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| 4. |
- Bombarda, F., et al.
(författare)
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Runaway electron beam control
- 2019
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Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 1361-6587 .- 0741-3335. ; 61:1
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Tidskriftsartikel (refereegranskat)
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| 5. |
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- 2018
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:1
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Forskningsöversikt (refereegranskat)
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| 6. |
- Krasilnikov, A., et al.
(författare)
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Evidence of 9 Be + p nuclear reactions during 2ω CH and hydrogen minority ICRH in JET-ILW hydrogen and deuterium plasmas
- 2018
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:2
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Tidskriftsartikel (refereegranskat)abstract
- The intensity of 9Be + p nuclear fusion reactions was experimentally studied during second harmonic (2ω CH) ion-cyclotron resonance heating (ICRH) and further analyzed during fundamental hydrogen minority ICRH of JET-ILW hydrogen and deuterium plasmas. In relatively low-density plasmas with a high ICRH power, a population of fast H+ ions was created and measured by neutral particle analyzers. Primary and secondary nuclear reaction products, due to 9Be + p interaction, were observed with fast ion loss detectors, γ-ray spectrometers and neutron flux monitors and spectrometers. The possibility of using 9Be(p, d)2α and 9Be(p, α)6Li nuclear reactions to create a population of fast alpha particles and study their behaviour in non-active stage of ITER operation is discussed in the paper.
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- Joffrin, E., et al.
(författare)
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Overview of the JET preparation for deuterium-tritium operation with the ITER like-wall
- 2019
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 59:11
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Forskningsöversikt (refereegranskat)abstract
- For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des. 82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D-T mixtures since 1997 and the first ever D-T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D-T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D-T preparation. This intense preparation includes the review of the physics basis for the D-T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D-T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the three-ions scheme), new diagnostics (neutron camera and spectrometer, active Alfven eigenmode antennas, neutral gauges, radiation hard imaging systems...) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D-T campaign provides an incomparable source of information and a basis for the future D-T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.
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| 29. |
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- 2018
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:9
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Tidskriftsartikel (refereegranskat)
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| 34. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 35. |
- Chen, Yu, 1990, et al.
(författare)
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Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0
- 2024
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Ingår i: Nature Protocols. - 1754-2189 .- 1750-2799. ; 19:3, s. 629-667
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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| 36. |
- Domenzain Del Castillo Cerecer, Iván, 1991, et al.
(författare)
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Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
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| 37. |
- Gershman, Alex B., et al.
(författare)
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The stochastic CRB for array processing in unknown noise fields
- 2001
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Ingår i: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. - : IEEE. - 0780370414 ; , s. 2989-2992
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Konferensbidrag (refereegranskat)abstract
- The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve this bound asymptotically. In this paper, we study the stochastic CRB on DOA estimation accuracy in the general case of arbitrary unknown noise field parametrized by a vector of unknowns. We derive explicit closed-form expressions for the CRB and examine its properties theoretically and by representative numerical examples.
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| 38. |
- Gustafsson, Johan, 1976, et al.
(författare)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2023
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
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Tidskriftsartikel (refereegranskat)abstract
- Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 39. |
- Li, Feiran, 1993, et al.
(författare)
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GotEnzymes: an extensive database of enzyme parameter predictions
- 2023
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 51:D1, s. D583-D586
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Tidskriftsartikel (refereegranskat)abstract
- Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (Al) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by Al approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.
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| 40. |
- Lu, Hongzhong, 1987, et al.
(författare)
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A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
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| 41. |
- Muneer, Sidra, et al.
(författare)
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Handling PA Nonlinearity in Massive MIMO : What are the Tradeoffs between System Capacity and Power Consumption
- 2020
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Ingår i: Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. - 1058-6393. - 9780738131269 ; 2020-November, s. 974-978
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Konferensbidrag (refereegranskat)abstract
- Massive MIMO enables a very high spectral efficiency by spatial multiplexing and opens opportunities to reduce transmit power per antenna. On the other side, it also introduces new challenges in tackling the power amplifier nonlinearity due to the increased number of antennas. The behavior of Out-of-Band radiation from PAs in Massive MIMO is non-trivial depending upon the applied precoding scheme, propagation environment, and how the users are distributed spatially. In this paper, we analyze the in-band and Out-of-Band power distribution in space for a Massive MIMO base station with nonlinear power amplifiers. We also study the tradeoff between amplifier power consumption and system performance (in terms of error vector magnitude at intended receivers) for PAs with different backoff operating levels. The spatial analysis of Out-of-Band under more realistic channel conditions is more reflective of the situation, to determine system emission requirements. The tradeoff between performance and power consumption will provide a basis for future investigations into the design of efficient Massive MIMO systems, taking nonlinearities into account.
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| 42. |
- Stoica, Petre, et al.
(författare)
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The stochastic CRB for array processing: a textbook derivation
- 2001
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 8:5, s. 148-150
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Tidskriftsartikel (refereegranskat)abstract
- The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.
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| 43. |
- Wang, Hao, 1975, et al.
(författare)
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Genome-scale metabolic network reconstruction of model animals as a platform for translational research
- 2021
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 118:30
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.
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