| 1. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 3. |
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| 4. |
- Kliest, Tessa, et al.
(author)
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Clinical trials in pediatric ALS: a TRICALS feasibility study
- 2022
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In: Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. - : Taylor & Francis Group. - 2167-8421 .- 2167-9223. ; 23:7-8, s. 481-488
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Journal article (peer-reviewed)abstract
- Background: Pediatric investigation plans (PIPs) describe how adult drugs can be studied in children. In 2015, PIPs for Amyotrophic Lateral Sclerosis (ALS) became mandatory for European marketing-authorization of adult treatments, unless a waiver is granted by the European Medicines Agency (EMA).Objective: To assess the feasibility of clinical studies on the effect of therapy in children (<18 years) with ALS in Europe.Methods: The EMA database was searched for submitted PIPs in ALS. A questionnaire was sent to 58 European ALS centers to collect the prevalence of pediatric ALS during the past ten years, the recruitment potential for future pediatric trials, and opinions of ALS experts concerning a waiver for ALS.Results: Four PIPs were identified; two were waived and two are planned for the future. In total, 49 (84.5%) centers responded to the questionnaire. The diagnosis of 44,858 patients with ALS was reported by 46 sites; 39 of the patients had an onset < 18 years (prevalence of 0.008 cases per 100,000 or 0.087% of all diagnosed patients). The estimated recruitment potential (47 sites) was 26 pediatric patients within five years. A majority of ALS experts (75.5%) recommend a waiver should apply for ALS due to the low prevalence of pediatric ALS.Conclusions: ALS with an onset before 18 years is extremely rare and may be a distinct entity from adult ALS. Conducting studies on the effect of disease-modifying therapy in pediatric ALS may involve lengthy recruitment periods, high costs, ethical/legal implications, challenges in trial design and limited information.
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| 5. |
- Roquet, Fabien, et al.
(author)
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Estimates of the Southern Ocean general circulation improved by animal-borne instruments
- 2013
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In: Geophysical Research Letters. - 0094-8276 .- 1944-8007. ; 40:23, s. 6176-6180
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Journal article (peer-reviewed)abstract
- Over the last decade, several hundred seals have been equipped with conductivity-temperature-depth sensors in the Southern Ocean for both biological and physical oceanographic studies. A calibrated collection of seal-derived hydrographic data is now available, consisting of more than 165,000 profiles. The value of these hydrographic data within the existing Southern Ocean observing system is demonstrated herein by conducting two state estimation experiments, differing only in the use or not of seal data to constrain the system. Including seal-derived data substantially modifies the estimated surface mixed-layer properties and circulation patterns within and south of the Antarctic Circumpolar Current. Agreement with independent satellite observations of sea ice concentration is improved, especially along the East Antarctic shelf. Instrumented animals efficiently reduce a critical observational gap, and their contribution to monitoring polar climate variability will continue to grow as data accuracy and spatial coverage increase.
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| 6. |
- Hindell, Mark A., et al.
(author)
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Circumpolar habitat use in the southern elephant seal : implications for foraging success and population trajectories
- 2016
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In: Ecosphere. - : Wiley. - 2150-8925 .- 2150-8925. ; 7:5
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Journal article (peer-reviewed)abstract
- In the Southern Ocean, wide-ranging predators offer the opportunity to quantify how animals respond to differences in the environment because their behavior and population trends are an integrated signal of prevailing conditions within multiple marine habitats. Southern elephant seals in particular, can provide useful insights due to their circumpolar distribution, their long and distant migrations and their performance of extended bouts of deep diving. Furthermore, across their range, elephant seal populations have very different population trends. In this study, we present a data set from the International Polar Year project; Marine Mammals Exploring the Oceans Pole to Pole for southern elephant seals, in which a large number of instruments (N = 287) deployed on animals, encompassing a broad circum-Antarctic geographic extent, collected in situ ocean data and at-sea foraging metrics that explicitly link foraging behavior and habitat structure in time and space. Broadly speaking, the seals foraged in two habitats, the relatively shallow waters of the Antarctic continental shelf and the Kerguelen Plateau and deep open water regions. Animals of both sexes were more likely to exhibit area-restricted search (ARS) behavior rather than transit in shelf habitats. While Antarctic shelf waters can be regarded as prime habitat for both sexes, female seals tend to move northwards with the advance of sea ice in the late autumn or early winter. The water masses used by the seals also influenced their behavioral mode, with female ARS behavior being most likely in modified Circumpolar Deepwater or northerly Modified Shelf Water, both of which tend to be associated with the outer reaches of the Antarctic Continental Shelf. The combined effects of (1) the differing habitat quality, (2) differing responses to encroaching ice as the winter progresses among colonies, (3) differing distances between breeding and haul-out sites and high quality habitats, and (4) differing long-term -regional trends in sea ice extent can explain the differing population trends observed among elephant seal colonies.
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| 7. |
- Jaervinen, A. E., et al.
