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Achieving the long-term climate goals requires vehicle propulsion systems in all sectors with a CO2 and emissions footprint close to zero. Hydrogen as energy carrier and the corresponding fuel cell propulsion are a promising cornerstone for future ground transportation as well as for the aviation industry. Fuel cell propulsion systems introduce hybrid topologies with very complex operating strategies and many components interacting with each other. The development of safe and robust propulsion systems requires the application of systems engineering methods utilizing state-of-the-art multi-integration test environments. These enable the seamless transition of development activities from virtual design simulation to the physical testing of actual components as well as the scaling from one system design to a multitude of system variants. This paper describes state-of-the-art development and validation methods from automotive industry and how they can be applied for the development of fuel cell flight propulsion systems.

BACKGROUND: Arterial hypertension is animportant risk factor for cardiovascular diseases. Epidemiological studies typically perform three consecutive blood pressure measurements. The first measurement is discarded in subsequent analyses, as this value may be strongly affected by previous activities. Due to time constraints the German National Cohort (GNC NAKO) performed only two blood pressure measurements. OBJECTIVES: The present analysis examined the possible effects of methodological differences in blood pressure measurement by comparing the first 101,816 GNC participants (two blood pressure measurements) with those of five German population-based studies (three measurements). MATERIALS AND METHODS: Blood pressure data from participants aged 20 to 79 years from the GNC, the German Health Interview and Examination Survey for Adults by the Robert Koch Institute (DEGS1), and four regional population-based cohort studies (CARLA, HNR, KORA, SHIP) were used to calculate age- and sex-specific mean blood pressure values and hypertension frequencies based on the second blood pressure measurement, the arithmetic mean of the first and second value and of the second and third (the latter not available in the GNC). RESULTS: The mean blood pressure values of the two most recent studies (GNC, DEGS1) were very similar and lower than in the other studies. The difference of the second measurement and the mean of second and third measurement was small (maximum mean difference: 1.5mm Hg systolic blood pressure), but leads to higher estimated hypertension frequencies. CONCLUSIONS: The current results show that using the second blood pressure measurement should be recommended for scientific analyses of GNC data.

BACKGROUND: Noise annoyance is associated with adverse health-related conditions and reduced wellbeing. Thereby, subjective noise annoyance depends on the objective noise exposure and is modified by personal and regional factors. OBJECTIVE: How many participants of the German National Cohort Study (GNC; NAKO Gesundheitsstudie) were annoyed by transportation noise during nighttime and what factors were associated with noise annoyance? MATERIALS AND METHODS: This cross-sectional analysis included 86,080 participants from 18 study centers, examined from 2014 to 2017. We used multinomial logistic regression to investigate associations of personal and regional factors to noise annoyance (slightly/moderately or strongly/extremely annoyed vs. not annoyed) mutually adjusting for all factors in the model. RESULTS: Two thirds of participants were not annoyed by transportation noise during nighttime and one in ten reported strong/extreme annoyance with highest percentages for the study centers Berlin-Mitte and Leipzig. The strongest associations were seen for factors related to the individual housing situation like the bedroom being positioned towards a major road (OR of being slightly/moderately annoyed: 4.26 [95% CI: 4.01;4.52]; OR of being strongly/extremely annoyed: 13.36 [95% CI: 12.47;14.32]) compared to a garden/inner courtyard. Participants aged 40-60 years and those in low- and medium-income groups reported greater noise annoyance compared to younger or older ones and those in the high-income group. CONCLUSION: In this study from Germany, transportation noise annoyance during nighttime varied by personal and regional factors.

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.