| 1. |
- Colomé, Núria, et al.
(författare)
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Multi-laboratory experiment PME11 for the standardization of phosphoproteome analysis
- 2022
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Ingår i: Journal of Proteomics. - : Elsevier. - 1874-3919 .- 1876-7737. ; 251
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Tidskriftsartikel (refereegranskat)abstract
- Global analysis of protein phosphorylation by mass spectrometry proteomic techniques has emerged in the last decades as a powerful tool in biological and biomedical research. However, there are several factors that make the global study of the phosphoproteome more challenging than measuring non-modified proteins. The low stoichiometry of the phosphorylated species and the need to retrieve residue specific information require particular attention on sample preparation, data acquisition and processing to ensure reproducibility, qualitative and quantitative robustness and ample phosphoproteome coverage in phosphoproteomic workflows. Aiming to investigate the effect of different variables in the performance of proteome wide phosphoprotein analysis protocols, ProteoRed-ISCIII and EuPA launched the Proteomics Multicentric Experiment 11 (PME11). A reference sample consisting of a yeast protein extract spiked in with different amounts of a phosphomix standard (Sigma/Merck) was distributed to 31 laboratories around the globe. Thirty-six datasets from 23 laboratories were analyzed. Our results indicate the suitability of the PME11 reference sample to benchmark and optimize phosphoproteomics strategies, weighing the influence of different factors, as well as to rank intra and inter laboratory performance.
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| 2. |
- Forzieri, Giovanni, et al.
(författare)
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The Database of European Forest Insect and Disease Disturbances: DEFID2
- 2023
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Ingår i: Global Change Biology. - 1365-2486. ; 29:21, s. 6040-6065
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Tidskriftsartikel (refereegranskat)abstract
- Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral–temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.
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| 3. |
- Mantini, Cesare, et al.
(författare)
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Influence of image reconstruction parameters on cardiovascular risk reclassification by Computed Tomography Coronary Artery Calcium Score
- 2018
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X. ; 101, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To investigate the influence of different CT reconstruction parameters on coronary artery calcium scoring (CACS) values and reclassification of predicted cardiovascular (CV) risk. Methods: CACS was evaluated in 113 patients undergoing ECG-gated 64-slice CT. Reference CACS protocol included standard kernel filter (B35f) with slice thickness/increment of 3/1.5 mm, and field-of-view (FOV) of 150–180 mm. Influence of different image reconstruction algorithms (reconstructed slice thickness/increment 2.0/1.0–1.5/0.8–3.0/2.0–3.0/3.0 mm; slice kernel B30f-B45f; FOV 200–250 mm) on Agatston score was assessed by Bland-Altman plots and concordance correlation coefficient (CCC) analysis. Classification of CV risk was based on the Mayo Clinic classification. Results: Different CACS reconstruction parameters showed overall good accuracy and precision when compared with reference protocol. Protocols with larger FOV, thinner slices and sharper kernels were associated with significant CV risk reclassification. Use of kernel B45f showed a moderate positive correlation with reference CACS protocol (Agatston CCC = 0.67), and yielded significantly higher CACS values (p <.05). Reconstruction parameters using B30f or B45f kernels, 250 mm FOV, or slice thickness/increment of 2.0/1.0 mm or 1.5/0.8 mm, were associated with significant reclassification of CV risk (p <.05). Conclusions: Kernel, FOV, slice thickness and increment are major determinants of accuracy and precision of CACS measurement. Despite high agreement and overall good correlation of different reconstruction protocols, thinner slices thickness and increment, and sharper kernels were associated with significant upward reclassification of CV risk. Larger FOV determined both upward and downward reclassification of CV risk.
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| 4. |
- Mantini, Cesare, et al.
(författare)
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Vieussens’ ring coronary collateral circulation : a natural bypass history
- 2022
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Ingår i: Acta Biomedica. - 0392-4203. ; 93
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Tidskriftsartikel (refereegranskat)abstract
- “Vieussens’ ring” or “arterial circle of Vieussens” is a crucial hetero-coronaric pathway, bridging proximal right coronary artery (RCA) and left anterior descending artery (LAD) when a hemodynami-cally stenosis is established in the either of the vessel. In detail such coronary collateral circulation is usually supplied by branches of the conus artery. We present a case of a 62-year-old man who was admitted to our emergency department complaining of chest pain. Coronary angiography showed LAD occlusion at the mid tract with delayed and slight opacification of its distal segment sustained by Vieussens’ ring. Coronary computed tomography angiography (CCTA) was subsequently performed which confirmed the presence of such natural bypass and evaluated its relationship with adjacent structures. Imaging, particularly CCTA of-fers a valid tool in assessing the hetero-coronaric collateral vessel. Due to its high spatial resolution it may provide many information about the coronary anatomy by delineating their origin, course and termination. (www.actabiomedica.it).
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| 5. |
- Mohammadi, Mohammadreza, et al.
(författare)
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Anomaly Detection Using LSTM-Autoencoder in Smart Grid : A Federated Learning Approach
- 2023
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Ingår i: <em>ACM International Conference Proceeding Series</em>. - : Association for Computing Machinery. ; , s. 48-54
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Konferensbidrag (refereegranskat)abstract
- ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL’s long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.
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| 6. |
- Shrestha, Rakesh, et al.
(författare)
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Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid
- 2024
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Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press Inc.. - 0743-7315 .- 1096-0848. ; 193
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Tidskriftsartikel (refereegranskat)abstract
- In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.
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