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Viral RNAs can be uridylated in eucaryotic hosts. However, our knowledge of uridylation patterns and roles remains rudimentary for phytoviruses. Here, we report global 3’ terminal RNA uridylation profiles for representatives of the main families of positive single-stranded RNA phytoviruses. We detected uridylation in all 47 viral RNAs investigated here, revealing its prevalence. Yet, uridylation levels of viral RNAs varied from 0.2% to 90%. Unexpectedly, most poly(A) tails of grapevine fanleaf virus (GFLV) RNAs, including encapsidated tails, were strictly mono-uridylated, which corresponded to an unidentified type of viral genomic RNA extremity. This mono-uridylation appears beneficial for GFLV because it becomes dominant when plants are infected with non-uridylated GFLV transcripts. We found that GFLV RNA mono-uridylation is independent of the known TUTases HEN1 SUPPRESSOR 1 (HESO1) and UTP:RNA URIDYLYLTRANSFERASE 1 (URT1) in Arabidopsis (Arabidopsis thaliana). By contrast, both TUTases uridylate other viral RNAs like turnip crinkle virus (TCV) and turnip mosaic virus (TuMV) RNAs. Interestingly, TCV and TuMV degradation intermediates were differentially uridylated by HESO1 and URT1. Although the lack of both TUTases did not prevent viral infection, we detected degradation intermediates of TCV RNA at higher levels in a Arabidopsis heso1 urt1 mutant, suggesting that uridylation participates in clearing viral RNA. Collectively, our work unveils an extreme diversity of uridylation patterns across phytoviruses and constitutes a valuable resource to further decipher pro- and anti-viral roles of uridylation.

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultra-high frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast ultrasound imaging. Most of these approaches have been performed on radio frequency (RF) signals. However, in-phase/quadrature (I/Q) digital beamformers are now widely used as low-cost strategies. In this work, we used complex convolutional neural networks for reconstruction of ultrasound images from I/Q signals. We recently described a convolutional neural network architecture called ID-Net, which exploited an inception layer designed for reconstruction of RF diverging-wave ultrasound images. In the present study, we derive the complex equivalent of this network; i.e., the Complex-valued Inception for Diverging-wave Network (CID-Net) that operates on I/Q data. We provide experimental evidence that CID-Net provides the same image quality as that obtained from RF-trained convolutional neural networks; i.e., using only three I/Q images, the CID-Net produces high-quality images that can compete with those obtained by coherently compounding 31 RF images. Moreover, we show that CID-Net outperforms the straightforward architecture that consists of processing the real and imaginary parts of the I/Q signal separately, which thereby indicates the importance of consistently processing the I/Q signals using a network that exploits the complex nature of such signals.

In eukaryotes, general mRNA decay requires the decapping complex. The activity of this complex depends on its catalytic subunit, DECAPPING2 (DCP2), and its interaction with decapping enhancers, including its main partner DECAPPING1 (DCP1). Here, we report that in Arabidopsis thaliana, DCP1 also interacts with a NYN domain endoribonuclease, hence named DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1). Interestingly, we found DNE1 predominantly associated with DCP1, but not with DCP2, and reciprocally, suggesting the existence of two distinct protein complexes. We also showed that the catalytic residues of DNE1 are required to repress the expression of mRNAs in planta upon transient expression. The overexpression of DNE1 in transgenic lines led to growth defects and a similar gene deregulation signature than inactivation of the decapping complex. Finally, the combination of dne1 and dcp2 mutations revealed a functional redundancy between DNE1 and DCP2 in controlling phyllotactic pattern formation. Our work identifies DNE1, a hitherto unknown DCP1 protein partner highly conserved in the plant kingdom and identifies its importance for developmental robustness.

