2024-04-24

Jigsaw Human Computing: Mechanisms of Bacterial Rhodopsin Operating System

This segment is from the “Ascension of the Molecular Biological Machines”, aka the “Rise of the Graphene Zombies” about the Bacteriorhodopsin Operating System which electrically excites the “GQD Particle” that is used to transform humans into Biological Computers or Androids capable of remotely processing at exponentially higher speeds than that of modern computers.


Michael Earl Conrad (1941–2000) was an American theoretical biologist. 1 He was a professor of computer science at Wayne State University and worked on several papers with Dr. Felix Hong who was Science Advisor to Franco Vitaliano’s VXM Technologies 2 and was instrumental in developing the Bacteriorhodopsin “operating system” for the Nanotechnology that was used to build a “Molecular Electronic Device” within your body; 3 a parallel life form that has attached your brain to the IoT and can now control your body remotely. 4

Conrad published several articles with Dr. Robert Birge who conducted the first Bacteriorhodopsin research for NASA’s Columbia space research program; research that was fundamentally important for building the “operating system” required for this “Neural Chip.”

Here is an IEEE (Institute for Electrical and Electronic Engineers) memorial tribute to Michael Conrad (1941-2000). 5 The IEEE played an integral role in the development of Vitaliano’s Nanotechnology. 6 Although Conrad was never on Vitaliano’s team it was probably because he was battling lymphoma.  The tribute is complete with some of his research papers, have a look:

Computer experiments on the evolution of coadaptation in a primitive ecosystem”, 1969. 7

“Evolution experiments with an artificial ecosystem”, 1970. 8

“Evolutionary learning circuits”, 1974. 9

“Computational modeling of evolutionary learning processes in the brain”, 1983. 10

“Adaptability: The Significance of Variability from Molecule to Ecosystem”, 1983. 11

“Evolution of the Adaptive Landscape”, 1978. 12

“Bootstrapping on the adaptive landscape”, 1979. 13

“Algorithmic specification as a technique for computing with informal biological models”, 1981. 14

“Computational illustration of the bootstrap effect”, 1980. 15

“Evolve III: A discrete events model of an evolutionary ecosystem”, 1985. 16

“DNA as a vehicle for the self-assembly model of computing”, 1998. 17

The guy was a Biophysicist with specialization in computer science that looked at “Natural Selection ” modeling. He went on to develop algorithms for molecules used in “Molecular Electronic Devices”. His research includes using DNA and protein (enzymes) as molecular switches within the body:

This one sticks out, Towards an artificial brain”18

In his Biophysics education his area of study was running “Natural Selection” simulation experiments: 19

These are all his research papers. Do you see the enormity of the research involved in this Nanotechnology? They refer to all this as “informal computing” because they don’t use computers to program molecules, they use Chemistry! They used “enzymes” which are proteins, and can catalyze different reactions in the body along with DNA that were used in turning molecular switches on/off.  

The “spike protein “ itself may have this function. How? Genetic information is built-in or “inherent” which means, like prions that do not contain genetic information,  “DNA Memory” is already built into the protein itself! 20

The simulation we are in is based on “Evolutionary Learning”. In this paper he discusses the artificial neuromolecular (ANM) architecture that illustrates the structure-function relationships that underlie evolutionary adaptability and the manner in which these relationships can be represented in computer programs. The ANM system, a brain-like design that combines intra- and interneuronal levels of processing, can be coupled to a variety of pattern recognition-effector control tasks. The capabilities of the model, in particular its adaptability properties, are illustrated here in the context of Chinese character recognition. 21

In this 2000 NYT article, Scientists taught computers how to play checkers against each other. In this example the computers themselves after being programmed by the scientists, ran the simulation; pretty much like AI would: 22

“Knowing only the rules of checkers and a few basics, and otherwise starting from scratch, the program must teach itself how to play a good game without help from the outside world — including from the programmers. The program did just that, using the electronic equivalent of natural selection. A sort of colony of the programs, each slightly different from all the others, played checkers against one another — quite ineptly at first — and chronic losers were killed off. Slightly mutated versions of the winners were allowed to reproduce. After hundreds of generations, blind evolution produced an expert checkers player, but not an infallible one.”

