Molecular monitoring of Staphylococcus aureus in chronic lesions: a descriptive study

Barreto, Bruna Maiara Ferreira; Pessanha, Fernanda Soares; Oliveira, Beatriz Guitton Renaud Baptista de; Teixeira, Lenise Arneiro; Paula, Geraldo Renato de; Marques, Ana Clara Silveira

doi:10.17665/1676-4285.20155108

Cited by 3 publications

(23 citation statements)

References 2 publications

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“…For Ultuna, simulated SOC loss in all cases underestimated measured loss, while for Bad Lauchstädt, simulated SOC losses consistently overestimated measured losses. At Kraichgau sites, SOC loss was underestimated by the models but with the Bruun et al (2003) parameter set yielding simulated values closer to actual measurements. In the Swabian Jura, both parameter sets underestimated SOC loss.…”

Section: Comparison Of the Different Model Initializationsmentioning

confidence: 66%

“…The three-way interaction of initialization, parameter set and time γ 1ij t k was significant for all but Bad Lauchstädt SOC, where only the parameter set had a significant effect. In the case of Bad Lauchstädt, the model error was significantly lower with the slower Muelle (1997) SOM turnover parameter set, while for the rest of the tested cases, the faster Bruun et al (2003) set performed significantly better (Table 4). For Ultuna and Kraichgau and Swabian Jura SOC, the steady-state assumption with Mueller et al (1997) parameters had the highest model error, while the steady-state assumption with Bruun et al (2003) parameters had the lowest model error of all simulations, being similar to DSI initializations at Kraichgau and Swabian Jura.…”

Section: Comparison Of the Different Model Initializationsmentioning

confidence: 99%

“…The first set was from Mueller et al (1997) and was a modification of the original parameter set of turnover rates reported by Jensen et al (1997). The second set was established after calibrations made by Bruun et al (2003) using the Askov long-term experiments, in which they introduced considerable changes to the turnover rates of the slow SOM pool and the humification efficiency. An equation developed by Bruun and Jensen (2002) was used to compute the proportions of the slow-and fast-cycling SOM pools for both parameter sets at a steady state (see next section).…”

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

“…with k SOM_slow and k SOM_fast representing the turnover (per day) of the slow and fast SOM pools, respectively, and f SOM_slow representing the fraction of the fast SOM pool directed towards the slow SOM pool (humification efficiency). This resulted in 83 % of SOM in the slow pool for the original Daisy turnover rates and 49 % in the slow pool for the Bruun et al (2003) turnover rates (Table 2). For the DSI initialization, the ratio of the area below the aliphatic absorption bands to the area below the aromatic-carboxylate absorption band was used as the ratio of SOM in the fast-cycling SOM pool to SOM in the slow-cycling SOM pool:…”

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

“…1. Parameter Mueller et al (1997) Bruun et al (2003 Unit using Eq. (1) were only simulated with the corresponding parameter set from which they were calculated.…”

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

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DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

et al. 2020

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Abstract. Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the area below the aliphatic absorption band (2930 cm−1) to the area below the aromatic–carboxylate absorption band (1620 cm−1). For pool partitioning, the DSI (2930 cm−1 ∕ 1620 cm−1) was set equal to the ratio of fast-cycling ∕ slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstädt extreme farmyard manure experiment in Germany (Chernozem, 25 years), the Ultuna continuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and Swabian Jura regions in southwest Germany (Luvisols). All experiments were at sites that were agricultural fields for centuries before fallow establishment, so classical theory would suggest that a steady state can be assumed for initializing SOM pools. Hence, steady-state and DSI initializations were compared, using two published parameter sets that differed in turnover rates and humification efficiency. Initialization using the DSI significantly reduced Daisy model error for total soil organic carbon and microbial carbon in cases where assuming a steady state had poor model performance. This was irrespective of the parameter set, but faster turnover performed better for all sites except for Bad Lauchstädt. These results suggest that soils, although under long-term agricultural use, were not necessarily at a steady state. In a next step, Bayesian-calibration-inferred best-fitting turnover rates for Daisy using the DSI were evaluated for each individual site or for all sites combined. Two approaches significantly reduced parameter uncertainty and equifinality in Bayesian calibrations: (1) adding physicochemical meaning with the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnover rates were strongly site specific. The Bayesian calibration combining all sites suggested a potential for rapid SOM loss with 95 % credibility intervals for the slow SOM pools' half-life being 278 to 1095 years (highest probability density at 426 years). The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar two-pool SOM models, i.e., with turnover rates being faster than earlier model calibrations suggested; hence they likely underestimated potential SOM losses.

