In patients with Alzheimer’s disease,gamma-glutamyl transferase 5(GGT5)expression has been observed to be downregulated in cerebrovascular endothelial cells.However,the functional role of GGT5 in the development of A...In patients with Alzheimer’s disease,gamma-glutamyl transferase 5(GGT5)expression has been observed to be downregulated in cerebrovascular endothelial cells.However,the functional role of GGT5 in the development of Alzheimer’s disease remains unclear.This study aimed to explore the effect of GGT5 on cognitive function and brain pathology in an APP/PS1 mouse model of Alzheimer’s disease,as well as the underlying mechanism.We observed a significant reduction in GGT5 expression in two in vitro models of Alzheimer’s disease(Aβ_(1-42)-treated hCMEC/D3 and bEnd.3 cells),as well as in the APP/PS1 mouse model.Additionally,injection of APP/PS1 mice with an adeno-associated virus encoding GGT5 enhanced hippocampal synaptic plasticity and mitigated cognitive deficits.Interestingly,increasing GGT5 expression in cerebrovascular endothelial cells reduced levels of both soluble and insoluble amyloid-βin the brains of APP/PS1 mice.This effect may be attributable to inhibition of the expression ofβ-site APP cleaving enzyme 1,which is mediated by nuclear factor-kappa B.Our findings demonstrate that GGT5 expression in cerebrovascular endothelial cells is inversely associated with Alzheimer’s disease pathogenesis,and that GGT5 upregulation mitigates cognitive deficits in APP/PS1 mice.These findings suggest that GGT5 expression in cerebrovascular endothelial cells is a potential therapeutic target and biomarker for Alzheimer’s disease.展开更多
Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The ...Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.展开更多
This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallo...This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract new展开更多
By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can descri...By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can describe services,can also combine the service,but the method of combination service is not automatic. So a method is presented by using Situation Calculus for automatic service composition based on the OWL-S model. Finally through the example analysis,the method of automatic service combination was validated.展开更多
In this paper, we proposed an improved hybrid semantic matching algorithm combining Input/Output (I/O) semantic matching with text lexical similarity to overcome the disadvantage that the existing semantic matching al...In this paper, we proposed an improved hybrid semantic matching algorithm combining Input/Output (I/O) semantic matching with text lexical similarity to overcome the disadvantage that the existing semantic matching algorithms were unable to distinguish those services with the same I/O by only performing I/O based service signature matching in semantic web service discovery techniques. The improved algorithm consists of two steps, the first is logic based I/O concept ontology matching, through which the candidate service set is obtained and the second is the service name matching with lexical similarity against the candidate service set, through which the final precise matching result is concluded. Using Ontology Web Language for Services (OWL-S) test collection, we tested our hybrid algorithm and compared it with OWL-S Matchmaker-X (OWLS-MX), the experimental results have shown that the proposed algorithm could pick out the most suitable advertised service corresponding to user's request from very similar ones and provide better matching precision and efficiency than OWLS-MX.展开更多
Background: There is increasing evidence that the failure to recover from proactive semantic interference (frPSI) may be an early cognitive marker of preclinical Alzheimer’s disease (AD). However, it is unclear wheth...Background: There is increasing evidence that the failure to recover from proactive semantic interference (frPSI) may be an early cognitive marker of preclinical Alzheimer’s disease (AD). However, it is unclear whether frPSI effects reflect deficiencies in an individual’s initial learning capacity versus the actual inability to learn new semantically related targets. Objective: The current study was designed to adjust for learning capacity and then to examine the extent to which frPSI, proactive semantic interference (PSI) and retroactive semantic interference (RSI) effects could differentiate between older adults who were cognitively normal (CN), and those diagnosed with either Pre-Mild Cognitive Impairment (PreMCI) or amnestic MCI (aMCI). Methods: We employed the LASSI-L cognitive stress test to examine frPSI, PSI and RSI effects while simultaneously controlling for the participant’s initial learning capacity among 50 CN, 35 aMCI, and 16 PreMCI participants who received an extensive diagnostic work-up. Results: aMCI and PreMCI participants showed greater frPSI deficits (50% and 43.8% respectively) compared to only 14% of CNparticipants. PSI effects were observed for aMCI but not PreMCI participants relative to their CN counterparts. RSI failed to differentiate between any of the study groups. Conclusion: By using participants as their own controls and adjusting for overall learning and memory, it is clear that frPSI deficits occur with much greater frequency in individuals at higher risk for Alzheimer’s disease (AD), and likely reflect a failure of brain compensatory mechanisms.展开更多
基金supported by STI2030-Major Projects,No.2021ZD 0201801(to JG)Shanxi Province Basic Research Program,No.20210302123429(to QS).