International audience; Color Doppler imaging (CDI) is the modality of choice for simultaneous visualization of myocardium and intracavitary flow over a wide scan area. This visualization modality is subject to several sources of error, the main ones being aliasing and clutter. Mitigation of these artifacts is a major concern for better analysis of intracardiac flow. One option to address these issues is through simulations. In this article, we present a numerical framework for generating clinical-like CDI. Synthetic blood vector fields were obtained from a patient-specific computational fluid dynamics CFD model. Realistic texture and clutter artifacts were simulated from real clinical ultrasound cineloops. We simulated several scenarios highlighting the effects of 1) flow acceleration; 2) wall clutter; and 3) transmit wavefronts, on Doppler velocities. As a comparison, an “ideal” color Doppler was also simulated, without these harmful effects. This synthetic dataset is made publicly available and can be used to evaluate the quality of Doppler estimation techniques. Besides, this approach can be seen as a first step toward the generation of comprehensive datasets for training neural networks to improve the quality of Doppler imaging

Viral infections impose extraordinary RNA stress, triggering cellular RNA surveillance pathways such as RNA decapping, nonsense-mediated decay, and RNA silencing. Viruses need to maneuver among these pathways to establish infection and succeed in producing high amounts of viral proteins. Processing bodies (PBs) are integral to RNA triage in eukaryotic cells, with several distinct RNA quality control pathways converging for selective RNA regulation. In this study, we investigated the role of Arabidopsis thaliana PBs during Cauliflower mosaic virus (CaMV) infection. We found that several PB components are co-opted into viral factories that support virus multiplication. This pro-viral role was not associated with RNA decay pathways but instead, we established that PB components are helpers in viral RNA translation. While CaMV is normally resilient to RNA silencing, dysfunctions in PB components expose the virus to this pathway, which is similar to previous observations for transgenes. Transgenes, however, undergo RNA quality control-dependent RNA degradation and transcriptional silencing, whereas CaMV RNA remains stable but becomes translationally repressed through decreased ribosome association, revealing a unique dependence among PBs, RNA silencing, and translational repression. Together, our study shows that PB components are co-opted by the virus to maintain efficient translation, a mechanism not associated with canonical PB functions.Arabidopsis RNA processing body components LSM1 and DCP5 are co-opted by Cauliflower mosaic virus to maintain efficient virus translation in the presence of RNA DEPENDENT POLYMERASE6-governed silencing.

In eukaryotes, general mRNA decay requires the decapping complex. The activity of this complex depends on its catalytic subunit, DCP2 and its interaction with decapping enhancers, including its main partner DCP1. Here, we report that in Arabidopsis, DCP1 also interacts with a NYN domain endoribonuclease, hence named DCP1-ASSOCIATED NYN ENDORIBONUCLEASE 1 (DNE1). Interestingly, we find DNE1 predominantly associated with DCP1 but not with DCP2 and reciprocally, suggesting the existence of two distinct protein complexes. We also show that the catalytic residues of DNE1 are required to repress the expression of mRNAs in planta upon transient expression. The overexpression of DNE1 in transgenic lines leads to growth defects and transcriptomic changes related to the one observed upon inactivation of the decapping complex. Finally, the combination of dne1 and dcp2 mutations, revealed a functional redundancy between DNE1 and DCP2 in controlling phyllotactic pattern formation in Arabidopsis. Our work identifies DNE1, a hitherto unknown DCP1 protein partner highly conserved in the plant kingdom and identifies its importance for developmental robustness.One-sentence summaryDNE1, a NYN domain protein interacts with the decapping activator DCP1 and, together with DCP2, specify phyllotactic patterns in Arabidopsis.

International audience; Medical ultrasound imaging requires recurring use of elementary principles, namely, the choice of the transducer, the design of the emitted waves and their wavefronts (e.g. focused or wide), the demodulation of the received signals, their beamforming, and their post-processing to generate B-mode or flow images. The ultrasound signals used for research purposes can be syntheticor acquired. To make the whole pipeline easily accessible to many researchers and students, the objective was to provide an open-access toolbox, widely documented, and adapted to ultrasound imaging research involving experimental or simulation methods. The MUST Matlab UltraSound Toolbox contains algorithms that focus on the development, simulation, and analysis of ultrasound signals for medical imaging. The user can design various ultrasound-imaging scenarios and analyze their performance using simulated or acquired data. The MUST functions allow studyingthe characteristics of transducers and waveforms, analyze signals, and construct ultrasound images. The many examples provide a starting point for students and researchers to quickly gain an understanding of the essentials of ultrasound imaging. The simulators integrated into MUST provide very realistic acoustic pressure fields and ultrasound images. The Matlab MUST toolbox is freely available at https://www.biomecardio.com/MUST. Before engaging in advanced ultrasound techniques and comparing them with so-called standard methods, it is necessary to have a good understanding of the latter and their advantages and limitations. With this in mind, the MUST toolbox includes everything needed for comprehensive ultrasound imaging: simulators of acoustic pressure fields and backscattered RF signals, delay-and-sum beamforming, B-mode imaging, wall filtering, color or vector Doppler, speckle tracking... The documentation and open access facilitate easy and intuitive use. If the MUST toolbox proves to be interesting, the author plans to integrate advanced features depending on the demands of the ultrasound community.

