Learning-based Framework for US Signals Super-resolution

We propose a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. We up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $1.7\%$ on obstetric 2X raw images, $6.1\%$ on cardiac 2X raw images, and $4.4\%$ on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $9.0\%$ on obstetric 4X raw images, $5.2\%$ on cardiac 4X raw images, and $6.2\%$ on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, our super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices

Effects of Lorentz invariance violation on cosmic ray photon emission and gamma ray decay processes

In this work, we use Lorentz invariance violation (LIV) introduced as a generic modification to particle dispersion relations to study some consequences of single photon emission, known as vacuum Cherenkov radiation, and photon decay processes in cosmic and gamma rays. These processes are forbidden in a Lorentz invariant theory but allowed under the hypothesis of LIV. We show that the emission rate have a dependency on the cosmic ray primary mass and the electric charge that could modify the UHECR spectrum. Furthermore, LIV dramatically enhances photon decay into an electro-positron pair above certain energy threshold. This last effect can then be used to set limits to the LIV energy scale from the direct observation of very high energy cosmic photon events by telescopes of gamma-rays.Comment: Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Kore

IgA N- and O-glycosylation profiling reveals no association with the pregnancy-related improvement in rheumatoid arthritis

Background: The Fc glycosylation of immunoglobulin G (IgG) is well known to associate with rheumatoid arthritis (RA) disease activity. The same may be true for other classes of Igs. In the present study, we sought to determine whether the glycosylation of IgA was different between healthy subjects and patients with RA, as well as whether it was associated with RA disease activity, in particular with the pregnancy-associated improvement thereof or the flare after delivery. Methods: A recently developed high-throughput method for glycoprofiling of IgA1 was applied to affinity-captured IgA from sera of patients with RA (n = 252) and healthy control subjects (n = 32) collected before, during and after pregnancy. Results: IgA1 O-glycans bore more sialic acids in patients with RA than in control subjects. In addition, levels of bisecting N-acetylglucosamine of the N-glycans at asparagine 144 were higher in the patients with RA. The levels of several N-glycosylation traits were shown to change with pregnancy, similar to what has been shown before for IgG. However, the changes in IgA glycosylation were not associated with improvement or a flare of disease activity. Conclusions: The glycosylation of IgA differs between patients with RA and healthy control subjects. However, our data suggest only a minor, if any, association of IgA glycosylation with RA disease activity

Apolipoprotein-CIII O-Glycosylation, a Link between GALNT2 and Plasma Lipids

Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII0a), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII0c, apo-CIII1 and apo-CIII2). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease.</p

Apolipoprotein-CIII O-Glycosylation Is Associated with Micro- and Macrovascular Complications of Type 2 Diabetes

Apolipoprotein-CIII (apo-CIII) inhibits the clearance of triglycerides from circulation and is associated with an increased risk of diabetes complications. It exists in four main proteoforms: O-glycosylated variants containing either zero, one, or two sialic acids and a non-glycosylated variant. O-glycosylation may affect the metabolic functions of apo-CIII. We investigated the associations of apo-CIII glycosylation in blood plasma, measured by mass spectrometry of the intact protein, and genetic variants with micro- and macrovascular complications (retinopathy, nephropathy, neuropathy, cardiovascular disease) of type 2 diabetes in a DiaGene study (n = 1571) and the Hoorn DCS cohort (n = 5409). Mono-sialylated apolipoprotein-CIII (apo-CIII1) was associated with a reduced risk of retinopathy (β = −7.215, 95% CI −11.137 to −3.294) whereas disialylated apolipoprotein-CIII (apo-CIII2) was associated with an increased risk (β = 5.309, 95% CI 2.279 to 8.339). A variant of the GALNT2-gene (rs4846913), previously linked to lower apo-CIII0a, was associated with a decreased prevalence of retinopathy (OR = 0.739, 95% CI 0.575 to 0.951). Higher apo-CIII1 levels were associated with neuropathy (β = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII0a with macrovascular complications (β = −9.195, 95% CI −15.847 to −2.543). In conclusion, apo-CIII glycosylation was associated with the prevalence of micro- and macrovascular complications of diabetes. Moreover, a variant in the GALNT2-gene was associated with apo-CIII glycosylation and retinopathy, suggesting a causal effect. The findings facilitate a molecular understanding of the pathophysiology of diabetes complications and warrant consideration of apo-CIII glycosylation as a potential target in the prevention of diabetes complications.</p

Apolipoprotein-CIII O-Glycosylation Is Associated with Micro- and Macrovascular Complications of Type 2 Diabetes

Apolipoprotein-CIII (apo-CIII) inhibits the clearance of triglycerides from circulation and is associated with an increased risk of diabetes complications. It exists in four main proteoforms: O-glycosylated variants containing either zero, one, or two sialic acids and a non-glycosylated variant. O-glycosylation may affect the metabolic functions of apo-CIII. We investigated the associations of apo-CIII glycosylation in blood plasma, measured by mass spectrometry of the intact protein, and genetic variants with micro- and macrovascular complications (retinopathy, nephropathy, neuropathy, cardiovascular disease) of type 2 diabetes in a DiaGene study (n = 1571) and the Hoorn DCS cohort (n = 5409). Mono-sialylated apolipoprotein-CIII (apo-CIII1) was associated with a reduced risk of retinopathy (β = −7.215, 95% CI −11.137 to −3.294) whereas disialylated apolipoprotein-CIII (apo-CIII2) was associated with an increased risk (β = 5.309, 95% CI 2.279 to 8.339). A variant of the GALNT2-gene (rs4846913), previously linked to lower apo-CIII0a, was associated with a decreased prevalence of retinopathy (OR = 0.739, 95% CI 0.575 to 0.951). Higher apo-CIII1 levels were associated with neuropathy (β = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII0a with macrovascular complications (β = −9.195, 95% CI −15.847 to −2.543). In conclusion, apo-CIII glycosylation was associated with the prevalence of micro- and macrovascular complications of diabetes. Moreover, a variant in the GALNT2-gene was associated with apo-CIII glycosylation and retinopathy, suggesting a causal effect. The findings facilitate a molecular understanding of the pathophysiology of diabetes complications and warrant consideration of apo-CIII glycosylation as a potential target in the prevention of diabetes complications.</p

Lipopolysaccharide O-antigen molecular and supramolecular modifications of plant root microbiota are pivotal for host recognition

11 pags., 5 figs.Lipopolysaccharides, the major outer membrane components of Gram-negative bacteria, are crucial actors of the host-microbial dialogue. They can contribute to the establishment of either symbiosis or bacterial virulence, depending on the bacterial lifestyle. Plant microbiota shows great complexity, promotes plant health and growth and assures protection from pathogens. How plants perceive LPS from plant-associated bacteria and discriminate between beneficial and pathogenic microbes is an open and urgent question. Here, we report on the structure, conformation, membrane properties and immune recognition of LPS isolated from the Arabidopsis thaliana root microbiota member Herbaspirillum sp. Root189. The LPS consists of an O-methylated and variously acetylated D-rhamnose containing polysaccharide with a rather hydrophobic surface. Plant immunology studies in A. thaliana demonstrate that the native acetylated O-antigen shields the LPS from immune recognition whereas the O-deacylated one does not. These findings highlight the role of Herbaspirillum LPS within plant-microbial crosstalk, and how O-antigen modifications influence membrane properties and modulate LPS host recognition.This study was supported by PRIN 2017 "Glytunes" (2017XZ2ZBK, 2019-2022) to AS; by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 851356 to RM. Neutron Reflectivity (NR) measurements were performed at the INTER instrument at ISIS Pulsed Neutron and Muon Source, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, UK. The authors thank the ISIS facility for provision of beam time. MACR and DS gratefully acknowl- edge financial support from the Spanish Ministry of Science, Innovation, and Universities (RTI2018-099985-B-I00), and the CIBER of Respiratory Diseases (CIBERES), an initiative from the Spanish Institute of Health Carlos III (ISCIII). AZ and LM acknowledge support from the Cluster of Excellence on Plant Sciences (CEPLAS) funded by the Deutsche For- schungsgemeinschaft (DFG, German Research Foundation) under Ger- many’s Excellence Strategy-EXC 2048/1-Project ID: 390686111 and project ZU 263/11-1 (SPP DECRyPT)Peer reviewe

A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces a speckle noise in the signal, that corrupts the resulting image and affects further processing operations, and the visual analysis that medical experts conduct to estimate patient diseases. Our main goal is to define a universal deep learning framework for real-time denoising of ultrasound images. We analyse and compare state-of-the-art methods for the smoothing of ultrasound images (e.g., spectral, low-rank, and deep learning denoising algorithms), in order to select the best one in terms of accuracy, preservation of anatomical features, and computational cost. Then, we propose a tuned version of the selected state-of-the-art denoising methods (e.g., WNNM), to improve the quality of the denoised images, and extend its applicability to ultrasound images. To handle large data sets of ultrasound images with respect to applications and industrial requirements, we introduce a denoising framework that exploits deep learning and HPC tools, and allows us to replicate the results of state-of-the-art denoising methods in a real-time execution.Comment: 21 pages, 10 figures, 3 table

Developments in FTICR-MS and Its Potential for Body Fluid Signatures

Fourier transform mass spectrometry (FTMS) is the method of choice for measurements that require ultra-high resolution. The establishment of Fourier transform ion cyclotron resonance (FTICR) MS, the availability of biomolecular ionization techniques and the introduction of the Orbitrap™ mass spectrometer have widened the number of FTMS-applications enormously. One recent example involves clinical proteomics using FTICR-MS to discover and validate protein biomarker signatures in body fluids such as serum or plasma. These biological samples are highly complex in terms of the type and number of components, their concentration range, and the structural identity of each species, and thus require extensive sample cleanup and chromatographic separation procedures. Clearly, such an elaborate and multi-step sample preparation process hampers high-throughput analysis of large clinical cohorts. A final MS read-out at ultra-high resolution enables the analysis of a more complex sample and can thus simplify upfront fractionations. To this end, FTICR-MS offers superior ultra-high resolving power with accurate and precise mass-to-charge ratio (m/z) measurement of a high number of peptides and small proteins (up to 20 kDa) at isotopic resolution over a wide mass range, and furthermore includes a wide variety of fragmentation strategies to characterize protein sequence and structure, including post-translational modifications (PTMs). In our laboratory, we have successfully applied FTICR “next-generation” peptide profiles with the purpose of cancer disease classifications. Here we will review a number of developments and innovations in FTICR-MS that have resulted in robust and routine procedures aiming for ultra-high resolution signatures of clinical samples, exemplified with state-of-the-art examples for serum and saliva