Anomalous Higgs-boson coupling effects in HWW production at the LHC

We study the LHC associated production of a Higgs boson and a W^+W^- vector-boson pair at 14 TeV, in the Standard Model and beyond. We consider different signatures corresponding to the cleanest H and W decay channels, and discuss the potential of the high-luminosity phase of the LHC. In particular, we investigate the sensitivity of the HWW production to possible anomalous Higgs couplings to vector bosons and fermions. Since the b-quark initiated partonic channel contributes significantly to this process, we find a moderate sensitivity to both the size and sign of an anomalous top-quark Yukawa coupling, because perturbative unitarity in the standard model implies a destructive interference in the b b-bar subprocess. We show that a combination of various signatures can reach a ~9 standard-deviation sensitivity in the presently allowed negative region of the top-Higgs coupling, if not previously excluded.Comment: 13 pages, 3 figure

A Thermal Analysis of Direct Driven Hydraulics

This paper focuses on thermal analysis of a direct driven hydraulic setup (DDH). DDH combines the benefits of electric with hydraulic technology in compact package with high power density, high performance and good controllability. DDH enables for reduction of parasitic losses for better fuel efficiency and lower operating costs. This one-piece housing design delivers system simplicity and lowers both installation and maintenance costs. Advantages of the presented architecture are the reduced hydraulic tubing and the amount of potential leakage points. The prediction of the thermal behavior and its management represents an open challenge for the system as temperature is a determinant parameter in terms of performance, lifespan and safety. Therefore, the electro-hydraulic model of a DDH involving a variable motor speed, fixed-displacement internal gear pump/motors was developed at system level for thermal analysis. In addition, a generic model was proposed for the electric machine, energy losses dependent on velocity, torque and temperature was validated by measurements under various operative conditions. Results of model investigation predict ricing of temperature during lifting cycle, and flattened during lowering in pimp/motor. Conclusions are drawn concerning the DDH thermal behavior

Deep Neural Networks for Inverse Problems with Pseudodifferential Operators : An Application to Limited-Angle Tomography

We propose a novel convolutional neural network (CNN), called Psi DONet, designed for learning pseudodifferential operators (Psi DOs) in the context of linear inverse problems. Our starting point is the iterative soft thresholding algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow us to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling, and convolution, which characterize our Psi DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited-angle X-ray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of Psi DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are Psi DOs or Fourier integral operators.Peer reviewe

Learning the optimal Tikhonov regularizer for inverse problems

Publisher Copyright: © 2021 Neural information processing systems foundation. All rights reserved.In this work, we consider the linear inverse problem y = Ax+ε, where A: X → Y is a known linear operator between the separable Hilbert spaces X and Y, x is a random variable in X and ε is a zero-mean random process in Y . This setting covers several inverse problems in imaging including denoising, deblurring and X-ray tomography. Within the classical framework of regularization, we focus on the case where the regularization functional is not given a priori, but learned from data. Our first result is a characterization of the optimal generalized Tikhonov regularizer, with respect to the mean squared error. We find that it is completely independent of the forward operator A and depends only on the mean and covariance of x. Then, we consider the problem of learning the regularizer from a finite training set in two different frameworks: one supervised, based on samples of both x and y, and one unsupervised, based only on samples of x. In both cases we prove generalization bounds, under some weak assumptions on the distribution of x and ε, including the case of sub-Gaussian variables. Our bounds hold in infinite-dimensional spaces, thereby showing that finer and finer discretizations do not make this learning problem harder. The results are validated through numerical simulations.Peer reviewe

Scripting for large-scale sequencing based on Hadoop

The large volumes of data generated by modern sequencing experiments present significant challenges in their manipulation and analysis. Traditional approaches are often found to be complicated to scale. We describe our ongoing work on SeqPig, a tool that facilitates the use of the Pig Latin distributed scripting language to manipulate, analyze and query sequencing data applying the advances motivated by the “big data revolution” in data-intensive activities. SeqPig provides access to popular data formats and implements a number of custom sequencing-specific functions. Most importantly, it grants users access to the scalable Hadoop platform from a high level scripting language84-85Pubblicat

