Specific and reciprocal interactions with the bone marrow microenvironment (BMM) govern the course of hematological malignancies. Matrix metalloproteinase-9 (MMP-9), secreted by leukemia cells, facilitates tumor progression via remodeling of the extracellular matrix (ECM) of the BMM. Hypothesizing that leukemias may instruct the BMM to degrade the ECM, we show, that MMP-9-deficiency in the BMM prolongs survival of mice with BCR-ABL1-induced B-cell acute lymphoblastic leukemia (B-ALL) compared with controls and reduces leukemia-initiating cells. MMP-9-deficiency in the BMM leads to reduced degradation of proteins of the ECM and reduced invasion of BALL. Using various in vivo and in vitro assays, as well as recipient mice deficient for the receptor for tumor necrosis factor (TNF) α (TNFR1) we demonstrate that BALL cells induce MMP-9-expression in mesenchymal stem cells (MSC) and possibly other cells of the BMM via a release of TNFα. MMP-9-expression in MSC is mediated by activation of nuclear factor kappa B (NF-κB) downstream of TNFR1. Consistently, knockdown of TNF-α in BALL initiating cells or pharmacological inhibition of MMP-9 led to significant prolongation of survival in mice with BALL. In summary, leukemia cell-derived Tnfα induced MMP-9-expression by the BMM promoting BALL progression. Inhibition of MMP-9 may act as an adjunct to existing therapies.

Deviations between system current measurements and reality can cause severe problems in the power train of electric vehicles (EVs) like inaccurate performance coordination and unnecessary power limitations. In this work, we propose a fleet-based framework to detect such deviations. Our main assumption is that the real value is the mean of all identically constructed EVs' measurements for the same input. Under this assumption, we train individual on-board models to predict the current of the electric machine (EM) and transmit the model parameters to a back-end. There, we compare individual deviations of the predicted current against the fleet in the same scenario. We use the results to classify three fault sources. As models we choose two different Machine Learning algorithms: State Models and Long Short-Term Memory Neural Networks (LSTMs). These are evaluated on an artificial fleet of 34 EVs derived from real drive data containing three different kinds of faults. Results show that our proposed approach correctly classifies major measurement faults. LSTMs are more accurate, whereas state models are less computationally complex, and thus better suited for electronic control units (ECUs).

Deviations between High Voltage (HV) current measurements and the corresponding real values provoke serious problems in the power trains of Electric Vehicle (EVs). Examples for these problems have inaccurate performance coordinations and unnecessary power limitations during driving or charging. The main reason for the deviations are time delays. By correcting these delays with accurate Time Delay Estimation (TDE), our data shows that we can reduce the measurement deviations from 25% of the maximum current to below 5%. In this paper, we present three different approaches for TDE. We evaluate all approaches with real data from power trains of EVs. To enable an execution on automotive Electronic Control Unit (ECUs), the focus of our evaluation lies not only on the accuracy of the TDE, but also on the computational efficiency. The proposed Linear Regression (LR) approach suffers even from small noise and offsets in the measurement data and is unsuited for our purpose. A better alternative is the Variance Minimization (VM) approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filter (AFs), introduced by Emadzadeh et al. Unfortunately, AFs do not reach the accuracy and efficiency of VM in our experiments. Thus, we recommend VM for TDE of HV current signals in the power train of EVs and present an additional optimization to enable its execution on ECUs.