| 1. |
- Einberg, Afrodite Psaros, et al.
(författare)
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Lack of association between interleukin 28B polymorphism and vertical transmission of hepatitis C
- 2017
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Ingår i: Journal of Pediatric Gastroenterology and Nutrition - JPGN. - : Lippincott Williams & Wilkins. - 0277-2116 .- 1536-4801. ; 65:6, s. 608-612
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: Single genetic nucleotide polymorphism (rs12979860) near the gene for Interleukin 28B (IL28B), is known to be of importance for frequency of spontaneous clearance and treatment outcome in interferon based therapies in patients with hepatitis C virus (HCV) infection. The aim of this study was to investigate if IL28B polymorphism in children and/or their mothers plays a role in vertical transmission of HCV (HCV-VT).METHODS: Plasma samples from 59 infected women, 76 uninfected children born to infected mothers, and 47 children with known vertically transmitted HCV infection, were analysed for IL28B polymorphism and classified by the IL28B genotype (C/C, C/T and T/T) as well as by viral genotype.RESULTS: The proportion of children with genotype C/C was the same in the vertically infected (36%, 17/47) and the exposed uninfected children (38%, 29/76). No difference was seen when stratifying for viral genotype. There was no association between mothers' IL28B genotype and the risk of vertical transmission.CONCLUSION: Regardless of viral genotype we found no association between IL28B genotype and the risk of HCV-VT. The IL28B genotype CC, which has been shown to be favourable in other settings, was not protective of HCV-VT. Thus, other factors possibly associated with the risk of HCV-VT need to be explored.
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| 2. |
- Lindgren, Nils, et al.
(författare)
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Data assimilation in stand level forest inventory – first results
- 2015
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Ingår i: Natural resources and bioeconomy studies. - 2342-7639. ; 29, s. 37-37
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)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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| 3. |
- Lindgren, Nils, et al.
(författare)
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Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data
- 2017
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Ingår i: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 43, s. 374-383
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Tidskriftsartikel (refereegranskat)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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| 4. |
- Nyström, Mattias, et al.
(författare)
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Assimilating remote sensing data with forest growth models
- 2015
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Konferensbidrag (refereegranskat)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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| 5. |
- Nyström, Mattias, et al.
(författare)
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Data assimilation in forest inventory: first empirical results
- 2015
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Ingår i: Forests. - : MDPI AG. - 1999-4907. ; 6, s. 4540-4557
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Nyström, Mattias, et al.
(författare)
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Data assimilation in forest inventory, first empirical results using ALS data
- 2015
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Konferensbidrag (refereegranskat)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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| 7. |
- Ehlers, Sarah, et al.
(författare)
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Data assimilation in stand-level forest inventories
- 2013
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 43, s. 1104-1113
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Tidskriftsartikel (refereegranskat)abstract
- The development of remote sensing methods through research and large-scale application nowadays makes it possible to obtain stand-level estimates of forest variables at short intervals and at low cost. This offers substantial possibilities to forestry practitioners, but it also poses challenges regarding how cost-efficient data acquisition strategies should be developed. For example, should cheap but low-quality data be acquired and discarded whenever new data become available or should investments be made in high-quality data that are continuously updated to last over a longer period of time? We suggest that the solution could be to establish data assimilation (DA) procedures linked to forest inventories to make appropriate use of data from several sources. With DA, old information is updated through growth forecasts and when new information becomes available it is assimilated with the old information; the different sources of information are made use of to the extent motivated by their accuracy. In this study we made a general assessment of the usefulness of DA in connection with stand-level forest inventories and we compared two different methodological approaches, the extended Kalman filter and the Bayesian method. Not surprisingly, the relative advantage of DA was found to be largest for cases where low-precision estimates of growing stock volume were obtained at short intervals and forecasts were made with accurate growth prediction models. The methodological comparison revealed a tendency of the extended Kalman filter to underestimate the variance of the estimates.
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| 8. |
- Ekström, Ylva, 1971-, et al.
(författare)
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En studie om att leda digitala transformationer i komplexa verksamheter : Institutionella normer, fysiska rum och digitala verktyg
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Föreliggande rapport sammanfattar forskningsprojektet “Att leda digital transformation av fysiska rum” som genomförts av forskare vid Uppsala universitet i samverkan med tre partnerorganisationer: Uppsala stadsteater, Svenska Kyrkan, och en nordeuropeisk finanskoncern med huvudkontor i Sverige; projektet samfinansierades av Vinnova under åren 2019-2020. Projektets syfte var att utveckla kunskap om, och verktyg för förståelse av, hur det fysiska rummet och dess digitalisering påverkar interaktionen mellan aktörer bland annat med avseende på kommunikation, tillit och legitimitet. Projektet har genomförts som en komparativ studie av dessa tre tämligen skilda organisationer, som kommit olika långt i relation till digital transformation av deras respektive verksamheter. Forskarna har genomfört intervjuer och observationer på de tre partnerorganisationerna samt gemensamma och organisations-överskridande diskussioner och analyser vid ett flertal workshoppar, där såväl forskare som representanter från partnerorganisationerna deltagit. Rapporten presenterar resultat i form av beskrivningar av specifika situationer i respektive partnerorganisation med betoning på digitaliseringsprocesser med direkt inverkan på det fysiska rummet och de utmaningar som detta leder till. Vidare presenteras analyser och insikter i relation till utmaningarna som digitaliseringen kan ge upphov till och generella, normativa rekommendationer för att vägleda organisationer genom digitala transformationsprocesser. I analysen har framför allt institutionella logiker tillämpats som teoretiskt perspektiv. Studien påvisar betydelsen som de fysiska platserna (t.ex. kontor, scen) utgör för skilda verksamheter, och att användningen av digitala verktyg med syfte att eliminera användningen av fysiska platser kan ge upphov till oförutsedda konsekvenser.
