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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Generative Code-Script Source

5. Common Code: A Further Report of Reliable, Invariant Principles

Levin, Simon. The Evolution of Ecology. The Chronicle Review. August 13, 2010. The Princeton ecologist notes how this environmental science, since a systems view was necessary from its inception, has grown to be an exemplary model for studies across the ranges of nature and society.

Ecology views biological systems as wholes, not as independent parts, while seeking to elucidate how the wholes emerge from and affect the parts. Increasingly, such a holistic perspective, rechristened at places like the Santa Fe Institute as "the theory of complex adaptive systems," has informed understanding and improved management of economic and financial systems, social systems, complex materials, and even physiology and medicine. Essentially, that means little more than taking an ecological approach to such systems. (13)

Locey, Ken and Jay Lennon. Scaling Laws Predict Global Microbial Diversity. Proceedings of the National Academy of Sciences. 113/5970, 2016. Indiana University biologists attest to the natural presence of a consistent mathematical organization across a widest range of bacterial entities and colonies.

Ecological scaling laws are intensively studied for their predictive power and universal nature but often fail to unify biodiversity across domains of life. Using a global-scale compilation of microbial and macrobial data, we uncover relationships of commonness and rarity that scale with abundance at similar rates for microorganisms and macroscopic plants and animals. We then show a unified scaling law that predicts the abundance of dominant species across 30 orders of magnitude to the scale of all microorganisms on Earth. Using this scaling law combined with the lognormal model of biodiversity, we predict that Earth is home to as many as 1 trillion (1012) microbial species. (Significance)

Luque, Jordi, et al. Speech Earthquakes: Scaling and Universality in Human Voice. arXiv:1408.0985. We cite this report by Spanish and British researchers as a good example of current discoveries of how the same generic patterns and processes recur across the widest domains from peoples to the planet. As the quote describes, disparate phenomena such as conversation and seismic events are yet found to exemplify and repeat in kind everywhere, which quite implies a universal, independent, mathematical source.

Speech is a distinctive complex feature of human capabilities. In order to understand the physics underlying speech production, in this work we empirically analyse the statistics of large human speech datasets ranging several languages. We first show that during speech the energy is unevenly released and power-law distributed, reporting a universal robust Gutenberg-Richter-like law in speech. We further show that such earthquakes in speech show temporal correlations, as the interevent statistics are again power-law distributed. Since this feature takes place in the intra-phoneme range, we conjecture that the responsible for this complex phenomenon is not cognitive, but it resides on the physiological speech production mechanism.

Moreover, we show that these waiting time distributions are scale invariant under a renormalisation group transformation, suggesting that the process of speech generation is indeed operating close to a critical point. These results are put in contrast with current paradigms in speech processing, which point towards low dimensional deterministic chaos as the origin of nonlinear traits in speech fluctuations. As these latter fluctuations are indeed the aspects that humanize synthetic speech, these findings may have an impact in future speech synthesis technologies. Results are robust and independent of the communication language or the number of speakers, pointing towards an universal pattern and yet another hint of complexity in human speech. (Abstract)

Magliocca, Nicholas, et al. Modeling Cocaine Traffickers and Counterdrug Interdiction Forces as a Complex Adaptive System. Proceedings of the National Academy of Sciences. Early online April 1, 2019. Eight systems geographers posted in Alabama, Arizona, Wyoming, Texas, Oregon, and Ohio identify a common mathematical patterning that even criminal chaos seems to hold to and be constrained by. Our interest extends to a concurrent paper, Structure, Spatial Dynamics of Novel Seed Dispersal Mutualistic Networks in Hawaii (Visentin herein), which notes similar structuring dynamics across ecosystems. Within our 21st century scan, it is increasingly evident to a point of proof and discovery that an independent generative source is in exemplary presence everywhere.

The US government’s cocaine interdiction mission in the transit zone of Central America is now in its fifth decade despite its long-demonstrated ineffectiveness, both in cost and results. We developed a model that builds an interdisciplinary understanding of the structure and function of narco-trafficking networks and their coevolution with interdiction efforts as a complex adaptive system. The model produced realistic predictions of where and when narco-traffickers move in and around Central America in response to interdiction. The model demonstrated that narco-trafficking is as widespread and difficult to eradicate as it is because of interdiction, and increased interdiction will continue to spread traffickers into new areas, allowing them to continue to move drugs north. (Significance)

Makarieva, Anastassia, et al. Mean Mass-Specific Metabolic Rates are Strikingly Similar Across Life's Major Domains: Evidence for Life's Metabolic Optimum. Proceedings of the National Academy of Sciences. 105/16004, 2008. An international research team finds, as the quotes aver, a consistent, universal repetition across all spatial and evolutionary natural taxa. From our late global vantage might it seem in retrospect the entirety of earth life appears as a single developing organism?

