Etude structurale des mécanismes de photoblanchiment des protéines fluorescentes photocommutables

The discovery of phototransformable FPs (PTFPs) from Anthozoa species, thanks totheir photophysical properties, has opened a large field in biological fluorescence imaging.One of the PTFPs’ sub-groups consists of Reversible Photoswitchable Fluorescent Proteins(RSFPs), which can be reversibly switched between nonfluorescent and fluorescent states.Photobleaching is the permanent loss of the fluorescence-emitting capacity under excitation,which is a common phenomenon among all the fluorescent molecules. Photobleaching has alarge impact on the microscopy image quality, notably on super-resolution imaging.Photoswitchable fluorescent proteins have a tendency to lose performance within everyswitching cycle, a process referred to as “photofatigue”. Our interest of study is focused onthe photobleaching mechanisms of RSFPs.We have reported the crystallographic structure of photobleached IrisFP under highand low illumination intensity at room temperature as well as its spectroscopic modifications.We found that different illumination intensities can result in different photobleachingpathways. Under low illumination intensity, an oxygen-dependent photobleaching pathwaywas evidenced. Structural modifications induced by singlet-oxygen production within thechromophore pocket revealed the oxidation of two sulfur-containing residues, Met159 andCys171, locking the chromophore in a nonfluorescent protonated state. Under highillumination intensity, a completely different, oxygen-independent photobleaching pathwaywas found. The conserved Glu212 underwent decarboxylation concomitantly with anextensive rearrangement of the H-bond network around the chromophore, and an sp2-to-sp3hybridization change of the carbon atom bridging the chromophore cyclic moieties wasobserved. As Met159 is the key residue involved in low-intensity illumination photobleaching,we have then mutated Met159 into Alanine in order to avoid sulfoxidation. We found that theIrisFP-M159A mutant display an enhanced photostability in solution, in PVA gel and inE.coli cells.La découverte des Protéines Fluorescentes Phototransformables (PTFPs) issuesd’espèces anthozoaires a ouvert, grâce à leurs propriétés photophysiques particulières, unvaste champ d’investigation pour l'imagerie biologique de fluorescence. L'un des sousgroupesdes PTFPs est formé des protéines fluorescentes réversiblement photocommutables(RSFPs), qui peuvent être commutées réversiblement entre des états non-fluorescent etfluorescent. Le photoblanchiment est la perte définitive d’émission de fluorescence sousexcitation et est un phénomène commun à toutes les molécules fluorescentes. Lephotoblanchiment a un impact important sur la qualité des images de microscopie, notammenten imagerie de super-résolution. Les RSFPs ont tendance à perdre de leur performance àchaque cycle de commutation, un processus dénommé “photofatigue”. Notre intérêt est centrésur l'étude des mécanismes de photofatigue des RSFPs.Nous avons rapporté les structures cristallographiques d’IrisFP photoblanchie par uneforte et une basse intensité d’illumination à température ambiante ainsi que les modificationsspectroscopiques associées. Nos résultats démontrent que différentes intensités d'excitationpeuvent donner lieu à différentes voies de photoblanchiment. Sous faible intensité d'excitation,une voie de photoblanchiment dépendante de l'oxygène a été mise en évidence. Lesmodifications structurales induites par la production d'oxygène singulet à l'intérieur de lapoche du chromophore ont révélé l'oxydation de deux résidus soufrés, Met159 et Cys171,piégeant le chromophore dans un état protoné non-fluorescent. Sous haute intensitéd'excitation, une voie de photoblanchiment oxygène-indépendante totalement différente a ététrouvée. Le Glu212, strictement conservé, subit une décarboxylation associée à un importantréarrangement du réseau de liaisons hydrogènes autour du chromophore, et un changementd’hybridation sp2 vers sp3 du carbone reliant les cycles du chromophore est observé. En tantque résidu clé impliqué dans le photoblanchiment induit par faible intensité d'excitation, nousavons muté Met159 en alanine afin d'éviter une sulfoxydation. Nous avons trouvé que lemutant IrisFP-M159A démontre une photostabilité améliorée en solution, en gel PVA et dansdes cellules E. coli

Etude structurale des mécanismes de photoblanchiment des protéines fluorescentes photocommutables

