Purpose: Semantic feature analysis (SFA) was used to determine whether training contextually related words would improve the discourse of individuals with nonfluent aphasia in preselected contexts.
Method: A modified multiple-probes-across-behaviors design was used to train target words using SFA in 3 adults with nonfluent aphasia. Pretreatment, posttreatment, and follow-up sessions obtained language samples for the preselected contexts. Contexts included 4 story retellings and 4 procedure explanations.
Results: All participants improved naming ability for treated words. No generalization to untrained items was found. Within discourse samples, participants increased number of target words produced from pretreatment to posttreatment sessions but did not increase lexical diversity across samples. Participants maintained performance on standardized measures from the beginning to the end of the study.
Conclusions: Results support and extend previous research by indicating that SFA improves confrontational naming ability and may benefit word retrieval in discourse production of closed-set contexts.
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http://dx.doi.org/10.1044/1058-0360(2008/016) | DOI Listing |
Diabetes Metab Res Rev
January 2025
Rush Alzheimer's Disease Centre, Rush University Medical Center, Chicago, Illinois, USA.
Diabetes increases the risk of dementia, and insulin resistance (IR) has emerged as a potential unifying feature. Here, we review published findings over the past 2 decades on the relation of diabetes and IR to brain health, including those related to cognition and neuropathology, in the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study (ROS/MAP/MARS), three harmonised cohort studies of ageing and dementia at the Rush Alzheimer's Disease Center (RADC). A wide range of participant data, including information on medical conditions such as diabetes and neuropsychological tests, as well as other clinical and laboratory-based data collected annually.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFBehav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer.
View Article and Find Full Text PDFComput Biol Med
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address:
Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information.
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