Given humans' habitual use of screens, they rarely consider potential differences when viewing two-dimensional (2D) stimuli and real-world versions of dimensional stimuli. Dogs also have access to many forms of screens and touchpads, with owners even subscribing to dog-directed content. Humans understand that 2D stimuli are representations of real-world objects, but do dogs? In canine cognition studies, 2D stimuli are almost always used to study what is normally 3D, like faces, and may assume that both 2D and 3D stimuli are represented in the brain the same way. Here, we used awake fMRI in 15 dogs to examine the neural mechanisms underlying dogs' perception of two- and three-dimensional objects after the dogs were trained on either two- or three-dimensional versions of the objects. Activation within reward processing regions and parietal cortex of the dog brain to 2D and 3D versions of objects was determined by their training experience, as dogs trained on one dimensionality showed greater differential activation within the dimension on which they were trained. These results show that dogs do not automatically generalize between two- and three-dimensional versions of object stimuli and suggest that future research consider the implicit assumptions when using pictures or videos.
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http://dx.doi.org/10.1007/s10071-021-01506-3 | DOI Listing |
FASEB J
January 2025
State Key Laboratory of Swine and Poultry Breeding Industry, Sichuan Agricultural University, Chengdu, China.
Triglyceride (TG) metabolism is a complex and highly coordinated biological process regulated by a series of genes, and its dysregulation can lead to the occurrence of disorders in lipid metabolism. However, the transcriptional regulatory mechanisms of crucial genes in TG metabolism mediated by enhancer-promoter interactions remain elusive. Here, we identified candidate enhancers regulating the Agpat2, Dgat1, Dgat2, Pnpla2, and Lipe genes in 3T3-L1 adipocytes by integrating epigenomic data (H3K27ac, H3K4me1, and DHS-seq) with chromatin three-dimensional interaction data.
View Article and Find Full Text PDFSurg Radiol Anat
January 2025
Anatomy Department, University of Western Brittany (UBO), Brest, France.
Purpose: The aim was to establish a functional MRI protocol for analyzing human stereoscopic vision in clinical practice. The feasibility was established in a cohort of 9 healthy subjects to determine the functional cortical areas responsible for virtually relief vision.
Methods: Nine healthy right-handed subjects underwent orthoptic examination and functional MRI.
J Opt Soc Am A Opt Image Sci Vis
August 2024
A three-dimensional (3D) waveguide model is applied in extreme ultraviolet (EUV) lithography simulations. The 3D waveguide model is equivalent to rigorous coupled-wave analysis, but fewer field components are used to solve Maxwell's equations. The 3D waveguide model uses two components of vector potential, and , corresponding to the two polarizations.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
University of Twente, Faculty of Engineering Technology, Applied Mechanics and Data Analysis, Drienerlolaan 5, 7522 NG Enschede, The Netherlands.
A solution method to improve an anechoic chamber at low frequencies with the use of active noise control is presented. The approach uses the Kirchhoff-Helmholtz integral to compute the reflected sound field resulting from the primary sources together with an algorithm to compute the filter coefficients of a controller driving secondary sources on the walls of the enclosure using reference signals as inputs, which are measured on a contour enclosing the primary sources. A causal frequency domain method with conjugate gradient iterations is derived to determine the controller.
View Article and Find Full Text PDFInt Endod J
January 2025
OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, Leuven, Belgium.
Aim: To develop and validate an artificial intelligence (AI)-powered tool based on convolutional neural network (CNN) for automatic segmentation of root canals in single-rooted teeth using cone-beam computed tomography (CBCT).
Methodology: A total of 69 CBCT scans were retrospectively recruited from a hospital database and acquired from two devices with varying protocols. These scans were randomly assigned to the training (n = 31, 88 teeth), validation (n = 8, 15 teeth) and testing (n = 30, 120 teeth) sets.
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