Agricultural data management and sharing: Best practices and case study

Agricultural data are crucial to many aspects of production, commerce, and research involved in feeding the global community. However, in most agricultural research disciplines standard best practices for data management and publication do not exist. Here we propose a set of best practices in the areas of peer review, minimal dataset development, data repositories, citizen science initiatives, and support for best data management. We illustrate some of these best practices with a case study in dairy agroecosystems research. While many common, and increasingly disparate data management and publication practices are entrenched in agricultural disciplines, opportunities are readily available for promoting and adopting best practices that better enable and enhance data-intensive agricultural research and production

Designing a suite of measurements to understand the critical zone

Many scientists have begun to refer to the earth surface environment from the upper canopy to the depths of bedrock as the critical zone (CZ). Identification of the CZ as an integral object worthy of study implicitly posits that the study of the whole earth surface will provide benefits that do not arise when studying the individual parts. To study the CZ, however, requires prioritizing among the measurements that can be made – and we do not generally agree on the priorities. Currently, the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) is expanding from a small original focus area (0.08 km2 , Shale Hills catchment), to a larger watershed (164 km2 , Shavers Creek watershed) and is grappling with the prioritization. This effort is an expansion from a monolithologic first-order forested catchment to a watershed that encompasses several lithologies (shale, sandstone, limestone) and land use types (forest, agriculture). The goal of the project remains the same: to understand water, energy, gas, solute, and sediment (WEGSS) fluxes that are occurring today in the context of the record of those fluxes over geologic time as recorded in soil profiles, the sedimentary record, and landscape morphology. Given the small size of the Shale Hills catchment, the original design incorporated measurement of as many parameters as possible at high temporal and spatial density. In the larger Shavers Creek watershed, however, we must focus the measurements. We describe a strategy of data collection and modeling based on a geomorphological and land use framework that builds on the hillslope as the basic unit. Interpolation and extrapolation beyond specific sites relies on geophysical surveying, remote sensing, geomorphic analysis, the study of natural integrators such as streams, groundwaters or air, and application of a suite of CZ models. We hypothesize that measurements of a few important variables at strategic locations within a geomorphological framework will allow development of predictive models of CZ behavior. In turn, the measurements and models will reveal how the larger watershed will respond to perturbations both now and into the future

Faint High Latitude Carbon Stars Discovered by the Sloan Digital Sky Survey: Methods and Initial Results

We report the discovery of 39 Faint High Latitude Carbon Stars (FHLCs) from Sloan Digital Sky Survey commissioning data. The objects, each selected photometrically and verified spectroscopically, range over 16.6 < r* < 20.0, and show a diversity of temperatures as judged by both colors and NaD line strengths. At the completion of the Sloan Survey, there will be many hundred homogeneously selected and observed FHLCs in this sample. We present proper motion measures for each object, indicating that the sample is a mixture of extremely distant (>100 kpc) halo giant stars, useful for constraining halo dynamics, plus members of the recently-recognized exotic class of very nearby dwarf carbon (dC) stars. Motions, and thus dC classification, are inferred for 40-50 percent of the sample, depending on the level of statistical significance invoked. The new list of dC stars presented here, although selected from only a small fraction of the final SDSS, doubles the number of such objects found by all previous methods. (Abstract abridged).Comment: Accepted for publication in The Astronomical Journal, Vol. 124, Sep. 2002, 40 pages, 7 figures, AASTeX v5.

Inhibition of microbial biofuel production in drought-stressed switchgrass hydrolysate

Additional file 2. Maps of significant gene ontology terms for chemical genomics data. Untreated biomass composition. Detailed hydrolysate composition

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Report from the Annual Conference of the British Society of Echocardiography, November 2016, Queen Elizabeth II Conference Centre, London.

Peer reviewedPublisher PD

New Insight into the Antifibrotic Effects of Praziquantel on Mice in Infection with Schistosoma japonicum

Schistosomiasis is a parasitic disease infecting more than 200 million people in the world. Although chemotherapy targeting on killing schistosomes is one of the main strategies in the disease control, there are few effective ways of dealing with liver fibrosis caused by the parasite infection in the chronic and advanced stages of schistosomiasis. For this reason, new strategies and prospective drugs, which exert antifibrotic effects, are urgently required.-induced liver fibrosis was inhibited by PZQ treatment for 30 days. Furthermore, we analyzed the effects of praziquantel on mouse primary hepatic stellate cells (HSCs). It is indicated that mRNA expressions of Col1α1, Col3α1, α-SMA, TGF-β, MMP9 and TIMP1 of HSCs were all inhibited after praziquantel anti-parasite treatments.The significant amelioration of hepatic fibrosis by praziquantel treatment validates it as a promising drug of anti-fibrosis and offers potential of a new chemotherapy for hepatic fibrosis resulting from schistosomiasis

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495