The literature suggests that deaf individuals lag behind their hearing peers in terms of mathematical abilities. However, it is still unknown how unique sensorimotor experiences, like deafness, might shape number-space interactions. We still do not know either the spatial frame of reference deaf individuals use to map numbers onto space in different numerical tasks. To examine these issues, deaf, hearing signer and hearing control adults were asked to perform a number comparison and a parity judgment task with the hands uncrossed and crossed over the body midline. Deafness appears to selectively affect the performance of the numerical task relying on verbal processes while keeping intact the task relying on visuospatial processes. Indeed, while a classic SNARC effect was found in all groups and in both hand postures of the number comparison task, deaf adults did not show the SNARC effect in both hand postures of the parity judgment task. These results are discussed in light of the spatial component characterizing the counting system used in sign language.
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http://dx.doi.org/10.1038/s41598-022-14728-3 | DOI Listing |
Sci Rep
December 2024
Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Amblyopia affects more than visual acuity. To compare the performances of visual selective attention and numerical processing in children with anisometropic amblyopia and children with normal vision, and investigate whether performance would be improved after visual acuity recovery, we performed 3 visual attention tasks (identifying number location task, numerical comparison task, and specific number comparison task) in children with anisometropic amblyopia, children who had recovered from anisometropic amblyopia, and children with normal vision in 6-8 and 9-11 years groups. The numerical processing ability, visual selective attention, and numerical distance effect were assessed by their reaction time of different tasks.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Tokyo University of Science, Shinjuku, Tokyo, 162-8601, Japan.
Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of reservoir computing is that only linear output, given by a linear combination of reservoir variables, is trained. Inspired by recent mathematical studies of generalized synchronization, we propose a novel reservoir computing framework with a generalized readout, including a nonlinear combination of reservoir variables.
View Article and Find Full Text PDFAust J Rural Health
February 2025
Western Australian Centre for Rural Health, University of Western Australia, Crawley, Western Australia, Australia.
Objective: To explore changes to rural nursing and allied health placements during the latter stage of the COVID-19 public health emergency.
Setting: Regional, rural and remote Australia.
Participants: Nursing and allied health students with a scheduled University Department of Rural Health (UDRH) facilitated rural placement between 1 January 2022 and 31 October 2022.
Front Public Health
December 2024
Division of Community Internal Medicine, Geriatrics, and Palliative Care, Department of Medicine, Mayo Clinic, Rochester, MN, United States.
Objective: Individuals with Limited English Proficiency (LEP), including Spanish-preferred patients, face healthcare challenges due to language barriers. Despite the potential of digital health technologies to improve access and outcomes, there is a "digital divide" with underutilization among vulnerable populations, including Spanish-speaking LEP individuals, highlighting a need for increased understanding and equitable digital health solutions.
Materials And Methods: A multi-mode, multi-language cross-sectional survey was built based on the Technology Acceptance Model and deployed from a multi-state healthcare practice.
Objective: Delays in discharges have a downstream effect on emergency department admissions, wait times, intensive care unit transfers, and elective admissions. This quality improvement project's aim was to increase the percentage of discharges before noon from a hospital medicine service from 19% to 30% over a 6-month period and sustain the increase for 6 months.
Methods: Interventions included introduction of a dedicated patient flow provider (PFP), optimization of workflow, technology assistance with discharge tasks, and multidisciplinary education on patient flow.
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