Skeletal muscle effects of antisense oligonucleotides targeting glycogen synthase 1 in a mouse model of Pompe disease.

Pompe disease (PD) is a progressive myopathy caused by the aberrant accumulation of glycogen in skeletal and cardiac muscle resulting from the deficiency of the enzyme acid alpha-glucosidase (GAA). Administration of recombinant human GAA as enzyme replacement therapy (ERT) works well in alleviating the cardiac manifestations of PD but loses sustained benefit in ameliorating the skeletal muscle pathology. The limited efficacy of ERT in skeletal muscle is partially attributable to its inability to curb the accumulation of new glycogen produced by the muscle enzyme glycogen synthase 1 (GYS1). Substrate reduction therapies aimed at knocking down GYS1 expression represent a promising avenue to improve Pompe myopathy. However, finding specific inhibitors for GYS1 is challenging given the presence of the highly homologous GYS2 in the liver. Antisense oligonucleotides (ASOs) are chemically modified oligomers that hybridise to their complementary target RNA to induce their degradation with exquisite specificity. In the present study, we show that ASO-mediated Gys1 knockdown in the Gaa-/- mouse model of PD led to a robust reduction in glycogen accumulation in skeletal muscle. In addition, combining Gys1 ASO with ERT slightly further reduced glycogen content in muscle, eliminated autophagic buildup and lysosomal dysfunction, and improved motor function in Gaa-/- mice. Our results provide a strong foundation for validation of the use of Gys1 ASO, alone or in combination with ERT, as a therapy for PD. We propose that early administration of Gys1 ASO in combination with ERT may be the key to preventative treatment options in PD. KEY POINTS: Antisense oligonucleotide (ASO) treatment in a mouse model of Pompe disease achieves robust knockdown of glycogen synthase (GYS1). ASO treatment reduces glycogen content in skeletal muscle. Combination of ASO and enzyme replacement therapy (ERT) further improves motor performance compared to ASO alone in a mouse model of Pompe disease

Effects of alirocumab on types of myocardial infarction: insights from the ODYSSEY OUTCOMES trial

Aims  The third Universal Definition of Myocardial Infarction (MI) Task Force classified MIs into five types: Type 1, spontaneous; Type 2, related to oxygen supply/demand imbalance; Type 3, fatal without ascertainment of cardiac biomarkers; Type 4, related to percutaneous coronary intervention; and Type 5, related to coronary artery bypass surgery. Low-density lipoprotein cholesterol (LDL-C) reduction with statins and proprotein convertase subtilisin–kexin Type 9 (PCSK9) inhibitors reduces risk of MI, but less is known about effects on types of MI. ODYSSEY OUTCOMES compared the PCSK9 inhibitor alirocumab with placebo in 18 924 patients with recent acute coronary syndrome (ACS) and elevated LDL-C (≥1.8 mmol/L) despite intensive statin therapy. In a pre-specified analysis, we assessed the effects of alirocumab on types of MI. Methods and results  Median follow-up was 2.8 years. Myocardial infarction types were prospectively adjudicated and classified. Of 1860 total MIs, 1223 (65.8%) were adjudicated as Type 1, 386 (20.8%) as Type 2, and 244 (13.1%) as Type 4. Few events were Type 3 (n = 2) or Type 5 (n = 5). Alirocumab reduced first MIs [hazard ratio (HR) 0.85, 95% confidence interval (CI) 0.77–0.95; P = 0.003], with reductions in both Type 1 (HR 0.87, 95% CI 0.77–0.99; P = 0.032) and Type 2 (0.77, 0.61–0.97; P = 0.025), but not Type 4 MI. Conclusion  After ACS, alirocumab added to intensive statin therapy favourably impacted on Type 1 and 2 MIs. The data indicate for the first time that a lipid-lowering therapy can attenuate the risk of Type 2 MI. Low-density lipoprotein cholesterol reduction below levels achievable with statins is an effective preventive strategy for both MI types.For complete list of authors see http://dx.doi.org/10.1093/eurheartj/ehz299</p

Navigation aérienne basée sur l’imagerie SAR et sur des données géospatiales de référence

