The existence of multiple copies of genes is a well-known phenomenon. A gene family is a set of sufficiently similar genes, formed by gene duplication. In earlier works conducted on limited number of completely sequenced and annotated genomes it was found that size of gene family and size of genome are positively correlated. Additionally, it was found that several atypical microbes deviated from the observed general trend. In this study, we reexamined these associations on a larger dataset consisting of 1484 prokaryotic genomes and using several ranking approaches. We applied ranking methods in such a way that genomes with lower number of paralogs would have lower rank. Until now only simple ranking methods were used; we applied the Kemeny optimal aggregation approach as well. Regression and correlation analysis were utilized in order to accurately quantify and characterize the relationships between measures of paralog indices and genome size. In addition, boxplot analysis was employed as a method for outlier detection. We found that, in general, all paralog indexes positively correlate with an increase of genome size. As expected, different groups of atypical prokaryotic genomes were found for different types of paralog quantities.

Wrist shape varies greatly across primates and previous studies indicate that the numerous morphological differences among them are related to a complex mixture of phylogeny and function. However, little is known about whether the variation in these various anatomical differences is linked and to what extent the wrist bones vary independently. Here, we used 3D geometric morphometrics on a sample of extant hominids (Homo sapiens, Pan troglodytes, Gorilla gorilla, and Gorilla beringei), to find the model that best describes the covariation patterns among four of the eight carpals (i.e., capitate, lunate, scaphoid, and trapezium). For this purpose, 15 modular hypotheses were tested using the Covariation Coefficient. Results indicate that there is a covariation structure common to all hominids, which corresponds to stronger covariation within each carpal as compared to the covariation between carpals. However, the results also indicate that that there is a degree of codependence in the variation of some carpals, which is unique in humans, chimpanzees, and gorillas, respectively. In humans there is evidence of associated shape changes between the lunate and capitate, and between the scaphoid and trapezium. This covariation between lunate and capitate is also apparent in gorillas, while chimpanzees display the greatest disassociation among carpals, showing low covariation values in all pairwise comparisons. Our analyses indicate that carpals have an important level of variational independence which might suggest a high degree of independent evolvability in the wrists of hominids, and that although weak, the structure of associated changes of these four carpals varies across genera. To our knowledge this is the first report on the patterns of modularity between these four wrist bones in the Homininae and future studies might attempt to investigate whether the anatomical shape associations among carpals are functionally related to locomotion and manipulation.

In principle, modular or integral character of manufacturing lines depends on topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems, since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. Obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening knowledge and better understanding of the concept of optimal network modularity.

Developmental modularity has long been viewed as a hierarchical organization that facilitates evolution over macro-evolutionary time through modification or co-option of preexisting modules. More recently, developmental modularity has been proposed as a micro-evolutionary mechanism capable of driving rapid evolution of novel color pattern phenotypes between closely related taxa. In this scenario, swapping allelic variants of modular cis-regulatory elements (CREs) via recombination generates novel phenotypes by shuffling preexisting color pattern modules into new arrangements. Recent evidence from Drosophila and butterflies, however, provides a series of examples in which pleiotropic CREs function in multiple developmental contexts. The potential prevalence of pleiotropy in CRE function is a major barrier to the proposed evolutionary role of CRE modules and encourages us to reconsider the relative importance of modularity for microevolutionary change. Here we first review the case for the apparent frequent exchange of modular color pattern phenotypes as a mechanism facilitating diversification. We then contrast this with recent evidence of CRE pleiotropy and argue that exchange of CRE modules should not be the default assumption, even when phenotypes look modular. Finally, we review experimental data on Heliconius butterfly wing patterns—which appear modular—and introduce the concept of evolutionary modularity as an alternative to developmental modularity. Evolutionary modularity reconciles the appearance of modularity in comparative genomic studies of Heliconius color patterns with experimental data supporting a non-modular architecture. We propose that evolutionary modularity provides a potentially important pathway for exchange of phenotypic elements between hybridizing taxa independent of the underlying developmental architecture.

Fossils constitute the principal repository of data that allow for independent tests of hypotheses of biological evolution derived from observations of the extant biota. Traditionally, transformational series of structure, consisting of sequences of fossils of the same lineage through time, have been employed to reconstruct and interpret morphological evolution. More recently, a move toward an updated paradigm was fueled by the deliberate integration of developmental thinking in the inclusion of fossils in reconstruction of morphological evolution. The vehicle for this is provided by structural fingerprints – recognizable morphological and anatomical structures generated by (and reflective of) the deployment of specific genes and regulatory pathways during development. Furthermore, because the regulation of plant development is both modular and hierarchical in nature, combining structural fingerprints recognized in the fossil record with our understanding of the developmental regulation of those structures produces a powerful tool for understanding plant evolution. This is particularly true when the systematic distribution of specific developmental regulatory mechanisms and modules is viewed within an evolutionary (paleo-evo-devo) framework. Here, we discuss several advances in understanding the processes and patterns of evolution, achieved by tracking structural fingerprints with their underlying regulatory modules across lineages, living and fossil: the role of polar auxin regulation in the cellular patterning of secondary xylem and the parallel evolution of arborescence in lycophytes and seed plants; the morphology and life history of early polysporangiophytes and tracheophytes; the role of modularity in the parallel evolution of leaves in euphyllophytes; leaf meristematic activity and the parallel evolution of venation patterns among euphyllophytes; mosaic deployment of regulatory modules and the diverse modes of secondary growth of euphyllophytes; modularity and hierarchy in developmental regulation and the evolution of equisetophyte reproductive morphology. More generally, inclusion of plant fossils in the evo-devo paradigm has informed discussions on the evolution of growth patterns and growth responses, sporophyte body plans and their homology, sequences of character evolution, and the evolution of reproductive systems.

As the globe becomes more urban, the question how much natural biodiversity can subsist in cities becomes increasingly urgent to answer, and also how is urban diversity structured? To contribute to an answer, we studied the metacommunity of bees in a North European metropolitan area. The system consisted of 13 sites in the city of Aarhus, Denmark, censused from April to September during one year. Bees were sampled monthly in pan traps and a set of traits was related to their presence. In total, 40 species were collected–29 solitary species (40% of all sampled individuals), ten Bombus species (28%), and Apis mellifera (32%). Most sampled bees were soil-nesting, pollen generalists, and relatively common. Habitat diversity within a set of concentric circles with trap as centre and radius from 50 m to 1000 m was related to bee a diversity per site (total bee spp. with and without honeybee, solitary bees, and bumblebees). The relationship was only significant within the 1,000 m circle, and solitary bee diversity did not even correlate with habitat diversity at this large spatial scale level. The bee-site (space) and bee-month (time) networks were analyzed with respect to nestedness, modularity, and spatio-temporal b diversity. The two networks were weakly nested and strongly modular, being composed of five and six modules of tightly linked bees. Total b diversity, b_TOTAL, is the sum of species turnover, b_TURN, and species loss/gain or nestedness, b_NEST; and for both space and time, b_TURN was more important than b_NEST. Furthermore, b_TOTAL was higher than season b_TOTAL. Thus, the urban bee metacommunity was well-structured in both space and time and the high b_TURN indicated, that the network was strongly modular. We suggest the main reason is the high spatio-temporal patchiness of habitats, sustaining many unique species.

With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.

The apicomplexan parasite, Sarcocystis neurona causes the degenerative neurological equine protozoal myeloencephalitis (EPM) disease of horses. Due to its host range expansion, S. neurona is an emerging threat that requires close monitoring. In apicomplexans, protein kinases (PKs) have been implicated in a myriad of critical functions such as host cell invasion, cell cycle progression and host immune responses evasion. Here, we used various bioinformatics methods to define the kinome of S. neurona and phylogenetic relatedness of its PKs to other apicomplexans. Further, three-dimensional (3D) homology models for selected S. neurona putative PKs were constructed and evaluated for inhibitor docking. We identified 92 putative PKs clustering within the AGC, CAMK, CK1, CMGC, STE, TKL, aPK and OPK groups. Although containing the universally conserved PKA (AGC group), S. neurona kinome was devoid of PKB and PKC, but contained the six apicomplexan conserved CDPKs (CAMK group). The OPK group was represented by ROPKs 19A, 27, 30, 33, 35 and 37, but was devoid of the virulence-associated ROPKs 5, 6, 18 and 38. Two out of the three S. neurona CK1 enzymes had high sequence similarities to T. gondii TgCK1-α and TgCK1-β and the Plasmodium PfCK1. Docking of four inhibitors onto homology models of putative ROP27 and PKA indicated that inhibition of S. neurona PKs is feasible, but needs to be experimentally tested. The essentiality of apicomplexan PKs makes the elucidation of S. neurona kinome a key milestone for development of novel therapeutics for EPM.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Modularity and organizational hierarchy are important concepts in understanding the structure and evolution of interactions in complex biological systems. In this work, we introduce and use a spectral characterization measure (Spectral Entropy) to quantify modularity in protein-to-protein interaction (PPI) networks in species across the tree of life. We evaluated the relation between the size of a PPI network and its (Spectral Entropy-based) modularity, and found a sigmoidal response between the two. We also found significant differences in the distribution of Spectral Entropy values among the three domains of life (Bacteria, Archaea, Eukaryotes). To explore further correlations with biological traits, we focused solely on bacterial PPI networks, which are the most numerous among the three domains and had associated trait metadata, and investigated how modularity impacts or is impacted by growth, aerobicity, selection and location on the tree of life. We found no relation between maximal growth rate and Spectral Entropy, but a strong dependence between G-C content (a proxy for selection) and Spectral Entropy. We also discovered that Spectral Entropy is negatively affected by phylogenetic placement (evolutionary distance from the last universal common ancestor). The general nature of the Spectral Entropy measure of hierarchical modularity in networks suggests that it will be useful in other settings where structural properties of real-world networks are being compared.

Obsessive–compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insight of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown, with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need of additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances, while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and extraordinary supply of essential resources.

