Measurement of Soil Water Potential by Adsorption Conductivity

Current methods of measuring soil water potential are reviewed, and the limitations of each are noted. The need for a transducer that will measure soil water potential over a wide moisture range for long periods of time is delineated. The concept of utilizing an adsorptive surface that resembles the soil in its water holding capacity as a transducer is discussed. Various designs and materials are tested for such a transducer. All designs tested did not fulfill the requirements needed for a truly useful transducer. However, experimental results show that modification of the adsorptive surface should allow construction of a unit that will be useful in soil water research

Introducing Big Sagebrush into a Crested Wheatgrass Monoculture

Crested wheatgrass (Agropyron desertorum or A. cristatum) has been effectively used to stabilize arid and semi-arid range sites for decades. Reestablishing native plant materials into these areas is often desirable to increase wildlife habitat and ecological diversity. Due to its competitive nature, efforts to reestablish native plants into crested wheatgrass monocultures have had limited success. Tillage will control the grass but leaves the soil vulnerable to erosion and weed invasion. This publication will report on a trial conducted near Nephi, Utah to find a method of introducing native plants into a crested wheatgrass monoculture without subjecting the resource base to degradation in the conversion process. In this trial, the effect of chemically controlling crested wheatgrass before transplanting big sagebrush (Artemisia tridentata) was studied. Small container grown plants of sagebrush were transplanted either directly into a 60 year-old stand of crested wheatgrass or after chemically controlling the grass. Three different subspecies of big sagebrush; Basin big sagebrush (Artemisia tridentata Nutt. ssp. tridentata), Mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle) and Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young); were planted to see if there would be differences among subspecies. Four years of data indicate that controlling crested wheatgrass prior to transplanting resulted in higher sagebrush survival and faster establishment. There were some differences among sagebrush subspecies. Basin big sagebrush survived equally well with or without grass control but grew faster with grass control. Chemical control of the grass was important for both the survival and growth of Mountain big sage and Wyoming big sage

Managing Protein in Spring Wheat with Aerial and Satellite Imagery

Nitrogen fertilizer application can help wheat growers increase crop value and marketability by increasing grain quality. Nitrogen (N) is often applied at heading as a method of increasing protein content and therefore quality of wheat. Our objectives were to obtain spectral signatures of wheat under various N rates (0, 72, 180, 234 kg N ha-1), test various spectral methods of identifying crop stress, and observe the grain protein response to a midseason N application. Spectral data from satellite and aerial platforms were compared to preseason N treatments and flag-leaf tissue samples. Spectral data correlated well with preseason and flag leaf tissue analysis (r2 = 0.58-0.82). Grain protein increased on plots that received an additional 54 kg of N ha-1 at anthesis almost 2% in the N stressed plots (72 kg N ha-1) and 0.3-0.4% on plots with sufficient N (234 and 180 kg N ha-1). Wheat stress detected and managed with help from satellite and aerial platforms could help growers increase revenue and decrease N over-application

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimuli time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environmen