Heart failure affects the heart’s ability to meet the oxygen needs of the organs. It concerns more than one million people in France. It can be defined by the reduced capacity of the heart to be fi...

Viral infections impose extraordinary RNA stress on a cell, triggering cellular RNA surveillance pathways like RNA decapping, nonsense-mediated decay and RNA silencing. Viruses need to maneuver between these pathways to establish infection and succeed in producing high amounts of viral proteins. Processing bodies (PBs) are integral to RNA triage in eukaryotic cells with several distinct RNA quality control pathways converging for selective RNA regulation. In this study, we investigate the role of Arabidopsis thaliana PBs during Cauliflower Mosaic Virus (CaMV) infection. We find that several PB components are co-opted into viral replication factories and support virus multiplication. This pro-viral role was not associated with RNA decay pathways but instead, we could establish PB components as essential helpers in viral RNA translation. While CaMV is normally resilient to RNA silencing, PB dysfunctions expose the virus to this pathway, similar to previous observations on transgenes. Transgenes, however, undergo RNA Quality Control dependent RNA degradation, whereas CaMV RNA remains stable but becomes translationally repressed through decreased ribosome association, revealing a unique dependence between PBs, RNA silencing and translational repression. Together, our study shows that PB components are co-opted by the virus to maintain efficient translation, a mechanism not associated with canonical PB functions.

International audience; The Foveal Avascular Zone (FAZ) is commonly analyzed from OCT-A images to diagnose retinal diseases. When quantitative measures are required, ophthalmologists manually draw the contours of the FAZ and compute several anatomical features from these annotations. However, there is no clear consensus among experts on the FAZ definition in pathological cases. In this work, we developed a novel framework to automatically segment the FAZ based on three different expert annotations to be robust to interexpert variability. A loss based on the Hausdorff Distance was used to specifically address complex pathological FAZ that are usually poorly segmented by automatic methods. We used a dataset of more than 200 images to train and test our model, and we achieved similar or even better segmentation performance than individual experts.

Echocardiography is widely used to provide critical left ventricular indices describing myocardial motion and blood inflow velocity. Tissue motion and blood flow are strongly connected and interdependent in the ventricle. During cardiac relaxation, rapid filling leads to the formation of a vortical blood flow pattern. In this paper, we introduce a high-frame-rate method to track vortex dynamics alongside myocardium motion, in a single heartbeat. Cardiac triplex imaging (B-mode + tissue Doppler + color Doppler) was obtained by insonating the left ventricle with diverging waves. We used coherent compounding with integrated motion compensation to obtain high-quality B-mode images. Tissue Doppler was retrieved and the septal and lateral velocities of the mitral annulus were deduced. A rate of ~80 triplex images/s was obtained. Vortex dynamics was analyzed by Doppler vortography. Blood vortex signature maps were used to track the vortex and compute core vorticities. The sequence was implemented in a Verasonics scanner with a 2.5-MHz phased array and tested in vivo in 12 healthy volunteers. Two main peaks appeared on the vorticity curves. These peaks were synchronized with the mitral inflow velocities with a small delay. We observed a relationship between the tissue and vortex waveforms, though also with a delay, which denoted the lag between the wall and the flow motion. Clinical diastolic indices combining basal and mitral inflow velocities (E/A ratio and E/ $e^\prime $ ratio) were determined and compared with those measured using a conventional ultrasound scanner; a good correlation was obtained ( $r^{2} = 0.96$ ). High-frame-rate Doppler echocardiography enabled us to retrieve time-resolved dynamics of the myocardium and vortex flow within the same cardiac cycle. Coupling wall-flow analysis could be of clinical relevance for early diagnosis of filling impairment.

