First estimate of the time delay in HE 1104-1805

We present first results from five years of spectrophotometric monitoring of the bright double QSO and gravitational lens HE 1104-1805. The quasar has varied considerably over this time, while the emission line fluxes appear to have remained constant. We have constructed monochromatic continuum light curves for components A and B, finding that B leads the variability. A quantitative analysis with the Pelt method gives a best estimate for the light travel time delay of about 0.73 years, although a value as low as 0.3 cannot yet be excluded. We discuss possible models for the QSO-lens configuration and use our measured time delay to predict the redshift of the lens, z_d. Finding that most likely z_d < 1, we can rule out the hitherto favoured values of z_d = 1.32 or 1.66. A new candidate is an absorption system at z=0.73, but the lens could also be an elliptical not detected in absorption.Comment: 4 pages, 4 figures, accepted for A&A, Letters to the edito

A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP

Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multineuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, that enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, that allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multidimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor inhibitory cognitive task. The test exhibits good classification performance, indicating the adequateness of our approach. In addition to its effectiveness, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc

The role of miRNAs in peritoneal dialysis associated fibrogenesis

Peritoneal dialysis (PD) is a life-saving form of renal replacement therapy for those with End Stage Kidney Disease. Peritoneal fibrosis is a considerable problem for PD patients, and mesothelial cells, which line the peritoneal cavity, play a central role in response to injury and fibrogenesis within the peritoneum. Mesothelial cells may undergo mesothelial to mesenchymal transition (MMT) contributing to peritoneal fibrosis and treatment failure. miRNAs are important regulators of fibrosis but their roles in peritoneal fibrosis are largely unknown. Here, a detailed characterization of the MMT process was performed in primary human mesothelial cells (HPMCs) in response to Transforming Growth Factor beta-1 (TGF-β1). Hybridization array showed mesothelial miR-21 and miR-31 expression was up-regulated by TGF-β1 which was validated by RTqPCR in different PD associated MMT models. Mesothelial cells cultured ex vivo from PD patients exhibited phenotypic changes consistent with a progressive MMT process that correlated with an increase in miR-21 and miR-31 expression. Association of miRNA expression and MMT markers in 33 peritoneal biopsies from patients undergoing PD treatment and in PD effluent from 230 PD patients confirmed these results. In silico analysis combined 4 target prediction algorithms (Targetscan, miRanda, miRDB and Diana-microT) for miR-21 and integrated the resulting outcome with mRNA arrays comparing omentum vs PD effluent-derived HPMCs with epithelial (E) and non-epithelial (NE) phenotype. 13 possible miR-21 targets during the MMT process associated to PD therapy were identified and model scrutinized. Four of these were confirmed to be miR- 21 targets. Functional gene analysis indicated that selected targets may be downstream modulators of Snail and cooperate driving MMT during peritoneal fibrosis. Taken together, these data provide a detailed characterisation of mesothelial miRNA expression and responses to TGF-β1, and identify miR-21 and miR-31 as promising biomarkers for peritoneal fibrosis associated to PD therapy

Stable rotating dipole solitons in nonlocal optical media

We reveal that nonlocality can provide a simple physical mechanism for stabilization of multi-hump optical solitons, and present the first example of stable rotating dipole solitons and soliton spiraling, known to be unstable in all types of realistic nonlinear media with local response.Comment: 3 pages, 3 figure

Assessment of different machine learning methods for reservoir outflow forecasting

Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.Ministerio de Ciencia e Innovación | Ref. FPU2020/0614