Identification of a Novel Pseudo-Natural Product Type IV IDO1 Inhibitor Chemotype

Natural product (NP)-inspired design principles provide invaluable guidance for bioactive compound discovery. Pseudo-natural products (PNPs) are de novo combinations of NP fragments to target biologically relevant chemical space not covered by NPs. We describe the design and synthesis of apoxidoles, a novel pseudo-NP class, whereby indole- and tetrahydropyridine fragments are linked in monopodal connectivity not found in nature. Apoxidoles are efficiently accessible by an enantioselective [4+2] annulation reaction. Biological evaluation revealed that apoxidoles define a new potent type IV inhibitor chemotype of indoleamine 2,3-dioxygenase 1 (IDO1), a heme-containing enzyme considered a target for the treatment of neurodegeneration, autoimmunity and cancer. Apoxidoles target apo-IDO1, prevent heme binding and induce unique amino acid positioning as revealed by crystal structure analysis. Novel type IV apo-IDO1 inhibitors are in high demand, and apoxidoles may provide new opportunities for chemical biology and medicinal chemistry research

Sensitivity Studies for Third-Generation Gravitational Wave Observatories

Advanced gravitational wave detectors, currently under construction, are expected to directly observe gravitational wave signals of astrophysical origin. The Einstein Telescope, a third-generation gravitational wave detector, has been proposed in order to fully open up the emerging field of gravitational wave astronomy. In this article we describe sensitivity models for the Einstein Telescope and investigate potential limits imposed by fundamental noise sources. A special focus is set on evaluating the frequency band below 10Hz where a complex mixture of seismic, gravity gradient, suspension thermal and radiation pressure noise dominates. We develop the most accurate sensitivity model, referred to as ET-D, for a third-generation detector so far, including the most relevant fundamental noise contributions.Comment: 13 pages, 7 picture

Scientific Potential of Einstein Telescope

Einstein gravitational-wave Telescope (ET) is a design study funded by the European Commission to explore the technological challenges of and scientific benefits from building a third generation gravitational wave detector. The three-year study, which concluded earlier this year, has formulated the conceptual design of an observatory that can support the implementation of new technology for the next two to three decades. The goal of this talk is to introduce the audience to the overall aims and objectives of the project and to enumerate ET's potential to influence our understanding of fundamental physics, astrophysics and cosmology.Comment: Conforms to conference proceedings, several author names correcte

A double-sided, shield-less stave prototype for the ATLAS upgrade strip tracker for the high luminosity LHC

A detailed description of the integration structures for the barrel region of the silicon strips tracker of the ATLAS Phase-II upgrade for the upgrade of the Large Hadron Collider, the so-called High Luminosity LHC (HL-LHC), is presented. This paper focuses on one of the latest demonstrator prototypes recently assembled, with numerous unique features. It consists of a shortened, shield-less, and double sided stave, with two candidate power distributions implemented. Thermal and electrical performances of the prototype are presented, as well as a description of the assembly procedures and tools

Scientific Objectives of Einstein Telescope

The advanced interferometer network will herald a new era in observational astronomy. There is a very strong science case to go beyond the advanced detector network and build detectors that operate in a frequency range from 1 Hz-10 kHz, with sensitivity a factor ten better in amplitude. Such detectors will be able to probe a range of topics in nuclear physics, astronomy, cosmology and fundamental physics, providing insights into many unsolved problems in these areas.Comment: 18 pages, 4 figures, Plenary talk given at Amaldi Meeting, July 201

Anastrozole-related acute hepatitis with autoimmune features: a case report

<p>Abstract</p> <p>Background</p> <p>Two cases of acute hepatitis occurring during treatment with anastrozole have previously been reported, but the underlying mechanisms of liver injury are still uncertain. We report the case of anastrozole-related acute hepatitis with some autoimmune features.</p> <p>Case presentation</p> <p>A 70-year-old woman developed acute hepatitis associated with serum antinuclear antibodies during anastrozole treatment; after drug withdrawal, liver function parameters rapidly improved and serum auto-antibodies were no longer detectable.</p> <p>Conclusions</p> <p>Anastrozole-induced hepatotoxicity is a very rare event. Drug-drug interactions or metabolically-mediated damage might be involved, with a possible role of individual susceptibility. Our report suggests that an immune-mediated mechanism may also be considered in anastrozole-related liver injury.</p

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)