Background Hypothalamus hamartomas(HHs)are rare,congenital,tumor-like,and nonprogressive malformations resulting in drug-resistant epilepsy,mainly affecting children.Gelastic seizures(GS)are an early hallmark of epile...Background Hypothalamus hamartomas(HHs)are rare,congenital,tumor-like,and nonprogressive malformations resulting in drug-resistant epilepsy,mainly affecting children.Gelastic seizures(GS)are an early hallmark of epilepsy with HH.The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH.Methods We obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only,GS-plus,and no-GS subgroups and then applied contrasted trajectories inference(cTI)to calculate the pseudotime value and evaluate GS progression.Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS,and then voxelwise lesion network-symptom mapping(LNSM)was applied to explore GS-associated brain regions.Results cTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types.This was further validated via actual disease duration(Pearson R=0.532,P=0.028).Male sex[odds ratio(OR)=2.611,P=0.013],low age at seizure onset(OR=0.361,P=0.005),high normalized HH metabolism(OR=−1.971,P=0.037)and severe seizure burden(OR=−0.006,P=0.032)were significant neuroimaging clinical predictors.LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex(S1)represented a negative correlation.Conclusions This study sheds light on the clinical characteristics and progression of GS in children with HH.We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical–subcortical–cerebellar level.These valuable results contribute to our understanding of the neural correlates of GS.展开更多
A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectivel...A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.展开更多
基金supported by Capital’s Funds for Health Improvement and Research(2022-1-1071,2020-2-1076)the National Natural Science Foundation of China(82071457)the National Key R&D Program of China(2021YFC2401201).
文摘Background Hypothalamus hamartomas(HHs)are rare,congenital,tumor-like,and nonprogressive malformations resulting in drug-resistant epilepsy,mainly affecting children.Gelastic seizures(GS)are an early hallmark of epilepsy with HH.The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH.Methods We obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only,GS-plus,and no-GS subgroups and then applied contrasted trajectories inference(cTI)to calculate the pseudotime value and evaluate GS progression.Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS,and then voxelwise lesion network-symptom mapping(LNSM)was applied to explore GS-associated brain regions.Results cTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types.This was further validated via actual disease duration(Pearson R=0.532,P=0.028).Male sex[odds ratio(OR)=2.611,P=0.013],low age at seizure onset(OR=0.361,P=0.005),high normalized HH metabolism(OR=−1.971,P=0.037)and severe seizure burden(OR=−0.006,P=0.032)were significant neuroimaging clinical predictors.LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex(S1)represented a negative correlation.Conclusions This study sheds light on the clinical characteristics and progression of GS in children with HH.We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical–subcortical–cerebellar level.These valuable results contribute to our understanding of the neural correlates of GS.
基金Sci-Tech Planning Projects of Chongqing City,China(No.CSTC2007AA7003).
文摘A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.
