Heterozygous ANKRD17 loss-of-function variants cause a syndrome with intellectual disability, speech delay, and dysmorphism

ANKRD17 is an ankyrin repeat-containing protein thought to play a role in cell cycle progression, whose ortholog in Drosophila functions in the Hippo pathway as a co-factor of Yorkie. Here, we delineate a neurodevelopmental disorder caused by de novo heterozygous ANKRD17 variants. The mutational spectrum of this cohort of 34 individuals from 32 families is highly suggestive of haploinsufficiency as the underlying mechanism of disease, with 21 truncating or essential splice site variants, 9 missense variants, 1 in-frame insertion-deletion, and 1 microdeletion (1.16 Mb). Consequently, our data indicate that loss of ANKRD17 is likely the main cause of phenotypes previously associated with large multi-gene chromosomal aberrations of the 4q13.3 region. Protein modeling suggests that most of the missense variants disrupt the stability of the ankyrin repeats through alteration of core structural residues. The major phenotypic characteristic of our cohort is a variable degree of developmental delay/intellectual disability, particularly affecting speech, while additional features include growth failure, feeding difficulties, non-specific MRI abnormalities, epilepsy and/or abnormal EEG, predisposition to recurrent infections (mostly bacterial), ophthalmological abnormalities, gait/balance disturbance, and joint hypermobility. Moreover, many individuals shared similar dysmorphic facial features. Analysis of single-cell RNA-seq data from the developing human telencephalon indicated ANKRD17 expression at multiple stages of neurogenesis, adding further evidence to the assertion that damaging ANKRD17 variants cause a neurodevelopmental disorder.Neurolog

Generative modeling of spatio-temporal traffic sign trajectories ∗

We consider the task of automatic detection and recognition of traffic signs in video. We show that successful offthe-shelf detection (Viola-Jones) and classification (SVM) systems yield unsatisfactory results. Our main concern are high false positive detection rates which occur due to sparseness of the traffic signs in videos. We address the problem by enforcing spatio-temporal consistency of the detections corresponding to a distinct sign in video. We also propose a generative model of the traffic sign motion in the image plane, which is obtained by clustering the trajectories filtered by an appropriate procedure. The contextual information recovered by the proposed model will be employed in our future research on recognizing traffic signs in video. 1