Self domestication and the evolution of language

We set out an account of how self-domestication plays a crucial role in the evolution of language. In doing so, we focus on the growing body of work that treats language structure as emerging from the process ofcultural transmission. We argue that a full recognition of the importance of cultural transmission fundamentally changes the kind ofquestionswe should be asking regarding the biological basis of language structure. If we think of language structure as reflecting an accumulated set of changes in our genome, then we might ask something like, "What are the genetic bases of language structure and why were they selected?" However, if cultural evolution can account for language structure, then this question no longer applies. Instead, we face the task of accounting for the origin of the traits that enabled that process of structure-creating cultural evolution to get started in the first place. In light of work on cultural evolution, then, the new question for biological evolution becomes, "How did those precursor traits evolve?" We identify two key precursor traits: (1) the transmission of the communication system throughlearning; and (2) the ability to infer thecommunicative intentassociated with a signal or action. We then describe two comparative case studies-the Bengalese finch and the domestic dog-in which parallel traits can be seen emerging followingdomestication. Finally, we turn to the role of domestication in human evolution. We argue that the cultural evolution of language structure has its origin in an earlier process of self-domestication.</p

Land-use effects on local biodiversity in tropical forests vary between continents

The file attached is the Published/publisher’s pdf version of the article

Development and external validation of a head and neck cancer risk prediction model

\ua9 2024 The Author(s). Head &amp; Neck published by Wiley Periodicals LLC. Background: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. Methods: The IARC-ARCAGE European case–control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74–0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61–0.64). Conclusion: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction

Fermi surface dichotomy of the superconducting gap and pseudogap in underdoped pnictides

High-temperature superconductivity in iron-arsenic materials (pnictides) near an antiferromagnetic phase raises the possibility of spin-fluctuation-mediated pairing. However, the interplay between antiferromagnetic fluctuations and superconductivity remains unclear in the underdoped regime, which is closer to the antiferromagnetic phase. Here we report that the superconducting gap of the underdoped pnictides scales linearly with the transition temperature, and that a distinct pseudogap coexisting with the SC gap develops on underdoping. This pseudogap occurs on Fermi surface sheets connected by the antiferromagnetic wavevector, where the superconducting pairing is stronger as well, suggesting that antiferromagnetic fluctuations drive both the pseudogap and superconductivity. Interestingly, we found that the pseudogap and the spectral lineshape vary with the Fermi surface quasi-nesting conditions in a fashion that shares similarities with the nodal-antinodal dichotomous behaviour observed in underdoped copper oxide superconductors.Comment: Main Manuscript: 19 pages, 3 figures; Supplementary Information: 10 pages, 7 figure

Development and external validation of a head and neck cancer risk prediction model

Background: Head and Neck Cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. Methods: The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioural and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking and alcohol variables had moderate discrimination, with an Area Under Curve (AUC) value of 0.75 (95% CI, 0.74 - 0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384,616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61 - 0.64). Conclusions: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviours are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction