Demonstration of the Feasibility of High Temperature Bearing Lubrication From Carbonaceous Gases

Research has been conducted on silicon nitride pin-on-disk sliding contacts at temperatures of up to 520 C, and four-ball rolling contacts with silicon nitride balls and 52100 steel or silicon nitride races at 590 C. These tests were conducted in a variety of gaseous environments in order to determine the effects of simulated engine exhaust gas on the carbonaceous gas decomposition lubrication scheme. In rolling tests with steel races and exhaust gas the wear track depth was roughly half that of tests run in nitrogen gas alone. The deposition of lubricous microcrystalline graphitic carbon on the rolling surfaces, generated from the carbon monoxide within the exhaust gas mixture, was verified by microfocused Raman spectroscopy. Ten-fold reductions in rolling wear could be achieved by the exhaust gas atmosphere in cases where water vapor was removed or not present. The exhaust gas mixture alone was not found to provide any lubricating effect on silicon nitride sliding contacts, where the rate of wear greatly exceeds the rate of carbon deposition. Directed admixture of acetylene (as low as 5% of the exhaust gas flow rates), has provided reductions in both wear volume and coefficient of friction by factors of 60X and 20X respectively for sliding contacts during the initial 80 m of sliding distance. Exhaust gas atmosphere with the acetylene admixture provided 65OX reductions in steady state wear rate compared to that measured for sliding contacts in dry N2. Such acetylene admixture also augments the ability of the exhaust gas atmosphere to lubricate high-temperature rolling contacts, with up to 25-fold reductions in wear track depth compared to those measured in the presence of N2 alone. In addition to providing some lubricating benefit itself, an important potential role of the exhaust gas from rich mixtures would be to shield bearings from 02. Such shielding enables surface deposition of lubricous pyrolytic carbon from the acetylene admixture, instead of combustion, rendering feasible the continuously replenished solid lubrication of high-temperature bearing surfaces

A Simple Method for Estimation of Agonist Activity at Receptor Subtypes: Comparison of Native and Cloned M3 Muscarinic Receptors in Guinea Pig Ileum and Transfected Cells

We describe a simple method for calculating the pharmacological activity of an agonist (A) relative to a standard agonist (S) using only the concentration-response curves of the two agonists. In most situations, we show that the product of the ratios of maximal responses (E max − A/E max − S) and potencies (EC50 − S/EC50 − A) is equivalent to the product of the affinity and intrinsic efficacy of A expressed relative to that of S. We refer to this term as the IRA value of A. In a cooperative system where the concentration-response curve of the standard agonist is steep and that of the test agonist is flatter with a lower maximal response, the simple calculation of IRA described above underestimates agonist activity; however, we also describe a means of correcting the IRA in this situation. We have validated our analysis with modeling techniques and have shown experimentally that the IRA values of muscarinic agonists for stimulating contractions in the guinea pig ileum (M3 response) are in excellent agreement with those measured in the phosphoinositide assay on Chinese hamster ovary cells expressing the M3 muscarinic receptor

Contractile Roles of the M 2 and M 3 Muscarinic Receptors in the Guinea Pig Colon 1

ABSTRACT The contractile roles of the M 2 and M 3 muscarinic receptors were investigated in guinea pig longitudinal colonic smooth muscle. Prior treatment of the colon with N-(2-chloroethyl)-4-piperidinyl diphenylacetate (4-DAMP mustard) (40 nM) in combination with [[2-[(diethylamino)methyl]-1-piperidinyl]acetyl]-5,11-dihydro-6H-pyrido [2,3b][1,4]benzodiazepine-6-one (AF-DX 116) (1.0 M) caused a subsequent, irreversible inhibition of oxotremorine-M-induced contractions when measured after extensive washing. The estimate of the degree of receptor inactivation after 2 hr (97%) was not much greater than that measured after 1 hr (95%), which suggests that both 4-DAMP mustard-sensitive and -insensitive muscarinic subtypes contribute to the contractile response. Pertussis toxin treatment had no significant inhibitory effect on the control contractile response to oxotremorine-M, but caused an 8.8-fold increase in the EC 50 value measured after a 2-hr treatment with 4-DAMP mustard. These results suggest that, after elimination of most of the M 3 receptors with 4-DAMP mustard, the contractile response can be mediated by the pertussis toxin-sensitive M 2 receptor. After pertussis toxin treatment, the kinetics of alkylation of muscarinic receptors in the colon were consistent with a single, 4-DAMP mustard-sensitive, M 3 receptor subtype mediating the contractile response. When measured after a 2-hr treatment with 4-DAMP mustard and in the presence of histamine (0.30 M) and either forskolin (10 M) or isoproterenol (0.60 M), the contractile responses to oxotremorine-M were pertussis toxin-sensitive and potently antagonized by the M 2 selective antagonist, AF-DX 116. Collectively, our results indicate that the M 2 receptor elicits contraction through two mechanisms, a direct contraction and an indirect contraction by preventing the relaxant effects of cAMP-generating agents. Muscarinic receptors are expressed abundantly in smooth muscle throughout the gastrointestinal tract in a manner that approximates a three-to-one mixture of the M 2 and M 3 subtypes (see The M 2 muscarinic receptor has been shown to mediate a pertussis toxin-sensitive inhibition of adenylyl cyclase activity in the ileum and colon Muscarinic receptors have also been shown to induce a nonselective cation conductance in the longitudinal smooth muscle of the guinea pig ileu

