Objective: Although previous research shows that generalized and focal epilepsies have at least some distinct genetic influences, it remains uncertain why some families manifest both types of epilepsy. We tested two hypotheses: (1) families with both generalized and focal epilepsy carry separate risk alleles for both types; and (2) within mixed families, the type of epilepsy each individual manifests is influenced by the relative burden of separate risk alleles for generalized epilepsies and focal epilepsies.
Methods: The Epi4K cohort included 711 individuals with epilepsy from 257 families (113 generalized families, 66 focal families, 78 mixed families). We calculated polygenic risk scores (PRSs) for genetic generalized epilepsy (GGE_PRS) and for focal epilepsy (Focal_PRS). We used mixed-effects models to compare these PRSs between and within families, accounting for relatedness.
Results: Compared to population controls, individuals in generalized families had elevated GGE_PRS (p < .001) but not elevated Focal_PRS (p = .50); focal family individuals had elevated Focal_PRS (p = .008) but not elevated GGE_PRS (p = .22); and individuals in mixed families had both elevated GGE_PRS and elevated Focal_PRS (both p < .001). Within mixed families, GGE_PRS was higher in individuals with generalized epilepsy than in individuals with focal epilepsy (p < .001), whereas we did not detect a difference in Focal_PRS between individuals with generalized and focal epilepsy (p = .46). The GGE_PRS value explained 10% of the variance in phenotype within mixed families.
Significance: The occurrence of families with both generalized and focal epilepsy in separate individuals is explained at least partly by the chance co-occurrence of distinct genetic risk alleles for generalized and focal epilepsies. Within mixed families, an individual's epilepsy type can be explained at least in part by the relative burden of risk alleles for genetic generalized epilepsy.
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http://dx.doi.org/10.1111/epi.18348 | DOI Listing |
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Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples-unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques.
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