This article introduces the Special Issue and its focus on research in language evolution with emphasis on theory as well as computational and robotic modeling. A key theme is based on the growth of evolutionary developmental biology or evo-devo. The Special Issue consists of 13 articles organized in two sections: A) Theoretical foundations and B) Modeling and simulation studies. All the papers are interdisciplinary in nature, encompassing work in biological and linguistic foundations for the study of language evolution as well as a variety of computational and robotic modeling efforts shedding light on how language may be developed and may have evolved.
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http://dx.doi.org/10.1111/tops.12204 | DOI Listing |
PLoS One
January 2025
School of Foreign Languages, East China University of Science and Technology, Shanghai, China.
Language policy plays a pivotal role in sustaining language behaviors and transforming language ideologies into practices. While the analysis of language policies in international organizations has received increasing attention, the evolution of language policies in the Association of Southeast Asian Nations (ASEAN) has been understudied. Existing research on ASEAN's language policies has concentrated on its official language, often overlooking the language practices and ideologies embedded within these policies.
View Article and Find Full Text PDFExpert Rev Clin Pharmacol
January 2025
Department of Psychiatry, McGill University, Montréal, Canada.
Introduction: Since its synthesis in 1962, ketamine has been widely used in diverse medical contexts, from anesthesia to treatment-resistant depression. However, interpretations of ketamine's subjective effects remain polarized. Biomedical frameworks typically construe the drug's experiential effects as dissociative or psychotomimetic, while psychedelic paradigms emphasize the potential therapeutic merits of these non-ordinary states.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
School of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, China.
Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input.
View Article and Find Full Text PDFCurr Opin Struct Biol
January 2025
Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA; Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA. Electronic address:
There is an ever-increasing need for accurate and efficient methods to identify protein homologs. Traditionally, sequence similarity-based methods have dominated protein homolog identification for function identification, but these struggle when the sequence identity between the pairs is low. Recently, transformer architecture-based deep learning methods have achieved breakthrough performances in many fields.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Biological Sciences, University of Adelaide, Adelaide, SA 5000, Australia; The Environment Institute, University of Adelaide, Adelaide, SA 5000, Australia; Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark; Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark. Electronic address:
Human overexploitation contributed strongly to the loss of hundreds of bird species across Oceania, including nine giant, flightless birds called moa. The inevitability of anthropogenic moa extinctions in New Zealand has been fiercely debated. However, we can now rigorously evaluate their extinction drivers using spatially explicit demographic models capturing species-specific interactions between moa, natural climates and landscapes, and human colonists.
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