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- Hentrich, Lea, et al.
(författare)
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Novel Genetic and Phenotypic Expansion in GOSR2-Related Progressive Myoclonus Epilepsy
- 2023
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Ingår i: Genes. - 2073-4425. ; 14:10
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Tidskriftsartikel (refereegranskat)abstract
- Biallelic variants in the Golgi SNAP receptor complex member 2 gene ( GOSR2) have been reported in progressive myoclonus epilepsy with neurodegeneration. Typical clinical features include ataxia and areflexia during early childhood, followed by seizures, scoliosis, dysarthria, and myoclonus. Here, we report two novel patients from unrelated families with a GOSR2-related disorder and novel genetic and clinical findings. The first patient, a male compound heterozygous for the GOSR2 splice site variant c.336+1G>A and the novel c.364G>A,p.Glu122Lys missense variant showed global developmental delay and seizures at the age of 2 years, followed by myoclonus at the age of 8 years with partial response to clonazepam. The second patient, a female homozygous for the GOSR2 founder variant p.Gly144Trp, showed only mild fine motor developmental delay and generalized tonic-clonic seizures triggered by infections during adolescence, with seizure remission on levetiracetam. The associated movement disorder progressed atypically slowly during adolescence compared to its usual speed, from initial intention tremor and myoclonus to ataxia, hyporeflexia, dysmetria, and dystonia. These findings expand the genotype-phenotype spectrum of GOSR2-related disorders and suggest that GOSR2 should be included in the consideration of monogenetic causes of dystonia, global developmental delay, and seizures.
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| 2. |
- Quach, Kha Gia, et al.
(författare)
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Dyglip: A dynamic graph model with link prediction for accurate multi-camera multiple object tracking
- 2021
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Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 1063-6919. ; , s. 13779-13788
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Konferensbidrag (refereegranskat)abstract
- Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.
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