| 1. |
- Brouqui, P., et al.
(författare)
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Infection control in the management of highly pathogenic infectious diseases : consensus of the European Network of Infectious Disease
- 2009
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Ingår i: The Lancet Infectious Diseases. - 1473-3099. ; 9:5, s. 301-311
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Forskningsöversikt (refereegranskat)abstract
- The European Network for Infectious Diseases (EUNID) is a network of clinicians, public health epidemiologists, microbiologists, infection control, and critical-care doctors from the European member states, who are experienced in the management of patients with highly infectious diseases. We aim to develop a consensus recommendation for infection control during clinical management and invasive procedures in such patients. After an extensive literature review, draft recommendations were amended jointly by 27 partners from 15 European countries. Recommendations include repetitive training of staff to ascertain infection control, systematic use of cough and respiratory etiquette at admission to the emergency department, fluid sampling in the isolation room, and analyses in biosafety level 3/4 laboratories, and preference for point-of-care bedside laboratory tests. Children should be cared for by paediatricians and intensive-care patients should be cared for by critical-care doctors in high-level isolation units (HLIU). Invasive procedures should be avoided if unnecessary or done in the HLIU, as should chest radiography, ultrasonography, and renal dialysis. Procedures that require transport of patients out of the HLIU should be done during designated sessions or hours in secure transport. Picture archiving and communication systems should be used. Post-mortem examination should be avoided; biopsy or blood collection is preferred.
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| 2. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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| 3. |
- Kiencke, U., et al.
(författare)
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The impact of automatic control on recent developments in transportation and vehicle systems
- 2006
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Ingår i: Annual Reviews in Control. - : Elsevier BV. - 1367-5788 .- 1872-9088. ; 30:1, s. 81-89
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Tidskriftsartikel (refereegranskat)abstract
- Throughout the field of transportation and vehicle systems control is gaining importance. This paper focuses on the current key problems engineers in this field are facing and highlights some major recent accomplishments. The driving forces behind the increasing use of control are the rising need for transportation services and the demand for a higher safety level. While each domain takes specific approach to deal with these demands, a general trend towards automatic co-pilots or even autopilots is visible. In the automotive domain, this is aided by the design of drive by wire systems. In other fields like marine or aerospace systems, the focus of research is on the swarming behavior of multiple vessels. New sensors and networking will also enable more efficient traffic flow control which will allow for a better use of the resource network capacity. Another trend in the vehicle systems sector is the modeling of nonlinear system behavior which is starting to replace look-up tables in real time systems. A forecast on future trends is given at the end of the paper. © 2006 Elsevier Ltd. All rights reserved.
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| 4. |
- Maiwald, Thomas, et al.
(författare)
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In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems
- 2012
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Ingår i: BMC Systems Biology. - : BioMed Central. - 1752-0509. ; 6:13
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Tidskriftsartikel (refereegranskat)abstract
- Background: Mathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time. Results: Application to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties. Conclusions: Our approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).
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