| 1. |
- Bar, Tzachi, 1970, et al.
(author)
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Kinetic Outlier Detection (KOD) in real-time PCR.
- 2003
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In: Nucleic acids research. - 1362-4962 .- 0305-1048. ; 31:17
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Journal article (peer-reviewed)abstract
- Real-time PCR is becoming the method of choice for precise quantification of minute amounts of nucleic acids. For proper comparison of samples, almost all quantification methods assume similar PCR efficiencies in the exponential phase of the reaction. However, inhibition of PCR is common when working with biological samples and may invalidate the assumed similarity of PCR efficiencies. Here we present a statistical method, Kinetic Outlier Detection (KOD), to detect samples with dissimilar efficiencies. KOD is based on a comparison of PCR efficiency, estimated from the amplification curve of a test sample, with the mean PCR efficiency of samples in a training set. KOD is demonstrated and validated on samples with the same initial number of template molecules, where PCR is inhibited to various degrees by elevated concentrations of dNTP; and in detection of cDNA samples with an aberrant ratio of two genes. Translating the dissimilarity in efficiency to quantity, KOD identifies outliers that differ by 1.3-1.9-fold in their quantity from normal samples with a P-value of 0.05. This precision is higher than the minimal 2-fold difference in number of DNA molecules that real-time PCR usually aims to detect. Thus, KOD may be a useful tool for outlier detection in real-time PCR.
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| 2. |
- Höök+, Angelica, 1972, et al.
(author)
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Content validity of the electronic faces thermometer scale for pain in children: is a picture worth more than a thousand words?
- 2024
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In: Frontiers in pain research (Lausanne, Switzerland). - : Frontiers Media S.A.. - 2673-561X. ; 5
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Journal article (peer-reviewed)abstract
- Early recognition of pain in children is crucial, and their self-report is the primary source of information. However, communication about pain in healthcare settings can be challenging. For non-verbal communication regarding different symptoms, children prefer digital tools. The electronic Faces Thermometer Scale (eFTS) utilizes a universal design with colors, face emojis, and numbers on an 11-point scale (0-10) for pain assessment. The aim of this study was to establish content validity of the eFTS for pain assessments in children.A mixed methods design was used. The study took place at a university hospital in eastern Sweden, involving 102 children aged 8-17 years who visited outpatient clinics. Participants were presented with 17 pictures representing varying pain levels and asked to assess hypothetical pain using the eFTS. A think-aloud approach was employed, prompting children to verbalize their thoughts about assessments and the eFTS. Quantitative data were analyzed using descriptive and comparative statistics, together with a qualitative approach for analysis of think-aloud conversations.A total of 1,734 assessments of hypothetical pain using the eFTS were conducted. The eFTS differentiated between no pain (level 0-1) and pain (level 2-10). However, no clear agreement was found in the differentiation between hypothetical pain intensity levels (level 2-10). The analysis revealed that children utilized the entire scale, ranging from no pain to high pain, incorporating numbers, colors, and face emojis in their assessments.The variability in assessments was influenced by prior experiences, which had an impact on the statistical outcome in our study. However, employing the think-aloud method enhances our understanding of how children utilize the scale and perceive its design, including the incorporation of emotion-laden anchors. Children express a preference for using the eFTS to assess their pain during hospital visits.
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| 3. |
- Lindgren, Nils, et al.
(author)
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Data assimilation in stand level forest inventory – first results
- 2015
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In: Natural resources and bioeconomy studies. - 2342-7639. ; 29, s. 37-37
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Conference paper (other academic/artistic)abstract
- Data assimilation in stand-level forest inventory – first results Nils Lindgren 1 , Mattias Nyström1 , Jörgen Wallerman 1 , Sarah Ehlers 1 , Anton Grafström1 , Anders Muszta 1 , Kenneth Nyström1 , Erik Willen 2 , Johan Fransson 1 , Jonas Bohlin 1 , Håkan Olsson 1 , Göran Ståhl 1 1Swedish University of Agricultural Sciences, Umeå, Sweden 2Skogforsk, Uppsala, Sweden As we are entering an era of increased supply of remote sensing data, we believe that data assimilation has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013). Data assimilation combines forecasts of previous estimates with new observations of the current state in an optimal way based on the uncertainties in the forecast and the observations. These forecasting and updating steps can be repeated with new available observations to get improved estimations. In the present study, we use canopy height models obtained from matching of digital aerial photos over the test site Remningstorp in Sweden, acquired 2003, 2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning. Stem volume was estimated for each data acquisition and stand, using regression functions based on field reference data from sample plots. Forecasting was done with growth functions constructed from National Forest Inventory plots. The remote sensing estimates for each time point were assimilated with the forecasts of the previous estimates, using extended Kalman filtering. Validation was done on 40 m radius sample plots dominated by Norway spruce. Early results for three stands show that the variances were lower when using assimilation of new estimates and there was less fluctuation compared to repeated remote sensing estimates. The results for the assimilated data at year 2011 were also consistently closer to the validation data measured in 2011 compared to the remote sensing estimates from year 2011.