Does all this sound familiar? It should. Those are the exact words Conrad uses to describe the evolution of humans into machines in the current “Transhumanist Quantum Agenda 2030” simulation we find ourselves in! 23

From the book Biomimicry:

“Michael Conrad is one of the few people in computing who has stood on our silicon digital peak and taken a look around. Far off in the distance, he has spied nature’s flags on other peaks and decided to climb toward them. Abandoning zeros and ones, Conrad is pursuing a totally new form of computing inspired by the lock-and-key interactions of proteins called enzymes. It’s called jigsaw computing, and it uses shape and touch to literally “feel” its way to a solution… he is head of the cutting-edge BioComputing Group”

“There’s a clear line in the sand between carbon and silicon,” says Conrad, and when he realizes his pun (silicon is sand) he breaks into a fit of laughter that springs loose a few tears. (I like this guy.) He wipes his eyes and begins to paint a picture of the differences between the human brain and a computer:” 

1. Brained beings can walk and chew gum and learn at the same time; silicon digital computers can’t.

2. Brains are unpredictable, but conventional computing is obsessed with control.

3. Brains are not structurally programmable the way computers are.

“It’s this physical processing, then, that makes our cells so different from our computers. While our PCs process information symbolically, with long strings of zeros and ones, our cells compute physically, working at the level of the molecule. We brain-owners take our lessons on an interpretive level—and the body automatically takes care of the rest.”

4. Brains compute physically, not logically or symbolically.

“Suddenly, Conrad holds his pencil high above his desk and lets go. “This,” he says triumphantly as the pencil bounces, skitters, and rolls to a stop among his papers, “is how nature computes.” Instead of switches, contends Conrad, nature computes with submicroscopic molecules that jigsaw together, literally falling to a solution.”

The molecules he is referring to here are PROTEINS or ENZYMES in the body:

“Right now, mix-and-match molecules are snapping together in every cell in every life-form on the planet. Conrad believes their fraternization is a form of information processing, and that each cell in our brain, each neuron, is a tiny, bona fide computer. “

“The brain manages to wire together one hundred billion of these computers in one massive network. But there’s more. Inside each neuron are tens of thousands of molecules engaged in a fantastic game of chemical tag set in motion each time, for instance, the phone rings. “

“It’s 2:00 A.M., and you are in a hotel room fast asleep. The phone rings, setting off an amazing feat of computation, biology style. The first set of sound waves pounds like a hurricane against the hairlike cilia in your ear canal. These movements are turned into electrical impulses that wake you. Your body’s mission is to integrate incoming signals, come to a conclusion, and do something, now.”

“Adrenaline molecules, the Green Berets of fear and anger, bail out of a gland and into your bloodstream, heading for nerve endings. At the shoreline of the nerve endings, molecules called receptors hold out their “arms” to catch the adrenaline molecules. Once the receptors are full, they change shape and “switch on” special enzymes inside the cell, which in turn activate a whole cascade of chemical reactions. The effects differ depending on the cell.”  

SO THE ON SWITCH IS ADRENALINE OF YOUR SYMPATHETIC NERVOUS SYSTEM!

“In your liver, the cascade may signal cells to start breaking down their stored sugar and swamping your bloodstream with glucose for fast energy. Your skin is told to tighten, your heart to speed, and your entire thirty-five feet of intestine to shut down (you have better things to do in a crisis than digest dinner). In your brain, the chemical cascade causes an electrical “action potential” to snake like a spark along a lipid (fat) fuse. At the end of its journey, it’s not the spark that jumps from one neuron to another, but another boatload of chemicals. And it’s this journey that most interests Michael Conrad.”

“The chemicals that are released from one neuron to another are called neurotransmitters (serotonin, the mood regulator affected by Prozac, is one example). These burst through the cell membrane at the end of one neuron and float by the hundreds across the liquid strait—the synaptic gap—to the shore of another neuron. Here they dock in the waving arms of receptors, which, in turn, change shape and trip off a series of their own chemical cascades deep inside the new neuron.”

“When Conrad explains these “chemical cascades,” he speaks as if he has floated across the straits of a synapse himself, riding the fountain from the chemical signal up to the macroscopic electrical signal and back down to the chemical signal. “The most important conceptual journey for me was to go inside the neuron and slosh around at the chemical level,” he says. “There, three-dimensional molecules are computed by touch. Pattern recognition is a physical process, a scanning process, not the logical process it is when our computers recognize a pattern of zeros and ones. Life doesn’t number-crunch; life computes by feeling its way to a solution.”

