THE HIGH CADENCE TRANSIENT SURVEY (HITS). I. SURVEY DESIGN AND SUPERNOVA SHOCK BREAKOUT CONSTRAINTS

Indexación: Web of Science; Scopus.We present the first results of the High Cadence Transient Survey (HiTS), a survey for which the objective is to detect and follow-up optical transients with characteristic timescales from hours to days, especially the earliest hours of supernova (SN) explosions. HiTS uses the Dark Energy Camera and a custom pipeline for image subtraction, candidate filtering and candidate visualization, which runs in real-time to be able to react rapidly to the new transients. We discuss the survey design, the technical challenges associated with the real-time analysis of these large volumes of data and our first results. In our 2013, 2014, and 2015 campaigns, we detected more than 120 young SN candidates, but we did not find a clear signature from the short-lived SN shock breakouts (SBOs) originating after the core collapse of red supergiant stars, which was the initial science aim of this survey. Using the empirical distribution of limiting magnitudes from our observational campaigns, we measured the expected recovery fraction of randomly injected SN light curves, which included SBO optical peaks produced with models from Tominaga et al. (2011) and Nakar & Sari (2010). From this analysis, we cannot rule out the models from Tominaga et al. (2011) under any reasonable distributions of progenitor masses, but we can marginally rule out the brighter and longer-lived SBO models from Nakar & Sari (2010) under our best-guess distribution of progenitor masses. Finally, we highlight the implications of this work for future massive data sets produced by astronomical observatories, such as LSST.http://iopscience.iop.org/article/10.3847/0004-637X/832/2/155/meta;jsessionid=76BDFFFE378003616F6DBA56A9225673.c4.iopscience.cld.iop.or

Glutaredoxin GRXS17 associates with the cytosolic iron-sulfur cluster assembly pathway

Cytosolic monothiol glutaredoxins (GRXs) are required in iron-sulfur (Fe-S) cluster delivery and iron sensing in yeast and mammals. In plants, it is unclear whether they have similar functions. Arabidopsis (Arabidopsis thaliana) has a sole class II cytosolic monothiol GRX encoded by GRXS17. Here, we used tandem affinity purification to establish that Arabidopsis GRXS17 associates with most known cytosolic Fe-S assembly (CIA) components. Similar to mutant plants with defective CIA components, grxs17 loss-of-function mutants showed some degree of hypersensitivity to DNA damage and elevated expression of DNA damage marker genes. We also found that several putative Fe-S client proteins directly bind to GRXS17, such as XANTHINE DEHYDROGENASE1 (XDH1), involved in the purine salvage pathway, and CYTOSOLIC THIOURIDYLASE SUBUNIT1 and CYTOSOLIC THIOURIDYLASE SUBUNIT2, both essential for the 2-thiolation step of 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification of tRNAs. Correspondingly, profiling of the grxs17-1 mutant pointed to a perturbed flux through the purine degradation pathway and revealed that it phenocopied mutants in the elongator subunit ELO3, essential for the mcm5 tRNA modification step, although we did not find XDH1 activity or tRNA thiolation to be markedly reduced in the grxs17-1 mutant. Taken together, our data suggest that plant cytosolic monothiol GRXs associate with the CIA complex, as in other eukaryotes, and contribute to, but are not essential for, the correct functioning of client Fe-S proteins in unchallenged conditions

A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin

Integrative research on water resources requires a wide range of socio-environmental datasets to better understand human-water interactions and inform decision-making. However, in transboundary watersheds, integrating cross-disciplinary and multinational datasets is a daunting task due to the disparity of data sources and the inconsistencies in data format, content, resolution, and language. This paper introduces a socio-environmental geodatabase that transcends political and disciplinary boundaries in the Rio Grande/Río Bravo basin (RGB). The geodatabase aggregates 145 GIS data layers on five main themes: (i) Water & Land Governance, (ii) Hydrology, (iii) Water Use & Hydraulic Infrastructures, (iv) Socio-Economics, and (v) Biophysical Environment. Datasets were primarily collected from public open-access data sources, processed with ArcGIS, and documented through the FGCD metadata standard. By synthesizing a broad array of datasets and mapping public and private water governance, we expect to advance interdisciplinary research in the RGB, provide a replicable approach to dataset compilation for transboundary watersheds, and ultimately foster transboundary collaboration for sustainable resource management.The project was funded by Grant No. G15AP00132 from the United States Geological Survey. Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Scale invariance and universality of force networks in static granular matter

Force networks form the skeleton of static granular matter. They are the key ingredient to mechanical properties, such as stability, elasticity and sound transmission, which are of utmost importance for civil engineering and industrial processing. Previous studies have focused on the global structure of external forces (the boundary condition), and on the probability distribution of individual contact forces. The disordered spatial structure of the force network, however, has remained elusive so far. Here we report evidence for scale invariance of clusters of particles that interact via relatively strong forces. We analyzed granular packings generated by molecular dynamics simulations mimicking real granular matter; despite the visual variation, force networks for various values of the confining pressure and other parameters have identical scaling exponents and scaling function, and thus determine a universality class. Remarkably, the flat ensemble of force configurations--a simple generalization of equilibrium statistical mechanics--belongs to the same universality class, while some widely studied simplified models do not.Comment: 15 pages, 4 figures; to appear in Natur

Vav proteins are key regulators of Card9 signaling for innate antifungal immunity

