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Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

De les Coves, Gemma, et al. Universality and Complexity in Natural Languages: Mechanistic and Emergent.. C:/Users/Author/Downloads/preprints202402.1330.v1.pdf.. As this year becomes distinguished by integral syntheses across every realm and occasion, polyscholars GC, University of Innsbruck, Bernat Corominas-Murtra, Graz University, Germany and Ricard Sole, Universitat Pompeu Fabra, Barcelona prepost a subject instance whence spoken and textual communications, broadly conceived, can likewise be seen to inhere an exemplary universality of complex network systems. As the quotes say, these recursive, fractal-like attributes also refer to morphogenetic programs and traced onto physical phase transitions. As a summary it is wondered if Gottfried Leibniz’ proposal of an alphabetic Characteristica Universalis might at last be fulfilled

Human language exemplifies a complex system formed by multiple scales of description. Its origins and content have been well studied from grammatical standards to statistical analyses of word usage, which are seen imply universal patterns shared by all languages. Yet, a cohesive perspective remains elusive. In this paper we provide a basic structure of universality, and define recursion as a special case. We note generative grammars of formal languages (Chomsky) on the path toward universality and compare mathematical properties. We arrive at Zipf's law as a complexity attractor with a relation to common writing systems and Turing computations. Overall, we find two forms of universality, mechanistic and emergent, and cite some connections between them.

If anything characterizes linguistic phenomena is its ubiquity and diversity, spanning research fields and scales. Linguistics, psychology, population genetics [9], artificial intelligence, network science, statistical physics, evolutionary biology and cognitive science provide frameworks encompassing some aspect of language complexity. Yet, such explanations are not independent, but their relations give rise to a complex net of relations, as illustrated in Figure 1 (see next quote) exhibiting a hierarchy of such representations ranging from minimal components to the socio-cultural domain. (1)

Figure 1. Human language is a multiscale phenomenon with hierarchical levels from phonemes at the microscale, grammar and sentences at the mesoscale, and sociocultural dynamics at the macroscale. These phases include symbols to build the basic units of language (signs or words), the rules to organise words in sentences (grammar), neural substrate of these processes, and the role played by cultural change networks. One can approach the presence of universals in natural languages from the perspective of writing systems, whose history experienced marked transitions, or machines and recursion. (3)

The enscripted writing of languages was a major historic innovation which arose independently in various cultures and became a cornerstone of civilizations. The emergence of writing required a fortunate combination of already-present brain circuits; similarly, the transition to a reading brain involved a blend of contingency and inevitability. In this process, a revolutionary turn occurred: The transition to alphabets, by which as a small inventory of basic symbols can be combined to form syllables and words. As we shall discuss very soon, this transition can be seen as a jump to universality. (12)

Fortnow, Lance. Computation Is All Around Us and You Can See It if You Try. Quanta. June 12, 2024. The dean of the College of Computing at the Illinois Institute of Technology reflects on years of wondering how to experience and explain an extant, active reality that well seems as the result of a separate domain of immaterial, software-like, informational, maybe linguistic codings.

Do we have a way to manage this randomness and complexity? The recent progress we have seen in AI gives us a glimpse into what it would mean to do just that. Information can be split into a structured part and a random part. Take English for example. There is an underlying complex structure that describes the language, and the sentences that society has produced over time are, in effect, a random sampling from that structure. Recent advances in machine learning have allowed us to take these random samples and recover much of the orderly basics that inform.

I’ve been very lucky. I could build a research career around the machines that encompass the way I feel the world. Whether you hear the music, the algebra, computation, biology, magic, art, or some other way of understanding the world, listen to it. Who knows what secrets you may learn?

Ansell, Helen and Istvan Kovacs. Unveiling universal aspects of the cellular anatomy of the brain. Communications Physics. 7/184, 2024. . Northwestern University systems neuroscientists describe the latest neuroimaging insight findings which add strong support to a definitive self-organized, critically poised, invariance. They next view the relative neural architecture of other mammals and onto insects to observe the same definitive patterns and processes.

