3,787 research outputs found
The two Tasmanian species of calorophus (restionaceae)
Calorophus ater L Johnson & B. Briggs sp. nov. from the sedgelands of southwestern and western Tasmania is described. Description and synonymy of C. elongatus are also provided
The UK register of HIV seroconverters: Methods and analytical issues
A Register of HIV-infected persons who have had a negative antibody test within 3 years of their first antibody positive test (seroconverters) is being set up in the UK to monitor the distribution of times from HIV seroconversion to AIDS (the incubation period) and to death. It will also provide a national resource for use by those designing studies in this group of individuals. Clinicians caring for HIV-positive persons in Genito-Urinary Medicine, Infectious Disease and other departments throughout the UK were asked to participate by providing information on eligible subjects. Most laboratories undertaking HIV antibody testing were also contacted and asked to provide the name of the attending clinician for all seroconverters identified through the HIV laboratory reporting systems of the PHLS Communicable Disease Surveillance Centre (CDSC) and the Scottish Centre for Infection and Environmental Health (SCIEH) and for any other seroconverters known to them but not identified by CDSC or SCIEH. Data items sought for the Register include: sex, ethnic group, probable route of HIV transmission, annual CD4 counts, details of therapy and prophylaxis prescribed, AIDS-defining events and vital status. Follow up information is collected annually. Wherever possible, all seroconverters known to a clinic have been identified, whether currently alive or dead, either from clinic records or laboratory reporting or both. The objective is to establish and update a complete register of seroconverters on a long-term basis to provide reliable estimates of the incubation period on which future projections of AIDS cases in the UK can be made
Intrinsic Mitochondrial Membrane Potential and Associated Tumor Phenotype Are Independent of MUC1 Over-Expression
We have established previously that minor subpopulations of cells with stable differences in their intrinsic mitochondrial membrane potential (Δψm) exist within populations of mammary and colonic carcinoma cells and that these differences in Δψm are linked to tumorigenic phenotypes consistent with increased probability of participating in tumor progression. However, the mechanism(s) involved in generating and maintaining stable differences in intrinsic Δψm and how they are linked to phenotype are unclear. Because the mucin 1 (MUC1) oncoprotein is over-expressed in many cancers, with the cytoplasmic C-terminal fragment (MUC1 C-ter) and its integration into the outer mitochondrial membrane linked to tumorigenic phenotypes similar to those of cells with elevated intrinsic Δψm, we investigated whether endogenous differences in MUC1 levels were linked to stable differences in intrinsic Δψm and/or to the tumor phenotypes associated with the intrinsic Δψm. We report that levels of MUC1 are significantly higher in subpopulations of cells with elevated intrinsic Δψm derived from both mammary and colonic carcinoma cell lines. However, using siRNA we found that down-regulation of MUC1 failed to significantly affect either the intrinsic Δψm or the tumor phenotypes associated with increased intrinsic Δψm. Moreover, whereas pharmacologically mediated disruption of the Δψm was accompanied by attenuation of tumor phenotype, it had no impact on MUC1 levels. Therefore, while MUC1 over-expression is associated with subpopulations of cells with elevated intrinsic Δψm, it is not directly linked to the generation or maintenance of stable alterations in intrinsic Δψm, or to intrinsic Δψm associated tumor phenotypes. Since the Δψm is the focus of chemotherapeutic strategies, these data have important clinical implications in regard to effectively targeting those cells within a tumor cell population that exhibit stable elevations in intrinsic Δψm and are most likely to contribute to tumor progression
Genetic and environmental variation in condition, cutaneous immunity, and haematocrit in house wrens