Intraventricular vector flow mapping (iVFM) is a technique for retrieving 2-D velocity vector fields of blood flow in the left ventricle. This method is based on conventional color Doppler imaging, which makes iVFM compatible with the clinical setting. We have generalized the iVFM for a three-dimensional reconstruction (3D-iVFM). 3D-iVFM is able to recover three-component velocity vector fields in a full intraventricular volume by using a clinical echocardiographic triplane mode. As with the 2-D version, the method is based on the mass conservation, and free-slip boundary conditions on the endocardial wall. These mechanical constraints were imposed in a least-squares minimization problem that was solved through the method of Lagrange multipliers. We validated 3D-iVFM in silico in a patient-specific CFD (computational fluid dynamics) model of cardiac flow, and tested its feasibility in vivo on volunteers. In both in silico and in vivo investigations, the dynamics of the intraventricular vortex that forms during diastole was deciphered by 3D-iVFM. Our results tend to indicate that 3D-iVFM could provide full-volume echocardiographic information on left intraventricular hemodynamics from the clinical modality of triplane color Doppler., Comment: original paper

Color Doppler by transthoracic echocardiography creates two-dimensional fan-shaped maps of blood velocities in the cardiac cavities. It is a one-component velocimetric technique since it only returns the velocity components parallel to the ultrasound beams. Intraventricular vector flow mapping (iVFM) is a method to recover the blood velocity vectors from the Doppler scalar fields in an echocardiographic three-chamber view. We improved our iVFM numerical scheme by imposing physical constraints. The iVFM consisted in minimizing regularized Doppler residuals subject to the condition that two fluid-dynamics constraints were satisfied, namely planar mass conservation, and free-slip boundary conditions. The optimization problem was solved by using the Lagrange multiplier method. A finite-difference discretization of the optimization problem, written in the polar coordinate system centered on the cardiac ultrasound probe, led to a sparse linear system. The single regularization parameter was determined automatically for non-supervision considerations. The physics-constrained method was validated using realistic intracardiac flow data from a patient-specific computational fluid dynamics (CFD) model. The numerical evaluations showed that the iVFM-derived velocity vectors were in very good agreement with the CFD-based original velocities, with relative errors ranged between 0.3% and 12%. We calculated two macroscopic measures of flow in the cardiac region of interest, the mean vorticity and mean stream function, and observed an excellent concordance between physics-constrained iVFM and CFD. The capability of physics-constrained iVFM was finally tested with in vivo color Doppler data acquired in patients routinely examined in the echocardiographic laboratory. The vortex that forms during the rapid filling was deciphered. The physics-constrained iVFM algorithm is ready for pilot clinical studies and is expected to have a significant clinical impact on the assessment of diastolic function.

International audience; We generalized and improved our clinical technique of twodimensional intraventricular vector flow mapping (2D-iVFM) for a full-volume three-component analysis of the intraventricular blood flow (3D-iVFM). While 2D-iVFM uses three-chamber color Doppler images, 3D-iVFM is based on the clinical mode of triplane color Doppler echocardiography. As in the previous twodimensional version, 3D-iVFM relies on mass conservation and free-slip endocardial boundary conditions. For sake of robustness, the optimization problem was written as a constrained least-squares problem. We tested and validated 3D-iVFM in silico through a patient-specific heart-flow CFD (computational fluid dynamics) model, as well as in vivo in one healthy volunteer. The intraventricular vortex that forms during left ventricular filling was deciphered. After further validation, 3D-iVFM could offer clinically compatible 3-D echocardiographic insights into left intraventricular hemodynamics.

International audience; In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional neural network algorithms for motion estimation in ultrasound imaging. We evaluated and compared several networks derived from FlowNet2, one of the most efficient architectures in computer vision. The networks were tested with and without transfer learning, and the best configuration was compared against the particle imaging velocimetry method, a popular state-of-the-art block-matching algorithm. Rotations are known to be difficult to track from ultrasound images due to a significant speckle decorrelation. We thus focused on the images of rotating disks, which could be tracked through speckle features only. Our database consisted of synthetic and in vitro B-mode images after log compression and covered a large range of rotational speeds. One of the FlowNet2 subnetworks, FlowNet2SD, produced competitive results with a motion field error smaller than 1 pixel on real data after transfer learning based on the simulated data. These errors remain small for a large velocity range without the need for hyperparameter tuning, which indicates the high potential and adaptability of DL solutions to motion estimation in ultrasound imaging.

Aims Current European guidelines recommend the SCORE to estimate 10-year cardiovascular mortality in patients with moderate/low cardiovascular risk. SCORE was derived from the general population. The objective of this study was to investigate the estimated 10-year cardiovascular mortality according to the SCORE in a historic and a contemporary cohort of hypertensive patients. Methods and results After exclusion of secondary prevention and diabetes, 3086 patients were analysed in the OLD-HTA (1969–90) and 1081 in the NEW-HTA (1997–2014) Lyon cohorts. SCORE was calculated using the low and high cardiovascular risk equations and charts, and patients classified as being at low (0%), moderate (1–4%), high (5–9%), and very high (≥10%) risk. In the OLD-HTA cohort, 10-year cardiovascular mortality was higher (1.2%, 5.5%, 17.7%, and 27.0%) than that predicted by the low-risk equation (0%, 1.7%, 6.4%, and 14.8%). In the NEW-HTA cohort, similar results were observed (1.1%, 4.7%, 15.1%, and 15.2% vs. 0%, 1.9%, 6.2%, and 11.7%, respectively). Using the high-risk equation, mortality was underestimated in both cohorts, but the difference was smaller. The diagnostic performance of the high-risk equation was lower than the low-risk equation in both cohorts, considering the SCORE as a continuous or a categorical variable (Likelihood ratio test P Conclusion SCORE underestimates the 10-year cardiovascular mortality risk in hypertensive patients in a historic cohort and in a contemporary one. The algorithm to predict cardiovascular mortality in hypertensive patients needs an update given new information since its creation.

International audience; Single diverging wave (DW) imaging produces ultrasound (US) images at high frame rate (ultrafast) but of low quality. Conventional high-quality DW imaging relies on the coherent compounding of multiple consecutive steered emissions, which in turn reduces the gain in frame rate. Reconstructing high-quality US images for ultrafast imaging using deep learning techniques has recently raised a growing interest in the US community. We recently described a convolutional neural network (CNN) architecture called ID-Net, which exploited an inception layer devoted to the reconstruction of DW ultrasound images using radio frequency (RF) data. We derive in this work the complex equivalent of this network, i.e., the complex inception for DW network (CID-Net), operating on in-phase/quadrature (I/Q) data. We experimentally demonstrate that the CID-Net yields the same image quality as that obtained from the RF-trained CNN, i.e., using only three I/Q images, the CID-Net yields high-quality images competing with those obtained by coherently compounding 31 RF images

Synthetic sequences can be promising tools for advanced in silico evaluation and learning of intraventricular blood flow in echocardiographic imaging. In this paper, we propose a simulation framework that allows the generation of realistic 2D synthetic echocardiographic images. A template acquisition involving a B-mode sequence and Doppler information was combined with a physical simulator to generate synthetic data. A reference motion field of the blood flow was derived from the Doppler velocities. The simulated images were evaluated qualitatively by showing realistic B-mode textures, as well as Doppler velocities similar to those of the real echocardiographic template.

Delay-and-sum (DAS) is the most widespread digital beamformer in high-frame-rate ultrasound imaging. Its implementation is simple and compatible with real-time applications. In this viewpoint article, we describe the fundamentals of DAS beamforming. The underlying theory and numerical approach are detailed so that users can be aware of its functioning and limitations. In particular, we discuss the importance of the f-number and speed of sound on image quality, and propose one solution to set their values from a physical viewpoint. We suggest determining the f-number from the directivity of the transducer elements and the speed of sound from the phase dispersion of the delayed signals. Simplified Matlab codes are provided for the sake of clarity and openness. The effect of the f-number and speed of sound on the lateral resolution and contrast-to-noise ratio was investigated in vitro and in vivo. If not properly preset, these two factors had a substantial negative impact on standard metrics of image quality (namely CNR and FWHM). When beamforming with DAS in vitro or in vivo, it is recommended to optimize these parameters in order to use it wisely and prevent image degradation., Comment: There was a mistake in the Matlab function EZSOS (appendix). "IQ" has been replaced by "IQ0" in the "ezdas" line