Estimation of Load-Time Curves Using Recurrent Neural Networks Based On Can Bus Signals

Precise knowledge of the load history of safety-relevant structures is a central aspect within the fatigue strength design of modern vehicles. Since the experimental measurement of load variables is complex and therefore associated with high costs, vehicles require estimation of these variables in order to design even more customer-orientedly in the future and thus consistently pursue sustainable lightweight construction. Hence the data measured by sensors in today's standard production vehicles is based on vehicle bus system signals which can be permanently retrieved. Due to the increasing availability of large quantities of recorded vehicle data, machine learning methods are moving into the focus of application. In this work, the implementation of Recurrent Neural Networks for the estimation of loadtime curves is investigated. In order to close existing gaps in the state of the art, successful concepts of machine learning for sequential data, such as speech processing, are to be transferred to this application case. Long Short-Term Memory cells [1] play a central role for this type of problem. In addition to the adaptation of the network architecture, the integration of engineering knowledge is pursued within the method development process in order to increase the quality of the model. Relevant input variables are specifically selected by feature engineering and new meaningful variables are generated by filtering. Statistical analysis is used to investigate the correlation of these input signals with the estimated quantities. The development of a robust load estimation takes place in the course of model development on the basis of the torque of the left-hand rear drive shaft. Results reveal that the Recurrent Neural Networks approach is justified in estimating the highly nonlinear load curve of a complexly loaded part ­ as a component of the dynamic system ­ by means of available sensor signals [2]. Subsequently, the model is validated using recorded measurement data for different chassis settings of the same vehicle. Finally, the transferability of the designed network configuration to other chassis components of the same vehicle is investigated and evaluated

Long-term care need, loneliness, and perceived social isolation during the COVID-19 pandemic: evidence from the German Ageing Survey

There is a complete lack of studies focusing on the association between care degree (reflecting the long-term care need) and loneliness or social isolation in Germany. Aims: To investigate the association between care degree and loneliness as well as perceived social isolation during the COVID-19 pandemic. Methods: We used data from the nationally representative German Ageing Survey, which covers community-dwelling middle-aged and older individuals aged 40 years or over. We used wave 8 of the German Ageing Survey (analytical sample: n = 4334 individuals, mean age was 68.9 years, SD: 10.2 years; range 46–100 years). To assess loneliness, the De Jong Gierveld instrument was used. To assess perceived social isolation, the Bude and Lantermann instrument was used. Moreover, the level of care was used as a key independent variable (absence of care degree (0); care degree 1–5). Results: After adjusting for various covariates, regressions showed that there were no significant differences between individuals without a care degree and individuals with a care degree of 1 or 2 in terms of loneliness and perceived social isolation. In contrast, individuals with a care degree of 3 or 4 had higher loneliness (β = 0.23, p = 0.034) and higher perceived social isolation scores (β = 0.38, p < 0.01) compared to individuals without a care degree. Discussion/conclusions: Care degrees of 3 or 4 are associated with higher levels of both loneliness and perceived social isolation. Longitudinal studies are required to confirm this association

Finite Element Analysis for Pre-Clinical Testing of Custom-Made Knee Implants for Complex Reconstruction Surgery

In severe cases of total knee arthroplasty, where off-the-shelf implants are not suitable or available anymore (i.e., in cases with extended bone defects or periprosthetic fractures), custom-made knee implants represent one of the few remaining treatment options. Design verification and validation of such custom-made implants is very challenging. The aim of this study is to support surgeons and engineers in their decision on whether a developed design is suitable for the specific case. A novel method for the pre-clinical testing of custom-made knee implants is suggested, which relies on the biomechanical test and finite element analysis (FEA) of a comparable reference implant. The method comprises six steps: (1) identification of the main potential failure mechanism and its corresponding FEA quantity of interest, (2) reproduction of the biomechanical test of the reference implant via FEA, (3) identification of the maximum value of the corresponding FEA quantity of interest at the required load level, (4) definition of this value as the acceptance criterion for the FEA of the custom-made implant, (5) reproduction of the biomechanical test with the custom-made implant via FEA, (6) conclusion, whether the acceptance criterion is fulfilled or not. Two exemplary cases of custom-made knee implants were evaluated with this method. The FEA acceptance criterion derived from the reference implants was fulfilled in both custom-made implants. Subsequent biomechanical tests verified the FEA results. The suggested method allows a quantitative evaluation of the biomechanical properties of a custom-made knee implant without performing a biomechanical test with it. This represents an important contribution in the pre-clinical testing of custom-made implants in order to achieve a sustainable treatment of complex revision total knee arthroplasty patients in a timely manner

Finite Element Analysis for Pre-Clinical Testing of Custom-Made Knee Implants for Complex Reconstruction Surgery

In severe cases of total knee arthroplasty, where off-the-shelf implants are not suitable or available anymore (i.e., in cases with extended bone defects or periprosthetic fractures), custom-made knee implants represent one of the few remaining treatment options. Design verification and validation of such custom-made implants is very challenging. The aim of this study is to support surgeons and engineers in their decision on whether a developed design is suitable for the specific case. A novel method for the pre-clinical testing of custom-made knee implants is suggested, which relies on the biomechanical test and finite element analysis (FEA) of a comparable reference implant. The method comprises six steps: (1) identification of the main potential failure mechanism and its corresponding FEA quantity of interest, (2) reproduction of the biomechanical test of the reference implant via FEA, (3) identification of the maximum value of the corresponding FEA quantity of interest at the required load level, (4) definition of this value as the acceptance criterion for the FEA of the custom-made implant, (5) reproduction of the biomechanical test with the custom-made implant via FEA, (6) conclusion, whether the acceptance criterion is fulfilled or not. Two exemplary cases of custom-made knee implants were evaluated with this method. The FEA acceptance criterion derived from the reference implants was fulfilled in both custom-made implants. Subsequent biomechanical tests verified the FEA results. The suggested method allows a quantitative evaluation of the biomechanical properties of a custom-made knee implant without performing a biomechanical test with it. This represents an important contribution in the pre-clinical testing of custom-made implants in order to achieve a sustainable treatment of complex revision total knee arthroplasty patients in a timely manner

BAZ2A (TIP5) is involved in epigenetic alterations in prostate cancer and its overexpression predicts disease recurrence

Prostate cancer is driven by a combination of genetic and/or epigenetic alterations. Epigenetic alterations are frequently observed in all human cancers, yet how aberrant epigenetic signatures are established is poorly understood. Here we show that the gene encoding BAZ2A (TIP5), a factor previously implicated in epigenetic rRNA gene silencing, is overexpressed in prostate cancer and is paradoxically involved in maintaining prostate cancer cell growth, a feature specific to cancer cells. BAZ2A regulates numerous protein-coding genes and directly interacts with EZH2 to maintain epigenetic silencing at genes repressed in metastasis. BAZ2A overexpression is tightly associated with a molecular subtype displaying a CpG island methylator phenotype (CIMP). Finally, high BAZ2A levels serve as an independent predictor of biochemical recurrence in a cohort of 7,682 individuals with prostate cancer. This work identifies a new aberrant role for the epigenetic regulator BAZ2A, which can also serve as a useful marker for metastatic potential in prostate cancer

Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer

SummaryEarly-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with “classical” (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic “androgen-type” pathomechanism in EO-PCA