Results 51 to 60 of about 14,589,261 (282)
FEBS Letters, EarlyView.Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley +1 more source
Bi-directional image style transfer
, 2019 Multi-class image style transfer aims at keeping the content of source image while learning the abstract style from the target image. I present a method to complete multi-class transfer through a single generator which is conditioned on style feature ...
core +1 more source
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [PDF]
, 2022 In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve ...
Lee, Tong-Yee +6 more
core +1 more source
Degradation mechanism of the von Willebrand factor A2 domain by nattokinase
FEBS Letters, EarlyView.Nattokinase, a natto‐derived protease, exhibits potent antithrombotic effects. This study demonstrates that nattokinase directly cleaves the von Willebrand factor (vWF) A2 domain in vitro. Unlike the native regulator ADAMTS13, nattokinase degrades folded vWF independently of shear stress.
Ryuichi Hyakumoto +3 more
wiley +1 more source
BAUST-Net: Balance-Aware Universal Image Style Transfer
IEEE AccessThe current style transfer methods usually suffer from an imbalance in learning the content features and style features. Some of them tend to learn style information in the style domain, while others are more affinitive with the content domain.
Yawen Cheng, Zhisheng Yin
doaj +1 more source
Cross Modal Facial Image Synthesis Using a Collaborative Bidirectional Style Transfer Network
IEEE Access, 2022 In this paper, we present a novel collaborative bidirectional style transfer network based on generative adversarial network (GAN) for cross modal facial image synthesis, possibly with large modality gap.
Nizam Ud Din +4 more
doaj +1 more source
Image style transfer using deep neural networks
, 2021 U ovom završnom radu, obrađena je uporaba neuronskih mreži za prijenos stila slike. Prijenos stila slike, tehnika je računalnog vida, koja omogućuje ponovnu kompoziciju slike u stilu neke druge slike.
Gjenero, Mario
core
FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
wiley +1 more source
Sand Painting Generation Based on Convolutional Neural Networks
Journal of ImagingNeural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content.
Chin-Chen Chang, Ping-Hao Peng
doaj +1 more source
FEBS Letters, EarlyView.CT10 regulator of kinase (CRK) and CRK‐Like (CRKL) are signaling adaptors driving cell adhesion, motility, differentiation, and proliferation. SH2‐domain containing (SH) proteins are enriched in YXXP motifs which when phosphorylated create preferred binding sites for CRK family SH2 domains.
Phoebe M. Cousens +8 more
wiley +1 more source