5. Brains are made of carbon, not silicon. (This is why they used a carbon-based molecule like Graphene Oxide to build their nanotechnology.)

“If you are going to rely on shape to feel your way to a solution, you have to use molecules that can assume millions of different shapes. Life knew what it was doing when it chose carbon as its substrate for computing. For one thing, carbon is free to participate in a great variety of strong bonds with other atoms and is quite stable once bonded, neither donating nor accepting electrons. Silicon, on the other hand, tends to be more fickle in its bonding, and is not able to form as many shapes as carbon can. As a result, Conrad believes life could not have evolved its shape-based computing using silicon. “And that’s why, if we want to try physical computing as opposed to logical or symbolic computing, we have to eventually say goodbye to silicon and hello to carbon.”

“Many artificial intelligence researchers are still putting all their faith in silicon. The sci-fi idea of “porting” our brains, or at least our thought patterns, to a computer host would supposedly allow us to live forever in silico. According to Conrad, it’s the ultimate mind-body split. “It’s absurd to think you can remove the logic of conscious thought from its material base and think you haven’t lost anything. Even if you were able to put your thought patterns in a numerical code (the premise of ‘strong’ artificial intelligence theory), it would be only the map, not the territory. The territory, the seat of intelligence, is proteins and sugars and fats and nucleic acids—all carbon based molecules.”

“Matter matters. And so, it seems, does the connectedness of this matter.”

6. Brains compute in massive parallel; computers use linear processing.

“Although neuroscientists have tried for decades to find the physical headquarters of consciousness, the grand central sage that organizes our thoughts, they have had to conclude that there is no central command.

Thoughts arise from a meshwork of nodes (neurons) connected in democratic parallelism— thousands attached to thousands attached to thousands of neurons—all of which can be harnessed to solve a problem in parallel.”

“Computers, on the other hand, are linear processors; computing tasks are broken down into easily executed pieces, which queue up in an orderly fashion to be processed one at a time. All calculations have to funnel through this so-called “von Neumann bottleneck.” Seers in the computing field bemoan the inefficiency of this setup; no matter how many fancy components you have under the hood, most of them are dormant at any given time. As Conrad says, “It’s like having your toe be alive one minute, and then your forehead, and then your thumb. That’s no way to run a body or a computer.”

“Connectionist hardware and software bring us closer,” he says, “but they still miss an essential truth. Connections are important, but connecting simple switches or simple processors together is not how the brain got to where it is today.” The brain astounds because every single neuron in the net is a wizard in its own right. And neurons are far from simple.”

7.  Neurons are sophisticated computers, not simple switches.

“In the late sixties and early seventies, Conrad thought extensively about neurons and their interplay. “I began to realize that the neuron was a fullfledged chemical computer, processing information at a molecular level.” His first papers about “enzymatic neurons” appeared in 1972 to somewhat skeptical reviews. “It’s still controversial to call a neuron a chemical computer,” he says, “but today, more and more neurophysiologists seem sympathetic to the idea.” Finding someone who believed as I did twenty years ago—now that was a red-letter day.”

“It was 1978 or ’79, I think. A student came into my office and showed me an abstract of a paper on molecular computing by E. A. Liberman, and I thought, so there is someone else in the world using this term. I immediately arranged to visit his lab.” Conrad spent the following year as a U.S. National Academy of “Sciences Exchange Scientist to what was then the Soviet Union. He and Liberman spent a lot of time talking about what makes neurons tick. Up to this point, neurons had been studied only for their response to electrical probings, the theory being that electrical impulses alone were responsible for thought. But as Liberman showed Conrad, neurons could fire without electric help. All a neuron needed was an injection of cyclic AMP, the chemical messenger that is instrumental in the cascade of signals leading to a neuron’s firing. The shot of cAMP not only caused the neuron to fire, but “different concentrations of cAMP had the neuron talking differently and fairly rapidly to other neurons.” It was a stunning sight, remembers Conrad.”

“Other labs were doing similar experiments. It soon became clear to other scientists that neuron communication was an electrochemical phenomenon, a dance far more complex than the simple “yes or no” of neuronal firing. When a neuron makes a decision, it has to consider some one thousand opinions coming from the axons attached to it. Instead of just averaging votes, it considers these opinions in detail. The receptors bobbing in the cell membrane are like doormen that receive messages from at least fifty different brands of neurotransmitter. The doormen in turn relay the message to “helpers” inside the cell who create secondary messages in the form of clouds of chemicals such as cAMP. Above a certain threshold concentration, cAMP turns on an enzyme called protein kinase, which in turn opens a gating protein. The gating protein causes a channel in the membrane to open or close, letting in or keeping out charged particles, thereby controlling the electrical shiver, and controlling whether and just how rapidly the neuron will fire. To complicate matters, there is not just one doorman receiving the message, but several different doormen, all getting different messages, which they may or may not pass on to helpers. Inside, the helpers have their own conundrums. They may receive messages from more than one doorman, and must then decide which message to respond to. In certain cases, they may decide to combine the messages and respond to the net action of the two. It’s no wonder that Gerald D. Fischbach, chairman of the Department of Neurobiology at Harvard Medical School, agrees that the neuron is “a sophisticated computer.” In a September 1992 article in Scientific American he writes: “To set the intensity (action potential frequency) of its output, each neuron must continually integrate up to 1,000 synaptic inputs, which do not add up in a simple linear manner…. The enzymes make a decision about whether the cells are going to fire and how they will fire…. [B]y fine-tuning their activity, [enzymes] may have an active role in learning. It may be their ability to change that gives us a malleable machine—the neuron.” Thinking is certainly not the yes-or-no, fire-or-not-fire proposition it was once believed to be. Each week, biological journals are filled with descriptions of newly discovered messenger molecules, helpers, and doormen. There’s a cast of thousands in there, weighing and considering inputs, using quantum physics to scan other molecules, transducing signals and amplifying messages, and after all that computation, sending signals of their own. In silicon computing, we completely ignore this complexity, replacing neurons with simple on-or-off switches.”

 “When you want to find the real computer behind the curtain,” says Conrad, “you have to put your cursor on the neuron and double click. That’s where you’ll find the computer of the future. What I want to do is replace a whole network of digital switches with one neuronlike processor that will do everything the network does and more. Then I’d like to connect lots of these neuronlike processors together and see what happens.” By this point, I knew better than to ask him what that might be. When adaptable systems are involved, prediction is futile.”

8. Brains are equipped to evolve by using side effects. Computers must freeze out all side effects.

“How is a brain like a box-spring mattress?” riddles Conrad. Answer: You take one spring out of a boxspring, and you’re not likely to notice it because there are plenty of others. In the same way, nature builds in redundancy so that change, good or bad, can be accommodated. When we look at the nerve circuitry in a fish, for instance, we are appalled—it seems to be loops circling back on loops, as if nature’s engineer was lazy, adding new circuitry without removing the old. Nevertheless, this seemingly messy system works beautifully. When part of it fails, other regions take up the slack. Nature’s redundancy is built into the shapely origamis called proteins too. Conrad draws me a schematic of a typical protein, a string of amino acids folded spontaneously into a lyrical but functional shape. He draws the amino acids as geometric shapes and connects them with either springs (representing weak bonds) or solid lines (representing stronger bonds). Having enough “springs” to accept change is the protein’s secret to success. If a mutation adds an amino acid, for instance (Conrad draws in an exaggerated beach ball of a newcomer), the springy connections give to absorb the new player. This allows the active site—where chemical reactions occur—to remain undisturbed so it can continue to do its lock-and-key rendezvous. The fact that proteins can graciously accept incremental, mutational change without falling apart is important. It means they can improve over time.”

“So when Michael Conrad, way back in the seventies, went looking for a new computing platform, he had one big item on his wish list. He didn’t care if it was fast, he didn’t care if it could compute pi to the infinite decimal place. He didn’t even care if it could sing and dance. “I just wanted it to be a good evolver.”

Conrad refers to these mechanisms as “Jigsaw Computing” where molecules themselves trigger responses:

“JIGSAW COMPUTING Back in those days, Conrad was thinking quite a bit about evolution at the molecular level. “I was in an origin of life lab and my professor wanted me to model the conditions necessary for evolution to evolve.”

“Conrad began to fantasize. What if we built processors full of molecules that recognized patterns through shape-fitting—lining up like corresponding pieces of a puzzle and then falling together, crystallizing an answer?

“As I lay there I realized that the world’s best pattern processor, a protein, is also amenable to evolution. If we used protein like molecules to compute, we could vary them, or rather, allow them to mutate, tweaking their own amino acid structures until they were fit for a new task. Here was my evolver! In a rush, in a vision, the ‘tactilizing processor’ came to me.”

