Reticulon1-C modulates protein disulphide isomerase function

Endoplasmic reticulum (ER) is the primary site for the synthesis and folding of secreted and membrane-bound proteins. Accumulation of unfolded and misfolded proteins in ER underlies a wide range of human neurodegenerative disorders. Hence, molecules regulating the ER stress response represent potential candidates as drug targets for tackling these diseases. Protein disulphide isomerase (PDI) is a chaperone involved in ER stress pathway, its activity being an important cellular defense against protein misfolding. Here, we demonstrate that human neuroblastoma SH-SY5Y cells overexpressing the reticulon protein 1-C (RTN1-C) reticulon family member show a PDI punctuate subcellular distribution identified as ER vesicles. This represents an event associated with a significant increase of PDI enzymatic activity. We provide evidence that the modulation of PDI localization and activity does not only rely upon ER stress induction or upregulation of its synthesis, but tightly correlates to an alteration in its nitrosylation status. By using different RTN1-C mutants, we demonstrate that the observed effects depend on RTN1-C N-terminal region and on the integrity of the microtubule network. Overall, our results indicate that RTN1-C induces PDI redistribution in ER vesicles, and concomitantly modulates its activity by decreasing the levels of its S-nitrosylated form. Thus RTN1-C represents a promising candidate to modulate PDI function

Next Generation Cosmology: Constraints from the Euclid Galaxy Cluster Survey

We study the characteristics of the galaxy cluster samples expected from the European Space Agency's Euclid satellite and forecast constraints on cosmological parameters describing a variety of cosmological models. The method used in this paper, based on the Fisher Matrix approach, is the same one used to provide the constraints presented in the Euclid Red Book (Laureijs et al.2011). We describe the analytical approach to compute the selection function of the photometric and spectroscopic cluster surveys. Based on the photometric selection function, we forecast the constraints on a number of cosmological parameter sets corresponding to different extensions of the standard LambdaCDM model. The dynamical evolution of dark energy will be constrained to Delta w_0=0.03 and Delta w_a=0.2 with free curvature Omega_k, resulting in a (w_0,w_a) Figure of Merit (FoM) of 291. Including the Planck CMB covariance matrix improves the constraints to Delta w_0=0.02, Delta w_a=0.07 and a FoM=802. The amplitude of primordial non-Gaussianity, parametrised by f_NL, will be constrained to \Delta f_NL ~ 6.6 for the local shape scenario, from Euclid clusters alone. Using only Euclid clusters, the growth factor parameter \gamma, which signals deviations from GR, will be constrained to Delta \gamma=0.02, and the neutrino density parameter to Delta Omega_\nu=0.0013 (or Delta \sum m_\nu=0.01). We emphasise that knowledge of the observable--mass scaling relation will be crucial to constrain cosmological parameters from a cluster catalogue. The Euclid mission will have a clear advantage in this respect, thanks to its imaging and spectroscopic capabilities that will enable internal mass calibration from weak lensing and the dynamics of cluster galaxies. This information will be further complemented by wide-area multi-wavelength external cluster surveys that will already be available when Euclid flies. [Abridged]Comment: submitted to MNRA

Impact of N-tau on adult hippocampal neurogenesis, anxiety, and memory.

Different pathological tau species are involved in memory loss in Alzheimer’s disease, the most common cause of dementia among older people. However, little is known about how tau pathology directly affects adult hippocampal neurogenesis, a unique form of structural plasticity implicated in hippocampusdependent spatial learning and mood-related behavior. To this aim, we generated a transgenic mouse model conditionally expressing a pathological tau fragment (26e230 aa of the longest human tau isoform, or N-tau) in nestin-positive stem/progenitor cells. We found that N-tau reduced the proliferation of progenitor cells in the adult dentate gyrus, reduced cell survival and increased cell death by a caspase- 3eindependent mechanism, and recruited microglia. Although the number of terminally differentiated neurons was reduced, these showed an increased dendritic arborization and spine density. This resulted in an increase of anxiety-related behavior and an impairment of episodic-like memory, whereas less complex forms of spatial learning remained unaltered. Understanding how pathological tau species directly affect neurogenesis is important for developing potential therapeutic strategies to direct neurogenic instructive cues for hippocampal function repair

Joint halo-mass function for modified gravity and massive neutrinos - I. Simulations and cosmological forecasts

We present a halo-mass function accurate over the full relevant Hu-Sawicki f(R) parameter space based on spherical collapse calculations and calibrated to a suite of modified gravity N-body simulations that include massive neutrinos. We investigate the ability of current and forthcoming galaxy cluster observations to detect deviations from general relativity while constraining the total neutrino mass and including systematic uncertainties. Our results indicate that the degeneracy between massive neutrino and modify gravity effects is a limiting factor for the current searches for new gravitational physics with clusters of galaxies, but future surveys will be able to break the degeneracy

Cosmological constraints from the abundance, weak lensing, and clustering of galaxy clusters: Application to the SDSS