Recent cellular volumetric brain reconstructions have revealed even higher levels of anatomic complexity. But which aspects to focus on when by way of computational models remains a challenge. Our own work has now been able to perceive an intricate brain anatomy satisfies universal scaling laws to an extent as to reveal a structural criticality. To illustrate, we estimated critical exponents in human, mouse and fruit fly brains and show they are consistent between these organisms. Such universal quantities are robust to many microscopic details of the cellular structures of individual brains. This is a key step towards generative computational approaches and toward which sense one animal may be akin to another. (Abstract)

In regard, neuronal complexity can be described through its fractal dimension which exemplifies a scale invariance, or self-similarity which occurs in the structure and function of the cerebral cortex, human connectome, and synaptic network of multiple organisms. We next propose that statistical physics can provide a further guide to discern cellular complexity. An analysis of cell size, as well as pairwise and higher-order correlations, can then signify collective phenomena close to criticality. We estimate a set of exponents from for each subject organism and find critical scaling relations, again indicating that brains reside in the vicinity of criticality. (1,2)

Cai, Chao-Ran, et al. Epidemic criticality in temporal networks. Physics Reviews Research. 6/L022017 April, 2024. Northwest University, China, Shaanxi Key Laboratory for Physics Frontiers, Xi’an, China and Aalto University, Espoo, Finland (Petter Holme) theorists discern the deep presence of self-organized critical transitions even in public phenomena such as disease vectors amongst diverse, many body populations.

Analytical studies of network epidemiology often focus on the extreme situations where the timescales of network dynamics are well separated (longer or shorter) from that of propagation. In realistic scenarios, however, these timescales could be similar, which has profound implications for modeling. Combining Monte Carlo simulations and mean-field theory, we analyze the behavior of susceptible-infected epidemics in the vicinity of the critical threshold of temporal networks. Dynamic correlations from being close to infected nodes increases the likelihood of infection and drive the state in the opposite direction. (Excerpt)

Gonda, Tomas, et al. A Framework for Universality in Physics, Computer Science, and Beyond.. arXiv:2307.06851. This is a specific notice to date by University of Innsbruck and Technical University of Munich mathematicians including Gemma De les Coves as our 21st century worldly scientific revolution comes to realize a common evidential occurrence across the atomic, cosmic and personal infinities. A main emphasis on computational methods is then found to hold for quantum spin models, linguistic grammar, neural networks, and elsewhere. See also an introductory overview by this group at arXiv:2406.16607.

Turing machines and spin models share a notion of universality according to which some simulate all others. Is there a theory of universality that captures this notion? We set up a categorical framework for universality which includes as instances universal Turing machines, universal spin models, NP completeness, top of a preorder, denseness of a subset, and more. By identifying necessary conditions for universality, we show that universal spin models cannot be finite. We also characterize when universality can be distinguished from a trivial one and use it to show that universal Turing machines are non-trivial in this sense. Our framework allows not only to compare universalities within each instance, but also instances themselves.

Ken, Megan, et al. RNA conformational propensities determine cellular activity. Nature. 617/835, 2023. We post this entry by Duke University School of Medicine, Johns Hopkins University, Gladstone Institute of Virology, San Francisco, Stanford University, and Columbia University (Hashim Al-Hashimi) bioresearchers for itself and to record its constant citation of “propensities” to necessarily enact vital nucleotide biomolecular metabolic processes. In regard, such a perception can move beyond abstract “mechanisms” to a deeper sense of an innately organic procreativity.

Cellular processes are due to interactions between biomolecules and intermolecular contacts, which if disrupted, alter cell physiologies. As a result, binding affinity and cellular activity crucially depend both on the strength of the contacts and on the inherent propensities to form binding-competent conformational states. Here we altered the propensities for forming the protein-bound conformation of HIV-1 TAR RNA and the extent of HIV-1 Tat-dependent transactivation in cells. Our results establish the role of ensemble-based conformational propensities in cellular activity. (Excerpt)

Liu, Shuming, et al. From Nucleosomes to Compartments: Physicochemical Interactions Underlying Chromatin Organization. Annual Review of Biophysics.. Volume 53, 2024. MIT system biologists add a latest chapter about life’s serial metabolic developments which can be traced to informative and topological genomic expressions. See also The Geometry of Chromatin by Subhash Kak at arXiv:2402.09408.

Chromatin organization plays a critical role in cellular function by regulating access to genetic information. However, its folding is hard to analyze due to a complex, multiscale nature. Advances have been made in vitro systems, individual nucleosomes, and the role of physicochemical forces in stabilization. But the resemblance between in vitro and in vivo chromatin conformations and internucleosomal interactions are subjects of debate. This article reviews experimental and computational studies which highlight intrinsic interactions between nucleosomes and their roles in chromatin folding. (Abstract).

Chromatin is a complex of DNA and protein found in eukaryotic cells.[1] The primary function is to package long DNA molecules into compact, denser structures. A nucleosome is a section of DNA that is wrapped around a core of proteins.