Understanding the Functional Properties of Lipid Heterogeneity in Pulmonary Surfactant Monolayers at the Atomistic Level

Pulmonary surfactant is a complex mixture of lipids and proteins lining the interior of the alveoli, and constitutes the first barrier to both oxygen and pathogens as they progress toward blood circulation. Despite decades of study, the behavior of the pulmonary surfactant at the molecular scale is poorly understood, which hinders the development of effective surfactant replacement therapies, useful in the treatment of several lung-related diseases. In this work, we combined all-atom molecular dynamics simulations, Langmuir trough measurements, and AFM imaging to study synthetic four-component lipid monolayers designed to model protein-free pulmonary surfactant. We characterized the structural and dynamic properties of the monolayers with a special focus on lateral heterogeneity. Remarkably, simulations reproduce almost quantitatively the experimental data on pressure-area isotherms and the presence of lateral heterogeneities highlighted by AFM. Quite surprisingly, the pressure-area isotherms do not show a plateau region, despite the presence of liquid-condensed nanometer-sized domains at surface pressures larger than 20 mN/m. In the simulations, the liquid-condensed domains were small and transient, but they did not coalesce to yield a separate phase. They were only slightly enriched in DPPC and cholesterol, and their chemical composition remained very similar to the overall composition of the monolayer membrane. Instead, they differed from liquid-expanded regions in terms of membrane thickness (in agreement with AFM data), diffusion rates, as well as acyl chain packing and orientation. We hypothesize that such lateral heterogeneities are crucial for lung surfactant function, as they allow both efficient packing, to achieve low surface tension, and sufficient fluidity, critical for rapid adsorption to the air–liquid interface during the breathing cycle.Peer reviewe

Investigation on the role of biallelic variants in VEGF-C found in a patient affected by Milroy-like lymphedema

Background Milroy-like disease is the diagnostic definition used for patients with phenotypes that resemble classic Milroy disease (MD) but are negative to genetic testing forFLT4. In this study, we aimed at performing a genetic characterization and biochemical analysis of VEGF-C variations found in a female proband born with congenital edema consistent with Milroy-like disease. Methods The proband underwent next-generation sequencing-based genetic testing for a panel of genes associated with known forms of hereditary lymphedema. Segregation analysis was performed on family members by direct sequencing. In vitro studies were performed to evaluate the role of a novel identified variant. Results TwoVEGF-Cvariations were found in the proband, a novel p.(Ser65Arg) and a pathogenic c.148-3_148-2delCA, of paternal and maternal origin, respectively. Functional characterization of the p.(Ser65Arg) variation in vitro showed alterations in VEGF-C processing. Conclusions Our findings reveal an interesting case in which biallelic variants inVEGF-Care found in a patient with Milroy-like lymphedema. These data expand our understanding of the etiology of congenital Milroy-like lymphedema.Peer reviewe

N-Prenylation of Tryptophan by an Aromatic Prenyltransferase from the Cyanobactin Biosynthetic Pathway

Funding This work was supported by grants from the Academy of Finland (259505, D.P.F.), Helsinki University Research grant (490085, D.P.F.) ESCMID grant (4720572, D.P.F.), the Industrial Biotechnology Innovation Centre (IBioIC) studentship (L. D.), the Jane and Aatos Erkko Foundation (K.S.), the BBSRC FoF grant (no BB/M013669/1, W. E. H.), IBCatalyst grant (no. BB/M028526/1, W. E. H.), the Sarcoma UK grant (W. E. H.) and the SULSA Leaders and SULSA PECRE awards (W. E. H.). W. E. H. acknowledges the fund from the ERC grant no. 339367. ACKNOWLEDGEMENTS D.P.F. and K.S. are grateful to Lyudmila Saari, Department of Microbiology, University of Helsinki, for her valuable help in handling the Anabaena sp. UHCC-0232 culture. W. E. H. thanks the Aberdeen Proteomics Facility and the Marine Biodiscovery Centre Mass Spectrometry Facility for extensive MS analysis. W. E. H. is grateful to Mr. Russell Gray (Marine Biodiscovery Centre, University of Aberdeen) for the NMR analysis of our samples.Peer reviewedPostprin