9 research outputs found

    Tsukamurella pulmonis bloodstream infection identified by secA1 gene sequencing

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    Recurrent bloodstream infections caused by a Gram-positive bacterium affected an immunocompromised child. Tsukamurella pulmonis was the microorganism identified by secA1 gene sequencing. Antibiotic treatment in combination with removal of the subcutaneous port healed the patient

    In vitro activity of tigecycline against methicillin-resistant Staphylococcus aureus, including livestock-associated strains

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    The in vitro activity of tigecycline was determined using a well-defined collection of methicillin-resistant Staphylococcus aureus (MRSA) isolates (n = 202), including 33 livestock-associated strains. Susceptibility testing was performed using the Etest system. Among the 202 MRSA strains, three (1.5%) had a minimum inhibitory concentration (MIC) value for tigecycline greater than 0.5 mg/l, which are considered to be resistant. When these strains were tested using Iso-Sensitest medium, the MICs were substantially lower and no resistance was found. This discrepancy warrants further investigations into the preferred test conditions for tigecycline. In conclusion, tigecycline showed good activity against MRSA strains in vitro

    Global dataset of soil organic carbon in tidal marshes.

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    Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

    Global dataset of soil organic carbon in tidal marshes

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    Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

    Global dataset of soil organic carbon in tidal marshes

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    Funding: W.E.N.A. and C.S. would like to acknowledge funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1).Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2±38.1 Mg SOC ha−1 in the top 30cm and 231±134 Mg SOC ha−1 in the top 1m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.Publisher PDFPeer reviewe

    Database: Tidal Marsh Soil Organic Carbon (MarSOC) Dataset

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    The repository is formatted in the following structure: - README.md: markdown file with repository description - MarSOC-Dataset.Rproj: R project file - useful when using RStudio - Maxwell_MarSOC_dataset.csv: .csv file containing the final dataset. The data structure is described in the metadata file. It contains 17,454 records distributed amongst 29 countries. - Maxwell_MarSOC_dataset_metadata.csv: .csv file containing the main data file metadata (equivalent to Table 1). - data_paper/: folder containing the list of studies included in the dataset, as well as figures for this data paper (generated from the following R script: ‘reports/04_data_process/scripts/04_data-paper_data_clean.R’). - reports/01_litsearchr/: folder containing .bib files with references from the original naive search, a .Rmd document describing the litsearchr analysis using nodes to go from the naive search to the final search string, and the .bib files from this final search, which were then imported into sysrev for abstract screening. - reports/02_sysrev/: folder with .csv files exported from sysrev after abstract screening. These files contain the included studies with their various labels. - reports/03_data_format/: folder containing all original data, associated scripts, and exported data. - reports/04_data_process/: folder containing data processing scripts to bind and clean the exported data, as well as a script testing the different models for predicting soil organic carbon from organic matter and finalising the equation using all available data. A script testing and removing outliers is also included
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