We address the impact of consistent modifications of gravity on the largest observable scales, focusing on relativistic effects in galaxy number counts and the cross-correlation between the matter large scale structure (LSS) distribution and the cosmic microwave background (CMB). Our analysis applies to a very broad class of general scalar-tensor theories encoded in the Horndeski Lagrangian and is fully consistent on linear scales, retaining the full dynamics of the scalar field and not assuming quasi-static evolution. As particular examples we consider self-accelerating Covariant Galileons, Brans-Dicke theory and parameterizations based on the effective field theory of dark energy, using the hi class code to address the impact of these models on relativistic corrections to LSS observables. We find that especially effects which involve integrals along the line of sight (lensing convergence, time delay and the integrated Sachs-Wolfe effect - ISW) can be considerably modified, and even lead to O(1000%) deviations from General Relativity in the case of the ISW effect for Galileon models, for which standard probes such as the growth function only vary by O(10%). These effects become dominant when correlating galaxy number counts at different redshifts and can lead to ∼ 50% deviations in the total signal that might be observable by future LSS surveys. Because of their integrated nature, these deep-redshift cross-correlations are sensitive to modifications of gravity even when probing eras much before dark energy domination. We further isolate the ISW effect using the cross-correlation between LSS and CMB temperature anisotropies and use current data to further constrain Horndeski models. Forthcoming large-volume galaxy surveys using multiple-tracers will search for all these effects, opening a new window to probe gravity and cosmic acceleration at the largest scales available in our universe.

Cosmological models with Galileon gravity are an alternative to the standard ΛCDM paradigm with testable predictions at the level of its self-accelerating solutions for the expansion history, as well as large-scale structure formation. Here, we place constraints on the full parameter space of these models using data from the cosmic microwave background (CMB) (including lensing), baryonic acoustic oscillations (BAO) and the Integrated Sachs-Wolfe (ISW) effect. We pay special attention to the ISW effect for which we use the cross-spectra, C ℓTg , of CMB temperature maps and foreground galaxies from the WISE survey. The sign of C ℓTg is set by the time evolution of the lensing potential in the redshift range of the galaxy sample: it is positive if the potential decays (like in ΛCDM), negative if it deepens. We constrain three subsets of Galileon gravity separately known as the Cubic, Quartic and Quintic Galileons. The cubic Galileon model predicts a negative C ℓTg and exhibits a 7.8σ tension with the data, which effectively rules it out. For the quartic and quintic models the ISW data also rule out a significant portion of the parameter space but permit regions where the goodness-of-fit is comparable to ΛCDM. The data prefers a non zero sum of the neutrino masses (∑m ν 0.5eV) with ∼ 5σ significance in these models. The best-fitting models have values of H 0 consistent with local determinations, thereby avoiding the tension that exists in ΛCDM. We also identify and discuss a ∼ 2σ tension that Galileon gravity exhibits with recent BAO measurements. Our analysis shows overall that Galileon cosmologies cannot be ruled out by current data but future lensing, BAO and ISW data hold strong potential to do so.

Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle - a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

Walnut shell suture strength directly impacts the ability to maintain shell integrity during harvest and processing, susceptibility to insect damage and other contamination, and the proportion of kernel halves recovered during cracking. Suture strength is therefore an important breeding objective. Here, two methods of phenotyping this trait were investigated: 1) traditional, qualitative and rather subjective scoring on an interval scale by human observers, and; 2) quantitative and continuous measurements captured by a texturometer. The aim of this work was to increase the accuracy of suture strength phenotyping and to then apply two mapping approaches, quantitative trait loci (QTL) mapping and genome wide association (GWAS) models, in order to dissect the genetic basis of the walnut suture trait. Using data collected on trees within the UC Davis Walnut Improvement Program (n = 464), the genetic correlation between the texturometer method and qualitatively scored method was high (0.826). Narrow sense heritability calculated using quantitative measurements was 0.82. A major QTL for suture strength was detected on LG05, explaining 34% of the phenotypic variation; additionally, two minor QTLs were identified on LG01 and LG11. All three QTLs were confirmed with GWAS on corresponding chromosomes. The findings reported in this study are relevant for application towards a molecular breeding program in walnut.