Attempts to find and quantify the supposed low entropy of organisms and its preservation are revised. Absolute entropy of the mixed components of non-living biomass (around -1.6 x 103 J K-1 L-1) is the reference to which other entropy decreases would be ascribed to life. Compartmentation of metabolites and departure from the equilibrium of metabolic reactions account for 1 and 40-50 J K-1 L-1, respectively, decreases of entropy and, though small, are distinctive features of living tissues. DNA and proteins do not supply significant decreases of thermodynamic entropy, but their low informational entropy is relevant for life and its evolution. No other living feature contributes significantly to the low entropy associated to life. The photosynthetic conversion of radiant energy to biomass energy accounts for the most of entropy (2.8 x 105 J K-1 carbon kg-1) produced by living beings. The comparative very low entropy produced in other processes (around 4.8 x102 J K-1 L-1 day-1 in human body) must be rapidly exported outside as heat to preserve the low entropy decreases due to compartmentation and non-equilibrium metabolism. Enzymes and genes are described whose control minimize the rate of production of entropy and could explain selective pressures in biological evolution and the rapid proliferation of cancer cells.

A review of the concept of "fitness" as it is used in evolutionary theory.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

We have shown that three astrometric solar-system anomalies can be explained satisfactorily by using evolutionary gravitational constant G and speed of light c in the Einstein’s field equation. These are: a) the Pioneer acceleration anomaly; b) the anomalous secular increase of Moon-orbit eccentricity; and c) the anomalous secular change in the astronomical unit AU. The gravitational constant G and the speed of light c both increase as dG/dt = 5.4GH₀ and dc/dt = 1.8cH₀ with H₀ as the Hubble constant. We also show that the Planck’s constant ħ increases as dħ/dt = 1.8ħH₀. Additionally, the new approach fits the supernovae Ia redshift vs distance modulus data as well as the standard ΛCDM model with just one adjustable parameter H₀.

Managing Water, Energy, and Food (WEF) resources within socio-environmental systems demands innovative strategies that provide decision-makers with actionable insights. This article explores the challenges and potential solutions of employing Focus Maps and choremes as a novel approach for assessing anthropogenic impacts and enhancing the visualization of the WEF Nexus in specific geographic areas. By integrating geospatial data, multi-criteria analysis, and involving stakeholders, our research aims to develop Choreomatic Focus Maps (CFMs) that offer a comprehensive spatial representation of the WEF Nexus, facilitating better-informed decision-making at the community level. Our current work outlines visualization of WEF Nexus, as an initial stages of creating CFMs through a detailed step-by-step process. This includes identifying the main drivers for WEF nexus, such as anthropogenic activities and their distribution, evaluating environmental factors, linking anthropogenic impact with environmental indicators over time, and conducting on-site validation. Our focus is on advancing through these crucial steps, particularly emphasizing field validation within the Local Decision-Makers Tools, visualized here by using Focus Maps. This phase is vital for ensuring our approach is grounded in reality and meets the needs of the communities within the Danube Delta region. By doing so, we aim to provide a visual summary of the WEF Nexus that not only captures the complexity of this fragile ecosystem but also incorporates local insights and priorities, identifying key areas where human activities have significant impacts.

A new evolutionary game is introduced which incorporates states and actions into the strategies of the organisms of the evolving populations. The game centrally features actions that result in demographic flow between states that may not conserve organism numbers. It is by this feature that the game encapsulate a range of other evolutionary games, and can encode almost very complex interactions between organisms, species and populations. The game's formalism is expounded and the nature of the game's equilibrium is discussed. This discussion leads to an algorithm for numerically determining the stable equilibrium points which is exemplified in the context of a modified Hawk-Dove game. The game's flexibility for modeling population dynamics is evaluated and compared with other evolutionary games.

The relationship between living things and their respective environments highly dependent on body temperature regulation. The human capacity to effectively thermoregulate evolved at a time when the environmental temperature was likely around 25°C during the Eocene epoch, some ~ 50-60 million years ago. This effectively meant that homeothermy settled on a core temperature balance point of ~ 37°C. When Homo split from chimpanzee around 5 million years ago the Earth was entering a cooling period where the balance point temperature was always well above that of the environment and body heat balance could be maintained. Following this cooling period, the Earth’s rewarming by 7 °C took over approximately 5,000 years, whereas the current estimates indicate 0.7 °C over the past 100 years; ten times the rate of ice-age-recovery warming, or 20 times faster compared with the last 2 million years. As such, if the predicted continued rise in global temperature continues, and surface temperature reaches values where heat load cannot be dumped as the body temperature balance point is at or near the environmental temperature, areas of the Earth would become inhospitable. This effectively means that we will need to deal with both physiological and behavioral limitations since our ability to adapt will be limited by a thermoregulatory strategy that evolved over millions of years for a different kind of environment, not one that is predicted to change rapidly over the next century. This paper outlines the basis on which Homo settled on a thermoregulatory balance point and what limitations this presents for us in the future.

Today, we are all threatened by an unprecedented pandemic: COVID-19. How different is it from other coronaviruses? Will it be attenuated or become more virulent? Which animals may be its original host? In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus, the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue. We found strong similarities between the current circulating COVID-19 and SARS and MERS, as well as COVID-19 in rhinolophines and pangolins. On the contrary, COVID-19 shares little similarity with the flu-causing coronaviruses and the other P3/P4 viruses. Strikingly, we observed divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 is actively evolving in human hosts. From all existing human COVID-19 genome sequences, we calculated the first common model that represents the shared sequences of the human COVID-19 strains, which provides important information for vaccine and antibody development. Geographic and time-course analysis of the evolutionary trees of the human COVID-19 reveals possibly heterogeneous evolutional paths among strains from 21 countries. This finding has important implications to the management of COVID-19 and the development of vaccines.

We propose a novel interpretation of consciousness and an enlarged definition of locality, which provide a solution to the problem of the consistency of measurements in quantum mechanics: consciousness is a characteristics of the Universe as a whole. Besides its physical conse- quences, this interpretation has also moral implications: individuality comes out naturally to be just an accident functional to evolution which shaped past and present history through competition, and re- alizing this fact should enforce cooperation.

Transposable elements (TEs) ubiquitously exist in the human genome, and some have the ability to copy and paste themselves to other locations, resulting in new insertions. Organisms have evolved with mechanisms and machineries to repress such activity. TEs are also co-opted for beneficial functions by the host and are thus maintained in the genome. During the lifetime of humans, aberrant TE activity might cause or contribute towards diseases including neurological conditions such as Alzheimers Disease (AD) and cancer. While inflammatory pathways linked to TEs may be a part of the disease pathology, on the other hand altered TE activity involving inflammatory pathways could be recognised by disease suppression mechanisms. For this reason, TEs could be targeted for therapeutic applications aiming to prevent TE activity or reduce initial inflammatory pathways as well as to activate disease suppression mechanisms. In this review, we describe the contributory and potential preventative roles of TEs in neurological conditions and cancer from a molecular and evolutionary perspective. Evolutionary paradigms both at the unicellular and organismal level aid understanding the role of TEs in disease. These observations could pave the way for the development of novel therapeutic approaches targeting TEs.

Ruminants perhaps appeared about 50 million years ago (Ma). Five ruminant families had been extinct and about 200 species in 6 ruminant families are living today. The first ruminant family probably was small omnivore without functional ruminal microbiota to digest fiber. Subsequently, other ruminant families evolved around 18-23 Ma along with woodlands and grasslands. Probably, ruminants started to consume selective and highly nutritious plant leaves and grasses similar to concentrates. By 5-11 Ma, grasslands expanded and some ruminants used more grass in their diets with comparatively low nutritive values and high fibers. Historically, humans have domesticated 9 ruminant species that are mostly utilizer of low quality forages for human benefits. Thus, the non-functional rumen microbiota to predominantly concentrate fermenting microbiota, followed by predominantly fiber digesting microbiota had evolved for mutual complementary benefits of holobiont over the million years. The core microbiome of ruminant species seems the resultant of hologenome interaction in an evolutionary unit. The inertia and resilience properties of ruminal ecosystem seem to be due to this core microbiota, which makes the ecosystem most stable in response to perturbations because this core microbiota has evolutionary advantages with logically more generalists (i.e., wide metabolic versatile and redundancy). Also, a part of the ruminal microbiome shows highly plasticity, which is likely useful for evolutionary adaptability of holobiont. This review discusses ecological characteristics of ruminal microbial community in evolutionary perspectives. The updated understanding of ecological traits of ruminal microbiome would be helpful to better modulate the ruminal fermentation favorably for human benefits.

We propose the traditional goal of cancer therapists to develop a single drug or drug combination that can, by itself, eliminate all cancer cells within a host has neglected potential treatments that may achieve curative outcomes by strategically combining agents that are individually effective but non-curative. We derive basic principles for such an approach from the eco-evolutionary dynamics of background extinctions in which a “first strike” reduces the size and heterogeneity of the initial population and is followed immediately by demographic and ecological “second strikes” that push the population below an extinction threshold. This proposed strategy appears identical to the empirically-derived curative therapy in childhood Acute Lymphocytic Leukemia.

It is desirable to upgrade previous evolutionary theories, which have remained incomplete and controversial for decades. Here we employ the concept of carbon-based entities (CBEs), which include methane, amino acids, proteins, organisms, and other entities containing relatively many carbon atoms. We deduce the driving force, mechanisms, steps, modes, tempos of CBE evolution, through integration of biology, physics, and chemistry using logics for complex issues. We hence establish the Carbon-Based Evolutionary Theory (CBET). The CBET suggests that evolution is the increase in hierarchy, diversity, fitness of CBEs under natural selection and driven by thermodynamics due to the chemical effect of the thermodynamic features of the Earth on CBEs. It provides better explanations for life origin, macroevolution events, natural selection, sympatric speciation, and evolution tempos than previous evolutionary theories. It reveals the evolutionary basis of multiple important social notions, including diversity, collaboration, altruism, obeying rules, and proper increase in freedom. It refutes some wrong notions in thermodynamics, including negative entropy (negentropy) and that biological order is equal to thermodynamic order, which have misled many people. The CBET is supported by its deduction and application. It could be a rare bridge linking laws of thermodynamics, evolution of life, and development of human society, and could have great significance in various sciences.

