| 1. |
- Orgueira, Adrian Mosquera, et al.
(författare)
-
Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling
- 2024
-
Ingår i: Clinical Epigenetics. - : BioMed Central (BMC). - 1868-7083. ; 16:1
-
Tidskriftsartikel (refereegranskat)abstract
- Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian (n = 42) and Nordic (n = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling.
|
|
| 2. |
- Albert, Marie Christine, et al.
(författare)
-
Identification of FasL as a crucial host factor driving COVID-19 pathology and lethality
- 2024
-
Ingår i: Cell Death and Differentiation. - 1350-9047. ; 31:5, s. 544-557
-
Tidskriftsartikel (refereegranskat)abstract
- The dysregulated immune response and inflammation resulting in severe COVID-19 are still incompletely understood. Having recently determined that aberrant death-ligand-induced cell death can cause lethal inflammation, we hypothesized that this process might also cause or contribute to inflammatory disease and lung failure following SARS-CoV-2 infection. To test this hypothesis, we developed a novel mouse-adapted SARS-CoV-2 model (MA20) that recapitulates key pathological features of COVID-19. Concomitantly with occurrence of cell death and inflammation, FasL expression was significantly increased on inflammatory monocytic macrophages and NK cells in the lungs of MA20-infected mice. Importantly, therapeutic FasL inhibition markedly increased survival of both, young and old MA20-infected mice coincident with substantially reduced cell death and inflammation in their lungs. Intriguingly, FasL was also increased in the bronchoalveolar lavage fluid of critically-ill COVID-19 patients. Together, these results identify FasL as a crucial host factor driving the immuno-pathology that underlies COVID-19 severity and lethality, and imply that patients with severe COVID-19 may significantly benefit from therapeutic inhibition of FasL.
|
|
| 3. |
- Arias Chao, Manuel, et al.
(författare)
-
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
- 2021
-
Ingår i: Data. - : MDPI. - 2306-5729. ; 6:1, s. 5-5
-
Tidskriftsartikel (refereegranskat)abstract
- A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.
|
|
| 4. |
- Arias Chao, Manuel, et al.
(författare)
-
Fusing physics-based and deep learning models for prognostics
- 2022
-
Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 217
-
Tidskriftsartikel (refereegranskat)abstract
- Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited representativeness of the training dataset for data-driven models. Combining the advantages of these two approaches while overcoming some of their limitations, we propose a novel hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems. In the proposed framework, we use physics-based performance models to infer unobservable model parameters related to a system’s components health by solving a calibration problem. These parameters are subsequently combined with sensor readings and used as input to a deep neural network, thereby generating a data-driven prognostics model with physics-augmented features. The performance of the hybrid framework is evaluated on an extensive case study comprising run-to-failure degradation trajectories from a fleet of nine turbofan engines under real flight conditions. The experimental results show that the hybrid framework outperforms purely data-driven approaches by extending the prediction horizon by nearly 127%. Furthermore, it requires less training data and is less sensitive to the limited representativeness of the dataset as compared to purely data-driven approaches. Furthermore, we demonstrated the feasibility of the proposed framework on the original CMAPSS dataset, thereby confirming its superior performance.
|
|
| 5. |
- Bajarunas, Kristupas, et al.
(författare)
-
Health index estimation through integration of general knowledge with unsupervised learning
- 2024
-
Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 251
-
Tidskriftsartikel (refereegranskat)abstract
- Accurately estimating a Health Index (HI) from condition monitoring data (CM) is essential for reliable and interpretable prognostics and health management (PHM) in complex systems. In most scenarios, complex systems operate under varying operating conditions and can exhibit different fault modes, making unsupervised inference of an HI from CM data a significant challenge. Hybrid models combining prior knowledge about degradation with deep learning models have been proposed to overcome this challenge. However, previously suggested hybrid models for HI estimation usually rely heavily on system-specific information, limiting their transferability to other systems. In this work, we propose an unsupervised hybrid method for HI estimation that integrates general knowledge about degradation into the convolutional autoencoder’s model architecture and learning algorithm, enhancing its applicability across various systems. The effectiveness of the proposed method is demonstrated in two case studies from different domains: turbofan engines and lithium batteries. The results show that the proposed method outperforms other competitive alternatives, including residual-based methods, in terms of HI quality and their utility for Remaining Useful Life (RUL) predictions. The case studies also highlight the comparable performance of our proposed method with a supervised model trained with HI labels.
|
|
| 6. |
- Campillo-Cora, Claudia, et al.