This is the final version of the article. Available from BioMed Central via the DOI in this record.BACKGROUND: Life-history studies of wild bird populations often focus on the relationship between an individual's condition and its capacity to mount an immune response, as measured by a commonly-employed assay of cutaneous immunity, the PHA skin test. In addition, haematocrit, the packed cell volume in relation to total blood volume, is often measured as an indicator of physiological performance. A multi-year study of a wild population of house wrens has recently revealed that those exhibiting the highest condition and strongest PHA responses as nestlings are most likely to be recruited to the breeding population and to breed through two years of age; in contrast, intermediate haematocrit values result in the highest recruitment to the population. Selection theory would predict, therefore, that most of the underlying genetic variation in these traits should be exhausted resulting in low heritability, although such traits may also exhibit low heritability because of increased residual variance. Here, we examine the genetic and environmental variation in condition, cutaneous immunity, and haematocrit using an animal model based on a pedigree of approximately 2,800 house wrens. RESULTS: Environmental effects played a paramount role in shaping the expression of the fitness-related traits measured in this wild population, but two of them, condition and haematocrit, retained significant heritable variation. Condition was also positively correlated with both the PHA response and haematocrit, but in the absence of any significant genetic correlations, it appears that this covariance arises through parallel effects of the environment acting on this suite of traits. CONCLUSIONS: The maintenance of genetic variation in different measures of condition appears to be a pervasive feature of wild bird populations, in contradiction of conventional selection theory. A major challenge in future studies will be to explain how such variation persists in the face of the directional selection acting on condition in house wrens and other species.We thank the 2004–2006 Wren Crews for field assistance and the ParkLands Foundation (Merwin Preserve) and the Sears and Butler families for the use of their properties. Financial support was provided by NSF grants GK12-0086354, IBN-0316580, IOS-0718140 and IOS-1118160; NIH grant R15HD076308-01; a visiting professorship from the Leverhulme Trust (SKS); the School of Biological Sciences, Illinois State University; a BBSRC David Phillips Research Fellowship (AJW); and student-research grants from the Beta Lambda Chapter of the Phi Sigma Biological Sciences Honor Society (AMF)
Pollination: A key event controlling the expression of genes related to phytohormone biosynthesis during grapevine berry formation
Berry formation is the process of ovary conversion into a functional fruit, and is characterized by abrupt changes in the content of several phytohormones, associated with pollination and fertilization. Much effort has been made in order to improve our understanding of berry development, particularly from veraison to post-harvest time. However, the period of berry formation has been poorly investigated, despite its importance. Phytohormones are involved in the control of fruit formation; hence it is important to understand the regulation of their content at this stage. Grapevine is an excellent fleshy-fruit plant model since its fruits have particularities that differentiate them from those of commonly studied organisms. For instance, berries are prepared to cope with stress by producing several antioxidants and they are non-climacteric fruits. Also its genome is fully sequenced, which allows to identify genes involved in developmental processes. In grapevine, no link has been established between pollination and phytohormone biosynthesis, until recently. Here we highlight relevant findings regarding pollination effect on gene expression related to phytohormone biosynthesis, and present results showing how quickly this effect is achieved
A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios
[EN] Ecuador is worldwide considered as one of the main natural flower producers and exporters ¿being roses the most salient ones. Such a fact has naturally led the emergence of small and medium sized companies devoted to the production of quality roses in the Ecuadorian highlands, which intrinsically entails resource usage optimization. One of the first steps towards optimizing the use of resources is to forecast demand, since it enables a fair perspective of the future, in such a manner that the in-advance raw materials supply can be previewed against eventualities, resources usage can be properly planned, as well as the misuse can be avoided. Within this approach, the problem of forecasting the supply of roses was solved into two phases: the first phase consists of the macro-forecast of the total amount to be exported by the Ecuadorian flower sector by the year 2020, using multi-layer neural networks. In the second phase, the monthly demand for the main rose varieties offered by the study company was micro-forecasted by testing seven models. In addition, a Bayesian network model is designed, which takes into consideration macroeconomic aspects, the level of employability in Ecuador and weather-related aspects. This Bayesian network provided satisfactory results without the need for a large amount of historical data and at a low-computational cost.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS ¿Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems¿ (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015. In addition, the authors are greatly grateful by the support given by the SDAS Research Group (www.sdas-group.com)Herrera-Granda, ID.; Lorente-Leyva, LL.; Peluffo-Ordóñez, DH.; Alemany Díaz, MDM. (2021). A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under
Uncertain Scenarios. Lecture Notes in Computer Science. 12566:245-258. https://doi.org/10.1007/978-3-030-64580-9_21S24525812566Asociación de Productores y Exportadores de Flores: Inicio – Expoflores. https://expoflores.com/Palacios, J., Rosero, D.: Análisis de las condiciones climáticas registradas en el Ecuador continental en el año 2013 y su impacto en el sector agrícola. Estud. e Investig. meteorológicas. Ina. Inst. Nac. Meteorol. e Hidrol. Ecuador, 28, p. (2014)Hidalgo-Proaño, M.: Variabilidad climática interanual sobre el Ecuador asociada a ENOS. CienciAmérica 6, 42–47 (2017)Ritchie, J.W., Abawi, G.Y., Dutta, S.C., Harris, T.R., Bange, M.: Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance. Aust. J. Agric. Resour. Econ. 48, 65–93 (2004). https://doi.org/10.1111/j.1467-8489.2004.t01-1-00230.xLetson, D., Podesta, G.P., Messina, C.D., Ferreyra, R.A.: The uncertain value of perfect ENSO phase forecasts: Stochastic agricultural prices and intra-phase climatic variations. Clim. Change 69, 163–196 (2005). https://doi.org/10.1007/s10584-005-1814-9Weber, E.U., Laciana, C., Bert, F., Letson, D.: Agricultural decision making in the argentine Pampas: Modeling the interaction between uncertain and complex environments and heterogeneous and complex decision makers (2008)Loy, J.-P., Pieniadz, A.: Optimal grain marketing revisited a german and polish perspective. Outlook Agric. 38, 47–54 (2009). https://doi.org/10.5367/000000009787762761Wang, Q.J., Robertson, D.E., Haines, C.L.: A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development. WATER Resour. Res. 45 (2009). https://doi.org/10.1029/2006wr005419Keesman, K.J., Doeswijk, T.: uncertainty analysis of weather controlled systems (2010). https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960073961&doi=10.1007%2F978-3-642-03735-1_12&partnerID=40&md5=210525584472097e996a9f124f96fddbSchnepf, R.: U.S. livestock and poultry feed use and availability: background and emerging issues. In: Feed Market Dynamics and U.S. Livestock Implications. pp. 1–36. Nova Science Publishers, Inc., CRS, United States (2012)Medellín-Azuara, J., Howitt, R.E., MacEwan, D.J., Lund, J.R.: Economic impacts of climate-related changes to California agriculture. Clim. Change 109, 387–405 (2011). https://doi.org/10.1007/s10584-011-0314-3McCown, R.L., Carberry, P.S., Dalgliesh, N.P., Foale, M.A., Hochman, Z.: Farmers use intuition to reinvent analytic decision support for managing seasonal climatic variability. Agric. Syst. 106, 33–45 (2012). https://doi.org/10.1016/j.agsy.2011.10.005Scott, S.L., Varian, H.R.: Predicting the present with bayesian structural time series. Available SSRN 2304426 (2013)Prudhomme, C., Shaffrey, L., Woollings, T., Jackson, C., Fowler, H., Anderson, B.: IMPETUS: Improving predictions of drought for user decision-making. International Conference on Drought: Research and Science-Policy Interfacing, 2015. pp. 273–278. CRC Press/Balkema, Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom (2015)Wiles, P., Enke, D.: A hybrid neuro-fuzzy model to forecast the Soybean complex. International Annual Conference of the American Society for Engineering Management 2015, ASEM 2015. pp. 1–5. American Society for Engineering Management, Missouri University of Science and Technology, Engineering Management and Systems Engineering Department, United States (2015)Hansen, B.G., Li, Y.: An analysis of past world market prices of feed and milk and predictions for the future. Agribusiness 33, 175–193 (2017). https://doi.org/10.1002/agr.21474Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bedard, F.: Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agric. For. Meteorol. 