A fundamental but unanswered biological question asks how much energy, on average, Earth's different life forms spend per unit mass per unit time to remain alive. Here, using the largest database to date, for 3,006 species that includes most of the range of biological diversity on the planet—from bacteria to elephants, and algae to sapling trees—we show that metabolism displays a striking degree of homeostasis across all of life. We demonstrate that, despite the enormous biochemical, physiological, and ecological differences between the surveyed species that vary over 1020-fold in body mass, mean metabolic rates of major taxonomic groups displayed at physiological rest converge on a narrow range from 0.3 to 9 W kg−1. (16994)

We have demonstrated that across dramatically different life forms, mean mass-specific metabolic rates converge on a relatively narrow range that is striking in contrast to the 20 orders of magnitude difference in the body mass of the studied species. This remarkable and previously unappreciated phenomenon is likely associated with the pervasive biochemical universality of living matter. (16999)

Marcus, Gary. Startling Starlings. Nature. 440/1117, 2006. A review of a research article in the same issue by Timothy Gentner, et al: Recursive Syntactic Pattern Learning by Songbirds. Recursion, or hierarchical self-embedding, was long thought to distinguish human language. In this study, the nested building-up of intricate communication is found to also occur in avian species. And the universally recurrent, code-like, pattern of emergence appears again in regnant speech.

Marquet, Pablo, et al. Scaling and Power-laws in Ecological Systems. Journal of Experimental Biology. 208/9, 2005. Seven authors from the Pontificia Universidad Catolica de Chile, Universidad de Concepcion, Chile, and the Santa Fe Institute, describes how the same dynamic phenomena are found to repeat across a wide range of hierarchical levels. Universally recurrent patterns and processes are now seen to characterize an increasingly intelligible Nature because of this attribute. The entire journal issue is devoted to scaling in biology and ecology.

During the past decade or so, several empirical and theoretical investigations have suggested that biological systems in general, and ecological systems in particular, seem to operate near a critical state, which results in the ubiquity of power-law behavior in several descriptors of their dynamics and might even belong to the same universality class as other complex systems such as economic systems. Thus the analysis of power-law and scaling relationships can help us to identify general principles that apply across a wide range of scales and levels of organizations, revealing the existence of universal principles within the seeming idiosyncratic nature of ecological systems. (1750)

This non-trivial rescaling suggests that in spite of differences in body size, life history and ecology, all the species under study fall along a single power-law relationship, which suggests that they share a common universal probability density distribution of growth rates. This powerful statement is further strengthened by the fact that this universal distribution is a power-law. (1759-1760) This strongly suggests that there may indeed exist universal principles that underlie the growth dynamics of complex adaptive systems that are involved in the acquisition, transformation and storage of information, materials and/or energy. (1760)

Mondal, Shrabani, et al. Universal Dynamic Scaling in Chemical Reactions At and Away from Equilibrium. arXiv:2101.01613. UM Boston, Center for Quantum and Nonequilibrium Systems contribute to on-going endeavors which are finding the expansive presence of an invariant, active self-similarity beyond the usual realms of living phenomena. See also On the Conditions for Mimicking Natural Selection in Chemical Systems by Gregoire Danger, et al in Nature Reviews Chemistry (4/102, 2020) for another example. As these efforts grow an spread in the 2020s, they achieve still deeper evidence for an organic fertility with an independent generative source.

Physical kinetic processes are known to exhibit universal scaling of observables that fluctuate in space and time. Are there analogous dynamic scaling laws that are unique to the chemical reaction mechanisms available to and natural and synthetic conditions? Here, we formulate two complementary approaches to the dynamic scaling of stochastic fluctuations in thermodynamic phenomena at and away from an equilibrium state. A survey of common chemical mechanisms reveals classes that organize according to the molecularity of the reactions, (non) equilibrium phases, and the extent of autocatalysis in the reaction network. Altogether, these results establish dynamic universality for the thermodynamic fluctuations well-mixed chemical reactions. (Abstract excerpt)

