搜索结果：Evolution letters

We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy-tailed Student's t-distribution with proportions (68% deleterious, 19% neutral, 13% beneficial for major ablations, n=568) that place AI between compact viral genomes and simple eukaryotes. The DFE shape matches D. melanogaster (normalized KS=0.07) and S. cerevisiae (KS=0.09); the elevated beneficial fraction (13% vs. 1-6% in biology) quantifies the advantage of directed over blind search while preserving the distributional form. Architectural origination follows logistic dynamics (R^2=0.994) with punctuated equilibria and adaptive radiation into domain niches. Fourteen architectural traits were independently invented 3-5 times, paralleling biological convergences. These results demonstrate that the statistical structure of evolution is substrate-independent, determined by fitness landscape topology rather than the mechanism of selection.

According to the "hard-steps" model, the origin of humanity required "successful passage through a number of intermediate steps" (so-called "hard" or "critical" steps) that were intrinsically improbable with respect to the total time available for biological evolution on Earth. This model similarly predicts that technological life analogous to human life on Earth is "exceedingly rare" in the universe. Here, we critically reevaluate the core assumptions of the hard-steps model in light of recent advances in the Earth and life sciences. Specifically, we advance a potential alternative model where there are no hard steps, and evolutionary novelties (or singularities) required for human origins can be explained via mechanisms outside of intrinsic improbability. Furthermore, if Earth's surface environment was initially inhospitable not only to human life, but also to certain key intermediate steps in human evolution (e.g., the origin of eukaryotic cells, multicellular animals), then the "delay" in the appearance of humans can be best explained through the sequential opening of new global environmental windows of habitability over Earth history, with humanity arising relatively quickly o

In this article, I put forward the idea that the neoplastic process (NP) has deep evolutionary roots and make specific predictions about the connection between cancer and the formation of the first embryo, which allowed for the evolutionary radiation of metazoans. My main hypothesis is that the NP is at the heart of cellular mechanisms responsible for animal morphogenesis and, given its embryological basis, also at the center of animal evolution. It is thus understood that NP-associated mechanisms are deeply rooted in evolutionary history and tied to the formation of the first animal embryo. In my consideration of these arguments, I expound on how cancer biology is perfectly intertwined with evolutionary biology. I describe essential cellular components of unicellular holozoans that served as a basis for the formation of the neoplastic functional module (NFM) and its subsequent exaptation, which brought forth two great biophysical revolutions within the first embryo. Finally, I examine the role of Physics in the modeling of the NFM and its contribution to morphogenesis to reveal the totipotency of the zygote.

Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modelled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modelling both "smooth" conformational changes and "catastrophic" conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence-structure evolutionary motif present in a large number of

Evolution by natural selection can be seen an algorithm for generating creative solutions to difficult problems. More precisely, evolution by natural selection is a class of algorithms that share a set of properties. The question we address here is, what are the conditions that define this class of algorithms? There is a standard answer to this question: Briefly, the conditions are variation, heredity, and selection. We agree that these three conditions are sufficient for a limited type of evolution, but they are not sufficient for open-ended evolution. By open-ended evolution, we mean evolution that generates a continuous stream of creative solutions, without stagnating. We propose a set of conditions for open-ended evolution. The new conditions build on the standard conditions by adding fission, fusion, and cooperation. We test the proposed conditions by applying them to major transitions in the evolution of life and culture. We find that the proposed conditions are able to account for the major transitions.

Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of deep and reinforcement learning methods and summarizes their helpfulness for simulating language emergence. It also discusses the key findings, limitations, and recent attempts to build realistic simulations. This chapter targets linguists and cognitive scientists seeking an introduction to deep learning as a tool to investigate language evolution.

The Einstein evolution equations have been written in a number of symmetric hyperbolic forms when the gauge fields--the densitized lapse and the shift--are taken to be fixed functions of the coordinates. Extended systems of evolution equations are constructed here by adding the gauge degrees of freedom to the set of dynamical fields, thus forming symmetric hyperbolic systems for the combined evolution of the gravitational and the gauge fields. The associated characteristic speeds can be made causal (i.e. less than or equal to the speed of light) by adjusting 14 free parameters in these new systems. And 21 additional free parameters are available, for example to optimize the stability of numerical evolutions. The gauge evolution equations in these systems are generalizations of the ``K-driver'' and ``Gamma-driver'' conditions that have been used with some success in numerical black hole evolutions.