Nous cherchons les moyens algorithmiques de déterminer l’état cinématique d’un appareil aérien à partir d’une image RSO observée et de données géospatiales de référence qui peuvent être RSO, optiques ou vectorielles. Nous déterminons la transformation qui associe les coordonnées de l’observation et les coordonnées de la référence et dont les paramètres sont l’état cinématique. Nous poursuivons trois approches. La première repose sur la détection et l’appariement de structures telles que des contours. Nous proposons un algorithme de type Iterative Closest Point (ICP) et démontrons comment il peut servir à estimer l’état cinématique complet. Nous proposons ensuite un système complet qui inclue un détecteur de contours multimodal appris. La seconde approche repose sur une métrique de similarité multimodale, ce qui est un moyen de mesurer la vraisemblance que deux restrictions locales de données géospatiales représentent le même point géographique. Nous déterminons l’état cinématique sous l’hypothèse duquel l’image SAR est la plus similaire aux données géospatiales de référence. La troisième approche repose sur la régression de coordonnées de scène. Nous prédisons les coordonnées géographiques de morceaux d’images et déduisons l’état cinématique à partir des correspondances ainsi prédites. Cependant, dans cette approche, nous ne satisfaisons pas l’hypothèse de multimodalité.We seek the algorithmic means of determining the kinematic state of an aerial device from an observation SAR image and reference geospatial data that may be SAR, optical or vector. We determine a transform that relates the observation and reference coordinates and whose parameters are the kinematic state. We follow three approaches. The first one is based on detecting and matching structures such as contours. We propose an iterative closest point algorithm and demonstrate how it can serve to estimate the full kinematic state. We then propose a complete pipeline that includes a learned multimodal contour detector. The second approach is based on a multimodal similarity metric, which is the means of measuring the likelihood that two local patches of geospatial data represent the same geographic point. We determine the kinematic state under the hypothesis of which the SAR image is most similar to the reference geospatial data. The third approach is based on scene coordinates regression. We predict the geographic coordinates of random image patches and infer the kinematic state from these predicted correspondences. However, in this approach, we do not address the fact that the modality of the observation and the reference are different

A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation

International audienceWe present a template matching algorithm based on local descriptors for aligning two geospatial products of different modalities with a large area asymmetry. Our system is generic with regards to the modalities of the geospatial products and is applicable to the self-localization of aerial devices such as drones and missiles. This algorithm consists in finding a superposition such that the average dissimilarity of the superposed points is minimal. The dissimilarity of two points belonging to two different geospatial products is the distance between their respective local descriptors. These local descriptors are learned. We performed experiments consisting in estimating a translation between optical (Pléiades) and SAR (Miranda) images onto vector data (OpenStreetMap), onto optical images (DOP) and onto SAR images (KOMPSAT-5). Each remote sensing image to be aligned covered 0.64 km2, and each reference geospatial product spanned over 225 km2. We conducted a total of 381 alignment experiments, with six unique modality combinations. In aggregate, the precision reached was finer than 10 m with 72% probability and finer than 20 m with 96% probability. This is considerably more than with traditional methods such as normalized cross-correlation and mutual information

Simulation of wind-driven snow redistribution at a high-elevation alpine site with a mesoscale atmospheric model

[Departement_IRSTEA]Eaux [TR1_IRSTEA]RIVAGEInternational audienceIn alpine regions, blowing snow events strongly influence the temporal and spatial evolution of the snow depth distribution throughout the winter season. We recently developed a new simulation system to gain understanding on the complex processes that drive the redistribution of snow by the wind in complex terrain. This new system couples directly the detailed snow-pack model Crocus with the meso-scale atmospheric model Meso-NH. A blowing snow scheme allows Meso-NH to simulate the transport of snow particles in the atmosphere

Simulation of wind-driven snow redistribution at a high-elevation alpine site with a mesoscale atmospheric model

International audienceIn alpine regions, blowing snow events strongly influence the temporal and spatial evolution of the snow depth distribution throughout the winter season. We recently developed a new simulation system to gain understanding on the complex processes that drive the redistribution of snow by the wind in complex terrain. This new system couples directly the detailed snow-pack model Crocus with the meso-scale atmospheric model Meso-NH. A blowing snow scheme allows Meso-NH to simulate the transport of snow particles in the atmosphere

High resolution modeling of wind-induced snow transport using a fully coupled snowpack/atmosphere model

International audienceIn alpine regions, blowing snow events strongly influence the spatio-temporal evolution of the snow cover throughout the winter season

Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model

In alpine regions, wind-induced snow transport strongly influences the spatio-temporal evolution of the snow cover throughout the winter season. To gain understanding on the complex processes that drive the redistribution of snow, a new numerical model is developed. It directly couples the detailed snowpack model Crocus with the atmospheric model Meso-NH. Meso-NH/Crocus simulates snow transport in saltation and in turbulent suspension and includes the sublimation of suspended snow particles. The coupled model is evaluated against data collected around the experimental site of Col du Lac Blanc (2720ma.s.l., French Alps). First, 1-D simulations show that a detailed representation of the first metres of the atmosphere is required to reproduce strong gradients of blowing snow concentration and compute mass exchange between the snowpack and the atmosphere. Secondly, 3-D simulations of a blowing snow event without concurrent snowfall have been carried out. Results show that the model captures the main structures of atmospheric flow in alpine terrain. However, at 50m grid spacing, the model reproduces only the patterns of snow erosion and deposition at the ridge scale and misses smaller scale patterns observed by terrestrial laser scanning. When activated, the sublimation of suspended snow particles causes a reduction of deposited snow mass of 5.3% over the calculation domain. Total sublimation (surface + blowing snow) is three times higher than surface sublimation in a simulation neglecting blowing snow sublimation