Calculating ex post economic optimum rates of nitrogen fertilization for corn

Rates of nitrogen (N) fertilization usually are selected with the intent of maximizing profits for producers. The most commonly used method of estimating N fertilizer needs, however, does not directly relate rates to profits. This problem was addressed by developing methodology for calculating economic optimum rates (EORs) of N fertilization from large amounts of data collected in trials using precision farming technologies. Distinctions were made between ex post and ex ante EORs and between EORs for individual trials and for samples of trials. Emphasis was placed on collecting and identifying appropriate samples of yield responses to N. Because ex post EORs are calculated for the sole purpose of calculating ex ante EORs, an appropriate sample must adequately represent the spatial and temporal variability in yield responses within a specified area of interest. Cumulative distribution functions were used to characterize variability in yield responses. Discrete marginal analysis was used to identify break-even rates of fertilization and rates that gave a desired level of profit on the last increment of fertilizer. The complement of the relative variance method and analysis of profit increases were used to evaluate alternative methods for classifying variability in yield responses. The profitability of classification was estimated by calculating ex post EORs for the whole sample and for subdivisions of this sample. Evidence presented suggests that effective systems for classification may have to consider factors that have received little attention in the past. A new procedure for calculating ex post EORs was defined as four steps; (i) define the range of conditions under consideration, (ii) collect an appropriate sample of yield responses, (iii) perform economic analyses on this sample, and (iv) explore the possibility that profits could be increased by dividing the sample into two or more populations that have different ex post EORs. Steps iii and iv should be repeated many times to evaluate all possible systems of classification. Application of two near-optimal rates of N in alternating strips across large areas of land was identified as a simple, efficient, and effective way to generate data needed for calculating ex post EORs by this method

ROC Curves Within the Framework of Neural Network Assembly Memory Model: Some Analytic Results

On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.Comment: Proceedings of the KDS-2003 Conference held in Varna, Bulgaria on June 16-26, 2003, pages 138-146, 5 Figures, 18 reference

symmetries of the Ricci tensor of static space times with maximal symmetric transverse spaces

Static space times with maximal symmetric transverse spaces are classified according to their Ricci collineations. These are investigated for non-degenerate Ricci tensor ($det.(R_{\alpha}) \neq 0$). It turns out that the only collineations admitted by these spaces can be ten, seven, six or four. Some new metrics admitting proper Ricci collineations are also investigated.Comment: 11 page

IL-33 promotes increased replication of Theiler’s Murine Encephalomyelitis Virus in RAW264.7 macrophage cells with an IRF3-dependent response

Interleukin-33 (IL-33), which promotes M2 macrophage development, may influence the control of viruses, such as Theiler’s Murine Encephalomyelitis Virus (TMEV) that infect macrophages. Because Interferon Regulatory Factor-3 (IRF3) is also critical to control of TMEV infection in macrophages, information on the relationship between IL-33 and IRF3 is important. Thus, RAW264.7 Lucia murine macrophage lineage cells with an endogenous IRF3-ISRE promoter driving secreted luciferase and IRF3KO RAW Lucia, a subline deficient in IRF3, were challenged with TMEV. After the challenge, considerable TMEV RNA detected at 18 and 24 h in RAW cells was significantly elevated in IRF3KO RAW cells. TMEV induction of ISRE-IRF3 promoter activity, IFN-β and IL-33 gene expression, and IL-6 and IL-10 protein production, which was strong in RAW cells, was less in IRF3KO RAW cells. In contrast, expression of CD206 and ARG1, classical M2 macrophage markers, was significantly elevated in IRF3KO RAW cells. Moreover, RAW and IRF3KO RAW cells produced extracellular IL-33 prior to and after infection with TMEV and antibody blockade of the IL-33 receptor, ST2, reduced CD206 and ARG1 expression, but increased IL-6 gene expression. Pre-treating both RAW and IRF3KO RAW cells with IL-33 prior to challenge significantly increased TMEV infection, but also increased IL-33, IL-10, IL-6 mRNA expression, and NO production without increasing IFN-β. Notably, IL-33 induction of IL-33, IRF3-ISRE promoter activity, and IL-10 by TMEV or poly I:C/IFN-γ was significantly dependent upon IRF3. The results show that the expression of IL-33 and the repression of M2 macrophage phenotypic markers are dependent on IRF3 and that IL-33 decreases the ability of macrophages to control infection with macrophage-tropic viruses

IL-33 promotes increased replication of Theiler’s Murine Encephalomyelitis Virus in RAW264.7 macrophage cells with an IRF3-dependent response

Interleukin-33 (IL-33), which promotes M2 macrophage development, may influence the control of viruses, such as Theiler’s Murine Encephalomyelitis Virus (TMEV) that infect macrophages. Because Interferon Regulatory Factor-3 (IRF3) is also critical to control of TMEV infection in macrophages, information on the relationship between IL-33 and IRF3 is important. Thus, RAW264.7 Lucia murine macrophage lineage cells with an endogenous IRF3-ISRE promoter driving secreted luciferase and IRF3KO RAW Lucia, a subline deficient in IRF3, were challenged with TMEV. After the challenge, considerable TMEV RNA detected at 18 and 24 h in RAW cells was significantly elevated in IRF3KO RAW cells. TMEV induction of ISRE-IRF3 promoter activity, IFN-β and IL-33 gene expression, and IL-6 and IL-10 protein production, which was strong in RAW cells, was less in IRF3KO RAW cells. In contrast, expression of CD206 and ARG1, classical M2 macrophage markers, was significantly elevated in IRF3KO RAW cells. Moreover, RAW and IRF3KO RAW cells produced extracellular IL-33 prior to and after infection with TMEV and antibody blockade of the IL-33 receptor, ST2, reduced CD206 and ARG1 expression, but increased IL-6 gene expression. Pre-treating both RAW and IRF3KO RAW cells with IL-33 prior to challenge significantly increased TMEV infection, but also increased IL-33, IL-10, IL-6 mRNA expression, and NO production without increasing IFN-β. Notably, IL-33 induction of IL-33, IRF3-ISRE promoter activity, and IL-10 by TMEV or poly I:C/IFN-γ was significantly dependent upon IRF3. The results show that the expression of IL-33 and the repression of M2 macrophage phenotypic markers are dependent on IRF3 and that IL-33 decreases the ability of macrophages to control infection with macrophage-tropic viruses