Rett syndrome (RTT) is mainly caused by mutations in methyl CpG-binding protein 2, cyclin-dependent kinase-like 5, or forkhead box protein G1. These RTT-causing proteins harbor an intrinsically disordered region (IDR) whose conformation exhibits spatiotemporal heterogeneity, which not only confer versatility to the protein, but also implicates them in diseases. The IDR generally evolves more rapidly than an ordered structure. In this study, we examined the relationship between pathogenic RTT-associated point mutations in RTT-causing proteins and the evolutionary dynamics of sequence features including structural order–disorder, phosphorylation sites, and evolutionary rates. We also analyzed the molecular properties and evolution of proteins that interact with RTT-causing proteins in terms of phylogenetic profiles, tissue specificity, subcellular localization, expression level, and functions. The results indicate that constrained IDRs may function by forming contacts with other regions in the protein sequence causing pathogenic missense mutations likely to arise in the rapidly evolving IDR and affect molecular networks, leading to disease. The results also provide novel insights into the genetic basis for RTT and the evolution of the neocortex in higher vertebrates.

The first half of the 20th century was noteworthy for the introduction of a unique group of drugs – antibiotics. It drastically changed concepts of infectious diseases treatment, which for centuries remained a scourge of the human population. With improvement of the antibiotic treatment efficacy, humanity has faced the problem of a dramatic increase in antibiotic resistance in bacteria. The COVID-19 pandemic has only exacerbated the problem and posed a new challenge to the medical community in finding solutions, both clinical and organizational and methodological, to fight antibiotic resistance widespread all over the globe. This publication covers some aspects of evolutionary processes in either pathogens or diseases, including ethical perspective.

Molecular methods have revolutionised virtually every area of biology, and metazoan phylogenetics is no exception: molecular phylogenies, molecular clocks, comparative phylogenomics, and developmental genetics have collectively transformed our understanding of the evolutionary history of animals. Moreover, the diversity of methods and models within molecular phylogenetics has resulted in significant disagreement among molecular phylogenies as well as between these and traditional phylogenies. Here, I argue that tackling this multifaceted problem lies in integrating evidence to infer the best evolutionary scenario. I begin with an overview of recent developments in early metazoan phylogenetics, followed by a discussion of key conceptual issues in phylogenetics revolving around phylogenetic evidence and theory. I then argue that integration of different kinds of evidence is necessary for arriving at the best evolutionary scenario rather than the best-fitting cladogram. Finally, I discuss the prospects of this view in stimulating interdisciplinary cross-talk in early metazoan research and beyond.

We have shown that the use of evolutionary gravitational constants G, the speed of light c, and cosmological constant Λ in the Einstein’s field equations leads to a very simple model that fits the supernovae Ia data with a single parameter as well as the standard ΛCDM model with two parameters, and has the predictive capability superior to the latter. We have shown that at the current time G and c both increase as dG/dt = 5.4GH₀ and dc/dt = 1.8cH₀ with H₀ as the Hubble constant, and Λ decreases as dΛ/dt = −1.2ΛH₀. Negative finding on the variation of G with time derived from the analysis of lunar laser ranging data in several studies has been attributed to the fact that these studies did not consider the variation of c simultaneously with G.

Cancer is a complex and devastating disease that has engaged scientists and researchers for decades. Despite tremendous efforts, previous hypotheses about cancer development have not achieved significant breakthroughs. Evolutionary Cancer Cell Biology (ECCB) is a novel and emerging branch of oncological science that provides an evolutionary perspective on the origin of cancer. It reveals that the cancer genome evolved hundreds of millions of years ago, long before multicellular organisms such as metazoans and humans emerged. ECCB aims to unify all evolutionary insights, hypotheses, and theories into a cohesive framework. It investigates the intricate relationship between cancer genomics and ancient pre-metazoan genes that emerge when normal cells transform into cancer cells. It challenges the conventional wisdom of cancer research by suggesting that cancer could arise through intrinsic cellular mechanisms without genetic alterations and mutations. Furthermore, it postulates that somatic mutations are only secondary, downstream events in the process of oncogenesis.

Polycystic ovary syndrome (PCOS) is increasingly recognized as a complex metabolic disorder that manifests in genetically susceptible women following a range of negative exposures to nutritional and environmental factors related to contemporary lifestyle. The hypothesis that PCOS phenotypes are derived from a mismatch between ancient genetic survival mechanisms and modern lifestyle practices is supported by a diversity of research findings. The proposed evolutionary model of the pathogenesis of PCOS incorporates evidence related to evolutionary theory, genetic studies, in-utero developmental epigenetic programming, transgenerational inheritance, metabolic features including insulin resistance, obesity and the apparent paradox of lean phenotypes, reproductive effects and subfertility, the impact of the microbiome and dysbiosis, endocrine disrupting chemical exposure, and the influence of lifestyle factors such as poor quality diet and physical inactivity. Based on these premises, the diverse lines of research are synthesized into a composite evolutionary model of the pathogenesis of PCOS. It is hoped that this model will assist clinicians and patients to understand the importance of lifestyle interventions in the prevention and management of PCOS and provide a conceptual framework for future research. It is appreciated that this theory represents a synthesis of the current evidence and that it is expected to evolve and change over time.

The demographic history of anatomically modern humans (AMH) involves multiple migration events, population extinctions and genetic adaptations. As genome-wide data from complete genome sequencing becomes increasingly abundant and available even from extinct hominins, new insights of the evolutionary history of our species are discovered. It is currently known that AMH introgressed with archaic hominins once they left the African continent. Current out of African human genomes carry fragments of archaic origin. This review focuses on the fitness consequences of archaic interbreeding in current human populations. We discuss new insights and challenges that researchers face when interpreting the potential impact of introgression on fitness and testing hypotheses about the role of selection within the context of health and disease.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

Animals are distinguished by their complex developmental programs, in which transcription factors play essential roles in coordinating the regulation of the genome. Thus, it has been hypothesized that animal origins involved the evolution of increasingly complex transcriptional regulatory mechanisms that permit greater spatiotemporal control over gene expression. Here, we revisit this hypothesis in light of new genomic and functional data from diverse phylogenetically-relevant taxa, including early branching animals (sponges, ctenophores) and close relatives of animals (choanoflagellates, filastereans, ichthyosporeans). We argue that many of the mechanisms posited to explain animal transcriptional complexity, such as large increases in transcription factor numbers, new transcription factor families, and distal enhancers, did not factor significantly into animal origins. Instead, we propose that the re-purposing of pre-existing transcriptional regulatory modules through cis-regulatory evolution and gene duplication events, combined with new protein-protein interactions among TFs, may have been fundamental to the transcriptional regulatory architecture of the first animals.

Evolutionary Machine Learning (EML) has demonstrated its effectiveness in modeling various real-world application domains, including the dynamics of diseases and the creation of predictive models across diverse fields. However, the challenge of data imbalance adversely impacts the performance of EML models. While strategies involving misclassification costs or cost-sensitive learning exist to address this issue, their integration into EML has yet to be thoroughly explored. This paper aims to fill this gap by investigating the application of cost-sensitive EML for intelligent health risk prediction. More specifically, this paper delves into the utilization of cost-sensitive genetic programming (CSGP) and cost-sensitive genetic algorithms (CSGA), which are among the most widely used algorithms in the EML category. The study entails training CSGP and CSGA to predict the early onset of three chronic conditions (hypertension, diabetes, and fatty acid-related disorders) through the development of an EML model. Subsequently, the model's performance is evaluated through standard fitness functions such as sensitivity, specificity, and F1-score. From the experimental results, EML models demonstrate promising results in identifying the occurrence of chronic diseases. Further optimization of the cost-sensitive EML model holds the potential for enhanced outcomes in modeling medical problems.

Recent successes in the field of evolutionary cancer cell biology (ECCB) have brought to a head two of the most important controversies regarding cancer origin. The first concerns the question of whether carcinogenesis is initiated only by genetic alterations, mutations, and driver genes, as the mutational theory teaches. The ECCB, claims that a large number of polyploidy-related cancers (PGCC cancers) are not due to mutations, but rather to inadequate ancient repair mechanisms used by DNA-damaged stem cells for their repair. Somatic mutations are merely secondary. The second controversy, described in the previous article, concerns the non-mutational theory itself: Are non-mutational polyploid cancers an atavism - as previously thought - or rather the effect of an ancient gene regulatory network aGRN that controls genome reprogramming in precancerous DNA-damaged cells as well in the transformed cells. Finally, the unicellular gene module of cancer, of which the aGRN is a part, has evolved along with the multicellular genome during the last 1000 My of evolution. and non-mutated cancer cell systems are under the control of the aGRN. The atavistic theory relies on phylostratigraphic evidence to determine the age of cancer genes. It states that most cancer-like genes arose during the transition period from unicellularity to multicellularity, but also during the early metazoan era. In our opinion, the atavistic theory is flawed and suffers from many misunderstandings. It provides little insight into the origin and coevolution of the cancer cell system and its control by the ancient gene regulatory network aGRN.

Multicellular life forms have evolved many times in our planet, suggesting that this is a common evolutionary innovation. Multiple advantages have been proposed for multicellularity (MC) to emerge. In this paper we address the problem of how the first precondition for multicellularity, namely "stay together" might have occurred under spatially limited resources exploited by a population of unicellular agents. Using a minimal model of evolved cell-cell adhesion among growing and dividing cells that exploit a localised resource with a given size, we show that a transition occurs at a critical resource size separating a phase of evolved multicellular aggregates from a phase where unicellularity (UC) is favoured. The two phases are separated by an intermediate domain where where both UC and MC can be selected by evolution. This model provides a minimal approach to the early stages that were required to transition from Darwinian individuality to cohesive groups of cells associated with a physical cooperative effect: when resources are present only in a localised portion of the habitat, MC is a desirable property as it helps cells to keep close to the available local nutrients.

