decided that there is “no infallible criterion by which to distinguish species and well marked varieties.” By the end of the chapter he is confident that “varieties have the same general characters as species, for they cannot be distinguished from species.” – Tom Bethell


What is it that is inherited? The classical illustration of evolution at the top of the page gives the impression that some sort of template representing an adult human form is passed on down through the generations and that maladaptive phenotypes are weeded out via natural selection. This is one of the core ideas of Darwinism and it is emphatically false.

In order to achieve a completed adult form, any organism must pass through many stages of development, with the form at each stage remaining functional and even advantageous, whether within a uterus, egg, chrysalis or as a toddler within Nature itself.

What is inherited then is a complete developmental program from gamete to adult, along with instinctive behavioural patterns appropriate to that stage of development.

As an example, think about a toddler learning to walk. The joints, muscles and bones are accustomed to crawling and are not particularly well designed for walking so they must be reshaped by the toddler himself while they are still pliable. He needs to perceive of the need at around the correct time or it will be too late and needs to embark on a self developmental process that is scary and dangerous. He will no doubt be encouraged and comforted by his mother who therefore takes an active part in shaping the final form of her child.


Again, consider nest building in birds. They never even saw their parents building their own nest so it isn’t learned behaviour and it isn’t even a fixed behavioural pattern as many things can go wrong during construction and the bird will adapt its strategy accordingly to achieve the desired goal.

Cuckoos do not build nests so the skills are not simply emergent properties of ‘being a bird’ nor are they somehow imprinted on the developing chick via some bio-field hosted by the parents.

Injuries of any animal must be repaired which is again a structured sequential process akin to development which results in restoration of age-appropriate morphology. If I lose a fingertip then it will regenerate as an adult fingertip and not progress through embryonic and toddler stages first.

So what is inherited is not just a final form but a lifelong program for self-developing and self-repairing morphology plus a whole set of cognitive strategies that mature at an appropriate time and interact with both the surrounding natural environment and species-specific cultural practices.

And all of this information encoded in 20,000 genes?


D’Arcy Wentworth Thompson (1860-1948) pointed out that the variation in shape of fish bodies, say, or the beaks of birds could be accounted for by simple geometric transformations. The Darwinists will ask us to believe that these precise relations between shapes are nevertheless the result of random mutation and natural selection.


The doctrine of Darwinism encourages us to believe, without explicitly stating it, that almost anything is possible given millions of years of mutation and the appropriate environmental selection. However, in the case of these fish, what we see is a sort of parallel evolution where only a limited class of shapes are selected for and the class members are related via a precise geometric law. This is just not credible from a Darwinian perspective.

If we turn now to neo-Darwinism and evolution via DNA mutations, we find that we have not made things any easier for ourselves and we now have the additional problem of explaining how random alterations of base-pairs can result in precise elongations along specific axes. How does the developmental program ‘know’ in what direction to elongate and how does it maintain that throughout the whole developmental process?

Part of the task of science is to explain natural phenomena in a simple and comprehensible fashion as possible and here we have a choice between:

  1. Geometric transformation – simple to understand
  2. Local molecular activity in a noisy Brownian environment (gene expression) entailing precise global and modular geometric transformations – unexplained at present and probably unexplainable even if true.

The laws of physics must be obeyed whatever shape an organism adopts. Wentworth Thompson noted that the physical appearance of many biological forms is dominated almost entirely by physical laws such as a constant-angle spiral or ‘least energy’ shapes arising from the surface tension of cellular walls.

These constructions need no other explanation as to why they are that precise shape as that shape largely arises from the inevitable consequences of natural laws. It is a little inaccurate to say then that the outward form is inherited as it could hardly be anything else and since it cannot vary significantly, it cannot be selected for.

A snail did not have necessarily need to build a shell at all of course but once it started, the choice of shapes was severely limited by geometric restrictions.

Think of these shapes as being part of a fixed physical ‘toolkit’ available to the more general evolutionary process. They do not need to evolve by increment but come packaged as almost completed ‘modules’ in a larger design scheme.