文摘In patients with Alzheimer’s disease,gamma-glutamyl transferase 5(GGT5)expression has been observed to be downregulated in cerebrovascular endothelial cells.However,the functional role of GGT5 in the development of Alzheimer’s disease remains unclear.This study aimed to explore the effect of GGT5 on cognitive function and brain pathology in an APP/PS1 mouse model of Alzheimer’s disease,as well as the underlying mechanism.We observed a significant reduction in GGT5 expression in two in vitro models of Alzheimer’s disease(Aβ_(1-42)-treated hCMEC/D3 and bEnd.3 cells),as well as in the APP/PS1 mouse model.Additionally,injection of APP/PS1 mice with an adeno-associated virus encoding GGT5 enhanced hippocampal synaptic plasticity and mitigated cognitive deficits.Interestingly,increasing GGT5 expression in cerebrovascular endothelial cells reduced levels of both soluble and insoluble amyloid-βin the brains of APP/PS1 mice.This effect may be attributable to inhibition of the expression ofβ-site APP cleaving enzyme 1,which is mediated by nuclear factor-kappa B.Our findings demonstrate that GGT5 expression in cerebrovascular endothelial cells is inversely associated with Alzheimer’s disease pathogenesis,and that GGT5 upregulation mitigates cognitive deficits in APP/PS1 mice.These findings suggest that GGT5 expression in cerebrovascular endothelial cells is a potential therapeutic target and biomarker for Alzheimer’s disease.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1011198).
文摘Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.
文摘This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract new
文摘By analyzing of the existing Web services,an ontology based on OWL is presented,which has rich semantic information,and the service description language OWL-S based on OWL is put forward. OWL-S through IOPE can describe services,can also combine the service,but the method of combination service is not automatic. So a method is presented by using Situation Calculus for automatic service composition based on the OWL-S model. Finally through the example analysis,the method of automatic service combination was validated.
基金Supported by the National Natural Science Foundation of China (No. 60872018)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070293001)973 Project (No. 2007CB310607)
文摘In this paper, we proposed an improved hybrid semantic matching algorithm combining Input/Output (I/O) semantic matching with text lexical similarity to overcome the disadvantage that the existing semantic matching algorithms were unable to distinguish those services with the same I/O by only performing I/O based service signature matching in semantic web service discovery techniques. The improved algorithm consists of two steps, the first is logic based I/O concept ontology matching, through which the candidate service set is obtained and the second is the service name matching with lexical similarity against the candidate service set, through which the final precise matching result is concluded. Using Ontology Web Language for Services (OWL-S) test collection, we tested our hybrid algorithm and compared it with OWL-S Matchmaker-X (OWLS-MX), the experimental results have shown that the proposed algorithm could pick out the most suitable advertised service corresponding to user's request from very similar ones and provide better matching precision and efficiency than OWLS-MX.
基金NIH/NIA Grant Number 1RO1AG047649-01A1 Loewenstein, David (PI)
文摘Background: There is increasing evidence that the failure to recover from proactive semantic interference (frPSI) may be an early cognitive marker of preclinical Alzheimer’s disease (AD). However, it is unclear whether frPSI effects reflect deficiencies in an individual’s initial learning capacity versus the actual inability to learn new semantically related targets. Objective: The current study was designed to adjust for learning capacity and then to examine the extent to which frPSI, proactive semantic interference (PSI) and retroactive semantic interference (RSI) effects could differentiate between older adults who were cognitively normal (CN), and those diagnosed with either Pre-Mild Cognitive Impairment (PreMCI) or amnestic MCI (aMCI). Methods: We employed the LASSI-L cognitive stress test to examine frPSI, PSI and RSI effects while simultaneously controlling for the participant’s initial learning capacity among 50 CN, 35 aMCI, and 16 PreMCI participants who received an extensive diagnostic work-up. Results: aMCI and PreMCI participants showed greater frPSI deficits (50% and 43.8% respectively) compared to only 14% of CNparticipants. PSI effects were observed for aMCI but not PreMCI participants relative to their CN counterparts. RSI failed to differentiate between any of the study groups. Conclusion: By using participants as their own controls and adjusting for overall learning and memory, it is clear that frPSI deficits occur with much greater frequency in individuals at higher risk for Alzheimer’s disease (AD), and likely reflect a failure of brain compensatory mechanisms.
