Brain size variation is often attributed to energetic trade-offs with other metabolically expensive tissues and organs, which is a prediction of the expensive brain hypothesis (EBH). Here we examine Asiatic toads (Bufo gargarizans) along altitudinal gradients and test size trade-offs between the brain and four visceral organs (heart, liver, alimentary tract, and kidney) with altitude. Body size and scaled mass index (a proxy for total energy intake) decline with altitude, implying stronger energetic constraints at high altitudes. Relative brain size decreases along altitudinal gradients, while visceral organs mostly increase in relative sizes. Using structural equation modeling, a significant negative relationship between brain size and a latent variable "budget," which represents the energy allocation to the four visceral organs, is detected among high-altitudinal toads. Heart appears to have the largest and most consistent response to changes in energy allocation. No such relationships are observed among toads at middle- and low-altitudes, where high energy intake may allow individuals to forego energetic trade-offs. When applying EBH to poikilotherms, a great emphasis should be placed on total energy intake in addition to energy allocation. Future research on EBH will benefit from more intra-specific comparisons and the evaluation of fitness consequences beyond energy limitation.
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http://dx.doi.org/10.1093/evolut/qpae137 | DOI Listing |
J Tradit Complement Med
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Graduate Institute of Acupuncture Science, China Medical University, Taichung, Taiwan.
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View Article and Find Full Text PDFBr J Vis Impair
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The Laboratory for Visual Neuroplasticity, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, USA.
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View Article and Find Full Text PDFCureus
December 2024
Department of Radiology, Aichi Medical University, Nagakute, JPN.
Purpose In linac-based stereotactic radiosurgery (SRS) utilizing a multileaf collimator (MLC) for brain metastases (BMs), a volumetric-modulated arc (VMA) technique is indispensable for generating a suitable dose distribution with efficient planning and delivery. However, the optimal calculation grid spacing (GS) and statistical uncertainty (SU) of the Monte Carlo algorithm for VMA optimization have yet to be determined. This planning study aimed to examine the impacts of GS and GU settings on VMA-based SRS planning and to find the optimal combination for templating.
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January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
View Article and Find Full Text PDFAm J Stem Cells
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Razi Herbal Medicines Research Center, Lorestan University of Medical Sciences Khorramabad, Iran.
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