The discovery of phototransformable FPs (PTFPs) from Anthozoa species, thanks totheir photophysical properties, has opened a large field in biological fluorescence imaging.One of the PTFPs’ sub-groups consists of Reversible Photoswitchable Fluorescent Proteins(RSFPs), which can be reversibly switched between nonfluorescent and fluorescent states.Photobleaching is the permanent loss of the fluorescence-emitting capacity under excitation,which is a common phenomenon among all the fluorescent molecules. Photobleaching has alarge impact on the microscopy image quality, notably on super-resolution imaging.Photoswitchable fluorescent proteins have a tendency to lose performance within everyswitching cycle, a process referred to as “photofatigue”. Our interest of study is focused onthe photobleaching mechanisms of RSFPs.We have reported the crystallographic structure of photobleached IrisFP under highand low illumination intensity at room temperature as well as its spectroscopic modifications.We found that different illumination intensities can result in different photobleachingpathways. Under low illumination intensity, an oxygen-dependent photobleaching pathwaywas evidenced. Structural modifications induced by singlet-oxygen production within thechromophore pocket revealed the oxidation of two sulfur-containing residues, Met159 andCys171, locking the chromophore in a nonfluorescent protonated state. Under highillumination intensity, a completely different, oxygen-independent photobleaching pathwaywas found. The conserved Glu212 underwent decarboxylation concomitantly with anextensive rearrangement of the H-bond network around the chromophore, and an sp2-to-sp3hybridization change of the carbon atom bridging the chromophore cyclic moieties wasobserved. As Met159 is the key residue involved in low-intensity illumination photobleaching,we have then mutated Met159 into Alanine in order to avoid sulfoxidation. We found that theIrisFP-M159A mutant display an enhanced photostability in solution, in PVA gel and inE.coli cells.La découverte des Protéines Fluorescentes Phototransformables (PTFPs) issuesd’espèces anthozoaires a ouvert, grâce à leurs propriétés photophysiques particulières, unvaste champ d’investigation pour l'imagerie biologique de fluorescence. L'un des sousgroupesdes PTFPs est formé des protéines fluorescentes réversiblement photocommutables(RSFPs), qui peuvent être commutées réversiblement entre des états non-fluorescent etfluorescent. Le photoblanchiment est la perte définitive d’émission de fluorescence sousexcitation et est un phénomène commun à toutes les molécules fluorescentes. Lephotoblanchiment a un impact important sur la qualité des images de microscopie, notammenten imagerie de super-résolution. Les RSFPs ont tendance à perdre de leur performance àchaque cycle de commutation, un processus dénommé “photofatigue”. Notre intérêt est centrésur l'étude des mécanismes de photofatigue des RSFPs.Nous avons rapporté les structures cristallographiques d’IrisFP photoblanchie par uneforte et une basse intensité d’illumination à température ambiante ainsi que les modificationsspectroscopiques associées. Nos résultats démontrent que différentes intensités d'excitationpeuvent donner lieu à différentes voies de photoblanchiment. Sous faible intensité d'excitation,une voie de photoblanchiment dépendante de l'oxygène a été mise en évidence. Lesmodifications structurales induites par la production d'oxygène singulet à l'intérieur de lapoche du chromophore ont révélé l'oxydation de deux résidus soufrés, Met159 et Cys171,piégeant le chromophore dans un état protoné non-fluorescent. Sous haute intensitéd'excitation, une voie de photoblanchiment oxygène-indépendante totalement différente a ététrouvée. Le Glu212, strictement conservé, subit une décarboxylation associée à un importantréarrangement du réseau de liaisons hydrogènes autour du chromophore, et un changementd’hybridation sp2 vers sp3 du carbone reliant les cycles du chromophore est observé. En tantque résidu clé impliqué dans le photoblanchiment induit par faible intensité d'excitation, nousavons muté Met159 en alanine afin d'éviter une sulfoxydation. Nous avons trouvé que lemutant IrisFP-M159A démontre une photostabilité améliorée en solution, en gel PVA et dansdes cellules E. coli

