Showing posts with label brainbrane. Show all posts
Showing posts with label brainbrane. Show all posts

🧠brainbrane* ("lnq") is our kernelspace@ (ie. scalable^ technopathic n-manifold) 🧠brain [= central nervous system (cns) / nerve net (nn)] reagent to regulate+control cell/neural signals, among other userspace functions.** Fibor(s) as a cell matrix. @@ Printed from. ^^ Mapping a neural network.
/// +🧠'brainbrane (bb)' would be the generic name for the 'lnq' technology (reagent = natural logarithm (ln) of the reaction quotient (Q) of the species involved), differentiating it from the person.
+We should think of the reagent as a propagating kernel (the 'genetic code' that directs information+translation in our userspace [🧠brain] example). Here, 'kernelspace' (= proof of the Quantumquotient) is any gate that can be debugged and/or have its processes interrupted, and 'userspace' [host] is the address registrar (ie. what registers some subset of some kernelspace). The two form an aggregate operating system.
+In this case, it is a hormone (ie. organic material determining physiological responses by regulating any and all internal sinusoidals) whose biological target is the entire 🧠brain.😵🤯
+This is colloquially known as '🧑🏿lnq's drug💊' (whereas the gameshow is the 'high'), and is also offered as a class of tangible proofs.

As the formal outlier of gameplay mechanics (ie. think "gameboard" - standard board layout for juking), this is UUe's interface/jukebox kernel augmenter. This neurally networks (ie. maps@) 🧑🏿lnq's walk. Jukebox instancing* utilizes portable, peer-to-peer core batching (ie. cellular nodes on dial)~ for walk-tracing and execution.@@ A type of hyperlinking ** linker ~~ neuron
The range of the threading (being essentially the reach of orchestration^, and, hence, the network) is determined organically.^^ 'orchestrate' means sequence and map. Cybernetically, this is a set of automata augmenting (ie. stew patchwork) gameplay. (see joey, Egglepple, everywhere!, BOT)
/// Network growth means that scalability in spacetime complexity is sought. In jukespace, this may be virtual, physical, or hybrid.
+Hypothesis: kernelspace and userspace are self-similar.

open framework


Steps: (1) preimage the protein, (2a) construct a distributed supercomputer [lnq] to (2b) juke the polymer, and (3) solve the structure with machine learning (genetic algorithms).
Theorizing that DNA/RNA/protein bindings should be algebraically solvable, we are juking certain classes of macromolecules for better models+investigation of their related neuropathologies (sampleset: PD, HD, MS, Alzheimer's/diabetes, ALS), cotangent viral groups (sampleset: EBV, HIV, influenza, Ebola), and possible extrapolation to cancer🎗 (ie. kinase catalysis). Doing so requires decomposition of a large number of long random walks, to which we are applying a homebrew artificial intelligence (UUe.i) that runs a huge machine learning algorithm. Our compute is distributed over a mobile cluster, allowing for user engagement in PP. Furthermore, cinematic properties are addended for driver monitoring; assuming that the eye is an extension of the brain, we gather (using the camera system on mobile devices) large batches of photographs (still,motion video) of the human eye(s) in various settings/strains for mapping α-syn and β-amyloid (Aβ) plaques (+ other proteins).

My solution (theory, proof-of-concept) for imaging the protein is to treat intracellular folding pathways (trace) like an animation exercise, where plaque in the human retina is photographed at start/stop endpoints with an intermediate random walk acting as molecular frame interpolation. Conditioning the polymer to behave like a self-driving vehicle, we use fleet learning (crowdsourcing) for molecular/cellular navigation. A compression algorithm is then applied. This relies heavy on artificial intelligence [ai] (ie. genetic algorithms for predictive analytics applied to biopolymers+biometrics) and involves utilizing MMO/crowd computing for purposes of gamification, versus traditional labwares. Development is open to the community as a way to fast-track builds, and to honor its promise of UUe being a public utility en perpetuity.
We are not creating a chemical (tangible pharmaceutical) nor a therapy. This is strictly molecular modeling of peptide conjugates using distributed machine learning, the preliminary step in accommodating lab-based rational drug design+synthesis. We conjecture that the above technique is applicable to any and all macromolecular determinants.


by 🧑🏿Link Starbureiy et al.
The aggregation mechanism of alpha-synuclein (aka SNCA/NACP gene encoding) is undetermined, as to be expected since the protein is intrinsically disordered (ie. chemically unstable). It has been suggested that this unstructured tetramer is a combination of alpha helix and beta sheet conformers in equilibrium. There exists lab-produced evidence that the protein interacts with both phospholipids and tubulin[j] within presynaptic terminals to form (characteristic) Lewy bodies, thus, it is a pathological component in multiple system atrophy (an umbrella term used here for would-be neurodegenerative [inverted dopamine modulation] diseases).

