| 1. |
- Hlebowicz, Joanna
(författare)
-
Glycaemic Response in Relation to Gastric Emptying and Satiety in Health and Disease
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Dietary fibre and whole grains are recommended to prevent the development of type 2 diabetes. Low glycaemic index foods that are rich in fibre are recommended to control blood glucose levels. Gastric emptying, together with other factors, regulate the postprandial blood glucose response. A delay in the gastric emptying rate (GER) leads to a lower postprandial blood glucose concentration. However, 30-50% of diabetes patients have delayed gastric emptying.The aims of these studies were to evaluate the effect of different food factors on the GER, the postprandial blood glucose response, and satiety in healthy subjects and those with diabetes mellitus. The results show that inclusion of 6 g cinnamon in the diet lowers the postprandial blood glucose response, a change that is at least partially explained by delayed GER. Neither bran flakes nor wholemeal oat flakes has any effect on the total postprandial blood glucose response, GER or satiety compared with cornflakes. Muesli with 4 g oat β-glucan does not affect the GER or satiety, but lowers the postprandial blood glucose response, indicating that the GER is not involved in the blood glucose lowering mechanism. Whole-kernel wheat bread served with vinegar leads to higher satiety than wholemeal wheat bread with vinegar, or white wheat bread with or without vinegar in healthy subjects. This may be explained by increased antral distension caused by intact cereal kernels, but not by changes in GER or postprandial blood glucose responses. Vinegar affects insulin-dependent diabetes mellitus patients with diabetic gastroparesis by reducing the GER even further.
|
|
| 2. |
- Stephan, Jörg, et al.
(författare)
-
American foulbrood in a honeybee colony: spore-symptom relationship and feedbacks between disease and colony development
- 2020
-
Ingår i: BMC Ecology. - : Springer Science and Business Media LLC. - 1472-6785. ; 20
-
Tidskriftsartikel (refereegranskat)abstract
- Background The most severe bacterial disease of honeybees is American foulbrood (AFB). The epidemiology of AFB is driven by the extreme spore resilience, the difficulty of bees to remove these spores, and the considerable incidence of undetected spore-producing colonies. The honeybee collective defence mechanisms and their feedback on colony development, which involves a division of labour at multiple levels of colony organization, are difficult to model. To better predict disease outbreaks we need to understand the feedback between colony development and disease progression within the colony. We therefore developed Bayesian models with data from forty AFB-diseased colonies monitored over an entire foraging season to (i) investigate the relationship between spore production and symptoms, (ii) disentangle the feedback loops between AFB epidemiology and natural colony development, and (iii) discuss whether larger insect societies promote or limit within-colony disease transmission. Results Rather than identifying a fixed spore count threshold for clinical symptoms, we estimated the probabilities around the relationship between spore counts and symptoms, taking into account modulators such as brood amount/number of bees and time post infection. We identified a decrease over time in the bees-to-brood ratio related to disease development, which should ultimately induce colony collapse. Lastly, two contrasting theories predict that larger colonies could promote either higher (classical epidemiological SIR-model) or lower (increasing spatial nest segregation and more effective pathogen removal) disease prevalence. Conclusions AFB followed the predictions of the SIR-model, partly because disease prevalence and brood removal are decoupled, with worker bees acting more as disease vectors, infecting new brood, than as agents of social immunity, by removing infected brood. We therefore established a direct link between disease prevalence and social group size for a eusocial insect. We furthermore provide a probabilistic description of the relationship between AFB spore counts and symptoms, and how disease development and colony strength over a season modulate this relationship. These results help to better understand disease development within honeybee colonies, provide important estimates for further epidemiological modelling, and gained important insights into the optimal sampling strategy for practical beekeeping and honeybee research.
|
|
| 3. |
|
|
| 4. |
- Zhang, Huang, 1993, et al.
(författare)
-
Comparative Analysis of Battery Cycle Life Early Prediction Using Machine Learning Pipeline
- 2023
-
Ingår i: IFAC-PapersOnLine. - 2405-8963. ; 56:2, s. 3757-3763
-
Konferensbidrag (refereegranskat)abstract
- Lithium-Ion battery system is one of the most critical but expensive components for both electric vehicles and stationary energy storage applications. In this regard, accurate and reliable early prediction of battery lifetime is important for optimizing life cycle management of batteries from cradle to grave. In particular, accurate aging diagnostics and prognostics is crucial for ensuring longevity, performance, safety, uptime, productivity, and profitability over a battery's lifetime. However, current state-of-art methods do not provide satisfactory prediction performance (lack of uncertainty quantification) using early degradation data. In the present work, to produce the best model for both battery cycle life point prediction and range prediction (i.e., confidence intervals or prediction intervals), a pipeline-based approach is proposed, in which a full 33-feature set is generated manually based on battery degradation knowledge, and then used to learn the best model among five machine learning (ML) models that have been reported in the battery lifetime prediction literature, and two quantile regression models for battery cycle life prediction. The calibration and sharpness property of battery cycle life range prediction is properly evaluated by their coverage probability and width respectively. The experimental results show that the gradient boosting regression tree model provides the best point prediction performance, while the quantile regression forest model provides the best range prediction performance with both full 33-feature set and the MIT 6-feature set.
|
|
