A proportional hazard model for the estimation of ionosphere storm occurrence risk

Severe Ionosphere magnetic storms are feared events for integrity and continuity of navigation systems such as EGNOS, the European SBAS (Satellite-Based Augmentation System) complementing GPS and an accurate modelling of this event probability is necessary. Our aim for the work presented in this paper is to give an estimation of the frequency of such extreme magnetic storms per time unit (year) throughout a solar cycle. Thus, we develop an innovative approach based on a proportional hazard model, inspired by the Cox model, with time dependent covariates. The number of storms during a cycle is supposed to be a non-homogeneous Poisson process. The intensity of this process could be expressed as the product of a baseline risk and a risk factor. Contrary to what is done in the Cox model, the baseline risk is one parameter of interest (and not a nuisance one), it is the intensity to estimate. As in Extreme Value Theory, all the high level events will be used to make estimation and the results will be extrapolated to the extreme level ones. After a precise description of the model, we present the estimation results and a model extension. A prediction for the current solar cycle (24th) is also proposed

Discretization error for the maximum of a Gaussian field

International audienceA Gaussian field $X$ defined on a square $S$ of \mathds R^2 is considered. We assume that this field is only observed at some points of a regular grid with spacing $\frac{1}{n}$. We are interested in the normalized discretization error $n^2(M - M_n)$, with $M$ the global maximum of $X$ over $S$ and $M_n$ the maximum of $X$ over the observation grid. The density of the location of the maximum is given using Rice formulas and its regularity is studied. Joint densities with the value of the field and the value of the second derivative are also given.Then, a kind of Slepian model is used to study the field behavior around the unique point where the maximum is attained, called $t^*$. We show that the normalized discretization error can be bounded by a quantity that converges in distribution to a uniform variable. The set where this uniform variable lies principally depends on the second derivative of the field at $t^*$. The bound is a function of this quantity which is approached by finite differences in practice. The bound is applied both on simulated and real data. Real data are used in positioning by satellite systems quality assessment

What Matters in Piglets’ Exposure to Antibiotics Administered through Drinking Water?

A number of drugs are given in drinking water in piglet farming, although this way of administering drugs leads to significant and uncontrolled variability in exposures. Three main explanations for this variability have been described in the literature: (1) the drinking behavior of animals, (2) the drug concentration in water, and (3) the inter-individual variability in the pharmacokinetic (PK) parameters. This article assesses the relative importance of these three sources of exposure variability for doxycycline and amoxicillin using pharmacokinetic simulations and by observing watering behavior, and analyzes the consequences of this exposure variability. The water consumption behavior was by far the most important factor as it led to a variation in exposures of up to a factor of 7 between piglets. The second most influential factor was the drug concentration in the drinking water with variations ranging from −43.3% to +48.7% at the beginning and the end of the pipeline. Finally, the between-individual variation in PK parameters depends on the drug, but had a low impact on exposure variability. In the most variable case (doxycycline), the mean ratio between the 10% less exposed and the 10% most exposed piglets varied from 3.7 without PK parameters variability to 6 with PK variability. For both drugs, this study also showed that only a small percentage of the piglets (36%) could be considered as well exposed in case of infection by Actinobacillus pleuropneumoniae or Pasteurella multocida. There may be some existing technical ways to reduce this important variability. However, their cost and ease of implementation merit examination

Contribution of Reliable Chromatographic Data in QSAR for Modelling Bisphenol Transport across the Human Placenta Barrier

Regulatory measures and public concerns regarding bisphenol A (BPA) have led to its replacement by structural analogues, such as BPAF, BPAP, BPB, BPF, BPP, BPS, and BPZ. However, these alternatives are under surveillance for potential endocrine disruption, particularly during the critical period of fetal development. Despite their structural analogies, these BPs differ greatly in their placental transport efficiency. For predicting the fetal exposure of this important class of emerging contaminants, quantitative structure-activity relationship (QSAR) studies were developed to model and predict the placental clearance indices (CI). The most usual input parameters were molecular descriptors obtained by modelling, but for bisphenols (BPs) with structural similarities or heteroatoms such as sulfur, these descriptors do not contrast greatly. This study evaluated and compared the capacity of QSAR models based either on molecular or chromatographic descriptors or a combination of both to predict the placental passage of BPs. These chromatographic descriptors include both the retention mechanism and the peak shape on columns that reflect specific molecular interactions between solute and stationary and mobile phases and are characteristic of the molecular structure of BPs. The chromatographic peak shape such as the asymmetry and tailing factors had more influence on predicting the placental passage than the usual retention parameters. Furthermore, the QSAR model, having the best prediction capacity, was obtained with the chromatographic descriptors alone and met the criteria of internal and cross validation. These QSAR models are crucial for predicting the fetal exposure of this important class of emerging contaminants

Lymphocyte migration and retention properties affected by ibrutibnib in chronic lymphocytic leukemia

International audienceThe Bruton’s tyrosine kinase (BTK) inhibitor ibrutinib is widely used for treatment of patients with relapsed/refractory or treatment-naïve Chronic Lymphocytic Leukemia (CLL). A prominent effect of ibrutinib is to disrupt the retention of CLL cells from supportive lymphoid tissues, by altering BTK-dependent adhesion and migration. To further explore the mechanism of action of ibrutinib and its potential impact on non-leukemic cells, we quantified multiple motility and adhesion parameters of human primary CLL cells and non-leukemic lymphoid cells. In vitro, ibrutinib affected CCL19-, CXCL12- and CXCL13-evoked migration behavior of CLL cells and non-neoplastic lymphocytes, by reducing both motility speed and directionality. Dephosphorylation of BTK induced by ibrutinib in CLL cells was associated with defective polarization over fibronectin and inability to assemble the immunological synapse upon BCR engagement. In patient samples collected during a 6-month monitoring of therapy, chemokineevoked migration was repressed in CLL cells and marginally reduced in T cells. This was accompanied by profound modulation of the expression of chemokine receptors and adhesion molecules. Remarkably, the relative expression of the receptors governing lymph node entry (CCR7) versus exit (S1PR1) stood out as a reliable predictive marker of the clinically relevant treatment-induced lymphocytosis. Together, our data reveal a multifaceted modulation of motility and adhesive properties of ibrutinib on both CLL leukemic cell and T-cell populations and point to intrinsic differences in CLL recirculation properties as underlying cause for variability in treatment response