Allometry is the study of the relationship between, and regulation of, the scaling of various body parts during development.


In the chart above we see the relationship between the body size of the fiddler crab and the size of the larger claw during development. As the crab grows there is a clear and consistent relationship which is obtained by carefully controlling the rate of growth of the claw.

This, of itself, is interesting as it means that the growth rate is independent not only of the shape template but also of the physical composition of the claw. It isn’t just a case of putting the right chemicals together and a crab miraculously emerges after a few weeks, but that the chemical reactions are somehow carefully regulated by some other (inherited) process.

Moreover, this control system applies to the whole claw and only to that claw. It seems unlikely then that this is controlled only by local information such as DNA but rather that there is some sort of morphological field that spans a whole claw and that another global field exists to manage a series of separate modules that go to make up the organism as a whole.

This arrangement makes sense in explaining the usual (or unusual!) symmetrical structure of organisms. A combination of modular management with geometric transformation (mirror reversal) is an easy way to visualise this.

Again, try to imagine how this could be achieved by a small molecule that is:

  1. Assumed to be identical in each cell of the body
  2. Actually quite unstable and permanently changing

Features in Darwinism arise out of a necessity, a need to meet some selective advantage, but where is the advantage is such a precise symmetry in outward form when an approximate symmetry would suffice?


Gene expression is the conventional way of explaining evolution, development, morphology and pretty much everything else, so geneticists need to provide a coherent description of how molecular activity somehow organises a final form and they then need to provide some evidence that this is what actually happens.

Unfortunately: “Among the more surprising and, perhaps, counterintuitive (from a neo-Darwinian viewpoint) results of recent research in evolutionary developmental biology is that the diversity of body plans and morphology in organisms across many phyla are not necessarily reflected in diversity at the level of the sequences of genes, including those of the developmental genetic toolkit and other genes involved in development. Indeed, [..] there is an apparent paradox: Where we most expect to find variation, we find conservation, a lack of change“.(Gerhart et al)

(Even the genes involved in development aren’t really involved in development.)

So, if the observed morphological novelty between different clades does not come from changes in gene sequences (such as by mutation), , where does it come from? Novelty may arise by mutation-driven changes in gene regulation.” Wikipedia


Mice who given electric shocks when they were smelling cherry blossom soon became fearful at the smell even when no shock was given. These mice went on to bear children who were also afraid of the same smell. A ‘characteristic’ has been acquired and passed down to the next generation in Lamarckian fashion. [Dias et al]

We have an example then (there are many others) of the inheritance, not of outward form but of a cognitive pattern (instinctive behaviour) resulting in a measurably altered chemical regulation. There are an arbitrarily large number of cognitive processes available so it seems very unlikely that these could all be represented as digital information on a finite sized chunk of DNA; some other storage format is required.

Transgenerational epigenetic inheritance is the transmission [..of..] modifications from one generation to multiple subsequent generations without altering the primary structure of DNA.” (Wikipedia ) Methods include “self-sustaining metabolic loops” and “structural templating

It is of note that cognitive recognition takes place in neural network activity in the brain where electric currents form self-sustaining loops. Also of note is the fact that emotional states such as fear are accompanied again by metabolic network activity.


Telegony: In one experiment, male flies were fed nutritious diets and acquired a larger body size than average. They were mated with immature female flies (no eggs yet) who then went on to have larger than average children even from subsequent smaller partners. Genetic information had been retained and inherited even though no physical substance had been exchanged.

Pigeon breeders are again quite strict with any female caught mating with a wild male for this specific reason. Her breeding value has been permanently compromised and she is now a liability to the flock.

Epigenetic inheritance is common from the female line as information is funnelled down into the ova where it can easily be passed down to the next generation but here we have information from a male passed on with no exchange of DNA.

The information must be held in some informational ‘field’, a self-sustaining loop of energetic activity that can be transferred to the female uterosome where it can install itself as an attractor state hosted by molecular network activity.



The common thread linking all these types of inheritance and evolutionary changes consists of meaningful loops of dynamic network activity that form semi-stable attractor states. These packets of information evolve over time, providing variety of form, and can be transferred from parent (male or female) to children.

The fossil record shows a body type stable over many millennia that is subject to sudden changes. This, and the phenomenon of reversion to mean are immediately suggestive of a chaotic attractor. We can therefore conceptualise this as a morphogenetic template that evolves slowly over time but occasionally demonstrates a sudden phase change resulting in a new species.

The template is not actually for a static adult form however but for a complete developmental program including body shape, inherited behaviour and all manner of metabolic and molecular regulation,

The creation of a living being works by expression of this template via physical matter. This expression will use the laws of physics to its own advantage along with various ‘toolkits’ including ‘least energy’ laws and geometric transformations.

(Note in the following diagram that DNA, being comprised of matter, is at the conceptual bottom of the causal chain – not the top!)


This information is likely to be of a fractal or holographic nature, meaning that every part of the informational field will contain sufficient information to reproduce the entire organism.

Sexual reproduction consists of the confluence of two such informational fields and Keith Baverstock gives the example of a merger of two manufacturing companies producing similar products. The staff are all autonomous and know their jobs well so not much more is needed than to put them together in a big factory with familiar equipment (physical matter) and let them get on with it.

Asexual reproduction simply consists of a splitting of the physical form and a corresponding splitting of the evolutionary field. The splitting is neither here nor there as the field is fractal in nature and both halves will contain all of the information needed to reproduce an entire organism.

Perhaps imagine a whirlpool in a river which grows in size as it accumulates energy, even absorbing other smaller eddies. The shape is preserved yet evolving and when the vortex gets too big it can easily spawn smaller ‘children’ which will repeat the cycle. The vortex is robust to quite large disturbances but can change suddenly and unpredictably; it is an attractor.

The human cognitive system is assumed to be hosted in active neural networks within the brain and so again has the aspect of a self-sustaining attractor.

Information entering via the senses is therefore converted to a neural network format and in the case of mice and cherry blossom this information is further downscaled to a molecular attractor in the gametes and passed on to the next generation.

Information from the environment is funnelled down through an interpretive cognitive system to integrate with the main fractal-like attractor of the organism and persists throughout the generations as a part of the evolutionary line for the species.


The future of humanity according to Darwinism is decidedly bleak, as with the removal of selective pressure we are condemned by genetic drift to accumulate small maladaptive mutations and wander aimlessly into a second rate species.

Darwinism lends support to, and apparent moral justification for, selective breeding and eugenics and no good can come of that. According to this doctrine we have no active participation in our own directionless evolution and must rely upon random chance followed by ruthless pruning of the evolutionary tree to make progress as a species. Darwinism is non-reversible and a rudderless ship is in a bad way.

The Evolutionary Attractor hypothesis presents completely different options.

Evolution is largely by the unfolding of the attractor state and is deterministic although unpredictable. If we ‘do nothing’ we will be stable for some more generations before a sudden phase change causes us to morph into a new species. This may happen with very little warning and in a way that is beyond our comprehension.

Robert O Becker noted that almost all species have arisen at specific points in history when there was a significant change in geo-magnetic conditions. Changes in the Earth’s magnetic field led to sudden phase shifts in the attractor state across all species on Earth.

Selective breeding usually induces only temporary variation of morphology before reversion to wild-type.

We can actively participate in our own cultural and conscious evolution however with the inheritance of cognitive recognition, behavioural patterns and emotional responses.

Adverse responses to drugs or vaccines can also be inherited although it isn’t clear whether the changes are permanent or not. Exposure to WiFi radiation can cause altered gene expression which is again inheritable, possibly inducing a permanent change to the evolutionary genome.