Mazzucato, Camilla et al.. "A network of mutualities of being": socio-material archaeological networks and biological ties at Çatalhöyük.. arXiv:2406.19149.. University of Copenhagen and Middle East Technical University, Ankara paleogeneticists add a further dimension to retrospective studies with regard to social and cultural aspects by an ability to identify relative interactivities.

Recent advances in archaeogenomics have the potential to further our understanding of past social dynamics at a range of scales. In this paper we propose a Network Science framework to study and integrate genomic data and material culture about biological relatedness and social organization at the Neolithic site of Çatalhöyük. Methodologically, we propose the use of network variances to investigate the concentration of biological relatedness and material culture within neighborhood dwellings. This approach allowed us to observe how material culture similarity between residences gives valuable information on relationships between individuals and how biogenetic ties concentrate at specific localities.

Fang, Jing-Kai, et al. Divide-and-Conquer Quantum Algorithm for Hybrid de novo Genome Assembly of Short and Long Reads. PRX Life. 2/023006, 2024. We note this contribution by BGI Research, Shenzhen, China computational geneticists as a frontier instance of how genetic studies are being taken to a new dimension by virtue of quantum capabilities. The evidential result implies that life’s implicate genomic proscription can gain an affinity with this fundamental physical ground.

Researchers have begun to apply quantum computing in genome assembly implementation, but the issue of repetitive sequences remains unresolved. Here, we propose a hybrid assembly quantum algorithm using short reads and long reads which utilizes divide-and-conquer strategies to approximate the ground state of a larger Hamiltonian while conserving quantum resources. The convergence speed is improved via the problem-inspired Ansatz based on the known information. In addition, we verify that entanglement within quantum circuits may accelerate the assembly path optimization. (Excerpt)

Hwang, Yunha, et al. Genomic language model predicts protein co-regulation and function. Nature Communications.. 15/2880, 2024. We enter this work by Cornell, Harvard, Johns Hopkins, and MIT biologists including Sergey Ovchinnikov as another literate version of the textual affinity of nucleotides and narratives. See also ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering at arXiv:2405.06658.

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Deciphering the relationship between a gene and its genomic context is vital to understand and modify biological systems. Machine learning can study the sequence-structure-function paradigm but higher order genomic information remains elusive. Evolutionary processes dictate genomic contexts in which a gene occurs across phylogenetic distances, and these emergent patterns can be leveraged to uncover functional relationships. Here, we train a genomic language model (gLM) on metagenomic scaffolds to uncover regulatory relationships between genes. Our findings illustrate that gLM’s deep learning of metagenomes is an effective approach to encode the semantics and syntax of genes and uncover complex relationships in a genomic region. (Abstract)

The unprecedented amount and diversity of metagenomic data presents opportunities to learn hidden patterns and structures of biological systems. With larger amounts of data, these models can disentangle the complexity of organismal genomes and their encoded functions. The work presented here validates the concept of genomic language modeling. Our implementation of the masked genomic language modeling illustrates the feasibility of training such a model, and provides evidence that biologically meaningful information is being captured in learned contextualized embeddings. (9)

Fang, Jing-Kai, et al. Divide-and-Conquer Quantum Algorithm for Hybrid de novo Genome Assembly of Short and Long Reads.. PRX Life. 2/023006, 2024. We note this contribution by BGI Research, Shenzhen, China computational geneticists as a frontier example of how genetic studies are being taken to a new dimension by virtue of quantum capabilities. An evidential result implies that life’s genomic proscription can gain an affinity with this fundamental physical phase.

Researchers have begun to apply quantum computing in genome assembly implementation, but the issue of repetitive sequences remains unresolved. Here, we propose a hybrid assembly quantum algorithm using short reads and long reads which utilizes divide-and-conquer strategies to approximate the ground state of a larger Hamiltonian while conserving quantum resources. The convergence speed is improved via the problem-inspired Ansatz based on the known information. In addition, we verify that entanglement within quantum circuits may accelerate the assembly path optimization. (Excerpt)

Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

MacIver, Malcolm and Barbara Finlay. The neuroecology of the water-to-land transition and the evolution of the vertebrate brain. Philosophical Transactions of the Royal Society B. December, 2021. Veteran Northwestern University and Cornell University evolutionary neuroscientists make a case that this epochal movement of aquatic creatures onto dry, sunlit surfaces played a much more paramount role in life’s emergence than previously seen.

The water-to-land transition in vertebrate evolution offers a unique opportunity for computational affordances and a new ecology for the brain. As a result, a much enlarged visual sensorium owing to air versus water as a medium, then led to mobile eyes and neck. In water, the midbrain tectum coordinates approach/avoid decisions, due to water flow and the bodily state and learning. On land, the relative motions of sensory surfaces and effectors must be resolved, adding on computational architectures from the dorsal pallium. For the large-brained and long-living denizens, making the right decision allows animals to learn from experience. Integration of memorized panoramas in the basal ganglia/frontal cortex becomes a substantial cognitive habit-to-plan benefit. (Excerpt)

Linden-Santangeli, Nathaniel, et al. Increasing certainty in systems biology models using Bayesian multimodel inference.. Linden-Santangeli, Nathaniel, et al. Increasing certainty in systems biology models using Bayesian multimodel inference. arXiv:2406.11178.. UC San Diego bioscientists show how to integrate this popular research procedure with an holistic sense of metabolic vitalities.

Mathematical models are a good way to study the structure and behavior of intracellular signaling networks. As a result, the same signal pathway can be represented by multiple models, each with underlying assumptions. Here, we use Bayesian inference to develop a way to achieve increasing certainty. A case study of extracellular regulated kinase (ERK), we show that multimodel inference enhances predictive accuracy. Finally, we use multimodel inference to explain sub-cellular location-specific ERK activity dynamics. (Excerpt)

Pérez-Mercader., Juan. Making Biochemistry-Free Life in a Test Tube.. Di Mauro, Ernesto, ed.. The First Steps of Life. Wiley Online, 2023. In this chapter, a senior scientist (see below) now at Origins describes evidential results which may well imply an intrinsic organism-like ecosmic fertility. See also Competitive exclusion principle among synthetic non-biochemical protocells by Sai Krishna Katla, et al (J P-M) in Cell Reports Physical Science (4/4, 2024).

As we discover many extrasolar planets it is time to ask: Is biochemistry-based life the only chemical support for life? On Earth, all living systems (i) process information, (ii) metabolize, (iii) self-reproduce and (iv) evolve. But can processes (i)-(iv) take place in a non-biochemical chemical system? We present progress resulting from experiments on a reaction during the non-equilibrium synthesis of functional polymer vesicles from small, non-biochemical molecules. Their dynamical evolution integrates metabolism, growth, reproduction, and descent with modification by implementing a polymerization induced self-assembly (PISA) scenario. Together, these results offer insights into generic chemistry-based artificial life, as well as into the origin of proto-cells enroute to proto-life and pre-LUCA living systems. (Abstract)

Juan Pérez-Mercader earned his Ph.D. from the City College of New York. In 1998 he founded Spain's Centro de Astrobiología as its first Director. In 2010, he joined Harvard in the Earth and Planetary Sciences and Origins of Life Initiative, where he leads a project on the "Top-down Synthesis of an Ex-novo Chemical Artificial Living System". Some of his Research Interests are Physics of Self-organizing Behavior, Information in Non-equilibrium, Physico-chemical Systems, Chemical Computation, Quantum Field Theory, and Dynamical Renormalization Group,

D'Eugeno, Francesco, et al. D'Eugenio, Francesco, et al. JADES: Carbon enrichment 350 Myr after the Big Bang in a gas-rich galaxy.. arXiv:2311.09908. As the awesome Webb telescope continues to send fantastic images from the outermost reaches of space and time, UK, USA, Australia, France, Italy, Germany, Japan, Spain report a deeper probe into the onset appearance of metallic elements and compounds which can innately engender a fertile prebiotic milieu. Into 2024 then as one may be a viewer of this ecosmic planetarium, the whole show seems to have an essential, independent life and mind of its own

Finding the first generation of metals in the early Universe and identifying their origin is an important goal of modern astrophysics. In regard, we report deep JWST/NIRSpec spectroscopy of a GS-z12 galaxy as the most distant detection of a metal transition and redshift via emission lines. We derive a super-solar carbon-to-oxygen ratio higher than the C/O measured in galaxies discovered by JWST, and higher than Type-II supernovae enrichment. Such a high C/O in a galaxy observed 350 Myr after the Big Bang may be explained by the yields of metal poor stars, and may even be the heritage of the first generation of supernovae from Population III progenitors. (Excerpt)

The appearance of the first galaxies marks a key phase transition of the Universe with the start of stellar nucleosynthesis and the diffusion of metals. Extensive theoretical work has been devoted to predicting the properties of the first generation of stars and their supernova yield. The launch of JWST enabled, for the first time, the measurement of the physical properties of galaxies. These are generally understood in terms of decreasing gas metallicity and increasing density, ionisation parameter, temperature and stochasticity of their star-formation histories. (1)

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