| 1. |
- Kehoe, Laura, et al.
(författare)
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Make EU trade with Brazil sustainable
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
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| 3. |
- Thul, Peter J., et al.
(författare)
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A subcellular map of the human proteome
- 2017
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Ingår i: Science. - : American Association for the Advancement of Science. - 0036-8075 .- 1095-9203. ; 356:6340
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Tidskriftsartikel (refereegranskat)abstract
- Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
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| 4. |
- Almstrom, Peter, et al.
(författare)
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Networked state estimation over a Gilbert-Elliot type channel
- 2009
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. ; , s. 2711-2716
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Konferensbidrag (refereegranskat)abstract
- We characterize the stability and achievable performance of networked estimation under correlated packet losses described by the Gilbert-Elliot model. For scalar continuous-time linear systems, we derive closed-form expressions for the mean-square distortion of the optimal estimator. The conditions for stable mean square estimation error are equivalent to those obtained previously for stability of 'peak distortions' [3]. We study how the estimator performance depends on loss probability and loss burst length, and show that the mean-square distortion remains bounded if the average burst length does not exceeda calculated bound. The main new finding is that, once we fix the mean length of loss bursts, the average packet loss rate influences the estimator's performance but not its stability.
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| 5. |
- Arabi, A., et al.
(författare)
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Proteomic screen reveals Fbw7 as a modulator of the NF-kappa B pathway
- 2012
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 3, s. 976-
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Tidskriftsartikel (refereegranskat)abstract
- Fbw7 is a ubiquitin-ligase that targets several oncoproteins for proteolysis, but the full range of Fbw7 substrates is not known. Here we show that by performing quantitative proteomics combined with degron motif searches, we effectively screened for a more complete set of Fbw7 targets. We identify 89 putative Fbw7 substrates, including several disease-associated proteins. The transcription factor NF-κB2 (p100/p52) is one of the candidate Fbw7 substrates. We show that Fbw7 interacts with p100 via a conserved degron and that it promotes degradation of p100 in a GSK3 2 phosphorylation-dependent manner. Fbw7 inactivation increases p100 levels, which in the presence of NF-κB pathway stimuli, leads to increased p52 levels and activity. Accordingly, the apoptotic threshold can be increased by loss of Fbw7 in a p100-dependent manner. In conclusion, Fbw7-mediated destruction of p100 is a regulatory component restricting the response to NF-κB2 pathway stimulation.
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| 6. |
- Barucca, G., et al.
(författare)
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The potential of Λ and Ξ- studies with PANDA at FAIR
- 2021
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Ingår i: European Physical Journal A. - : Springer Nature. - 1434-6001 .- 1434-601X. ; 57:4
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Tidskriftsartikel (refereegranskat)abstract
- The antiproton experiment PANDA at FAIR is designed to bring hadron physics to a new level in terms of scope, precision and accuracy. In this work, its unique capability for studies of hyperons is outlined. We discuss ground-state hyperons as diagnostic tools to study non-perturbative aspects of the strong interaction, and fundamental symmetries. New simulation studies have been carried out for two benchmark hyperon-antihyperon production channels: p¯ p→ Λ¯ Λ and p¯ p→ Ξ¯ +Ξ-. The results, presented in detail in this paper, show that hyperon-antihyperon pairs from these reactions can be exclusively reconstructed with high efficiency and very low background contamination. In addition, the polarisation and spin correlations have been studied, exploiting the weak, self-analysing decay of hyperons and antihyperons. Two independent approaches to the finite efficiency have been applied and evaluated: one standard multidimensional efficiency correction approach, and one efficiency independent approach. The applicability of the latter was thoroughly evaluated for all channels, beam momenta and observables. The standard method yields good results in all cases, and shows that spin observables can be studied with high precision and accuracy already in the first phase of data taking with PANDA.
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| 7. |
- Baykov, Vitaly, 1977-
(författare)
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Point defect interactions and structural stability of compounds
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Theoretical studies of point defect interactions and structural stability of compounds have been performed using density functional theory. The defect-related properties, such as activation energy of diffusion, electronic and magnetic structure of selected materials have been studied. The major part of the present work is devoted to a very important material for semiconductor industry, GaAs. The formation energies of intrinsic point defects and the solution energies of 3d transitions in GaAs have been calculated from first principles. Based on the calculated energies, we analysed the site preference of defects in the crystal. The tendency of defects to form clusters has been investigated for the intrinsic defects as well as for impurities in GaAs. The magnetic moment of 3d impurities has been calculated as a function of the chemical environment. The possibility of increasing the Curie temperature in (Ga,Mn)As by co-doping it with Cr impurities has been examined on the basis of calculated total energy difference between the disordered local moment and the ferromagnetically ordered spin configurations. We found that, in order to reach the highest critical temperature, GaAs should be separately doped with either Cr or Mn impurities. Also, we have shown that diffusion barrier of interstitial Mn depends on the charge state of this impurity in (Ga, Mn)As. The formation of defect complexes between interstitial and substitutional Mn atoms, and their influence on the value of diffusion barrier for interstitial Mn, has been studied. The pair interactions energies between interstitial oxygen atoms in hcp Zr, Hf and Ti have been calculated using first principles. Based on the calculated energies, the oxygen ordering structures in IVB transition metal solid solutions have been explained. A prediction of nitrogen ordering in Hf-N solid solution has been made. The thermodynamic description of intermetallic compounds in the Zr-Sn binary system has been obtained. The conclusion has been made that Zr substitution on the Sn sites takes place in the Zr4Sn phase, which accounts for the unusual stoichiometry of this Cr3Si structure type compound. The influence of pressure on the phase stability in the Fe-Si system has been investigated. We have found instability of the hcp Fe0.9Si0.1 random alloy with respect to the decomposition onto the Si-poor hcp Fe alloy and the B2 FeSi under high pressure. The tendency of this decomposition becomes stronger with increasing the applied pressure.
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| 8. |
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| 9. |
- Blixt, L., et al.
(författare)
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Covid-19 in patients with chronic lymphocytic leukemia : clinical outcome and B- and T-cell immunity during 13 months in consecutive patients
- 2022
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Ingår i: Leukemia. - : Springer Nature. - 0887-6924 .- 1476-5551. ; 36:2, s. 476-481
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Tidskriftsartikel (refereegranskat)abstract
- We studied clinical and immunological outcome of Covid-19 in consecutive CLL patients from a well-defined area during month 1–13 of the pandemic. Sixty patients (median age 71 y, range 43–97) were identified. Median CIRS was eight (4–20). Patients had indolent CLL (n = 38), had completed (n = 12) or ongoing therapy (n = 10). Forty-six patients (77%) were hospitalized due to severe Covid-19 and 11 were admitted to ICU. Severe Covid-19 was equally distributed across subgroups irrespective of age, gender, BMI, CLL status except CIRS (p < 0.05). Fourteen patients (23%) died; age ≥75 y was the only significant risk factor (p < 0.05, multivariate analysis with limited power). Comparing month 1–6 vs 7–13 of the pandemic, deaths were numerically reduced from 32% to 18%, ICU admission from 37% to 15% whereas hospitalizations remained frequent (86% vs 71%). Seroconversion occurred in 33/40 patients (82%) and anti-SARS-CoV-2 antibodies were detectable at six and 12 months in 17/22 and 8/11 patients, respectively. Most (13/17) had neutralizing antibodies and 19/28 had antibodies in saliva. SARS-CoV-2-specific T-cells (ELISpot) were detected in 14/17 patients. Covid-19 continued to result in high admission even among consecutive and young early- stage CLL patients. A robust and durable B and/or T cell immunity was observed in most convalescents.
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| 10. |
- Carlsson, Leo, 1992-
(författare)
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Applied Machine Learning in Steel Process Engineering : Using Supervised Machine Learning Models to Predict the Electrical Energy Consumption of Electric Arc Furnaces
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The steel industry is in constant need of improving its production processes. This is partly due to increasing competition and partly due to environmental concerns. One commonly used method for improving these processes is through the act of modeling. Models are representations of the reality that can be used to study and test new processes and strategies without costly interventions. In recent years, Machine Learning (ML) has emerged as a promising modeling approach for the steel industry. This has partly been driven by the Industry 4.0 development, which highlights ML as one of the key technologies for its realization. However, these models are often difficult to interpret, which makes it impractical to validate if the model accurately represents reality. This can lead to a lack of trust in ML models by domain practitioners in the steel industry. Thus, the present work investigates the practical usefulness of ML models in the context steel process engineering. The chosen application to answer this research question is the prediction of the Electrical Energy (EE) consumption of Electric Arc Furnaces (EAF). The EAF process was chosen due to its widespread use in the steel industry and due to the difficulty to accurately model the EE consumption using physical modeling. In the present literature, the use of linear statistical models are commonly used even though the EE consumption is non-linearly dependant on multiple important EAF process variables. In addition, the literature does neither investigate the correlations between input variables nor attempts to find the most optimal model with respect to model complexity, predictive performance, stability, and generalizability. Furthermore, a consistent reporting of predictive performance metrics and interpreting the non-transparent models is lacking. These shortcomings motivated the development of a Model Construction methodology and a Model Evaluation methodology that eliminate these shortcomings by considering both the domain-specific (metallurgical) aspects as well as the challenges imposed by ML modeling. By using the developed methodologies, several important findings originated from the resulting ML models predicting the EE consumption of two disparate EAF. A high model complexity, governed by an elevated number of input variables and model coefficients, is not necessary to achieve a state-of-the-art predictive performance on test data. This was confirmed both by the extensive number of produced models and by the comparison of the selected models with the models reported in the literature. To improve the predictive performance of the models, the main focus should instead be on data quality improvements. Experts in both process metallurgy and the specific process under study must be utilized when developing practically useful ML models. They support both in the selection of input variables and in the evaluation of the contribution of the input variables on the EE consumption prediction in relation to established physico-chemical laws and experiences with the specific EAF under study. In addition, a data cleaning strategy performed by an expert at one of the two EAF resulted in the best performing model. The scrap melting process in the EAF is complex and therefore challenging to accurately model using physico-chemical modeling. Using ML modeling, it was demonstrated that a scrap categorization based on the surface-area-to-volume ratio of scrap produced ML models with the highest predictive performance. This agrees well with the physico-chemical phenomena that governs the melting of scrap; temperature gradients, alloying gradients, stirring velocity, and the freezing effect. Multiple different practical use cases of ML models were exemplified in the present work, since the model evaluation methodology demonstrated the possibility to reveal the true contributions by each input variable on the EE consumption. The most prominent example was the analysis of the contribution by various scrap categories on the EE consumption. Three of these scrap categories were confirmed by the steel plant engineers to be accurately interpreted by the model. However, to be able to draw specific conclusions, a higher model predictive performance is required. This can only be realized after significant data quality improvements. Lastly, the developed methodology is not limited to the case used in the present work. It can be used to develop supervised ML models for other processes in the steel industry. This is valuable for the steel industry moving forward in the Industry 4.0 development.
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