| 1. |
- Cascone, Claudia, et al.
(författare)
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AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring
- 2022
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Ingår i: Environmental Science: Water Research & Technology. - 2053-1419. ; 8:4, s. 836-848
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Tidskriftsartikel (refereegranskat)abstract
- The long-term trend of increasing natural organic matter (NOM) in boreal and north European surface waters represents an economic and environmental challenge for drinking water treatment plants (DWTPs). High-frequency measurements from absorbance-based online spectrophotometers are often used in modern DWTPs to measure the chromophoric fraction of dissolved organic matter (CDOM) over time. These data contain valuable information that can be used to optimise NOM removal at various stages of treatment and/or diagnose the causes of underperformance at the DWTP. However, automated monitoring systems generate large datasets that need careful preprocessing, followed by variable selection and signal processing before interpretation. In this work we introduce AbspectroscoPY (“Absorbance spectroscopic analysis inPython”), a Python toolbox for processing time-series datasets collected by in situ spectrophotometers. The toolbox addresses some of the main challenges in data preprocessing by handling duplicates, systematic time shifts, baseline corrections and outliers. It contains automated functions to compute a range of spectral metrics for the time-series data, including absorbance ratios, exponential fits, slope ratios and spectral slope curves. To demonstrate its utility, AbspectroscoPY was applied to 15-month datasets from three onlinespectrophotometers in a drinking water treatment plant. Despite only small variations in surface water quality over the time period, variability in the spectrophotometric profiles of treated water could be identified, quantified and related to lake turnover or operational changes in the DWTP. This toolboxrepresents a step toward automated early warning systems for detecting and responding to potential threats to treatment performance caused by rapid changes in incoming water quality.
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| 2. |
- Cascone, C., et al.
(författare)
-
AbspectroscoPY, a Python toolbox for absorbance-based sensor data in water quality monitoring
- 2022
-
Ingår i: Environmental Science. - : Royal Society of Chemistry (RSC). - 2053-1400 .- 2053-1419. ; 8:4, s. 836-848
-
Tidskriftsartikel (refereegranskat)abstract
- The long-term trend of increasing natural organic matter (NOM) in boreal and north European surface waters represents an economic and environmental challenge for drinking water treatment plants (DWTPs). High-frequency measurements from absorbance-based online spectrophotometers are often used in modern DWTPs to measure the chromophoric fraction of dissolved organic matter (CDOM) over time. These data contain valuable information that can be used to optimise NOM removal at various stages of treatment and/or diagnose the causes of underperformance at the DWTP. However, automated monitoring systems generate large datasets that need careful preprocessing, followed by variable selection and signal processing before interpretation. In this work we introduce AbspectroscoPY (“Absorbance spectroscopic analysis in Python”), a Python toolbox for processing time-series datasets collected by in situ spectrophotometers. The toolbox addresses some of the main challenges in data preprocessing by handling duplicates, systematic time shifts, baseline corrections and outliers. It contains automated functions to compute a range of spectral metrics for the time-series data, including absorbance ratios, exponential fits, slope ratios and spectral slope curves. To demonstrate its utility, AbspectroscoPY was applied to 15-month datasets from three online spectrophotometers in a drinking water treatment plant. Despite only small variations in surface water quality over the time period, variability in the spectrophotometric profiles of treated water could be identified, quantified and related to lake turnover or operational changes in the DWTP. This toolbox represents a step toward automated early warning systems for detecting and responding to potential threats to treatment performance caused by rapid changes in incoming water quality.
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| 3. |
- de Rojas, I., et al.
(författare)
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Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease. © 2021, The Author(s).
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| 4. |
- Fomalont, E. B., et al.
(författare)
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THE 2014 ALMA LONG BASELINE CAMPAIGN: AN OVERVIEW
- 2015
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Ingår i: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8213 .- 2041-8205. ; 808:1
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Tidskriftsartikel (refereegranskat)abstract
- A major goal of the Atacama Large Millimeter/submillimeter Array (ALMA) is to make accurate images with resolutions of tens of milliarcseconds, which at submillimeter (submm) wavelengths requires baselines up to similar to 15 km. To develop and test this capability, a Long Baseline Campaign (LBC) was carried out from 2014 September to late November, culminating in end-to-end observations, calibrations, and imaging of selected Science Verification (SV) targets. This paper presents an overview of the campaign and its main results, including an investigation of the short-term coherence properties and systematic phase errors over the long baselines at the ALMA site, a summary of the SV targets and observations, and recommendations for science observing strategies at long baselines. Deep ALMA images of the quasar 3C 138 at 97 and 241 GHz are also compared to VLA 43 GHz results, demonstrating an agreement at a level of a few percent. As a result of the extensive program of LBC testing, the highly successful SV imaging at long baselines achieved angular resolutions as fine as 19 mas at similar to 350 GHz. Observing with ALMA on baselines of up to 15 km is now possible, and opens up new parameter space for submm astronomy.
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| 5. |
- Fu, Z. J., et al.
(författare)
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Dyslipidemia in retinal metabolic disorders
- 2019
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Ingår i: Embo Molecular Medicine. - : EMBO. - 1757-4676 .- 1757-4684. ; 11:10
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Tidskriftsartikel (refereegranskat)abstract
- The light-sensitive photoreceptors in the retina are extremely metabolically demanding and have the highest density of mitochondria of any cell in the body. Both physiological and pathological retinal vascular growth and regression are controlled by photoreceptor energy demands. It is critical to understand the energy demands of photoreceptors and fuel sources supplying them to understand neurovascular diseases. Retinas are very rich in lipids, which are continuously recycled as lipid-rich photoreceptor outer segments are shed and reformed and dietary intake of lipids modulates retinal lipid composition. Lipids (as well as glucose) are fuel substrates for photoreceptor mitochondria. Dyslipidemia contributes to the development and progression of retinal dysfunction in many eye diseases. Here, we review photoreceptor energy demands with a focus on lipid metabolism in retinal neurovascular disorders.
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