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A polarimetric synthetic aperture radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning-based despeckling methods have started to evolve from single-channel SAR images to PolSAR images. To this aim, deep learning-based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in standard convolutional neural networks (CNNs). However, this approach neglects the mathematical relationship that exists between the real and imaginary components, resulting in suboptimal output. Here, we propose a multistream complex-valued fully convolutional network (FCN) (CV-deSpeckNet1) to reduce speckle and effectively estimate the PolSAR covariance matrix. To evaluate the performance of CV-deSpeckNet, we used Sentinel-1 dual polarimetric SAR images to compare against its real-valued counterpart that separates the real and imaginary parts of the complex covariance matrix. CV-deSpeckNet was also compared against the state of the art PolSAR despeckling methods. The results show that CV-deSpeckNet was able to be trained with a fewer number of samples, has a higher generalization capability, and resulted in higher accuracy than its real-valued counterpart and state-of-the-art PolSAR despeckling methods. These results showcase the potential of complex-valued deep learning for PolSAR despeckling.

RATIONALECardiac rhythm, conduction and synchronization of electrical activity require the coordinated action of different types of ion channels that differ according to transmural and regional specificities. Classical pharmacology affects these ion channels in a non-regionalized way which explains why treating arrhythmias, that often occur in specific foci, has often limited efficacy in addition to negative side-effects on non-targeted organs. Photopharmacology is an emergent technology that has the potential to counteract all the negative aspects of classical pharmacology by restricting drug activity in a spatio-temporal manner.OBJECTIVEWe tested the potential of photopharmacology in specifically regulating heart activity by using a caged derivative of a natural peptide inhibitor of the ERG channel, BeKm1. The peptide was uncaged and activity monitoredin vitroon a cell line expressing the hERG channel, on human cardiomyocytes derived from iPS cells, andex vivoandin vivoon zebrafish larvae and rat hearts.METHODS AND RESULTSCaged BeKm-1 is inactive and fully active upon uncaging. Uncaging of the peptide on human iPS-derived cardiomyocytes enlarges the action potential duration and triggers arrhythmias. Uncaging also triggers bradycardia and disturbs cardiac conduction within the atria in perfused rat hearts upon illumination. The potency of photopharmacology for cardiac electrical modulation was further validated in zebrafish larvae where illumination of the caged compound induces bradycardia and atrio-ventricular desynchrony. Finally, in anesthetized rats, illumination of the caged peptide in the right atria, containing the sino-atrial node, leads to bradycardia without arrhythmia.CONCLUSIONSThis report demonstrates that photopharmacology, using the caged peptide strategy, can be used for dynamically regulating cardiac electrical activityin vivoand that spatial illumination restriction can dissociate the bradycardic effect from the arrhythmic one. The technology is applicable to all kinds of cardiac ion channels and regions of interest to create arrhythmogenic models or investigate new clinical applications.

Glaciers are under pressure in the current climate warming trend. The aerial extent and the mass balance, a measure of mass loss or mass gain of a glacier are representative of the “health state” of a glacier. They are recognized as Essential Climate Variables by the World Meteorological Organization. Due to the sheer number of glaciers of roughly 200.000 worldwide and their inaccessibility, satellite data has been proven as a valuable source to map glaciers around the globe. Satellite imagery has enabled researchers to map the extent of glaciers and glacier surface types (glacier facies, i.e. snow, firn, ice). Multi-temporal information on the glacier surface elevation and mass balance proxies (snow cover, albedo) which are highly correlated with the glacier mass balance have been used to estimate mass changes. Today the ever-growing amount of satellite data motivates the development of new methodology to maximize the information retrieved (value-added) and ease the handling and storage of the data in data cubes. Recently, the field of machine learning has been revolutionized by deep learning. In the context of image analysis, Convolutional Neural Networks are among the most successful deep learning algorithms as they can learn a hierarchy of spatial features at different layers of the network associated to increasing levels of abstraction, i.e., from raw pixel values to parts of objects (edges and corners), local shapes, up to complex structures in satellite and aerial images. In MASSIVE the most promising network architectures for pixel-wise mapping in remote sensing, Fully Convolutional Networks, will be adapted to map glacier extend and estimate glacier mass balance.

Introduction. Septic shock is a systemic inflammatory response syndrome associated with organ failures. Earlier clinical diagnosis would be of benefit to a decrease in the mortality rate. However, there is currently a lack of predictive biomarkers. The secretome is the set of proteins secreted by a cell, tissue, or organism at a given time and under certain conditions. The plasma secretome is easily accessible from biological fluids and represents a good opportunity to discover new biomarkers that can be studied with nontargeted “omic” strategies. Aims. To identify relevant deregulated proteins (DEP) in the secretome of a rat endotoxemic shock model. Methods. Endotoxemic shock was induced in rats by intravenous injection of lipopolysaccharides (LPS, S. enterica typhi, 0.5 mg/kg) and compared to controls (Ringer Lactate, iv). Under isoflurane anesthesia, carotid cannulation allowed mean arterial blood pressure (MAP) and heart rate (HR) monitoring and blood sampling at different time points (T0 and T50 or T0 and T90, with EDTA and protease inhibitor). Samples were prepared for large-scale tandem mass spectrometry (MS-MS) based on a label-free quantification to allow identification of the proteins deregulated upon endotoxemic conditions. A Gene Ontology (GO) analysis defined several clusters of biological processes (BP) in which the DEP are involved. Results. Ninety minutes after shock induction, the LPS group presents a reduction in MAP (-45%, p < 0.05 ) and increased lactate levels (+27.5%, p < 0.05 ) compared to the control group. Proteomic analyses revealed 10 and 33 DEP in the LPS group, respectively, at 50 and 90 minutes after LPS injection. At these time points, GO-BP showed alterations in pathways involved in oxidative stress response and coagulation. Conclusion. This study proposes an approach to identify relevant DEP in septic shock and brings new insights into the understanding of the secretome adaptations upon sepsis.

In clinical practice, extracorporeal circulation (ECC) is associated with coagulopathy and inflammation, eventually leading to organ injuries without preventive systemic pharmacological treatment. Relevant models are needed to reproduce the pathophysiology observed in humans and preclinical tests. Rodent models are less expensive than large models but require adaptations and validated comparisons to clinics. This study aimed to develop a rat ECC model and to establish its clinical relevance. One hour of veno-arterial ECC or a sham procedure were achieved on mechanically ventilated rats after cannulations with a mean arterial pressure objective > 60 mmHg. Five hours post-surgery, the rats’ behavior, plasmatic/blood biomarkers, and hemodynamics were measured. Blood biomarkers and transcriptomic changes were compared in 41 patients undergoing on-pump cardiac surgery. Five hours post-ECC, the rats presented hypotension, hyperlactatemia, and behavioral alterations. The same patterns of marker measurements (Lactate dehydrogenase, Creatinine kinase, ASAT, ALAT, and Troponin T) were observed in both rats and human patients. Transcriptome analyses showed similarity in both humans and rats in the biological processes involved in the ECC response. This new ECC rat model seems to resemble both ECC clinical procedures and the associated pathophysiology, but with early organ injury corresponding to a severe phenotype. Although the mechanisms at stake in the post-ECC pathophysiology of rats or humans need to be described, this new rat model appears to be a relevant and costless preclinical model of human ECC.

O-GlcNAcylation is a post-translational modification which affects approximately 5000 human proteins. Its involvement has been shown in many if not all biological processes. Variations in O-GlcNAcylation levels can be associated with the development of diseases. Deciphering the role of O-GlcNAcylation is an important issue to (i) understand its involvement in pathophysiological development and (ii) develop new therapeutic strategies to modulate O-GlcNAc levels. Over the past 30 years, despite the development of several approaches, knowledge of its role and regulation have remained limited. This review proposes an overview of the currently available tools to study O-GlcNAcylation and identify O-GlcNAcylated proteins. Briefly, we discuss pharmacological modulators, methods to study O-GlcNAcylation levels and approaches for O-GlcNAcylomic profiling. This review aims to contribute to a better understanding of the methods used to study O-GlcNAcylation and to promote efforts in the development of new strategies to explore this promising modification.

The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.