| 1. |
- Freud, Lindsay R., et al.
(författare)
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Prenatal vs postnatal diagnosis of 22q11.2 deletion syndrome: cardiac and noncardiac outcomes through 1 year of age
- 2024
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Ingår i: American Journal of Obstetrics and Gynecology. - 0002-9378 .- 1097-6868. ; 230:3
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Tidskriftsartikel (refereegranskat)abstract
- Background: The 22q11.2 deletion syndrome is the most common microdeletion syndrome and is frequently associated with congenital heart disease. Prenatal diagnosis of 22q11.2 deletion syndrome is increasingly offered. It is unknown whether there is a clinical benefit to prenatal detection as compared with postnatal diagnosis. Objective: This study aimed to determine differences in perinatal and infant outcomes between patients with prenatal and postnatal diagnosis of 22q11.2 deletion syndrome. Study Design: This was a retrospective cohort study across multiple international centers (30 sites, 4 continents) from 2006 to 2019. Participants were fetuses, neonates, or infants with a genetic diagnosis of 22q11.2 deletion syndrome by 1 year of age with or without congenital heart disease; those with prenatal diagnosis or suspicion (suggestive ultrasound findings and/or high-risk cell-free fetal DNA screen for 22q11.2 deletion syndrome with postnatal confirmation) were compared with those with postnatal diagnosis. Perinatal management, cardiac and noncardiac morbidity, and mortality by 1 year were assessed. Outcomes were adjusted for presence of critical congenital heart disease, gestational age at birth, and site. Results: A total of 625 fetuses, neonates, or infants with 22q11.2 deletion syndrome (53.4% male) were included: 259 fetuses were prenatally diagnosed (156 [60.2%] were live-born) and 122 neonates were prenatally suspected with postnatal confirmation, whereas 244 infants were postnatally diagnosed. In the live-born cohort (n=522), 1-year mortality was 5.9%, which did not differ between groups but differed by the presence of critical congenital heart disease (hazard ratio, 4.18; 95% confidence interval, 1.56–11.18; P<.001) and gestational age at birth (hazard ratio, 0.78 per week; 95% confidence interval, 0.69–0.89; P<.001). Adjusting for critical congenital heart disease and gestational age at birth, the prenatal cohort was less likely to deliver at a local community hospital (5.1% vs 38.2%; odds ratio, 0.11; 95% confidence interval, 0.06–0.23; P<.001), experience neonatal cardiac decompensation (1.3% vs 5.0%; odds ratio, 0.11; 95% confidence interval, 0.03–0.49; P=.004), or have failure to thrive by 1 year (43.4% vs 50.3%; odds ratio, 0.58; 95% confidence interval, 0.36–0.91; P=.019). Conclusion: Prenatal detection of 22q11.2 deletion syndrome was associated with improved delivery management and less cardiac and noncardiac morbidity, but not mortality, compared with postnatal detection.
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| 2. |
- Fronzek, Stefan, et al.
(författare)
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Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
- 2018
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Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 159, s. 209-224
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Tidskriftsartikel (refereegranskat)abstract
- Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
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| 3. |
- Risseh, Arash, 1980-, et al.
(författare)
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Design of a Thermoelectric Generator for Waste Heat Recovery Application on a Drivable Heavy Duty Vehicle
- 2017
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Ingår i: SAE International Journal of Commercial Vehicles. - : SAE International. - 1946-391X .- 1946-3928. ; 10:1, s. 26-44
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Tidskriftsartikel (refereegranskat)abstract
- The European Union’s 2020 target aims to be producing 20 % of its energy from renewable sources by 2020, to achieve a 20 % reduction in greenhouse gas emissions and a 20 % improvement in energy efficiency compared to 1990 levels. To reach these goals, the energy consumption has to decrease which results in reduction of the emissions. The transport sector is the second largest energy consumer in the EU, responsible for 25 % of the emissions of greenhouse gases caused by the low efficiency (<40 %) of combustion engines. Much work has been done to improve that efficiency but there is still a large amount of fuel energy that converts to heat and escapes to the ambient atmosphere through the exhaust system. Taking advantage of thermoelectricity, the heat can be recovered, improving the fuel economy. A thermoelectric generator (TEG) consists of a number of thermoelectric elements, which advantageously can be built into modules, arranged thermally and electrically, in a way such that the highest possible thermal power can be converted into electrical power. In a unique waste heat recovery (WHR) project, five international companies and research institutes cooperated and equipped a fully drivable Scania prototype truck with two TEGs. The entire system, from the heat transfer in the exchangers to the electrical power system, was simulated, built and evaluated. The primary experimental results showed that approximately 1 kW electrical power could be generated from the heat energy. In this paper the entire system from design to experimental results is presented.
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