FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations

In recent years, language-driven artistic style transfer has emerged as a new type of style transfer technique, eliminating the need for a reference style image by using natural language descriptions of the style. The first model to achieve this, called CLIPstyler, has demonstrated impressive stylisation results. However, its lengthy optimisation procedure at runtime for each query limits its suitability for many practical applications. In this work, we present FastCLIPstyler, a generalised text-based image style transfer model capable of stylising images in a single forward pass for arbitrary text inputs. Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed for compatibility with resource-constrained devices. Through quantitative and qualitative comparisons with state-of-the-art approaches, we demonstrate that our models achieve superior stylisation quality based on measurable metrics while offering significantly improved runtime efficiency, particularly on edge devices.Comment: Accepted at the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024

A Deep Learning Approach To Identify MRNA Localization Patterns

International audienc

Visualization of Arenavirus RNA Species in Individual Cells by Single-Molecule Fluorescence In Situ Hybridization Suggests a Model of Cyclical Infection and Clearance during Persistence

International audienceLymphocytic choriomeningitis mammarenavirus (LCMV) is an enveloped, negative-strand RNA virus that causes serious disease in humans but establishes an asymptomatic, lifelong infection in reservoir rodents. Different models have been proposed to describe how arenaviruses regulate the replication and transcription of their bisegmented, single-stranded RNA genomes, particularly during persistent infection. However, these models were based largely on viral RNA profiling data derived from entire populations of cells. To better understand LCMV replication and transcription at the single-cell level, we established a high-throughput, single-molecule fluorescence in situ hybridization (smFISH) image acquisition and analysis pipeline and examined viral RNA species at discrete time points from virus entry through the late stages of persistent infection in vitro We observed the transcription of viral nucleoprotein and polymerase mRNAs from the incoming S and L segment genomic RNAs, respectively, within 1 h of infection, whereas the transcription of glycoprotein mRNA from the S segment antigenome required ∼4 to 6 h. This confirms the temporal separation of viral gene expression expected due to the ambisense coding strategy of arenaviruses and also suggests that antigenomic RNA contained in virions is not transcriptionally active upon entry. Viral replication and transcription peaked at 36 h postinfection, followed by a progressive loss of viral RNAs over the next several days. During persistence, the majority of cells showed repeating cyclical waves of viral transcription and replication followed by the clearance of viral RNA. Thus, our data support a model of LCMV persistence whereby infected cells can spontaneously clear infection and become reinfected by viral reservoir cells that remain in the population.IMPORTANCE Arenaviruses are human pathogens that can establish asymptomatic, lifelong infections in their rodent reservoirs. Several models have been proposed to explain how arenavirus spread is restricted within host rodents, including the periodic accumulation and loss of replication-competent, but transcriptionally incompetent, viral genomes. A limitation of previous studies was the inability to enumerate viral RNA species at the single-cell level. We developed a high-throughput, smFISH assay and used it to quantitate lymphocytic choriomeningitis mammarenavirus (LCMV) replicative and transcriptional RNA species in individual cells at distinct time points following infection. Our findings support a model whereby productively infected cells can clear infection, including viral RNAs and antigen, and later be reinfected. This information improves our understanding of the timing and possible regulation of LCMV genome replication and transcription during infection. Importantly, the smFISH assay and data analysis pipeline developed here is easily adaptable to other RNA viruses

A computational framework to study sub-cellular RNA localization

Automated analysis of RNA localisation in smFISH data has been elusive. Here, the authors simulate and use a large dataset of images to design and validate a framework for highly accurate classification of sub-cellular RNA localisation patterns from smFISH experiments

Smifish and fish-quant – a flexible single rna detection approach with super-resolution capability

Single molecule FISH (smFISH) allows studying transcription and RNA localization by imaging individual mRNAs in single cells. We present smiFISH (single molecule inexpensive FISH), an easy to use and flexible RNA visualization and quantification approach that uses unlabelled primary probes and a fluorescently labelled secondary detector oligonucleotide. The gene-specific probes are unlabelled and can therefore be synthesized at low cost, thus allowing to use more probes per mRNA resulting in a substantial increase in detection efficiency. smiFISH is also flexible since differently labelled secondary detector probes can be used with the same primary probes. We demonstrate that this flexibility allows multicolor labelling without the need to synthesize new probe sets. We further demonstrate that the use of a specific acrydite detector oligonucleotide allows smiFISH to be combined with expansion microscopy, enabling the resolution of transcripts in 3D below the diffraction limit on a standard microscope. Lastly, we provide improved, fully automated software tools fromprobe-design to quantitative analysis of smFISH images. In short, we provide a complete workflow to obtain automatically counts of individual RNA molecules in single cells

Smifish and fish-quant – a flexible single rna detection approach with super-resolution capability

Single molecule FISH (smFISH) allows studying transcription and RNA localization by imaging individual mRNAs in single cells. We present smiFISH (single molecule inexpensive FISH), an easy to use and flexible RNA visualization and quantification approach that uses unlabelled primary probes and a fluorescently labelled secondary detector oligonucleotide. The gene-specific probes are unlabelled and can therefore be synthesized at low cost, thus allowing to use more probes per mRNA resulting in a substantial increase in detection efficiency. smiFISH is also flexible since differently labelled secondary detector probes can be used with the same primary probes. We demonstrate that this flexibility allows multicolor labelling without the need to synthesize new probe sets. We further demonstrate that the use of a specific acrydite detector oligonucleotide allows smiFISH to be combined with expansion microscopy, enabling the resolution of transcripts in 3D below the diffraction limit on a standard microscope. Lastly, we provide improved, fully automated software tools fromprobe-design to quantitative analysis of smFISH images. In short, we provide a complete workflow to obtain automatically counts of individual RNA molecules in single cells

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.

Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts

A localization screen reveals translation factories and widespread co-translational RNA targeting

Publié sur BioRxiv le 21 mai 2020 : https://www.biorxiv.org/content/10.1101/2020.05.20.106989v1Local translation allows a spatial control of gene expression. Here, we performed a dual protein/mRNA localization screen, using smFISH on 523 human cell lines expressing GFP-tagged genes. A total of 32 mRNAs displayed specific cytoplasmic localizations, and we observed local translation at unexpected locations, including cytoplasmic protrusions, cell edges, endosomes, Golgi, the nuclear envelope and centrosomes, the latter being cell cycle dependent. Quantitation of mRNA distribution and automatic pattern classification revealed a high degree of localization heterogeneity between cells. Surprisingly, mRNA localization frequently required ongoing translation, indicating widespread co-translational RNA targeting. Interestingly, while P-body accumulation was frequent (15 mRNAs), four mRNAs accumulated in foci that were distinct structures. These foci lacked the mature protein, but nascent polypeptide imaging showed that they were specialized translation factories. For β-catenin, foci formation was regulated by Wnt, relied on APC-dependent polysome aggregation, and led to nascent protein degradation. Thus, translation factories uniquely regulate nascent protein metabolism and create a fine granular compartmentalization of translation