Two-pool Model Research Articles

Quantitative Magnetization Transfer Detects Renal Fibrosis in Murine Kidneys With Renal Artery Stenosis.

Renal fibrosis is a common pathway in tubulointerstitial injury and a major determinant of renal insufficiency. Collagen deposition, a key feature of renal fibrosis, may serve as an imaging biomarker to differentiate scarred from healthy kidneys. To test the feasibility of using quantitative magnetization transfer (qMT), which assesses tissue macromolecule content, to measure renal fibrosis. Prospective. Fifteen 129S1 mice were studied 4 weeks after either sham (n = 7) or unilateral renal artery stenosis (RAS, n = 8) surgeries. Magnetization transfer (MT)-weighted images were acquired at 16.4T using an MT-prepared fast-low-angle-shot sequence. Renal B0, B1, and T1 maps were also acquired, using a dual-echo gradient echo, an actual flip angle, and inversion recovery method, respectively. A two-pool model was used to estimate the bound water fraction (f) and other tissue imaging biomarkers. Masson's trichrome staining was subsequently performed ex vivo to evaluate renal fibrosis. Comparisons of renal parameters between sham and RAS were performed using independent samples t-tests. Pearson's correlation was conducted to investigate the relationship between renal fibrosis by histology and the qMT-derived bound pool fraction f. The two-pool model provided accurate fittings of measured MT signal. The qMT-derived f of RAS kidneys was significantly increased compared to sham in all kidney zones (renal cortex [CO], 7.6 ± 2.4% vs. 4.6 ± 0.6%; outer medulla [OM], 8.2 ± 4.2% vs. 4.2 ± 0.9%; inner medulla [IM] + P, 5.8 ± 1.6% vs. 2.9 ± 0.6%, all P < 0.05). Measured f correlated well with histological fibrosis in all kidney zones (CO, Pearson's correlation coefficient r = 0.95; OM, r = 0.93; IM + P, r = 0.94, all P < 0.05). The bound pool fraction f can be quantified using qMT at 16.4T in murine kidneys, increases significantly in fibrotic RAS kidneys, and correlates well with fibrosis by histology. Therefore, qMT may constitute a valuable tool for measuring renal fibrosis in RAS. 1 TECHNICAL EFFICACY STAGE: 3.

Linear two-pool models are insufficient to infer soil organic matter decomposition temperature sensitivity from incubations

Terrestrial carbon (C)-climate feedbacks depend strongly on how soil organic matter (SOM) decomposition responds to temperature. This dependency is often represented in land models by the parameter Q10, which quantifies the relative increase of microbial soil respiration per 10 °C temperature increase. Many studies have conducted paired laboratory soil incubations and inferred “active” and “slow” pool Q10 values by fitting linear two-pool models to measured respiration time series. Using a recently published incubation study (Qin et al. in Sci Adv 5(7):eaau1218, 2019) as an example, here we first show that the very high parametric equifinality of the linear two-pool models may render such incubation-based Q10 estimates unreliable. In particular, we show that, accompanied by the uncertain initial active pool size, the slow pool Q10 can span a very wide range, including values as high as 100, although all parameter combinations are producing almost equally good model fit with respect to the observations. This result is robust whether or not interactions between the active and slow pools are considered (typically these interactions are not considered when interpreting incubation data, but are part of the predictive soil carbon models). This very large parametric equifinality in the context of interpreting incubation data is consistent with the poor temporal extrapolation capability of linear multi-pool models identified in recent studies. Next, using a microbe-explicit SOM model (RESOM), we show that the inferred two pools and their associated parameters (e.g., Q10) could be artificial constructs and are therefore unreliable concepts for integration into predictive models. We finally discuss uncertainties in applying linear two-pool (or more generally multiple-pool) models to estimate SOM decomposition parameters such as temperature sensitivities from laboratory incubations. We also propose new observations and model structures that could enable better process understanding and more robust predictive capabilities of soil carbon dynamics.

Soil Properties Control Microbial Carbon Assimilation and Its Mean Residence Time

Microbial assimilation and stabilization of soil organic carbon (SOC) is an important process in global carbon cycling. For an improved understanding of climate-induced changes in ecosystem C dynamics, it is important to know the group-specific turnover of microbial C. Consequently, we wanted to answer the questions if fungi store newly assimilated C longer than bacteria and if climatic and edaphic properties of different regions affect microbial C assimilation and its subsequent release. This study presents results from a 112-day long field experiment where endogenous soil microorganisms were labelled with 13C labelled glucose to follow the dynamics of newly assimilated C in two study regions (Kraichgau and Swabian Alb). Whereas microbial assimilation of newly added C was higher in Kraichgau than in Swabian Alb, the opposite result was obtained for the mean residence time (MRT) of microbial biomass C (76 days in Kraichgau and 93 days in Swabian Alb). The accelerated turnover rates of microbial C in the warmer soils of Kraichgau with lower clay content might be an important mechanism explaining the differences in SOC content between both regions. Gram-positive bacteria assimilated more 13C-glucose into their biomass than fungi and the MRT of C was higher in bacteria as compared to fungi in both regions. Beside these dynamic substrate utilization strategies, we could proof cross feeding by gram-negative bacteria. Carbon MRT in fungi was region specific and was best represented by a two-pool model; the initial MRT ranged between 5 to 39 days and was, in the end, higher than 4 years. This provides evidence of a possible shift in fungal community composition; fast growing fungi dominant in the early phase and internal redistribution of C in the second phase of decomposition. Our study identified microbial group and region specific MRT of freshly assimilated C as an important parameter, which might help to explain the commonly found variation in soil respiration and SOC stabilization.

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

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.

B0-field dependence of MRI T1 relaxation in human brain

Tissue longitudinal relaxation characterized by recovery time T1 or rate R1 is a fundamental MRI contrast mechanism that is increasingly being used to study the brain’s myelination patterns in both health and disease. Nevertheless, the quantitative relationship between T1 and myelination, and its dependence on B0 field strength, is still not well known. It has been theorized that in much of brain tissue, T1 field-dependence is driven by that of macromolecular protons (MP) through a mechanism called magnetization transfer (MT). Despite the explanatory power of this theory and substantial support from in-vitro experiments at low fields (<3 ​T), in-vivo evidence across clinically relevant field strengths is lacking. In this study, T1-weighted MRI was acquired in a group of eight healthy volunteers at four clinically relevant field strengths (0.55, 1.5, 3 and 7 ​T) using the same pulse sequence at a single site, and jointly analyzed based on the two-pool model of MT. MP fraction and free-water pool T1 were obtained in several brain structures at 3 and 7 ​T, which allowed distinguishing between contributions from macromolecular content and iron to tissue T1. Based on this, the T1 of MP in white matter, indirectly determined by assuming a field independent T1 of free water, was shown to increase approximately linearly with B0. This study advances our understanding of the T1 contrast mechanism and its relation to brain myelin content across the wide range of currently available MRI strengths, and it has the potential to inform design of T1 mapping methods for improved reproducibility in the human brain.

Does the magnetization transfer effect bias chemical exchange saturation transfer effects? Quantifying chemical exchange saturation transfer in the presence of magnetization transfer.

PurposeChemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low‐concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements.MethodsCEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a two‐pool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates.ResultsWhen all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis.ConclusionsThese results indicate that current practices of simultaneously fitting both CEST and MT effects in model‐based analyses can lead to significant bias in all parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases.

Assessment of leaf water enrichment of Platycladus orientalis using numerical modeling with different isotopic models

Abstract Investigation of leaf water enrichment may improve our understanding of evapotranspiration partitioning and material exchange between the inside and the outside of leaves. In this study, with measurements of diel variations in δ18O of bulk leaf water (δL,b) of Platycladus orientalis, two theories (the Peclet effect and the two-pool model) describing leaf water enrichment were assessed. The proportional difference between observed and modeled leaf water enrichment, f, was estimated using both isotopic-steady-state (ISS) and non-steady-state (NSS) models to test the two theories. The results showed that differences between ISS- and NSS-modeled leaf water enrichment at the evaporative sites (Δl,e) were not significant (p > 0.05), thus estimated f was not significantly (p > 0.05) influenced by assuming ISS or NSS conditions. Under both ISS and NSS conditions, no significant (P > 0.05) positive f–Tr (leaf transpiration rate) relationships were found on either diel or diurnal time scales, indicating that the Peclet effect is not required to describe bulk leaf water enrichment. Therefore, it would be adequate to combine the simple two-pool model with the ISS model to predict δ18O of bulk leaf water of P. orientalis. These results may have implications for parameterizing the carbon cycle, past climates, and evapotranspiration partitioning models.

Stabilization of microbial residues in soil organic matter after two years of decomposition

Microbially-derived nitrogen (N) has been considered as one of important contributors to soil organic N, but few studies have quantified the rate of necromass N decomposition. Here, via an in situ incubation of 15N-labeled necromass, we found that 33.1–39.5% of the initial 15N stabilized in the soil as non-living organic N after 803 days of incubation. Bacterial, fungal, and actinobacterial necromass N showed similar decomposition pattern and mean residence time. The decomposition of microbial necromass N was best simulated by a two-pool model where a labile pool decomposed rapidly (0.4 years), and a more recalcitrant pool decomposed at a much slower rate. This finding contrasted with the decomposition of plant litter N, which was better simulated by a single-pool model. The stabilization of necromass N in soils after more than two years suggests the important contribution of microbial residues to soil organic N, which is most likely due to mineral protection from decomposition.

Magnetization transfer in magnetic resonance fingerprinting.

To study the effects of magnetization transfer (MT, in which a semi-solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF). Simulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T1 and T2 estimation. Subsequently, an MRF sequence implementing off-resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two-pool model, were used to estimate the fractional pool size in addition to the , T1 , and T2 values. The proposed method was evaluated in the human brain. Simulations and phantom experiments showed that an MRF signal obtained from a cross-linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off-resonance MT pulses can improve the differentiation of MT from T1 and T2 . In vivo results showed that MT affects the MRF signals from white matter (fractional pool-size ~16%) and gray matter (fractional pool-size ~10%). Furthermore, longer T1 (~1060 ms vs. ~860 ms) and T2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for. Our experiments demonstrated a potential influence of MT on the quantification of T1 and T2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.

7T quantitative magnetization transfer (qMT) of cortical gray matter in multiple sclerosis correlates with cognitive impairment

Cognitive impairment (CI) is a major manifestation of multiple sclerosis (MS) and is responsible for extensively hindering patient quality of life. Cortical gray matter (cGM) damage is a significant contributor to CI, but is poorly characterized by conventional MRI let alone with quantitative MRI, such as quantitative magnetization transfer (qMT). Here we employed high-resolution qMT at 7T via the selective inversion recovery (SIR) method, which provides tissue-specific indices of tissue macromolecular content, such as the pool size ratio (PSR) and the rate of MT exchange (kmf). These indices could represent expected demyelination that occurs in the presence of gray matter damage. We utilized selective inversion recovery (SIR) qMT which provides a low SAR estimate of macromolecular-bulk water interactions using a tailored, B1 and B0 robust inversion recovery (IR) sequence acquired at multiple inversion times (TI) at 7T and fit to a two-pool model of magnetization exchange. Using this sequence, we evaluated qMT indices across relapsing-remitting multiple sclerosis patients (N = 19) and healthy volunteers (N = 37) and derived related associations with neuropsychological measures of cognitive impairment. We found a significant reduction in kmf in cGM of MS patients (15.5%, p = 0.002), unique association with EDSS (ρ = -0.922, p = 0.0001), and strong correlation with cognitive performance (ρ = -0.602, p = 0.0082). Together these findings indicate that the rate of MT exchange (kmf) may be a significant biomarker of cGM damage relating to CI in MS.

Assessing cortical bone mechanical properties using collagen proton fraction from ultrashort echo time magnetization transfer (UTE-MT) MRI modeling

Cortical bone shows as a signal void when using conventional clinical magnetic resonance imaging (MRI). Ultrashort echo time MRI (UTE-MRI) can acquire high signal from cortical bone, thus enabling quantitative assessments. Magnetization transfer (MT) imaging combined with UTE-MRI can indirectly assess protons in the organic matrix of bone. This study aimed to examine UTE-MT MRI techniques to estimate the mechanical properties of cortical bone. A total of 156 rectangular human cortical bone strips were harvested from the tibial and femoral midshafts of 43 donors (62 ± 22 years old, 62 specimens from females, 94 specimens from males). Bone specimens were scanned using UTE-MT sequences on a clinical 3 T MRI scanner and on a micro-computed tomography (μCT) scanner. A series of MT pulse saturation powers (400°, 600°, 800°) and frequency offsets (2, 5, 10, 20, 50 kHz) was used to measure the macromolecular fraction (MMF) utilizing a two-pool MT model. Failure mechanical properties of the bone specimens were measured using 4-point bending tests. MMF from MRI results showed significant strong correlations with cortical bone porosity (R = -0.72, P < 0.01) and bone mineral density (BMD) (R = +0.71, P < 0.01). MMF demonstrated significant moderate correlations with Young modulus, yield stress, and ultimate stress (R = 0.60–0.61, P < 0.01). These results suggest that the two-pool UTE-MT model focusing on the organic matrix of bone can potentially serve as a novel tool to detect the variations of bone mechanical properties and intracortical porosity.