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Section: Comparison Of the Different Model Initializationsmentioning

confidence: 66%

Section: Comparison Of the Different Model Initializationsmentioning

confidence: 99%

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

“…1. Parameter Mueller et al (1997) Bruun et al (2003 Unit using Eq. (1) were only simulated with the corresponding parameter set from which they were calculated.…”

Section: Description Of the Simulation Model Daisymentioning

confidence: 99%

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DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

et al. 2020

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Monitoring and Molecular Characterization of Staphylococcus aureus Isolated from Chronic Wounds

Pires

Oliveira

et al. 2018

Adv Skin Wound Care

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DRIFTS peaks as measured pool size proxy to reduce parameter uncertainty of soil organic matter models

Laub¹,

Demyan²,

Nkwain³

et al. 2019

Preprint

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Abstract. The initialization of soil organic matter (SOM) turnover models has been a challenge for decades. Instead of using laborious and error prone size-density fractionation SOM pool partitioning, we propose the inexpensive, rapid, and non destructive Diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) technique on bulk soil samples to gain information on SOM pool partitioning from the spectra. Specifically, the DRIFTS stability index, defined as the ratio of aliphatic C-H (2930&#8201;cm&#8722;1) to aromatic C=C (1620&#8201;cm&#8722;1) stretching vibrations, was used to divide SOM into fast and slow cycling pools in the soil organic module of the DAISY model. Long-term bare fallow plots from Bad Lauchst&#228;dt (Chernozem, 25 years) and the Ultuna frame trial in Sweden (Cambisol, 50 years) were combined with bare fallow plots of 7 years duration in the Kraichgau and Swabian Jura region in Southwest Germany (Luvisols). All fields had been in agricultural use for centuries before fallow establishment, so classical theory would suggest an initial steady state of SOM, which was hence used to compare the performance of DAISY initializations against the newly established DRIFTS stability index. The test was done using two different published parameter sets (2.7&#8201;&#215;&#8201;10&#8722;6&#8201;d&#8722;1, 1.4&#8201;&#215;&#8201;10&#8722;4&#8201;d&#8722;1, 0.1 compared to 4.3&#8201;&#215;&#8201;10&#8722;5&#8201;d&#8722;1, 1.4&#8201;&#215;&#8201;10&#8722;4&#8201;d&#8722;1, 0.3 for the turnover rates of slow pool, fast pool and humification efficiency, respectively). The DRIFTS initialization of SOM pools significantly reduced model errors of poor performing model runs assuming steady state, irrespective of the turnover rates used, but the faster turnover parameter set fit better to all sites except Bad Lauchst&#228;dt. This suggests that soils under long-term agricultural use were not necessarily at steady state. A Bayesian calibration was applied in a next step to identify the best-fitting turnover rates for the DRIFTS stability index in DAISY, both for each site individually and for a combination of all sites. The two approaches which significantly reduced parameter uncertainty and equifinality were: (1) the addition of the physico-chemically based DRIFTS stability index, and (2) combining several sites into one Bayesian calibration, as derived turnover rates can be strongly site specific. The combination of all four sites showed that SOM is likely lost at relatively fast turnover rates with the 95&#8201;% credibility intervals of the slow SOM pools half life ranging from 278 to 1095 years, with 426 years as a value of highest probability density. The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar SOM models, all turnover rates were considerably faster than earlier models suggested. It is therefore likely that published turnover rates understimate the potential loss of SOM.

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Molecular monitoring of Staphylococcus aureus in chronic lesions: a descriptive study

Cited by 3 publications

References 2 publications

DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

Monitoring and Molecular Characterization of Staphylococcus aureus Isolated from Chronic Wounds

DRIFTS peaks as measured pool size proxy to reduce parameter uncertainty of soil organic matter models

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