〔研究ノート〕 ムスカリン性アセチルコリン受容体の細胞膜への トラフィッキングの観察

　　G protein-coupled receptors are cell-surface receptors, many of which have conserved motif F（x）6LL in their C-terminal intracellular region. The motif is known to function when the G protein-coupled receptors are exported from the endoplasmic reticulum to the cell surface. We reported previously that the amino acid mutations of the conserved leucines of M1 muscarinic acetylcholine receptor caused significant decrease in the cell-surface expression and significant increase in the endoplasmic reticulum expression of the mutant receptor, and that in the presence of antagonist atropine, the mutant receptor showed cell-surface expression, similar to the expression of the wild-type receptor. In this study, we investigated the export trafficking of the mutant receptor by the measurements of membrane-impermeable antagonist ［3H］N-methylscopolamine binding to the cell surface, and indicated that the cell-surface expression of the mutant receptor in the presence of atropine decreased to 20％ by the depletion of atropine for 3 days, and recovered to the same amount as before by the second addition of atropine. We also investigated the export trafficking of the mutant receptor using the total internal reflection fluorescence microscope, and indicated that the mutant receptor expressed time-dependently to the cell surface by the second addition of atropine

Second-order rotational effects on the r-modes of neutron stars

Techniques are developed here for evaluating the r-modes of rotating neutron stars through second order in the angular velocity of the star. Second-order corrections to the frequencies and eigenfunctions for these modes are evaluated for neutron star models. The second-order eigenfunctions for these modes are determined here by solving an unusual inhomogeneous hyperbolic boundary-value problem. The numerical techniques developed to solve this unusual problem are somewhat non-standard and may well be of interest beyond the particular application here. The bulk-viscosity coupling to the r-modes, which appears first at second order, is evaluated. The bulk-viscosity timescales are found here to be longer than previous estimates for normal neutron stars, but shorter than previous estimates for strange stars. These new timescales do not substantially affect the current picture of the gravitational radiation driven instability of the r-modes either for neutron stars or for strange stars.Comment: 13 pages, 5 figures, revte

Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at-planting and side-dress). Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was \u3e150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Winter Rye Cover Crop Biomass Production, Degradation, and Nitrogen Recycling

Winter rye (Secale cereale L.) cover crop (RCC) use in corn (Zea mays L.) and soybean [Glycine max. (L.) Merr.] production can alter N dynamics compared to no RCC. The objectives of this study were to evaluate RCC biomass production (BP) and subsequent RCC degradation (BD) and N recycling in a no-till corn–soybean (CS) rotation. Aboveground RCC was sampled at spring termination for biomass dry matter (DM), C, and N. To evaluate BD and remaining C and N, RCC biomass was put into nylon mesh bags, placed on the soil surface, and collected multiple times over 105 d. Treatments included rye cover crop following soybean (RCC-FS) and corn (RCC-FC), and prior-year N applied to corn. Overall, the RCC BP and N was low due to low soil profile NO3–N. Across sites and years, the greatest BP was with RCC-FC that received 225 kg N ha–1 (1280 kg DM ha–1), with similar N uptake as with RCC-FS (27 kg N ha–1). The RCC biomass and N remaining decreased over time following an exponential decay. An average 62% biomass with RCC-FS and RCC-FC degraded after 105 d; however, N recycled was greater with RCC-FS than RCC-FC [22 (80%) vs. 14 (64%) kg N ha–1, respectively], and was influenced by the RCC C/N ratio. The RCC did not recycle an agronomically meaningful amount of N, which limited N that could potentially be supplied to corn. Rye cover crops can conserve soil N, and with improved management and growth, recycling of crop-available N should increase

A Public–Industry Partnership for Enhancing Corn Nitrogen Research and Datasets: Project Description, Methodology, and Outcomes

Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project’s anticipated value. The project was initiated in a partnership between eight U.S. Midwest land-grant universities, USDA-ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools