Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 46 through 60 of 96 found.

Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

Di Marco, Niccolo, et al. Decoding Musical Evolution Through Network Science.. arXiv:2501.07557.. While most entries herein attest to the deep presence of complex, self-similar topologies in written prose, this entry by Sapienza University of Rome and University of Padova researchers proceeds to discern a similar intrinsic formative basis for multiplex melodious versions.

In this study, we use the latest Network Science to analyze musical complexity. Drawing on six macro-genres spanning four centuries, we represent each composition as a weighted directed network to study its structural properties. Our results show that Classical and Jazz compositions have higher complexity and melodic diversity than recently developed genres. This study highlights how digital tools and streaming platforms can shape musical evolution.

Monakhov, Sergei and Holger Diessel. Complex Words as Shortest Paths in the Network of Lexical Knowledge.. Cognitive Science. 48/11, 2024. Friedrich-Schiller University, Jena system linguists carry out a latest, comprehensive analysis of the English language to show how it is wholly characterized by complex network topologies and emergent behaviors. See also Composition as Nonlinear Combination in Semantic Space: A Computational Characterization of Compound Processing by Tianqi Wang and Xu Xu in this Journal (49/2, 2025) for similar findings in Chinese script. In regard, an extensive 2025 verification of this deeper, common ecode, textual dimension is again achieved, which then by turns implies a natural literary narrative.

Lexical models diverge on how to represent complex words. Under the morpheme-based approach, each morpheme is treated as a separate unit, while in the word-based methods, morphological structure is derived from complex words. In this paper, we propose a computational model for word-based networks to view how complex words are learned, stored, and processed. Our study shows that complex words can be segmented into morphemes through the shortest pathway and novel terms are often formed along optimal paths. Our empirical results are tested by a usage-based grammar which reveals that network science provides a deep language structure. (Excerpt)

ity of the network. In conclusion, network science provides a powerful framework for analyzing language. In this paper, we have focused on central aspects of morphological productivity. However, if we think of language as an encompassing network, the network approach can also be applied to many other phenomena in phonology, morphology, and syntax. This approach is consistent with the way psychologists and neuroscientists analyze the human mind and brain and resonates with the emergentist view of grammar. (28)

Gabriel, Nicholas, et al. Connecting the geometry and dynamics of many-body complex systems with message passing neural operators. arXiv:2502.15913. George Washington University and Brown University system mathematicians including Neil Johnson describe a real connection all the way from deep physical phenomena to cerebral and public realms by way of novel renormalization theories. Once again, a deep grounding in substantial, generative dynamics is achieved as they continuously instantiate and exemplify themselves everywhere.

The relationship between scale transformations and dynamics established by renormalization group techniques is a cornerstone is in effect from fluid mechanics to elementary particle physics. Integrating these methods into neural operators for many-body complex systems could enhance their utility and uncover a multiscale organization. In this regard, we introduce a scalable AI framework, ROMA (Renormalized Operators with Multiscale Attention), for learning evolution operators and apply it to large systems with 1M nodes, long-range interactions, and Kuramoto oscillators. (Excerpt)

Hecker, Nikolai, et al. Enhancer-driven cell type comparison re. Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium. Science. February 14, 2025. Twenty VIB Center for AI & Computational Biology. Leuven, Belgium biologists apply the latest neuroimage techniques to discern a constant recurrence of genomic sources and cerebral architectures throughout the Metazoan creatures. Once again our Earthumen acumen reveals how nature’s long developmental course reuses in kind the same patterns and processes.

Despite vast diversity in behavior and cognition, a consistent similarity in brain structures and even gene expression is being found to exist across the amniote group of reptiles, birds, and mammals. Three papers in this issue explore the development and evolution of the brain telencephalon. Rueda-Alana et al used single-cell resolution and mathematical modeling to investigate sensory circuits in chicken, gecko, and mouse. Zaremba et al generated a spatial cell atlas in chicken pallium. Hecker et al developed deep learning models to identify telencephalon cell types in chicken, human, and mouse. (Editorial)

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to compare cell types in the telencephalon across amniotes. To this end, we resolved transcriptomics data of the chicken telencephalon. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. (Hecker Excerpt)

Poggialini, Anna, et al. Networks with many structural scales: a Renormalization Group perspetive. arXiv:2406.19104. We cite this work by Università “Sapienza” Rome and Universidad de Granada (Miguel Munoz) system physicists as an example of the increasing avail of this foundational theory in many far removed cerebral, bioregion and public phases. As the second quotes advises, by the mid 2020s such a consistently apt utiliety can then be seen to imply a true universal invariance. See also, e.g., Laplacian renormalization group: heterogeneous coarse-graining by Guido Caldarelli, et al in the Journal of Statistical Mechanics: (August 2, 2024). Gabriel, Nicholas, et al. Connecting the geometry and dynamics of many-body complex systems by Nicholas Gabriel, et al. at (arXiv:2502.15913).

Scale invariance profoundly influences the dynamics and structure of complex systems from critical phenomena to network architecture. Here, we propose a precise definition of scale-invariant networks by leveraging the concept of a constant entropy-loss rate in a renormalization-group coarse-graining setting. This approach differentiates between scale-free and scale-invariant networks, revealing characteristics within each class. We then survey genuine networks to show that the human connectome exhibits true scale invariance. (Excerpt)

The network paradigm captures essential attributes of real-world complex systems, offering a natural framework for studying entangled interconnected systems across disciplines like neuroscience [1], ecology [2], and epidemiology [3], among others [4]. Understanding the evolution-ary dynamics of complex networks, as they adapt their connectivity patterns to achieve diverse goals, is crucial to understanding their long-term stability or other features influencing functional roles and performance [5, 6]. Notably, amidst the multitude of potential network structures, one organization ubiquitously arises in natural systems: the scale-free architecture. (1)

Kilgore, Henry, et al. Protein codes promote selective subcellular compartmentalization. Science. February 6, 2025. In our novel phase of AI assisted computational biology, twelve researchers at the Whitehead Institute for Biomedical Research and Computer Science and Artificial Intelligence Laboratory, MIT describe a language based code-script model in addition to functional aspects which can now predict which bounded places they locate in.

Cells have evolved mechanisms to distribute billions of protein molecules to subcellular phases where they are involved in shared functions. Here, we show that these proteins convey amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts their localization from the training set. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution to diverse subcellular compartments. (Excerpt)

McBride, John and Tsvi Tlusty. McBride, John and Tsvi Tlusty. The physical logic of protein machines.. Journal of Statistical Mechanics. Vol. 2024/Num. 2, 2025. This paper by Center for Soft and Living Matter, Institute for Basic Science, Ulsan, South Korea theorists was presented at the STATPHYS 28 conference in 2024 as another way to combine neural net learning, proteome programs and AI language methods. We also note usage of the machine word whence it is meant to infer, so to clarify, a computer rather than a lathe. This is traced herein to a Simple mechanics of protein machines by Holger Flechsig and Alexander Mikhailov in the Journal of the Royal Interface for June 2019.

Proteins are intricate biomolecules whose complexity arises from the heterogeneity of the amino acids and their dynamic network of many-body interactions. Their functionality was shaped by an evolutionary history through intertwined paths of selection and adaptation. However, their basic logic remains open. Here, we explore a physical approach that treats proteins as mechano-chemical machines, which are adapted via a concerted evolution of structure, motion, and chemical interactions. (Excerpt)

Subirana-Granés, Marc, et al. Genetic Studies Through the Lens of Gene Networks.. Annual Review of Biomedical Data Science. February, 2025. Into the mid 2020s entries like this by University of Colorado, Anschutz Medical Campus researchers report how they are taking appropriate advantage of AI capabilities with regard to GWAS studies so to gain new levels of insight and functional benefit.

Genome-wide association studies have identified many variant–trait associations, but most are located in noncoding regions, making the link to biological function elusive. Here, we review approaches to leverage machine learning methods that identify gene modules by coexpression and functional relationships. This integration provides a context-specific understanding of disease processes and enhances the interpretability of genetic studies in personalized medicine. (Excerpt)

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

Bich, Leonardo. Biological Organization. Online: Cambridge University Press, 2024. A Research Professor at the University of the Basque Country provides a latest (December) thorough survey of the insightful clarity and benefits that can be gained by a holistic vista which also includes the internal interactive subsystems and their own closures.

Complex living systems are made of components that tend to degrade, but nonetheless maintain themselves far from equilibrium. This requires an extraction of energy and materials from the environment in ways that allow them to keep living. The philosophical and theoretical approach discussed in this Element aims to explain these features by appealing to their biological organization. It addresses philosophic issues from origins and definitions of life to teleology and functions from a perspective focused on the whole organism, its physiology and behavior, rather than evolution. (Summary)

Doolittle, W. Ford. Darwinizing Gaia: Natural Selection and Multispecies Community Evolution. Cambridge: MIT Press,, 2024. The author is an esteemed biologist who for many years was a professor at Dalhousie University in Nova Scotia. But he was notably resistant since the 1980s to this view that living systems can regulate and maintain themselves on a planetary scale. Now four decades later when evolutionary understandings have moved beyond Darwinian strictures (W. Veit, et al), a more considerate acceptance is indeed possible. However, in so doing most of the book is a latest, thorough survey to date of these theoretical frontiers. Ten chapters such as Unresolved Conflicts in Darwinism, Holobiosis, Extended Phenotypes, Replicators and Interactors and especially Evolutionary Transitions in Individuality present a topical discussion of our mid-paradigm shift mix (per T. Kuhn) of both vested and replacement versions.

In the 1970s, James Lovelock's Gaia Hypothesis proposed that living organisms developed in tandem with their inorganic surroundings so to form a complex, self-regulating system. But evolutionary biologists still consider the theory problematic. In Darwinizing Gaia, W. Ford Doolittle, one of evolutionary and molecular biology's most prestigious thinkers, reformulates what evolution by natural selection is while legitimizing the controversial Gaia Hypothesis. As the first book attempting to reconcile Gaia with Darwinian thinking, and the first on persistence-based evolution, Doolittle's clear, innovative position broadens evolutionary theory by offering potential remedies for Gaia's theoretical challenges

Lala, Kevin, et al. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton: Princeton University Press, 2024. Five veteran authors, KL (nee Laland), Tobias Uller, Nathalie Feiner, Marcus Feldman and Scott Gilbert orient and advance a once and future proposal that life’s personal occasion and maturation, aka eco evo devo, across the extent of Metazoan animals should take the place of life’s central explanatory basis.

A new scientific view of evolution is emerging that questions and expands our understanding of how evolution works. Recent research shows that organisms differ in how effective they are at evolving because the process itself has changed over time. In this book, a group of leading biologists draw on the latest findings in evo-devo studies, as well as epigenetics, symbiosis and inheritance to examine the central role that developmental processes play.

Sokolowski, Thomas, et al. Deriving a genetic regulatory network from an optimization principle. PNAS. 122/1, 2025. Institute of Science and Technology Austria and Princeton University computational theorists including William Bialek and Gašper Tkačik post a latest, full scale representation of life’s evolution as some manner of a persistent, episodic, iterative improvement. Although at an early stage, it is far from Darwin and may presage, at last, an insightful verification of life’s actual ecosmic orthogenesis

Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. Here, we explore a detailed model of the gap gene network in the Drosophila embryo, made to maximize the information that gene expression levels provide. Our framework quantifies the tradeoffs involved in functional behavior and allows for the exploration of alternative network configurations. Our results suggest that multiple solutions to the optimization problem might exist across related organisms. (Abstract)

Thacik, Gaspar and Pieter Rein ten Wolde. Information Processing in Biochemical Networks. Annual Review of Biophysics. February, 2025. Institute of Science and Technology, Austria and AMOLF, Amsterdam biotheorists suggest better ways to understand and include the vital metabolic conveyance of encoded, instructional content. In the course of elucidating this advance, they also contribute new appreciations of how natural evolution can be seen to proceed and develop by way a constant optimization procedure See also Deriving a genetic regulatory network from an optimization principle by Thomas Sokolowski, et al in PNAS (122/1, 2025).

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has engaged with the first two, addressing information issues has been harder to achieve. In regard, studies at the interface of biophysics, quantitative biology, and engineering have led to a mathematical language at the molecular scale. Here we review how flows of information through biochemical networks use information-theoretic data computed within modeling frameworks. Optimization is presented as a design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation.
(Excerpt)

AMOLF conducts leading research on the fundamental physics and design principles of natural and man-made animate complex matter. The institute applies this knowledge to create novel functional materials, and to solve societal challenges in renewable energy, green ICT, sustainable materials, and healthcare. AMOLF is home to some 150 scientists in 19 research groups and located at Amsterdam Science Park. See also the AMOLF Magazine on this site.

Veit, Walter, et al. Evolution, Complexity, and Life History Theory. Biological Theory.. January, 2025.
Along with new admissions of a teleologic course (see section), if common convergence, homology, transitions and more are factored in, a growing sense accrues that an oriented emergence seems to be going on by itself which does not square with the neoDarwinian view. This paper by University of Reading and Oxford University biologists is instance as a younger generation entering the theoretic fray. An earlier version was presented at the Paradox of the Organism meeting at Georgetown University in November 2022 (Google for much more) where similar concerns and proposals were broached.

As the evidence builds and the paradigm shifts see also Metabolic complementation between cells drives the evolution of tissues and organs by Mihaela Pavlicev, et al in Biology Letters (November 2024), Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity by Kevin Lala et al (Princeton 2024) and especially Darwinizing Gaia: Natural Selection and Multispecies Community Evolution by Ford Doolittle (MIT Press, 2024).

In this article, we revisit the longstanding debate of whether there is a pattern in the evolution of organisms towards greater complexity, and how this hypothesis could be tested using an interdisciplinary lens. We argue that this debate remains alive today due to the lack of a quantitative measure of complexity that is related to the teleonomic nature of living systems. We propose that an ideal method to quantify this complexity lies within life history strategies for they are under selection to optimize the organism’s fitness. In this context, we consider how this complexity can be measured mathematically, and how to engage in a comparative analysis of this complexity across species to investigate the evolutionary forces driving this orientation.

Pavlicev, Mihaela, et al. Metabolic complementation between cells drives the evolution of tissues and organs.. Biology Letters. November, 2024. As expansive evolutionary understandings continue to presage a 2020s genesis synthesis, University of Vienna, James DiFrisco, Francis Crick Institute, London, Alan Love, University of Minnesota and Günter Wagner, Yale University describe a further array of recurrent, nested transitional domains that appear to occur and distinguish within creaturely anatomies and physiologies.

Although evolutionary transitions of individuality have been extensively theorized, little attention has been paid to the origin of levels of organization within organisms. We propose a hypothesis for this version based on metabolic constraints on functional performance and the capacity for complementation between cells. We illustrate this process of ‘supra-functionalization’ using the nervous system and pancreas. (Excerpt)Although evolutionary transitions of individuality have been extensively theorized, little attention has been paid to the origin of levels of organization within organisms. We propose a hypothesis for this version based on metabolic constraints on functional performance and the capacity for complementation between cells. We illustrate this process of ‘supra-functionalization’ using the nervous system and pancreas. (Excerpt)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7  Next