Noninvasive quantitative magnetization transfer MRI reveals tubulointerstitial fibrosis in murine kidney.

Excessive tissue scarring, or fibrosis, is a critical contributor to end stage renal disease, but current clinical tests are not sufficient for assessing renal fibrosis. Quantitative magnetization transfer (qMT) MRI provides indirect information about the macromolecular composition of tissues. We evaluated measurements of the pool size ratio (PSR, the ratio of immobilized macromolecular to free water protons) obtained by qMT as a biomarker of tubulointerstitial fibrosis in a well-established murine model with progressive renal disease. MR images were acquired from 16-week-old fibrotic hHB-EGFTg/Tg mice and normal wild-type (WT) mice (N=12) at 7T. QMT parameters were derived using a two-pool five-parameter fitting model. A normal range of PSR values in the cortex and outer stripe of outer medulla (CR+OSOM) was determined by averaging across voxels within WT kidneys (mean±2SD). Regions in diseased mice whose PSR values exceeded the normal range above a threshold value (tPSR) were identified and measured. The spatial distribution of fibrosis was confirmed using picrosirius red stains. Compared with normal WT mice, scattered clusters of high PSR regions were observed in the OSOM of hHB-EGFTg/Tg mouse kidneys. Moderate increases in mean PSR (mPSR) of CR+OSOM regions were observed across fibrotic kidneys. The abnormally high PSR regions (% area) detected by the tPSR were significantly increased in hHB-EGFTg/Tg mice, and were highly correlated with regions of fibrosis detected by histological fibrosis indices measured from picrosirius red staining. Renal tubulointerstitial fibrosis in OSOM can thus be assessed by qMT MRI using an appropriate analysis of PSR. This technique may be used as an imaging biomarker for chronic kidney diseases.

The &amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;O ecohydrology of a grassland ecosystem – predictions and observations

Abstract. The oxygen isotope composition (δ18O) of leaf water (δ18Oleaf) is an important determinant of environmental and physiological information found in biological archives, but the system-scale understanding of the propagation of the δ18O of rain through soil and xylem water to δ18Oleaf has not been verified for grassland. Here we report a unique and comprehensive dataset of fortnightly δ18O observations in soil, stem and leaf waters made over seven growing seasons in a temperate, drought-prone, mixed-species grassland. Using the ecohydrology part of a physically based, 18O-enabled soil–plant–atmosphere transfer model (MuSICA), we evaluated our ability to predict the dynamics of δ18O in soil water, the depth of water uptake, and the effects of soil and atmospheric moisture on 18O enrichment of leaf water (Δ18Oleaf) in this ecosystem. The model accurately predicted the δ18O dynamics of the different ecosystem water pools, suggesting that the model generated realistic predictions of the vertical distribution of soil water and root water uptake dynamics. Observations and model predictions indicated that water uptake occurred predominantly from shallow (&lt;20 cm) soil depths throughout dry and wet periods in all years, presumably due (at least in part) to the effects of high grazing pressure on root system turnover and placement. Δ18Oleaf responded to both soil and atmospheric moisture contents and was best described in terms of constant proportions of unenriched and evaporatively enriched water (two-pool model). The good agreement between model predictions and observations is remarkable as model parameters describing the relevant physical features or functional relationships of soil and vegetation were held constant with one single value for the entire mixed-species ecosystem.

Rapid framework for quantitative magnetization transfer imaging with interslice magnetization transfer and dictionary-driven fitting approaches.

To develop a rapid framework for quantitative magnetization transfer (qMT) imaging based on the 2D interslice MT and dictionary-driven fitting approaches. Sequential balanced steady-state free precession (bSSFP) scanning was performed on a whole human brain in a total of 12 conditions from six different interslice gaps and two different flip angles. To obtain qMT maps, the acquired 12 datasets were fitted to a dictionary predefined by using Bloch equation simulations based on the two-pool MT model. The proposed qMT method was compared to the conventional qMT methods, in terms of qMT parameter maps and processing time. The proposed method yielded qMT maps similar to those of the conventional method, indicating feasibility of modulating MT saturation frequency and power with the interslice gap and flip angle. The whole-brain qMT imaging could be completed in 8min because of the absence of separate MT pulses. The time for processing qMT parameters was significantly reduced through the dictionary-driven approach; it is 1000 times shorter than that without the dictionary-driven approach and 3 times shorter than that with the spoiled gradient recalled echo-qMT method that uses an analytical solution. The proposed dictionary-driven interslice qMT imaging provides qMT maps close to those from the conventional method with significantly reduced scan time and postprocessing time, which can make qMT imaging more clinically acceptable.

Effects of Cyclical Temperature Fluctuations on Organic Carbon Mineralization in Purple Soil

In the natural state, the soil temperature changes periodically throughout the day, but there is no unified opinion on whether the daily variation of soil temperature affects the mineralization of soil organic carbon. Therefore, the effect of cyclical temperature fluctuations on organic carbon mineralization of purple soil, widely distributed in southwest China, was studied by an incubation experiment in the laboratory. These soils were incubated at three constant temperatures (15, 20, and 25℃) with cyclically fluctuating temperatures (the range was within 15/25℃) and two moisture levels (70% water holding capacity (WHC) and submerged condition). In the entire incubation period (66 d), aerobic and submerged condition, variable temperature (15/25℃) cultivation of purple soil, SOC cumulative mineralization and the mineralization intensity had no obvious differences with constant temperature of 20℃. This indicated that the periodic variable temperature treatment (15/25℃) of SOC mineralization of purple soil with the same accumulated temperature of constant temperature processing (20℃). In addition, except for constant temperature of 15℃, the temperature processing of submerged condition of purple soil SOC cumulative mineralization was significantly higher than 70% WHC treatment (P<0.05). During the entire incubation period, soil microorganism carbon content could not reflect the SOC mineralization differences under constant temperature and cyclically fluctuating temperature. According to two-pool first-order model, the submerged condition can effectively increase the labile SOC pool size of purple soil, but the cyclically fluctuating temperature cannot effectively affect the labile SOC pool size of purple soil.

Cross-relaxation parameters in cortical bone assessed with different MR sequences.

This study aimed to show evidence of MR cross-relaxation effects in cortical bone and to compare different MR sequences for the quantification of cross-relaxation parameters. Measurements were performed on bovine diaphysis samples with spectroscopic methods (inversion-recovery, off-resonance saturation) and with a variable flip angle (VFA) UTE imaging method on a 4.7T laboratory-assembled scanner. Cross-relaxation parameter assessment was carried out via a two-pool model simulation with a matrix algebra approach. A proton signal amplitude of 28 Mol/L was observed (equivalent water fraction of 25%). It was attributed to collagen-bound water, with values of ~ 0.3ms, a "long-T2 " proton pool, in exchange with protons from the collagen macromolecules ( of 10-20μs). Magnetization transfer (MT) effects were detected with all sequences. The best precision of model parameters was obtained with off-resonance saturation; the fraction of collagen methylene protons was found in the range of 22-28% and the transverse relaxation time for collagen methylene protons was 11μs (1% precision). The model parameters obtained were compatible with VFA-UTE results but could not be assessed with acceptable accuracy and precision using this method. In vivo MT quantification using off-resonance saturation with a single B1 amplitude and offset frequency may provide information about the relative amount of collagen per unit volume in cortical bone.

Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation

Multicomponent driven equilibrium steady-state observation of T1 and T2 (mcDESPOT) aims to quantify the Myelin Water Fraction (MWF) using a two-pool microstructural model. The MWF has been used to track neurodevelopment and neurodegeneration and has been histologically correlated to myelin content. mcDESPOT has a clinically feasible acquisition time and high signal-to-noise ratio (SNR) relative to other MWF techniques. However, disagreement exists in the literature between experimental studies that show MWF maps with plausible grey matter-white matter (GM-WM) contrast and theoretical work that questions the accuracy and precision of mcDESPOT. We demonstrate that mcDESPOT parameter estimation is inaccurate and imprecise if intercompartmental exchange is included in the microstructural model, but that significant bias results if exchange is neglected. The source of apparent MWF contrast is likely due to the complex convergence behaviour of the Stochastic Region Contraction (SRC) method commonly used to fit the mcDESPOT model. mcDESPOT-derived parameter estimates are hence not directly relatable to the underlying microstructural model and are only comparable to others using similar acquisition schemes and fitting constraints.

Selective inversion recovery quantitative magnetization transfer imaging: Toward a 3 T clinical application in multiple sclerosis

Background: Assessing the degree of myelin injury in patients with multiple sclerosis (MS) is challenging due to the lack of magnetic resonance imaging (MRI) methods specific to myelin quantity. By measuring distinct tissue parameters from a two-pool model of the magnetization transfer (MT) effect, quantitative magnetization transfer (qMT) may yield these indices. However, due to long scan times, qMT has not been translated clinically. Objectives: We aim to assess the clinical feasibility of a recently optimized selective inversion recovery (SIR) qMT and to test the hypothesis that SIR-qMT-derived metrics are informative of radiological and clinical disease-related changes in MS. Methods: A total of 18 MS patients and 9 age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla (3 T) brain MRI, including clinical scans and an optimized SIR-qMT protocol. Four subjects were re-scanned at a 2-week interval to determine inter-scan variability. Results: SIR-qMT measures differed between lesional and non-lesional tissue (p < 0.0001) and between normal-appearing white matter (NAWM) of patients with more advanced disability and normal white matter (WM) of HCs (p < 0.05). SIR-qMT measures were associated with lesion volumes, disease duration, and disability scores (p ⩽ 0.002). Conclusion: SIR-qMT at 3 T is clinically feasible and predicts both radiological and clinical disease severity in MS.

Long-Term Nitrogen Addition Does Not Increase Soil Carbon Storage or Cycling Across Eight Temperate Forest and Grassland Sites on a Sandy Outwash Plain

Experimental nitrogen (N) deposition generally inhibits decomposition and promotes carbon (C) accumulation in soils, but with substantial variation among studies. Differences in ecosystem properties could help explain this variability: N could have distinct effects on decomposition and soil C due to differences in vegetation characteristics (that is, root C inputs and chemistry) that influence microbial biomass or soil properties like pH that can affect organic matter stabilization. We used a 12-year N addition experiment to determine effects of sustained N addition on soil C pool sizes and cycling across different grassland, conifer and deciduous forest sites in Minnesota, USA, while controlling for soil type and climate. We conducted a year-long soil incubation, and fit one- and two-pool decay models to respiration data to identify C pool sizes and decay rates. Contrary to previous studies, we found no consistent effects of N on soil C across sites: soil C stocks, microbial respiration, soil C decay rates and pool sizes all showed no general response to N in these sandy soils. Nevertheless, microbial biomass, microbial respiration, and the root biomass C pool responses to N addition were highly correlated, suggesting that soil C responses were ultimately driven by fine root biomass C responses to N addition, which in turn affected microbial biomass. However, the inconsistent directional responses to N among sites with similar vegetation cover highlight that N addition effects can be site-specific and raise caution for broad extrapolation of results from individual systems to global models.

Evaluation of models utilized in in vitro gas production from tropical feedstuffs

This study aimed to evaluate the adequacy of seven non-linear models (France, Orskov and McDonald, Gompertz, exponential, logistic, two-pool exponential and two-pool logistic) in the adjustment of the curve and in the generation of parameters of cumulative gas production from five tropical feedstuffs (rice hulls, sugarcane, cassava chips, turnip by-product, and peach-palm by-product) used in ruminant nutrition. To this end, the feedstuffs were incubated in vitro in graduated glass syringes together with a buffer inoculum solution, in triplicate. Gas production was read at 0, 2, 4, 6, 8, 10, 12, 24, 26, 28, 30, 32, 36, 48, 52, 54, 56, 60 and 72 h of incubation. The data were used to generate the parameters of each model using the SAS statistical package. After the parameters were generated, the gas volume values were obtained at the aforementioned times, for each model, and these were compared with the values observed at incubation by using Model Evaluation System (MES) software. For the comparison, regression parameters were tested using Mayer’s test, in addition to the evaluation of the mean bias (MS), concordance correlation coefficient (CCC), and mean squared prediction error (MSPE). The France, Logistic, and Gompertz models, for rice hulls, and the Orskov and McDonald model, for cassava chips, were significant (P &lt; 0.1) according to Mayer’s test, indicating lack of fit of the model. Besides presenting the lowest MSPE, the models that showed fit according to Mayer’s test were the two-pool logistic for rice hulls and cassava chips, and the two-pool exponential for sugarcane, turnip, and peach palm. Thus, the non-linear two-pool models are the most efficient in adjusting to the curve and in the generation of parameters of cumulative production of gases from the tested feedstuffs.