Enabling a Privacy-Preserving Synthesis of Representative Driving Cycles from Fleet Data using Data Aggregation

Driving cycles are of fundamental relevance in the design of vehicle components, in the optimization of control strategies for different drivetrain topologies and the identification of vehicle properties. Ideally, a high quantity of real fleet driving data including varying operation conditions is used to generate representative driving cycles that are the basis for further investigations. Traditionally, a specific testing fleet is employed to gather the driving data. Nevertheless, driving data can nowadays also be gathered from regular production cars, as they are already equipped with the required sensors. This approach would be more real-driving representative and cost efficient, but on the other side imposes new challenges. In particular, gathered driving data has to be handled efficiently and the privacy of individuals must be guaranteed. In this work, an approach to synthesize representative driving cycles using data aggregation is presented. It is shown that the approach is efficient and generates driving cycles with excellent quality when compared to classical approaches, thus acting as an enabler for privacy-preserving techniques.</p

Low risk HLA-DQ and increased body mass index in newly diagnosed type 1 diabetes children in the Better Diabetes Diagnosis study in Sweden.

Objective:Type 1 diabetes and obesity has increased in childhood. We therefore tested the hypothesis that type 1 diabetes human leukocyte antigen DQ (HLA-DQ) risk genotypes may be associated with increased body mass index (BMI).Design:The type 1 diabetes high-risk HLA-DQ A1(*)05:01-B1(*)02:01/A1(*)03:01-B1(*)03:02 genotype along with lower risk DQ genotypes were determined at the time of clinical onset by PCR and hybridization with allele-specific probes. BMI was determined after diabetes was stabilized.Subjects:A total of 2403 incident type 1 diabetes children below 18 years of age were ascertained in the Swedish national Better Diabetes Diagnosis (BDD) study between May 2005 to September 2009. All children classified with type 1 diabetes, including positivity for at least one islet autoantibody, were investigated.Results:Overall, type 1 diabetes HLA-DQ risk was negatively associated with BMI (P<0.0008). The proportion of the highest risk A1(*)05:01-B1(*)02:01/A1(*)03:01-B1(*)03:02 genotype decreased with increasing BMI (P<0.0004). However, lower risk type 1 diabetes DQ genotypes were associated with an increased proportion of patients who were overweight or obese (P<0.0001). Indeed, the proportion of patients with the low-risk A1(*)05:01-B1(*)02:01/A1(*)05:01-B1(*)02:01 genotype increased with increasing BMI (P<0.003). The magnitude of association on the multiplicative scale between the A1(*)05:01-B1(*)02:01/A1(*)05:01-B1(*)02:01 genotype and increased BMI was significant (P<0.006). The odds ratio in patients with this genotype of being obese was 1.80 (95% confidence interval 1.21-2.61; P<0.006). The increased proportion of overweight type 1 diabetes children with the A1(*)05:01-B1(*)02:01 haplotype was most pronounced in children diagnosed between 5 and 9 years of age.Conclusions:Susceptibility for childhood type 1 diabetes was unexpectedly found to be associated with the A1(*)05:01-B1(*)02:01/A1(*)05:01-B1(*)02:01 genotype and an increased BMI. These results support the hypothesis that overweight may contribute to the risk of type 1 diabetes in children positive for HLA-DQ A1(*)05:01-B1(*)02:01.International Journal of Obesity advance online publication, 28 June 2011; doi:10.1038/ijo.2011.122