Differential cross section of the reaction [pi]+ + p --> K+ + Y* (1385) at 4.0 and 5.05 GeV/c

Approximately 1900 events were obtained in the differential cross section measurements of the reaction [pi]+ + p --> K+ + Y* (1385). Forward peaks were found with slopes of 2.3 +/- 0.3 and 2.6 +/- 0.4 (GeV/c)-2 for 4 and 5.05 GeV/c respectively. This data together with that in other decuplet production reactions gives evidence of a large SU(3) violation as would be expected from the mass splitting of the strange and non-strange exchanged particles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32884/1/0000262.pd

Sources of variation in simulated ecosystem carbon storage capacity from the 5th Climate Model Intercomparison Project (CMIP5)

Ecosystem carbon (C) storage strongly regulates climate-C cycle feedback and is largely determined by both C residence time and C input from net primary productivity (NPP). However, spatial patterns of ecosystem C storage and its variation have not been well quantified in earth system models (ESMs), which is essential to predict future climate change and guide model development. We intended to evaluate spatial patterns of ecosystem C storage capacity simulated by ESMs as part of the 5th Climate Model Intercomparison Project (CMIP5) and explore the sources of multi-model variation from mean residence time (MRT) and/or C inputs. Five ESMs were evaluated, including C inputs (NPP and [gross primary productivity] GPP), outputs (autotrophic/heterotrophic respiration) and pools (vegetation, litter and soil C). ESMs reasonably simulated the NPP and NPP/GPP ratio compared with Moderate Resolution Imaging Spectroradiometer (MODIS) estimates except NorESM. However, all of the models significantly underestimated ecosystem MRT, resulting in underestimation of ecosystem C storage capacity. CCSM predicted the lowest ecosystem C storage capacity (~10 kg C m−2) with the lowest MRT values (14 yr), while MIROC-ESM estimated the highest ecosystem C storage capacity (~36 kg C m−2) with the longest MRT (44 yr). Ecosystem C storage capacity varied considerably among models, with larger variation at high latitudes and in Australia, mainly resulting from the differences in the MRTs across models. Our results indicate that additional research is needed to improve post-photosynthesis C-cycle modelling, especially at high latitudes, so that ecosystem C residence time and storage capacity can be appropriately simulated

Temporal origin of nitrogen in the grain of irrigated rice in the dry season: the outcome of uptake, cycling, senescence and competition studied using a \u3csup\u3e15\u3c/sup\u3eN-point placement technique

It is often suggested that nitrogen absorbed in the vegetative stage of growth acts as a “reservoir” to supply the shortfall in demand during grain filling. The main objective of the work described in this paper was to investigate how effectively nitrogen absorbed at different stages of the growing season was retained and used for grain growth. The total nitrogen in the grain is the integral of the product of the total nitrogen absorbed at any instant and the eventual allocation of a fraction of that nitrogen to the grain. A point-placement technique was used to deliver small amounts of 15N to the roots of the rice plant at different growth stages. The total nitrogen content of the crop was measured by growth analysis throughout its duration and the measurements used to calculate the rate of total nitrogen uptake. Using 15N as a tracer enabled the fate of nitrogen taken up at any time to be determined. In the short-term, the labeled nitrogen was distributed between the various plant organs depending on their demand for nitrogen during the period of absorption. In the long-term, transfers of 15N occurred between organs, in particular to the developing panicle (rice inflorescence). The rate of nitrogen absorption of the panicle exceeded the rate of absorption by the whole plant from 68 DAT onwards. Surprisingly, in the context of rice as an annual plant, the distribution patterns suggested that towards maturity, the perennial nature of the rice plant led to competition for nitrogen between the panicles and the next generation of developing tillers. The results showed that the total nitrogen absorption by the plant was high when the fractional allocation to the grain was low and vice-versa. About 30% of the total nitrogen in the grain was acquired before panicle initiation (45 days after transplanting, DAT) and the leaves acted as the main “reservoir” for nitrogen. Losses of labeled nitrogen acquired by the plants after 35 DAT were not significant, suggesting that there was no large loss of nitrogen through volatilization, once the nitrogen had been incorporated in the plant biomass

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

Introduction Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved.Methods and analysis The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0–17 years only (component A), three centres conduct surveillance in young adults aged 18–44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression.Ethics and dissemination The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA