Williams syndrome (WS) is a genetic condition characterized by atypical brain structure, cognitive deficits, and a life-long fascination with faces. Face recognition is relatively spared in WS, despite abnormalities in aspects of face processing and structural alterations in the fusiform gyrus, part of the ventral visual stream. Thus, face recognition in WS may be subserved by abnormal neural substrates in the ventral stream. To test this hypothesis, we used functional magnetic resonance imaging and examined the fusiform face area (FFA), which is implicated in face recognition in typically developed (TD) individuals, but its role in WS is not well understood. We found that the FFA was approximately two times larger among WS than TD participants (both absolutely and relative to the fusiform gyrus), despite apparently normal levels of face recognition performance on a Benton face recognition test. Thus, a larger FFA may play a role in face recognition proficiency among WS.
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http://dx.doi.org/10.1523/JNEUROSCI.4268-09.2010 | DOI Listing |
J Am Acad Dermatol
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Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL.
Frontal Fibrosing Alopecia (FFA) is a primary lymphocytic cicatricial alopecia predominantly affecting postmenopausal Caucasian women. It is characterized by a progressive frontotemporal hairline recession that presents as a scarring hairless band and is often accompanied by eyebrow and body hair loss. Although initially described in postmenopausal women, FFA has been observed in a broader demographic, including premenopausal women and occasionally men.
View Article and Find Full Text PDFS Afr J Physiother
December 2024
Department of Physiotherapy, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Background: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of morbidity and mortality in South Africa. Physiotherapy practice and factors that influence management of patients with AECOPD are unknown.
Objectives: To explore physiotherapy practice in the management of patients with AECOPD in South African private healthcare settings and to identify and describe factors that influence physiotherapy patient management.
Atten Percept Psychophys
January 2025
Department of Psychology, Rutgers University - New Brunswick, 152 Frelinghuysen Rd, Piscataway, NJ, 08854, USA.
Human observers can often judge emotional or affective states from bodily motion, even in the absence of facial information, but the mechanisms underlying this inference are not completely understood. Important clues come from the literature on "biological motion" using point-light displays (PLDs), which convey human action, and possibly emotion, apparently on the basis of body movements alone. However, most studies have used simplified and often exaggerated displays chosen to convey emotions as clearly as possible.
View Article and Find Full Text PDFPLoS One
January 2025
Xinjiang Institute of Technology, Aksu, China.
Facial expression recognition faces great challenges due to factors such as face similarity, image quality, and age variation. Although various existing end-to-end Convolutional Neural Network (CNN) architectures have achieved good classification results in facial expression recognition tasks, these network architectures share a common drawback that the convolutional kernel can only compute the correlation between elements of a localized region when extracting expression features from an image. This leads to difficulties for the network to explore the relationship between all the elements that make up a complete expression.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical and Control Engineering, North China University of Technology, Beijing, China.
This paper proposes a new strategy for analysing and detecting abnormal passenger behavior and abnormal objects on buses. First, a library of abnormal passenger behaviors and objects on buses is established. Then, a new mask detection and abnormal object detection and analysis (MD-AODA) algorithm is proposed.
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