Sparse demand systems: corners and complements

We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good. The model nests multinomial discrete choice and continuous demand systems as special cases. Goods can be substitutes or complements. Random coefficients are employed to capture the wide variation in the composition of consumption baskets. Non-negativity constraints produce corners that account for different consumers purchasing different numbers of types of goods. We show semiparametric identification of the model. We apply the model to the demand for fruit in the United Kingdom. We estimate the model’s parameters using UK scanner data for 2008 from the Kantar World Panel. Using our parameter estimates, we estimate a matrix of demand elasticities for 27 categories of fruit and analyze a range of tax and policy change scenarios

Semiparametric estimation of random coefficients in structural economic models

This paper discusses nonparametric estimation of the distribution of random coefficients in a structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function (pdf) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the structural model from estimation. We first discuss nonparametric identification. Then, we propose two alternative estimation procedures to estimate the density and derive their asymptotic properties. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure

A Control Switch for Prothrombinase: Characterization of a Hirudin-Like Pentapeptide From the COOH Terminus of Factor VA Heavy Chain That Regulates the Rate and Pathway for Prothrombin Activation

Membrane-bound factor Xa alone catalyzes prothrombin activation following initial cleavage at Arg(271) and prethrombin 2 formation (pre2 pathway). Factor Va directs prothrombin activation by factor Xa through the meizothrombin pathway, characterized by initial cleavage at Arg(320) (meizo pathway). We have shown previously that a pentapeptide encompassing amino acid sequence 695-699 from the COOH terminus of the heavy chain of factor Va (Asp-Tyr-Asp-Tyr-Gln, DYDYQ) inhibits prothrombin activation by prothrombinase in a competitive manner with respect to substrate. To understand the mechanism of inhibition of thrombin formation by DYDYQ, we have studied prothrombin activation by gel electrophoresis. Titration of plasma-derived prothrombin activation by prothrombinase, with increasing concentrations of peptide, resulted in complete inhibition of the meizo pathway. However, thrombin formation still occurred through the pre2 pathway. These data demonstrate that the peptide preferentially inhibits initial cleavage of prothrombin by prothrombinase at Arg(320). These findings were corroborated by studying the activation of recombinant mutant prothrombin molecules rMZ-II (R155A/R284A/R271A) and rP2-II (R155A/R284A/R320A) which can be only cleaved at Arg(320) and Arg(271), respectively. Cleavage of rMZ-II by prothrombinase was completely inhibited by low concentrations of DYDYQ, whereas high concentrations of pentapeptide were required to inhibit cleavage of rP2-II. The pentapeptide also interfered with prothrombin cleavage by membrane-bound factor Xa alone in the absence of factor Va increasing the rate for cleavage at Arg(271) of plasma-derived prothrombin or rP2-II. Our data demonstrate that pentapeptide DYDYQ has opposing effects on membrane-bound factor Xa for prothrombin cleavage, depending on the incorporation of factor Va in prothrombinase

Incorporation of Factor VA Into Prothrombinase Is Required for Coordinated Cleavage of Prothrombin by Factor Xa

Prothrombin is activated to thrombin by two sequential factor Xa-catalyzed cleavages, at Arg271 followed by cleavage at Arg320. Factor Va, along with phospholipid and Ca2+, enhances the rate of the process by 300,000-fold, reverses the order of cleavages, and directs the process through the meizothrombin pathway, characterized by initial cleavage at Arg320. Previous work indicated reduced rates of prothrombin activation with recombinant mutant factor Va defective in factor Xa binding (E323F/Y324F and E330M/V331I, designated factor VaFF/MI). The present studies were undertaken to determine whether loss of activity can be attributed to selective loss of efficiency at one or both of the two prothrombin-activating cleavage sites. Kinetic constants for the overall activation of prothrombin by prothrombinase assembled with saturating concentrations of recombinant mutant factor Va were calculated, prothrombin activation was assessed by SDS-PAGE, and rate constants for both cleavages were analyzed from the time course of the concentration of meizothrombin. Prothrombinase assembled with factor VaFF/MI had decreased k(cat) for prothrombin activation with Km remaining unaffected. Prothrombinase assembled with saturating concentrations of factor VaFF/MI showed significantly lower rate for cleavage of plasma-derived prothrombin at Arg320 than prothrombinase assembled with saturating concentrations of wild type factor Va. These results were corroborated by analysis of cleavage of recombinant prothrombin mutants rMz-II (R155A/R284A/R271A) and rP2-II (R155A/R284A/R320A), which can be cleaved only at Arg320 or Arg271, respectively. Time courses of these mutants indicated that mutations in the factor Xa binding site of factor Va reduce rates for both bonds. These data indicate that the interaction of factor Xa with the heavy chain of factor Va strongly influences the catalytic activity of the enzyme resulting in increased rates for both prothrombin-activating cleavages

The Structural Integrity of Anion Binding Exosite I of Thrombin Is Required and Sufficient for Timely Cleavage and Activation of Factor V and Factor VIII

Alpha-thrombin has two separate electropositive binding exosites (anion binding exosite I, ABE-I and anion binding exosite II, ABE-II) that are involved in substrate tethering necessary for efficient catalysis. Alpha-thrombin catalyzes the activation of factor V and factor VIII following discrete proteolytic cleavages. Requirement for both anion binding exosites of the enzyme has been suggested for the activation of both procofactors by alpha-thrombin. We have used plasma-derived alpha-thrombin, beta-thrombin (a thrombin molecule that has only ABE-II available), and a recombinant prothrombin molecule rMZ-II (R155A/R284A/R271A) that can only be cleaved at Arg(320) (resulting in an enzymatically active molecule that has only ABE-I exposed, rMZ-IIa) to ascertain the role of each exosite for procofactor activation. We have also employed a synthetic sulfated pentapeptide (DY(SO(3)(-))DY(SO(3)(-))Q, designated D5Q1,2) as an exosite-directed inhibitor of thrombin. The clotting time obtained with beta-thrombin was increased by approximately 8-fold, whereas rMZ-IIa was 4-fold less efficient in promoting clotting than alpha-thrombin under similar experimental conditions. Alpha-thrombin readily activated factor V following cleavages at Arg(709), Arg(1018), and Arg(1545) and factor VIII following proteolysis at Arg(372), Arg(740), and Arg(1689). Cleavage of both procofactors by alpha-thrombin was significantly inhibited by D5Q1,2. In contrast, beta-thrombin was unable to cleave factor V at Arg(1545) and factor VIII at both Arg(372) and Arg(1689). The former is required for light chain formation and expression of optimum factor Va cofactor activity, whereas the latter two cleavages are a prerequisite for expression of factor VIIIa cofactor activity. Beta-thrombin was found to cleave factor V at Arg(709) and factor VIII at Arg(740), albeit less efficiently than alpha-thrombin. The sulfated pentapeptide inhibited moderately both cleavages by beta-thrombin. Under similar experimental conditions, membrane-bound rMZ-IIa cleaved and activated both procofactor molecules. Activation of the two procofactors by membrane-bound rMZ-IIa was severely impaired by D5Q1,2. Overall the data demonstrate that ABE-I alone of alpha-thrombin can account for the interaction of both procofactors with alpha-thrombin resulting in their timely and efficient activation. Because formation of meizothrombin precedes that of alpha-thrombin, our findings also imply that meizothrombin may be the physiological activator of both procofactors in vivo in the presence of a procoagulant membrane surface during the early stages of coagulation

Pesticide use policy for conservation use acreage - (PIK Program)

The Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311

Cooperative Regulation of the Activity of Factor Xa within Prothrombinase by Discrete Amino Acid Regions from Factor Va Heavy Chain†

ABSTRACT: The prothrombinase complex catalyzes the activation of prothrombin to R-thrombin. We have repetitively shown that amino acid region 695DYDY698 from the COOH terminus of the heavy chain of factor Va regulates the rate of cleavage of prothrombin at Arg271 by prothrombinase. We have also recently demonstrated that amino acid region 334DY335 is required for the optimal activity of prothrombinase. To assess the effect of these six amino acid residues on cofactor activity, we created recombinant factor Va molecules combining mutations at amino acid regions 334–335 an

Cleavage Requirements for Activation of Factor V by Factor Xa

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65728/1/j.1432-1033.1997.00012.x.pd

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Background Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. Methods More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. Findings 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. Interpretation A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes