Impact of traffic management on black carbon emissions: a microsimulation study

This paper investigates the effectiveness of traffic management tools, includ- ing traffic signal control and en-route navigation provided by variable message signs (VMS), in reducing traffic congestion and associated emissions of CO2, NOx, and black carbon. The latter is among the most significant contributors of climate change, and is associated with many serious health problems. This study combines traffic microsimulation (S-Paramics) with emission modeling (AIRE) to simulate and predict the impacts of different traffic management measures on a number traffic and environmental Key Performance Indicators (KPIs) assessed at different spatial levels. Simulation results for a real road network located in West Glasgow suggest that these traffic management tools can bring a reduction in travel delay and BC emission respectively by up to 6 % and 3 % network wide. The improvement at local levels such as junctions or corridors can be more significant. However, our results also show that the potential benefits of such interventions are strongly dependent on a number of factors, including dynamic demand profile, VMS compliance rate, and fleet composition. Extensive discussion based on the simulation results as well as managerial insights are provided to support traffic network operation and control with environmental goals. The study described by this paper was conducted under the support of the FP7-funded CARBOTRAF project

No significant improvement of cardiovascular disease risk indicators by a lifestyle intervention in people with familial hypercholesterolemia compared to usual care: results of a randomised controlled trial

Background: People with Familial Hypercholesterolemia (FH) may benefit from lifestyle changes supporting their primary treatment of dyslipidaemia. This project evaluated the efficacy of an individualised tailored lifestyle intervention on lipids (low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), total cholesterol (TC) and triglycerides), systolic blood pressure, glucose, body mass index (BMI) and waist circumference in people with FH. Methods: Adults with FH (n= 340), recruited from a Dutch cascade screening program, were randomly assigned to either a control group or an intervention group. The personalised intervention consisted of web-based tailored lifestyle advice and personal counselling. The control group received care as usual. Lipids, systolic blood pressure, glucose, BMI, and waist circumference were measured at baseline and after 12 months. Regression analyses were conducted to examine differences between both groups. Results: After 12 months, no significant between-group differences of cardiovascular disease (CVD) risk indicators were observed. LDL-C levels had decreased in both the intervention and control group. This difference between intervention and control group was not statistically significant. Conclusions: This project suggests that an individually tailored lifestyle intervention did not have an additional effect in improving CVD risk indicators among people with FH. The cumulative effect of many small improvements in all indicators on long term CVD risk remains to be assessed in future studies. Trial registration: NTR1899 at ww.trialregister.nl.© 2012 Broekhuizen et al

Reducing environmental impact by adaptive traffic control and management for urban road networks

This paper investigates the effectiveness of traffic signal control and variable message sign (VMS) as environmental traffic management tool. The focus is on black carbon and CO2, which are among the highest contributors to climate change. The modelling tool chain adopted to support this study includes traffic microsimulation, emission modelling and dispersion modelling. A number of scenarios have been simulated with different levels of demand and VMS compliance rates. The results demonstrate the potential of these interventions in reducing black carbon and CO2 emissions and improving air quality, as well as reducing traffic congestion and travel delays

Correlates of absolute and excessive weight gain during pregnancy

OBJECTIVE: Factors associated with weight gain during pregnancy that may be linked to maternal overweight and obesity were examined. METHODS: In this observational study, 144 women reported on demographics, (prepregnancy) body weight, and lifestyles in self-reported questionnaires at 30 weeks gestation. Body weight at the end of pregnancy (self-reported at 6 weeks postpartum) was used to determine total gestational weight gain. Multivariate prediction models were developed to identify factors associated with total gestational weight gain and excessive gestational weight gain (i.e., higher weight gain than recommended by the Institute of Medicine). RESULTS: Women gained 14.4 (+/-5.0) kg during pregnancy. Obese women gained almost 4 kg less than normal weight women. Pregnant women judging themselves to be less physically active or women who reported increased food intakes during pregnancy gained significantly more weight. Over one third of women (38%) gained more weight than recommended. Being overweight, judging yourself to be less physically active than others, and a perceived elevated food intake during pregnancy were significantly associated with excessive weight gain (odds ratio [OR] = 6.33, 95% confidence interval [CI]: 2.01-19.32; OR = 3.96, 95% CI: 1.55l, 10.15; and OR = 3.14, 95% CI: 1.18, 8.36, respectively). A higher age at menarche and hours of sleep reduced the odds for excessive weight gain (OR = 0.75, 95% CI: 0.57, 0.99; and OR = 0.35, 95% CI: 0.57, 0.93, respectively). CONCLUSIONS: Mean hours of sleep, perceived physical activity, and measures of food intake at 30 weeks gestation were identified as modifiable behavioral correlates for excessive gestational weight gain. Strategies to optimize gestational weight gain need to be explored, with a focus on the identified factors

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:01

Changing current practice in urology: Improving guideline development and implementation through stakeholder engagement

Effective stakeholder integration for guideline development should improve outcomes and adherence to clinical practice guidelines