Though the cancer research community has used mouse xenografts for decades more than zebrafish xenografts, zebrafish have much to offer: they are cheap, easy to work with, and the embryonic model is relatively easy to use in high-throughput assays. Zebrafish can be imaged live, allowing us to observe cellular and molecular processes in vivo in real time. Opponents dismiss the zebrafish model due to the evolutionary distance between zebrafish and humans, as compared to mice, but proponents argue for the zebrafish xenograft’s superiority to cell culture systems and its advantages in imaging. This review places the zebrafish xenograft in the context of current views on cancer and gives an overview of how several aspects of this evolutionary disease can be addressed in the zebrafish model. Zebrafish are missing homologs of some human proteins and (of particular interest) several members of the matrix metalloproteinase (MMP) family of proteases, which are known for their importance in tumour biology. This review draws attention to the implicit evolutionary experiment taking place when the molecular ecology of the xenograft host is significantly different than that of the donor.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

Human language and social cognition are two key disciplines that have traditionally been studied as separate domains. However, emerging research from detailed studies of comparative animal behavior, human social behavior, language acquisition in children, social cognitive neuroscience, and the cognitive neuroscience of language suggest that language and social cognition are two deeply interrelated capacities of the human species. For instance, recent developmental studies on the acquisition of language in infants and young children, draw attention to the important crucial benefits associated with several different aspects of social stimulation for youngsters, including the quantity and quality of linguistic input, dyadic infant/child-to-parent verbal and non-verbal social interactions, and other important social cues integral for facilitating language learning and social bonding. Studies of the adult human brain further suggest a high degree of specialization for sociolinguistic information processing, memory retrieval, and comprehension, suggesting the function of these regions may link language with social cognition and social bonding. In effect, the social brain and social complexity hypotheses may jointly help to explain how neurotypical children and adults learn language, why autistic adults and children exhibit simultaneous deficits in language and social cognition, and why nonhuman primates and other animals with significant computational abilities do not acquire the capacity for language. But perhaps most critically, the following article argues that this and related research will allow scientists to generate a holistic profile and deeper understanding of the healthy adult social brain, while developing more innovative and effective diagnoses, prognoses, and treatments for maladies and deficits also associated with the social brain.

Promoting close and sustainable cooperation between schools, enterprises, and government has become an important concern in many countries. Based on evolutionary game theory, this paper constructs a tripartite evolutionary game model of schools, enterprises, and government in order to analyze the stability of strategies of the different players. The results show that the main factor that influences the stability of the strategies of schools and enterprises is the reward of positive cooperation from sources other than the government, and the main factor that influences the stability of the strategy of the government is the benefit of positive cooperation strategy under the scenario where schools cooperate with enterprises. Therefore, the government should focus on implementing more effective policies, such as increasing the incentives and penalties, improving the mechanism for managing conflicts, ensuring the fairness of benefits distribution, and clarifying the responsibilities of different departments; schools should focus on providing more practical curricula and programs for students, training high-quality teachers, and perfecting talent cultivation to meet the needs of enterprises; and enterprises should focus on providing job experiences for students and transforming the results of schools’ teaching and theoretical research into practical productivity.

The genetic systems of Paspalum species have not been extensively studied. We analysed the ploidy, reproductive mode, mating system, and fertility of four Paspalum species - P. durifolium, P. ionan-thum, P. regnellii, and P. urvillei. An analysis of 378 individuals from 20 populations of north-eastern Argentina was conducted. All populations of the four Paspalum species were pure tetraploid and had a sexual and stable reproductive mode. However, some populations of P. durifolium and P. ionanthum showed low levels of apospory. Populations of P. durifolium and P. ionanthum had low seed sets under self-pollination but were fertile under open pollination, showing that self-incompatibility likely caused self-sterility. In contrast, populations of P. regnellii or P. urvillei showed no evidence of apospory and seed set in both self- and open pollination conditions were high, suggesting that they are self-compatible, due to the absence of pollen-pistil molecular in-compatibility mechanisms. The evolutionary origin of the four Paspalum species could explain these differences. This study supplies valuable insights into the genetic systems of Paspalum species, which could have implications for their conservation and management.

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

In order to reconstruct the origin and pathways of variola virus (VARV) dispersion, we analyzed 47 VARV isolates available in public databases and their SNPs. The mean substitution rate of the whole genomes was 9.41x10-6 (95%HPD:8.5-11.3x10-6) substitutions/site/year. The time of the tree root was estimated to be a mean 68 years (95%HPD:60.5–75.9). The phylogeographical analysis showed that the Far East and India were the most probable locations of the tree root and of the inner nodes, respectively, whereas for the outer nodes it corresponded to the sampling locations. The Bayesian Skyline plot showed that the effective number of infections started to grow exponentially in 1915-1920, peaked in the 1940s, and then decreased to zero. Our results suggests that the VARV major strains circulating between 1940s-1970s probably shared a common ancestor originated in the Far East; subsequently moved to India, which became the center of its dispersion to eastern and southern Africa, and then to central Africa and the Middle East, probably following the movements of people between south-eastern Asia and the other places with a common colonial history. These findings may help to explain the controversial reconstructions of the history of VARV obtained using long- and short- term calibrations.

Purpose: This study's objective was to examine international research trends in behavior management that had been written about in journals that were indexed by Scopus and authored by scientists from all around the world.Patients and methods: We examined publications in the Scopus database using bibliometric and content analysis. We only retrieved English-language articles. The VOSviewer program was used for content analysisResults: We found 808 papers on behavior management during the previous 50 years by following the study strategy. The study concentrated more on authors, nations, institutions, journals, documents, and keywords, as well as the annual scientific production (classification by subject area and document type)Conclusion: The finding prompts a discussion regarding behavior management for next research.

The evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach.

Cooperative localization under complex urban environments has become a solution to replace the Global Navigation Satellite System (GNSS) positioning. Due to utilizing distance measured and information exchanged between vehicles, cooperative localization results in high computational complexity and heavy communication overhead. This paper proposes a cooperative localization method based on evolutionary coalitional game theory, which implements vehicles' location estimation with less communication cost. We select the neighboring vehicles to form a coalition based on the node's square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method holds low communication overhead while maintaining localization accuracy.

Transporter proteins, P-glycoprotein (P-gp) and P4ATPase-CDC50, are responsible for the transport of Miltefosine drug across cell membrane of a protozoan parasite Leishmania major. Mutations or change in activity of these proteins may lead to emergence of resistance in the parasite. Owing to the structural and functional importance of these transporter proteins, in this ppaper, we have tried to decipher the evolutionary divergence of these Miltefosine transporter proteins across different forms of life including Protists, Fungi, Plants and Animals. We retrieved 96, 207, and 189 sequences of P-gp, P4ATPase and CDC50 proteins respectively, across diverse variety of organisms for the conserved analysis. Phylogenetic trees were constructed for these three transporter proteins based on Bayesian posterior probability inference. The evolutionary analysis concluded that these proteins remain highly conserved throughout the species diversity but still substantial differences in the proteins for host (Homo sapiens) and parasite (L. major) were observed which have led in targeting these Miltefosine transporter proteins in a parasite specific manner.

Machine learning research has been able to solve problems in multiple aspects. An open area of research is machine learning for solving optimisation problems. An optimisation problem can be solved using a metaheuristic algorithm, which is able to find a solution in a reasonable amount of time. However, there is a problem, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly. A solution approach is shown here, using a proposal that automatically creates metaheuristic algorithms aided by a reinforced learning approach. Based on the experiments performed, the approach succeeded in creating a metaheuristic algorithm that managed to solve a large number of different continuous domain optimisation problems. This work's implications are immediate because they describe a basis for the generation of metaheuristic algorithms in real-time.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

The constraint equations in Maxwell theory are investigated. In analogy with some recent results on the constraints of general relativity it is shown, regardless of the signature and dimension of the ambient space, that the "divergence of a vector field" type constraints can always be put into linear first order hyperbolic form for which global existence and uniqueness of solutions to an initial-boundary value problem is guaranteed.

We introduced a novel biotin-binding peptide for sensing biotin, biotinylated proteins, and nu-cleotides. From a 15-mer library displayed on the RNA coliphage Qβ, a 15-amino acid long peptide (HGHGWQIPVWPWGQG) hereby referred to as nanotag was identified to selectively bind biotin. The targets selection was achieved through panning with elution by infection. The selected peptide was tested as a transducer for an immunogenic epitope of the foot-and-mouth disease virus (FMDV) on Qβ phage platform separated by a linker. The biotin-tag showed no significant in-fluence on the affinity of the epitope to its cognate antibody (SD6). The nanotag bound biotin selectively when fused either to the C- or N-terminus of the epitope. The epitope would not bind or recognize SD6 while positioned at the N-terminus of the nanotag. Additionally, the biotin com-peted linearly with the SD6 antibody in a competitive ELISA. Competition assays using the se-lected recombinant phage itself as a probe, or transducer, enable the operationalization of this technology as a biosensor toolkit to sense and quantify SD6 analyte. Herein, the published Strep II nanotag (DVEWLDERVPLVET) was used as a control and has similar functionalities to our pro-posed novel biotin-tag thereby providing a new platform for developing devices for diagnostic purposes.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

The role of lactic acid (lactate) in cell metabolism has been significantly revised in recent dec-ades. Initially, lactic acid was attributed to the role of a toxic end product of metabolism, which accumulation in the cell and extracellular space leads to acidosis, muscle pain and other adverse effects. However, it has now become obvious that lactate is not only a universal fuel molecule and the main substrate for gluconeogenesis, but also one of the most ancient metabolites with signaling function, which has a wide range of regulatory activity. The Warburg effect described 100 years ago (that means intensification of glycolysis associated with high lactate production), which is characteristic of many malignant tumors, confirms the key role of lactate not only in physiological conditions, but also in pathologies. The study of lactate’s role in the malignant transformation becomes more relevant in the light of the “atavistic theory of carcinogenesis,” which suggests that tumor cells return to a more primitive hereditary phenotype during micro-evolution. In this review, we attempted to summarize the accumulated knowledges about the functions of lactate in cell metabolism and its role in the process of carcinogenesis, and to con-sider the possible evolutionary significance of the Warburg effect.

