An adaptation of roughness discrimination task successfully used on rats was performed on mice. It was found that mice can master discrimination of rough surfaces using only mystacial vibrissae. This task can be used for studying sensory abilities of genetically modified mice as well as dynamics and pharmacology of complex sensory learning.
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http://dx.doi.org/10.55782/ane-2001-1386 | DOI Listing |
Forensic Sci Int
January 2025
Departamento de Química. Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto. Universidade de São Paulo, Avenida Bandeirantes, 3900, Ribeirão Preto, São Paulo CEP 14040-901, Brazil; Instituto Nacional de Ciência e Tecnologia Ciências Forenses (INCT-Forense), Departamento de Química, Faculdade de Filosofia Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brasil; Programa de Cooperação Acadêmica - Segurança Pública e Ciências Forenses (PROCAD-SPCF), Departamento de Química, Faculdade de Filosofia Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brasil. Electronic address:
Forensic gemstone analysis faces many challenges. One of the most critical aspects of gemological research is the classification of a gemstone. It is necessary to understand the mineral species, purity, origin, and identification of treatments to identify and classify a gem correctly, as well as assign a monetary value to it.
View Article and Find Full Text PDFSci Rep
December 2024
School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.
Feeling a texture typically involves sliding the fingers of a hand across that surface or rubbing the surface between the thumb and another digit. Texture signals appear to be integrated across the digits of a hand with perceived roughness at one finger swayed in the direction of texture touched by another finger of the same hand. To date, one study has reported similar integrative effects when the pairs of digits belong to different hands.
View Article and Find Full Text PDFBiomed Opt Express
December 2024
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (UESTC), Huzhou 313001, China.
The problems of complex background, low quality of finger vein images, and poor discriminative features have been the bottleneck of feature extraction and finger vein recognition. To this end, we propose a feature extraction algorithm based on the open-set testing protocol. In order to eliminate the interference of irrelevant areas, this paper proposes the idea of segmentation-assisted classification, that is, using the rough mask of the finger vein to constrain the feature learning process so that the network can focus on the vein area and learn greater weight for the vein.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Mathematics, University of Management and Technology, Lahore, 54000, Pakistan.
Selecting the best power source that is legal, affordable, environmentally friendly, and able to ensure long-term viability is a difficult but vital task. Existing frameworks based on traditional fuzzy and soft sets are unable to adequately capture the complexity of the optimal energy system selection (ESS). These decision models may also be complex, especially when rough data and integrity need to be taken into account.
View Article and Find Full Text PDFJ Voice
December 2024
Department of Otolaryngology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Unlabelled: This cross-sectional study aimed to examine acoustic voice parameters among older Thai individuals in Chiang Mai-a northern Thai province with the third-largest elderly population and significant PM exposure-and to compare these measures between normal and abnormal voice groups within this population.
Method: The study involved 125 Thai elderly with normal voices, who were selected through a multi-stage sampling process and assessed by using the GRBASI scale (Grade, Roughness, Breathiness, Asthenia, Strain, Instability), and 45 elderly Thai individuals with abnormal voices, selected through purposive sampling and diagnosed by an Ear, Nose, and Throat (ENT) doctor or assessed by using the GRBASI scale. Additionally, participants with normal voices underwent a speech discrimination test before voice recording.
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