Aims. The clustering of galaxy clusters is a powerful cosmological tool. When it is combined with other cosmological observables, it can help to resolve parameter degeneracies and improve constraints, especially on Ωm and Ï8. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak-lensing mass information. As a case study, we use the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). Methods. We extended a previous analysis of the number counts and weak-lensing signal by the two-point correlation function. We derived cosmological and scaling relation posteriors for all possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. Results. We find no evidence for tensions between the three data sets we analyzed. We demonstrate that the constraining power of the sample can be greatly improved by including the clustering statistics because this can break the Ωmâ â â Ï8 degeneracy that is characteristic of cluster abundance studies. In particular, for a flat Î CDM model with massive neutrinos, we obtain Ωmâ =â 0.28â ±â 0.03 and Ï 8â =â 0.82â ±â 0.05, which is an improvement of 33% and 50% compared to the posteriors derived by combining cluster abundance and weak-lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, and also with Planck results for the cosmic microwave background (CMB) and DES-Y3 galaxy clustering and weak-lensing analysis

Neutrino cosmology and Planck

Relic neutrinos play an important role in the evolution of the Universe, modifying some of the cosmological observables. We summarize the main aspects of cosmological neutrinos and describe how the precision of present cosmological data can be used to learn about neutrino properties. In particular, we discuss how cosmology provides information on the absolute scale of neutrino masses, complementary to beta decay and neutrinoless double-beta decay experiments. We explain why the combination of Planck temperature data with measurements of the baryon acoustic oscillation angular scale provides a strong bound on the sum of neutrino masses, 0.23 eV at the 95% confidence level, while the lensing potential spectrum and the cluster mass function measured by Planck are compatible with larger values. We also review the constraints from current data on other neutrino properties. Finally, we describe the very good perspectives from future cosmological measurements, which are expected to be sensitive to neutrino masses close the minimum values guaranteed by flavour oscillations

Dark Energy Surveyed Year 1 results: calibration of cluster mis-centring in the redMaPPer catalogues

The centre determination of a galaxy cluster from an optical cluster finding algorithm can be offset from theoretical prescriptions or N-body definitions of its host halo centre. These offsets impact the recovered cluster statistics, affecting both richness measurements and the weak lensing shear profile around the clusters. This paper models the centring performance of the redMaPPer cluster finding algorithm using archival X-ray observations of redMaPPer selected clusters. Assuming the X-ray emission peaks as the fiducial halo centres, and through analysing their offsets to the redMaPPer centres, we find that ∼75 ± 8 per cent of the redMaPPer clusters are well centred and the mis-centred offset follows a Gamma distribution in normalized, projected distance. These mis-centring offsets cause a systematic underestimation of cluster richness relative to the well-centred clusters, for which we propose a descriptive model. Our results enable the DES Y1 cluster cosmology analysis by characterizing the necessary corrections to both the weak lensing and richness abundance functions of the DES Y1 redMaPPer cluster catalogue

Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters

We constrain the mass--richness scaling relation of redMaPPer galaxy clusters identified in the Dark Energy Survey Year 1 data using weak gravitational lensing. We split clusters into 4×3 bins of richness λ and redshift z for λ≥20 and 0.2≤z≤0.65 and measure the mean masses of these bins using their stacked weak lensing signal. By modeling the scaling relation as ⟨M 200m |λ,z⟩=M 0 (λ/40) F ((1+z)/1.35) G , we constrain the normalization of the scaling relation at the 5.0 per cent level as M 0 =[3.081±0.075(stat)±0.133(sys)]⋅10 14 M ⊙ at λ=40 and z=0.35 . The richness scaling index is constrained to be F=1.356±0.051 (stat)±0.008 (sys) and the redshift scaling index G=−0.30±0.30 (stat)±0.06 (sys) . These are the tightest measurements of the normalization and richness scaling index made to date. We use a semi-analytic covariance matrix to characterize the statistical errors in the recovered weak lensing profiles. Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects. We discuss prospects for reducing this systematic error budget, which dominates the uncertainty on M 0. Our result is in excellent agreement with, but has significantly smaller uncertainties than, previous measurements in the literature, and augurs well for the power of the DES cluster survey as a tool for precision cosmology and upcoming galaxy surveys such as LSST, Euclid and WFIRST

Detection and classification of man-made objects for the autonomy of underwater robots

Recent developments in marine technologies allow underwater vehicles to perform survey missions for data collection in an automatic way. The scientific community is now focusing on endowing these vehicles with strong perception capabilities, aiming at full autonomy and decision-making skills. Such abilities would bring benefits to a wide range of field applications, e.g. Inspection and Maintenance (I&M) of man-made structures, port security, and marine rescue. Indeed, most of these tasks are currently carried out employing remotely operated vehicles, making the presence of humans in water necessary. Projects like Metrological Evaluation and Testing of Robots in International CompetitionS (METRICS), funded by the European Commission, are promoting research on this field by organising events such as the Robotics for Asset Maintenance and Inspection (RAMI) competition. In particular, this competition requires participants to develop perception techniques capable of identifying a set of specific targets. Within such context, this paper presents an algorithm able to detect and classify Objects of Potential Interest (OPIs) in underwater camera images. First, the proposed solution compensates for the quality degradation of underwater images by applying color enhancement and restoration procedures. Then, it exploits deep-learning techniques, as well as color and shape based methods, to recognize and correctly label the predefined OPIs. Preliminary results of the implemented neural network using restored images are provided, and a mean Average Precision (mAP) of about 92% was achieved on the dataset provided to the RAMI competition participating teams by the NATO Science and Technology Organization Centre for Maritime Research and Experimentation (STO CMRE)