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- Ahearn, Thomas U., et al.
(författare)
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Common variants in breast cancer risk loci predispose to distinct tumor subtypes
- 2022
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Ingår i: Breast Cancer Research. - : Springer Nature. - 1465-5411 .- 1465-542X. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundGenome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.MethodsAmong 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.ResultsEighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.ConclusionThis report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
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- Alawadi, Sadi, et al.
(författare)
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A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings
- 2020
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Ingår i: Energy Systems, Springer Verlag. - : Springer. - 1868-3967 .- 1868-3975. ; 13, s. 689-705
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Tidskriftsartikel (refereegranskat)abstract
- The international community has largely recognized that the Earth's climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.
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- Alawadi, Sadi, 1983-, et al.
(författare)
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A Federated Interactive Learning IoT-Based Health Monitoring Platform
- 2021
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Ingår i: New Trends in Database and Information Systems. - Cham : Springer. ; , s. 235-246, s. 235-246
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Konferensbidrag (refereegranskat)abstract
- Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld.
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- Bagni, Tommaso, et al.
(författare)
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Modeling Results of the Quench Behavior of a Nb-Ti Canted-Cosine-Theta Corrector Magnet for LHC
- 2024
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Ingår i: IEEE transactions on applied superconductivity (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1051-8223 .- 1558-2515. ; 34:5, s. 1-5
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Tidskriftsartikel (refereegranskat)abstract
- A newly designed superconducting magnet of the Canted-Cosine-Theta (CCT) type was developed as a result of a collaboration between Swedish universities (Uppsala and Linneaus) and Swedish industries. This magnet was designed to function as a replacement of the present LHC orbit corrector magnets, which are approaching their end of life due to the radiation load. As a result, the new CCT magnet was developed to be more radiation tolerant and to constitute a one-to-one replacement to the currently installed version, which is a 1 m long 70 mm double aperture dipole magnet. The final magnet, which is currently under construction, will be tested at FREIA laboratory at Uppsala University and generate a magnetic field of 3.3 T and an integrated field of 2.8 Tm at about 85 A. To examine the magnet quench behavior and to identify a suitable quench protection system, the 3D electro-magnetic and thermal behavior of the coil was modeled using the RAT-Raccoon software. Based on the simulation results, a Metrosil varistor was selected to protect the magnet during the test. In this article, we report the results of the numerical analysis. The magnet model is equipped with a spot heater to initialize the quench and the temperature and voltages are monitored during the avalanche effect. The simulated current decay and the hot-spot temperature are analyzed with a focus on the impact of quench-back on the magnet protection.
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- Bergman, Frida, Medicine doktor, 1984-, et al.
(författare)
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Walking Time Is associated With Hippocampal Volume in Overweight and Obese Office Workers
- 2020
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Ingår i: Frontiers in Human Neuroscience. - : Frontiers Media S.A.. - 1662-5161. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To investigate the long-term effects on cognition and brain function after installing treadmill workstations in offices for 13 months.Methods: Eighty healthy overweight or obese office workers aged 40–67 years were individually randomized to an intervention group, receiving a treadmill workstation and encouraging emails, or to a control group, continuing to work as usual. Effects on cognitive function, hippocampal volume, prefrontal cortex (PFC) thickness, and circulating brain-derived neurotrophic factor (BDNF) were analyzed. Further, mediation analyses between changes in walking time and light-intensity physical activity (LPA) on changes in BDNF and hippocampal volume between baseline and 13 months, and multivariate analyses of the baseline data with percentage sitting time as the response variable, were performed.Results: No group by time interactions were observed for any of the outcomes. In the mediation analyses, positive associations between changes in walking time and LPA on changes in hippocampal volume were observed, although not mediated by changes in BDNF levels. In the multivariate analyses, a negative association between percentage sitting time and hippocampal volume was observed, however only among those older than 51 years of age.Conclusion: Although no group by time interactions were observed, our analyses suggest that increased walking and LPA may have positive effects on hippocampal volume and that sedentary behavior is associated with brain structures of importance for memory functions.Trial Registration: www.ClinicalTrials.gov as NCT01997970.
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- Brondin, Anna, et al.
(författare)
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Open source step counter algorithm for wearable devices
- 2020
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Ingår i: Companion Proceedings of the 10th International Conference on the Internet of Things (IoT 2020). - New York, United States : ACM Digital Library. - 9781450388207
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Konferensbidrag (refereegranskat)abstract
- Commercial wearable devices and fitness trackers are commonly sold as black boxes of which little is known about their accuracy. This poses serious issues especially in health-related contexts such as clinical research, where transparency about accuracy and reliability are paramount.We present a validated algorithm for computing step counting that is optimised for use in constrained computing environments. Released as open source, the algorithm is based on the windowed peak detection approach, which has previously shown high accuracy on smartphones. The algorithm is optimised to run on a programmable smartwatch (Pine Time) and tested on 10 subjects in 8 scenarios, with varying varying positions of the wearable and walking paces.Our approach achieves a 89% average accuracy, with the highest average accuracy when walking outdoor (98%) and the lowest in a slow-walk scenario (77%). This result can be compared with the built-in step counter of the smartwatch (Bosch BMA421), which yielded a 94% average accuracy for the same use cases. Our work thus shows that an open-source approach for extracting physical activity data from wearable devices is possible and achieves an accuracy comparable to the one produced by proprietary embedded algorithms.
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