We wish to have a walkable model of synuclein. Current popular molecular dynamics programs on the market[a][b][c][d] are strictly brute-forcing chemical force fields, and not accounting for twistorspaces, thus missing a more potent subset of polynomials in the protein folding problem. Because medical resonance techniques (especially those in radiolabeling) require datasets of preimages/images, it only makes sense that a positron emission tomography (PET) tracer (as requested) should be constructed from a camera utilizing volumetric* pixel-level signal compression. For this (the signal sampling scheme), we'll use the Stewdio to hash EGP. Right now, the api is public and the math works, but it needs substantial computing power for realtime implementation.with depthnets+segnets

The secret to curing any disease is with cell division (ie. iteration). Think of it in the way of an automotive analogy -- if you wanted to teach a car how to drive itself, the instinct may be to program its 'eyes' (camera+radar) to adhere to maps. A smarter way is to just let the car follow patterns it has learned from other drivers. In other words, the best way to train the system to drive is to simply show it what driving actually looks like by creating an agent that clones behavior. That's how computers are trained; give them big data sets of what is correct and incorrect, and then write a classifier. Which is precisely what we are going to do here (in our case, include animation modifiers); take live strategies (an array of preimages, ie. divided quanta) derived from juker activity [genetic algorithms] and apply them to known protein structures (specifically, SNCA, parkin, HTT, and LRRK2). Over time, we'll get tighter bounds on errors that will eventually only produce optimized images.

This is done using UUe, a 'smart' (artificially intelligent) preimage-image compositor native to distributed computing (ie. distributed memory). The ai will do two (2) things here: (1) be a background embed that pulls+loads idle compute resources for processing, and (2) provide visual ventriloquy [+training] for image classification+mapping onto an actor [ie. string] in the capture pipeline. It is designed as a distributed framework that uses the processors from mobile devices (eg. phones, tablets, VR goggles, watches, etc.) to power the compute. Today's machines tend to be more game-centric, so the GPU is a premium, which is good since the ai will be performing specialized 3D renderings. It can augment opensource libraries (eg. OpenMM, +GROMACS, BOINC, TensorFlow) in addition to the Stewdio. In theory, this will be the largest and most powerful supercomputer ever constructed. Once ready, it will be freely available for uses similar to this one en perpetuity.


No doubt, drug companies have done astonishing work thusfar considering that their modalities for quantitative predictability of synthetic chemicals and bioorthogonals have very limited data-driven processes for determining structural motifs (assumed macromolecular architecture). The proliferation of genome sequencing projects (eg. Human Genome Project) simplifies cellular polymer/protein primary structure (amino acid sequence) inference. However, that information doesn't reveal much about protein function, which is necessary for our ultimate goal of rational drug design (albeit via computational biochemistry). That comes from knowing macromolecular three-dimensional tertiary structure. It is also important to note that protein folding happens in solution versus isolation, so any serious contender for a structural motif must incorporate at least hydrogen (usually with oxygen: H + O2) in its formula (ie. amino acid residues are surrounded by water). This is because protein structure conforms to thermodynamics (/energy landscape)[w]; as temperatures drop, said structures collapse, whereas in higher temperatures, hydrogen bonds [amino acids-H-residues] break and re-form (mimicry of symmetry-breaking).

UUe.i can dramatically reduce discovery-to-classification time (and thereby remove the evaluation bottleneck) precisely because it is (in theory) the heaviest duty program on the market geared towards molecular dynamics and numerical analysis. To satisfy a certain rewards committee, this is being advertised as a radiotracer~, but on a deeper level, it is a generic solution to protein structure prediction, meaning that it has much broader application (biology, ecology, etc.) for consideration.~~ A radiotracer is a chemical compound which, having dropped (proton) or absorbed (neutron) an isotope (radionuclide) from an atom, behaves as a mechanism for nuclear reactions/chemical processes in accordance with radioactive decay. In laymen terms, we're highlighting only part of a chemical element (say hydrogen) to track it through various pathways, at the same time revealing different imagery. The protein is traced by voxelating its longitudinal pathways in the retina using the camera system on a mobile device. Another bennie is that the program is at the forefront of scientific innovation in regards to supercomputing.

Solutions to protein folding and similar RNA folding problems are largely computational. Engineering+maintaining a supercomputer is expensive, but the payoff is near-immediate and widely useful for everyone. This is probably going to be a long-term endeavor, which is why the project has been opened up to the community at-large. Implementation needs boil down to underwriting. 🧀Back this to enable development (improved core features, bug fixes) continuity.
Function map: 🧠brainbraneavatar