(författare)
-
Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator
- 2022
-
Ingår i: agronomy. - : MDPI AG. - 2073-4395. ; 12:10
-
Tidskriftsartikel (refereegranskat)abstract
- The assessment of remediation on metal-polluted soils is usually focused on total and/or bioavailable metal content. However, these chemical variables do not provide direct information about reductions in heavy metals pressure on soil microorganisms. We propose the use of bacterial communities to evaluate the efficiency of three remediation techniques: crushed mussel shell (CMS) and pine bark (PB) as soil amendments and EDTA-washing. A soil sample was polluted with different doses of Cu, Ni, and Zn (separately). After 30 days of incubation, the remediation techniques were applied, and bacterial community tolerance to heavy metals determined. If bacterial communities develop tolerance, it is an indicator that the metal is exerting toxicity on them. Soil bacterial communities developed tolerance to Cu, Ni, and Zn in response to metal additions. After remediation, bacterial communities showed decreases in bacterial community tolerance to Cu, Ni, and Zn for all remediation techniques. For Cu and Ni, soil EDTA-washing showed the greatest reduction of bacterial community tolerance to Cu and Ni, respectively, while for Zn the soil amendment with PB was the most effective remediation technique. Thus, bacterial community tolerance to heavy metals successfully detect differences in the effectiveness of the three remediation techniques.
|
|
| 7. |
- Campillo-Cora, Claudia, et al.
(författare)
-
Bacterial community tolerance to Cu in soils with geochemical baseline concentrations (GBCs) of heavy metals : Importance for pollution induced community tolerance (PICT) determinations using the leucine incorporation method
- 2021
-
Ingår i: Soil Biology and Biochemistry. - : Elsevier BV. - 0038-0717. ; 155
-
Tidskriftsartikel (refereegranskat)abstract
- PICT (Pollution Induced Community Tolerance) to Cu is a useful and sensitive tool to assess the effects of Cu pollution in soils under laboratory conditions. However, in field situations, the absence of reference values, i.e. bacterial community tolerance to Cu baseline from non-polluted soils, make the method uncertain when we want to know if a soil is or is not polluted from a microbiological point of view. In order to shed some light on this topic, the PICT (Pollution Induced Community Tolerance) concept was used to determine the bacterial community tolerance to Cu in uncontaminated soils developed on five different parent materials, using log IC50 as a tolerance index. IC50 was calculated as the amount of Cu that inhibit 50% of bacterial growth (estimated via the leucine incorporation method) in a bacterial suspension extracted from soil. With physico-chemical soil characteristics and type of parent material, a linear multiple regression equation was fitted explaining 80% of the variance in log IC50 values. This equation provides a useful tool to estimate the bacterial community tolerance to Cu baseline in a soil using the general soil characteristics, allowing for the more general use of PICT without the need of reference soils.
|
|
| 8. |
- Campillo-Cora, Claudia, et al.
