| 1. |
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| 2. |
- Holm, Lars, et al.
(författare)
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An exploration of the methods to determine the protein-specific synthesis and breakdown rates in vivo in humans.
- 2019
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Ingår i: Physiological Reports. - : John Wiley & Sons. - 2051-817X. ; 7:17
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Tidskriftsartikel (refereegranskat)abstract
- The present study explores the methods to determine human in vivo protein-specific myofibrillar and collagenous connective tissue protein fractional synthesis and breakdown rates. We found that in human myofibrillar proteins, the protein-bound tracer disappearance method to determine the protein fractional breakdown rate (FBR) (via 2 H2 O ingestion, endogenous labeling of 2 H-alanine that is incorporated into proteins, and FBR quantified by its disappearance from these proteins) has a comparable intrasubject reproducibility (range: 0.09-53.5%) as the established direct-essential amino acid, here L-ring-13 C6 -phenylalanine, incorporation method to determine the muscle protein fractional synthesis rate (FSR) (range: 2.8-56.2%). Further, the determination of the protein breakdown in a protein structure with complex post-translational processing and maturation, exemplified by human tendon tissue, was not achieved in this experimentation, but more investigation is encouraged to reveal the possibility. Finally, we found that muscle protein FBR measured with an essential amino acid tracer prelabeling is inappropriate presumably because of significant and prolonged intracellular recycling, which also may become a significant limitation for determination of the myofibrillar FSR when repeated infusion trials are completed in the same participants.
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| 3. |
- Holm, Sören, et al.
(författare)
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Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar
- 2017
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 197, s. 85-97
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Tidskriftsartikel (refereegranskat)abstract
- Previous studies have utilized ground plots, airborne lidar scanning or profiling data, and space lidar profiling data to estimate biomass across large regions, but these studies have failed to take into account the variance components associated with multiple models because the proper variance equations were not available. Previous large-domain studies estimated the variances of their biomass density estimates as the sum of the GLAS sampling variability plus the model variability associated with the models that predict airborne lidar estimates of biomass density (Y) as a function of satellite lidar measurements (X). This approach ignores the additional variability associated with the predictive models used to estimate ground biomass density as a function of airborne lidar measurements. This paper addresses that shortcoming. Analytic variance expressions are provided that include sampling variability and model variability in situations where multiple models are employed to generate estimates of biomass. As an example, the forest biomass of the continental US is estimated, by forest stratum within state, using a space lidar system (ICESat/GLAS). An airborne laser system (ALS) is used as an intermediary to tie the GLAS measurements of forest height to a small subset of US Forest Service (USFS) ground plots by flying the ALS over the ground plots and, independently, over individual GLAS footprints. Two sets of models are employed to relate satellite measurements to the ground plots. The first set of equations relates USFS ground plot estimates of total aboveground dry biomass density (Y-1) to spatially coincident ALS forest canopy measurements (X-1). The second set of models predicts those ALS canopy height measurements (X-1) used in the first set of models to GLAS waveform measurements (X2). The following important conclusions are noted. (1) The variability associated with estimation of the plot-ALS model coefficients is significant and should be included in the overall estimate of biomass density variance. In the continental US, the total variance of mean forest biomass density (98.06 t/ha) increases by a factor of 3.6x, i.e., from 1.91 to 6.94 t(2)/ha(2), when plot-ALS model variance is included in the calculation of total variance. (2) State-level results are more variable, but on average, the percent model variance at the state level, i.e., (model variance / total variance) * 100, increases from 16% to 59% when plot-ALS model variance is included. (3) The overall model variance is driven in large part by the number of plots overflown by the ALS and the number of GLAS pulses overflown by the ALS. Given a choice of improving precision by either increasing the number of plot-ALS observations or increasing ALS-GLAS observations, there is no obvious benefit to selecting one over the other. However, typically the number of ground plots overflown is the limiting factor. (4) If heteroskedasticity is evident in either the ground-air or air-satellite models, it can modeled using weighted regression techniques and incorporated into these model variance formulas in straightforward fashion. The results are unambiguous; in a hybrid three-phase sampling framework, both the ground-air and air-satellite model variance components are significant and should be taken into account. Published by Elsevier Inc.
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| 4. |
- Ilander, M, et al.
(författare)
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Increased proportion of mature NK cells is associated with successful imatinib discontinuation in chronic myeloid leukemia.
- 2017
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Ingår i: Leukemia. - : Springer Science and Business Media LLC. - 0887-6924 .- 1476-5551. ; 31:5, s. 1108-1116
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Tidskriftsartikel (refereegranskat)abstract
- Recent studies suggest that a proportion of chronic myeloid leukemia (CML) patients in deep molecular remission can discontinue the tyrosine kinase inhibitor (TKI) treatment without disease relapse. In this multi-center, prospective clinical trial (EURO-SKI, NCT01596114) we analyzed the function and phenotype of T and NK cells and their relation to successful TKI cessation. Lymphocyte subclasses were measured from 100 imatinib-treated patients at baseline and 1 month after the discontinuation, and functional characterization of NK and T cells was done from 45 patients. The proportion of NK cells was associated with the molecular relapse-free survival as patients with higher than median NK-cell percentage at the time of drug discontinuation had better probability to stay in remission. Similar association was not found with T or B cells or their subsets. In non-relapsing patients the NK-cell phenotype was mature, whereas patients with more naïve CD56(bright) NK cells had decreased relapse-free survival. In addition, the TNF-α/IFN-γ cytokine secretion by NK cells correlated with the successful drug discontinuation. Our results highlight the role of NK cells in sustaining remission and strengthen the status of CML as an immunogenic tumor warranting novel clinical trials with immunomodulating agents.Leukemia advance online publication, 16 December 2016; doi:10.1038/leu.2016.360.
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| 5. |
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| 6. |
- Pedersen, Helle Krogh, et al.
(författare)
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Human gut microbes impact host serum metabolome and insulin sensitivity
- 2016
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Ingår i: Nature. - : Nature Publishing Group. - 0028-0836 .- 1476-4687. ; 535:7612, s. 376-381
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Tidskriftsartikel (refereegranskat)abstract
- Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
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| 7. |
- Petersson, Hans, et al.
(författare)
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Assessing Uncertainty: Sample Size Trade-Offs in the Development and Application of Carbon Stock Models
- 2017
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Ingår i: Forest Science. - 0015-749X. ; 63, s. 402-412
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Tidskriftsartikel (refereegranskat)abstract
- Many parties to the United Nation's Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing new national forest inventories and biomass models in the REDD + context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.
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| 8. |
- Ramezani, Habib, et al.
(författare)
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Sample-based estimation of "contagion metric" using line intersect sampling method (LIS)
- 2015
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Ingår i: Landscape and Ecological Engineering. - : Springer Science and Business Media LLC. - 1860-1871 .- 1860-188X. ; 11, s. 239-248
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Tidskriftsartikel (refereegranskat)abstract
- Quantification of landscape pattern is of primary interest in landscape ecological studies. For quantification purposes, a large number of landscape metrics have been developed, with definitions based on measurable patch attributes. Calculation of these metrics is commonly conducted on wall-to-wall maps, whereas a new interest is to use sample data. It is argued that a sample survey takes less time and results are more reliable. The overall objective in this paper was to present the potential of the line interest sampling method for estimating a special contagion metric. The specific objective was to assess statistical properties in terms of root mean square error (RMSE) and bias of the contagion metric estimator. This study was conducted on 50.1 km(2) already manually delineated land cover maps from the National Inventory of Landscape in Sweden. Monte-Carlo sampling simulation was employed to assess the statistical properties of the estimator. The simulation was conducted for different combinations of two sampling designs, four sample sizes, five lines transect configurations, three lines transect lengths, and two classification systems. The systematic sampling design resulted in lower RMSE and bias compared to a simple random one. Both RMSE and bias of the contagion estimator tended to decrease with increasing sample size and line transect length. We recommend using a combination of systematic sampling design, straight line configuration and long line transect. We conclude that there is no need to use mapped data and thus polygon delineation errors can considerably be reduced or eliminated.
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| 9. |
- Saarela, Svetlana, et al.
(författare)
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Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data
- 2018
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.
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| 10. |
- Saarela, Svetlana, et al.
(författare)
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Hierarchical model-based inference for forest inventory utilizing three sources of information
- 2016
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Ingår i: Annals of Forest Science. - : Springer Science and Business Media LLC. - 1286-4560 .- 1297-966X. ; 73, s. 895-910
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Tidskriftsartikel (refereegranskat)abstract
- The study presents novel model-based estimators for growing stock volume and its uncertainty estimation, combining a sparse sample of field plots, a sample of laser data, and wall-to-wall Landsat data. On the basis of our detailed simulation, we show that when the uncertainty of estimating mean growing stock volume on the basis of an intermediate ALS model is not accounted for, the estimated variance of the estimator can be biased by as much as a factor of three or more, depending on the sample size at the various stages of the design.This study concerns model-based inference for estimating growing stock volume in large-area forest inventories, combining wall-to-wall Landsat data, a sample of laser data, and a sparse subsample of field data.We develop and evaluate novel estimators and variance estimators for the population mean volume, taking into account the uncertainty in two model steps.Estimators and variance estimators were derived for two main methodological approaches and evaluated through Monte Carlo simulation. The first approach is known as two-stage least squares regression, where Landsat data were used to predict laser predictor variables, thus emulating the use of wall-to-wall laser data. In the second approach laser data were used to predict field-recorded volumes, which were subsequently used as response variables in modeling the relationship between Landsat and field data.a (TM) The estimators and variance estimators are shown to be at least approximately unbiased. Under certain assumptions the two methods provide identical results with regard to estimators and similar results with regard to estimated variances.We show that ignoring the uncertainty due to one of the models leads to substantial underestimation of the variance, when two models are involved in the estimation procedure.
