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| 2. |
- Winkler, Nicolas P., 1991-, et al.
(författare)
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Learning From the Past : Sequential Deep Learning for Gas Distribution Mapping
- 2022
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Ingår i: ROBOT2022. - Cham : Springer. - 9783031210617 - 9783031210624 ; , s. 178-188
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Konferensbidrag (refereegranskat)abstract
- To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
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| 3. |
- Dieleman, J., et al.
(författare)
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Evolution and patterns of global health financing 1995-2014 : Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries
- 2017
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Ingår i: The Lancet. - : Lancet Publishing Group. - 0140-6736 .- 1474-547X. ; 389:10083, s. 1981-2004
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Tidskriftsartikel (refereegranskat)abstract
- Background: An adequate amount of prepaid resources for health is important to ensure access to health services and for the pursuit of universal health coverage. Previous studies on global health financing have described the relationship between economic development and health financing. In this study, we further explore global health financing trends and examine how the sources of funds used, types of services purchased, and development assistance for health disbursed change with economic development. We also identify countries that deviate from the trends. Methods: We estimated national health spending by type of care and by source, including development assistance for health, based on a diverse set of data including programme reports, budget data, national estimates, and 964 National Health Accounts. These data represent health spending for 184 countries from 1995 through 2014. We converted these data into a common inflation-adjusted and purchasing power-adjusted currency, and used non-linear regression methods to model the relationship between health financing, time, and economic development. Findings: Between 1995 and 2014, economic development was positively associated with total health spending and a shift away from a reliance on development assistance and out-of-pocket (OOP) towards government spending. The largest absolute increase in spending was in high-income countries, which increased to purchasing power-adjusted $5221 per capita based on an annual growth rate of 3.0%. The largest health spending growth rates were in upper-middle-income (5.9) and lower-middle-income groups (5.0), which both increased spending at more than 5% per year, and spent $914 and $267 per capita in 2014, respectively. Spending in low-income countries grew nearly as fast, at 4.6%, and health spending increased from $51 to $120 per capita. In 2014, 59.2% of all health spending was financed by the government, although in low-income and lower-middle-income countries, 29.1% and 58.0% of spending was OOP spending and 35.7% and 3.0% of spending was development assistance. Recent growth in development assistance for health has been tepid; between 2010 and 2016, it grew annually at 1.8%, and reached US$37.6 billion in 2016. Nonetheless, there is a great deal of variation revolving around these averages. 29 countries spend at least 50% more than expected per capita, based on their level of economic development alone, whereas 11 countries spend less than 50% their expected amount. Interpretation: Health spending remains disparate, with low-income and lower-middle-income countries increasing spending in absolute terms the least, and relying heavily on OOP spending and development assistance. Moreover, tremendous variation shows that neither time nor economic development guarantee adequate prepaid health resources, which are vital for the pursuit of universal health coverage. © The Author(s). Published by Elsevier Ltd.
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| 4. |
- Fan, Y., et al.
(författare)
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The gut microbiota contributes to the pathogenesis of anorexia nervosa in humans and mice
- 2023
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Ingår i: Nature Microbiology. - : Nature Publishing Group. - 2058-5276. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Anorexia nervosa (AN) is an eating disorder with a high mortality. About 95% of cases are women and it has a population prevalence of about 1%, but evidence-based treatment is lacking. The pathogenesis of AN probably involves genetics and various environmental factors, and an altered gut microbiota has been observed in individuals with AN using amplicon sequencing and relatively small cohorts. Here we investigated whether a disrupted gut microbiota contributes to AN pathogenesis. Shotgun metagenomics and metabolomics were performed on faecal and serum samples, respectively, from a cohort of 77 females with AN and 70 healthy females. Multiple bacterial taxa (for example, Clostridium species) were altered in AN and correlated with estimates of eating behaviour and mental health. The gut virome was also altered in AN including a reduction in viral-bacterial interactions. Bacterial functional modules associated with the degradation of neurotransmitters were enriched in AN and various structural variants in bacteria were linked to metabolic features of AN. Serum metabolomics revealed an increase in metabolites associated with reduced food intake (for example, indole-3-propionic acid). Causal inference analyses implied that serum bacterial metabolites are potentially mediating the impact of an altered gut microbiota on AN behaviour. Further, we performed faecal microbiota transplantation from AN cases to germ-free mice under energy-restricted feeding to mirror AN eating behaviour. We found that the reduced weight gain and induced hypothalamic and adipose tissue gene expression were related to aberrant energy metabolism and eating behaviour. Our 'omics' and mechanistic studies imply that a disruptive gut microbiome may contribute to AN pathogenesis. Faecal metagenomics and serum metabolomics reveal compositional and functional alterations in the gut microbiota of women with anorexia nervosa, and faecal transplants could transfer an anorexia-associated phenotype to germ-free mice.
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| 5. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots
- 2017
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 2:2, s. 1117-1123
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Tidskriftsartikel (refereegranskat)abstract
- For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback–Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
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| 6. |
- Singh, Jagmeet P., et al.
(författare)
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Phased target trial design and meta-analysis in a head-to-head treatment comparison
- 2023
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Ingår i: Pharmacoepidemiology and Drug Safety. - : John Wiley & Sons. - 1053-8569 .- 1099-1557. ; 32:Suppl. 1, s. 444-444
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: For conditions with rare clinical outcomes, real-world treatment comparisons are challenging to design and prone to confounding.Objectives: To present a robust methodologic approach for rigorous and transparent assessment of rare outcomes using real-world data.Methods: We emulated a target trial using an active comparator, new-user design to compare dronedarone to sotalol for rhythm control in atrial fibrillation (AF) as both are recommended for similar patient phenotypes. Using one protocol, a pre-specified stepwise approach was implemented across 4 datasets (Optum CDM; IBM MarketScan; Veterans Affairs Electronic Health Records; Swedish National Patient Register). Meta-analysis was used to ensure sufficient capture of specific, rare primary outcomes (cardiovascular (CV) hospitalization and ventricular proarrhythmia) and to evaluate consistency of findings across patient populations. Steps 1–3 focused on cohort selection, propensity score matching (PSM), baseline equipoise and residual confounding assessment via negative control outcome analyses. In steps 4–6, outcomes in the individual cohorts were analyzed using an as-treated approach and Cox proportional hazards models. Step 7 included a heterogeneity assessment, meta-analysis using fixed effects models, and hypothesis testing using a hierarchical approach. In step 8, sensitivity analyses, including E-values and Inverse Probability of Censoring Weighting, were conducted to verify the robustness of findings.Results: In step 1, 35,467 sotalol and 27,955 dronedarone patients with AF who were antiarrhythmic drug-naive were identified across databases. In steps 2–3, 23,275 dronedarone patients were PS-matched to 23,275 sotalol patients. Baseline covariates were well-balanced and little-to-no residual confounding was observed via the negative control analyses. Individual HRs were estimated in steps 4–6, and, when no significant heterogeneity between databases was observed, hazard ratios (HRs) were pooled across datasets in step 7. For example, for CV hospitalization, dronedarone was superior to sotalol with no heterogeneity (HR: 0.91; 95% CI: 0.85, 0.97; Cochran Q p-value: 0.32). Eleven sensitivity analyses were conducted in step 8 and confirmed that findings were generally robust.Conclusions: An active comparator, new-user design using the target trial approach coupled with meta-analysis generated consistent findings across databases and countries using one protocol. Similar methods, including a pre-specified stepwise approach, negative control outcome, and tests for robustness should be considered for real-world studies where specific, rare outcomes need to be examined in a rigorous and transparent way.
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