Robust stop-and-go control strategy: an algebraic approach for nonlinear estimation and control

International audienceThis paper describes a robust stop-and-go control strategy for vehicles. Since sensors used in a real automotive context are generally low cost, measurements are quite noisy. Furthermore, many vehicle/road interaction factors (road slope, rolling resistance, aerodynamic forces) are very poorly known. Hence, a robust strategy to noise and parameters is proposed within the same theoretical framework: algebraic nonlinear estimation and control techniques. On the one hand, noisy signals will be processed in order to obtain accurate derivatives, and thereafter, variable estimates. On the other hand, a grey-box closedloop control will be implemented to reject all kind of disturbances caused by exogenous parameter uncertainties

Model-free control of automotive engine and brake for Stop-and-Go scenarios

International audienceIn this paper we propose a complete strategy for the longitudinal control of automotive vehicles in Stop-and- Go situations. Firstly, a upper level grey-box torque control is proposed to compensate for neglected dynamics at chassis level (due for example to road slopes, aerodynamic forces, rolling resistance forces, etc.). Secondly, to obtain the desired torque, we have considered a model-free approach to elaborate the suitable low level engine or braking torque. Convincing simulation results are presented to validate our metho

spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data

Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types

Diagnostic markers of serious bacterial infections in infants aged 29 to 90 days

Objectives: The diagnosis of serious bacterial infection (SBI) is difficult due to a lack of clinical evidence. The purpose of this study was to determine which inflammatory markers can be used to detect SBI in febrile infants. Methods: This retrospective cohort study included infants aged 29 to 90 days who visited a tertiary hospital emergency department in Korea between July 2016 and June 2018. The diagnostic characteristics of the neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT), C-reactive protein (CRP), white blood cell (WBC) count, and absolute neutrophil cell (ANC) count for detecting SBI were described. Their cutoff values were calculated based on receiver operating characteristic (ROC) curve analysis. Results: Among 528 infants, 199 were finally enrolled. SBI was detected in 68 (34.2%) of these infants. The median values of all investigated diagnostic markers were significantly higher in infants with SBI than the values in those without: WBC (12.72 vs. 9.91 k/μL), ANC (6.28 vs. 3.14 k/μL), CRP (26.6 vs. 2.8 mg/L), NLR (1.29 vs. 0.78), and PCT (0.5 vs. 0 ng/mL). The areas under the ROC curves for discriminating SBI were: 0.705 (95% confidence interval [CI], 0.629-0.781), 0.793 (95% CI, 0.731-0.856), 0.832 (95% CI, 0.775-0.889), 0.722 (95% CI, 0.651-0.792), and 0.695 (95% CI, 0.611-0.780) for WBC, ANC, CRP, NLR, and PCT, respectively. Using a cutoff value of 0.67 for NLR, the negative predictive value was 90.8% for identifying SBI. Conclusions: CRP was the best single discriminatory marker of SBI, while NLR was the best parameter for considering discharge

Update on B→D∗ℓνB\to D^\ast \ell \nu form factor at zero-recoil using the Oktay-Kronfeld action

We present an update on the calculation of $\bar{B}\to D^\ast \ell \bar{\nu}$ semileptonic form factor at zero recoil using the Oktay-Kronfeld bottom and charm quarks on $N_f=2+1+1$ flavor HISQ ensembles generated by the MILC collaboration. Preliminary results are given for two ensembles with $a\approx 0.12$ and $0.09$ fm and $M_\pi\approx 310$ MeV. Calculations have been done with a number of valence quark masses, and the dependence of the form factor on them is investigated on the $a\approx 0.12$ fm ensemble. The excited state is controlled by using multistate fits to the three-point correlators measured at 4--6 source-sink separations.Comment: 7 pages and 4 figures. Talk at The 36th Annual International Symposium on Lattice Field Theory - LATTICE201

Assessing and enhancing migration of human myogenic progenitors using directed iPS cell differentiation and advanced tissue modelling

Muscle satellite stem cells (MuSCs) are responsible for skeletal muscle growth and regeneration. Despite their differentiation potential, human MuSCs have limited in vitro expansion and in vivo migration capacity, limiting their use in cell therapies for diseases affecting multiple skeletal muscles. Several protocols have been developed to derive MuSC-like progenitors from human induced pluripotent stem (iPS) cells (hiPSCs) to establish a source of myogenic cells with controllable proliferation and differentiation. However, current hiPSC myogenic derivatives also suffer from limitations of cell migration, ultimately delaying their clinical translation. Here we use a multi-disciplinary approach including bioinformatics and tissue engineering to show that DLL4 and PDGF-BB improve migration of hiPSC-derived myogenic progenitors. Transcriptomic analyses demonstrate that this property is conserved across species and multiple hiPSC lines, consistent with results from single cell motility profiling. Treated cells showed enhanced trans-endothelial migration in transwell assays. Finally, increased motility was detected in a novel humanised assay to study cell migration using 3D artificial muscles, harnessing advanced tissue modelling to move hiPSCs closer to future muscle gene and cell therapies