Lesinurad, a novel, oral compound for gout, acts to decrease serum uric acid through inhibition of urate transporters in the kidney.

BackgroundExcess body burden of uric acid promotes gout. Diminished renal clearance of uric acid causes hyperuricemia in most patients with gout, and the renal urate transporter (URAT)1 is important for regulation of serum uric acid (sUA) levels. The URAT1 inhibitors probenecid and benzbromarone are used as gout therapies; however, their use is limited by drug-drug interactions and off-target toxicity, respectively. Here, we define the mechanism of action of lesinurad (Zurampic®; RDEA594), a novel URAT1 inhibitor, recently approved in the USA and Europe for treatment of chronic gout.MethodssUA levels, fractional excretion of uric acid (FEUA), lesinurad plasma levels, and urinary excretion of lesinurad were measured in healthy volunteers treated with lesinurad. In addition, lesinurad, probenecid, and benzbromarone were compared in vitro for effects on urate transporters and the organic anion transporters (OAT)1 and OAT3, changes in mitochondrial membrane potential, and human peroxisome proliferator-activated receptor gamma (PPARγ) activity.ResultsAfter 6 hours, a single 200-mg dose of lesinurad elevated FEUA 3.6-fold (p < 0.001) and reduced sUA levels by 33 % (p < 0.001). At concentrations achieved in the clinic, lesinurad inhibited activity of URAT1 and OAT4 in vitro, did not inhibit GLUT9, and had no effect on ABCG2. Lesinurad also showed a low risk for mitochondrial toxicity and PPARγ induction compared to benzbromarone. Unlike probenecid, lesinurad did not inhibit OAT1 or OAT3 in the clinical setting.ConclusionThe pharmacodynamic effects and in vitro activity of lesinurad are consistent with inhibition of URAT1 and OAT4, major apical transporters for uric acid. Lesinurad also has a favorable selectivity and safety profile, consistent with an important role in sUA-lowering therapy for patients with gout

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Les avant-dernières choses (2). Deux monodrames de Samuel Beckett suivi de « Pierrot Lunaire » d'Arnold Schoenberg. Compte-rendu.

Dimanche 13 décembre, le gouvernement allemand annonçait un nouveau renforcement des mesures de confinement et des consignes sanitaires à partir de ce mercredi 16 décembre, limitant notamment le nombre de personnes autorisées à se réunir, et prolongeant du même coup la fermeture des lieux culturels, qui fait encore débat en France, après la réouverture annoncée des magasins. À la suite du dernier billet publié ici, et dans l'impatience de voir rouvrir les salles de concert et de théâtre, on d..

« L’expérience métaphysique » ou le non-identique contre la réification

L’article porte sur le sens des exemples historiques « extrêmes » évoqués par Adorno dans la Dialectique négative [1966]. Ces exemples manifestent le caractère paradoxal du non-identique, le fait qu’il puisse se manifester dans l’expérience tout en ne se réduisant jamais à elle. À partir de ce problème, Adorno donne ainsi un sens nouveau à la notion d’« expérience métaphysique ». En s’interrogeant notamment sur le sens historique qu’on peut conférer à la mort « après Auschwitz » et sur les moyens de préserver la part d’« étrangeté » du non-identique, Adorno se réfère en fait explicitement à des enjeux politiques qui lui sont directement contemporains, comme les procès d’Auschwitz et l’usage de la torture d’une part, la question des régimes socialistes d’autre part. Loin de désigner quelque chose d’intemporel, la notion d’« expérience métaphysique » vient donc pointer la présence paradoxale du non-identique, qui serait à reconnaître dans l’histoire.Der Artikel befasst sich mit dem Sinn „extremer“ historischer Beispiele, die in Adornos Negativer Dialektik [1966] vorkommen. Diese Beispiele zeigen den paradoxen Charakter des Nichtidentischen, das in der Erfahrung erscheinen kann und ohne sich je auf sie reduzieren zu lassen. Von diesem Problem aus gibt Adorno dem Begriff der „metaphysischen Erfahrung“ eine neue Bedeutung, vor allem durch die Frage nach der historischen Bedeutung des Todes „nach Auschwitz“ und über die Frage, wie der Anteil der „Fremdheit“ des Nichtidentischen erhalten werden kann. Adorno verweist explizit auf politische Themen, die für ihn unmittelbar aktuell sind, den Auschwitz-Prozess und den Einsatz von Folter einerseits, die Frage der sozialistischen Regime andererseits. Weit davon entfernt, etwas Zeitloses zu bezeichnen, weist der Begriff der „metaphysischen Erfahrung“ auf die paradoxe Präsenz des Nicht-Identischen hin, die es in der Geschichte zu erkennen gilt

