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Background: All-terrain vehicles are mostly used in poor driving environments. A key part of the suspension mechanism of all-terrain vehicles, the lower control arm, bears various loads when the vehicle is driving. This component is prone to be fatigue and failure, which affects the performance of the entire vehicle. Therefore, in order to improve the performance of all-terrain vehicles, the fatigue life of the lower control arm was studied based on the measured force load spectrum. Methods: Firstly, the finite element model of the lower control arm is established, the free modal simulation analysis is carried out, and the experimental research is carried out by building a modal test system. Then combining the calculated modal and experimental modal results, the finite element model is verified. Next, through the road load spectrum acquisition test in the automobile proving ground, the force time history of the lower control arm is obtained, and the signal is processed and analyzed to verify the reliability of the force load signal. On this basis, the boundary constraints of the lower control arm are established based on the actual working conditions of the all-terrain vehicle, and the dynamics simulation analysis is carried out with the measured force as input. Finally, according to stress-strain signal in dynamic analysis results, combining the modified local stress-strain method and the Landgrave damage criterion, the fatigue life of the lower control arm is calculated. Results: The minimum fatigue cycle life of the lower control arm on the test roads is 3.56×105 km, and its fatigue life meets the design and use requirements. Conclusions: The result shows that based on the actual driving load spectrum, the actual driving fatigue life can be calculated and forecasted more accurately.

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.