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| 9. |
- Gericke, Martin, et al.
(författare)
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The European Polysaccharide Network of Excellence (EPNOE) research roadmap 2040: Advanced strategies for exploiting the vast potential of polysaccharides as renewable bioresources
- 2024
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Ingår i: Carbohydrate Polymers. - : Elsevier BV. - 0144-8617 .- 1879-1344. ; 326
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Tidskriftsartikel (refereegranskat)abstract
- Polysaccharides are among the most abundant bioresources on earth and consequently need to play a pivotal role when addressing existential scientific challenges like climate change and the shift from fossil-based to sustainable biobased materials. The Research Roadmap 2040 of the European Polysaccharide Network of Excellence (EPNOE) provides an expert's view on how future research and development strategies need to evolve to fully exploit the vast potential of polysaccharides as renewable bioresources. It is addressed to academic researchers, companies, as well as policymakers and covers five strategic areas that are of great importance in the context of polysaccharide related research: (I) Materials & Engineering, (II) Food & Nutrition, (III) Biomedical Applications, (IV) Chemistry, Biology & Physics, and (V) Skills & Education. Each section summarizes the state of research, identifies challenges that are currently faced, project achievements and developments that are expected in the upcoming 20 years, and finally provides outlines on how future research activities need to evolve.
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| 10. |
- Nyström, Mattias, et al.
(författare)
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Data assimilation : a prototype system to assimilate forest stand information
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of this report is to describe a data assimilation prototype program(Appendix A) developed to estimate forest stand data. The program was developed and tested on data col-lected on the forest estate Remningstorp in southern Sweden. Data assimilation can be used to sequentially combine remote sensing based estimates of forest variables with predictions from growth models. The assimilation routine implemented was the extended Kalman Filter. The program supports two different ways to assimilate the forest data: (1) pixel-wise and (2)stand-wise. In the pixel-wise way, raster cells are used as assimilation unit and can beaggregated to a stand for evaluation. In the stand-wise way, the whole stand is assimilatedas one unit. The two methods has pros and cons. The pixel-wise way is simple to use as nostand-delineation is needed, but might be subject to boundary effects and noise due to geo-metric errors. Using the developed code, it has been shown in three case studies that thecombination of time series of remote sensing data and forest growth functions provides bet-ter estimates of forest variables than only using forecasting, or only using the latest remotesensing data. This opens up for a new way to keep forest stand registers up to date.
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| 11. |
- Sator, Lea, et al.
(författare)
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Overdiagnosis of COPD in Subjects With Unobstructed Spirometry A BOLD Analysis
- 2019
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Ingår i: Chest. - : Elsevier BV. - 0012-3692 .- 1931-3543. ; 156:2, s. 277-288
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There are several reports on underdiagnosis of COPD, while little is known about COPD overdiagnosis and overtreatment. We describe the overdiagnosis and the prevalence of spirometrically defined false positive COPD, as well as their relationship with overtreatment across 23 population samples in 20 countries participating in the BOLD Study between 2003 and 2012.METHODS: A false positive diagnosis of COPD was considered when participants reported a doctor's diagnosis of COPD, but postbronchodilator spirometry was unobstructed (FEV1/FVC > LLN). Additional analyses were performed using the fixed ratio criterion (FEV1/FVC < 0.7).RESULTS: Among 16,177 participants, 919 (5.7%) reported a previous medical diagnosis of COPD. Postbronchodilator spirometry was unobstructed in 569 subjects (61.9%): false positive COPD. A similar rate of overdiagnosis was seen when using the fixed ratio criterion (55.3%). In a subgroup analysis excluding participants who reported a diagnosis of "chronic bronchitis" or "emphysema" (n = 220), 37.7% had no airflow limitation. The site-specific prevalence of false positive COPD varied greatly, from 1.9% in low- to middle-income countries to 4.9% in high-income countries. In multivariate analysis, overdiagnosis was more common among women, and was associated with higher education; former and current smoking; the presence of wheeze, cough, and phlegm; and concomitant medical diagnosis of asthma or heart disease. Among the subjects with false positive COPD, 45.7% reported current use of respiratory medication. Excluding patients with reported asthma, 34.4% of those with normal spirometry still used a respiratory medication.CONCLUSIONS: False positive COPD is frequent. This might expose nonobstructed subjects to possible adverse effects of respiratory medication.
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| 12. |
- Studnicka, Michael, et al.
(författare)
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COPD : Should Diagnosis Match Physiology?
- 2020
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Ingår i: Chest. - : Elsevier BV. - 0012-3692 .- 1931-3543. ; 157:2, s. 473-475
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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