Refined investigation of infrequent dissociations within remote memory, such as preservation of autobiographical episodic memory and selective impairment of public semantic memory could provide some insight on the interactions of long-term memory systems and their underlying brain correlates. Combining clinical neuropsychological and neuroimaging methods in the present study, we examined a patient surgically treated for temporal lobe epilepsy showing this rare pattern of dissociation. Specifically, we investigated along the two temporal directions, past and future, his autobiographical episodic memory, semantic memory for public events and famous people and their interaction through the concept of autobiographical significance (AS). The results showed impaired ability not only to recall past but also to imagine future public events in a context of preserved past and future personal episodic memory. Remarkably, impersonal future thinking was impaired regardless of AS, while the autobiographical-significant public past knowledge relied exclusively on the patient's spared autobiographical episodic memory. These results were corroborated by neuroimaging data showing the absence of brain activation for public knowledge devoid of personal significance and activation of the autobiographical memory cerebral network for personally significant public knowledge. Our findings suggest that AS did not 'restore' the code to access public semantic memory, but bypassed it by using personal memory sources successful only for past public recollections. Therefore, remembering impersonal and imagining public events seems to require the contribution of public semantic knowledge per se. The patient's cognitive profile suggested a reorganization of memory systems.
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http://dx.doi.org/10.1016/j.cortex.2012.11.007 | DOI Listing |
Front Psychol
December 2024
Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China.
Objective: This study proposes an emotion correlation-enhanced sentiment analysis model (ECO-SAM), a sentiment correlation modeling-based multi-label sentiment analysis model.
Methods: The ECO-SAM utilizes a pre-trained BERT encoder to obtain semantic embedding of input texts and then leverages a self-attention mechanism to model the semantic correlation between emotions. Additionally, it utilizes a text emotion matching neural network to make sentiment analysis for input texts.
J Oral Biol Craniofac Res
December 2024
Department of Dentistry, All India Institute of Medical Sciences, Bathinda, India.
Background: This systematic review and meta-analysis compared the accuracy of robotic-assisted dental implant placement (r-CAIS) with conventional freehand, static computer-assisted (s-CAIS), and dynamic computer-assisted (d-CAIS) techniques.
Methods: A comprehensive search was conducted in PubMed, Google Scholar, Semantic Scholar, and the Cochrane Library from January 2000 to January 2024. Studies meeting PICOST criteria, including clinical and in vitro studies, were included.
Sci Rep
January 2025
Ministry of Higher Education & Scientific Research, Industrial Technical Institute in Mataria, Cairo, 11718, Egypt.
"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer and Control Engineering, Qiqihar University, Qiqihar, 161003, China.
In semantic segmentation research, spatial information and receptive fields are essential. However, currently, most algorithms focus on acquiring semantic information and lose a significant amount of spatial information, leading to a significant decrease in accuracy despite improving real-time inference speed. This paper proposes a new method to address this issue.
View Article and Find Full Text PDFJ Biomed Inform
January 2025
Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address:
Motivation: The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data.
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