2,113 research outputs found
The mRNA encoding the yeast ARE-binding protein Cth2 is generated by a novel 3 ' processing pathway
No full textMicroarray analyses of mRNAs over-expressed in strains lacking the nuclear exosome component Rrp6 identified the transcript encoding the ARE-binding protein Cth2, which functions in cytoplasmic mRNA stability. Subsequent northern analyses revealed that exosome mutants accumulate a 3'-extended transcript at the expense of the mature CTH2 mRNA. The 3' ends of the CTH2 mRNA were mapped to a [GU(3-5)](5) repeat, unlike any previously characterized polyadenylation site. CTH2 mRNA accumulation was not inhibited by mutations in 3'-cleavage and polyadenylation factors, Rna14, Rna15 and Pap1, which block accumulation of other mRNAs. The 3'-extended CTH2 pre-mRNA strongly accumulated in strains with mutations in the TRAMP4 polyadenylation complex or the Nrd1/Nab3/Sen1 complex, and contains multiple Nrd1 and Nab3 binding sites. CTH2 carries a consensus ARE element and levels of the pre-mRNA and mRNA were elevated by mutation of the ARE or inactivation of the nuclear 5'-exonuclease Rat1. We propose that CTH2 mRNA is processed from a 3'-extended primary transcript by the exosome, TRAMP and Nrd1/Nab3/ Sen1 complexes. This unusual pathway may allow time for nuclear, ARE-mediated regulation of CTH2 levels involving Rat1.</p
The cost of antibiotic therapy and antibiotic resistance for complicated intra-abdominal infections (cIAIs) and complicated urinary tract infections (cUTIs) in Italy
No full textThe cost of antibiotic therapy and antibiotic resistance for complicated intra-abdominal infections (cIAIs) and complicated urinary tract infections (cUTIs) in ItalyObjectiveTo assess costs associated with the treatment of community-acquired cIAIs and cUTIs, from the Italian National Health Service perspective.MethodsA retrospective observational cohort study was conducted. This study analyzed the charts of patients discharged from one Italian hospital between January 1 and December 31, 2015, with a primary diagnosis of community-acquired cIAIs or cUTIs. Patient characteristics, diagnosis, cost and length of antibiotic therapy, cost and length of hospital stay and antibiotic resistance were all recorded. Costs were calculated in Euros at 2015 values.ResultsThe records of 324 patients (mean age 59.0 years; 48.8% males) were analyzed, 239 with cIAIs (mean age 59.9 years; males 53.2%) and 85 with cUTIs (mean age 56.5 years; males 38.8%). The average cost of care for a patient hospitalized due to cIAI was €8,519 (±€7,239) and due to cUTI was €3,866 (±€2,915). The average cost of the antibiotic therapy for a patient with cIAI was €503 (±€1,059) and with cUTI was €239 (±€526). Patients with antibiotic resistance received additional days of antibiotic therapy (cIAI: +9.5 days - p<0.0001; cUTI: +17.3 days - p<0.0001). Furthermore, they incurred in additional hospitalization costs (cIAI: €933 - p<0.0001; cUTI: €272 - p<0.0001).ConclusionsThese findings show that antibiotic resistance could increase the cost of care of community-acquired cIAIs or cUTIs
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: ORCHIDEE-crop rice
No full textThis is model output from ORCHIDEE-crop for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.
The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).
