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- Pöntinen, M., et al.
(författare)
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Euclid: Identification of asteroid streaks in simulated images using deep learning
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 679
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Tidskriftsartikel (refereegranskat)abstract
- The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition of asteroids, but we currently have such data, especially in the near-infrared wavelengths, for only a limited number of asteroids. This is a significant limitation considering the complex orbital structures of the asteroid populations. Up to 150 000 asteroids will be visible in the images of the upcoming ESA Euclid space telescope, and the instruments of Euclid will offer multiband visual to near-infrared photometry and slitless near-infrared spectra of these objects. Most of the asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-machine-learning approach based on the Streak Det software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample. The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0.25–0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.
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| 2. |
- Le, L. T., et al.
(författare)
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Correlation between CD4/CD8 ratio and neurocognitive performance during early HIV infection
- 2023
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Ingår i: Hiv Medicine. - : Wiley. - 1464-2662 .- 1468-1293. ; 24:4, s. 442-452
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Tidskriftsartikel (refereegranskat)abstract
- Introduction CD4/CD8 ratio is a marker of immune activation in HIV infection and has been associated with neurocognitive performance during chronic infection, but little is known about the early phases. The aim of this study was to examine the relationship between blood CD4/CD8 ratio and central nervous system endpoints in primary HIV infection (PHI) before and after antiretroviral treatment (ART). Methods This was a retrospective analysis of the Primary Infection Stage CNS Events Study (PISCES) cohort. We longitudinally assessed blood and cerebrospinal fluid (CSF) markers of inflammation, immune activation and neuronal injury, and neuropsychological testing performance (NPZ4, an average of three motor and one processing speed tests, and a summarized total score, NPZ11, including also executive function, learning and memory) in ART-naive participants enrolled during PHI. Spearman correlation and linear mixed models assessed the relationships between the trajectory of CD4/CD8 ratio over time and neurocognitive performance, blood and CSF markers of immune activation and neuronal injury. Results In all, 109 PHI participants were enrolled. The mean CD4/CD8 ratio decreased with longer time from infection to starting treatment (p < 0.001). Every unit increase in NPZ4 score was independently associated with a 0.15 increase in CD4/CD8 ratio (95% CI: 0.002-0.29; p = 0.047), whereas no correlation was found between CD4/CD8 ratio and NPZ11. Among the cognitive domains, only a change in processing speed was correlated with CD4/CD8 ratio over time (p = 0.03). The trajectory of the CD4/CD8 ratio was negatively correlated with change in CSF neurofilament light chain (p = 0.04). Conclusions The trajectory of CD4/CD8 ratio was independently associated with motor/psychomotor speed performance, suggesting that immune activation is involved in brain injury during the early stages of the infection.
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