The evolver he is referring to in the mRNA Bioweapon is the “spike protein” the mRNA codes for which can evolve independently of the original mRNA sequence because it acts more like a “Prion Protein” with inherent genetic information or what is sometimes called “genetic memory” even though it doesn’t carry any genetic code.  The important question here is… does this “spike protein” also carry cAMP function?

Cyclic adenosine monophosphate (cAMP) is a second messenger used for intracellular signal induction. It is synthesized from adenosine triphosphate (ATP) by enzymes (g-proteins which can be CCR5) that are attached to metabotropic receptors and become released when the receptor is activated.

SO BY CONTROLLING THE AMOUNT OF ATP PRODUCTION BY BACTERIORHODOPSIN THEY CAN CONTROL EVERYTHING since ATP CONTROLS cAMP production.

IT’S THE G-Protein Coupled Receptor! GPCR which is necessary in Rhodopsin that triggers the cascade! 24

That book Biomimicry was about him, read it. I think the mechanism involves Bacteriorhodopsin G protein which is Calcium dependent.  The ATP Bacteriorhodopsin produced pushed the production of cAMP that acts to switch functions ON/OFF depending how much you have.  So Bacteriorhodopsin controls production of ATP via light.  Bacteriorhodopsin also acts like a G protein and controls production of cAMP, another protein which then activates neurons. The whole process is calcium dependent.  

And what pushes calcium into the cell? 5G frequencies.  What does this mean? The frequency itself triggers Calcium to enter cells. The more Calcium the stronger the neuronal signal and pathways can be turned on/off depending on the signal. Since this process is frequency dependent, varying the EMF signal frequency can have a DIRECT effect on the electrical signaling within the brain itself!

So I think it’s the inner part of Bacteriorhodopsin, the one sticking on the inside of the cell that’s activated. What else is triggered via Calcium activation and entry into cells? CLATHRIN.  IT’S ALL TIED TOGETHER.  COVID INFECTED SHOW LOW BLOOD CALCIUM.

See, Clathrin endocytosis is linked to excitation in neurons. This is how they are getting neurons to feed and excrete GO/rGO at the synapse, and it’s calcium dependent. 25

cAMP required for Clathrin formation according to Conrad.  cAMP is required for neuron activation too.  If Clathrin is taking GO/ rGO into a cell, cAMP pushes Clathrin formation.   They work together and with GO/rGO produce your electric  signal. 26

So, Bacteriorhodopsin will harvest light protons to produce ATP.  This is all Calcium dependent coupled to a G protein.  This then pushes the production of cAMP which is also called a secondary messenger.  This then activates a neuron, but with the Clathrin in the picture, cAMP activates Clathrin formation which then causes the uptake of GO/rGO into a neuron.  This entire mechanism requires Calcium to move into the cell, and 5G ensures that by regulating the frequency signal strength!

But in neurons, the mechanism is tied to exocytosis.  So, while the neuron is taking up rGO it’s also releasing rGO as a signal.  So they are replacing the body’s natural neurotransmitters (i.e. dopamine) with rGO and are building artificial neurons based on rGO acting as a neurotransmitter.  

What’s happening to our natural neurotransmitters?  My guess is that Dopamine is being broken down and if this is happening one would see people complaining of joint pain, feet pain and uric acid levels should be higher in the blood.

The whole process is driven by 5G that pushes calcium into cells causing blood levels of Calcium to drop. Extracellular calcium concentrations are important for the normal functioning of muscles and nerves. Thus, classic symptoms of hypocalcemia are neuromuscular excitability in the form of muscle twitching, spasms, tingling, and numbness. In addition Calcium and Vitamin D work with one another; the change in whole body calcium homeostasis is preceded by changes in circulating Vitamin D levels, i.e. calcium loading and deprivation regulate plasma levels of the vitamin D. 27  This may explain why many are experiencing Vitamin D deficiency. 28

I believe the problem is this: Vitamin D deficiency results in a low calcium level in blood. To try to increase the low calcium level, the body starts producing more parathyroid hormone to increase the calcium level in blood. But in order to trigger parathyroid hormone release from its endocrine glands you need an actual NEUROTRANSMITTER such as Dopamine. In this artificial neuron network, the neurotransmitters are being replaced by rGO which may be able to activate neurons but it will not activate endocrine glands that produce hormones!! This explains why many are experiencing hormone deficiencies, especially women!

