24 days agoLiverpool boss Klopp prepared to be dumped from Carabao Cup

first_imgAbout the authorPaul VegasShare the loveHave your say Liverpool boss Klopp prepared to be dumped from Carabao Cupby Paul Vegas24 days agoSend to a friendShare the loveLiverpool are still waiting to learn of their Carabao Cup status.The Daily Mail says Liverpool have yet to receive any indication from the EFL when they will discover their fate after fielding an ineligible player in the Carabao Cup win over MK Dons. The strongest sanction is expulsion from the competition – manager Jurgen Klopp says Liverpool must accept whatever punishment is given.”If it was our fault,” said Klopp, “we need to get punished.” last_img read more

Study Radial or femoral approaches for PCI are equal in terms of

first_imgReviewed by James Ives, M.Psych. (Editor)Mar 19 2019Doctors can use either an artery in the arm (the radial approach) or in the groin (the femoral approach) to safely perform percutaneous coronary intervention (PCI) on patients presenting with a heart attack, according to research presented at the American College of Cardiology’s 68thAnnual Scientific Session. The research, which was stopped early, suggests the radial and femoral approach are equivalent in terms of the risk of death at 30 days.”Based on these findings, we feel you can achieve similar results with either approach if you have an efficient system for getting patients into the procedure quickly and a good team to perform it,” said Michel Le May, MD, director of the STEMI program at the University of Ottawa Heart Institute and the study’s lead author. “Furthermore, we believe it is important for interventionists to be familiar with both radial and femoral access in order to be able to shift gears from one strategy to the other without hesitation.”Le May said that while some operators may have a preference for the radial or the femoral approach, it can become necessary to switch approaches for certain patients, sometimes in the middle of a procedure. For this reason, it is valuable for operators to routinely practice both methods.”I think it will be important for medical training programs to emphasize the need to be proficient at both the radial and femoral access,” Le May said. “It is possible to become deskilled at doing one of the procedures, and a consistent emphasis on one approach over the other can lead to an increase in complications.”PCI is performed to clear blocked arteries responsible for a heart attack. During the procedure, a doctor threads a narrow tube through the artery until it reaches the heart. The operator then uses the tube to manipulate small surgical tools and insert a stent to prop open the artery, thereby restoring blood flow.Related StoriesCutting around 300 calories a day protects the heart even in svelte adultsTeam approach to care increases likelihood of surviving refractory cardiogenic shockStudy explores role of iron in over 900 diseasesWhen PCI was first developed, doctors accessed the heart using the femoral approach. With the advent of smaller surgical equipment, it became feasible to use smaller-diameter arteries, leading some doctors to use the radial approach instead. Previous trials have suggested the radial approach may reduce the risk of bleeding and improve survival. However, no large, randomized trial has provided definitive evidence on which approach is superior in terms of survival in patients presenting with an acute heart attack.This study, which sought to fill that void, aimed to enroll nearly 5,000 patients at five medical centers across Canada but stopped after enrolling 2,2929. All patients underwent PCI after ST-elevation myocardial infarction (STEMI), the most severe type of heart attack. Half were randomly assigned to radial access and half to femoral access. Most of the patients received bivalirudin and ticagrelor, medications commonly used to prevent blood clots during and after PCI, respectively.The study was stopped early after an analysis indicated it would not be possible to reach the primary endpoint, an expected 1.5 percent difference in mortality at 30 days, as survival rates between the radial and femoral approaches were roughly equal (1.5 percent in the radial access group and 1.3 percent in the femoral access group, an absolute difference of 0.2 percent). Rates of other outcomes including subsequent heart attack, blood clotting at the stent and bleeding complications were not significantly different between the two groups either.One unique aspect of the design of this study was the inclusion of a homogenous population of STEMI patients, according to researchers. It is possible that patients without STEMI, or certain STEMI patient subgroups, may see different benefits from the two approaches. The trial also used updated procedure protocols in terms of medications and surgical equipment compared to previous trials. Source:https://www.ottawaheart.ca/last_img read more

