Convergent evolution is an important phenomenon in the history of life. Despite this, there is no common definition of convergence used by biologists. Instead, several conceptually different definitions are employed. The primary dichotomy is between pattern-based definitions, where independently evolved similarity is sufficient for convergence, and process-based definitions, where convergence requires a certain process to produce this similarity. The unacknowledged diversity of definitions can lead to problems in evolutionary research. Process-based definitions may bias researchers away from studying or recognizing other sources of independently evolved similarity, or lead researchers to interpret convergent patterns as necessarily caused by a given process. Thus, pattern-based definitions are recommended. Existing measures of convergence are reviewed, and two new measures are developed. Both are pattern based and conceptually minimal, quantifying nothing but independently evolved similarity. One quantifies the amount of phenotypic distance between two lineages that is closed by subsequent evolution; the other simply counts the number of lineages entering a region of phenotypic space. The behavior of these measures is explored in simulations; both show acceptable Type I and Type II error. The study of convergent evolution will be facilitated if researchers are explicit about working definitions of convergence and adopt a standard toolbox of convergence measures.
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http://dx.doi.org/10.1111/evo.12729 | DOI Listing |
Cell Commun Signal
January 2025
Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
One hallmark of cancer is the upregulation and dependency on glucose metabolism to fuel macromolecule biosynthesis and rapid proliferation. Despite significant pre-clinical effort to exploit this pathway, additional mechanistic insights are necessary to prioritize the diversity of metabolic adaptations upon acute loss of glucose metabolism. Here, we investigated a potent small molecule inhibitor to Class I glucose transporters, KL-11743, using glycolytic leukemia cell lines and patient-based model systems.
View Article and Find Full Text PDFMol Plant Pathol
January 2025
Faculty of Bioscience Engineering, Ghent University, Gent, Belgium.
In the coevolutionary process between plant pathogens and hosts, pathogen effectors, primarily proteinaceous, engage in interactions with host proteins, such as plant transcription factors (TFs), during the infection process. This review delves into the intricate interplay between TFs and effectors, a key aspect in the prolonged and complex battle between plants and pathogens. Effectors strategically manipulate TFs using diverse tactics.
View Article and Find Full Text PDFGenome Biol Evol
January 2025
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA 15219.
Homology is a key concept underpinning the comparison of sequences across organisms. Sequence-level homology is based on a statistical framework optimized over decades of work. Recently, computational protein structure prediction has enabled large-scale homology inference beyond the limits of accurate sequence alignment.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
School of Petroleum Engineering, Yangtze University, Wuhan 430100, China.
Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO's search capabilities.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
Technology Research and Development Centre, Xuelong Group Co., Ltd., Ningbo 315899, China.
To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm's global optimization capability.
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