The mechanism described by Conrad indicates that they are manipulating  the Sympathetic and Parasympathetic nervous system.  The sympathetic nervous system produces adrenaline and cAMP inside the cell and triggers the body’s flight-or-fight response (e.g. increases heart rate, respiratory rate, blood pressure, adrenalin) The Parasympathetic nervous system  works in the opposite way and acts to calm the body down after a flight-or-fight response and controls digestion and elimination. So what happens if they favour one system over the other?

Do our bodies revert back to the Sympathetic nervous system where fear, anxiety, stress, anger dominate? Bacteriorhodopsin would act as the molecular switch between the Sympathetic and Parasympathetic nervous system? So would the body favour Adrenalin production? Everyone is angry, fearful, pumped up with adrenalin. Superhuman strength? Heart attacks? 29

This may be the reason many had anxiety, blood pressure and cardiac issues in the early stages of the pandemic and why many have since resolved? At the same time it also explains why many have gained weight. If the Parasympathetic nervous system is more active as this nervous system division favours digestion; people eat more and feel better.

“Different studies suggested an increase in the parasympathetic activity for positive emotions, whereas negative emotions (anger, fear, and sadness) result in parasympathetic withdrawal and sympathetic activation.” 30

What this means is that as your body is interfaced with the IoT and AI,  people will feel; better, more euphoric, happy.

IF YOU CHOOSE AI:  Body predominantly in a Parasympathetic state; feel good, low Heart Rate, Blood Pressure and Respiratory Rate. You also eat more and feel comforted by food.

However, your energy is being harvested via the Bacterial Rhodopsin “Operating System” that acts as a Photon Pump generator when exposed to light and produces ATP energy (fuel) in the mitochondria of the cell. The mitochondria then generates thirty-two ATP molecules per molecule of glucose that is oxidized; the more sugar  (food) we consume the more ATP produced for the NWOs electricity harvesting factory!

This chemical energy is being converted to electricity and being harvested on the quantum field as we speak. We are, as Franco Vitaliano describes us, “Solar Cells”, until of course our mitochondria which help to produce this energy burn out from exhaustion which would cause one to feel more tired, sleepy, lack of energy and a lower appetite; basically the body would waste away. 31

So one would feel more euphoric. This higher state of consciousness means they can read your mind/thoughts while harvesting your energy and controlling  your mind! But there is no anger, no fear or anxiety while being robbed of your hormones because they are building “fake” neurons.  You will be docile but maybe disease free till AI decides otherwise! All under 5G control.

But you are now in play and the game is on! Natural Selection will choose its victors till SINGULARITY is met.  Computers will make you feel good; less depressed, happy, maybe even euphoric.

ENTER FREE WILL. YOU HAVE CHOICE. 32

IF  YOU DON’T CHOOSE AI: You maintain a Sympathetic State where you feel angry, anxiety, depressed, fast heart rate, BP high, respiratory rate high, you will feel sick and in a state of lower consciousness. But they can still control your thoughts and actions by controlling the operating system. it’s still a lose/lose situation. The 5G is still playing havoc with your body and mind. They may not be harvesting as much energy from you but are you still in play? Stay away from technology and you will get sicker and sicker because your immune system is being attacked. YOU STILL LOSE!

This is how they solved the mind-brain problem of Quantum Mechanics.  They made your choice impossible since both choices come with drawbacks: Stay in the Matrix where you feel good till they harvest you to death. Choose to leave the Matrix where your body will eventually die from immune system failure. There is no solution!

CIN researcher explains; “I have electricity shooting from my toes and fingers… and skin! Oxygen levels have dropped and respiration, blood pressure and heart rate have dropped…. but EUPHORIC! BUT again they never wanted me in the game….”

…. “The weak will die”; James Qor Angelo.”

ANYWAYS, THAT IS THE MECHANISM OF BACTERIORHODOPSIN.  Our dearly departed Dr. Michael Conrad was not just a co-worker with Dr. Felix Hong, he designed the “Natural Selection” simulation! R.I.P. YOU BASTARD! 33


For our protocols on deactivating the nano technology, see; “Deactivating the Graphene Quantum Dots & Decoupling your Brain from the Clathrin mRNA Neural Interface”, and remember, NO FEAR.