A new form of beta coronavirus called severe acute respiratory disease coronavirus type 2 (SARS-CoV-2) causing a recent pandemic outbreak possesses a linear positive ss-RNA genome with a length of 29,903 nt. Here, the genomes of SARS-CoV-2 from 821 samples were characterised for its better understanding of the genomic and evolutionary patterns. The phylogeny of SARS-CoV-2 was reconstructed using concatenated dataset consisting of all peptide encoding sequences under Bayesian Inference (BI) and Maximum Likelihood (ML) approaches. Comparison of all peptide encoding sequences reveals high divergence of amino acid sequences proportional to divergence of nucleotides, indicating that the viral genomic evolution has not been strictly neutral. The most part of the genome was under neutral evolution, however, the specific sites for peptide encoding sequences were evolved under positive selection. As well as providing reliable evidence on transmission routes of the SARS-CoV-2 outbreak, the phylogenetics and network analyses suggest the sample reported from Guangdong province is likely ancestor of the pandemic virus form. The overall substitution rate of SARS-CoV-2 genome was estimated to be 1.65 x 10^-3 per site per year, falling within the range for previously reported RNA viruses. Median estimation of tMRCA from Bayesian coalescent analyses corresponds to 10 September 2019. The exponential growth rate (r), doubling time (Td) and R₀ were estimated to be 47.43 per year, 5.39 days and 2.72, respectively. These findings convincingly emphasise that the use of more comprehensive genome data improves robustness and also enhances understanding of the demographic history of the outbreak.

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control over the flow of electricity between the source and the load, they are often used passive or active filters. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtain high quality solutions that could be generalized to similar problems of any dimension.

Closely related to the model plant Arabidopsis thaliana, the genus Boechera is known to contain both sexual and apomictic species or accessions. Boechera retrofracta is a diploid sexually reproducing species and is thought to be an ancestral parent species of the apomictic species Boechera divaricarpa. Here we report the de novo assembly of the B. retrofracta genome using short Illumina and Roche reads from 1 paired-end and 3 mate pair libraries. The distribution of 23-mers from the paired end library has indicated a low level of heterozygosity and the presence of detectable duplications and triplications. The genome size was estimated to be equal 227 Mb. N50 of the assembled scaffolds was 2.3 Mb. 27048 protein-coding genes were predicted using a hybrid approach that combines homology-based and de novo methods. Also repeats, tRNA and rRNA genes were annotated. Finally, genes of B. retrofracta and 6 other Brassicaceae species were used for phylogenetic tree reconstruction. Also, a detailed analysis of evolution of the APOLLO apomixis-associated locus was performed. An assembled genome of B. retrofracta will help in the challenging assembly of the highly heterozygous genomes of hybrid apomictic species such as B. divaricarpa.

The shoot apical meristem (SAM) gives rise to the aerial structure of plants by producing lateral organs and secondary meristems. The SAM is responsible for plant developmental patterns, thus determining plant morphology and, consequently, many agronomic traits such as the number and size of fruits and flowers and kernel yield. Our current understanding of SAM morphology and regulation is based on studies conducted mainly on some angiosperms, including economically important crops such as maize (Zea mays) and rice (Oryza sativa), and the model species Arabidopsis (Arabidopsis thaliana). However, studies in other plant species from the gymnosperm class are scant, making difficult comparative analyses that help us understand SAM regulation in diverse plant species. This limitation prevents deciphering the mechanisms by which evolution gave rise to the multiple plant structures within the plant kingdom, and determined the conserved mechanisms involved in SAM maintenance and operation. This review aims to integrate and analyze the current knowledge of SAM evolution by combining the morphological and molecular information recently reported from the plant kingdom.

Polycystic ovary syndrome (PCOS) is a common endocrinopathy of reproductive-aged women characterized by hyperandrogenism, oligo-anovulation and insulin resistance closely linked with preferential abdominal fat accumulation. As an ancestral primate trait, PCOS was likely further selected in humans when scarcity of food in hunter-gatherers of the late Pleistocene additionally programmed for enhanced fat storage to meet the metabolic demands of reproduction in later life. As an evolutional model for PCOS, healthy normal-weight women with hyperandrogenic PCOS have subcutaneous (SC) abdominal adipose stem cells that favor fat storage through exaggerated lipid accumulation during development to adipocytes in vitro. In turn, fat storage is counterbalanced by reduced insulin sensitivity and preferential accumulation of highly-lipolytic intra-abdominal fat in vivo. This metabolic adaptation in PCOS balances energy storage with glucose availability and fatty acid oxidation for optimal energy use during reproduction; its accompanying oligo-anovulation allowed PCOS women from antiquity sufficient time and strength for childrearing of fewer offspring with a greater likelihood of childhood survival. Heritable PCOS characteristics are now affected by today’s contemporary environment through epigenetic events that predispose to lipotoxicity with excess weight gain and pregnancy complications, calling for an emphasis on preventive healthcare to optimize the long-term, endocrine-metabolic health of PCOS women in today’s obesogenic environment.

Supervised machine learning (SML) is a powerful method for predicting a small number of well-defined output groups (e.g., potential buyers of a certain product) by taking as input a large number of known well-defined measurements (e.g., past purchases, income, ethnicity, gender, credit record, age, favorite color, favorite chewing gum). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known to be true. SML has had enormous success in the world of commerce, and this success has prompted a few scientists to employ it in the study of molecular and genome evolution. Here, we list the properties of SML that make it an unsuitable tool in evolutionary studies. In particular, we argue that SML cannot be used in an evolutionary exploratory context for the simple reason that training datasets that are known to be a priori true do not exist. As a case study, we use an SML study in which it was concluded that most human genomes evolve by positive selection through soft selective sweeps (Schrider and Kern 2017). We show that in the absence of legitimate training datasets, Schrider and Kern (2017) used (1) simulations that employ many manipulatable variables and (2) a system of cherry-picking data that would put to shame most modern evangelical exegeses of the Bible. These two factors, in addition to the lack of methodological detail and the lack of either negative controls or corrections for multiple comparisons, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., discoal) should be taken with a huge shovel of salt.

Contemporary climate change is exposing plant populations to novel combinations of temperatures, drought stress, [CO₂] and other abiotic and biotic conditions. These changes are rapidly disrupting the evolutionary dynamics of plants. Despite the multifactorial nature of climate change, most studies typically manipulate only one climatic factor. In this opinion piece, we seek to explore how climate change factors interact with each other and with biotic pressures to alter evolutionary processes. We first explore the ramifications of climate change for key life history stages (germination, growth and reproduction). We then examine how mating system variation influences population persistence under rapid environmental change and propose that mixed mating could be advantageous in future climates. Furthermore, we discuss how spatial and temporal mismatches between plants and their mutualists and antagonists could promote or constrain adaptive responses to climate change. For example, plant-virus interactions vary from highly pathogenic to mildly facilitative, and are partly mediated by temperature, moisture availability and [CO₂]. Will host plants exposed to novel, stressful abiotic conditions be more susceptible to viral pathogens? Finally, we propose novel experimental approaches that could illuminate how plants will cope with unprecedented global change, such as resurrection studies combined with experimental evolution, genomics or epigenetics.

The ongoing mutations in the structural proteins of SARS-CoV-2 is the major impediment for prevention and control of the COVID-19 disease. The envelope (E) protein of SARS-CoV-2 is a structural protein existing in both monomeric and homopentameric forms, associated with a multitude of functions including virus assembly, replication, dissemination, release of virions, infection, pathogenesis, and immune response stimulation. In the present study, 81,818 high quality E protein sequences retrieving from the GISAID were subjected to mutational analyses. Our analysis revealed that only 0.012 % (982/81818) stains possessed amino acid (aa) substitutions in 63 sites of the genome while 58.77% mutations in the primary structure of nucleotides in 134 sites. We found the V25A mutation in the transmembrane domain which is a key factor for the homopentameric conformation of E protein. We also observed a triple cysteine motif harboring mutations (L39M, A41S, A41V, C43F, C43R, C43S, C44Y, N45R) which may hinder the binding of E protein with spike glycoprotein. These results therefore suggest the continuous monitoring of each structural protein of SARS-CoV-2 since the number of genome sequences from across the world are continuously increasing.

The corona (COVID-19) pandemic offers an opportunity for dealing with persistent problems, through a transformative recovery process. It is a crisis that offers opportunities for dealing with three interrelated crises: the ecological crisis (climate change, loss of biodiversity, resource depletion, pollution and ecosystem destruction), the confidence crisis (people losing trust in government, politics, companies, regular news channels, science, each other and the future), and the inequality crisis (the widening of the gap between rich and poor). Our argument is that sustainability transitions will not succeed without a different economy and another social contract with the associated rights and duties of care (for the environment and the well-being of others, including future generations). A different social contract is not only desirable from the point of view of sustainability and fairness, justice and equality, but is also necessary to restore citizens' trust in politics, government, companies and each other. In the paper we discuss mechanisms towards a Natural Social Contract, systemic leverage points for system transformations and possibilities for co-evolutionary governance by actor coalitions interested in transformative change. The combination of those three elements helps to synchronize different agendas and reduce the chance that they will work against each other.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identi cation, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

Chironomus riparius is a well-established model organism in various fields such as ecotoxicology and ecology, and therefore environmental preferences, ecological interactions and metabolic traits are well-studied. With the recent publication of a high-quality draft genome, as well as different population genetic parameters such as mutation and recombination rate, the species can be used as an alternative to the Drosophila models in experimental population genomics or molecular ecology. To facilitate access to this promising experimental model species for a wider range of researchers, we describe experimental methods to first create and sustain long term cultures of C. riparius and then use them to perform repeatable and comparable experiments for various research questions.

