Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers

Humanoid push recovery control in case of multiple non-coplanar contacts

Conference of 2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:102443International audienceThis paper presents a method for humanoid push recovery in the general context of multiple non-coplanar contacts. The method consists of a controller that minimizes the kinetic energy of a perturbed whole body humanoid system, while controlling the support change to achieve the stabilization (or push recovery) of the system. The controller uses a simple model based approach to determine the necessity of support change and in this case to approximate a new contact position that allows to stabilize the system. The controller is tested on a simulated humanoid robot and it succeeds in stabilizing the robot in coplanar and non-coplanar environments

HuR-targeted agents: An insight into medicinal chemistry, biophysical, computational studies and pharmacological effects on cancer models

The Human antigen R (HuR) protein is an RNA-binding protein, ubiquitously expressed in human tissues, that orchestrates target RNA maturation and processing both in the nucleus and in the cytoplasm. A survey of known modulators of the RNA-HuR interactions is followed by a description of its structure and molecular mechanism of action – RRM domains, interactions with RNA, dimerization, binding modes with naturally occurring and synthetic HuR inhibitors. Then, the review focuses on HuR as a validated molecular target in oncology and briefly describes its role in inflammation. Namely, we show ample evidence for the involvement of HuR in the hallmarks and enabling characteristics of cancer, reporting findings from in vitro and in vivo studies; and we provide abundant experimental proofs of a beneficial role for the inhibition of HuR-mRNA interactions through silencing (CRISPR, siRNA) or pharmacological inhibition (small molecule HuR inhibitors)