Ikerbasque

About: Ikerbasque is a other organization based out in Bilbao, Spain. It is known for research contribution in the topics: Graphene & Quantum. The organization has 713 authors who have published 7967 publications receiving 231990 citations. The organization is also known as: Basque Foundation for Science.

The MUSIC of CLASH: Predictions on the Concentration-Mass Relation

Abstract: We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies and masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ~11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.

A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

Abstract: Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.

Ultrasensitive and label-free molecular-level detection enabled by light phase control in magnetoplasmonic nanoantennas

Abstract: Systems allowing label-free molecular detection are expected to have enormous impact on biochemical sciences. Research focuses on materials and technologies based on exploiting localized surface plasmon resonances in metallic nanostructures. The reason for this focused attention is their suitability for single-molecule sensing, arising from intrinsically nanoscopic sensing volume and the high sensitivity to the local environment. Here we propose an alternative route, which enables radically improved sensitivity compared with recently reported plasmon-based sensors. Such high sensitivity is achieved by exploiting the control of the phase of light in magnetoplasmonic nanoantennas. We demonstrate a manifold improvement of refractometric sensing figure-of-merit. Most remarkably, we show a raw surface sensitivity (that is, without applying fitting procedures) of two orders of magnitude higher than the current values reported for nanoplasmonic sensors. Such sensitivity corresponds to a mass of similar to 0.8 ag per nanoantenna of polyamide-6.6 (n = 1.51), which is representative for a large variety of polymers, peptides and proteins.

Methods for Separation and Characterization of Extracellular Vesicles: Results of a Worldwide Survey Performed by the ISEV Rigor and Standardization Subcommittee

Abstract: Research on extracellular vesicles (EVs) is growing exponentially due to an increasing appreciation of EVs as disease biomarkers and therapeutics, an expanding number of EV-containing materials under study, and application of new preparation, detection, and cargo analysis methods. Diversity of both sources and methodologies imposes challenges on the comparison of measurement results between studies and laboratories. While reference guidelines and minimal requirements for EV research have achieved the important objective of assembling community consensus, it is also essential to understand which methodologies and quality controls are currently being applied, and how usage trends are evolving. As an initial response to this need, the International Society for Extracellular Vesicles (ISEV) performed a worldwide survey in 2015 on “Techniques used for the isolation and characterization of extracellular vesicles” and published the results from this survey in 2016. In 2019, a new survey was performed to assess the changing state of the field. The questionnaire received more than 600 full or partial responses, and the present manuscript summarizes the results of this second worldwide survey. The results emphasize that separation methods such as ultracentrifugation and density gradients are still the most commonly used methods, the use of size exclusion chromatography has increased, and techniques based on tangential flow and microfluidics are now being used by more than 10% of respondents. The survey also reveals that most EV researchers still do not perform sample quality controls before or after isolation of EVs. Finally, the majority of EV researchers emphasize that separation and characterization of EVs should receive more attention.

Genome-wide analysis of the human p53 transcriptional network unveils a lncRNA tumour suppressor signature

Abstract: Despite the inarguable relevance of p53 in cancer, genome-wide studies relating endogenous p53 activity to the expression of lncRNAs in human cells are still missing. Here, by integrating RNA-seq with p53 ChIP-seq analyses of a human cancer cell line under DNA damage, we define a high-confidence set of 18 lncRNAs that are p53 transcriptional targets. We demonstrate that two of the p53-regulated lncRNAs are required for the efficient binding of p53 to some of its target genes, modulating the p53 transcriptional network and contributing to apoptosis induction by DNA damage. We also show that the expression of p53-lncRNAs is lowered in colorectal cancer samples, constituting a tumour suppressor signature with high diagnostic power. Thus, p53-regulated lncRNAs establish a positive regulatory feedback loop that enhances p53 tumour suppressor activity. Furthermore, the signature defined by p53-regulated lncRNAs supports their potential use in the clinic as biomarkers and therapeutic targets.