Team breaks exaop barrier with deep learning application

first_img This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. Improved Scalability and CommunicationOn the software side, in addition to providing the climate dataset, the Berkeley Lab team developed pattern-recognition algorithms for training the DeepLabv3+ neural network to extract pixel-level classifications of extreme weather patterns, which could aid in the prediction of how extreme weather events are changing as the climate warms. According to Thorsten Kurth, an application performance specialist at NERSC who led this project, the team made modifications to DeepLabv3+ that improved the network’s scalability and communications capabilities and made the exaops achievement possible. This included tweaking the network to train it to extract pixel-level features and per-pixel classification and improve node-to-node communication.”What is impressive about this effort is that we could scale a high-productivity framework like TensorFlow, which is technically designed for rapid prototyping on small to medium scales, to 4,560 nodes on Summit,” he said. “With a number of performance enhancements, we were able to get the framework to run on nearly the entire supercomputer and achieve exaop-level performance, which to my knowledge is the best achieved so far in a tightly coupled application.”Other innovations included high-speed parallel data staging, an optimized data ingestion pipeline and multi-channel segmentation. Traditional image segmentation tasks work on three-channel red/blue/green images. But scientific datasets often comprise many channels; in climate, for example, these can include temperature, wind speeds, pressure values and humidity. By running the optimized neural network on Summit, the additional computational capabilities allowed the use of all 16 available channels, which dramatically improved the accuracy of the models.”We have shown that we can apply deep-learning methods for pixel-level segmentation on climate data, and potentially on other scientific domains,” said Prabhat. “More generally, our project has laid the groundwork for exascale deep learning for science, as well as commercial applications.” A team of computational scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) and Oak Ridge National Laboratory (ORNL) and engineers from NVIDIA has, for the first time, demonstrated an exascale-class deep learning application that has broken the exaop barrier. High-quality segmentation results produced by deep learning on climate datasets. Credit: Berkeley Lab Explore further Using a climate dataset from Berkeley Lab on ORNL’s Summit system at the Oak Ridge Leadership Computing Facility (OLCF), they trained a deep neural network to identify extreme weather patterns from high-resolution climate simulations. Summit is an IBM Power Systems AC922 supercomputer powered by more than 9,000 IBM POWER9 CPUs and 27,000 NVIDIA Tesla V100 Tensor Core GPUs. By tapping into the specialized NVIDIA Tensor Cores built into the GPUs at scale, the researchers achieved a peak performance of 1.13 exaops and a sustained performance of 0.999—the fastest deep learning algorithm reported to date and an achievement that earned them a spot on this year’s list of finalists for the Gordon Bell Prize.”This collaboration has produced a number of unique accomplishments,” said Prabhat, who leads the Data & Analytics Services team at Berkeley Lab’s National Energy Research Scientific Computing Center and is a co-author on the Gordon Bell submission. “It is the first example of deep learning architecture that has been able to solve segmentation problems in climate science, and in the field of deep learning, it is the first example of a real application that has broken the exascale barrier.”These achievements were made possible through an innovative blend of hardware and software capabilities. On the hardware side, Summit has been designed to deliver 200 petaflops of high-precision computing performance and was recently named the fastest computer in the world, capable of performing more than three exaops (3 billion billion calculations) per second. The system features a hybrid architecture; each of its 4,608 compute nodes contains two IBM POWER9 CPUs and six NVIDIA Volta Tensor Core GPUs, all connected via the NVIDIA NVLink high-speed interconnect.The NVIDIA GPUs are a key factor in Summit’s performance, enabling up to 12 times higher peak teraflops for training and 6 times higher peak teraflops for inference in deep learning applications compared to its predecessor, the Tesla P100.”Our partnering with Berkeley Lab and Oak Ridge National Laboratory showed the true potential of NVIDIA Tensor Core GPUs for AI and HPC applications,” said Michael Houston, senior distinguished engineer of deep learning at NVIDIA. “To make exascale a reality, our team tapped into the multi-precision capabilities packed into the thousands of NVIDIA Volta Tensor Core GPUs on Summit to achieve peak performance in training and inference in deep learning applications.” Researchers use Titan to accelerate design, training of deep learning networks Citation: Team breaks exaop barrier with deep learning application (2018, October 9) retrieved 17 July 2019 from https://phys.org/news/2018-10-team-exaop-barrier-deep-application.html Provided by Lawrence Berkeley National Laboratorylast_img read more