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| 11. |
- Ståhl, Göran, et al.
(författare)
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Statistical properties of hybrid estimators proposed for GEDI-NASA's global ecosystem dynamics investigation
- 2019
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of similar to 25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI's primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha(-1)), covering the latitudes overflown by ISS (51.6 degrees S to 51.6 degrees N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI's sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.
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| 12. |
- Ståhl, Göran, et al.
(författare)
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Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation
- 2016
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Ingår i: Forest Ecosystems. - : Elsevier BV. - 2095-6355 .- 2197-5620. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data. We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.
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| 13. |
- Trier, Nicole Hartwig, et al.
(författare)
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Application of synthetic peptides for detection of anti-citrullinated peptide antibodies.
- 2016
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Ingår i: Peptides. - : Elsevier BV. - 1873-5169 .- 0196-9781. ; 76, s. 87-95
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Tidskriftsartikel (refereegranskat)abstract
- Anti-citrullinated protein antibodies (ACPAs) are a hallmark of rheumatoid arthritis (RA) and represent an important tool for the serological diagnosis of RA. In this study, we describe ACPA reactivity to overlapping citrullinated Epstein-Barr virus nuclear antigen-1 (EBNA-1)-derived peptides and analyze their potential as substrates for ACPA detection by streptavidin capture enzyme-linked immunosorbent assay. Using systematically overlapping peptides, containing a 10 amino acid overlap, labelled with biotin C-terminally or N-terminally, sera from 160 individuals (RA sera (n=60), healthy controls (n=40), systemic lupus erythematosus (n=20), Sjögren's syndrome (n=40)) were screened for antibody reactivity. Antibodies to a panel of five citrullinated EBNA-1 peptides were found in 67% of RA sera, exclusively of the IgG isotype, while 53% of the patient sera reacted with a single peptide, ARGGSRERARGRGRG-Cit-GEKR, accounting for more than half of the ACPA reactivity alone. Moreover, these antibodies were detected in 10% of CCP2-negative RA sera. In addition, 47% of the RA sera reacted with two or three citrullinated EBNA-1 peptides from the selected peptide panel. Furthermore, a negative correlation between the biotin attachment site and the location of citrulline in the peptides was found, i.e. the closer the citrulline was located to biotin, the lower the antibody reactivity. Our data suggest that citrullinated EBNA-1 peptides may be considered a substrate for the detection of ACPAs and that the presence of Epstein-Barr virus may play a role in the induction of these autoantibodies.
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| 14. |
- Trier, Nicole Hartwig, et al.
(författare)
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The use of synthetic peptides for detection of anti-citrullinated protein antibodies in rheumatoid arthritis
- 2018
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Ingår i: Journal of Immunological Methods. - : Elsevier BV. - 0022-1759. ; 454, s. 6-14
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Tidskriftsartikel (refereegranskat)abstract
- Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology. A characteristic feature of RA is the presence of anti-citrullinated protein antibodies (ACPA). Since ACPAs are highly specific for RA and are often present before the onset of RA symptoms, they have become valuable diagnostic and prognostic. As a result, several assays for detection of ACPAs exist, which vary in sensitivity and specificity. In this study, we analyzed the reactivity of RA sera to selected peptides by solid-phase immunoassays in order to develop an ACPA assay with improved sensitivity and specificity. ACPA levels were determined with respect to sensitivity and specificity in 332 serum samples using the newly developed peptide panel, which was compared to the commercial assays CCPlus (Eurodiagnostica) and CCP3.1 (Inova Diagnostics).A primary panel (peptides 814, 33062 and 33156) was identified, which obtained a sensitivity of 71%, while the complete peptide panel reacted with 79% of RA sera screened. Total specificities of 89% and 80% were obtained for the primary peptide panel and the complete peptide panel. Sensitivities for the commercial assays ranged between 71% and 76% and specificities between 88% and 90%. These findings indicate that the generated peptide panel is optimal for ACPA detection and able to compete with commercial available assays. Collectively, this study may contribute to characterize autoimmunity towards citrullinated proteins and to the development of new and improved diagnostic assays for detection of ACPA and determination of RA.
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