(author)
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Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG
- 2024
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In: Physics of Plasmas. - : AIP Publishing. - 1070-664X .- 1089-7674. ; 31:3
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Journal article (peer-reviewed)abstract
- Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles from JET and ASDEX Upgrade tokamaks. Representation learning aims to establish a useful representation that characterizes the dataset. In the context of magnetic confinement fusion devices, a useful representation could be considered to map the high-dimensional observations to a manifold that represents the actual degrees of freedom of the plasma scenario. A desired property for these representations is organization of the information into disentangled variables, enabling interpretation of the latent variables as representations of semantically meaningful characteristics of the data. The representation learning algorithms in this work are based on VAE that encodes the pedestal profile information into a reduced dimensionality latent space and learns to reconstruct the full profile information given the latent representation. Attaching an auxiliary regression objective for the machine control parameter configuration, broadly following the architecture of the domain invariant variational autoencoder (DIVA), the model learns to associate device control parameters with the latent representation. With this multimachine dataset, the representation does encode density scaling with device size that is qualitatively consistent with Greenwald density limit scaling. However, if the major radius of the device is given through a common regression objective with the other machine control parameters, the latent state of the representation struggles to clearly disentangle the device size from changes of the other machine control parameters. When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and class labels in the original DIVA publication, the latent space becomes well organized as a function of the device size.
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| 8. |
- Kit, A., et al.
(author)
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Developing deep learning algorithms for inferring upstream separatrix density at JET
- 2023
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In: Nuclear Materials and Energy. - : Elsevier BV. - 2352-1791. ; 34
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Journal article (peer-reviewed)abstract
- Predictive and real-time inference capability for the upstream separatrix electron density, ne, sep, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi -supervised machine learning algorithms are explored to establish direct mapping as well as indirect compressed representation of the pedestal profiles for predictions and inference of ne, sep. Based on the EUROfusion pedestal database for JET (Frassinetti et al., 2021), a tabular dataset was created, consisting of machine parameters, fraction of ELM cycle, high resolution Thomson scattering profiles of electron density and temperature, and ne, sep for 608 JET shots. Using the tabular dataset, the direct mapping approach provides a mapping of machine parameters and ELM percentage to ne, sep. Through representation learning, a compressed representation of the experimental pedestal electron density and temperature profiles is established. By conditioning the representation with machine control parameters, a probabilistic generative predictive model is established. For prediction, the machine parameters can be used to establish a conditional distribution of the compressed pedestal profiles, and the decoder that is trained as part of the algorithm can be used to decode the compressed representation back to full pedestal profiles. Although, in this work, a proof-of-principle for predicting and inferring ne, sep is given, such a representation learning can be used also for many other applications as the full pedestal profile is predicted. An implementation of this work can be found at https://github.com/ fusionby2030/psi_2022.
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| 9. |
- Kit, A., et al.
(author)
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Supervised learning approaches to modeling pedestal density
- 2023
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In: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 0741-3335 .- 1361-6587. ; 65:4
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Journal article (peer-reviewed)abstract
- Pedestals are the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by tokamak pedestals. The leading paradigm for predicting the pedestal top pressure is encompassed by EPED-like models. However, EPED does not predict the pedestal top density, n(e,ped), but requires it as an input. EUROPED (Saarelma et al 2019 Phys. Plasmas 26 072501) employs simplified models, such as log-linear regression, to constrain n(e,ped) with tokamak machine control parameters in an EPED-like model. However, these simplified models for n(e,ped) often show disagreements with experimental observations and do not use all of the available numerical and categorical machine control information. In this work it is observed that using the same input parameters, decision tree ensembles and deep learning models improves the predictive quality of n(e,ped) by about 23% relative to that obtained with log-linear scaling laws, measured by root mean square error. Including all of the available tokamak machine control parameters, both numerical and categorical, leads to further improvement of about 13%. Finally, predictive quality was tested when including global normalized plasma pressure and effective charge state as inputs, as these parameters are known to impact pedestals. Surprisingly, these parameters lead to only a few percent further improvement of the predictive quality. The corresponding code for this analysis can be found at github.com/fusionby2030/supervised_learning_jetpdb.
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| 10. |
- Treasure, Anne M., et al.
(author)
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Marine Mammals Exploring the Oceans Pole to Pole A Review of the MEOP Consortium
- 2017
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In: Oceanography. - : The Oceanography Society. - 1042-8275. ; 30:2, s. 132-138
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Journal article (peer-reviewed)abstract
- Polar oceans are poorly monitored despite the important role they play in regulating Earth's climate system. Marine mammals equipped with biologging devices are now being used to fill the data gaps in these logistically difficult to sample regions. Since 2002, instrumented animals have been generating exceptionally large data sets of oceanographic CTD casts (>500,000 profiles), which are now freely available to the scientific community through the MEOP data portal (http://meop.net). MEOP (Marine Mammals Exploring the Oceans Pole to Pole) is a consortium of international researchers dedicated to sharing animal-derived data and knowledge about the polar oceans. Collectively, MEOP demonstrates the power and cost-effectiveness of using marine mammals as data-collection platforms that can dramatically improve the ocean observing system for biological and physical oceanographers. Here, we review the MEOP program and database to bring it to the attention of the international community.
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