Background

Both a source of diversity and the development of genomic tools, such as reference genomes and molecular markers, are equally important to enable faster progress in plant breeding. Pear (Pyrus spp.) lags far behind other fruit and nut crops in terms of employment of available genetic resources for new cultivar development. To address this gap, we designed a high-density, high-efficiency and robust single nucleotide polymorphism (SNP) array for pear, with the main objectives of conducting genetic diversity and genome-wide association studies.

Results

By applying a two-step design process, which consisted of the construction of a first 'draft' array for the screening of a small subset of samples, we were able to identify the most robust and informative SNPs to include in the Applied Biosystems™ Axiom™ Pear 70 K Genotyping Array, currently the densest SNP array for pear. Preliminary evaluation of this 70 K array in 1416 diverse pear accessions from the USDA National Clonal Germplasm Repository (NCGR) in Corvallis, OR identified 66,616 SNPs (93% of all the tiled SNPs) as high quality and polymorphic (PolyHighResolution). We further used the Axiom Pear 70 K Genotyping Array to construct high-density linkage maps in a bi-parental population, and to make a direct comparison with available genotyping-by-sequencing (GBS) data, which suggested that the SNP array is a more robust method of screening for SNPs than restriction enzyme reduced representation sequence-based genotyping.

Conclusions

The Axiom Pear 70 K Genotyping Array, with its high efficiency in a widely diverse panel of Pyrus species and cultivars, represents a valuable resource for a multitude of molecular studies in pear. The characterization of the USDA-NCGR collection with this array will provide important information for pear geneticists and breeders, as well as for the optimization of conservation strategies for Pyrus.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Liquid Argon Time Projection Chamber (LAr TPC) detectors offer charged particle imaging capability with remarkable spatial resolution. Precise event reconstruction procedures are critical in order to fully exploit the potential of this technology. In this paper we present a new, general approach to 3D reconstruction for the LAr TPC with a practical application to the track reconstruction. The efficiency of the method is evaluated on a sample of simulated tracks. We present also the application of the method to the analysis of stopping particle tracks collected during the ICARUS T600 detector operation with the CNGS neutrino beam. © 2013 M. Antonello et al.

We report an early result from the ICARUS experiment on the search for a νμ → νe signal due to the LSND anomaly. The search was performed with the ICARUS T600 detector located at the Gran Sasso Laboratory, receiving CNGS neutrinos from CERN at an average energy of about 20 GeV, after a flight path of ~730 km. The LSND anomaly would manifest as an excess of νe events, characterized by a fast energy oscillation averaging approximately to sin2(1. 27Δ mnew2L/Eν) ≈ 1/2 with probability Pνμ → νe = 1/2 sin2(2θnew. The present analysis is based on 1091 neutrino events, which are about 50 % of the ICARUS data collected in 2010-2011. Two clear νe events have been found, compared with the expectation of 3. 7±0. 6 events from conventional sources. Within the range of our observations, this result is compatible with the absence of a LSND anomaly. At 90 % and 99 % confidence levels the limits of 3. 4 and 7. 3 events corresponding to oscillation probabilities 〈Pνμ → νe〉 ≤ 5.4 × 10-3 and 〈Pνμ → νe〉 ≤ 1.1 × 10-2 are set respectively. The result strongly limits the window of open options for the LSND anomaly to a narrow region around (Δm2,sin2(2θ))new=(0. 5 eV2,0. 005), where there is an overall agreement (90 % CL) between the present ICARUS limit, the published limits of KARMEN and the published positive signals of LSND and MiniBooNE Collaborations. © 2013 The Author(s).