| 1. |
- Autio, U.A., et al.
(författare)
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Magnetotelluric array in the central Finnish Lapland II : 3-D inversion and tectonic implications
- 2020
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Ingår i: Tectonophysics. - : Elsevier. - 0040-1951 .- 1879-3266. ; 794
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Tidskriftsartikel (refereegranskat)abstract
- The northern part of the Fennoscandian shield records several Palaeoproterozoic phases of rifting, crustal shortening, metamorphism and structural reactivation. The geologic history has left behind a complex crustal geoelectric structure as evidenced by magnetotelluric (MT) data from the central Finnish Lapland acquired in the context of the MaSca-project in 2014. The data are characterized by strong 3-D effects such as high phase tensor skew values and anomalous induction vectors. Interestingly, however, at the same time a dominant E-W principal direction from the phase tensor data can be inferred. 3-D conductivity models derived using the ModEM code display, high crustal conductance (> 10,000 S) in the vicinity of the Central Lapland Greenstone Belt, the Peräpohja Belt and the Kuusamo Belt. Conductors in the northern and southern part of the study area are separated by a resistor coinciding with the Central Lapland Granitoid Complex. A remarkable feature is an arc-shaped conductor inside the northern part of the Central Lapland Granitoid Complex, which continues into the Central Lapland Greenstone Belt in the north. The conductor is associated with a major induction vector anomaly and also coincides with extreme responses (negative phase tensor principal values), which are discussed in an accompanying paper. The conductive structures in the models are interpreted as deeply buried graphite and sulphide bearing metasedimentary rocks or as reactivated Archaean shear zones. The revealed geoelectric structures also partly correlate with seismic reflection and other geophysical data from the area. A possible explanation for the observed pervasive E-W principal direction of the phase tensor data could be the aulacogen (failed rift) suggested in recent tectonic evolution models.
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| 2. |
- Raj, R, et al.
(författare)
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Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm
- 2022
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Ingår i: NPJ digital medicine. - : Springer Science and Business Media LLC. - 2398-6352. ; 5:1, s. 96-
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Tidskriftsartikel (refereegranskat)abstract
- Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.
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