| 1. |
- Kiefer, Johannes, et al.
(författare)
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Laser-induced breakdown flame thermometry
- 2012
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Ingår i: Combustion and Flame. - : Elsevier BV. - 0010-2180. ; 159:12, s. 3576-3582
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Tidskriftsartikel (refereegranskat)abstract
- The determination of temperature distribution is an essential task when flames are characterized. We propose a new approach for flame thermometry based on laser-induced breakdown spectroscopy (LIBS) utilizing the temperature dependency of the breakdown threshold laser pulse energy. Calibration measurements are carried out in heated gas flows and post-combustion gases. Compositional effects on the breakdown threshold are corrected employing a straightforward procedure. For this purpose, the elemental composition is derived from the LIBS spectra and this information is then used for correcting the measured threshold laser pulse energy. A series of proof-of-concept measurements in a laminar methane/air flame on a Bunsen burner is conducted and compared to reference data from coherent anti-Stokes Raman scattering (CARS). The corrected LIBS temperatures show excellent agreement with those obtained by CARS. Therefore, our approach represents a simple and straightforward alternative to traditionally used thermometry methods. (C) 2012 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
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| 2. |
- Dorr, Felix, et al.
(författare)
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Dissociating memory and executive function impairment through temporal features in a word list verbal learning task
- 2023
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Ingår i: NEUROPSYCHOLOGIA. - 0028-3932 .- 1873-3514. ; 189
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Tidskriftsartikel (refereegranskat)abstract
- The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment-as quantified e.g. by the RAVLT-at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes. 675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario. Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.
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| 3. |
- Schaefer, Simona, et al.
(författare)
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Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts
- 2023
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Ingår i: JOURNAL OF ALZHEIMERS DISEASE. - 1387-2877 .- 1875-8908. ; 91:3, s. 1165-1171
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Tidskriftsartikel (refereegranskat)abstract
- Background: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. Objective: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. Methods: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N= 121) and a Swedish birth cohort (N= 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. Results: The algorithms achieved a performance of AUC similar to 0.73 and AUC similar to 0.77 in the respective training cohorts and AUC similar to 0.81 in the unseen validation cohorts. Conclusion: The results indicate that a ki:e SB-C based algorithm robustly detectsMCIacross different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.
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