We present the Prime Focus Spectrograph (PFS) Galaxy Evolution pillar of the 360-night PFS Subaru Strategic Program. This 130-night program will capitalize on the wide wavelength coverage and massive multiplexing capabilities of PFS to study the evolution of typical galaxies from cosmic dawn to the present. From Lyman alpha emitters at z~7 to probe reionization, drop-outs at z~3 to map the inter-galactic medium in absorption, and a continuum-selected sample at z~1.5, we will chart the physics of galaxy evolution within the evolving cosmic web. This article is dedicated to the memory of Olivier Le Fevre, who was an early advocate for the construction of PFS, and a key early member of the Galaxy Evolution Working Group.

Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that: traceability supports 11 maintenance and evolution activities, among which change management is the most frequently supported activity; strong empirical evidence from industry is needed to validate the impact of traceability on maintenance and evolution; easing the process of change management is the main benefit of deploying traceability practices; establishing and maintaining traceability links is the main cost of deploying traceability practices; 13 approaches and 32 tools that support traceability in maintenance and evolution were identified; improving the quality of traceability links, the performance of using traceability approaches and tools are the main traceability challenges in maintenance and evolution. The findings of this study provide a comprehensive understanding of deploying traceabi

The possibility of complicated dynamic behaviour driven by non-linear feedbacks in dynamical systems has revolutionized science in the latter part of the last century. Yet despite examples of complicated frequency dynamics, the possibility of long-term evolutionary chaos is rarely considered. The concept of "survival of the fittest" is central to much evolutionary thinking and embodies a perspective of evolution as a directional optimization process exhibiting simple, predictable dynamics. This perspective is adequate for simple scenarios, when frequency-independent selection acts on scalar phenotypes. However, in most organisms many phenotypic properties combine in complicated ways to determine ecological interactions, and hence frequency-dependent selection. Therefore, it is natural to consider models for the evolutionary dynamics generated by frequency-dependent selection acting simultaneously on many different phenotypes. Here we show that complicated, chaotic dynamics of long-term evolutionary trajectories in phenotype space is very common in a large class of such models when the dimension of phenotype space is large, and when there are epistatic interactions between the pheno

Environmental and genetic mutations can transform the cells in a co-operating healthy tissue into an ecosystem of individualistic tumour cells that compete for space and resources. Various selection forces are responsible for driving the evolution of cells in a tumour towards more malignant and aggressive phenotypes that tend to have a fitness advantage over the older populations. Although the evolutionary nature of cancer has been recognised for more than three decades (ever since the seminal work of Nowell) it has been only recently that tools traditionally used by ecological and evolutionary researchers have been adopted to study the evolution of cancer phenotypes in populations of individuals capable of co-operation and competition. In this chapter we will describe game theory as an important tool to study the emergence of cell phenotypes in a tumour and will critically review some of its applications in cancer research. These applications demonstrate that game theory can be used to understand the dynamics of somatic cancer evolution and suggest new therapies in which this knowledge could be applied to gain some control over the evolution of the tumour.

Software Visualization encompasses the development and evaluation of methods for graphically representing different aspects of methods of software, including its structure, execution and evolution. Creating visualizations helps the user to better understand complex phenomena. It is also found by the software engineering community that visualization is essential and important. In order to visualize the evolution of the models in Model-Driven Software Evolution, authors have proposed a framework which consists of 7 key areas (views) and 22 key features for the assessment of Model Driven Software Evolution process and addresses a number of stakeholder concerns. The framework is derived by the application of the Goal Question Metric Paradigm. This paper aims to describe an application of the framework by considering different visualization tools/CASE tools which are used to visualize the models in different views and to capture the information of models during their evolution. Comparison of such tools is also possible by using the framework.

Simulations of the coevolution of many interacting species are performed using the Webworld model. The model has a realistic set of predator-prey equations that describe the population dynamics of the species for any structure of the food web. The equations account for competition between species for the same resources, and for the diet choice of predators between alternative prey according to an evolutionarily stable strategy. The set of species present undergoes long-term evolution due to speciation and extinction events. We summarize results obtained on the macro-evolutionary dynamics of speciations and extinctions, and on the statistical properties of the food webs that are generated by the model. Simulations begin from small numbers of species and build up to larger webs with relatively constant species number on average. The rate of origination and extinction of species are relatively high, but remain roughly balanced throughout the simulations. When a `parent' species undergoes speciation, the `child' species usually adds to the same trophic level as the parent. The chance of the child species surviving is significantly higher if the parent is on the second or third trophic

The basic principles underlying galactic chemical evolution and the most important results of chemical evolution models are discussed. In particular, the chemical evolution of the Milky Way galaxy, for which we possess the majority of observational constraints, is described. Then, it is shown how different star formation histories influence the chemical evolution of galaxies of different morphological type. Finally, the role of abundances and abundance ratios as cosmic clocks is emphasized and a comparison between model predictions and abundance patterns in high redshift objects is used to infer the nature and the age of these systems.