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization

In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow are not only sparse but also smooth along the horizontal and vertical direction in images while the clean images are smooth along the temporal direction between images. Therefore, the sparsity norm is used to boost the sparsity of the cloud and cloud shadow, and unidirectional total variation (UTV) regularizers are applied to ensure the unidirectional smoothness. This paper utilizes alternation direction method of multipliers to solve the presented model and generate the cloud and cloud shadow element as well as the clean element. The cloud and cloud shadow element is purified to get the cloud area and cloud shadow area. Then, the clean area of the original cloud-contaminated images is replaced to the corresponding area of the clean element. Finally, the reference image is selected to reconstruct details of the cloud area and cloud shadow area using the information cloning method. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints

Efficient Hybrid Transformer: Learning Global-local Context for Urban Scene Segmentation

Semantic segmentation of fine-resolution urban scene images plays a vital role in extensive practical applications, such as land cover mapping, urban change detection, environmental protection and economic assessment. Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has dominated the semantic segmentation task for many years. Convolutional neural networks adopt hierarchical feature representation, demonstrating strong local information extraction. However, the local property of the convolution layer limits the network from capturing global context that is crucial for precise segmentation. Recently, Transformer comprise a hot topic in the computer vision domain. Transformer demonstrates the great capability of global information modelling, boosting many vision tasks, such as image classification, object detection and especially semantic segmentation. In this paper, we propose an efficient hybrid Transformer (EHT) for real-time urban scene segmentation. The EHT adopts a hybrid structure with and CNN-based encoder and a transformer-based decoder, learning global-local context with lower computation. Extensive experiments demonstrate that our EHT has faster inference speed with competitive accuracy compared with state-of-the-art lightweight models. Specifically, the proposed EHT achieves a 66.9% mIoU on the UAVid test set and outperforms other benchmark networks significantly. The code will be available soon

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Although the Convolutional Neural Network (CNN) has shown great potential for land cover classification, the frequently used single-scale convolution kernel limits the scope of information extraction. Therefore, we propose a Multi-Scale Fully Convolutional Network (MSFCN) with a multi-scale convolutional kernel as well as a Channel Attention Block (CAB) and a Global Pooling Module (GPM) in this paper to exploit discriminative representations from two-dimensional (2D) satellite images. Meanwhile, to explore the ability of the proposed MSFCN for spatio-temporal images, we expand our MSFCN to three-dimension using three-dimensional (3D) CNN, capable of harnessing each land cover category’s time series interaction from the reshaped spatio-temporal remote sensing images. To verify the effectiveness of the proposed MSFCN, we conduct experiments on two spatial datasets and two spatio-temporal datasets. The proposed MSFCN achieves 60.366% on the WHDLD dataset and 75.127% on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753% and 77.156%. Extensive comparative experiments and ablation studies demonstrate the effectiveness of the proposed MSFCN. Code will be available at https://github.com/lironui/MSFCN

A2-FPN for semantic segmentation of fine-resolution remotely sensed images

The thriving development of earth observation technology makes more and more high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial and spectral complexity leads to the automation of semantic segmentation becoming a challenging task. Addressing such an issue represents an exciting research field, which paves the way for scene-level landscape pattern analysis and decision-making. To tackle this problem, we propose an approach for automatic land segmentation based on the Feature Pyramid Network (FPN). As a classic architecture, FPN can build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion hinder FPN from further aggregating more discriminative features. Hence, we propose an Attention Aggregation Module (AAM) to enhance multiscale feature learning through attention-guided feature aggregation. Based on FPN and AAM, a novel framework named Attention Aggregation Feature Pyramid Network (A2-FPN) is developed for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on four datasets demonstrate the effectiveness of our A2-FPN in segmentation accuracy. Code is available at https://github.com/lironui/A2-FPN

Scale-aware neural network for semantic segmentation of multi-resolution remote sensing images

Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: (1) increased scale variation of geo-objects and (2) loss of detailed information at coarse spatial resolutions. To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed imagery. SaNet deploys a densely connected feature network (DCFFM) module to capture high-quality multi-scale context, such that the scale variation is handled properly and the quality of segmentation is increased for both large and small objects. A spatial feature recalibration (SFRM) module was further incorporated into the network to learn intact semantic content with enhanced spatial relationships, where the negative effects of information loss are removed. The combination of DCFFM and SFRM allows SaNet to learn scale-aware feature representation, which outperforms the existing multi-scale feature representation. Extensive experiments on three semantic segmentation datasets demonstrated the effectiveness of the proposed SaNet in cross-resolution segmentation