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Pathogenic missense and truncating variants in the GABRG2 gene cause a spectrum of epilepsies, from Dravet syndrome to milder simple febrile seizures. In most cases, pathogenic missense variants in the GABRG2 gene segregate with a febrile seizure phenotype. In this case series, we report a recurrent, de novo missense variant (c0.316 G > A; p.A106T) in the GABRG2 gene that was identified in five unrelated individuals. These patients were described to have a more severe phenotype than previously reported for GABRG2 missense variants. Common features include variable early-onset seizures, significant motor and speech delays, intellectual disability, hypotonia, movement disorder, dysmorphic features and vision/ocular issues. Our report further explores a recurrent pathogenic missense variant within the GABRG2 variant family and broadens the spectrum of associated phenotypes for GABRG2-associated disorders.

Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age‐invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age‐invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state‐of‐the‐art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research. [ABSTRACT FROM AUTHOR]

Abstract. Deep learning is revolutionizing the already rapidly developing field of computer vision. The convolutional neural network (CNN) is a state-of-the-art deep learning tool that learns high level features directly from a huge dataset of labeled images. In document image processing, ink analysis allows for determination of ink age and forgery and identification of pen or writer. The spectral information of inks in hyperspectral document images provides valuable information about the underlying material and thus helps in identification and discrimination of inks based on their unique spectral signatures even if they have the same color. Ink mismatch detection is a key step in document forgery detection. Although various ink mismatch detection techniques are available in the recent literature, there is a constant need for development of more accurate and effective methods to empower automated document forgery detection. A state-of-the-art deep learning method for ink mismatch detection in hyperspectral document images is proposed. The spectral responses of ink pixels are extracted from a hyperspectral document image, reshaped to a CNN-friendly image format and fed to the CNN for classification. The proposed method effectively identifies different ink types in a hyperspectral document image for forgery detection and achieves an overall accuracy of 98.2% for blue and 88% for black inks, which is the highest accuracy among the latest techniques of ink mismatch detection on the UWA Writing Ink Hyperspectral Images (WIHSI) database and differentiates between the highest number of inks mixed in unbalanced proportions in a hyperspectral document image. Furthermore, a detailed discussion on selection of appropriate CNN architecture and classification results are presented in this paper along with comparison with the former methods of ink mismatch detection. This research opens a new window for research on automated forgery detection in hyperspectral document images using deep learning. [ABSTRACT FROM AUTHOR]