218, 74–84 (2016). https://doi.org/10.1016/j.agrformet.2015.11.003Chen, J., Yang, J., Zhao, J., Xu, F., Shen, Z., Zhang, L.: Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing 174, 1087–1100 (2016). https://doi.org/10.1016/j.neucom.2015.09.105Fodor, N., et al.: Integrating plant science and crop modeling: assessment of the impact of climate change on soybean and maize production. Plant Cell Physiol. 58, 1833–1847 (2017). https://doi.org/10.1093/pcp/pcx141Chapman, R., et al.: Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis. Comput. Electron. Agric. 151, 338–348 (2018). https://doi.org/10.1016/j.compag.2018.06.006Lara-Estrada, L., Rasche, L., Sucar, L.E., Schneider, U.A.: Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks. LAND. 7 (2018). https://doi.org/10.3390/land7010004Abdelaal, H.S.A., Thilmany, D.: Grains production prospects and long run food security in Egypt. Sustain. 11 (2019). https://doi.org/10.3390/su11164457Kusunose, Y., Ma, L., Van Sanford, D.: User responses to imperfect forecasts: findings from an experiment with Kentucky wheat farmers. Weather. Clim. Soc. 11, 791–808 (2019). https://doi.org/10.1175/wcas-d-18-0135.1Kadigi, I.L., et al.: Forecasting yields, prices and net returns for main cereal crops in Tanzania as probability distributions: a multivariate empirical (MVE) approach. Agric. Syst. 180 (2020). https://doi.org/10.1016/j.agsy.2019.102693McGrath, G., Rao, P.S.C., Mellander, P.-E., Kennedy, I., Rose, M., van Zwieten, L.: Real-time forecasting of pesticide concentrations in soil. Sci. Total Environ. 663, 709–717 (2019). https://doi.org/10.1016/j.scitotenv.2019.01.401Yang, B., Xie, L.: Bayesian network modelling for “direct farm” mode based agricultural supply chain risk. Ekoloji 28, 2361–2368 (2019)Zaporozhtseva, L.A., Sabetova, T. V, Yu Fedulova, I.: Assessment of the uncertainty factors in computer modelling of an agricultural company operation. International Conference on Information Technologies in Business and Industries, ITBI 2019. Institute of Physics Publishing, Voronezh State Agrarian University, Michurina Str. 30, Voronezh, 394087, Russian Federation (2019)Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. Wiley (2015)Hanke, J., Wichern, D.: Business forecast. Pearson Educación (2010)Novagric: Invernaderos para Cultivo de Rosas. https://www.novagric.com/es/invernaderos-rosasWeather Spark: Clima promedio en Quito, Ecuador, durante todo el año - Weather Spark. https://es.weatherspark.com/y/20030/Clima-promedio-en-Quito-Ecuador-durante-todo-el-añoInstituto Nacional de Estadísticas y Censos-INEC: Encuesta Nacional de Empleo, Desempleo y subempleo-ENEMDU. https://www.ecuadorencifras.gob.ec/empleo-diciembre-2019/Central Bank of Ecuador: Central Bank of Ecuador. www.bce.fin.ecHyndman, R., Athnasopoulos, G.: Forecasting: Principles and Practice. OTexts, Australia (2018)Herrera-Granda, I.D., et al.: Artificial neural networks for bottled water demand forecasting: a small business case study. In: Rojas, I., Joya, G.C.A. (eds.) International Work-Conference on Artificial Neural Networks, pp. 362–373. Springer, Canaria (2019
Estimating pneumonia deaths of post-neonatal children in countries of low or no death certification in 2008
BACKGROUND: Pneumonia is the leading cause of child deaths globally. The aims of this study were to: a) estimate the number and global distribution of pneumonia deaths for children 1-59 months for 2008 for countries with low (85% coverage of death certification countries was used. For 87 high child-mortality countries pneumonia death estimates were obtained by applying a regression model developed from published and unpublished verbal autopsy data from high child-mortality settings. The total number of 1-59 months pneumonia deaths for the year 2008 for these 122 countries was estimated to be 1.18 M (95% CI 0.77 M-1.80 M), which represented 23.27% (95% CI 17.15%-32.75%) of all 1-59 month child deaths. The country level estimation correlation coefficient between these two methods was 0.40. INTERPRETATION: Although the overall number of post-neonatal pneumonia deaths was similar irrespective to the method of estimation used, the country