7 research outputs found
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Modern driver assistance systems as well as autonomous vehicles take their
decisions based on local maps of the environment. These maps include, for
example, surrounding moving objects perceived by sensors as well as routes and
navigation information. Current research in the field of environment mapping is
concerned with two major challenges. The first one is the integration of
information from different sources e.g. on-board sensors like radar, camera,
ultrasound and lidar, offline map data or backend information. The second
challenge comprises in finding an abstract representation of this aggregated
information with suitable interfaces for different driving functions and
traffic situations. To overcome these challenges, an extended environment model
is a reasonable choice. In this paper, we show that role-based motion
predictions in combination with v2x-extended environment models are able to
contribute to increased traffic safety and driving comfort. Thus, we combine
the mentioned research areas and show possible improvements, using the example
of a threading process at a motorway access road. Furthermore, it is shown that
already an average v2x equipment penetration of 80% can lead to a significant
improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance
compared to non v2x-equipped vehicles during the threading process.Comment: the article has been accepted for publication during the 16th IEEE
International Conference on Intelligent Computer Communication and Processing
(ICCP 2020), 8 pages, 8 figures, 1 tabl
Using Springs as Sentinels of Climate Change in Nature Parks North and South of the Alps: A Critical Evaluation of Methodological Aspects and Recommendations for Long-Term Monitoring
Spring ecosystems are diverse transition zones between ground- and surface-water habitats. Due to their characteristics and vulnerable species assemblages, springs are considered indicator systems for monitoring environmental change. In particular, climate change is expected to alter spring-ecosystem features, such as water temperature and discharge, affecting otherwise typically stable biotic and abiotic conditions. However, reliable trend-development recognition and analysis require a uniform methodology and comparable data series over long periods of time. Spring research findings in the Berchtesgaden National Park and the Adamello-Brenta Nature Park have been consolidated to develop methodological recommendations to create lasting societal-added value. The successful transfer of the methodology to the Bavarian Forest National Park and the experienced contribution of the Bavarian Association for the Protection of Nature (Bavarian Climate Alliance) strongly improved method validations. Our resulting, newly developed recommendations for long-term spring monitoring have a focus on climate change impacts and aim at providing a decision-making basis for establishing programs in similar ecological and climatic zones. Uniform site-selection criteria and selected climate-sensitive parameters are indicated. This includes documenting the spring's environment and structure, measuring abiotic parameters, and determining selected floristic and faunistic groups. We recommend measurement and sampling-survey intervals ranging from 3(4) times yearly to every 5 years, depending on the parameter. We further suggest a database system that integrates all monitoring parameters to ensure consistent data management and storage. Analysing the data resulting from our new holistic spring monitoring methodology should provide critical knowledge about putatively changing ecosystems that can then be used as evidence of climate-change impact on spring ecosystems
Metabolic network failures in Alzheimer's disease-A biochemical road map
IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results
Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.Analytical BioScience
\u3ci\u3eDrosophila\u3c/i\u3e Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution
The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu