It is controversial whether providing visual word segmentation cues can improve Chinese reading performance. This study investigated this topic by examining how visual word segmentation cues such as grey highlighting, red colour and interword spacing influence global sentence reading and local word recognition during reading Chinese text in three experiments. The results showed that interword spacing could facilitate local word recognition but could not increase reading speed. In contrast, grey highlighting and red colour could improve neither local word recognition nor global sentence reading performance. Instead, these cues increased the number of fixations and saccades, resulting in slower reading speed. These results suggest that even red colour is not a practically visual cue for Chinese word segmentation and the corresponding mechanisms were discussed. We studied how visual cues such as grey highlighting, red colour and interword spacing influenced Chinese reading performance. Our data showed that even the red colour was not an efficient cue for Chinese word segmentation. The corresponding mechanisms and future direction were discussed regarding how to improve Chinese reading performance.
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http://dx.doi.org/10.1080/00140139.2019.1608315 | DOI Listing |
Cardiovasc Diagn Ther
December 2024
Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
Background: Computed tomography pulmonary angiography (CTPA) is frequently performed in patients with pulmonary hypertension (PH) and may aid non-invasive estimation of pulmonary hemodynamics. We, therefore, investigated automated volumetry of intrapulmonary vasculature on CTPA, separated into core and peel fractions of the lung volume and its potential to differentially reflect pulmonary hemodynamics in patients with pre- and postcapillary PH.
Methods: A retrospective case-control study of 72 consecutive patients with PH according to the 2022 joint guidelines of the European Society of Cardiology and the European Respiratory Society who underwent right heart catheterization (RHC) and CTPA within 7 days between August 2013 and February 2016 at Thoraxklinik at Heidelberg University Hospital (Heidelberg, Germany) was conducted.
JMIR Form Res
January 2025
Centre for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
Background: With the development of artificial intelligence (AI), medicine has entered the era of intelligent medicine, and various aspects, such as medical education and talent cultivation, are also being redefined. The cultivation of clinical thinking abilities poses a formidable challenge even for seasoned clinical educators, as offline training modalities often fall short in bridging the divide between current practice and the desired ideal. Consequently, there arises an imperative need for the expeditious development of a web-based database, tailored to empower physicians in their quest to learn and hone their clinical reasoning skills.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
Background/objectives: Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences.
View Article and Find Full Text PDFDev Sci
March 2025
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants.
View Article and Find Full Text PDFRev Cardiovasc Med
December 2024
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment.
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