Universal scaling behavior has been found in biochemical networks [8], the stochastic exponential growth and division of bacterial cells [9, 10], the growth of human cancers [11], and dissipative self-assembly [12]. Formal analogies have expanded the scope of kinetic roughening theory [13, 14] even further by treating the fluctuations of mathematical functions as surrogates for the physical interface [1]. Examples include biological systems such as DNA [15], complex networks [16], crudeoil prices [17], heartbeat signals [18], strongly interacting gases [19], and material fracture [20). (1)

Universal behaviors have been extensively explored for physical phenomena, and here we have shown that universal dynamical scaling extends to the thermodynamic observables of chemical phenomena at and away from equilibrium. These observables satisfy three interconnected dynamic scaling classes we have tested of chemistry from simple, elementary reactions to complex, coupled autocatalytic reactions. Dynamical universality classes are typically determined by the dimensionality, conservation laws, symmetry of the order parameter, range of the interactions, and the coupling of the order parameter to conserved quantities. (16)

Moret, Marcelo. Self-Organized Critical Model for Protein Folding. Physica A. In Press, April, 2011. In accord with contemporary papers that report upon quantum to neural and astral dynamic complexities, a Brazilian biophysicist finds the realm of protein topologies to similarly exhibit a fractally scaled self-organized criticality.

We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. (Abstract)

Mumford,, David, et al. Indra’s Pearls. Cambridge: Cambridge University Press, 2002. A visually impressive book to convey how fast computers can now give colorful exposition to the intricate mathematics of the early 20th century, especially those of Felix Klein. By these insights and methods, an intricate fractal self-similarity seems to pervade nature at every scale. These findings are next seen to affirm the ancient Buddhist vision of reality as a net or web of jewels whence the entire universe is reflected in each pearl. These equations and images convey a universally repeated interrelationship, a mutual identity, among every domain and member of the cosmos.

Making a statement equally faithful to both mathematics and religion, we can say that each part of our pictures contains within itself the essence of the whole. (xix)

Nacher, Jose and T. Ochiai. Emergent Principles in Gene Expression Dynamics. Open Bioinformatics Journal. 5/34, 2011. We place this online contribution by Future University-Hakodate, Complex and Intelligent Systems, and Toyama Prefectural University, Japan, bioengineers in Universal Principles as an example of how complex network phenomena is being found to infuse this genomic domain, and as many papers today wherein the authors trace their exemplary presence and role to an implied independent, archetypal source. By so doing, akin to more articles in Nature Scientific Reports and such journals, a once and future doubleness is verified of a manifest phenotype, and these waxing admissions of an informative genotype.

Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emergence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to reconstruct, characterize and complete gene networks. (Abstract)

Universality in Systems Biology The molecular interactions within a cell are very complex and their direct study poses enormous difficulties from experimental and theoretical view point. However, the cell is not the only example of complexity. We are surrounded by many disparate complex systems like, for example, financial systems, social networks, fluid dynamics and Internet evolution. In these cases, it is simply impracticable to solve and predict the behavior of single stock prices, individuals, water atoms and web pages, respectively. In spite of that, these systems often show a remarkably simple behavior and commonalities. (35)

Criticality If we think in terms of atomic matter, fluids or even larger-scales like social networks, populations, cities or ecosystems, we observe that these systems are composed of multiple fundamental elements or individuals that interplay by means of physical forces, social relationships or information exchange. While these interactions are originated by intrinsic features of the systems, external forces, like electromagnetic and gravitational fields, social rules as well as drastic and severe climate changes, may also drive the evolution of the system. An intriguing phenomena is that even though intrinsic and extrinsic forces co-exist, it seems that systems share a high degree of commonality and behavior, which seems to be independent of the nature and details of the system itself. (36)

Nakamura, Eita and Kunihiko Kaneko. Statistical Evolutionary Laws in Music Styles. arXiv:1809.05832. Kyoto University and University of Tokyo complexity theorists perceive and quantify the same stochastic motifs and movements within musical compositions as those that are constantly present across life’s developmental course. See also Higor Sigaki, et al (below) for similar appearances throughout artistic schools.

If a cultural feature is transmitted over generations and exposed to stochastic selection when spreading in a population, its evolution may be governed by statistical laws, as in the case of genetic evolution. Music exhibits steady changes of styles over time, with new characteristics developing from traditions. Recent studies have found trends in the evolution of music styles, but little is known about quantitative laws and theories. Here we analyze Western classical music data and find statistical evolutionary laws.. The model reproduces the observed statistical laws and its predictions are in good agreement with real data. We conclude that some trends in music culture can be formulated as statistical evolutionary laws and explained by the evolutionary model incorporating statistical learning and the novelty-typicality bias. (Abstract)

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