estimate correlation coefficient was low, and therefore country-specific estimates should be interpreted with caution. Pneumonia remains the leading cause of child deaths and is greatest in regions of poverty and high child-mortality. Despite the concerns about gender inequity linked with childhood mortality we could not estimate sex-specific pneumonia mortality rates due to the inadequate data. Life-saving interventions effective in preventing and treating pneumonia mortality exist but few children in high pneumonia disease burden regions are able to access them. To achieve the United Nations Millennium Development Goal 4 target to reduce child deaths by two-thirds in year 2015 will require the scale-up of access to these effective pneumonia interventions
Strategies used as spectroscopy of financial markets reveal new stylized facts
We propose a new set of stylized facts quantifying the structure of financial
markets. The key idea is to study the combined structure of both investment
strategies and prices in order to open a qualitatively new level of
understanding of financial and economic markets. We study the detailed order
flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This
enormous dataset allows us to compare (i) a closed national market (A-shares)
with an international market (B-shares), (ii) individuals and institutions and
(iii) real investors to random strategies with respect to timing that share
otherwise all other characteristics. We find that more trading results in
smaller net return due to trading frictions. We unveiled quantitative power
laws with non-trivial exponents, that quantify the deterioration of performance
with frequency and with holding period of the strategies used by investors.
Random strategies are found to perform much better than real ones, both for
winners and losers. Surprising large arbitrage opportunities exist, especially
when using zero-intelligence strategies. This is a diagnostic of possible
inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl
The properties, origin and evolution of stellar clusters in galaxy simulations and observations
Published onlineWe investigate the properties and evolution of star particles in two simulations of isolated spiral galaxies, and two galaxies from cosmological simulations. Unlike previous numerical work, where typically each star particle represents one ‘cluster’, for the isolated galaxies we are able to model features we term ‘clusters’ with groups of particles. We compute the spatial distribution of stars with different ages, and cluster mass distributions, comparing our findings with observations including the recent LEGUS survey. We find that spiral structure tends to be present in older (100s Myr) stars and clusters in the simulations compared to the observations. This likely reflects differences in the numbers of stars or clusters, the strength of spiral arms, and whether the clusters are allowed to evolve. Where we model clusters with multiple particles, we are able to study their evolution. The evolution of simulated clusters tends to follow that of their natal gas clouds. Massive, dense, long-lived clouds host massive clusters, whilst short-lived clouds host smaller clusters which readily disperse. Most clusters appear to disperse fairly quickly, in basic agreement with observational findings. We note that embedded clusters may be less inclined to disperse in simulations in a galactic environment with continuous accretion of gas on to the clouds than isolated clouds and correspondingly, massive young clusters which are no longer associated with gas tend not to occur in the simulations. Caveats of our models include that the cluster densities are lower than realistic clusters, and the simplistic implementation of stellar feedback.We thank the referee for a useful report. The calculations for this paper were performed primarily on the DiRAC machine ‘Complexity’, as well as the supercomputer at Exeter, which is jointly funded by STFC, the Large Facilities Capital Fund of BIS, and the University of Exeter. We would like to thank Michele Fumagalli for work putting together the LEGUS cluster catalogues. CLD and CGF acknowledge funding from the European Research Council for the FP7 ERC starting grant project LOCALSTAR. CGF thanks Ben Thompson for performing data reduction. DG kindly acknowledges financial support by the German Research Foundation (DFG) through grant GO 1659/3-2. Figures in this paper were produced using splash (Price 2007)
- …