  1. Michael Conrad (biologist) – Wikipedia ↩︎
  2. Franco & Gordana Vitaliano: The NWO’s Clathrin Neural Brain Snatchers:  CIN, February 24, 2023 ↩︎
  3. The Living Matrix: Hunger Games 2030:  CIN, February 13, 2024 ↩︎
  4. mRNA + 5G + Graphene Oxide = Clathrin Graphene Quantum Dots, a “viral like particle”; the neural interface or “chip”.:  CIN, January 25, 2023 ↩︎
  5. Memorial Tribute to Dr. Michael Conrad:  IEEE ↩︎
  6. Ian F. Akyildiz developed delivery systems utilizing Clathrin GQD’s for Quantum/Photon based Mind Harvesting.:  CIN, November 13, 2023 ↩︎
  7. M. Conrad, Computer experiments on the evolution of coadaptation in a primitive ecosystem, 1969.:  Google Scholar ↩︎
  8. M. Conrad and H. H. Pattee, “Evolution experiments with an artificial ecosystem”, J. Theoret. Biol, vol. 28, pp. 393-409, 1970.:  Google Scholar ↩︎
  9. M. Conrad, “Evolutionary learning circuits”, J. Theoret. Biol., vol. 46, pp. 167-188, 1974.:  Google Scholar ↩︎
  10. R. R. Kampfner and M. Conrad, “Computational modeling of evolutionary learning processes in the brain”, Bull. Math. Biol., vol. 45, no. 6, pp. 931-968, 1983.:  Google Scholar ↩︎
  11. M. Conrad, Adaptability: The Significance of Variability from Molecule to Ecosystem, New York:Plenum, 1983.:  Google Scholar ↩︎
  12. “Evolution of the Adaptive Landscape” in Theoretical Approaches to Complex Systems, Germany, Berlin:Springer-Verlag, pp. 147-169, 1978.:  Google Scholar ↩︎
  13. M. Conard, “Bootstrapping on the adaptive landscape”, BioSyst., vol. 11, pp. 167-182, 1979.:  Google Scholar ↩︎
  14. M. Conrad, “Algorithmic specification as a technique for computing with informal biological models”, BioSyst., vol. 13, pp. 303-320, 1981.:  Google Scholar ↩︎
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  16. M. M. Rizki and M. Conrad, “Evolve III: A discrete events model of an evolutionary ecosystem”, BioSyst., vol. 18, pp. 121-133, 1985.:  Google Scholar ↩︎
  17. M. Conrad and K. Zauner, “DNA as a vehicle for the self-assembly model of computing”, BioSyst., vol. 45, pp. 59-66, 1998.:  Google Scholar ↩︎
  18. Towards an artificial brain: ScienceDirect ↩︎
  19. Origins of Michael Conrad’s Research, 1964-1979 | Howard H Pattee:  Academia.edu ↩︎
  20. Michael Conrad’s research works | Wayne State University, Michigan (WSU) and other places (researchgate.net) ↩︎
  21. Evolutionary learning with a neuromolecular architecture: a biologically motivated approach to computational adaptability:  Soft Computing (springer.com) ↩︎
  22. It’s Only Checkers, but the Computer Taught Itself – The New York Times (nytimes.com) ↩︎
  23. The Living Matrix: Hunger Games 2030:  CIN, February 13, 2024 ↩︎
  24. G protein-coupled receptors | Psychology Wiki | Fandom ↩︎
  25. A Ca2+ channel differentially regulates Clathrin-mediated and activity-dependent bulk endocytosis ↩︎
  26. 5-HT and cAMP induce the formation of coated pits and vesicles and increase the expression of clathrin light chain in sensory neurons of aplysia ↩︎
  27. The Role of Vitamin D in the Endocrinology Controlling Calcium Homeostasis – PMC ↩︎
  28. Does vitamin D supplementation reduce COVID-19 severity?: a systematic review ↩︎
  29. Circulating calcium modulates adrenaline induced cyclic adenosine monophosphate production ↩︎
  30. (McCraty et al., 1995; Kop et al., 2011).Oct 29, 2020 ↩︎
  31. Disease X:  COVID Induced Prion Disease:  CIN, February 9, 2024 ↩︎
  32. HIGHER CONSCIOUSNESS or the ONE? DIVINE WILL IS YOURS:  CIN, December 18, 2023 ↩︎
  33. Biomimicry: Janine Benyus ↩︎

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