The Esophageal Pressure-Guided Ventilation 2 (EPVent2) trial protocol: a multicentre, randomised clinical trial of mechanical ventilation guided by transpulmonary pressure

Introduction: Optimal ventilator management for patients with acute respiratory distress syndrome (ARDS) remains uncertain. Lower tidal volume ventilation appears to be beneficial, but optimal management of positive end-expiratory pressure (PEEP) remains unclear. The Esophageal Pressure-Guided Ventilation 2 Trial (EPVent2) aims to examine the impact of mechanical ventilation directed at maintaining a positive transpulmonary pressure (PTP) in patients with moderate-to-severe ARDS. Methods and analysis EPVent2 is a multicentre, prospective, randomised, phase II clinical trial testing the hypothesis that the use of a PTP-guided ventilation strategy will lead to improvement in composite outcomes of mortality and time off the ventilator at 28 days as compared with a high-PEEP control. This study will enrol 200 study participants from 11 hospitals across North America. The trial will utilise a primary composite end point that incorporates death and days off the ventilator at 28 days to test the primary hypothesis that adjusting ventilator pressure to achieve positive PTP values will result in improved mortality and ventilator-free days. Ethics and dissemination Safety oversight will be under the direction of an independent Data and Safety Monitoring Board (DSMB). Approval of the protocol was obtained from the DSMB prior to enrolling the first study participant. Approvals of the protocol as well as informed consent documents were also obtained from the Institutional Review Board of each participating institution prior to enrolling study participants at each respective site. The findings of this investigation, as well as associated ancillary studies, will be disseminated in the form of oral and abstract presentations at major national and international medical specialty meetings. The primary objective and other significant findings will also be presented in manuscript form. All final, published manuscripts resulting from this protocol will be submitted to PubMed Central in accordance with the National Institute of Health Public Access Policy. Trial registration number ClinicalTrials.gov under number NCT01681225

Transverse mass and invariant mass observables for measuring the mass of a semi-invisibly decaying heavy particle

Formulae are derived for the positions of end-points in the invariant mass and transverse mass distributions obtained from the products of heavy states decaying to pairs of semi-invisibly decaying lighter states. Formulae are derived both for the special case where the two decay chains are identical and the more general case where they are different. The formulae are tested with a simple case study of heavy SUSY higgs particles decaying to gauginos at the LHC.Comment: 13 pages, 8 eps figure

Lung Injury Prevention with Aspirin (LIPS-A): a Protocol for a Multicentre Randomised Clinical Trial in Medical Patients at High Risk of Acute Lung Injury

Introduction: Acute lung injury (ALI) is a devastating condition that places a heavy burden on public health resources. Although the need for effective ALI prevention strategies is increasingly recognised, no effective preventative strategies exist. The Lung Injury Prevention Study with Aspirin (LIPS-A) aims to test whether aspirin (ASA) could prevent and/or mitigate the development of ALI. Methods and analysis LIPS-A is a multicentre, double-blind, randomised clinical trial testing the hypothesis that the early administration of ASA will result in a reduced incidence of ALI in adult patients at high risk. This investigation will enrol 400 study participants from 14 hospitals across the USA. Conditional logistic regression will be used to test the primary hypothesis that early ASA administration will decrease the incidence of ALI. Ethics and dissemination Safety oversight will be under the direction of an independent Data and Safety Monitoring Board (DSMB). Approval of the protocol was obtained from the DSMB prior to enrolling the first study participant. Approval of both the protocol and informed consent documents were also obtained from the institutional review board of each participating institution prior to enrolling study participants at the respective site. In addition to providing important clinical and mechanistic information, this investigation will inform the scientific merit and feasibility of a phase III trial on ASA as an ALI prevention agent. The findings of this investigation, as well as associated ancillary studies, will be disseminated in the form of oral and abstract presentations at major national and international medical specialty meetings. The primary objective and other significant findings will also be presented in manuscript form. All final, published manuscripts resulting from this protocol will be submitted to Pub Med Central in accordance with the National Institute of Health Public Access Policy

Protein dynamics with off-lattice Monte Carlo moves

A Monte Carlo method for dynamics simulation of all-atom protein models is introduced, to reach long times not accessible to conventional molecular dynamics. The considered degrees of freedom are the dihedrals at C$_\alpha$-atoms. Two Monte Carlo moves are used: single rotations about torsion axes, and cooperative rotations in windows of amide planes, changing the conformation globally and locally, respectively. For local moves Jacobians are used to obtain an unbiased distribution of dihedrals. A molecular dynamics energy function adapted to the protein model is employed. A polypeptide is folded into native-like structures by local but not by global moves.Comment: 10 pages, 4 Postscript figures, uses epsf.sty and a4.sty; scheduled tentatively for Phys.Rev.E issue of 1 March 199

Minimum Variance Unbiased N:M Sparsity for the Neural Gradients

In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) up to x2, and doubles throughput by skipping computation of zero values. So far, it was mainly only used to prune weights to accelerate the forward and backward phases. We examine how this method can be used also for the neural gradients (i.e., loss gradients with respect to the intermediate neural layer outputs). To this end, we first establish a tensor-level optimality criteria. Previous works aimed to minimize the mean-square-error (MSE) of each pruned block. We show that while minimization of the MSE works fine for pruning the weights and activations, it catastrophically fails for the neural gradients. Instead, we show that accurate pruning of the neural gradients requires an unbiased minimum-variance pruning mask. We design such specialized masks, and find that in most cases, 1:2 sparsity is sufficient for training, and 2:4 sparsity is usually enough when this is not the case. Further, we suggest combining several such methods together in order to potentially speed up training even more

Status and Prospects of Top-Quark Physics

The top quark is the heaviest elementary particle observed to date. Its large mass of about 173 GeV/c^2 makes the top quark act differently than other elementary fermions, as it decays before it hadronises, passing its spin information on to its decay products. In addition, the top quark plays an important role in higher-order loop corrections to standard model processes, which makes the top quark mass a crucial parameter for precision tests of the electroweak theory. The top quark is also a powerful probe for new phenomena beyond the standard model. During the time of discovery at the Tevatron in 1995 only a few properties of the top quark could be measured. In recent years, since the start of Tevatron Run II, the field of top-quark physics has changed and entered a precision era. This report summarises the latest measurements and studies of top-quark properties and gives prospects for future measurements at the Large Hadron Collider (LHC).Comment: 76 pages, 35 figures, submitted to Progress in Particle and Nuclear Physic