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| http://dx.doi.org/10.2340/actadv.v105.42730 | DOI Listing |
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Hemispheric asymmetries in face recognition in health and dysfunction.
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Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States. Electronic address:
A defining characteristic of the human brain is that, notwithstanding the clear anatomic similarities, the two cerebral hemispheres have several different functional superiorities. The focus of this chapter is on the hemispheric asymmetry associated with the function of face identity processing, a finely tuned and expert behavior for almost all humans that is acquired incidentally from birth and continues to be refined through early adulthood. The first section lays out the well-accepted doctrine that face perception is a product of the right hemisphere, a finding based on longstanding behavioral data from healthy adult human observers.
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Corporations, as major contributors to carbon emissions, face mounting pressure to adopt genuine sustainability practices. However, the rise of Environmental, Social, and Governance (ESG) disclosures has concurrently given rise to greenwashing-misleading claims that erode stakeholder trust and obstruct sustainability progress. Despite the widespread acknowledgment of greenwashing, there is a notable gap in research identifying the key factors that can mitigate it.
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Garcin syndrome is an exceptional clinical condition characterized by progressive multiple ipsilateral cranial nerve involvement which is caused by malignant osteoclastic lesion at skull base. We report a rare case of Garcin syndrome which was misdiagnosed as Bell's palsy. A 69-year-old lady, presented with generalized headache and right facial nerve palsy in which progressively worsened even after treatment with corticosteroid.
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National Academy of Engineering, Washington, DC 20001, USA.
Recent developments in artificial intelligence (AI) and machine learning (ML), driven by unprecedented data and computing capabilities, have transformed fields from computer vision to medicine, beginning to influence culture at large. These advances face key challenges: accuracy and trustworthiness issues, security vulnerabilities, algorithmic bias, lack of interpretability, and performance degradation when deployment conditions differ from training data. Fields lacking large datasets have yet to see similar impacts.
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