2,019 research outputs found
Regularity of the minimiser of one-dimensional interaction energies
We consider both the minimisation of a class of nonlocal interaction energies
over non-negative measures with unit mass and a class of singular integral
equations of the first kind of Fredholm type. Our setting covers applications
to dislocation pile-ups, contact problems, fracture mechanics and random matrix
theory. Our main result shows that both the minimisation problems and the
related singular integral equations have the same unique solution, which
provides new regularity results on the minimiser of the energy and new
positivity results on the solutions to singular integral equations.Comment: 46 page
Upscaling of dislocation walls in finite domains
We wish to understand the macroscopic plastic behaviour of metals by
upscaling the micro-mechanics of dislocations. We consider a highly simplified
dislocation network, which allows our microscopic model to be a one dimensional
particle system, in which the interactions between the particles (dislocation
walls) are singular and non-local.
As a first step towards treating realistic geometries, we focus on
finite-size effects rather than considering an infinite domain as typically
discussed in the literature. We derive effective equations for the dislocation
density by means of \Gamma-convergence on the space of probability measures.
Our analysis yields a classification of macroscopic models, in which the size
of the domain plays a key role
ICU at home, with the use of mobile IC unit services:intensive care goes that extra mile
In this report we describe a patient with a long ICU stay because of severe Guillain Barré syndrome. Treatment was patient-centred and Mobile ICU facilities were used to facilitate an ICU at home for one day. Early focus on individual needs and wishes and close communication with and within ICU treatment teams can help to improve the long-term consequences of ICU admission. Research on which interventions are effective and most cost-effective need to be performed
Inhaled Nitric Oxide Therapy for Pulmonary Disorders of the Term and Preterm Infant
The 21st century began with the FDA approval of inhaled nitric oxide therapy for the treatment of neonatal hypoxic respiratory failure associated with pulmonary hypertension in recognition of the two randomized clinical trials demostrating a significant reduction in the need for extracorporeal support in the term and near-term infant. Inhaled nitric oxide is one of only a few therapeutic agents approved for use through clinical investigations primarily in the neonate. This article provides an overview of the pertinent biology and chemistry of nitric oxide, discusses potential toxicities, and reviews the results of pertinent clinical investigations and large randomized clinical trials including neurodevelopmental follow-up in term and preterm neonates. The clinical investigations conducted by the Eunice Kennedy Shriver NICHD Neonatal Research Network will be discussed and placed in context with other pertinent clinical investigations exploring the efficacy of inhaled nitric oxide therapy in neonatal hypoxic respiratory failure
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a
cure, and current treatment options are limited to symptomatic relief.
Prediction of OA progression is a very challenging and timely issue, and it
could, if resolved, accelerate the disease modifying drug development and
ultimately help to prevent millions of total joint replacement surgeries
performed annually. Here, we present a multi-modal machine learning-based OA
progression prediction model that utilizes raw radiographic data, clinical
examination results and previous medical history of the patient. We validated
this approach on an independent test set of 3,918 knee images from 2,129
subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81)
and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference
approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP
of 0.62 (0.60-0.64). The proposed method could significantly improve the
subject selection process for OA drug-development trials and help the
development of personalized therapeutic plans
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