Les avant-dernières choses

Alors même que les mesures de confinement partiel ont été récemment durcies et prolongées en Allemagne (jusqu'à Noël au moins), on se propose ici de revenir sur les jours qui ont précédé l'application de ces mesures, à la fin du mois d'octobre dernier, en évoquant quelques questions méthodologiques liées à limitation de l'accès aux bibliothèques et aux fonds d'archives. Ce vendredi 30 Octobre, après les annonces de la chancelière Angela Merkel diffusées le mercredi précédent, l'incertitude ..

Generating Efficient FPGA-based CNN Accelerators from High-Level Descriptions

International audienceThe wide landscape of memory-hungry and compute-intensive Convolutional Neural Networks (CNNs) is quickly changing. CNNs are continuously evolving by introducing new layers or optimization strategies to either improve accuracy, reduce memory and computational needs or both. Moving such algorithms to on-device enables smarter edge products. However, hardware designers find this constant evolution hard to master, which keeps CNN accelerators one step behind. More approaches are using reconfigurable hardware, such as FPGAs, to design customized inference accelerators that are more suited to the newly-emerging CNN algorithms. Moreover, high-level design techniques, such as High-Level Synthesis (HLS), are adopted to address the time-consuming RTL-based design and the design space exploration problems. HLS allows generating RTL source code from high-level descriptions. This paper presents a hardware accelerator generation framework targeting FPGAs that relies on two steps. The first step characterizes the input CNN and produces hardware-aware metrics. The second step exploits the generated metrics to produce an optimized C-HLS source code for each layer of the input CNN, then it uses an HLS tool to generate a synthesizable RTL representation of the inference accelerator. The main goal of this approach is to reduce the gap between the evolving CNNs and the hardware accelerators, thus reducing design time of new systems

Exploration and Generation of Efficient FPGA-based Deep Neural Network Accelerators

International audienceConvolutional Neural Networks (CNNs) have emerged as an answer to next-generation applications such as complex image recognition and object detection. Embeddingsuch compute-intensive and memory-hungry algorithms on edge systems will lead to smarter high-value applications. However, the algorithmic innovations in the CNN field leave the hardware accelerators one step behind. Reconfigurable hardware (e.g. FPGAs) allows designing custom accelerators adapted to new algorithms. Furthermore, new design approaches such as highlevel synthesis (HLS) enable to generate RTL code based on highlevel function descriptions. This paper presents a high-level CNN accelerator generation framework for FPGAs. A first phase of the framework characterizes CNN descriptions using hardwareaware metrics. These metrics then drive a hardware generation phase which builds the proper C source code implementationfor each layer of the network. Finally, an HLS tool outputs the synthesizable RTL code of the accelerator. This approach aims at reducing the gap between the evolving applications based on artificial intelligence and hardware accelerators, thus reducing time-to-market of new systems

Deep Neural Networks Characterization Framework for Efficient Implementation on Embedded Systems

International audienceBio-inspired machine learning algorithms, such as Convolutional Neural Networks (CNNs), offer interesting solutions to complex real-life problems that cannot be simply modeled. Applications involving image recognition and object detection can greatly benefit from these approaches. Furthermore, their intrinsic and regular parallel structure offer opportunities regarding hardware acceleration. However, moving compute and memory-intensive CNNs to embedded systems while maintaining high energy-efficiency remains challenging. This paper presents the first step of a generic framework targeting the characterization of neural network algorithms to improve their implementation on embedded systems. The presented approach aims at reducing the gap between the fast-changing landscape of applications based on artificial intelligence and the hardware targets. The framework computes different metrics from neural network descriptions (such as computation and memory needs or data locality and reuse) to derive appropriate implementation strategies, or configurations of target architectures. Based on the outputs of the framework, new neural networks topologies can be quickly studied to reduce time-to-market of new systems

Sur la tradition

Traduction collective du texte "Über Tradition" de Theodor W. Adorn