Thyroid hormones are essential for regulating the metabolic rate, growth, development, and maintenance of many physiological processes in vertebrates. They are synthesized by the thyroid gland, which is an ancient organ that evolved early in vertebrate evolution. This study aims to characterize the evolutionary path of polymorphic variants associated with the hypothalamus-pituitary-thyroid (HPT) axis by comparing ancient DNA (aDNA) sequences of Late Pleistocene-Holocene hominin populations with those of modern humans. We evaluated the genetic sequences of several populations, including Neanderthals, Denisovans, Palaeolithic hunter-gatherers from European Russia and Sunghir, and Neolithic hunter-gatherers/farmers from Anatolia focusing on genes involved in the development of the thyroid gland and thyroid hormone (TH) biosynthesis, secretion, and transport. Results showed interesting variants in the DIO2 (rs225014), TPO (rs4927611), and TG (rs2069556 and rs1133076) genes. All SNPs seemed to confirm that Neanderthals, and in part Denisovas, were physiologically hypothyroidic. Probably they had lower T3 levels due to defective production or peripheral conversion. From the moment that the most favourable alleles in terms of T3 production appear during the Paleolithic, we are inclined to assume that their selection was linked to environmental pressure.Our study supports insight into the evolutionary history of the endocrine system and can help in providing understanding into the evolution of physiological systems and their adaptation to changing environments. Understanding the evolution of thyroid hormones can also shed light on the mechanisms underlying thyroid disorders in humans.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) at-tacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands or more to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantitative and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms Opti-mization with TOPOT-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using evolutionary algo-rithms, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

Eyes are quintessential complex traits and our understanding of their evolution guides models of trait evolution in general. A long-standing account of eye evolution argues natural selection favors morphological variations that allow increased functionality for sensing light (Darwin 1859; v. Salvini-Plawen and Mayr 1977; Nilsson and Pelger 1994; Nilsson 2013). While certainly true in part, this focus on visual performance does not entirely explain why diffuse photosensitivity persists even after eyes evolve, or why eyes evolved many times, each time using similar building blocks. Here we briefly review a vast literature indicating most genetic components of eyes historically responded to stress caused directly by light, including UV damage of DNA, oxidative stress, and production of aldehydes. We propose light-induced stress had a direct and prominent role in the evolution of eyes by bringing together genes to repair and prevent damage from light-stress, both before and during the evolution of eyes themselves. Stress-repair and stress-prevention genes were perhaps originally deployed as plastic responses to light and/or as beneficial mutations genetically driving expression where light was prominent. These stress-response genes sense, shield, and refract light but only as reactions to ongoing light stress. Once under regulatory-genetic control, they could be expressed before light stress appeared, evolve as a module, and be influenced by natural selection to increase functionality for sensing light, ultimately leading to complex eyes and behaviors. Recognizing the potentially prominent role of stress in eye evolution invites discussions of plasticity and assimilation and provides a hypothesis for why similar genes are repeatedly used in convergent eyes. Broadening the drivers of eye evolution encourages consideration of multi-faceted mechanisms of plasticity/assimilation and mutation/selection for complex novelties and innovations in general.

Inteins, often referred to as protein introns, are highly mobile genetic elements that invade conserved genes throughout the tree of life. Inteins have been found to invade a wide variety of key genes within actinophages. While in the process of conducting a survey of these inteins in actinophages we discovered that one protein family of methylases contained a putative intein, and two other unique insertion elements. These methylases are known to occur commonly in phages as orphan methylases (possibly as a form of resistance to restriction-modification systems). We found that the methylase family is not conserved within phage clusters and has a disparate distribution across divergent phage groups. We determined that two of the three insertion elements have a patchy distribution within the methylase protein family. We also found that the third insertion element is likely a second homing endonuclease, and that all three elements (the intein, the homing endonuclease, and what we refer to as the ShiLan domain) all have different insertion sites that are conserved in the methylase gene family. Furthermore, we find strong evidence that both the intein and ShiLan domain are partaking in long distance horizontal gene transfer events between divergent methylases in disparate phage hosts within the already dispersed methylase distribution. The reticulate evolutionary history of methylases and their insertion elements reveals high rates of gene transfer and within-gene recombination in actinophages.

Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.

The diversification of life-histories is mediated by cooperation, innovations of biological information, modularity, and heterochrony in developmental processes. These processes are defined, contextualized, and exemplified, studying the evolution of coloniality (i.e. life-history involving modularization of the multicellular individual) in the family of benthic tunicates Styelidae. This study proposes that in these colonial tunicates there is an inter-generational division of labor, where one generation is feeding, a second is developing by morphogenetic processes, and a third is aging by programmed cell death and phagocytosis. The communication system developed in these colonies is mediated, by changes in proportion, location, and gene expression of specialized blood cells. Colonial life-history in animals is related to the reduction of individual size; development of extra-corporeal tissues to interconnect zooids; the inter-generational division of labor; and the reduction of zooid’s individuality. Processes analogous with the widely accepted major evolutionary transitions (METs), suggesting that coloniality could be studied as a MET. The understanding of colonial life-histories could provide information about key mechanisms for life diversification.

Background: Given the potential for additional development to clarify a better knowledge of the overall impact of COVID-19 on the pediatric population, the clinical symptoms of SARS-CoV-2 infection in children and adolescents are still being explored. Morbidity in children is characterized by a variable clinical course. Our study's goal was to compare clinical aspects of 230 pediatric patients who tested positive for SARS-CoV-2 and were hospitalized between April 2020 and March 2022. Methods: In a retrospective analysis, we compared two groups hospitalized in the infectious diseases clinical ward IX at the National Institute for Infectious Diseases "Prof. Dr. Matei Bals," Bucharest, Romania. The first group of 88 patients was admitted between (April–December 2020) and their clinical manifestations were compared with the second group of 142 children followed between July 2021 and March 2022. Results: Of 230 children, the median age was 4.5 (interquartile range 0.6-17) years, 53.9% were male. 88 (36.21%) patients (first group) were admitted during the second wave in Romania, mostly aged < 5 years old, and experienced digestive manifestations like fever (p=0.001), and diarrhoea (p=0.004). The second group experienced different clinical signs when compared with the first group, with higher temperature and increased respiratory symptoms analogous to those of acute respiratory viral infections. The proportion in the second group increased, and 64.5% had symptoms for a median interval of 5 days; age (0-4 -years old) and length of stay were both proportionally inversely (p<0.01) and with correlation with hospital admission (p = 0.04). We report two Paediatric Inflammatory Multisystem Syndrome (PIMS) in the second group, with favourable evolution under treatment. Comorbidities were risk factors for complications appear (p < 0.001) in both groups. All paediatric cases admitted to our clinic evolved favourably and no death was recorded. Conclusions: In the first group children experienced digestive symptoms, whereas the second group experienced mild and moderate respiratory symptoms. We confirmed risk factors for severe cases as manifestations across the age spectrum, 0-4 (digestive symptoms) and 5-12 years old (for respiratory symptoms), associated comorbidities, fever, and male gender. The potential effects of COVID-19 infection in children older than 5 years should encourage caregivers to vaccinate and improve the prognosis among pediatric patients at risk.

Two welcome extensions of evolutionary thinking have come to prominence over the last thirty years: the so-called “extended evolutionary synthesis” (EES) and debate about biological kinds and individuals. These two agendas have, however, remained orthogonal to one another. The EES has mostly restricted itself to widening the explanations of adaptation offered by the preceding “modern evolutionary synthesis” by including additional mechanisms of inheritance and variation; while discussion of biological kinds has turned toward philosophical questions of essential vs. contingent properties of life forms and realist vs. epistemological approaches to categorization and classification. Here we attempt to broaden the explanatory scope of evolutionary theory by linking these two agendas. We expand on the mechanistic orientation of the EES, using new understandings of networked systems of components in order to engage the distinct intellectual challenge of the origination of historical kinds. With this phrase we designate a subset of natural kinds that acquires, through evolutionary processes, a quasi-independent lineage-history. Such kinds emerge in both biology and culture, and we enlarge the limited number of historical kinds that have thus far been recognized in evolutionary biology in a series of paradigmatic exemplars, from genes and cell types to rituals and music. For each exemplar we discern specific mechanisms by which it arose and persists; comparing these, we suggest a general unity in the ways in which diverse historical kinds originate.

No single evolutionary event has been identified as the cause for the final emergence of our species. I propose that a mutation on CD155 receptor gene occurred to establish a symbiosis with poliovirus, which exerted its beneficial impact via RNA dependent non-genetic transgenerational inheritance, which caused a qualitative enhancement of cognitive functions. I posit that this mutation occurred in what we call, Anatomically Modern Humans, our immediate ancestor species and that the disruption of this symbiosis causes autism spectrum disorder. Positive selection of CD155 to the extent of becoming a species defining characteristic, the chronology of autism spectrum disorder prevalence increase and continued increase, the multigenerational nature of RNA inheritance, the universal infection of humans by poliovirus and a very low associated mortality rate, and several other factors support this hypothesis. Specific genetic, epidemiological and sperm miRNA content studies are suggested to test this hypothesis.

Since its inception, evolutionary theory has experienced a number of extensions. The most important of these took the forms of the Modern Evolutionary Synthesis (MES), embracing genetics and population biology in the early 20^th century, and the Extended Evolutionary Synthesis (EES) of the last thirty years, embracing, among other factors, non-genetic forms of inheritance. While we appreciate the motivation for this recent extension, we argue that it does not go far enough, since it restricts itself to widening explanations of adaptation by adding mechanisms of inheritance and variation. A more thoroughgoing extension is needed, one that widens the explanatory scope of evolutionary theory. In addition to adaptation and its various mechanisms, evolutionary theory must recognize as a distinct intellectual challenge the origin of what we call “historical kinds.” Under historical kinds we include any process that acquires a quasi-independent and traceable lineage-history in biological and cultural evolution. We develop the notion of a historical kind in a series of paradigmatic exemplars, from genes and homologues to rituals and music, and we propose a preliminary characterization.

We hypothesize that the first ancestral "ante-cell" molecular structures, i.e., first RNAs and peptides which gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear there, have leaved traces in RNAs and genes of present cells. We propose a circular RNA which could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the ante-cells have begun to join the living organisms. In particular, we propose that in present RNAs (ribosomal or messenger) playing an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) belonging to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in evolution of species.