(författare)
-
Estimation of baseline levels of bacterial community tolerance to Cr, Ni, Pb, and Zn in unpolluted soils, a background for PICT (pollution-induced community tolerance) determination
- 2022
-
Ingår i: Biology and Fertility of Soils. - : Springer Science and Business Media LLC. - 0178-2762 .- 1432-0789. ; 58:1, s. 49-61
-
Tidskriftsartikel (refereegranskat)abstract
- The PICT method (pollution-induced community tolerance) can be used to assess whether changes in soil microbial response are due to heavy metal toxicity or not. Microbial community tolerance baseline levels can, however, also change due to variations in soil physicochemical properties. Thirty soil samples (0–20 cm), with geochemical baseline concentrations (GBCs) of heavy metals and from five different parent materials (granite, limestone, schist, amphibolite, and serpentine), were used to estimate baseline levels of bacterial community tolerance to Cr, Ni, Pb, and Zn using the leucine incorporation method. General equations (n = 30) were determined by multiple linear regression using general soil properties and parent material as binary variables, explaining 38% of the variance in log IC50 (concentration that inhibits 50% of bacterial growth) values for Zn, with 36% for Pb, 44% for Cr, and 68% for Ni. The use of individual equations for each parent material increased the explained variance for all heavy metals, but the presence of a low number of samples (n = 6) lead to low robustness. Generally, clay content and dissolved organic C (DOC) were the main variables explaining bacterial community tolerance for the tested heavy metals. Our results suggest that these equations may permit applying the PICT method with Zn and Pb when there are no reference soils, while more data are needed before using this concept for Ni and Cr.
|
|
| 9. |
- Chao, Manuel Arias, et al.
(författare)
-
Hybrid deep fault detection and isolation : Combining deep neural networks and system performance models
- 2019
-
Ingår i: International Journal of Prognostics and Health Management. - : PHM Society. - 2153-2648. ; 10:11
-
Tidskriftsartikel (refereegranskat)abstract
- With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on fault isolation often suffers from the fact that fault conditions affect a large portion of the measured signals thereby masking the fault source. To overcome this limitation, we propose a hybrid approach combining physical performance models with deep learning algorithms. Unobserved process variables are inferred with a physics-based performance model to enhance the input space of a data-driven diagnostics model. The resulting increased input space gains representation power enabling more accurate fault detection and isolation. To validate the effectiveness of the proposed method, we generate a condition monitoring dataset of an advanced gas turbine during flight conditions under healthy and four faulty operative conditions based on the Aero-Propulsion System Simulation (C-MAPSS) dynamical model. We evaluate the performance of the proposed hybrid methodology in combination with two different deep learning algorithms: deep feed forward neural networks and Variational Autoencoders, both of which demonstrate a significant improvement when applied within the hybrid fault detection and diagnostics framework. The proposed method is able to outperform pure data-driven solutions, particularly for systems with a high variability of Manuel Arias Chao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. operating conditions. It provides superior results both for fault detection as well as for fault isolation. For the fault isolation task, it overcomes the smearing effect that is commonly observed in pure data-driven approaches and enables a precise isolation of the affected signal. We also demonstrate that deep learning algorithms provide a better performance on the fault detection tas
|
|
| 10. |
- Curtis, Bruce A., et al.
(författare)
-
Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs
- 2012
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 492:7427, s. 59-65
-
Tidskriftsartikel (refereegranskat)abstract
- Cryptophyte and chlorarachniophyte algae are transitional forms in the widespread secondary endosymbiotic acquisition of photosynthesis by engulfment of eukaryotic algae. Unlike most secondary plastid-bearing algae, miniaturized versions of the endosymbiont nuclei (nucleomorphs) persist in cryptophytes and chlorarachniophytes. To determine why, and to address other fundamental questions about eukaryote-eukaryote endosymbiosis, we sequenced the nuclear genomes of the cryptophyte Guillardia theta and the chlorarachniophyte Bigelowiella natans. Both genomes have >21,000 protein genes and are intron rich, and B. natans exhibits unprecedented alternative splicing for a single-celled organism. Phylogenomic analyses and subcellular targeting predictions reveal extensive genetic and biochemical mosaicism, with both host-and endosymbiont-derived genes servicing the mitochondrion, the host cell cytosol, the plastid and the remnant endosymbiont cytosol of both algae. Mitochondrion-to-nucleus gene transfer still occurs in both organisms but plastid-to-nucleus and nucleomorph-to-nucleus transfers do not, which explains why a small residue of essential genes remains locked in each nucleomorph.
|
|