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| 4. |
- Lindgren, Nils, et al.
(author)
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Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data
- 2017
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In: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 43, s. 374-383
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Journal article (peer-reviewed)abstract
- The statistical framework of data assimilation provides methods for utilizing new data for obtaining up-to-date forest data: existing forest data are forecasted and combined with each new remote sensing data set. This new paradigm for updating forest database, well known from other fields of study, will provide a framework for utilizing all available remote sensing data in proportion to their quality to improve prediction. It also solves the problem that not all remote sensing data sets provide information for the entire area of interest, since areas with no remote sensing data can be forecasted until new remote sensing data become available. In this study, extended Kalman filtering was used for assimilating data from 19 TanDEM-X InSAR images on 137 sample plots, each of 10-meter radius at a test site in southern Sweden over a period of 4 years. At almost all time points data assimilation resulted in predictions closer to the reference value than predictions based on data from that single time point. For the study variables Lorey's mean height, basal area, and stem volume, the median reduction in root mean square error was 0.4 m, 0.9 m(2)/ha, and 15.3 m(3)/ha (2, 3, and 6 percentage points), respectively.
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| 5. |
- Muszta, Anders, 1974
(author)
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Contributions to Numerical Solution of Stochastic Differential Equations
- 2005
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Doctoral thesis (other academic/artistic)abstract
- This thesis consists of four papers: Paper I is an overview of recent techniques in strong numerical solutions of stochastic differential equations, driven by Wiener processes, that have appeared the last then 10 years, or so. Paper II studies theoretical and numerical aspects of stochastic differential equations with so called volatility induced stationarity. While being of great importance in contemporary applications, these equations are particularly difficult from a numerical point of view, to the extent that most or even all standard numerical procedures fail. Paper III develops numerical procedures for stochastic differential equations driven by Levy processes. A general scheme for stochastic Taylor expansions is developed, together with an analysis of convergence properties. Paper IV shows how to reduce the common global Lipschitz condition for numerical procedures for stochastic differential equations, to a local Lipschitz condition.
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| 6. |
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| 7. |
- Nyström, Mattias, et al.
(author)
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Assimilating remote sensing data with forest growth models
- 2015
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Conference paper (peer-reviewed)abstract
- As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011.
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| 8. |
- Nyström, Mattias, et al.
(author)
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Data assimilation in forest inventory: first empirical results
- 2015
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In: Forests. - : MDPI AG. - 1999-4907. ; 6, s. 4540-4557
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Journal article (peer-reviewed)abstract
- Data assimilation techniques were used to estimate forest stand data in 2011 bysequentially combining remote sensing based estimates of forest variables with predictions fromgrowth models. Estimates of stand data, based on canopy height models obtained from imagematching of digital aerial images at six different time-points between 2003 and 2011, served asinput to the data assimilation. The assimilation routines were built on the extended Kalman filter.The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden(lat. 13˝371 N; long. 58˝281 E). The assimilation results were compared with two other methodsused in practice for estimation of forest variables: the first was to use only the most recent estimateobtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003)to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots,which were carefully measured in the field. The results showed that the data assimilation approachprovided better results than the two alternative methods. Data assimilation of remote sensing timeseries has been used previously for calibrating forest ecosystem models, but, to our knowledge,this is the first study with real data where data assimilation has been used for estimating forestinventory data. The study constitutes a starting point for the development of a framework usefulfor sequentially utilizing all types of remote sensing data in order to provide precise and up-to-dateestimates of forest stand parameters.
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| 9. |
- Nyström, Mattias, et al.
(author)
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Data assimilation in forest inventory, first empirical results using ALS data
- 2015
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Conference paper (peer-reviewed)abstract
- A first data assimilation case study using a time series of ALS for updating forest stand data is presented. Forest stand data are predicted from each ALS acquisition. Kalman filtering and growth models are then used to combine each new ALS based prediction with forecasts from the previous data acquisition.
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| 10. |
- Petersson, Hans, et al.
(author)
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Assessing Uncertainty: Sample Size Trade-Offs in the Development and Application of Carbon Stock Models
- 2017
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In: Forest Science. - 0015-749X. ; 63, s. 402-412
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Journal article (peer-reviewed)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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