The evolutionary cancer cell biology ECCB explains the deep homology existent between the life cycle of cancer and protists. Due to their origin and sensitivity to oxygen, all non-gametogenic germ lines and germ stem cells (GSCs), including human adult stem cells (ASCs) and cancer stem cells (CSCs), suffer severe DNADSB damage when exposed to stress oxygen content above 6.0% O2 (Urgermline hyperoxia) as occurring in tissue and bloodstream. As a result, GSCs/ASCs/ CSCs lose their stemness potential and convert from a functional asymmetric cyclic phenotype (ACD phenotype) to a dysfunctional symmetric phenotype capable of defective symmetric proliferation (DSCD) that is, however, unable to repair genomic degradation. Genome reconstruction occurs through evolutionary mechanisms evolved by the common ancestor of amoebozoans, metazoans, and fungi. They complete the cycle by removing the damage, reconstructing the functional genome, and reinstating the ASC phenotype. The stemness life cycle acts not only during carcinogenesis, tumorigenesis, and recurrence but also in the life cycle of protists.

Genetic adaptation of Hermetia illucens (BSF) to suboptimal single sourced wastes can open new perspectives for insect production. Here, four replicate BSF lines were maintained on a single sourced, low-quality wheat bran diet (WB) or on a high-quality chicken feed diet (CF) for 13 generations. We continuously evaluated evolutionary responses in several performance traits to rearing at the two diets. Subsequently, we tested responses to interchanged diets, i.e. of larvae that had been adapted to low-quality feed and to high quality feed and vice versa to evaluate costs associated with diet adaptation. BSF were found to experience rapid adaptation to the diet composition. While performances on WB diet were always inferior to CF diet, the adaptive re-sponses were stronger to the former diet. This stronger response was likely due to stronger se-lection pressure experienced by BSF fed on the low-quality single sourced diet. The interchanged diet experiment found no costs associated with diet adaptation but confirmed diet adaptation for the larval biomass and insect frass traits for the WB diet treatment. Our results revealed that BSF can rapidly adapt to diet through evolutionary responses, which has potential to be utilized to produce lines tailored to specific diets.

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

Cooperation is widely recognized to be challenging for the well-balanced development of human societies. The emergence of cooperation in populations has been largely studied in the context of the Prisoner's Dilemma game, where temptation to defect and fear to be betrayed by others often activate defective strategies. In this paper we analyze the decision making mechanisms fostering cooperation in the two-strategy Stag-Hunt and Chicken games, which include the mixed strategy Nash equilibrium, describing partially cooperative behavior. We find the conditions for which cooperation is asymptotically stable in both full and partial cases, and we show that the partially cooperative steady state is also globally stable in the simplex. Furthermore, we show that the last can be more rewarding than the first, thus making the mixed strategy effective, although people cooperate at a lower level with respect to the maximum allowed, as it is reasonably expected in real situations. Our findings highlight the importance of Stag-Hunt and Chicken games in understanding the emergence of cooperation in social networks.

The outbreak of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused severe damage to the world. With the support of classic evolutionary theories and population genetics principles, many studies on the origin of SARS-CoV-2 have revealed encouraging results but meanwhile are still under debate. We are concerned with the validity of applying classic evolutionary theories and formula to the evolution of RNA viruses. We have raised several factors like the RNA replication feature and the RNA modification systems of the hosts, which might jeopardize the validity of the application of classic methods to analyze the SARS-CoV-2 data.

The establishment and spread of exotic species is a contemporary major concern. Alien species may become invasive in their new habitat, leading to both/either environmental and/or economic impacts. I briefly reviewed the literature in the last decade about the relationship of exotic species and native communities. I identified that professionals usually approach the subject in two main points of view: (1) researchers tend to point out the impacts of alien species on entire communities, evaluating if the relationship is positive, negative or neutral; (2) they focus on the eco-evolutionary processes involved in the introductions, the dynamics of invasion, and individual study cases. When evaluating the response of introductions to entire communities, evidence seems to be ambiguous and may support positive, negative or neutral relationship, especially depending on the scale approached. The unique eco-evolutionary pathways of each introduction may be a great shortcoming in the searching for generalities. On the other hand, advances have been made in understanding the dynamics of invasion on different lineages through a more selective/individualized approach. I suggest that the dynamics of invasion might be studied through a perspective in which different eco-evolutionary processes, levels of organization (from gene to entire communities), the history of the organism(s) and time are taken into account. Individual cases might be compared in attempt to understand how the relationship exotic and native works and in the search for generalities.

Understanding the evolutionary role of environmentally-induced phenotypic variation (i.e., environmental plasticity) is an important issue in developmental evolution. One of the major physiological responses to environmental changes is cellular stress, which is counteracted by a generic stress reaction that detoxifies the cell, refolds proteins, and repairs DNA damage. In this paper, we elaborate on a previous finding suggesting that the cell differentiation cascade of human decidual stromal cells, a cell type critical for embryo implantation and the maintenance of pregnancy, evolved from a cellular stress reaction. We hypothesize that the stress reaction in these cells was elicited ancestrally through the inflammation caused by embryo attachment and invasion. We describe a model, Stress-Induced Evolutionary Innovation (SIEI), whereby ancestral stress reactions and their corresponding pathways can be transformed into novel structural components of body plans, such as new cell types. After reviewing similarities and differences between SIEI and the “plasticity first hypothesis” (PFH) of evolution, we argue that SIEI is a distinct form of plasticity-based evolutionary change because it leads to the origin of novel structures rather than the adaptive transformation of a pre-existing character.

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

It is advisable to avoid and, even better, demystify such grandiose terms as "infinity" or "singularity" in the description of the cosmos. Its proliferation does not positively contribute to the understanding of key concepts that are essential for an updated account of its origin and evolutionary history. It will be here argued that, as a matter of fact, there are no infinities in physics, in the real world: all which appear, in any given formulation of nature by means of mathematical equations, actually arise from extrapolations, which are made beyond the bounds of validity of the equations themselves. Such crucial point is rather well-known, but too often forgotten, as discussed in this paper with several examples; namely, the famous Big Bang singularity and others, which appeared before in classical mechanics and electrodynamics, and notably in the quantization of field theories. A brief description of the Universe’s history and evolution follows. Special emphasis is put on what is presently known, from detailed observations of the cosmos and, complementarily, from advanced experiments of very-high-energy physics. To conclude, a future perspective on how this knowledge might soon improve is given.

The provenance of the aminoacyl-tRNA synthetases (aaRS) poses challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant “scaffold” shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics—residue-by-residue conservation, its variance, and row-by-row cladistic congruence—imply that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Phylogenetic metrics of different modules vary in accordance with their presumed functionality. A 46-residue Class I “protozyme” roots the Class I molecular tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.

The implementation of One Health/EcoHealth/Planetary Health approaches has been identified as key (i) to address the strong interconnections between risk for pandemics, climate change and biodiversity loss, and (ii) to develop and implement solutions to these interlinked crises. As a response to the multiple calls of scientists in that direction, we have put forward seven long term research questions regarding COVID-19 and emerging infectious diseases (EIDs) that are based on an effective integration of environmental, ecological, evolutionary, and social sciences to better anticipate and mitigate EIDs. Research needs cover the social-ecology of infectious disease agents, their evolution, the determinants of susceptibility of humans and animals to infections, and the human and ecological factors accelerating infectious disease emergence. For comprehensive investigation, they include the development of nature-based solutions to interlinked global planetary crises, addressing ethical and philosophical questions regarding the relationship of humans to nature and regarding transformative changes to safeguard the environment and human health. In support of this research, we propose the implementation of innovative multidisciplinary facilities embedded in social-ecosystems locally: the “ecological health observatories” and the “living laboratories”. This work has been carried out in the frame of the EC project HERA (www.HERAresearchEU.eu) that aims to set the priorities for an environment, climate and health research agenda in the EU by adopting a systemic approach in the face of global environmental change.

The present paper reviews evidence for ecological evolution of Montiaceae. Montiaceae (Portulacineae) comprise a family of ca. 275 species and ca. 25 subspecific taxa of flowering plants distributed mainly in extreme western America, with additional endemism elsewhere, including other continents and islands. They have diversified repeatedly across steep ecological gradients. Based on narrative analysis, I argue that phylogenetic transitions from annual to perennial life history have been more frequent than suggested by computational phylogenetic reconstructions. I suggest that a reported phylogenetic correlation between the evolution of life history and temperature niche is coincidental and not causal. I demonstrate how statistical phylogenetic comparative analysis (PhCA) missed evidence for marked moisture niche diversification among Montiaceae. I discount PhCA evidence for the relation between Montiaceae genome duplication and ecological diversification. Based on the present analysis of Montiaceae evolution, I criticize the premise of the prevalent statistical approach to PhCA, which tests Darwinian deterministic hypotheses against stochastic evolutionary null models. I discuss theoretical/empirical evidence that evolution is neither stochastic, nor Darwinistically-determined, but idiosyncratic. Idiosyncraticity describes the outcome of a stochastically perturbed nonlinear chaos-like process. The Principle of Evolutionary Idiosyncraticity (PEI) is based on the evolutionary theory of Natural Drift, which maintains that determinism in evolution is a property of the organism and not, as maintained by the theory of Natural Selection, its traits or its milieu. This determinism is characteristic of chaotic functions, which are absolutely determinate, generate self-similarity, but remain absolutely unpredictable. PEI explains precisely observations that evolution proceeds not linearly, but chaotically, producing both quasi-linear fractal-like patterns and non-linear jumps. PEI has ramifications for all areas of macroevolutionary research. In particular, it demonstrates both the fallacy and futility of the statistical PhCA approach that interprets evolutionary causes in terms of evolutionary correlations. However, statistical methods of PhCA can be applied heuristically and fruitfully to reveal idiosyncraticity and discover evolutionary novelty. This, in turn, is demonstrated by the emergence of statistical anomalies in evolutionary analyses of Montiaceae.