Fungal infections are major causes of morbidity and mortality, especially in immunocompromised individuals. The innate immune system senses fungal pathogens through Syk-coupled C-type lectin receptors (CLRs), which signal through the conserved immune adaptor Card9. Although Card9 is essential for antifungal defense, the mechanisms that couple CLR-proximal events to Card9 control are not well defined. Here, we identify Vav proteins as key activators of the Card9 pathway. Vav1, Vav2, and Vav3 cooperate downstream of Dectin-1, Dectin-2, and Mincle to engage Card9 for NF-κB control and proinflammatory gene transcription. Although Vav family members show functional redundancy, Vav1/2/3(-/-) mice phenocopy Card9(-/-) animals with extreme susceptibility to fungi. In this context, Vav3 is the single most important Vav in mice, and a polymorphism in human VAV3 is associated with susceptibility to candidemia in patients. Our results reveal a molecular mechanism for CLR-mediated Card9 regulation that controls innate immunity to fungal infections

The logic of the floral transition: reverse-engineering the switch controlling the identity of lateral organs

Much laboratory work has been carried out to determine the gene regulatory network (GRN) that results in plant cells becoming flowers instead of leaves. However, this also involves the spatial distribution of different cell types, and poses the question of whether alternative networks could produce the same set of observed results. This issue has been addressed here through a survey of the published intercellular distribution of expressed regulatory genes and techniques both developed and applied to Boolean network models. This has uncovered a large number of models which are compatible with the currently available data. An exhaustive exploration had some success but proved to be unfeasible due to the massive number of alternative models, so genetic programming algorithms have also been employed. This approach allows exploration on the basis of both data-fitting criteria and parsimony of the regulatory processes, ruling out biologically unrealistic mechanisms. One of the conclusions is that, despite the multiplicity of acceptable models, an overall structure dominates, with differences mostly in alternative fine-grained regulatory interactions. The overall structure confirms the known interactions, including some that were not present in the training set, showing that current data are sufficient to determine the overall structure of the GRN. The model stresses the importance of relative spatial location, through explicit references to this aspect. This approach also provides a quantitative indication of how likely some regulatory interactions might be, and can be applied to the study of other developmental transitions

Studying the functional conservation of cis-regulatory modules and their transcriptional output

<p>Abstract</p> <p>Background</p> <p><it>Cis</it>-regulatory modules (CRMs) are distinct, genomic regions surrounding the target gene that can independently activate the promoter to drive transcription. The activation of a CRM is controlled by the binding of a certain combination of transcription factors (TFs). It would be of great benefit if the transcriptional output mediated by a specific CRM could be predicted. Of equal benefit would be identifying <it>in silico </it>a specific CRM as the driver of the expression in a specific tissue or situation. We extend a recently developed biochemical modeling approach to manage both prediction tasks. Given a set of TFs, their protein concentrations, and the positions and binding strengths of each of the TFs in a putative CRM, the model predicts the transcriptional output of the gene. Our approach predicts the location of the regulating CRM by using predicted TF binding sites in regions near the gene as input to the model and searching for the region that yields a predicted transcription rate most closely matching the known rate.</p> <p>Results</p> <p>Here we show the ability of the model on the example of one of the CRMs regulating the <it>eve </it>gene, MSE2. A model trained on the MSE2 in <it>D. melanogaster </it>was applied to the surrounding sequence of the <it>eve </it>gene in seven other <it>Drosophila </it>species. The model successfully predicts the correct MSE2 location and output in six out of eight <it>Drosophila </it>species we examine.</p> <p>Conclusion</p> <p>The model is able to generalize from <it>D. melanogaster </it>to other <it>Drosophila </it>species and accurately predicts the location and transcriptional output of MSE2 in those species. However, we also show that the current model is not specific enough to function as a genome-wide CRM scanner, because it incorrectly predicts other genomic regions to be MSE2s.</p

Effect of clopidogrel discontinuation at 1 year after drug eluting stent placement on soluble CD40L, P-selectin and C-reactive protein levels: DECADES (Discontinuation Effect of Clopidogrel After Drug Eluting Stent): a multicenter, open-label study

Antiplatelet therapy with clopidogrel has been shown to reduce major adverse cardiac events in acute coronary syndromes and after percutaneous interventions. This effect is not only due to its anti-platelet effect but also possibly due to an anti-inflammatory effect. The effect of clopidogrel cessation after one year of therapy on markers of inflammation has been investigated in diabetics and showed an increase in platelet aggregation as well as hsCRP and surface P-selectin levels. This was an exploratory multicenter prospective open-label single arm study of 98 non-diabetic patients who had received one or more drug eluting stents and were coming to the end of their 12 months course of clopidogrel therapy. The effect of clopidogrel cessation on expression of biomarkers: sCD40L, soluble P-selectin and hsCRP was measured right before clopidogrel cessation (day 0), and subsequently at 1, 2, 3 and 4 weeks after drug withdrawal. A median increase in sCD40L expression from 224 to 324.5 pg/ml was observed between baseline and 4 weeks after clopidogrel cessation, which corresponded to a 39% mean percent change based on an ANCOVA model (P < 0.001). Over the 4 weeks observation period the change in sCD40L expression correlated weakly with soluble P-selectin levels (at 4 weeks Spearman’s correlation coefficient = 0.32; P = 0.0024). Increase in P-selectin expression from baseline was statistically significant at week 1 and 2. Conversely, hsCRP level decreased by 21% at 1 week (P = 0.008) and was still reduced by 18% by 4 weeks (P = 0.062). The change in sCD40L expression appeared to vary with the type of drug eluting stent. Patients treated with drug eluting stents at 1 year after implantation display significant increase in sCD40L and decrease in hsCRP after clopidogrel cessation. Further studies should elucidate if this increase in sCD40L levels reflects solely the removal of the inhibitory effects of clopidogrel on platelet activity or rather an increase in pro-inflammatory state. The latter hypothesis may be less likely given decrease in hsCRP levels. Randomized studies are urgently needed to establish potential link of clopidogrel discontinuation and vascular outcomes