CLASH-VLT: A Highly Precise Strong Lensing Model of the Galaxy Cluster RXC J2248.7-4431 (Abell S1063) and Prospects for Cosmography

We perform a comprehensive study of the total mass distribution of the galaxy cluster RXCJ2248 ($z=0.348$) with a set of high-precision strong lensing models, which take advantage of extensive spectroscopic information on many multiply lensed systems. In the effort to understand and quantify inherent systematics in parametric strong lensing modelling, we explore a collection of 22 models where we use different samples of multiple image families, parametrizations of the mass distribution and cosmological parameters. As input information for the strong lensing models, we use the CLASH HST imaging data and spectroscopic follow-up observations, carried out with the VIMOS and MUSE spectrographs, to identify bona-fide multiple images. A total of 16 background sources, over the redshift range $1.0-6.1$, are multiply lensed into 47 images, 24 of which are spectroscopically confirmed and belong to 10 individual sources. The cluster total mass distribution and underlying cosmology in the models are optimized by matching the observed positions of the multiple images on the lens plane. We show that with a careful selection of a sample of spectroscopically confirmed multiple images, the best-fit model reproduces their observed positions with a rms of $0.3$ in a fixed flat $\Lambda$CDM cosmology, whereas the lack of spectroscopic information lead to biases in the values of the model parameters. Allowing cosmological parameters to vary together with the cluster parameters, we find (at $68\%$ confidence level) $\Omega_m=0.25^{+0.13}_{-0.16}$ and $w=-1.07^{+0.16}_{-0.42}$ for a flat $\Lambda$CDM model, and $\Omega_m=0.31^{+0.12}_{-0.13}$ and $\Omega_\Lambda=0.38^{+0.38}_{-0.27}$ for a universe with $w=-1$ and free curvature. Using toy models mimicking the overall configuration of RXCJ2248, we estimate the impact of the line of sight mass structure on the positional rms to be $0.3\pm 0.1$.(ABRIDGED)Comment: 23 pages, 13 figures, accepted for publication in A&

Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristo

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Structure-activity relationship of graphene-related materials: A meta-analysis based on mammalian in vitro toxicity data

To support a safe application of graphene-related materials (GRMs) it is necessary to understand the potential negative impacts they could have on human health, in particular on the lung -one of the most sensitive exposure routes. Machine learning (ML) approaches can help analyse the results of multiple toxicity studies to understand the structure-activity relationship and the effect of experimental conditions, thus supporting predictive nano -toxicology. In this work we collected in vitro cytotoxicity data obtained from studies using lung cells; we then fitted multiple regression models to predict this endpoint based on the material properties and experimental conditions. Moreover, the data set was used to calculate the Benchmark Dose Lower Confidence Interval (BMDL), a dose descriptor widely used in risk assessment. Regression and classification models were applied for the prediction of the BMDL value and BMDL range. The analyses show that both cytotoxicity and the BMDL range can be predicted well (Q2 = 0.77 and accuracy = 0.71, respectively). Both physico-chemical characteristics such as the lateral size, number of layers, and functionalization, and experimental conditions such as the assay and media used were important predicting features, confirming the need for thorough characterization and reporting of these parameters