References:
Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015
Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017
Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p
Supporting information for Nandintsetseg, B., Chang, J., Sen, O.L., Reyer, C.P.O., Kong, K., Yetemen, O., Ciais, P., Davaadalai, J., Future drought risk and adaptation of pastoralism in Eurasian rangelands. <i>npj Climate and Atmospheric Science</i>. 2024, DOI: 10.1038/s41612-024-00624-2
No full textSupporting information for Nandintsetseg, B., Chang, J., Sen, O.L., Reyer, C.P.O., Kong, K., Yetemen, O., Ciais, P., Davaadalai, J., Future drought risk and adaptation of pastoralism in Eurasian rangelands. npj Climate and Atmospheric Science. 2024.</p
Amplifying effects of land-use change on future atmospheric CO2 levels
No full textWe constructed a model to analyze the interactions between land-use change and atmospheric CO2 during the recent past and for the future. The primary impact of the conversion of forested lands to cultivated lands is to increase atmospheric CO2, via losses of biomass and soil carbon to the atmosphere. This increase is likely to continue in the next decades, but its magnitude can vary according to each land-use scenario. We show that this first-order effect is further amplified by the correlated diminution of terrestrial sinks, because when croplands replace forests, the turnover time of excess carbon in the biosphere decreases, and hence the sink capacity of terrestrial ecosystems decreases. This effect acts to further increase by up to 100 ppm the CO2 level reached by 2100, and it is ofthe same order of magnitude, although smaller, than climate-carbon feedbacks. Uncertainties on the magnitude of this land-use induced effect are large, because of uncertainties in the sink role of terrestrial ecosystems in the future and because of uncertainties inherent to the modeling of land-use induced carbon emissions. Such an extra rise in atmospheric CO2 is however partially offset by the ocean reservoir and by sinks operating over undisturbed, pristine ecosystems, suggesting that conserving pristine forests with long turnover times might be efficient in mitigating the greenhouse effectland-use change; carbon cycle; future scenarios
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: ORCHIDEE-crop soy
No full textThis is model output from ORCHIDEE-crop for soy as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.
The data has been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper is in preparation.
References:
Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015
Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: ORCHIDEE-crop wheat
No full textThis is model output from ORCHIDEE-crop for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.
The data has been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper is in preparation.
References:
Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015
Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017
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AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: ORCHIDEE-crop maize
No full textThis is model output from ORCHIDEE-crop for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.
The data has been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper is in preparation.
References:
Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015
Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p
Landform-driven human reliance on rivers in imperial China
No full textProximity to rivers and flat areas are two of the main factors that determine the location of human settlements. Despite empirical relationships between human settlements, river dynamics and the corresponding landforms, the underlying mechanism remains speculative. Here, we present the first millennium-scale, quantitative temporal analysis of human population dynamics and its relationship with river location and landforms in imperial China across four dynasties (Han – 2 CE, Tang – 742 CE, Song – 1102 CE and Ming – 1522 CE) over the last 2000 years. Our results show less human reliance (measured by the number of people living close to a water course) on rivers for people living in lowland areas, which we interpret to be related to flood risk and the availability of groundwater from alluvial aquifers distant from rivers, used for agriculture. Conversely, people living in mountainous and hilly areas appear to have a stronger reliance on rivers in imperial China. Specifically, behind the strong variations of human-river relation across millennia, we infer a general principle highlighting the role of landforms in human-river interactions. These results shed light on how geomorphology shape settlement and urban patterns, with important implications for sustainable lifeways in riverine environments
Is the recent build-up of atmospheric CO 2 over Europe reproduced by models. Part 2: an overview with the atmospheric mesoscale transport model CHIMERE
No full textInternational audienceTo cite this article: C. Aulagnier, P. Rayner, P. Ciais, R. Vautard, L. Rivier & M. Ramonet (2010) Is the recent build-up of atmospheric CO 2 over Europe reproduced by models. Part 2: an overview with the atmospheric mesoscale transport model CHIMERE, A B S T R A C T In this issue, Ramonet et al. revealed a positive trend in European, atmospheric CO 2 concentrations relative to a marine, North Atlantic reference baseline, for the years 2001-2006. The observed build up mainly occurred during the cold season where it reaches a 0.8 ppm yr −1 at low-altitude stations to a 0.3 ppm yr −1 at mid-altitude stations. We explore the cause of this build-up using the mesoscale model CHIMERE. We first model the observed trends, using interannually varying fluxes and transport, then suppress the interannual variability in fluxes or aspects of transport to elucidate the cause. The run with no interannual variability in fluxes still matches observed trends suggesting that transport is the major cause. Separate runs varying either boundary layer height or winds show that changes in boundary layer height explain the trends at low-altitude stations within the continents while changes in wind regimes drive changes elsewhere
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