Velocity estimates for signal propagation leading to systemic jasmonic acid accumulation in wounded Arabidopsis.

The wound response prohormone jasmonic acid (JA) accumulates rapidly in tissues both proximal and distal to injury sites in plants. Using quantitative liquid chromatography-mass spectrometry after flash freezing of tissues, we found that JA accumulated within 30 s of injury in wounded Arabidopsis leaves (p = 3.5 e(-7)). JA augmentation distal to wounds was strongest in unwounded leaves with direct vascular connections to wounded leaves wherein JA levels increased significantly within 120 s of wounding (p = 0.00027). This gave conservative and statistically robust temporal boundaries for the average velocity of the long distance signal leading to distal JA accumulation in unwounded leaves of 3.4-4.5 cm min(-1). Like JA, transcripts of the JA synthesis gene LIPOXYGENASE2 (LOX2) and the jasmonate response gene JAZ10.3 also accumulated to higher levels in directly interconnected leaves than in indirectly connected leaves. JA accumulation in a lox2-1 mutant plant was initiated rapidly after wounding then slowed progressively compared with the wild type (WT). Despite this, JAZ10.3 expression in the two genotypes was similar. Free cyclopentenone jasmonate levels were similar in both resting WT and lox2-1. In contrast, bound cyclopentenone jasmonates (arabidopsides) were far lower in lox2-1 than in the WT. The major roles of LOX2 are to generate arabidopsides and the large levels of JA that accumulate proximal to the wound. LOX2 is not essential for some of the most rapid events elicited by wounding.

Variable Kernel estimates: On the impossibility of tuning the parameters

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any data-based bandwidth, there exists a densityfor which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem of tuning the variable bandwidth in an optimal manner is ``too hard''. Moreover, from the class of counterexamples exhibited in the paper, it appears thatplacing conditions on the densities (monotonicity, convexity, smoothness) does not help.

Nutritional ecology of blowflies (Diptera, Calliphoridae): estimates of critical larval weight for pupation on two different

Under natural environmental conditions, blowflies utilize discrete and ephemeral feeding resources such as decaying carcasses. Competition for food on such feeding substrates is usually very severe, and only the individuals that are capable of attaining the critical larval weight for pupation will be able to survive. This critical weight is hitherto unknown for several blowfly species; therefore, the current work is aimed at obtaining such a critical value for four blowfly species of the genera Chrysomya and Lucilia, deploying two types of feeding substrate, namely, artificial diet and macerated bovine meat. On the whole, the critical weights ranged from 30 to 35 mg. The lowest larval weight which permitted pupation was 30.0 mg for Chrysomya megacephala reared on macerated bovine meat. This species was also the best adapted to pupation at low larval weights in relation to the maximum larval weight for males. Regarding the pupation of females, the best-adapted individual was a C. albiceps specimen exhibiting a critical weight that was equal to 39.20 % of the maximum value obtained. Concerning all the species and diet types, the female individuals exhibited the lowest critical weights that produced viable pupae, probably representing an evolutionary strategy that favoured the survival of females, responsible for the egg formation, contributing to the establishment of future generations. Regarding the loss (in percentage) of adult biomass in relation to the third instar larvae, the females of C. megacephala lost less weight than males in both feeding substrates. On the other hand, such a loss of weight occurred in males of C. albiceps and L. cuprina.

Estimates of patient costs related with population morbidity: Can indirect costs affect the results?

A number of health economics works require patient cost estimates as a basic information input.However the accuracy of cost estimates remains in general unspecified. We propose to investigate howthe allocation of indirect costs or overheads can affect the estimation of patient costs in order to allow forimprovements in the analysis of patient costs estimates. Instead of focusing on the costing method, thispaper proposes to highlight changes in variance explained observed when a methodology is chosen. Wecompare three overhead allocation methods for a specific Spanish population adjusted using the ClinicalRisk Groups (CRG), and we obtain different series of full-cost group estimates. As a result, there aresignificant gains in the proportion of the variance explained, depending upon the methodology used.Furthermore, we find that the global amount of variation explained by risk adjustment models dependsmainly on direct costs and is independent of the level of aggregation used in the classification system.

Spatial and temporal variability of chlorophyll distributions and geostrophic estimates on the Peru shelf at 9°S

Estudio del perfil de la plataforma del Peru a los 9°S. Datos colectados mediante Batfish en zig zag

Inequalities for a new data-based method for selecting nonparametric density estimates

We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provideexplicit non-asymptotic density-free inequalities that relate the $L_1$ error of the selected estimate with that of the best possible estimate,and study in particular the connection between the richness of the classof density estimates and the performance bound. For example, our methodallows one to pick the bandwidth and kernel order in the kernel estimatesimultaneously and still assure that for {\it all densities}, the $L_1$error of the corresponding kernel estimate is not larger than aboutthree times the error of the estimate with the optimal smoothing factor and kernel plus a constant times $\sqrt{\log n/n}$, where $n$ is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variablekernel estimates.

Iowa County Precipitation Estimates, 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

County Precipitation Estimates, January 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

Iowa County Precipitation Estimates, Departure from Normal 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

Iowa County Precipitation Estimates, Departure from Normal 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

Iowa Preliminary County Precipitation Estimates, July 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

Iowa Preliminary County Precipitation Estimates, July 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau.

County Precipitation Estimates, October 2001

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Precipitation Estimates, November 2001

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Precipitation Estimates, December 2001

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.