A brief review of various methods to calculate radiative accelerations for stellar evolution and an analysis of their limitations are followed by applications to Pop I and Pop II stars. Recent applications to Horizontal Branch (HB) star evolution are also described. It is shown that models including atomic diffusion satisfy Schwarzschild's criterion on the interior side of the core boundary on the HB without the introduction of overshooting. Using stellar evolution models starting on the Main Sequence and calculated throughout evolution with atomic diffusion, radiative accelerations are shown to lead to abundance anomalies similar to those observed on the HB of M15.

In the present work we analyze the problem of adaptation and evolution of RNA virus populations, by defining the basic stochastic model as a multivariate branching process in close relation with the branching process advanced by Demetrius, Schuster and Sigmund ("Polynucleotide evolution and branching processes", Bull. Math. Biol. 46 (1985) 239-262), in their study of polynucleotide evolution. We show that in the absence of beneficial forces the model is exactly solvable. As a result it is possible to prove several key results directly related to known typical properties of these systems like (i) proof, in the context of the theory of branching processes, of the lethal mutagenesis criterion proposed by Bull, Sanjuán and Wilke ("Theory of lethal mutagenesis for viruses", J. Virology 18 (2007) 2930-2939); (ii) a new proposal for the notion of relaxation time with a quantitative prescription for its evaluation and (iii) the quantitative description of the evolution of the expected values in four distinct regimes: transient, "stationary" equilibrium, extinction threshold and lethal mutagenesis. Moreover, new insights on the dynamics of evolving virus populations can be foreseen.

Metals are ideal tracers of the baryonic cycle within halos. Their composition is a fossil record connecting the evolution of the various stellar components of galaxies to the interaction with the environment by in- and outflows. The Magneticum simulations allow to study halos across a large range of masses and environments, from massive galaxy clusters containing hundreds of galaxies down to isolated field galaxies. They include a detailed treatment of the chemo-energetic feedback from the stellar component and its evolution as well as feedback from the evolution of supermassive black holes. Following the detailed evolution of various metal species and their relative composition due to continuing enrichment of the IGM and ICM by SNIa, SNII and AGB winds of the evolving stellar population reveals the complex interplay of local star formation processes, mixing, global baryonic flows, secular galactic evolution and environmental processes. We present results from the Magneticum simulations on the chemical properties of simulated galaxies and galaxy clusters, carefully comparing them to observations. We show that the simulations already reach a very high level of realism within their

In celebration of the 2025 UN International Year of Quantum Science and Technology, this Resource Letter surveys the rapidly-growing field of scholarship in quantum information science and engineering (QISE) education. It is primarily written as a guide for educators wishing to get started teaching QISE using research-based teaching methods, as well as for discipline-based education research (DBER) practitioners looking to get started in this field. Topics covered include scoping the field of QISE education, research into student reasoning in QISE, research-based and research-inspired curricular materials from the high school to graduate level, research-based assessments, simulation and gamification tools, and tools for incorporating discussion of the societal and ethical implications of quantum technologies into the classroom.

In the recent paper by Teys [JETP Letters 105 (8), 477-483 (2017)], an atomic model for the Si(331) reconstructed surface (hereby referred to as T-model) was proposed on the basis of high-resolution scanning tunneling microscopy (STM) images. While detailing the virtues against previous and abandoned models, the author avoids any reference to the rather distinct 8P-model advocated few weeks earlier by Zhachuk and Teys [R. Zhachuk, S. Teys, Phys. Rev. B 95, 041412 (2017)], casting doubts to his own work. Formulated that way, findings from Ref. [JETP Letters 105 (8), 477-483 (2017)] leave readers of JETP Letters with a partial and confusing view of the problem, and above all, leaves the observations open to ambiguous interpretation. The 8P-model is also based on STM measurements, and unlike the T-model, passed through the scrutiny of first-principles calculations. The present comment reconciles Ref. [JETP Letters 105 (8), 477-483 (2017)] with the literature by supplementing the discussion with a missing and critical account on the stability and electronic structure of the T- versus 8P-models of Si(331).