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Synthetic rational design of live-attenuated Zika viruses based on a computational model.

Nucleic Acids Res

January 2025

SynVaccine Ltd, Ramat Hachayal, 3 Golda Meir Street, Science Park, Nes Ziona 7403648, Israel.

Many viruses of the Flaviviridae family, including the Zika virus (ZIKV), are human pathogens of significant public health concerns. Despite extensive research, there are currently no approved vaccines available for ZIKV and specifically no live-attenuated Zika vaccine. In this current study, we suggest a novel computational algorithm for generating live-attenuated vaccines via the introduction of silent mutation into regions that undergo selection for strong or weak local RNA folding or into regions that exhibit medium levels of sequence conservation.

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Background: There is a global need for synthetic speech development in multiple languages and dialects, as many children who cannot communicate using their natural voice struggle to find synthetic voices on high-technology devices that match their age, social and linguistic background.

Aims: To document multiple stakeholders' perspectives surrounding the quality, acceptability and utility of newly created synthetic speech in three under-resourced South African languages, namely South African English, Afrikaans and isiXhosa.

Methods & Procedures: A mixed methods research design was selected.

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Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.

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EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application.

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División de Sistemas e Ingeniería Electrónica (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.

This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle.

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This narrative review explores the benefits and risks of cannabinoids in kidney health, particularly in individuals with pre-existing renal conditions. It discusses the roles of cannabinoid receptor ligands (phytocannabinoids, synthetic cannabinoids, and endocannabinoids) in kidney physiology. The metabolism and excretion of these substances are also highlighted, with partial elimination occurring via the kidneys.

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