The ship in this case is not rudderless by any means but is steered in part by a continual influx of information into our cognitive systems which helps tune us to our environment. It follows therefore that we should take particular care over the nature of that information. See: Evolution and cognition



References:

The gene: an appraisal – Keith Baverstock
An analysis of the results of the Long-Term Evolution Experiment (LTEE) with E. coli bacteria, grown over 60,000 generations, does not support spontaneous gene mutation as the source of variance for natural selection. https://pubmed.ncbi.nlm.nih.gov/33979646/

Von Baer’s law for the ages: lost and found principles of developmental evolution – Arhat Abzhanov
https://pubmed.ncbi.nlm.nih.gov/24120296/

Parental olfactory experience influences behaviour and neural structure in subsequent generations – Dias, Ressier
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923835/

“Within a taxon, most animals share a common body plan (or “bauplan” — German for ‘blueprint” or “builder’s plan”) that comprises a certain number of body parts arranged in a particular way”

Consequently, the evolution of morphology is arguably the evolution of static allometry, which is in turn a consequence of changes in ontogenetic allometry “

Allometry: The study of biological scaling – Alexander W. Shingleton 
https://www.nature.com/scitable/knowledge/library/allometry-the-study-of-biological-scaling-13228439/

Von Baer’s law – Encyclopedia.com
Mutations that alter early development are usually lethal because they can introduce drastic changes to subsequent development.
https://www.encyclopedia.com/science/news-wires-white-papers-and-books/von-baers-law

Cells, Embryos and Evolution. – Gerhart, John; Kirschner, Marc (1997). Blackwell Science. ISBN 978-0-86542-574-3.
So, if the observed morphological novelty between different clades does not come from changes in gene sequences (such as by mutation), where does it come from? Novelty may arise by mutation-driven changes in gene regulation.
https://en.wikipedia.org/wiki/Evolutionary_developmental_biology#The_origins_of_novelty

Evolutionary developmental biology – Wikipedia
Evolutionary innovation may sometimes begin in Lamarkian style with epigenetic alterations of gene regulation or phenotype generation, subsequently  consolidated by changes at the gene level.
https://en.wikipedia.org/wiki/Evolutionary_developmental_biology#Consolidation_of_epigenetic_changes

Transgenerational epigenetic inheritance – Wikipedia
Transgenerational epigenetic inheritance is the transmission of epigenetic markers and modifications from one generation to multiple subsequent generations without altering the primary structure of DNA. Thus, the regulation of genes via epigenetic mechanisms can be heritable; the amount of transcripts and proteins produced can be altered by inherited epigenetic changes
https://en.wikipedia.org/wiki/Transgenerational_epigenetic_inheritance

Genetic assimilation – Wikipedia
Conrad Waddington “supposed that the organism’s genetics evolved to ensure that development proceeded in a certain way regardless of normal environmental variations.”
https://en.wikipedia.org/wiki/Genetic_assimilation

“A long time ago, we assumed that to make a creature as wonderful and attractive as a human being would take millions of different genes, but in fact, we now know that there are about 32000 – far fewer than we expected. That is not much genetic information for evolution to work with.”

Even more annoying is to find that around 99% of our genome is what is called ‘junk DNA’, which we got from parasites, repeats, and a lot of it does not work.” – Steve jones

“Evolutionists and geneticists still have a slightly uneasy relationship; there are still arguments about big mutations versus small mutations, how often mutations happen, and why is the mutation rate so low – around 1 in a million. But we know that nearly all mutations are repaired. The question here is – why doesn’t it fix all mutations? We do not know.

https://serious-science.org/theory-of-evolution-6344

Natural Limits to Variation, or Reversion to the Mean: Is Evolution Just Extrapolation by Another Name?
“In spite of intensive and long continued efforts, breeders have failed to give the world blue roses and black tulips. A bluish purple and a deep bronze in the tulip are the limits reached. True blue and jet black have proved impossible. ” – [J. Huxley, Evolution: the Modern Synthesis, London, Allen and Unwin, 1942, p. 519]
https://evolutionnews.org/2012/04/natural_limits/