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Introduction: Clinical understanding of primary progressive aphasia (PPA) has been primarily derived from Indo-European languages. Generalizing certain linguistic findings across languages is unfitting due to contrasting linguistic structures. While PPA patients showed noun classes impairments, Chinese languages lack noun classes. Instead, Chinese languages are classifier language, and how PPA patients manipulate classifiers is unknown.
Methods: We included 74 native Chinese speakers (22 controls, 52 PPA). For classifier production task, participants were asked to produce the classifiers of high-frequency items. In a classifier recognition task, participants were asked to choose the correct classifier.
Results: Both semantic variant (sv) PPA and logopenic variant (lv) PPA scored significantly lower in classifier production task. In classifier recognition task, lvPPA patients outperformed svPPA patients. The classifier production scores were correlated to cortical volume over left temporal and visual association cortices.
Discussion: This study highlights noun classifiers as linguistic markers to discriminate PPA syndromes in Chinese speakers.
Highlights: Noun classifier processing varies in the different primary progressive aphasia (PPA) variants. Specifically, semantic variant PPA (svPPA) and logopenic variant PPA (lvPPA) patients showed significantly lower ability in producing specific classifiers. Compared to lvPPA, svPPA patients were less able to choose the accurate classifiers when presented with choices. In svPPA, classifier production score was positively correlated with gray matter volume over bilateral temporal and left visual association cortices in svPPA. Conversely, classifier production performance was correlated with volumetric changes over left ventral temporal and bilateral frontal regions in lvPPA. Comparable performance of mass and count classifier were noted in Chinese PPA patients, suggesting a common cognitive process between mass and count classifiers in Chinese languages.
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http://dx.doi.org/10.1002/alz.13701 | DOI Listing |
PLoS One
September 2025
Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national accreditation system.
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J Vis Exp
August 2025
Chitkara University Institute of Engineering & Technology, Chitkara University.
Emotion annotation in code-mixed languages like Hinglish (Hindi-English) presents unique challenges due to linguistic complexity and resource constraints. This study introduces a hybrid active learning framework that combines lexical rules, machine learning, and iterative expert feedback to achieve cost-efficient, high-accuracy emotion annotation. Grounded in psychological theories of emotion, including Discrete Emotions Theory and Cognitive Appraisal Theory, the framework employs bilingual emotion dictionaries (e.
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September 2025
Department of Pathology, Hospital Tuanku Fauziah, Jalan Tun Abdul Razak, Kangar, Perlis, Malaysia.
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July 2025
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center, Qingdao 266237, China. Electronic address: jinxianliu@gmail
Determination of evolutionary mechanisms underlying innovative traits is crucial for understanding the vast diversity of species and phenotypes. Given their respiratory physiologies, fishes are compelling subjects for evolutionary analysis of the hemoprotein-based oxygen-transport systems. Asian noodlefishes (Osmeriformes: Salangidae) and Antarctic icefishes (Notothenioidei: Channichthyidae) are examples of fish clades that generally do not express myoglobin or hemoglobin.
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Department of Surgery, Federal University of Santa Catarina, Florianópolis, SC, Brazil.
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