The challenge posed by multi-drug resistance (MDR) of pathogenic organisms, spectacularly manifested in the 6 “ESKAPE” bacterium (two Gram-positive, four Gram-negative) species, should invoke new comprehensive strategies, and needs cooperation of scientists with medical, veterinary and natural science background. This review is aimed at informing newcomers, coming from the field of biology and genetics, about problems related to rapidly emerging, new multi-drug resistant, pathogenic, bacteria. Unlike persistence, the antibiotic resistance is inherited. A functioning “resistance gene” makes a susceptible organism resistant to a given antibiotic, encoding for polypeptides capable of acting either as decomposing enzymes, or acting as trans-membrane pumps, or membrane structure components capable of modifying the permeability implementing a «by pass» mechanism enabling the antibiotic molecule to reach its cellular target(s). A functioning “sensitivity gene” encode for a polypeptide, capable (directly or indirectly) of transferring toxic molecules into target cells, or of metabolizing non-transferable to transferable, or non-toxic molecules to toxic derivatives. A gene of a normal function could act as a “sensitivity” gene in the presence of antibiotics of chemical structures similar to the natural substrate of the gene product, (enzyme or binding/ trans-membrane protein). The Agrocin 84 story is a good example. Multi-drug resistance is a phenotypic consequence of the sequential accumulation of mutations, and/or up-take of plasmids or genomic islands carrying resistance genes from the environment via horizontal gene transfer, mediated by conjugative plasmid or bacteriophage carrying mobile genetic elements. Both multi-drug resistance and collateral sensitivity are evolutionary products. Some revealed evolutionary process and their Lamarckian and Darwinian interpretations are discussed. Toolkits of comparative full-genome sequencing, genomics, experimental evolution and population genetics may provide perspectives for overcoming the invincibility of multi-drug panresistance. The status of some recently emerging pathogenic bacterium species with zoonic features and of veterinary background is also discussed.

Evolutionary Cancer Cell Biology (ECCB) reveals that the majority of cancer hallmarks trace their origins back to the premetazoic era, approximately 2300 to 1750 million years ago. These cancer hallmarks share deep homology with the oxygen-sensitive non-gametogenic NG (Urgermline), which evolved from the common ancestor of Amoebozoan, Metazoan, and Fungi (AMF). The genes, gene modules, and gene regulatory networks of the premetazoic cell system are preserved in the ancestral genome compartment of metazoans and humans. The Urgermline serves as a blueprint for all germ and stem cell lineages, including parasitic amoebae. As observed in amoebae, DNA double-strand breaks (DSBs) manifest in the homologous recombination (HR) genes of NG germlines and stem cell lineages when exposed to specific hyperoxic conditions, referred to as AMF hyperoxia, characterized by an oxygen content exceeding 6.0%. The cells lose their stemness and differentiation potential but persist in proliferation as low-grade polyploids (4n) through defective symmetric cell division (DSCD phenotype). Genomic integrity can be restored through homotypic cell and nuclear fusion, resulting in the formation of high-grade polyploids known as multinuclear genome repair syncytia (MGRSs), or by inductive hyperpolyploidization of more than 64n, as observed in single-celled polyploid giant cancer cells (PGCCs). Interestingly, low-, middle-, and high-grade polyploidization are not exclusive to cancer. Therefore, we investigate (i) functional polyploidies that occur in healthy cells, including humans, mammals, and protists, (ii) dysfunctional polyploidies that occur in cells with impaired homologous recombination (HR) and irreparable DNA DSB defects, and (iii) the restoration of genome integrity through cyst-like and high-grade polyploidization events. Additionally, we explore dysfunction in aging stem cells, hepatocytes, and cardiomyocytes

Evolutionary algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1 and Part2, the author previously reported experimental evaluations on Genetic Algorithm and Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part3 of the series of comparative research. In this study, Differential Evolution is adopted as the third instance of major evolutionary algorithms. The experimental results show a statistically significant superiority of DE over both GA and ES (p < .01). In addition, DE exhibits its robustness to variations in hyperparameter configurations (the number of offsprings and generations). In the previous experiments, both ES and GA showed significant performance differences depending on the configurations, whereas in the experiment reported in this article, such differences are not detected for DE.

Using metaheuristics, such as the Evolutionary Algorithm (EA), within control structures is a realistic approach for certain optimal control problems. Their role is often predicting the optimal control values over a prediction horizon using a process model (PM). The big computational effort sometimes causes problems. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA, PM). A positive answer is given by proposing datasets apprehending this couple's optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA, PM) and yielded efficient controllers.

Evolutionary algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. In this study, Genetic Algorithm is adopted as another instance of major evolutionary algorithms. Experimental results demonstrate that there was no statistically significant difference between the experimental performances of GA and ES, but the priority of generations and offsprings was different; GA performed better with a greater number of generations while ES performed better with a greater number of offsprings. Eight hidden units were the best among four variations (4, 8, 16 or 32 units), which aligns with previous study using ES.

Evolutionary algorithms find applicability in the reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the pendulum control task. In this study, the Acrobot control task is adopted as another task. Experimental results demonstrate that ES successfully trained a Multi-Layer Perceptron to achieve a remarkable height of 99.85% concerning the maximum height. However, the trained MLP failed to maintain the chain end in an upright position throughout an episode. In this study, it was observed that employing 8 hidden units in the neural network yielded better results with statistical significance compared to using 4, 16, or 32 hidden units. Furthermore, the findings indicate that a larger population size in ES led to a more extensive exploration of potential solutions over a greater number of generations, which aligns with the previous study.

The factors that determine the relative rates of amino acid substitution during protein evolution are complex and they are known to vary among taxa. We estimated relative exchangeabilities for pairs of amino acids from clades spread across the tree of life and assessed the historical signal in the distances among these clade-specific models. We trained these models separately on collections of arbitrarily selected protein alignments and on ribosomal protein alignments. In both cases we found a clear separation between the models trained using multiple sequence alignments from bacterial clades and the models trained on archaeal and eukaryotic data. We assessed the predictive power of our novel clade-specific models of sequence evolution by asking whether fit to the models could be used to identify the source of multiple sequence alignments. Model fit was generally able to classify protein alignments correctly at the level of domain (bacterial versus archaeal), but the accuracy of classification at finer scales was much lower. The only exceptions to this were the relatively high classification accuracy for two archaeal lineages: Halobacteriaceae and Thermoprotei. Genomic GC content had a modest impact on relative exchangeabilities despite having a large impact on amino acid frequencies. Relative exchangeabilities involving aromatic residues exhibited the largest differences among models. There were a small number of exchangeabilities that exhibited large differences in comparisons among major clades and between generalized models and ribosomal protein models. Taken as a whole, these results reveal that a small number of relative exchangeabilities are responsible for much of the structure of the “model space” for protein sequence evolution. If we look beyond the information that these clade-specific models reveal about protein evolution the models themselves are likely to be useful tools for phylogenomic inference across the tree of life.

The history of life on Earth has been shaped by a series of major evolutionary innovations. While some of these innovations occur repeatedly, some of the most important innovations are evolutionary singularities, arising only once. This fact has often been interpreted to mean that singularities are particularly difficult, low-probability evolutionary events, implying the long-term course of life on Earth is highly contingent. Alternatively, singularities could arise from evolutionary priority effects, where first-movers suppress subsequent independent origins. Here, we disentangle these hypotheses by examining the origins of phototrophy. The ability to use light for energy evolved independently twice, preserving information about their origins that is lost when examining singularities. We show that the two forms of phototrophy occupy opposite ends of key trade-offs: most importantly efficiency of light capture vs. return on investment. Our results suggest that the dual singularity of phototrophy exists due to evolutionary interactions between nascent phototrophs, within a phototrophic niche space too large for a single first mover to fill all niches and fully suppress all future innovation. While often ignored over geological time scales, ecological interactions and evolutionary priority effects may play a fundamental role in the tempo and mode of major evolutionary innovations.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

A few empirical examples document fixed alternative male mating strategies in animals. Here we focus on the polymorphism of male mating strategies in the ruff (Calidris pugnax, Aves Charadriiformes). In ruffs, three fixed alternative male mating strategies coexist and are signaled by extreme plumage polymorphism. We first present relevant data on the biology of the species. Then we review the available knowledge of the behavioral ecology of ruffs during the breeding season and we detail the characteristics of each of the three known fixed male mating strategies. We next turn to the exceptional quality results accumulated on both the structural and functional genomics of the ruff over the past few years. We show how much these genomic data can shed a new, mechanistic light on the evolution and maintenance of the three fixed alternative male mating strategies. We then look if there are sufficient indication to support frequency-dependent selection as key mechanism in maintaining these three strategies. Specifically, we search for evidences of equal fitness among individuals using each of the three strategies. Finally, we propose three lines of research avenues that will help to understand the eco-evolutionary dynamics of phenotypic differences within natural populations of this iconic model species.

This work deals with aerofoil aerodynamic features optimization, not only to improve flight features, but also to improve economy, ecology and safety of parameters of flight technique. In cruise mission, occupying the most flight time, the most important parameter is aerodynamic drag, which directly influences the aeroplane operational economy of transportation. Drag reduction is adequately reflected in the fuel consumption reduction. Consumption reduction is also adequately reflected in the flight ecology. In take-off and landing mission, the safety is priority and directly influences the aerofoil geometry. For cruise mission the new modified evolutionary algorithms (EA) are used to parameters incoming to Bezier-PARSEC 3434 parametrization. Such aerofoil is processed and evaluated by the Xfoil program. The change of model parameters results to optimal aerofoil shape. The DCAG (Direct Control Aerofoil Geometry) is unique developed mechanical device, makes possible the change of curvature of aerofoil, and also aerofoil geometry. DCAG is based on the rotary principle, which makes it possible to define the curvature of aerofoil for every roll as well as defining the geometry in the variable parts of aerofoil. For take-off and landing mission the best combination of slots and flaps is choosed. To improve of laminarity and reduce turbulent flow the DCAG is used. The work results to optimization, which is 50 times faster in comparison to ordinary optimization, with minimum of input parameters (flight speed, chord length, range of angles of attack and fitness function). The optimized aerofoil can achieve savings in fuel consumption up to 44% in comparison with unoptimized aerofoil, the aerodynamic drag reduction up to 44%. The output was checked by ANSYS Fluent simulation.