Ehbp1 orchestrates orderly sorting of Wnt/Wingless to the basolateral and apical cell membranes.

Wingless (Wg)/Wnt signaling plays a critical role in both development and adult tissue homeostasis. In the Drosophila larval wing disc epithelium, the orderly delivery of Wg/Wnt to the apical and basal cell surfaces is essential for wing development. Here, we identified Ehbp1 as the switch that dictates the direction of Wg/Wnt polarized intracellular transport: the Adaptor Protein complex 1 (AP-1) delivers Wg/Wnt to the basolateral cell surface, and its sequestration by Ehbp1 redirects Wg/Wnt for apical delivery.

Feature Interaction-Based Face De-Morphing Factor Prediction for Restoring Accomplice's Facial Image.

Face morphing attacks disrupt the essential correlation between a face image and its identity information, posing a significant challenge to face recognition systems. Despite advancements in face morphing attack detection methods, these techniques cannot reconstruct the face images of accomplices. Existing deep learning-based face de-morphing techniques have mainly focused on identity disentanglement, overlooking the morphing factors inherent in the morphed images.

ConvMedSegNet: A multi-receptive field depthwise convolutional neural network for medical image segmentation.

In order to achieve highly precise medical image segmentation, this paper presents ConvMedSegNet, a novel convolutional neural network designed with a U-shaped architecture that seamlessly integrates two crucial modules: the multi-receptive field depthwise convolution module (MRDC) and the guided fusion module (GF). The MRDC module's primary function is to capture texture information of varying sizes through multi-scale convolutional layers. This information is subsequently utilized to enhance the correlation of global feature data by expanding the network's width.

A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification.

Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training.