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- Aarrestad, Thea, et al.
(författare)
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Benchmark data and model independent event classification for the large hadron collider
- 2022
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Ingår i: SciPost Physics. - 2542-4653. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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- Fervier, Veronica, et al.
(författare)
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Evaluating Nutrient Reduction, Grazing and Barley Straw as Measures Against Algal Growth
- 2020
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Ingår i: Wetlands (Wilmington, N.C.). - : SPRINGER. - 0277-5212 .- 1943-6246. ; 40:1, s. 193-202
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Tidskriftsartikel (refereegranskat)abstract
- The aim of our study was to experimentally investigate whether it is possible to reduce nuisance growth of filamentous algae in freshwater ecosystems. We used an experimental set-up mimicking a shallow pond system and performed a field investigation in the eutrophic moat of Krapperup castle (Southern Sweden), which exemplifies an extremely impaired ecosystem with ample growth of filamentous green algae. The indoor experiment tested three treatments: I) reduced nutrient concentrations, II) invertebrate grazers and III) addition of barley straw, which may constitute measures against filamentous algal growth and thereby improve the quality of the ecosystem services provided by water bodies. Our results show a decrease in cyanobacteria and diatom abundances in all mesocosms as filamentous algae biomass increased, suggesting that the microalgae suffered from nutrient and light competition with filamentous algae. A tendency for lower filamentous algae final biomass, as well as coverage, was observed in the treatment where the concentration of nutrients was reduced. Grazers treatment showed a tendency to inhibit filamentous algae growth on artificial macrophytes towards the end of the experiment, suggesting that snails initially fed on their preferred food source (diatoms), until it was almost depleted and then started to feed on filamentous algae. Interestingly, the barley straw treatment was the only treatment promoting macrophytes growth and enhancing diatom biomass, but this did not affect filamentous algae biomass. However, the ratio between filamentous algae and macrophyte final biomasses was significantly lower in the straw treatment. In a broader context, it is likely that in a long-term perspective the positive effect of barley straw on macrophyte growth will promote a shift from dominance by filamentous algae to macrophytes as main primary producer. Moreover, our experiment shows that barley straw may be effective in reducing cyanobacterial growth, which may lead to improved water quality and thereby ecosystem services, such as supporting and cultural ecosystem services, since cyanobacteria may produce potent toxins and pose a serious risk to human and animal health. Altogether, our experimental results have important implications for the challenge of reversing nuisance filamentous algal blooms in highly eutrophic systems.
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- Johnson, Kara A., et al.
(författare)
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Basal Ganglia Pathways Associated With Therapeutic Pallidal Deep Brain Stimulation for Tourette Syndrome
- 2021
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Ingår i: Biological Psychiatry. - : Elsevier. - 2451-9022. ; 6:10, s. 961-972
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Tidskriftsartikel (refereegranskat)abstract
- Background: Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) can improve tics and comorbid obsessive-compulsive behavior (OCB) in patients with treatment-refractory Tourette syndrome (TS). However, some patients’ symptoms remain unresponsive, the stimulation applied across patients is variable, and the mechanisms underlying improvement are unclear. Identifying the fiber pathways surrounding the GPi that are associated with improvement could provide mechanistic insight and refine targeting strategies to improve outcomes.Methods: Retrospective data were collected for 35 patients who underwent bilateral GPi DBS for TS. Computational models of fiber tract activation were constructed using patient-specific lead locations and stimulation settings to evaluate the effects of DBS on basal ganglia pathways and the internal capsule. We first evaluated the relationship between activation of individual pathways and symptom improvement. Next, linear mixed-effects models with combinations of pathways and clinical variables were compared in order to identify the best-fit predictive models of tic and OCB improvement.Results: The best-fit model of tic improvement included baseline severity and the associative pallido-subthalamic pathway. The best-fit model of OCB improvement included baseline severity and the sensorimotor pallido-subthalamic pathway, with substantial evidence also supporting the involvement of the prefrontal, motor, and premotor internal capsule pathways. The best-fit models of tic and OCB improvement predicted outcomes across the cohort and in cross-validation.Conclusions: Differences in fiber pathway activation likely contribute to variable outcomes of DBS for TS. Computational models of pathway activation could be used to develop novel approaches for preoperative targeting and selecting stimulation parameters to improve patient outcomes.
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